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Tinney EM, España-Irla G, Warren AEL, Whitehurst LN, Stillman AM, Hillman CH, Morris TP. Axonal injury, sleep disturbances, and memory following traumatic brain injury. Ann Clin Transl Neurol 2024. [PMID: 39031956 DOI: 10.1002/acn3.52145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/21/2024] [Accepted: 06/25/2024] [Indexed: 07/22/2024] Open
Abstract
OBJECTIVES Traumatic brain injury (TBI) is associated with sleep deficits, but it is not clear why some report sleep disturbances and others do not. The objective of this study was to assess the associations between axonal injury, sleep, and memory in chronic and acute TBI. METHODS Data were acquired from two independent datasets which included 156 older adult veterans (69.8 years) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with prior moderate-to-severe TBIs and 90 (69.2 years) controls and 374 (39.6 years) from Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) with a recent mild TBI (mTBI) and 87 controls (39.6 years), all who completed an MRI, memory assessment, and sleep questionnaire. RESULTS Older adults with a prior TBI had a significant association between axonal injury and sleep disturbances [β = 9.52, 95% CI (4.1, 14.9), p = 0.01]. Axonal injury predicted changes in memory over 1-year in TBI [β = -8.72, 95% CI (-18, -2.7), p = 0.03]. We externally validated those findings in TRACK-TBI where axonal injury within 2 weeks after mTBI was significantly associated with higher sleep disturbances in the TBI group at 2 weeks[β = -7.2, 95% CI (-14, -0.50), p = 0.04], 6 months [β = -16, 95% CI (-24, -7.6), p ≤ 0.01], and 12 months post-injury [β = -11, 95% CI (-19, -0.85), p = 0.03]. These associations were not significant in controls. INTERPRETATIONS Axonal injury, specifically to the left anterior internal capsule is robustly associated with sleep disturbances in multiple TBI populations. Early assessment of axonal injury following mTBI could identify those at risk for persistent sleep disturbances following injury.
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Affiliation(s)
- Emma M Tinney
- Department of Psychology, Northeastern University, Boston, Massachusetts, USA
- Center for Cognitive & Brain Health, Northeastern University, Boston, Massachusetts, USA
| | - Goretti España-Irla
- Center for Cognitive & Brain Health, Northeastern University, Boston, Massachusetts, USA
- Department of Physical Therapy, Movement & Rehabilitation Sciences, Northeastern University, Boston, Massachusetts, USA
| | - Aaron E L Warren
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Alexandra M Stillman
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Charles H Hillman
- Department of Psychology, Northeastern University, Boston, Massachusetts, USA
- Center for Cognitive & Brain Health, Northeastern University, Boston, Massachusetts, USA
- Department of Physical Therapy, Movement & Rehabilitation Sciences, Northeastern University, Boston, Massachusetts, USA
| | - Timothy P Morris
- Center for Cognitive & Brain Health, Northeastern University, Boston, Massachusetts, USA
- Department of Physical Therapy, Movement & Rehabilitation Sciences, Northeastern University, Boston, Massachusetts, USA
- Department of Applied Psychology, Northeastern University, Boston, Massachusetts, USA
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Liu Q, Ning L, Shaik IA, Liao C, Gagoski B, Bilgic B, Grissom W, Nielsen JF, Zaitsev M, Rathi Y. Reduced cross-scanner variability using vendor-agnostic sequences for single-shell diffusion MRI. Magn Reson Med 2024; 92:246-256. [PMID: 38469671 PMCID: PMC11055665 DOI: 10.1002/mrm.30062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/31/2024] [Accepted: 02/05/2024] [Indexed: 03/13/2024]
Abstract
PURPOSE To reduce the inter-scanner variability of diffusion MRI (dMRI) measures between scanners from different vendors by developing a vendor-neutral dMRI pulse sequence using the open-source vendor-agnostic Pulseq platform. METHODS We implemented a standard EPI based dMRI sequence in Pulseq. We tested it on two clinical scanners from different vendors (Siemens Prisma and GE Premier), systematically evaluating and comparing the within- and inter-scanner variability across the vendors, using both the vendor-provided and Pulseq dMRI sequences. Assessments covered both a diffusion phantom and three human subjects, using standard error (SE) and Lin's concordance correlation to measure the repeatability and reproducibility of standard DTI metrics including fractional anisotropy (FA) and mean diffusivity (MD). RESULTS Identical dMRI sequences were executed on both scanners using Pulseq. On the phantom, the Pulseq sequence showed more than a 2.5× reduction in SE (variability) across Siemens and GE scanners. Furthermore, Pulseq sequences exhibited markedly reduced SE in-vivo, maintaining scan-rescan repeatability while delivering lower variability in FA and MD (more than 50% reduction in cortical/subcortical regions) compared to vendor-provided sequences. CONCLUSION The Pulseq diffusion sequence reduces the cross-scanner variability for both phantom and in-vivo data, which will benefit multi-center neuroimaging studies and improve the reproducibility of neuroimaging studies.
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Affiliation(s)
- Qiang Liu
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Lipeng Ning
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Imam Ahmed Shaik
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Congyu Liao
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Borjan Gagoski
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States
| | - Berkin Bilgic
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
- Harvard/MIT Health Sciences and Technology, Cambridge, MA, United States
| | - William Grissom
- Department of Biomedical Engineering, Case School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Jon-Fredrik Nielsen
- fMRI Laboratory and Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Maxim Zaitsev
- Division of Medical Physics, Department of Radiology, University Medical Center Freiburg, Freiburg, Germany
| | - Yogesh Rathi
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
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Ligneul C, Najac C, Döring A, Beaulieu C, Branzoli F, Clarke WT, Cudalbu C, Genovese G, Jbabdi S, Jelescu I, Karampinos D, Kreis R, Lundell H, Marjańska M, Möller HE, Mosso J, Mougel E, Posse S, Ruschke S, Simsek K, Szczepankiewicz F, Tal A, Tax C, Oeltzschner G, Palombo M, Ronen I, Valette J. Diffusion-weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling. Magn Reson Med 2024; 91:860-885. [PMID: 37946584 DOI: 10.1002/mrm.29877] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/18/2023] [Accepted: 09/08/2023] [Indexed: 11/12/2023]
Abstract
Brain cell structure and function reflect neurodevelopment, plasticity, and aging; and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to noninvasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MRS (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modeling, and interpretation of results. These challenges were the motivation behind the organization of the Lorentz Center workshop on "Best Practices & Tools for Diffusion MR Spectroscopy" held in Leiden, the Netherlands, in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting, and modeling dMRS data, and provides links to useful resources.
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Affiliation(s)
- Clémence Ligneul
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Chloé Najac
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - André Döring
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Christian Beaulieu
- Departments of Biomedical Engineering and Radiology, University of Alberta, Alberta, Edmonton, Canada
| | - Francesca Branzoli
- Paris Brain Institute-ICM, Sorbonne University, UMR S 1127, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - William T Clarke
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Cristina Cudalbu
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Guglielmo Genovese
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minnesota, Minneapolis, USA
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Ileana Jelescu
- Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Dimitrios Karampinos
- Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany
| | - Roland Kreis
- MR Methodology, Department for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager anf Hvidovre, Hvidovre, Denmark
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Małgorzata Marjańska
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minnesota, Minneapolis, USA
| | - Harald E Möller
- NMR Methods & Development Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jessie Mosso
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- LIFMET, EPFL, Lausanne, Switzerland
| | - Eloïse Mougel
- Université Paris-Saclay, CEA, CNRS, MIRCen, Laboratoires des Maladies Neurodégénératives, Fontenay-aux-Roses, France
| | - Stefan Posse
- Department of Neurology, University of New Mexico School of Medicine, New Mexico, Albuquerque, USA
- Department of Physics and Astronomy, University of New Mexico School of Medicine, New Mexico, Albuquerque, USA
| | - Stefan Ruschke
- Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany
| | - Kadir Simsek
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | | | - Assaf Tal
- Department of Chemical and Biological Physics, The Weizmann Institute of Science, Rehovot, Israel
| | - Chantal Tax
- University Medical Center Utrecht, Utrecht, The Netherlands
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Maryland, Baltimore, USA
- F. M. Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, Maryland, Baltimore, USA
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | - Itamar Ronen
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton, UK
| | - Julien Valette
- Université Paris-Saclay, CEA, CNRS, MIRCen, Laboratoires des Maladies Neurodégénératives, Fontenay-aux-Roses, France
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Cai LT, Brett BL, Palacios EM, Yuh EL, Bourla I, Wren-Jarvis J, Wang Y, Mac Donald C, Diaz-Arrastia R, Giacino JT, Okonkwo DO, Levin HS, Robertson CS, Temkin N, Markowitz AJ, Manley GT, Stein MB, McCrea MA, Zafonte RD, Nelson LD, Mukherjee P. Emotional Resilience Predicts Preserved White Matter Microstructure Following Mild Traumatic Brain Injury. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:164-175. [PMID: 36152948 PMCID: PMC10065831 DOI: 10.1016/j.bpsc.2022.08.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/12/2022] [Accepted: 08/31/2022] [Indexed: 01/31/2023]
Abstract
BACKGROUND Adult patients with mild traumatic brain injury (mTBI) exhibit distinct phenotypes of emotional and cognitive functioning identified by latent profile analysis of clinical neuropsychological assessments. When discerned early after injury, these latent clinical profiles have been found to improve prediction of long-term outcomes from mTBI. The present study hypothesized that white matter (WM) microstructure is better preserved in an emotionally resilient mTBI phenotype compared with a neuropsychiatrically distressed mTBI phenotype. METHODS The present study used diffusion magnetic resonance imaging to investigate and compare WM microstructure in major association, projection, and commissural tracts between the two phenotypes and over time. Diffusion magnetic resonance images from 172 patients with mTBI were analyzed to compute individual diffusion tensor imaging maps at 2 weeks and 6 months after injury. RESULTS By comparing the diffusion tensor imaging parameters between the two phenotypes at global, regional, and voxel levels, emotionally resilient patients were shown to have higher axial diffusivity compared with neuropsychiatrically distressed patients early after mTBI. Longitudinal analysis revealed greater compromise of WM microstructure in neuropsychiatrically distressed patients, with greater decrease of global axial diffusivity and more widespread decrease of regional axial diffusivity during the first 6 months after injury compared with emotionally resilient patients. CONCLUSIONS These results provide neuroimaging evidence of WM microstructural differences underpinning mTBI phenotypes identified from neuropsychological assessments and show differing longitudinal trajectories of these biological effects. These findings suggest that diffusion magnetic resonance imaging can provide short- and long-term imaging biomarkers of resilience.
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Affiliation(s)
- Lanya T Cai
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California
| | - Benjamin L Brett
- Departments of Neurosurgery and Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Eva M Palacios
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California
| | - Esther L Yuh
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California
| | - Ioanna Bourla
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California
| | - Jamie Wren-Jarvis
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California
| | - Yang Wang
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Christine Mac Donald
- Department of Neurological Surgery, University of Washington, Seattle, Washington
| | - Ramon Diaz-Arrastia
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Joseph T Giacino
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, Massachusetts
| | - David O Okonkwo
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Harvey S Levin
- Department of Physical Medicine & Rehabilitation, Baylor College of Medicine, Houston, Texas
| | | | - Nancy Temkin
- Department of Neurological Surgery, University of Washington, Seattle, Washington
| | - Amy J Markowitz
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California
| | - Geoffrey T Manley
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California
| | - Murray B Stein
- Department of Psychiatry, University of California, San Diego, San Diego, California
| | - Michael A McCrea
- Departments of Neurosurgery and Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Ross D Zafonte
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, Massachusetts
| | - Lindsay D Nelson
- Departments of Neurosurgery and Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin.
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California.
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Davies-Jenkins CW, Döring A, Fasano F, Kleban E, Mueller L, Evans CJ, Afzali M, Jones DK, Ronen I, Branzoli F, Tax CMW. Practical considerations of diffusion-weighted MRS with ultra-strong diffusion gradients. Front Neurosci 2023; 17:1258408. [PMID: 38144210 PMCID: PMC10740196 DOI: 10.3389/fnins.2023.1258408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 11/03/2023] [Indexed: 12/26/2023] Open
Abstract
Introduction Diffusion-weighted magnetic resonance spectroscopy (DW-MRS) offers improved cellular specificity to microstructure-compared to water-based methods alone-but spatial resolution and SNR is severely reduced and slow-diffusing metabolites necessitate higher b-values to accurately characterize their diffusion properties. Ultra-strong gradients allow access to higher b-values per-unit time, higher SNR for a given b-value, and shorter diffusion times, but introduce additional challenges such as eddy-current artefacts, gradient non-uniformity, and mechanical vibrations. Methods In this work, we present initial DW-MRS data acquired on a 3T Siemens Connectom scanner equipped with ultra-strong (300 mT/m) gradients. We explore the practical issues associated with this manner of acquisition, the steps that may be taken to mitigate their impact on the data, and the potential benefits of ultra-strong gradients for DW-MRS. An in-house DW-PRESS sequence and data processing pipeline were developed to mitigate the impact of these confounds. The interaction of TE, b-value, and maximum gradient amplitude was investigated using simulations and pilot data, whereby maximum gradient amplitude was restricted. Furthermore, two DW-MRS voxels in grey and white matter were acquired using ultra-strong gradients and high b-values. Results Simulations suggest T2-based SNR gains that are experimentally confirmed. Ultra-strong gradient acquisitions exhibit similar artefact profiles to those of lower gradient amplitude, suggesting adequate performance of artefact mitigation strategies. Gradient field non-uniformity influenced ADC estimates by up to 4% when left uncorrected. ADC and Kurtosis estimates for tNAA, tCho, and tCr align with previously published literature. Discussion In conclusion, we successfully implemented acquisition and data processing strategies for ultra-strong gradient DW-MRS and results indicate that confounding effects of the strong gradient system can be ameliorated, while achieving shorter diffusion times and improved metabolite SNR.
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Affiliation(s)
- Christopher W. Davies-Jenkins
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - André Döring
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- CIBM Center for Biomedical Imaging, EPFL CIBM-AIT, EPFL Lausanne, Lausanne, Switzerland
| | - Fabrizio Fasano
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Siemens Healthcare Ltd., Camberly, United Kingdom
| | - Elena Kleban
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Department of Radiology, Universität Bern, Bern, Switzerland
| | - Lars Mueller
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Leeds Institute of Cardiovascular & Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| | - C. John Evans
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - Maryam Afzali
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Leeds Institute of Cardiovascular & Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - Itamar Ronen
- Clinical Sciences Institue, Brighton and Sussex Medical School, Brighton, United Kingdom
| | - Francesca Branzoli
- Center for NeuroImaging Research (CENIR), Paris Brain Institute (ICM), Pitié-Salpêtrière Hospital, Paris, France
- Inserm U1127, CNRS U7225, Sorbonne Universités, Paris, France
| | - Chantal M. W. Tax
- Brain Research Imaging Centre, School Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
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Weygand J, Armstrong T, Bryant JM, Andreozzi JM, Oraiqat IM, Nichols S, Liveringhouse CL, Latifi K, Yamoah K, Costello JR, Frakes JM, Moros EG, El Naqa IM, Naghavi AO, Rosenberg SA, Redler G. Accurate, repeatable, and geometrically precise diffusion-weighted imaging on a 0.35 T magnetic resonance imaging-guided linear accelerator. Phys Imaging Radiat Oncol 2023; 28:100505. [PMID: 38045642 PMCID: PMC10692914 DOI: 10.1016/j.phro.2023.100505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/04/2023] [Accepted: 10/30/2023] [Indexed: 12/05/2023] Open
Abstract
Background and purpose Diffusion weighted imaging (DWI) allows for the interrogation of tissue cellularity, which is a surrogate for cellular proliferation. Previous attempts to incorporate DWI into the workflow of a 0.35 T MR-linac (MRL) have lacked quantitative accuracy. In this study, accuracy, repeatability, and geometric precision of apparent diffusion coefficient (ADC) maps produced using an echo planar imaging (EPI)-based DWI protocol on the MRL system is illustrated, and in vivo potential for longitudinal patient imaging is demonstrated. Materials and methods Accuracy and repeatability were assessed by measuring ADC values in a diffusion phantom at three timepoints and comparing to reference ADC values. System-dependent geometric distortion was quantified by measuring the distance between 93 pairs of phantom features on ADC maps acquired on a 0.35 T MRL and a 3.0 T diagnostic scanner and comparing to spatially precise CT images. Additionally, for five sarcoma patients receiving radiotherapy on the MRL, same-day in vivo ADC maps were acquired on both systems, one of which at multiple timepoints. Results Phantom ADC quantification was accurate on the 0.35 T MRL with significant discrepancies only seen at high ADC. Average geometric distortions were 0.35 (±0.02) mm and 0.85 (±0.02) mm in the central slice and 0.66 (±0.04) mm and 2.14 (±0.07) mm at 5.4 cm off-center for the MRL and diagnostic system, respectively. In the sarcoma patients, a mean pretreatment ADC of 910x10-6 (±100x10-6) mm2/s was measured on the MRL. Conclusions The acquisition of accurate, repeatable, and geometrically precise ADC maps is possible at 0.35 T with an EPI approach.
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Affiliation(s)
- Joseph Weygand
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | | | | | | | | | - Steven Nichols
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Kujtim Latifi
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Kosj Yamoah
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Jessica M. Frakes
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Eduardo G. Moros
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | - Issam M. El Naqa
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA
| | - Arash O. Naghavi
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Gage Redler
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA
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Nasrallah F, Bellapart J, Walsham J, Jacobson E, To XV, Manzanero S, Brown N, Meyer J, Stuart J, Evans T, Chandra SS, Ross J, Campbell L, Senthuran S, Newcombe V, McCullough J, Fleming J, Pollard C, Reade M. PREdiction and Diagnosis using Imaging and Clinical biomarkers Trial in Traumatic Brain Injury (PREDICT-TBI) study protocol: an observational, prospective, multicentre cohort study for the prediction of outcome in moderate-to-severe TBI. BMJ Open 2023; 13:e067740. [PMID: 37094888 PMCID: PMC10151972 DOI: 10.1136/bmjopen-2022-067740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/26/2023] Open
Abstract
INTRODUCTION Traumatic brain injury (TBI) is a heterogeneous condition with a broad spectrum of injury severity, pathophysiological processes and variable outcomes. For moderate-to-severe TBI survivors, recovery is often protracted and outcomes can range from total dependence to full recovery. Despite advances in medical treatment options, prognosis remains largely unchanged. The objective of this study is to develop a machine learning predictive model for neurological outcomes at 6 months in patients with a moderate-to-severe TBI, incorporating longitudinal clinical, multimodal neuroimaging and blood biomarker predictor variables. METHODS AND ANALYSIS A prospective, observational, cohort study will enrol 300 patients with moderate-to-severe TBI from seven Australian hospitals over 3 years. Candidate predictors including demographic and general health variables, and longitudinal clinical, neuroimaging (CT and MRI), blood biomarker and patient-reported outcome measures will be collected at multiple time points within the acute phase of injury. The predictor variables will populate novel machine learning models to predict the Glasgow Outcome Scale Extended 6 months after injury. The study will also expand on current prognostic models by including novel blood biomarkers (circulating cell-free DNA), and the results of quantitative neuroimaging such as Quantitative Susceptibility Mapping and Dynamic Contrast Enhanced MRI as predictor variables. ETHICS AND DISSEMINATION Ethical approval has been obtained by the Royal Brisbane and Women's Hospital Human Research Ethics Committee, Queensland. Participants or their substitute decision-maker/s will receive oral and written information about the study before providing written informed consent. Study findings will be disseminated by peer-review publications and presented at national and international conferences and clinical networks. TRIAL REGISTRATION NUMBER ACTRN12620001360909.
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Affiliation(s)
- Fatima Nasrallah
- The Queensland Brain Institute, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Judith Bellapart
- Intensive Care Unit, Royal Brisbane and Women's Hospital, Metro North Health Service District, Herston, Queensland, Australia
| | - James Walsham
- Intensive Care Unit, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia
- Faculty of Medicine, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Esther Jacobson
- Jamieson Trauma Institute, Metro North Health Service District, Herston, Queensland, Australia
| | - Xuan Vinh To
- The Queensland Brain Institute, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Silvia Manzanero
- Jamieson Trauma Institute, Metro North Health Service District, Herston, Queensland, Australia
| | - Nathan Brown
- Intensive Care Unit, Royal Brisbane and Women's Hospital, Metro North Health Service District, Herston, Queensland, Australia
- Faculty of Medicine, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Jason Meyer
- Intensive Care Unit, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia
| | - Janine Stuart
- Intensive Care Unit, Royal Brisbane and Women's Hospital, Metro North Health Service District, Herston, Queensland, Australia
| | - Tracey Evans
- The Queensland Brain Institute, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Shekhar S Chandra
- School of Information Technology and Electrical Engineering, Architecture and Information Technology, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Jason Ross
- Health and Biosecurity, CSIRO, Westmead, New South Wales, Australia
| | - Lewis Campbell
- Intensive Care Unit, Royal Darwin Hospital, Casuarina, Darwin, Australia
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Siva Senthuran
- Intensive Care Unit, Townsville Hospital and Health Service, Townsville, Queensland, Australia
| | - Virginia Newcombe
- University Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - James McCullough
- Intensive Care Unit, Gold Coast Hospital and Health Service, Gold Coast, Queensland, Australia
| | - Jennifer Fleming
- School of Health and Rehabilitation Sciences, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Clifford Pollard
- School of Information Technology and Electrical Engineering, Architecture and Information Technology, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Michael Reade
- Intensive Care Unit, Royal Brisbane and Women's Hospital, Metro North Health Service District, Herston, Queensland, Australia
- Faculty of Medicine, The University of Queensland, Saint Lucia, Queensland, Australia
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Wen G, Shim V, Holdsworth SJ, Fernandez J, Qiao M, Kasabov N, Wang A. Machine Learning for Brain MRI Data Harmonisation: A Systematic Review. Bioengineering (Basel) 2023; 10:bioengineering10040397. [PMID: 37106584 PMCID: PMC10135601 DOI: 10.3390/bioengineering10040397] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/16/2023] [Accepted: 03/21/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND Magnetic Resonance Imaging (MRI) data collected from multiple centres can be heterogeneous due to factors such as the scanner used and the site location. To reduce this heterogeneity, the data needs to be harmonised. In recent years, machine learning (ML) has been used to solve different types of problems related to MRI data, showing great promise. OBJECTIVE This study explores how well various ML algorithms perform in harmonising MRI data, both implicitly and explicitly, by summarising the findings in relevant peer-reviewed articles. Furthermore, it provides guidelines for the use of current methods and identifies potential future research directions. METHOD This review covers articles published through PubMed, Web of Science, and IEEE databases through June 2022. Data from studies were analysed based on the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Quality assessment questions were derived to assess the quality of the included publications. RESULTS a total of 41 articles published between 2015 and 2022 were identified and analysed. In the review, MRI data has been found to be harmonised either in an implicit (n = 21) or an explicit (n = 20) way. Three MRI modalities were identified: structural MRI (n = 28), diffusion MRI (n = 7) and functional MRI (n = 6). CONCLUSION Various ML techniques have been employed to harmonise different types of MRI data. There is currently a lack of consistent evaluation methods and metrics used across studies, and it is recommended that the issue be addressed in future studies. Harmonisation of MRI data using ML shows promises in improving performance for ML downstream tasks, while caution should be exercised when using ML-harmonised data for direct interpretation.
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Affiliation(s)
- Grace Wen
- Auckland Bioengineering Institute, University of Auckland, Auckland 1142, New Zealand
| | - Vickie Shim
- Auckland Bioengineering Institute, University of Auckland, Auckland 1142, New Zealand
- Centre for Brain Research, University of Auckland, Auckland 1142, New Zealand
| | - Samantha Jane Holdsworth
- Centre for Brain Research, University of Auckland, Auckland 1142, New Zealand
- Mātai Medical Research Institute, Tairāwhiti-Gisborne 4010, New Zealand
- Department of Anatomy & Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1142, New Zealand
| | - Justin Fernandez
- Auckland Bioengineering Institute, University of Auckland, Auckland 1142, New Zealand
| | - Miao Qiao
- Department of Computer Science, University of Auckland, Auckland 1142, New Zealand
| | - Nikola Kasabov
- Auckland Bioengineering Institute, University of Auckland, Auckland 1142, New Zealand
- Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1010, New Zealand
- Intelligent Systems Research Centre, Ulster University, Londonderry BT52 1SA, UK
- Institute for Information and Communication Technologies, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
| | - Alan Wang
- Auckland Bioengineering Institute, University of Auckland, Auckland 1142, New Zealand
- Centre for Brain Research, University of Auckland, Auckland 1142, New Zealand
- Department of Anatomy & Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1142, New Zealand
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9
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Korte JC, Chin Z, Carr M, Holloway L, Franich R. Magnetic resonance biomarker assessment software (MR-BIAS): an automated open-source tool for the ISMRM/NIST system phantom. Phys Med Biol 2023; 68. [PMID: 36796102 DOI: 10.1088/1361-6560/acbcbb] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 02/16/2023] [Indexed: 02/18/2023]
Abstract
Objective.To provide an open-source software for repeatable and efficient quantification ofT1andT2relaxation times with the ISMRM/NIST system phantom. Quantitative magnetic resonance imaging (qMRI) biomarkers have the potential to improve disease detection, staging and monitoring of treatment response. Reference objects, such as the system phantom, play a major role in translating qMRI methods into the clinic. The currently available open-source software for ISMRM/NIST system phantom analysis, Phantom Viewer (PV), includes manual steps that are subject to variability.Approach.We developed the Magnetic Resonance BIomarker Assessment Software (MR-BIAS) to automatically extract system phantom relaxation times. The inter-observer variability (IOV) and time efficiency of MR-BIAS and PV was observed in six volunteers analysing three phantom datasets. The IOV was measured with the coefficient of variation (CV) of percent bias (%bias) inT1andT2with respect to NMR reference values. The accuracy of MR-BIAS was compared to a custom script from a published study of twelve phantom datasets. This included comparison of overall bias and %bias for variable inversion recovery (T1VIR), variable flip angle (T1VFA) and multiple spin-echo (T2MSE) relaxation models.Main results.MR-BIAS had a lower mean CV withT1VIR(0.03%) andT2MSE(0.05%) in comparison to PV withT1VIR(1.28%) andT2MSE(4.55%). The mean analysis duration was 9.7 times faster for MR-BIAS (0.8 min) than PV (7.6 min). There was no statistically significant difference in the overall bias, or the %bias for the majority of ROIs, as calculated by MR-BIAS or the custom script for all models.Significance.MR-BIAS has demonstrated repeatable and efficient analysis of the ISMRM/NIST system phantom, with comparable accuracy to previous studies. The software is freely available to the MRI community, providing a framework to automate required analysis tasks, with the flexibility to explore open questions and accelerate biomarker research.
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Affiliation(s)
- James C Korte
- Peter MacCallum Cancer Centre, Department of Physical Sciences, Melbourne, Australia.,The University of Melbourne, Department of Biomedical Engineering, Melbourne, Australia
| | - Zachary Chin
- RMIT University, School of Science, Melbourne, Australia
| | - Madeline Carr
- University of Wollongong, Centre for Medical Radiation Physics, Wollongong, Australia.,Liverpool and Macarthur Cancer Therapy Centres and Ingham Institute, Liverpool, Sydney, Australia.,GenesisCare, Sydney, New South Wales, Australia
| | - Lois Holloway
- University of Wollongong, Centre for Medical Radiation Physics, Wollongong, Australia.,Liverpool and Macarthur Cancer Therapy Centres and Ingham Institute, Liverpool, Sydney, Australia
| | - Rick Franich
- Peter MacCallum Cancer Centre, Department of Physical Sciences, Melbourne, Australia.,RMIT University, School of Science, Melbourne, Australia
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10
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Diffusion-Weighted Imaging in Mild Traumatic Brain Injury: A Systematic Review of the Literature. Neuropsychol Rev 2023; 33:42-121. [PMID: 33721207 DOI: 10.1007/s11065-021-09485-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 02/09/2021] [Indexed: 12/14/2022]
Abstract
There is evidence that diffusion-weighted imaging (DWI) is able to detect tissue alterations following mild traumatic brain injury (mTBI) that may not be observed on conventional neuroimaging; however, findings are often inconsistent between studies. This systematic review assesses patterns of differences in DWI metrics between those with and without a history of mTBI. A PubMed literature search was performed using relevant indexing terms for articles published prior to May 14, 2020. Findings were limited to human studies using DWI in mTBI. Articles were excluded if they were not full-length, did not contain original data, if they were case studies, pertained to military populations, had inadequate injury severity classification, or did not report post-injury interval. Findings were reported independently for four subgroups: acute/subacute pediatric mTBI, acute/subacute adult mTBI, chronic adult mTBI, and sport-related concussion, and all DWI acquisition and analysis methods used were included. Patterns of findings between studies were reported, along with strengths and weaknesses of the current state of the literature. Although heterogeneity of sample characteristics and study methods limited the consistency of findings, alterations in DWI metrics were most commonly reported in the corpus callosum, corona radiata, internal capsule, and long association pathways. Many acute/subacute pediatric studies reported higher FA and lower ADC or MD in various regions. In contrast, acute/subacute adult studies most commonly indicate lower FA within the context of higher MD and RD. In the chronic phase of recovery, FA may remain low, possibly indicating overall demyelination or Wallerian degeneration over time. Longitudinal studies, though limited, generally indicate at least a partial normalization of DWI metrics over time, which is often associated with functional improvement. We conclude that DWI is able to detect structural mTBI-related abnormalities that may persist over time, although future DWI research will benefit from larger samples, improved data analysis methods, standardized reporting, and increasing transparency.
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11
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Malyarenko D, Amouzandeh G, Pickup S, Zhou R, Manning HC, Gammon ST, Shoghi KI, Quirk JD, Sriram R, Larson P, Lewis MT, Pautler RG, Kinahan PE, Muzi M, Chenevert TL. Evaluation of Apparent Diffusion Coefficient Repeatability and Reproducibility for Preclinical MRIs Using Standardized Procedures and a Diffusion-Weighted Imaging Phantom. Tomography 2023; 9:375-386. [PMID: 36828382 PMCID: PMC9964373 DOI: 10.3390/tomography9010030] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 02/10/2023] Open
Abstract
Relevant to co-clinical trials, the goal of this work was to assess repeatability, reproducibility, and bias of the apparent diffusion coefficient (ADC) for preclinical MRIs using standardized procedures for comparison to performance of clinical MRIs. A temperature-controlled phantom provided an absolute reference standard to measure spatial uniformity of these performance metrics. Seven institutions participated in the study, wherein diffusion-weighted imaging (DWI) data were acquired over multiple days on 10 preclinical scanners, from 3 vendors, at 6 field strengths. Centralized versus site-based analysis was compared to illustrate incremental variance due to processing workflow. At magnet isocenter, short-term (intra-exam) and long-term (multiday) repeatability were excellent at within-system coefficient of variance, wCV [±CI] = 0.73% [0.54%, 1.12%] and 1.26% [0.94%, 1.89%], respectively. The cross-system reproducibility coefficient, RDC [±CI] = 0.188 [0.129, 0.343] µm2/ms, corresponded to 17% [12%, 31%] relative to the reference standard. Absolute bias at isocenter was low (within 4%) for 8 of 10 systems, whereas two high-bias (>10%) scanners were primary contributors to the relatively high RDC. Significant additional variance (>2%) due to site-specific analysis was observed for 2 of 10 systems. Base-level technical bias, repeatability, reproducibility, and spatial uniformity patterns were consistent with human MRIs (scaled for bore size). Well-calibrated preclinical MRI systems are capable of highly repeatable and reproducible ADC measurements.
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Affiliation(s)
- Dariya Malyarenko
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ghoncheh Amouzandeh
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
- Neuro42, Inc., San Francisco, CA 94105, USA
| | - Stephen Pickup
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Rong Zhou
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Henry Charles Manning
- Department of Cancer Systems Imaging, The University of Texas MDACC, Houston, TX 77030, USA
| | - Seth T. Gammon
- Department of Cancer Systems Imaging, The University of Texas MDACC, Houston, TX 77030, USA
| | - Kooresh I. Shoghi
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - James D. Quirk
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Renuka Sriram
- UCSF Department of Radiology & Biomedical Imaging, San Francisco, CA 94158, USA
| | - Peder Larson
- UCSF Department of Radiology & Biomedical Imaging, San Francisco, CA 94158, USA
| | | | | | - Paul E. Kinahan
- Department of Radiology, University of Washington, Seattle, WA 98195, USA
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, WA 98195, USA
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12
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Richter S, Winzeck S, Correia MM, Kornaropoulos EN, Manktelow A, Outtrim J, Chatfield D, Posti JP, Tenovuo O, Williams GB, Menon DK, Newcombe VF. Validation of cross-sectional and longitudinal ComBat harmonization methods for magnetic resonance imaging data on a travelling subject cohort. NEUROIMAGE. REPORTS 2022; 2:None. [PMID: 36507071 PMCID: PMC9726680 DOI: 10.1016/j.ynirp.2022.100136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 09/07/2022] [Accepted: 09/14/2022] [Indexed: 11/05/2022]
Abstract
Background The growth in multi-center neuroimaging studies generated a need for methods that mitigate the differences in hardware and acquisition protocols across sites i.e., scanner effects. ComBat harmonization methods have shown promise but have not yet been tested on all the data types commonly studied with magnetic resonance imaging (MRI). This study aimed to validate neuroCombat, longCombat and gamCombat on both structural and diffusion metrics in both cross-sectional and longitudinal data. Methods We used a travelling subject design whereby 73 healthy volunteers contributed 161 scans across two sites and four machines using one T1 and five diffusion MRI protocols. Scanner was defined as a composite of site, machine and protocol. A common pipeline extracted two structural metrics (volumes and cortical thickness) and two diffusion tensor imaging metrics (mean diffusivity and fractional anisotropy) for seven regions of interest including gray and (except for cortical thickness) white matter regions. Results Structural data exhibited no significant scanner effect and therefore did not benefit from harmonization in our particular cohort. Indeed, attempting harmonization obscured the true biological effect for some regions of interest. Diffusion data contained marked scanner effects and was successfully harmonized by all methods, resulting in smaller scanner effects and better detection of true biological effects. LongCombat less effectively reduced the scanner effect for cross-sectional white matter data but had a slightly lower probability of incorrectly finding group differences in simulations, compared to neuroCombat and gamCombat. False positive rates for all methods and all metrics did not significantly exceed 5%. Conclusions Statistical harmonization of structural data is not always necessary and harmonization in the absence of a scanner effect may be harmful. Harmonization of diffusion MRI data is highly recommended with neuroCombat, longCombat and gamCombat performing well in cross-sectional and longitudinal settings.
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Affiliation(s)
- Sophie Richter
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
- Corresponding author.
| | - Stefan Winzeck
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
- BioMedIA Group, Department of Computing, Imperial College London, London, UK
| | - Marta M. Correia
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | | | - Anne Manktelow
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Joanne Outtrim
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Doris Chatfield
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Jussi P. Posti
- Turku Brain Injury Center, Turku University Hospital & University of Turku, Turku, Finland
- Department of Neurosurgery, Turku University Hospital, Turku, Finland
| | - Olli Tenovuo
- Turku Brain Injury Center, Turku University Hospital & University of Turku, Turku, Finland
| | - Guy B. Williams
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - David K. Menon
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
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13
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Spilling CA, Howe FA, Barrick TR. Optimization of quasi-diffusion magnetic resonance imaging for quantitative accuracy and time-efficient acquisition. Magn Reson Med 2022; 88:2532-2547. [PMID: 36054778 PMCID: PMC9804504 DOI: 10.1002/mrm.29420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 07/17/2022] [Accepted: 07/30/2022] [Indexed: 01/05/2023]
Abstract
PURPOSE Quasi-diffusion MRI (QDI) is a novel quantitative technique based on the continuous time random walk model of diffusion dynamics. QDI provides estimates of the diffusion coefficient, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mspace/> <mml:msub><mml:mi>D</mml:mi> <mml:mrow><mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {D}_{1,2} $$</mml:annotation></mml:semantics> </mml:math> in mm2 s-1 and a fractional exponent, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>α</mml:mi></mml:mrow> <mml:annotation>$$ \upalpha $$</mml:annotation></mml:semantics> </mml:math> , defining the non-Gaussianity of the diffusion signal decay. Here, the b-value selection for rapid clinical acquisition of QDI tensor imaging (QDTI) data is optimized. METHODS Clinically appropriate QDTI acquisitions were optimized in healthy volunteers with respect to a multi-b-value reference (MbR) dataset comprising 29 diffusion-sensitized images arrayed between <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>b</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0</mml:mn></mml:mrow> <mml:annotation>$$ b=0 $$</mml:annotation></mml:semantics> </mml:math> and 5000 s mm-2 . The effects of varying maximum b-value ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>b</mml:mi> <mml:mi>max</mml:mi></mml:msub> </mml:mrow> <mml:annotation>$$ {b}_{\mathrm{max}} $$</mml:annotation></mml:semantics> </mml:math> ), number of b-value shells, and the effects of Rician noise were investigated. RESULTS QDTI measures showed <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>b</mml:mi> <mml:mi>max</mml:mi></mml:msub> </mml:mrow> <mml:annotation>$$ {b}_{\mathrm{max}} $$</mml:annotation></mml:semantics> </mml:math> dependence, most significantly for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>α</mml:mi></mml:mrow> <mml:annotation>$$ \upalpha $$</mml:annotation></mml:semantics> </mml:math> in white matter, which monotonically decreased with higher <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>b</mml:mi> <mml:mi>max</mml:mi></mml:msub> </mml:mrow> <mml:annotation>$$ {b}_{\mathrm{max}} $$</mml:annotation></mml:semantics> </mml:math> leading to improved tissue contrast. Optimized 2 b-value shell acquisitions showed small systematic differences in QDTI measures relative to MbR values, with overestimation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mspace/> <mml:mspace/> <mml:msub><mml:mi>D</mml:mi> <mml:mrow><mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ \kern0.50em {D}_{1,2} $$</mml:annotation></mml:semantics> </mml:math> and underestimation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>α</mml:mi></mml:mrow> <mml:annotation>$$ \upalpha $$</mml:annotation></mml:semantics> </mml:math> in white matter, and overestimation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>D</mml:mi> <mml:mrow><mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {D}_{1,2} $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>α</mml:mi></mml:mrow> <mml:annotation>$$ \upalpha $$</mml:annotation></mml:semantics> </mml:math> anisotropies in gray and white matter. Additional shells improved the accuracy, precision, and reliability of QDTI estimates with 3 and 4 shells at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>b</mml:mi> <mml:mi>max</mml:mi></mml:msub> <mml:mo>=</mml:mo> <mml:mn>5000</mml:mn></mml:mrow> <mml:annotation>$$ {b}_{\mathrm{max}}=5000 $$</mml:annotation></mml:semantics> </mml:math> s mm-2 , and 4 b-value shells at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>b</mml:mi> <mml:mi>max</mml:mi></mml:msub> <mml:mo>=</mml:mo> <mml:mn>3960</mml:mn></mml:mrow> <mml:annotation>$$ {b}_{\mathrm{max}}=3960 $$</mml:annotation></mml:semantics> </mml:math> s mm-2 , providing minimal bias in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>D</mml:mi> <mml:mrow><mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {D}_{1,2} $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>α</mml:mi></mml:mrow> <mml:annotation>$$ \upalpha $$</mml:annotation></mml:semantics> </mml:math> compared to the MbR. CONCLUSION A highly detailed optimization of non-Gaussian dMRI for in vivo brain imaging was performed. QDI provided robust parameterization of non-Gaussian diffusion signal decay in clinically feasible imaging times with high reliability, accuracy, and precision of QDTI measures.
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Affiliation(s)
- Catherine A. Spilling
- Neurosciences Research Section, Molecular and Clinical Sciences Research InstituteSt George's University of London
LondonUnited Kingdom
- Centre for Affective Disorders, Department of Psychological Medicine, Division of Academic PsychiatryInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
| | - Franklyn A. Howe
- Neurosciences Research Section, Molecular and Clinical Sciences Research InstituteSt George's University of London
LondonUnited Kingdom
| | - Thomas R. Barrick
- Neurosciences Research Section, Molecular and Clinical Sciences Research InstituteSt George's University of London
LondonUnited Kingdom
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Paquier Z, Chao SL, Bregni G, Sanchez AV, Guiot T, Dhont J, Gulyban A, Levillain H, Sclafani F, Reynaert N, Bali MA. Pre-trial quality assurance of diffusion-weighted MRI for radiomic analysis and the role of harmonisation. Phys Med 2022; 103:138-146. [DOI: 10.1016/j.ejmp.2022.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 09/30/2022] [Accepted: 10/08/2022] [Indexed: 11/17/2022] Open
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15
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Palacios EM, Yuh EL, Mac Donald CL, Bourla I, Wren-Jarvis J, Sun X, Vassar MJ, Diaz-Arrastia R, Giacino JT, Okonkwo DO, Robertson CS, Stein MB, Temkin N, McCrea MA, Levin HS, Markowitz AJ, Jain S, Manley GT, Mukherjee P. Diffusion Tensor Imaging Reveals Elevated Diffusivity of White Matter Microstructure that Is Independently Associated with Long-Term Outcome after Mild Traumatic Brain Injury: A TRACK-TBI Study. J Neurotrauma 2022; 39:1318-1328. [PMID: 35579949 PMCID: PMC9529303 DOI: 10.1089/neu.2021.0408] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Diffusion tensor imaging (DTI) literature on single-center studies contains conflicting results regarding acute effects of mild traumatic brain injury (mTBI) on white matter (WM) microstructure and the prognostic significance. This larger-scale multi-center DTI study aimed to determine how acute mTBI affects WM microstructure over time and how early WM changes affect long-term outcome. From Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI), a cohort study at 11 United States level 1 trauma centers, a total of 391 patients with acute mTBI ages 17 to 60 years were included and studied at two weeks and six months post-injury. Demographically matched friends or family of the participants were the control group (n = 148). Axial diffusivity (AD), fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) were the measures of WM microstructure. The primary outcome was the Glasgow Outcome Scale Extended (GOSE) score of injury-related functional limitations across broad life domains at six months post-injury. The AD, MD, and RD were higher and FA was lower in mTBI versus friend control (FC) at both two weeks and six months post-injury throughout most major WM tracts of the cerebral hemispheres. In the mTBI group, AD and, to a lesser extent, MD decreased in WM from two weeks to six months post-injury. At two weeks post-injury, global WM AD and MD were both independently associated with six-month incomplete recovery (GOSE <8 vs = 8) even after accounting for demographic, clinical, and other imaging factors. DTI provides reliable imaging biomarkers of dynamic WM microstructural changes after mTBI that have utility for patient selection and treatment response in clinical trials. Continued technological advances in the sensitivity, specificity, and precision of diffusion magnetic resonance imaging hold promise for routine clinical application in mTBI.
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Affiliation(s)
- Eva M. Palacios
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, California, USA
| | - Esther L. Yuh
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, California, USA
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, California, USA
| | | | - Ioanna Bourla
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, California, USA
| | - Jamie Wren-Jarvis
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, California, USA
| | - Xiaoying Sun
- Biostatistics Research Center, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, California, USA
| | - Mary J. Vassar
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, California, USA
- Department of Neurological Surgery, UCSF, San Francisco, California, USA
| | - Ramon Diaz-Arrastia
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Joseph T. Giacino
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Charlestown, Massachusetts, USA
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, Massachusetts, USA
| | - David O. Okonkwo
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | | | - Murray B. Stein
- Biostatistics Research Center, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, California, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, California, USA
| | - Nancy Temkin
- Department of Neurological Surgery, University of Washington, Seattle, Washington, USA
| | - Michael A. McCrea
- Department of Neurosurgery and Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Harvey S. Levin
- Department of Neurology, Baylor College of Medicine, Houston, Texas, USA
| | - Amy J. Markowitz
- Department of Neurological Surgery, University of Washington, Seattle, Washington, USA
- Biostatistics Research Center, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, California, USA
| | - Sonia Jain
- Biostatistics Research Center, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, California, USA
| | - Geoffrey T. Manley
- Department of Neurological Surgery, University of Washington, Seattle, Washington, USA
- Biostatistics Research Center, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, California, USA
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, California, USA
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, California, USA
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, California, USA
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16
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Neri JP, Koff MF, Koch KM, Tan ET. Validating the accuracy of multispectral metal artifact suppressed diffusion-weighted imaging. Med Phys 2022; 49:6538-6546. [PMID: 35953390 PMCID: PMC9588535 DOI: 10.1002/mp.15925] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 06/29/2022] [Accepted: 08/07/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Diffusion-weighted imaging (DWI) provides quantitative measurement of random water displacement in tissue as calculated by the apparent diffusion coefficient (ADC). While heavily utilized in stroke and oncology applications, DWI is a promising tool to map microstructural changes in musculoskeletal applications including evaluation of synovial reactions resulting from total hip arthroplasty (THA). One major challenge facing the application of DWI in THA is the significant artifacts related to the conventional echo-planar imaging (EPI) readout used. Multispectral imaging (MSI) techniques, including the multiacquisition with variable resonance image combination (MAVRIC), have been shown to effectively reduce metallic susceptibility artifacts around total joint replacements to render clinically useful images. Recently, a 2D periodically rotated overlapping parallel line with enhanced reconstruction (PROPELLER) FSE acquisition that incorporates a diffusion preparation pulse with 2D-MAVRIC has been developed to mitigate both distortion and dropout artifacts. While there have been some preliminary assessments of DWI-MAVRIC, the repeatability of DWI-MAVRIC and the effects of key parameters, such as the number of spectral bins, are unknown. PURPOSE To evaluate the quantitative accuracy of DWI-MAVRIC as compared to conventional diffusion sequences. METHODS A diffusion phantom with different reference diffusivities (ADC = 113-1123 μm2 /s) was used. Scans were performed on two 1.5T MRI scanners. DWI-EPI and DWI-MAVRIC were acquired in both the axial and coronal planes. Three spatial offsets (0 cm, 10 cm left, and 10 cm right off iso-center) were used to evaluate effects of off-isocenter positioning. To assess intraday and interday repeatability, DWI-EPI and DWI-MAVRIC acquisitions were repeated on one scanner at same-day and 9-month intervals. To assess inter-scanner repeatability, DWI-EPI and DWI-MAVRIC acquisitions were compared between two scanners. ADC maps were generated with and without gradient nonlinearity correction (GNC). Linear regression, correlation, and error statistics were determined between calculated and reference ADC values. Bland-Altman plots were generated to evaluate intraday, interday, and interscanner repeatability. RESULTS DWI-MAVRIC had excellent correlation to reference values but at reduced linearity (r = 1.00, slope = 0.91-0.94) as compared to DWI-EPI (r = 1.00, slope = 0.99-1.01). A greater than 5% ADC bias was observed at the lowest ADC values, predominantly in the DWI-MAVRIC scans. ADC values did not vary with DWI-MAVRIC parameters. DWI-EPI acquisitions had intraday, interday, and interscanner repeatability of 3.18 μm2 /s, 19.2 μm2 /s, and 20.2 μm2 /s, respectively. DWI-MAVRIC acquisitions had inferior intraday, interday, and interscanner repeatability of 13.3 μm2 /s, 44.7 μm2 /s, 110 μm2 /s, respectively. Lower ADC errors were found at isocenter, as compared to the left and right positions. GNC reduced the absolute error by 0.31% ± 0.89%, 3.6% ± 1.4%, 0.65% ± 2.4% for the center, left, and right positions, respectively. CONCLUSIONS DWI-MAVRIC provides good linearity with respect to reference values and good intra- and interday repeatability.
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Affiliation(s)
- John P Neri
- MRI Research Laboratory, Hospital for Special Surgery, New York, New York, USA
| | - Matthew F Koff
- MRI Research Laboratory, Hospital for Special Surgery, New York, New York, USA
| | - Kevin M Koch
- Center for Imaging Research, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Ek T Tan
- MRI Research Laboratory, Hospital for Special Surgery, New York, New York, USA
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17
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Behler A, Lulé D, Ludolph AC, Kassubek J, Müller HP. Longitudinal monitoring of amyotrophic lateral sclerosis by diffusion tensor imaging: Power calculations for group studies. Front Neurosci 2022; 16:929151. [PMID: 36117627 PMCID: PMC9479493 DOI: 10.3389/fnins.2022.929151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 07/20/2022] [Indexed: 11/21/2022] Open
Abstract
Introduction Diffusion tensor imaging (DTI) can be used to map disease progression in amyotrophic lateral sclerosis (ALS) and therefore is a promising candidate for a biomarker in ALS. To this end, longitudinal study protocols need to be optimized and validated regarding group sizes and time intervals between visits. The objective of this study was to assess the influences of sample size, the schedule of follow-up measurements, and measurement uncertainties on the statistical power to optimize longitudinal DTI study protocols in ALS. Patients and methods To estimate the measurement uncertainty of a tract-of–interest-based DTI approach, longitudinal test-retest measurements were applied first to a normal data set. Then, DTI data sets of 80 patients with ALS and 50 healthy participants were analyzed in the simulation of longitudinal trajectories, that is, longitudinal fractional anisotropy (FA) values for follow-up sessions were simulated for synthetic patient and control groups with different rates of FA decrease in the corticospinal tract. Monte Carlo simulations of synthetic longitudinal study groups were used to estimate the statistical power and thus the potentially needed sample sizes for a various number of scans at one visit, different time intervals between baseline and follow-up measurements, and measurement uncertainties. Results From the simulation for different longitudinal FA decrease rates, it was found that two scans per session increased the statistical power in the investigated settings unless sample sizes were sufficiently large and time intervals were appropriately long. The positive effect of a second scan per session on the statistical power was particularly pronounced for FA values with high measurement uncertainty, for which the third scan per session increased the statistical power even further. Conclusion With more than one scan per session, the statistical power of longitudinal DTI studies can be increased in patients with ALS. Consequently, sufficient statistical power can be achieved even with limited sample sizes. An improved longitudinal DTI study protocol contributes to the detection of small changes in diffusion metrics and thereby supports DTI as an applicable and reliable non-invasive biomarker in ALS.
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Affiliation(s)
- Anna Behler
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Dorothée Lulé
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Albert C Ludolph
- Department of Neurology, University of Ulm, Ulm, Germany.,Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Ulm, Germany
| | - Jan Kassubek
- Department of Neurology, University of Ulm, Ulm, Germany.,Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Ulm, Germany
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18
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Hubbard Cristinacce PL, Keaveney S, Aboagye EO, Hall MG, Little RA, O'Connor JPB, Parker GJM, Waterton JC, Winfield JM, Jauregui-Osoro M. Clinical translation of quantitative magnetic resonance imaging biomarkers - An overview and gap analysis of current practice. Phys Med 2022; 101:165-182. [PMID: 36055125 DOI: 10.1016/j.ejmp.2022.08.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 08/05/2022] [Accepted: 08/17/2022] [Indexed: 10/14/2022] Open
Abstract
PURPOSE This overview of the current landscape of quantitative magnetic resonance imaging biomarkers (qMR IBs) aims to support the standardisation of academic IBs to assist their translation to clinical practice. METHODS We used three complementary approaches to investigate qMR IB use and quality management practices within the UK: 1) a literature search of qMR and quality management terms during 2011-2015 and 2016-2020; 2) a database search for clinical research studies using qMR IBs during 2016-2020; and 3) a survey to ascertain the current availability and quality management practices for clinical MRI scanners and associated equipment at research institutions across the UK. RESULTS The analysis showed increased use of all qMR methods between the periods 2011-2015 and 2016-2020 and diffusion-tensor MRI and volumetry to be popular methods. However, the "translation ratio" of journal articles to clinical research studies was higher for qMR methods that have evidence of clinical translation via a commercial route, such as fat fraction and T2 mapping. The number of journal articles citing quality management terms doubled between the periods 2011-2015 and 2016-2020; although, its proportion relative to all journal articles only increased by 3.0%. The survey suggested that quality assurance (QA) and quality control (QC) of data acquisition procedures are under-reported in the literature and that QA/QC of acquired data/data analysis are under-developed and lack consistency between institutions. CONCLUSIONS We summarise current attempts to standardise and translate qMR IBs, and conclude by outlining the ideal quality management practices and providing a gap analysis between current practice and a metrological standard.
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Affiliation(s)
| | - Sam Keaveney
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey SM2 5PT, UK; Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Eric O Aboagye
- Department of Surgery & Cancer, Division of Cancer, Imperial College London, W12 0NN London, UK
| | - Matt G Hall
- National Physical Laboratory, Hampton Road, Teddington TW11 0LW, UK
| | - Ross A Little
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PT, UK
| | - James P B O'Connor
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PT, UK; Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Geoff J M Parker
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, 90 High Holborn, London WC1V 6LJ, UK; Bioxydyn Ltd, Manchester M15 6SZ, UK
| | - John C Waterton
- Bioxydyn Ltd, Manchester M15 6SZ, UK; Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester M13 9PT, UK
| | - Jessica M Winfield
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey SM2 5PT, UK; Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Maite Jauregui-Osoro
- Department of Surgery & Cancer, Division of Cancer, Imperial College London, W12 0NN London, UK
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19
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Morgan CA, Roberts RP, Chaffey T, Tahara-Eckl L, van der Meer M, Günther M, Anderson TJ, Cutfield NJ, Dalrymple-Alford JC, Kirk IJ, Rose Addis D, Tippett LJ, Melzer TR. Reproducibility and repeatability of magnetic resonance imaging in dementia. Phys Med 2022; 101:8-17. [PMID: 35849909 DOI: 10.1016/j.ejmp.2022.06.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/09/2022] [Accepted: 06/27/2022] [Indexed: 01/01/2023] Open
Abstract
PURPOSE Individualised predictive models of cognitive decline require disease-monitoring markers that are repeatable. For wide-spread adoption, such markers also need to be reproducible at different locations. This study assessed the repeatability and reproducibility of MRI markers derived from a dementia protocol. METHODS Six participants were scanned at three different sites with a 3T MRI scanner. The protocol employed: T1-weighted (T1w) imaging, resting state functional MRI (rsfMRI), arterial spin labelling (ASL), diffusion-weighted imaging (DWI), T2-weighted fluid attenuation inversion recovery (FLAIR), T2-weighted (T2w) imaging, and susceptibility weighted imaging (SWI). Participants were scanned repeatedly, up to six times over a maximum period of five years. One participant was also scanned a further three times on sequential days on one scanner. Fifteen derived metrics were computed from the seven different modalities. RESULTS Reproducibility (coefficient of variation; CoV, across sites) was best for T1w derived grey matter, white matter and hippocampal volume (CoV < 1.5%), compared to rsfMRI and SWI derived metrics (CoV, 19% and 21%). For a given metric, long-term repeatability (CoV across time) was comparable to reproducibility, with short-term repeatability considerably better. CONCLUSIONS Reproducibility and repeatability were assessed for a suite of markers calculated from a dementia MRI protocol. In general, structural markers were less variable than functional MRI markers. Variability over time on the same scanner was comparable to variability measured across different scanners. Overall, the results support the viability of multi-site longitudinal studies for monitoring cognitive decline.
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Affiliation(s)
- Catherine A Morgan
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand; Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand; Centre for Advanced MRI, Auckland UniServices Limited, Auckland, New Zealand.
| | - Reece P Roberts
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand; Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand
| | - Tessa Chaffey
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Lenore Tahara-Eckl
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Meghan van der Meer
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Matthias Günther
- Fraunhofer Institute for Digital Medicine and University of Bremen, Bremen, Germany
| | - Timothy J Anderson
- Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand; Department of Medicine, University of Otago, Christchurch, New Zealand; NZ Brain Research Institute, Christchurch, New Zealand
| | - Nicholas J Cutfield
- Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand; Department of Medicine, University of Otago, Dunedin, New Zealand
| | - John C Dalrymple-Alford
- Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand; Department of Medicine, University of Otago, Christchurch, New Zealand; NZ Brain Research Institute, Christchurch, New Zealand; School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Ian J Kirk
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand; Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand
| | - Donna Rose Addis
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand; Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand; Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada; Department of Psychology, University of Toronto, Toronto, Canada
| | - Lynette J Tippett
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand; Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand
| | - Tracy R Melzer
- Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand; Department of Medicine, University of Otago, Christchurch, New Zealand; NZ Brain Research Institute, Christchurch, New Zealand; School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand
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20
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Teh I, Romero R. WA, Boyle J, Coll‐Font J, Dall'Armellina E, Ennis DB, Ferreira PF, Kalra P, Kolipaka A, Kozerke S, Lohr D, Mongeon F, Moulin K, Nguyen C, Nielles‐Vallespin S, Raterman B, Schreiber LM, Scott AD, Sosnovik DE, Stoeck CT, Tous C, Tunnicliffe EM, Weng AM, Croisille P, Viallon M, Schneider JE. Validation of cardiac diffusion tensor imaging sequences: A multicentre test-retest phantom study. NMR IN BIOMEDICINE 2022; 35:e4685. [PMID: 34967060 PMCID: PMC9285553 DOI: 10.1002/nbm.4685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 11/19/2021] [Accepted: 12/24/2021] [Indexed: 05/23/2023]
Abstract
Cardiac diffusion tensor imaging (DTI) is an emerging technique for the in vivo characterisation of myocardial microstructure, and there is a growing need for its validation and standardisation. We sought to establish the accuracy, precision, repeatability and reproducibility of state-of-the-art pulse sequences for cardiac DTI among 10 centres internationally. Phantoms comprising 0%-20% polyvinylpyrrolidone (PVP) were scanned with DTI using a product pulsed gradient spin echo (PGSE; N = 10 sites) sequence, and a custom motion-compensated spin echo (SE; N = 5) or stimulated echo acquisition mode (STEAM; N = 5) sequence suitable for cardiac DTI in vivo. A second identical scan was performed 1-9 days later, and the data were analysed centrally. The average mean diffusivities (MDs) in 0% PVP were (1.124, 1.130, 1.113) x 10-3 mm2 /s for PGSE, SE and STEAM, respectively, and accurate to within 1.5% of reference data from the literature. The coefficients of variation in MDs across sites were 2.6%, 3.1% and 2.1% for PGSE, SE and STEAM, respectively, and were similar to previous studies using only PGSE. Reproducibility in MD was excellent, with mean differences in PGSE, SE and STEAM of (0.3 ± 2.3, 0.24 ± 0.95, 0.52 ± 0.58) x 10-5 mm2 /s (mean ± 1.96 SD). We show that custom sequences for cardiac DTI provide accurate, precise, repeatable and reproducible measurements. Further work in anisotropic and/or deforming phantoms is warranted.
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Affiliation(s)
- Irvin Teh
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - William A. Romero R.
- Univ Lyon, INSA‐Lyon, Université Claude Bernard Lyon 1UJM‐Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F‐42023Saint EtienneFrance
| | - Jordan Boyle
- School of Mechanical EngineeringUniversity of LeedsLeedsUK
| | - Jaume Coll‐Font
- Cardiovascular Research Center and A. A. Martinos Center for Biomedical ImagingMassachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Erica Dall'Armellina
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Daniel B. Ennis
- Division of RadiologyVA Palo Alto Health Care SystemPalo AltoCaliforniaUSA
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
| | - Pedro F. Ferreira
- Cardiovascular Magnetic Resonance UnitThe Royal Brompton and Harefield NHS Foundation TrustLondonUK
- National Heart and Lung InstituteImperial College LondonLondonUK
| | - Prateek Kalra
- Department of RadiologyThe Ohio State University Wexner Medical CenterColumbusOhioUSA
| | - Arunark Kolipaka
- Department of RadiologyThe Ohio State University Wexner Medical CenterColumbusOhioUSA
| | - Sebastian Kozerke
- Institute for Biomedical EngineeringUniversity and ETH ZurichZurichSwitzerland
| | - David Lohr
- Department of Cardiovascular ImagingComprehensive Heart Failure CenterWürzburgGermany
| | | | - Kévin Moulin
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
| | - Christopher Nguyen
- Cardiovascular Research Center and A. A. Martinos Center for Biomedical ImagingMassachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Sonia Nielles‐Vallespin
- Cardiovascular Magnetic Resonance UnitThe Royal Brompton and Harefield NHS Foundation TrustLondonUK
- National Heart and Lung InstituteImperial College LondonLondonUK
| | - Brian Raterman
- Department of RadiologyThe Ohio State University Wexner Medical CenterColumbusOhioUSA
| | - Laura M. Schreiber
- Department of Cardiovascular ImagingComprehensive Heart Failure CenterWürzburgGermany
| | - Andrew D. Scott
- Cardiovascular Magnetic Resonance UnitThe Royal Brompton and Harefield NHS Foundation TrustLondonUK
- National Heart and Lung InstituteImperial College LondonLondonUK
| | - David E. Sosnovik
- Cardiovascular Research Center and A. A. Martinos Center for Biomedical ImagingMassachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Christian T. Stoeck
- Institute for Biomedical EngineeringUniversity and ETH ZurichZurichSwitzerland
| | - Cyril Tous
- Department of Radiology, Radiation‐Oncology and Nuclear Medicine and Institute of Biomedical EngineeringUniversité de MontréalMontréalCanada
| | - Elizabeth M. Tunnicliffe
- Radcliffe Department of MedicineUniversity of OxfordOxfordUK
- Oxford NIHR Biomedical Research CentreOxfordUK
| | - Andreas M. Weng
- Department of Diagnostic and Interventional RadiologyUniversity Hospital WürzburgWürzburgGermany
| | - Pierre Croisille
- Univ Lyon, INSA‐Lyon, Université Claude Bernard Lyon 1UJM‐Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F‐42023Saint EtienneFrance
| | - Magalie Viallon
- Univ Lyon, INSA‐Lyon, Université Claude Bernard Lyon 1UJM‐Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F‐42023Saint EtienneFrance
| | - Jürgen E. Schneider
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
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21
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De Stefano N, Battaglini M, Pareto D, Cortese R, Zhang J, Oesingmann N, Prados F, Rocca MA, Valsasina P, Vrenken H, Gandini Wheeler-Kingshott CAM, Filippi M, Barkhof F, Rovira À. MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies. Neuroimage Clin 2022; 34:102972. [PMID: 35245791 PMCID: PMC8892169 DOI: 10.1016/j.nicl.2022.102972] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 02/22/2022] [Accepted: 02/23/2022] [Indexed: 11/24/2022]
Abstract
Sharing data from cooperative studies is essential to develop new biomarkers in MS. Differences in MRI acquisition, analysis, storage represent a substantial constraint. We review the state of the art and developments in the harmonization of MRI. We provide recommendations to harmonize large MRI datasets in the MS field.
There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resources.
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Affiliation(s)
- Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy.
| | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Rosa Cortese
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Jian Zhang
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | | | - Ferran Prados
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, UCL, London, WC1V 6LJ, United Kingdom; e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Hugo Vrenken
- Amsterdam Neuroscience, MS Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Brain MRI 3T Research Center, C. Mondino National Neurological Institute, Pavia, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy; Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Frederik Barkhof
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, UCL, London, WC1V 6LJ, United Kingdom; Amsterdam Neuroscience, MS Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
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22
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Moon JY, Mukherjee P, Madduri RK, Markowitz AJ, Cai LT, Palacios EM, Manley GT, Bremer PT. The Case for Optimized Edge-Centric Tractography at Scale. Front Neuroinform 2022; 16:752471. [PMID: 35651721 PMCID: PMC9148990 DOI: 10.3389/fninf.2022.752471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
The anatomic validity of structural connectomes remains a significant uncertainty in neuroimaging. Edge-centric tractography reconstructs streamlines in bundles between each pair of cortical or subcortical regions. Although edge bundles provides a stronger anatomic embedding than traditional connectomes, calculating them for each region-pair requires exponentially greater computation. We observe that major speedup can be achieved by reducing the number of streamlines used by probabilistic tractography algorithms. To ensure this does not degrade connectome quality, we calculate the identifiability of edge-centric connectomes between test and re-test sessions as a proxy for information content. We find that running PROBTRACKX2 with as few as 1 streamline per voxel per region-pair has no significant impact on identifiability. Variation in identifiability caused by streamline count is overshadowed by variation due to subject demographics. This finding even holds true in an entirely different tractography algorithm using MRTrix. Incidentally, we observe that Jaccard similarity is more effective than Pearson correlation in calculating identifiability for our subject population.
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Affiliation(s)
- Joseph Y. Moon
- Lawrence Livermore National Laboratory, Livermore, CA, United States
- *Correspondence: Joseph Y. Moon
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
- Pratik Mukherjee
| | | | - Amy J. Markowitz
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Lanya T. Cai
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Eva M. Palacios
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Geoffrey T. Manley
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Peer-Timo Bremer
- Lawrence Livermore National Laboratory, Livermore, CA, United States
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23
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Nigri A, Ferraro S, Gandini Wheeler-Kingshott CAM, Tosetti M, Redolfi A, Forloni G, D'Angelo E, Aquino D, Biagi L, Bosco P, Carne I, De Francesco S, Demichelis G, Gianeri R, Lagana MM, Micotti E, Napolitano A, Palesi F, Pirastru A, Savini G, Alberici E, Amato C, Arrigoni F, Baglio F, Bozzali M, Castellano A, Cavaliere C, Contarino VE, Ferrazzi G, Gaudino S, Marino S, Manzo V, Pavone L, Politi LS, Roccatagliata L, Rognone E, Rossi A, Tonon C, Lodi R, Tagliavini F, Bruzzone MG. Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN-Neuroimaging Network. Front Neurol 2022; 13:855125. [PMID: 35493836 PMCID: PMC9047871 DOI: 10.3389/fneur.2022.855125] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 03/17/2022] [Indexed: 11/13/2022] Open
Abstract
Neuroimaging studies often lack reproducibility, one of the cardinal features of the scientific method. Multisite collaboration initiatives increase sample size and limit methodological flexibility, therefore providing the foundation for increased statistical power and generalizable results. However, multisite collaborative initiatives are inherently limited by hardware, software, and pulse and sequence design heterogeneities of both clinical and preclinical MRI scanners and the lack of benchmark for acquisition protocols, data analysis, and data sharing. We present the overarching vision that yielded to the constitution of RIN-Neuroimaging Network, a national consortium dedicated to identifying disease and subject-specific in-vivo neuroimaging biomarkers of diverse neurological and neuropsychiatric conditions. This ambitious goal needs efforts toward increasing the diagnostic and prognostic power of advanced MRI data. To this aim, 23 Italian Scientific Institutes of Hospitalization and Care (IRCCS), with technological and clinical specialization in the neurological and neuroimaging field, have gathered together. Each IRCCS is equipped with high- or ultra-high field MRI scanners (i.e., ≥3T) for clinical or preclinical research or has established expertise in MRI data analysis and infrastructure. The actions of this Network were defined across several work packages (WP). A clinical work package (WP1) defined the guidelines for a minimum standard clinical qualitative MRI assessment for the main neurological diseases. Two neuroimaging technical work packages (WP2 and WP3, for clinical and preclinical scanners) established Standard Operative Procedures for quality controls on phantoms as well as advanced harmonized quantitative MRI protocols for studying the brain of healthy human participants and wild type mice. Under FAIR principles, a web-based e-infrastructure to store and share data across sites was also implemented (WP4). Finally, the RIN translated all these efforts into a large-scale multimodal data collection in patients and animal models with dementia (i.e., case study). The RIN-Neuroimaging Network can maximize the impact of public investments in research and clinical practice acquiring data across institutes and pathologies with high-quality and highly-consistent acquisition protocols, optimizing the analysis pipeline and data sharing procedures.
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Affiliation(s)
- Anna Nigri
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Stefania Ferraro
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Claudia A. M. Gandini Wheeler-Kingshott
- Unità di Neuroradiologia, IRCCS Mondino Foundation, Pavia, Italy
- NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Michela Tosetti
- Medical Physics and MR Lab, Fondazione IRCCS Stella Maris, Pisa, Italy
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Gianluigi Forloni
- Medical Physics and MR Lab, Fondazione IRCCS Stella Maris, Pisa, Italy
| | - Egidio D'Angelo
- Unità di Neuroradiologia, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Domenico Aquino
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Laura Biagi
- Medical Physics and MR Lab, Fondazione IRCCS Stella Maris, Pisa, Italy
| | - Paolo Bosco
- Medical Physics and MR Lab, Fondazione IRCCS Stella Maris, Pisa, Italy
| | - Irene Carne
- Neuroradiology Unit, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Silvia De Francesco
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Greta Demichelis
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Ruben Gianeri
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | | | - Edoardo Micotti
- Laboratory of Biology of Neurodegenerative Disorders, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Antonio Napolitano
- Medical Physics, IRCCS Istituto Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Fulvia Palesi
- Unità di Neuroradiologia, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | | | - Giovanni Savini
- Neuroradiology Unit, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Elisa Alberici
- Neuroradiology Unit, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Carmelo Amato
- Unit of Neuroradiology, Oasi Research Institute-IRCCS, Troina, Italy
| | - Filippo Arrigoni
- Neuroimaging Unit, Scientific Institute, IRCCS E. Medea, Bosisio Parini, Italy
| | | | - Marco Bozzali
- Neuroimaging Laboratory, Santa Lucia Foundation, IRCCS, Rome, Italy
| | | | | | - Valeria Elisa Contarino
- Unità di Neuroradiologia, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | | | - Simona Gaudino
- Istituto di Radiologia, UOC Radiologia e Neuroradiologia, IRCCS Fondazione Policlinico Universitario Agostino Gemelli, Rome, Italy
| | - Silvia Marino
- IRCCS Centro Neurolesi “Bonino-Pulejo”, Messina, Italy
| | - Vittorio Manzo
- Department of Radiology, Istituto Auxologico Italiano, IRCCS, Milan, Italy
| | | | - Letterio S. Politi
- Neuroradiology Unit, IRCCS Humanitas Research Hospital, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Luca Roccatagliata
- Neuroradiologia IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Dipartimento di Scienze della Salute Università di Genova, Genoa, Italy
| | - Elisa Rognone
- Unità di Neuroradiologia, IRCCS Mondino Foundation, Pavia, Italy
| | - Andrea Rossi
- Dipartimento di Scienze della Salute Università di Genova, Genoa, Italy
- UO Neuroradiologia, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Caterina Tonon
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Raffaele Lodi
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Fabrizio Tagliavini
- Scientific Direction, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Maria Grazia Bruzzone
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
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Carr ME, Keenan KE, Rai R, Boss MA, Metcalfe P, Walker A, Holloway L. Conformance of a 3T Radiotherapy MRI Scanner to the QIBA Diffusion Profile. Med Phys 2022; 49:4508-4517. [PMID: 35365884 PMCID: PMC9543906 DOI: 10.1002/mp.15645] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 03/12/2022] [Accepted: 03/16/2022] [Indexed: 11/11/2022] Open
Abstract
Purpose To assess the technical performance of the apparent diffusion coefficient (ADC) on a dedicated 3T radiotherapy scanner, using a standardized phantom and sequences. Investigations into factors that could impact the technical performance of ADC in the clinic were also completed, including changing the slice‐encoded imaging direction and the reference sample ADC value. Methods ADC acquisitions were performed monthly on an isotropic diffusion phantom over 1 year. Measurements of ADC %bias, coefficients of variation for short‐/long‐term repeatability and precision (CVST/CVLT and CVP), and b‐value dependency (Depb) were calculated. The measurements were then assessed according to the Quantitative Imaging Biomarker Alliance (QIBA) Diffusion Profile specifications. Results The average of all measurements over the year was within Profile recommended ranges. This included when testing was performed in different imaging directions, and on samples that had different ADC reference values (0.4–1.1 μm2/ms). Results in the axial plane for the central water vial included a bias of +0.05%, CVST /CVLT/CVP = 0.1%/ 0.9%/0.4% and Depb = 0.4%. Conclusions The technical performance of ADC on a radiotherapy dedicated MRI scanner over the course of 12 months was considered conformant to the QIBA Profile. Quantifying these metrics and factors that may affect the performance is essential in progressing the use of ADC clinically: ensuring that the observed change of ADC in a tissue is due to a physiological response and not measurement variability.
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Affiliation(s)
- Madeline E Carr
- Centre for Medical and Radiation Physics, University of Wollongong, Wollongong, Australia.,Ingham Institute for Applied Medical Research, Liverpool, Australia.,Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - Kathryn E Keenan
- National Institute of Standards and Technology, Boulder, United States
| | - Robba Rai
- Ingham Institute for Applied Medical Research, Liverpool, Australia.,Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia.,Institute of Medical Physics, University of Sydney, Camperdown, Australia
| | - Michael A Boss
- American College of Radiology, Philadelphia, United States
| | - Peter Metcalfe
- Centre for Medical and Radiation Physics, University of Wollongong, Wollongong, Australia.,Ingham Institute for Applied Medical Research, Liverpool, Australia
| | - Amy Walker
- Centre for Medical and Radiation Physics, University of Wollongong, Wollongong, Australia.,Ingham Institute for Applied Medical Research, Liverpool, Australia.,Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia.,Institute of Medical Physics, University of Sydney, Camperdown, Australia
| | - Lois Holloway
- Centre for Medical and Radiation Physics, University of Wollongong, Wollongong, Australia.,Ingham Institute for Applied Medical Research, Liverpool, Australia.,Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia.,Institute of Medical Physics, University of Sydney, Camperdown, Australia.,South Western Sydney Clinical School, University of New South Wales, Liverpool, Australia
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25
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Subashi E, Dresner A, Tyagi N. Longitudinal assessment of quality assurance measurements in a 1.5 T MR-linac: Part II-Magnetic resonance imaging. J Appl Clin Med Phys 2022; 23:e13586. [PMID: 35332990 PMCID: PMC9398228 DOI: 10.1002/acm2.13586] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 02/05/2022] [Accepted: 02/25/2022] [Indexed: 11/08/2022] Open
Abstract
PURPOSE To describe and report longitudinal quality assurance (QA) measurements for the magnetic resonance imaging (MRI) component of the Elekta Unity MR-linac during the first year of clinical use in our institution. MATERIALS AND METHODS The performance of the MRI component of Unity was evaluated with daily, weekly, monthly, and annual QA testing. The measurements monitor image uniformity, signal-to-noise ratio (SNR), resolution/detectability, slice position/thickness, linearity, central frequency, and geometric accuracy. In anticipation of routine use of quantitative imaging (qMRI), we characterize B0/B1 uniformity and the bias/reproducibility of longitudinal/transverse relaxation times (T1/T2) and apparent diffusion coefficient (ADC). Tolerance levels for QA measurements of qMRI biomarkers are derived from weekly monitoring of T1, T2, and ADC. RESULTS The 1-year assessment of QA measurements shows that daily variations in each MR quality metric are well below the threshold for failure. Routine testing procedures can reproducibly identify machine issues. The longitudinal three-dimensional (3D) geometric analysis reveals that the maximum distortion in a diameter of spherical volume (DSV) of 20, 30, 40, and 50 cm is 0.4, 0.6, 1.0, and 3.1 mm, respectively. The main source of distortion is gradient nonlinearity. Maximum peak-to-peak B0 inhomogeneity is 3.05 ppm, with gantry induced B0 inhomogeneities an order of magnitude smaller. The average deviation from the nominal B1 is within 2%, with minimal dependence on gantry angle. Mean ADC, T1, and T2 values are measured with high reproducibility. The median coefficient of variation for ADC, T1, and T2 is 1.3%, 1.1%, and 0.5%, respectively. The median bias for ADC, T1, and T2 is -0.8%, -0.1%, and 3.9%, respectively. CONCLUSION The MRI component of Unity operates within the guidelines and recommendations for scanner performance and stability. Our findings support the recently published guidance in establishing clinically acceptable tolerance levels for image quality. Highly reproducible qMRI measurements are feasible in Unity.
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Affiliation(s)
- Ergys Subashi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Alex Dresner
- Philips Healthcare MR Oncology, Cleveland, Ohio, USA
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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26
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Amouzandeh G, Chenevert TL, Swanson SD, Ross BD, Malyarenko DI. Technical Note: Temperature and concentration dependence of water diffusion in polyvinylpyrrolidone solutions. Med Phys 2022; 49:3325-3332. [PMID: 35184316 PMCID: PMC9090959 DOI: 10.1002/mp.15556] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/27/2022] [Accepted: 02/08/2022] [Indexed: 11/26/2022] Open
Abstract
Objective The goal of this work is to provide temperature and concentration calibration of water diffusivity in polyvinylpyrrolidone (PVP) solutions used in phantoms to assess system bias and linearity in apparent diffusion coefficient (ADC) measurements. Method ADC measurements were performed for 40 kDa (K40) PVP of six concentrations (0%, 10%, 20%, 30%, 40%, and 50% by weight) at three temperatures (19.5°C, 22.5°C, and 26.4°C), with internal phantom temperature monitored by optical thermometer (±0.2°C). To achieve ADC measurement and fit accuracy of better than 0.5%, three orthogonal diffusion gradients were calibrated using known water diffusivity at 0°C and system gradient nonlinearity maps. Noise‐floor fit bias was also controlled by limiting the maximum b‐value used for ADC calculation of each sample. The ADC temperature dependence was modeled by Arrhenius functions of each PVP concentration. The concentration dependence was modeled by quadratic function for ADC normalized by the theoretical water diffusion values. Calibration coefficients were obtained from linear regression model fits. Results Measured phantom ADC values increased with temperature and decreasing PVP concentration, [PVP]. The derived Arrhenius model parameters for [PVP] between 0% and 50%, are reported and can be used for K40 ADC temperature calibration with absolute ADC error within ±0.016 μm2/ms. Arrhenius model fit parameters normalized to water value scaled with [PVP] between 10% and 40%, and proportional change in activation energy increased faster than collision frequency. ADC normalization by water diffusivity, DW, from the Speedy–Angell relation accounted for the bulk of temperature dependence (±0.035 μm2/ms) and yielded quadratic calibration for ADCPVP/DW = (12.5 ± 0.7) ·10−5·[PVP]2 − (23.2 ± 0.3)·10−3·[PVP]+1, nearly independent of PVP molecular weight and temperature. Conclusion The study provides ground‐truth ADC values for K40 PVP solutions commonly used in diffusion phantoms for scanning at ambient room temperature. The described procedures and the reported calibration can be used for quality control and standardization of measured ADC values of PVP at different concentrations and temperatures.
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Affiliation(s)
| | | | | | - Brian D. Ross
- Department of Radiology University of Michigan Ann Arbor MI USA
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27
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Examining brain white matter after pediatric mild traumatic brain injury using neurite orientation dispersion and density imaging: An A-CAP study. Neuroimage Clin 2021; 32:102887. [PMID: 34911193 PMCID: PMC8633364 DOI: 10.1016/j.nicl.2021.102887] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 10/26/2021] [Accepted: 11/16/2021] [Indexed: 12/04/2022]
Abstract
We examined white matter microstructure after pediatric mTBI using NODDI and DTI. Children with mTBI did not significantly differ from those with OI on any metrics. Minor alterations, if any, may be present in children at the post-acute stage after mTBI. Large longitudinal studies are needed to understand long-term brain changes post injury.
Background Pediatric mild traumatic brain injury (mTBI) affects millions of children annually. Diffusion tensor imaging (DTI) is sensitive to axonal injuries and white matter microstructure and has been used to characterize the brain changes associated with mild traumatic brain injury (mTBI). Neurite orientation dispersion and density imaging (NODDI) is a diffusion model that can provide additional insight beyond traditional DTI metrics, but has not been examined in pediatric mTBI. The goal of this study was to employ DTI and NODDI to gain added insight into white matter alterations in children with mTBI compared to children with mild orthopedic injury (OI). Methods Children (mTBI n = 320, OI n = 176) aged 8–16.99 years (12.39 ± 2.32 years) were recruited from emergency departments at five hospitals across Canada and underwent 3 T MRI on average 11 days post-injury. DTI and NODDI metrics were calculated for seven major white matter tracts and compared between groups using univariate analysis of covariance controlling for age, sex, and scanner type. False discovery rate (FDR) was used to correct for multiple comparisons. Results Univariate analysis revealed no significant group main effects or interactions in DTI or NODDI metrics. Fractional anisotropy and neurite density index in all tracts exhibited a significant positive association with age and mean diffusivity in all tracts exhibited a significant negative association with age in the whole sample. Conclusions Overall, there were no significant differences between mTBI and OI groups in brain white matter microstructure from either DTI or NODDI in the seven tracts. This indicates that mTBI is associated with relatively minor white matter differences, if any, at the post-acute stage. Brain differences may evolve at later stages of injury, so longitudinal studies with long-term follow-up are needed.
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28
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Cai LY, Yang Q, Kanakaraj P, Nath V, Newton AT, Edmonson HA, Luci J, Conrad BN, Price GR, Hansen CB, Kerley CI, Ramadass K, Yeh FC, Kang H, Garyfallidis E, Descoteaux M, Rheault F, Schilling KG, Landman BA. MASiVar: Multisite, multiscanner, and multisubject acquisitions for studying variability in diffusion weighted MRI. Magn Reson Med 2021; 86:3304-3320. [PMID: 34270123 PMCID: PMC9087815 DOI: 10.1002/mrm.28926] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 06/21/2021] [Accepted: 06/23/2021] [Indexed: 12/18/2022]
Abstract
PURPOSE Diffusion-weighted imaging allows investigators to identify structural, microstructural, and connectivity-based differences between subjects, but variability due to session and scanner biases is a challenge. METHODS To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de-identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi-compartment neurite orientation dispersion and density model, (3) white-matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region-wise fractional anisotropy, mean diffusivity, and principal eigenvector; region-wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle-wise shape, volume, fractional anisotropy, and length; and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length. RESULTS We plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability. CONCLUSIONS This study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects.
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Affiliation(s)
- Leon Y. Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Qi Yang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Praitayini Kanakaraj
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Vishwesh Nath
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Allen T. Newton
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
| | | | - Jeffrey Luci
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, USA
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, New Jersey, USA
| | - Benjamin N. Conrad
- Neuroscience Graduate Program, Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, Tennessee, USA
| | - Gavin R. Price
- Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, Tennessee, USA
| | - Colin B. Hansen
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Cailey I. Kerley
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Karthik Ramadass
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - Maxime Descoteaux
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Francois Rheault
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Kurt G. Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Bennett A. Landman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
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29
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de Brito Robalo BM, Biessels GJ, Chen C, Dewenter A, Duering M, Hilal S, Koek HL, Kopczak A, Yin Ka Lam B, Leemans A, Mok V, Onkenhout LP, van den Brink H, de Luca A. Diffusion MRI harmonization enables joint-analysis of multicentre data of patients with cerebral small vessel disease. Neuroimage Clin 2021; 32:102886. [PMID: 34911192 PMCID: PMC8609094 DOI: 10.1016/j.nicl.2021.102886] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 11/16/2021] [Indexed: 01/10/2023]
Abstract
OBJECTIVES Acquisition-related differences in diffusion magnetic resonance imaging (dMRI) hamper pooling of multicentre data to achieve large sample sizes. A promising solution is to harmonize the raw diffusion signal using rotation invariant spherical harmonic (RISH) features, but this has not been tested in elderly subjects. Here we aimed to establish if RISH harmonization effectively removes acquisition-related differences in multicentre dMRI of elderly subjects with cerebral small vessel disease (SVD), while preserving sensitivity to disease effects. METHODS Five cohorts of patients with SVD (N = 397) and elderly controls (N = 175) with 3 Tesla MRI on different systems were included. First, to establish effectiveness of harmonization, the RISH method was trained with data of 13 to 15 age and sex-matched controls from each site. Fractional anisotropy (FA) and mean diffusivity (MD) were compared in matched controls between sites using tract-based spatial statistics (TBSS) and voxel-wise analysis, before and after harmonization. Second, to assess sensitivity to disease effects, we examined whether the contrast (effect sizes of FA, MD and peak width of skeletonized MD - PSMD) between patients and controls within each site remained unaffected by harmonization. Finally, we evaluated the association between white matter hyperintensity (WMH) burden, FA, MD and PSMD using linear regression analyses both within individual cohorts as well as with pooled scans from multiple sites, before and after harmonization. RESULTS Before harmonization, significant differences in FA and MD were observed between matched controls of different sites (p < 0.05). After harmonization these site-differences were removed. Within each site, RISH harmonization did not alter the effect sizes of FA, MD and PSMD between patients and controls (relative change in Cohen's d = 4 %) nor the strength of association with WMH volume (relative change in R2 = 2.8 %). After harmonization, patient data of all sites could be aggregated in a single analysis to infer the association between WMH volume and FA (R2 = 0.62), MD (R2 = 0.64), and PSMD (R2 = 0.60). CONCLUSIONS We showed that RISH harmonization effectively removes acquisition-related differences in dMRI of elderly subjects while preserving sensitivity to SVD-related effects. This study provides proof of concept for future multicentre SVD studies with pooled datasets.
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Affiliation(s)
- Bruno M de Brito Robalo
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands; Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Christopher Chen
- Memory, Aging and Cognition Center, Department of Pharmacology, National University of Singapore, Singapore.
| | - Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany.
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany; Medical Image Analysis Center (MIAC AG) and qbig, Department of Biomedical Engineering, University of Basel, Basel, Switzerland.
| | - Saima Hilal
- Memory, Aging and Cognition Center, Department of Pharmacology, National University of Singapore, Singapore.
| | - Huiberdina L Koek
- Department of Geriatric Medicine, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Anna Kopczak
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany.
| | - Bonnie Yin Ka Lam
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region.
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Vincent Mok
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region.
| | - Laurien P Onkenhout
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Hilde van den Brink
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Alberto de Luca
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands; Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
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Same Brain, Different Look?-The Impact of Scanner, Sequence and Preprocessing on Diffusion Imaging Outcome Parameters. J Clin Med 2021; 10:jcm10214987. [PMID: 34768507 PMCID: PMC8584364 DOI: 10.3390/jcm10214987] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 10/21/2021] [Accepted: 10/23/2021] [Indexed: 11/17/2022] Open
Abstract
In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or mask pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from, e.g., diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19–54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyrafit). After different preprocessing steps, fractional anisotropy (FA) and mean diffusivity (MD) maps, obtained by tensor fitting, were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Skeletonised MD values differed up to 14% between scanners. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability.
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31
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Graham NSN, Zimmerman KA, Moro F, Heslegrave A, Maillard SA, Bernini A, Miroz JP, Donat CK, Lopez MY, Bourke N, Jolly AE, Mallas EJ, Soreq E, Wilson MH, Fatania G, Roi D, Patel MC, Garbero E, Nattino G, Baciu C, Fainardi E, Chieregato A, Gradisek P, Magnoni S, Oddo M, Zetterberg H, Bertolini G, Sharp DJ. Axonal marker neurofilament light predicts long-term outcomes and progressive neurodegeneration after traumatic brain injury. Sci Transl Med 2021; 13:eabg9922. [PMID: 34586833 DOI: 10.1126/scitranslmed.abg9922] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Neil S N Graham
- Department of Brain Sciences, Imperial College London, London W12 0NN, UK.,UK DRI Centre for Care Research and Technology, Imperial College London, London W12 0BZ, UK
| | - Karl A Zimmerman
- Department of Brain Sciences, Imperial College London, London W12 0NN, UK.,UK DRI Centre for Care Research and Technology, Imperial College London, London W12 0BZ, UK
| | - Federico Moro
- Laboratory of Acute Brain Injury and Therapeutic Strategies, Department of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo 24126, Italy.,Fondazione IRCCS, Ca' Granda Ospedale Maggiore Policlinico, Dipartimento di Anestesia e Rianimazione, 20122, Milan, Italy
| | - Amanda Heslegrave
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK.,UK Dementia Research Institute at UCL, London WC1N 3BG, UK
| | - Samia Abed Maillard
- Neuroscience Critical Care Research Group, Department of Intensive Care Medicine, CHUV Lausanne University Hospital and University of Lausanne, Lausanne 1011, Switzerland
| | - Adriano Bernini
- Neuroscience Critical Care Research Group, Department of Intensive Care Medicine, CHUV Lausanne University Hospital and University of Lausanne, Lausanne 1011, Switzerland
| | - John-Paul Miroz
- Neuroscience Critical Care Research Group, Department of Intensive Care Medicine, CHUV Lausanne University Hospital and University of Lausanne, Lausanne 1011, Switzerland
| | - Cornelius K Donat
- Department of Brain Sciences, Imperial College London, London W12 0NN, UK
| | - Maria Yanez Lopez
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
| | - Niall Bourke
- Department of Brain Sciences, Imperial College London, London W12 0NN, UK.,UK DRI Centre for Care Research and Technology, Imperial College London, London W12 0BZ, UK
| | - Amy E Jolly
- Department of Brain Sciences, Imperial College London, London W12 0NN, UK.,UK DRI Centre for Care Research and Technology, Imperial College London, London W12 0BZ, UK
| | - Emma-Jane Mallas
- Department of Brain Sciences, Imperial College London, London W12 0NN, UK.,UK DRI Centre for Care Research and Technology, Imperial College London, London W12 0BZ, UK
| | - Eyal Soreq
- Department of Brain Sciences, Imperial College London, London W12 0NN, UK.,UK DRI Centre for Care Research and Technology, Imperial College London, London W12 0BZ, UK
| | - Mark H Wilson
- Department of Neurosurgery, Imperial College Healthcare NHS Trust, London W6 8RF, UK.,Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK
| | - Gavin Fatania
- Department of Imaging, Imperial College Healthcare NHS Trust, London W6 8RF, UK
| | - Dylan Roi
- Department of Imaging, Imperial College Healthcare NHS Trust, London W6 8RF, UK
| | - Maneesh C Patel
- Department of Imaging, Imperial College Healthcare NHS Trust, London W6 8RF, UK
| | - Elena Garbero
- Laboratory of Clinical Epidemiology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo 24126, Italy
| | - Giovanni Nattino
- Laboratory of Clinical Epidemiology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo 24126, Italy
| | - Camelia Baciu
- Neurorianimazione, ASST Grande Ospedale Metropolitano Niguarda, Milano 20162, Italy
| | - Enrico Fainardi
- Department of Experimental and Clinical Sciences, Careggi University Hospital, University of Firenze, Florence 50139, Italy
| | - Arturo Chieregato
- Neurorianimazione, ASST Grande Ospedale Metropolitano Niguarda, Milano 20162, Italy
| | - Primoz Gradisek
- Clinical Department of Anaesthesiology and Intensive Therapy, University Medical Center, Ljubljana 1000, Slovenia
| | - Sandra Magnoni
- Department of Anesthesia and Intensive Care, Santa Chiara Hospital, Trento 38122, Italy
| | - Mauro Oddo
- Neuroscience Critical Care Research Group, Department of Intensive Care Medicine, CHUV Lausanne University Hospital and University of Lausanne, Lausanne 1011, Switzerland.,Medical Direction, CHUV Lausanne University Hospital, Lausanne 1011, Switzerland
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Mölndal 431 41, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal 431 41, Sweden
| | - Guido Bertolini
- Laboratory of Clinical Epidemiology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo 24126, Italy
| | - David J Sharp
- Department of Brain Sciences, Imperial College London, London W12 0NN, UK.,UK DRI Centre for Care Research and Technology, Imperial College London, London W12 0BZ, UK.,Centre for Injury Studies, Imperial College London, London SW7 2AZ, UK
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Huie JR, Mondello S, Lindsell CJ, Antiga L, Yuh EL, Zanier ER, Masson S, Rosario BL, Ferguson AR. Biomarkers for Traumatic Brain Injury: Data Standards and Statistical Considerations. J Neurotrauma 2021; 38:2514-2529. [PMID: 32046588 PMCID: PMC8403188 DOI: 10.1089/neu.2019.6762] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Recent biomarker innovations hold potential for transforming diagnosis, prognostic modeling, and precision therapeutic targeting of traumatic brain injury (TBI). However, many biomarkers, including brain imaging, genomics, and proteomics, involve vast quantities of high-throughput and high-content data. Management, curation, analysis, and evidence synthesis of these data are not trivial tasks. In this review, we discuss data management concepts and statistical and data sharing strategies when dealing with biomarker data in the context of TBI research. We propose that application of biomarkers involves three distinct steps-discovery, evaluation, and evidence synthesis. First, complex/big data has to be reduced to useful data elements at the stage of biomarker discovery. Second, inferential statistical approaches must be applied to these biomarker data elements for assessment of biomarker clinical utility and validity. Last, synthesis of relevant research is required to support practice guidelines and enable health decisions informed by the highest quality, up-to-date evidence available. We focus our discussion around recent experiences from the International Traumatic Brain Injury Research (InTBIR) initiative, with a specific focus on four major clinical projects (Transforming Research and Clinical Knowledge in TBI, Collaborative European NeuroTrauma Effectiveness Research in TBI, Collaborative Research on Acute Traumatic Brain Injury in Intensive Care Medicine in Europe, and Approaches and Decisions in Acute Pediatric TBI Trial), which are currently enrolling subjects in North America and Europe. We discuss common data elements, data collection efforts, data-sharing opportunities, and challenges, as well as examine the statistical techniques required to realize successful adoption and use of biomarkers in the clinic as a foundation for precision medicine in TBI.
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Affiliation(s)
- J. Russell Huie
- Brain and Spinal Injury Center, Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
| | - Stefania Mondello
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy
| | - Christopher J. Lindsell
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - Esther L. Yuh
- Brain and Spinal Injury Center, Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
| | - Elisa R. Zanier
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Serge Masson
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Bedda L. Rosario
- Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania, USA
| | - Adam R. Ferguson
- Brain and Spinal Injury Center, Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
- San Francisco Veterans Affairs Medical Center (SFVAMC), San Francisco, California, USA
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Pang Y, Malyarenko DI, Amouzandeh G, Barberi E, Cole M, Vom Endt A, Peeters J, Tan ET, Chenevert TL. Empirical validation of gradient field models for an accurate ADC measured on clinical 3T MR systems in body oncologic applications. Phys Med 2021; 86:113-120. [PMID: 34107440 PMCID: PMC8268998 DOI: 10.1016/j.ejmp.2021.05.030] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 04/28/2021] [Accepted: 05/21/2021] [Indexed: 12/20/2022] Open
Abstract
PURPOSE To empirically corroborate vendor-provided gradient nonlinearity (GNL) characteristics and demonstrate efficient GNL bias correction for human brain apparent diffusion coefficient (ADC) across 3T MR systems and spatial locations. METHODS Spatial distortion vector fields (DVF) were mapped in 3D using a surface fiducial array phantom for individual gradient channels on three 3T MR platforms from different vendors. Measured DVF were converted into empirical 3D GNL tensors and compared with their theoretical counterparts derived from vendor-provided spherical harmonic (SPH) coefficients. To illustrate spatial impact of GNL on ADC, diffusion weighted imaging using three orthogonal gradient directions was performed on a volunteer brain positioned at isocenter (as a reference) and offset superiorly by 10-17 cm (>10% predicted GNL bias). The SPH tensor-based GNL correction was applied to individual DWI gradient directions, and derived ADC was compared with low-bias reference for human brain white matter (WM) ROIs. RESULTS Empiric and predicted GNL errors were comparable for all three studied 3T MR systems, with <1.0% differences in the median and width of spatial histograms for individual GNL tensor elements. Median (±width) of ADC (10-3mm2/s) histograms measured at isocenter in WM reference ROIs from three MR systems were: 0.73 ± 0.11, 0.71 ± 0.14, 0.74 ± 0.17, and at off-isocenters (before versus after GNL correction) were respectively 0.63 ± 0.14 versus 0.72 ± 0.11, 0.53 ± 0.16 versus 0.74 ± 0.18, and 0.65 ± 0.16 versus 0.76 ± 0.18. CONCLUSION The phantom-based spatial distortion measurements validated vendor-provided gradient fields, and accurate WM ADC was recovered regardless of spatial locations and clinical MR platforms using system-specific tensor-based GNL correction for routine DWI.
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Affiliation(s)
- Yuxi Pang
- Radiology, University of Michigan, Ann Arbor, MI, United States.
| | | | | | - Enzo Barberi
- Modus Medical Devices Inc., London, ON, CA, Canada
| | - Michael Cole
- Modus Medical Devices Inc., London, ON, CA, Canada
| | | | | | - Ek T Tan
- Radiology and Imaging, Hospital for Special Surgery, New York, NY, United States
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Di Sapia R, Moro F, Montanarella M, Iori V, Micotti E, Tolomeo D, Wang KKW, Vezzani A, Ravizza T, Zanier ER. In-depth characterization of a mouse model of post-traumatic epilepsy for biomarker and drug discovery. Acta Neuropathol Commun 2021; 9:76. [PMID: 33902685 PMCID: PMC8073903 DOI: 10.1186/s40478-021-01165-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 03/19/2021] [Indexed: 12/11/2022] Open
Abstract
Post-traumatic epilepsy (PTE) accounts for 5% of all epilepsies and 10–20% of the acquired forms. The latency between traumatic brain injury (TBI) and epilepsy onset in high-risk patients offers a therapeutic window for intervention to prevent or improve the disease course. However, progress towards effective treatments has been hampered by the lack of sensitive prognostic biomarkers of PTE, and of therapeutic targets. There is therefore a pressing clinical need for preclinical PTE models suitable for biomarker discovery and drug testing. We characterized in-depth a model of severe TBI induced by controlled cortical impact evolving into PTE in CD1 adult male mice. To identify sensitive measures predictive of PTE development and severity, TBI mice were longitudinally monitored by video-electrocorticography (ECoG), examined by MRI, and tested for sensorimotor and cognitive deficits and locomotor activity. At the end of the video-ECoG recording mice were killed for brain histological analysis. PTE occurred in 58% of mice with frequent motor seizures (one seizure every other day), as determined up to 5 months post-TBI. The weight loss of PTE mice in 1 week after TBI correlated with the number of spontaneous seizures at 5 months. Moreover, the recovery rate of the sensorimotor deficit detected by the SNAP test before the predicted time of epilepsy onset was significantly lower in PTE mice than in those without epilepsy. Neuroscore, beam walk and cognitive deficit were similar in all TBI mice. The increase in the contusion volume, the volume of forebrain regions contralateral to the lesioned hemisphere and white matter changes over time assessed by MRI were similar in PTE and no-PTE mice. However, brain histology showed a more pronounced neuronal cell loss in the cortex and hippocampus contralateral to the injured hemisphere in PTE than in no-PTE mice. The extensive functional and neuropathological characterization of this TBI model, provides a tool to identify sensitive measures of epilepsy development and severity clinically useful for increasing PTE prediction in high-risk TBI patients. The high PTE incidence and spontaneous seizures frequency in mice provide an ideal model for biomarker discovery and for testing new drugs.
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Boursianis T, Kalaitzakis G, Pappas E, Karantanas AH, Maris TG. MRI diffusion phantoms: ADC and relaxometric measurement comparisons between polyacrylamide and agarose gels. Eur J Radiol 2021; 139:109696. [PMID: 33865063 DOI: 10.1016/j.ejrad.2021.109696] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 03/30/2021] [Accepted: 04/05/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE The aim of this study is to compare polyacrylamide and agarose gels, as components of a simple MRI phantom, for the measurements of Apparent Diffusion Coefficient (ADC), T1 and T2 relaxation times. MATERIALS AND METHODS Five (5) test tubes with polyacrylamide gels of different monomer concentrations and six (6) test tubes of different agarose gel concentrations were used as a phantom for ADC, T1 and T2 measurements, which were expressed as 2D color parametric maps, on a 1.5 T clinical MRI system. ADC and T2 maps were calculated utilizing a Weighted Linear (WL) regression fitting algorithm. T1 maps were calculated utilizing a standard non-linear fitting algorithm. RESULTS In agarose gels, ADC measurements are independent of the agarose concentration, whereas the T1 and T2 relaxation times decrease with increasing agarose concentration. On the contrary, in polyacrylamide gels, ADC measurements decrease quadratically while increasing the monomer concentration, whereas the T1 and T2 relaxation times reveal a linear decrease with increasing monomer concentration. CONCLUSION Polyacrylamide gels can serve as a better means for simulating ADC values, as compared with the agarose gels used in this study.
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Affiliation(s)
- T Boursianis
- Department of Medical Physics, University of Crete, Heraklion, Crete, Greece.
| | - G Kalaitzakis
- Department of Medical Physics, University of Crete, Heraklion, Crete, Greece.
| | - E Pappas
- Department of Biomedical Sciences, Radiology & Radiotherapy Sector, University of West Attica, Athens, Greece.
| | - A H Karantanas
- Department of Radiology, University of Crete, Heraklion, Crete, Greece; Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece; Department of Medical Imaging, University Hosptital, Heraklion, Crete, Greece.
| | - T G Maris
- Department of Medical Physics, University of Crete, Heraklion, Crete, Greece; Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), Heraklion, Crete, Greece.
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Turner S, Lazarus R, Marion D, Main KL. Molecular and Diffusion Tensor Imaging Biomarkers of Traumatic Brain Injury: Principles for Investigation and Integration. J Neurotrauma 2021; 38:1762-1782. [PMID: 33446015 DOI: 10.1089/neu.2020.7259] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The last 20 years have seen the advent of new technologies that enhance the diagnosis and prognosis of traumatic brain injury (TBI). There is recognition that TBI affects the brain beyond initial injury, in some cases inciting a progressive neuropathology that leads to chronic impairments. Medical researchers are now searching for biomarkers to detect and monitor this condition. Perhaps the most promising developments are in the biomolecular and neuroimaging domains. Molecular assays can identify proteins indicative of neuronal injury and/or degeneration. Diffusion imaging now allows sensitive evaluations of the brain's cellular microstructure. As the pace of discovery accelerates, it is important to survey the research landscape and identify promising avenues of investigation. In this review, we discuss the potential of molecular and diffusion tensor imaging (DTI) biomarkers in TBI research. Integration of these technologies could advance models of disease prognosis, ultimately improving care. To date, however, few studies have explored relationships between molecular and DTI variables in patients with TBI. Here, we provide a short primer on each technology, review the latest research, and discuss how these biomarkers may be incorporated in future studies.
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Affiliation(s)
- Stephanie Turner
- Defense and Veterans Brain Injury Center, Silver Spring, Maryland, USA.,General Dynamics Information Technology, Falls Church, Virginia, USA
| | - Rachel Lazarus
- Defense and Veterans Brain Injury Center, Silver Spring, Maryland, USA.,General Dynamics Information Technology, Falls Church, Virginia, USA
| | - Donald Marion
- Defense and Veterans Brain Injury Center, Silver Spring, Maryland, USA.,General Dynamics Information Technology, Falls Church, Virginia, USA
| | - Keith L Main
- Defense and Veterans Brain Injury Center, Silver Spring, Maryland, USA.,General Dynamics Information Technology, Falls Church, Virginia, USA
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Quantitative MRI: Defining repeatability, reproducibility and accuracy for prostate cancer imaging biomarker development. Magn Reson Imaging 2021; 77:169-179. [PMID: 33388362 DOI: 10.1016/j.mri.2020.12.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 11/25/2020] [Accepted: 12/29/2020] [Indexed: 01/09/2023]
Abstract
INTRODUCTION Quantitative MRI (qMRI) parameters have been increasingly used to develop predictive models to accurately monitor treatment response in prostate cancer after radiotherapy. To reliably detect changes in signal due to treatment response, predictive models require qMRI parameters with high repeatability and reproducibility. The purpose of this study was to measure qMRI parameter uncertainties in both commercial and in-house developed phantoms to guide the development of robust predictive models for monitoring treatment response. MATERIALS AND METHODS ADC, T1, and R2* values were acquired across three 3 T scanners with a prostate-specific qMRI protocol using the NIST/ISMRM system phantom, RSNA/NIST diffusion phantom, and an in-house phantom. A B1 field map was acquired to correct for flip angle inhomogeneity in T1 maps. All sequences were repeated in each scan to assess within-session repeatability. Weekly scans were acquired on one scanner for three months with the in-house phantom. Between-session repeatability was measured with test-retest scans 6-months apart on all scanners with all phantoms. Accuracy, defined as percentage deviation from reference value for ADC and T1, was evaluated using the system and diffusion phantoms. Repeatability and reproducibility coefficients of variation (%CV) were calculated for all qMRI parameters on all phantoms. RESULTS Overall, repeatability CV of ADC was <2.40%, reproducibility CV was <3.98%, and accuracy ranged between -8.0% to 2.7% across all scanners. Applying B1 correction on T1 measurements significantly improved the repeatability and reproducibility (p<0.05) but increased error in accuracy (p<0.001). Repeatability and reproducibility of R2* was <4.5% and <7.3% respectively in the system phantom across all scanners. CONCLUSION Repeatability, reproducibility, and accuracy in qMRI parameters from a prostate-specific protocol was estimated using both commercial and in-house phantoms. Results from this work will be used to identify robust qMRI parameters for use in the development of predictive models to longitudinally monitor treatment response for prostate cancer in current and future clinical trials.
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Cancer Detection and Quantification of Treatment Response Using Diffusion-Weighted MRI. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00068-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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Barucci A, Flori A, Carpi R, Marchi DD, Esposito M, Menichetti L, Pini R, Ratto F, Belli G, Piazza F. Ficoll as testing material for diffusion weighted imaging-quality assurance phantoms. Magn Reson Imaging 2020; 76:1-7. [PMID: 33161101 DOI: 10.1016/j.mri.2020.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 11/03/2020] [Accepted: 11/03/2020] [Indexed: 10/23/2022]
Abstract
PURPOSE The aim of this work is to test the use of aqueous solutions of Ficoll®**, a highly branched polymer displaying crowding properties, to build a phantom suitable for Diffusion Weighted Imaging (DWI) in Magnetic Resonance Imaging (MRI). METHODS We developed a test object made of a cylindrical plastic container with a precise geometrical arrangement suitable for measuring several samples at the same time. The container was designed to host single vials with variable geometry and number, and to fit inside common commercial head coils for MRI scanners. In our experiments, vials were filled with 8 aqueous solutions of Ficoll 70 and Ficoll 400 spanning a range of polymer concentration from 5 to 30% by weight. Vials containing ultra-pure water were also used as reference. Experiments were performed on both 1.5 and 3 T clinical scanners (GE, Philips and Siemens), under the conditions of a standard clinical examination. RESULTS The geometry of the phantom provided reduced imaging artifacts, especially image distortions at magnetic interfaces. We found that the Apparent Diffusion Coefficient (ADC) varied in the range of 0.00125-0.00223 mm2/s and decreased with Ficoll concentration. ADC vs Ficoll concentration exhibited a linear trend. Results were consistent over time and among different MRI clinical scanners, showing an average variability of 3% at 1.5 T and of 7.5% at 3 T. Moreover, no substantial difference was found between Ficoll 70 and 400. By varying Ficoll concentration, ADC can be modulated to approach tissue-mimicking values. Preliminary results for relaxation measurements proved that both T1 and T2 decreased with Ficoll concentration in the ranges 1.3-2.4 s and 150-800 ms respectively. CONCLUSIONS In this work, we propose a 3D phantom design based on the widespread crowding agent Ficoll, which is suitable for DWI quality assurance purposes in MRI acquisitions. Aqueous Ficoll solutions provide good performance in terms of stability, ease of preparation, and safety.
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Affiliation(s)
- Andrea Barucci
- "Nello Carrara" Institute of Applied Physics, CNR Florence Research Area, Via Madonna del Piano 10, 50019 Sesto Fiorentino, FI, Italy.
| | - Alessandra Flori
- Bioengineering and clinical engineering unit, Fondazione CNR/Regione Toscana G. Monasterio, Via G. Moruzzi 1, 56124 Pisa, Italy
| | - Roberto Carpi
- USL Toscana Centro, Piazza Santa Maria Nuova 1, 50121 Firenze, Italy
| | - Daniele De Marchi
- MRI unit, Fondazione CNR/Regione Toscana G. Monasterio, Via G. Moruzzi 1, 56124 Pisa, Italy
| | - Marco Esposito
- USL Toscana Centro, Piazza Santa Maria Nuova 1, 50121 Firenze, Italy
| | - Luca Menichetti
- Institute of Clinical Physiology, CNR, Via G. Moruzzi 1, 56124 Pisa, Italy
| | - Roberto Pini
- "Nello Carrara" Institute of Applied Physics, CNR Florence Research Area, Via Madonna del Piano 10, 50019 Sesto Fiorentino, FI, Italy
| | - Fulvio Ratto
- "Nello Carrara" Institute of Applied Physics, CNR Florence Research Area, Via Madonna del Piano 10, 50019 Sesto Fiorentino, FI, Italy
| | - Giacomo Belli
- Health Physics Unit, AOU Careggi, Largo Brambilla 3, 50134 Firenze, Italy
| | - Francesco Piazza
- Université d'Orléans and Centre de Biophysique Moléculaire (CBM), CNRS UPR 4301, rue Charles Sadron, 45071 Orléans, France
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Tong Q, Gong T, He H, Wang Z, Yu W, Zhang J, Zhai L, Cui H, Meng X, Tax CWM, Zhong J. A deep learning-based method for improving reliability of multicenter diffusion kurtosis imaging with varied acquisition protocols. Magn Reson Imaging 2020; 73:31-44. [PMID: 32822818 DOI: 10.1016/j.mri.2020.08.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 07/13/2020] [Accepted: 08/14/2020] [Indexed: 01/02/2023]
Abstract
Multicenter magnetic resonance imaging is gaining more popularity in large-sample projects. Since both varying hardware and software across different centers cause unavoidable data heterogeneity across centers, its impact on reliability in study outcomes has also drawn much attention recently. One fundamental issue arises in how to derive model parameters reliably from image data of varying quality. This issue is even more challenging for advanced diffusion methods such as diffusion kurtosis imaging (DKI). Recently, deep learning-based methods have been demonstrated with their potential for robust and efficient computation of diffusion-derived measures. Inspired by these approaches, the current study specifically designed a framework based on a three-dimensional hierarchical convolutional neural network, to jointly reconstruct and harmonize DKI measures from multicenter acquisition to reformulate these to a state-of-the-art hardware using data from traveling subjects. The results from the harmonized data acquired with different protocols show that: 1) the inter-scanner variation of DKI measures within white matter was reduced by 51.5% in mean kurtosis, 65.9% in axial kurtosis, 53.7% in radial kurtosis, and 61.5% in kurtosis fractional anisotropy, respectively; 2) data reliability of each single scanner was enhanced and brought to the level of the reference scanner; and 3) the harmonization network was able to reconstruct reliable DKI values from high data variability. Overall the results demonstrate the feasibility of the proposed deep learning-based method for DKI harmonization and help to simplify the protocol setup procedure for multicenter scanners with different hardware and software configurations.
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Affiliation(s)
- Qiqi Tong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China; Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, Zhejiang, China.
| | - Ting Gong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Hongjian He
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Zheng Wang
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China.
| | - Wenwen Yu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China.
| | - Jianjun Zhang
- Department of Radiology, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Lihao Zhai
- Department of Radiology, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Hongsheng Cui
- Department of Radiology, The Third Affiliated Hospital of Qiqihar Medical University, Qiqihar, Heilongjiang, China
| | - Xin Meng
- Department of Radiology, The Third Affiliated Hospital of Qiqihar Medical University, Qiqihar, Heilongjiang, China
| | - Chantal W M Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom.
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China; Department of Imaging Sciences, University of Rochester, Rochester, NY, USA.
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Gregory S, Lohse KR, Johnson EB, Leavitt BR, Durr A, Roos RAC, Rees G, Tabrizi SJ, Scahill RI, Orth M. Longitudinal Structural MRI in Neurologically Healthy Adults. J Magn Reson Imaging 2020; 52:1385-1399. [PMID: 32469154 PMCID: PMC8425332 DOI: 10.1002/jmri.27203] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 05/07/2020] [Accepted: 05/07/2020] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Structural brain MRI measures are frequently examined in both healthy and clinical groups, so an understanding of how these measures vary over time is desirable. PURPOSE To test the stability of structural brain MRI measures over time. POPULATION In all, 112 healthy volunteers across four sites. STUDY TYPE Retrospective analysis of prospectively acquired data. FIELD STRENGTH/SEQUENCE 3 T, magnetization prepared - rapid gradient echo, and single-shell diffusion sequence. ASSESSMENT Diffusion, cortical thickness, and volume data from the sensorimotor network were assessed for stability over time across 3 years. Two sites used a Siemens MRI scanner, two sites a Philips scanner. STATISTICAL TESTS The stability of structural measures across timepoints was assessed using intraclass correlation coefficients (ICC) for absolute agreement, cutoff ≥0.80, indicating high reliability. Mixed-factorial analysis of variance (ANOVA) was used to examine between-site and between-scanner type differences in individuals over time. RESULTS All cortical thickness and gray matter volume measures in the sensorimotor network, plus all diffusivity measures (fractional anisotropy plus mean, axial and radial diffusivities) for primary and premotor cortices, primary somatosensory thalamic connections, and the cortico-spinal tract met ICC. The majority of measures differed significantly between scanners, with a trend for sites using Siemens scanners to produce larger values for connectivity, cortical thickness, and volume measures than sites using Philips scanners. DATA CONCLUSION Levels of reliability over time for all tested structural MRI measures were generally high, indicating that any differences between measurements over time likely reflect underlying biological differences rather than inherent methodological variability. LEVEL OF EVIDENCE 4. TECHNICAL EFFICACY STAGE 1.
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Affiliation(s)
- Sarah Gregory
- Huntington's Disease Research Centre, Institute of Neurology, University College London, London, UK.,Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK
| | - Keith R Lohse
- Department of Health, Kinesiology, and Recreation, University of Utah, Salt Lake City, Utah, USA.,Department of Physical Therapy and Athletic Training, University of Utah, Salt Lake City, Utah, USA
| | - Eileanoir B Johnson
- Huntington's Disease Research Centre, Institute of Neurology, University College London, London, UK
| | - Blair R Leavitt
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Alexandra Durr
- APHP Department of Genetics, Pitié-Salpêtrière University Hospital, and Institut du Cerveau et de la Moell épinière (ICM), Sorbonne Université, Paris, France
| | - Raymund A C Roos
- Department of Neurology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Geraint Rees
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK.,Institute of Cognitive Neuroscience, University College London, London, UK
| | - Sarah J Tabrizi
- Huntington's Disease Research Centre, Institute of Neurology, University College London, London, UK
| | - Rachael I Scahill
- Huntington's Disease Research Centre, Institute of Neurology, University College London, London, UK
| | - Michael Orth
- Department of Neurology, Ulm University Hospital, Ulm, Germany.,Neurozentrum Siloah, Bern, Switzerland
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42
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Pinto MS, Paolella R, Billiet T, Van Dyck P, Guns PJ, Jeurissen B, Ribbens A, den Dekker AJ, Sijbers J. Harmonization of Brain Diffusion MRI: Concepts and Methods. Front Neurosci 2020; 14:396. [PMID: 32435181 PMCID: PMC7218137 DOI: 10.3389/fnins.2020.00396] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 03/30/2020] [Indexed: 11/13/2022] Open
Abstract
MRI diffusion data suffers from significant inter- and intra-site variability, which hinders multi-site and/or longitudinal diffusion studies. This variability may arise from a range of factors, such as hardware, reconstruction algorithms and acquisition settings. To allow a reliable comparison and joint analysis of diffusion data across sites and over time, there is a clear need for robust data harmonization methods. This review article provides a comprehensive overview of diffusion data harmonization concepts and methods, and their limitations. Overall, the methods for the harmonization of multi-site diffusion images can be categorized in two main groups: diffusion parametric map harmonization (DPMH) and diffusion weighted image harmonization (DWIH). Whereas DPMH harmonizes the diffusion parametric maps (e.g., FA, MD, and MK), DWIH harmonizes the diffusion-weighted images. Defining a gold standard harmonization technique for dMRI data is still an ongoing challenge. Nevertheless, in this paper we provide two classification tools, namely a feature table and a flowchart, which aim to guide the readers in selecting an appropriate harmonization method for their study.
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Affiliation(s)
- Maíra Siqueira Pinto
- Department of Radiology, Antwerp University Hospital, University of Antwerp, Antwerp, Belgium.,imec-Vision Lab, University of Antwerp, Antwerp, Belgium
| | - Roberto Paolella
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium.,Icometrix, Leuven, Belgium
| | | | - Pieter Van Dyck
- Department of Radiology, Antwerp University Hospital, University of Antwerp, Antwerp, Belgium
| | | | - Ben Jeurissen
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium
| | | | | | - Jan Sijbers
- imec-Vision Lab, University of Antwerp, Antwerp, Belgium
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43
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Siless V, Hubbard NA, Jones R, Wang J, Lo N, Bauer CCC, Goncalves M, Frosch I, Norton D, Vergara G, Conroy K, De Souza FV, Rosso IM, Wickham AH, Cosby EA, Pinaire M, Hirshfeld-Becker D, Pizzagalli DA, Henin A, Hofmann SG, Auerbach RP, Ghosh S, Gabrieli J, Whitfield-Gabrieli S, Yendiki A. Image acquisition and quality assurance in the Boston Adolescent Neuroimaging of Depression and Anxiety study. Neuroimage Clin 2020; 26:102242. [PMID: 32339824 PMCID: PMC7184183 DOI: 10.1016/j.nicl.2020.102242] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 02/19/2020] [Accepted: 03/10/2020] [Indexed: 12/18/2022]
Abstract
The Connectomes Related to Human Diseases (CRHD) initiative was developed with the Human Connectome Project (HCP) to provide high-resolution, open-access, multi-modal MRI data to better understand the neural correlates of human disease. Here, we present an introduction to a CRHD project, the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) study, which is collecting multimodal neuroimaging, clinical, and neuropsychological data from 225 adolescents (ages 14-17), 150 of whom are expected to have a diagnosis of depression and/or anxiety. Our transdiagnostic recruitment approach samples the full spectrum of depressed/anxious symptoms and their comorbidity, consistent with NIMH Research Domain Criteria (RDoC). We focused on an age range that is critical for brain development and for the onset of mental illness. This project sought to harmonize imaging sequences, hardware, and functional tasks with other HCP studies, although some changes were made to canonical HCP methods to accommodate our study population and questions. We present a thorough overview of our imaging sequences, hardware, and scanning protocol. We detail similarities and differences between this study and other HCP studies. We evaluate structural-, diffusion-, and functional-image-quality measures that may be influenced by clinical factors (e.g., disorder, symptomatology). Signal-to-noise and motion estimates from the first 140 adolescents suggest minimal influence of clinical factors on image quality. We anticipate enrollment of an additional 85 participants, most of whom are expected to have a diagnosis of anxiety and/or depression. Clinical and neuropsychological data from the first 140 participants are currently freely available through the National Institute of Mental Health Data Archive (NDA).
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Affiliation(s)
- Viviana Siless
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Nicholas A Hubbard
- Massachusetts Institute of Technology, Cambridge, MA, United States; University of Nebraska, Lincoln, Lincoln, NE, United States
| | - Robert Jones
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Jonathan Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Nicole Lo
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Clemens C C Bauer
- Massachusetts Institute of Technology, Cambridge, MA, United States; Northeastern University, Department of Psychology, Boston, MA, United States
| | | | - Isabelle Frosch
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Daniel Norton
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | | | | | | | - Isabelle M Rosso
- McLean Hospital, Belmont, MA, United States; Harvard Medical School, Boston, MA, United States
| | | | | | | | | | | | - Aude Henin
- Massachusetts General Hospital, Boston, MA, United States
| | | | | | - Satrajit Ghosh
- Harvard Medical School, Boston, MA, United States; Massachusetts Institute of Technology, Cambridge, MA, United States
| | - John Gabrieli
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Susan Whitfield-Gabrieli
- Massachusetts Institute of Technology, Cambridge, MA, United States; Northeastern University, Department of Psychology, Boston, MA, United States
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
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44
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Melzer TR, Keenan RJ, Leeper GJ, Kingston-Smith S, Felton SA, Green SK, Henderson KJ, Palmer NJ, Shoorangiz R, Almuqbel MM, Myall DJ. Test-retest reliability and sample size estimates after MRI scanner relocation. Neuroimage 2020; 211:116608. [PMID: 32032737 DOI: 10.1016/j.neuroimage.2020.116608] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 01/30/2020] [Accepted: 02/03/2020] [Indexed: 12/21/2022] Open
Abstract
OBJECTIVE Many factors can contribute to the reliability and robustness of MRI-derived metrics. In this study, we assessed the reliability and reproducibility of three MRI modalities after an MRI scanner was relocated to a new hospital facility. METHODS Twenty healthy volunteers (12 females, mean age (standard deviation) = 41 (11) years, age range [25-66]) completed three MRI sessions. The first session (S1) was one week prior to the 3T GE HDxt scanner relocation. The second (S2) occurred nine weeks after S1 and at the new location; a third session (S3) was acquired 4 weeks after S2. At each session, we acquired structural T1-weighted, pseudo-continuous arterial spin labelled, and diffusion tensor imaging sequences. We used longitudinal processing streams to create 12 summary MRI metrics, including total gray matter (GM), cortical GM, subcortical GM, white matter (WM), and lateral ventricle volume; mean cortical thickness; total surface area; average gray matter perfusion, and average diffusion tensor metrics along principal white matter pathways. We compared mean MRI values and variance at the old scanner location to multiple sessions at the new location using Bayesian multi-level regression models. K-fold cross validation allowed identification of important predictors. Whole-brain analyses were used to investigate any regional differences. Furthermore, we calculated within-subject coefficient of variation (wsCV), intraclass correlation coefficient (ICC), and dice similarity index (SI) of cortical segmentations across scanner relocation and within-site. Additionally, we estimated sample sizes required to robustly detect a 4% difference between two groups across MRI metrics. RESULTS All global MRI metrics exhibited little mean difference and small variability (bar cortical gray matter perfusion) both across scanner relocation and within-site repeat. T1- and DTI-derived tissue metrics showed < |0.3|% mean difference and <1.2% variance across scanner location and <|0.4|% mean difference and <0.8% variance within the new location, with between-site intraclass correlation coefficient (ICC) > 0.80 and within-subject coefficient of variation (wsCV) < 1.4%. Mean cortical gray matter perfusion had the highest between-session variability (6.7% [0.3, 16.7], estimate [95% uncertainty interval]), and hence the smallest ICC (0.71 [0.44,0.92]) and largest wsCV (13.4% [5.4, 18.1]). No global metric exhibited evidence of a meaningful mean difference between scanner locations. However, surface area showed evidence of a mean difference within-site repeat (between S2 and S3). Whole-brain analyses revealed no significant areas of difference between scanner relocation or within-site. For all metrics, we found no support for a systematic difference in variance across relocation sites compared to within-site test-retest reliability. Necessary sample sizes to detect a 4% difference between two independent groups varied from a maximum of n = 362 per group (cortical gray matter perfusion), to total gray matter volume (n = 114), average fractional anisotropy (n = 23), total gray matter volume normalized by intracranial volume (n = 19), and axial diffusivity (n = 3 per group). CONCLUSION Cortical gray matter perfusion was the most variable metric investigated (necessitating large sample sizes to identify group differences), with other metrics showing substantially less variability. Scanner relocation appeared to have a negligible effect on variability of the global MRI metrics tested. This manuscript reports within-site test-retest variability to act as a tool for calculating sample size in future investigations. Our results suggest that when all other parameters are held constant (e.g., sequence parameters and MRI processing), the effect of scanner relocation is indistinguishable from within-site variability, but may need to be considered depending on the question being investigated.
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Affiliation(s)
- Tracy R Melzer
- Department of Medicine, University of Otago, Christchurch, New Zealand; New Zealand Brain Research Institute, Christchurch, New Zealand; Brain Research New Zealand - Rangahau Roro Aotearoa Centre of Research Excellence, New Zealand.
| | - Ross J Keenan
- New Zealand Brain Research Institute, Christchurch, New Zealand; Department of Radiology, Christchurch Hospital, Christchurch, New Zealand; Pacific Radiology Group, Christchurch, New Zealand.
| | | | | | | | | | | | | | - Reza Shoorangiz
- New Zealand Brain Research Institute, Christchurch, New Zealand; Department of Electrical and Computer Engineering, University of Canterbury, Christchurch, New Zealand.
| | - Mustafa M Almuqbel
- Department of Medicine, University of Otago, Christchurch, New Zealand; New Zealand Brain Research Institute, Christchurch, New Zealand; Pacific Radiology Group, Christchurch, New Zealand.
| | - Daniel J Myall
- New Zealand Brain Research Institute, Christchurch, New Zealand.
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45
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Paudyal R, Konar AS, Obuchowski NA, Hatzoglou V, Chenevert TL, Malyarenko DI, Swanson SD, LoCastro E, Jambawalikar S, Liu MZ, Schwartz LH, Tuttle RM, Lee N, Shukla-Dave A. Repeatability of Quantitative Diffusion-Weighted Imaging Metrics in Phantoms, Head-and-Neck and Thyroid Cancers: Preliminary Findings. ACTA ACUST UNITED AC 2020; 5:15-25. [PMID: 30854438 PMCID: PMC6403035 DOI: 10.18383/j.tom.2018.00044] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
The aim of this study was to establish the repeatability measures of quantitative Gaussian and non-Gaussian diffusion metrics using diffusion-weighted imaging (DWI) data from phantoms and patients with head-and-neck and papillary thyroid cancers. The Quantitative Imaging Biomarker Alliance (QIBA) DWI phantom and a novel isotropic diffusion kurtosis imaging phantom were scanned at 3 different sites, on 1.5T and 3T magnetic resonance imaging systems, using standardized multiple b-value DWI acquisition protocol. In the clinical component of this study, a total of 60 multiple b-value DWI data sets were analyzed for test–retest, obtained from 14 patients (9 head-and-neck squamous cell carcinoma and 5 papillary thyroid cancers). Repeatability of quantitative DWI measurements was assessed by within-subject coefficient of variation (wCV%) and Bland–Altman analysis. In isotropic diffusion kurtosis imaging phantom vial with 2% ceteryl alcohol and behentrimonium chloride solution, the mean apparent diffusion (Dapp × 10−3 mm2/s) and kurtosis (Kapp, unitless) coefficient values were 1.02 and 1.68 respectively, capturing in vivo tumor cellularity and tissue microstructure. For the same vial, Dapp and Kapp mean wCVs (%) were ≤1.41% and ≤0.43% for 1.5T and 3T across 3 sites. For pretreatment head-and-neck squamous cell carcinoma, apparent diffusion coefficient, D, D*, K, and f mean wCVs (%) were 2.38%, 3.55%, 3.88%, 8.0%, and 9.92%, respectively; wCVs exhibited a higher trend for papillary thyroid cancers. Knowledge of technical precision and bias of quantitative imaging metrics enables investigators to properly design and power clinical trials and better discern between measurement variability versus biological change.
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Affiliation(s)
- Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Nancy A Obuchowski
- Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, OH
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | - Scott D Swanson
- Department of Radiology, University of Michigan, Ann Arbor, MI
| | - Eve LoCastro
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Sachin Jambawalikar
- Department of Radiology, Columbia University Irving Medical Center, and New York Presbyterian Hospital, New York, NY
| | - Michael Z Liu
- Department of Radiology, Columbia University Irving Medical Center, and New York Presbyterian Hospital, New York, NY
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University Irving Medical Center, and New York Presbyterian Hospital, New York, NY
| | | | - Nancy Lee
- Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY.,Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
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Andica C, Kamagata K, Hayashi T, Hagiwara A, Uchida W, Saito Y, Kamiya K, Fujita S, Akashi T, Wada A, Abe M, Kusahara H, Hori M, Aoki S. Scan-rescan and inter-vendor reproducibility of neurite orientation dispersion and density imaging metrics. Neuroradiology 2019; 62:483-494. [PMID: 31883043 PMCID: PMC7093343 DOI: 10.1007/s00234-019-02350-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 12/15/2019] [Indexed: 02/06/2023]
Abstract
Purpose The reproducibility of neurite orientation dispersion and density imaging (NODDI) metrics in the human brain has not been explored across different magnetic resonance (MR) scanners from different vendors. This study aimed to evaluate the scan–rescan and inter-vendor reproducibility of NODDI metrics in white and gray matter of healthy subjects using two 3-T MR scanners from two vendors. Methods Ten healthy subjects (7 males; mean age 30 ± 7 years, range 23–37 years) were included in the study. Whole-brain diffusion-weighted imaging was performed with b-values of 1000 and 2000 s/mm2 using two 3-T MR scanners from two different vendors. Automatic extraction of the region of interest was performed to obtain NODDI metrics for whole and localized areas of white and gray matter. The coefficient of variation (CoV) and intraclass correlation coefficient (ICC) were calculated to assess the scan–rescan and inter-vendor reproducibilities of NODDI metrics. Results The scan–rescan and inter-vendor reproducibility of NODDI metrics (intracellular volume fraction and orientation dispersion index) were comparable with those of diffusion tensor imaging (DTI) metrics. However, the inter-vendor reproducibilities of NODDI (CoV = 2.3–14%) were lower than the scan–rescan reproducibility (CoV: scanner A = 0.8–3.8%; scanner B = 0.8–2.6%). Compared with the finding of DTI metrics, the reproducibility of NODDI metrics was lower in white matter and higher in gray matter. Conclusion The lower inter-vendor reproducibility of NODDI in some brain regions indicates that data acquired from different MRI scanners should be carefully interpreted.
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Affiliation(s)
- Christina Andica
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Takuya Hayashi
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Yuya Saito
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Kouhei Kamiya
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shohei Fujita
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Toshiaki Akashi
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Akihiko Wada
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Masahiro Abe
- Canon Medical Systems Corporation, Kanagawa, Japan
| | | | - Masaaki Hori
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
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Lerma-Usabiaga G, Mukherjee P, Ren Z, Perry ML, Wandell BA. Replication and generalization in applied neuroimaging. Neuroimage 2019; 202:116048. [PMID: 31356879 PMCID: PMC6819246 DOI: 10.1016/j.neuroimage.2019.116048] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 04/29/2019] [Accepted: 07/22/2019] [Indexed: 10/26/2022] Open
Abstract
There is much interest in translating neuroimaging findings into meaningful clinical diagnostics. The goal of scientific discoveries differs from clinical diagnostics. Scientific discoveries must replicate under a specific set of conditions; to translate to the clinic we must show that findings using purpose-built scientific instruments will be observable in clinical populations and instruments. Here we describe and evaluate data and computational methods designed to translate a scientific observation to a clinical setting. Using diffusion weighted imaging (DWI), Wahl et al. (2010) observed that across subjects the mean fractional anisotropy (FA) of homologous pairs of tracts is highly correlated. We hypothesize that this is a fundamental biological trait that should be present in most healthy participants, and deviations from this assessment may be a useful diagnostic metric. Using this metric as an illustration of our methods, we analyzed six pairs of homologous white matter tracts in nine different DWI datasets with 44 subjects each. Considering the original FA measurement as a baseline, we show that the new metric is between 2 and 4 times more precise when used in a clinical context. Our framework to translate research findings into clinical practice can be applied, in principle, to other neuroimaging results.
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Affiliation(s)
- Garikoitz Lerma-Usabiaga
- Department of Psychology, Stanford University, 450 Serra Mall, Jordan Hall Building, 94305, Stanford, CA, USA; BCBL. Basque Center on Cognition, Brain and Language, Mikeletegi Pasealekua 69, Donostia - San Sebastián, 20009, Gipuzkoa, Spain.
| | - Pratik Mukherjee
- Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA; Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Zhimei Ren
- Department of Statistics, Stanford University, 390 Serra Mall, Sequoia Hall Building, 94305, Stanford, CA, USA
| | - Michael L Perry
- Department of Psychology, Stanford University, 450 Serra Mall, Jordan Hall Building, 94305, Stanford, CA, USA
| | - Brian A Wandell
- Department of Psychology, Stanford University, 450 Serra Mall, Jordan Hall Building, 94305, Stanford, CA, USA
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48
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Wassenaar TM, Yaffe K, van der Werf YD, Sexton CE. Associations between modifiable risk factors and white matter of the aging brain: insights from diffusion tensor imaging studies. Neurobiol Aging 2019; 80:56-70. [PMID: 31103633 PMCID: PMC6683729 DOI: 10.1016/j.neurobiolaging.2019.04.006] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 03/26/2019] [Accepted: 04/05/2019] [Indexed: 01/13/2023]
Abstract
There is increasing interest in factors that may modulate white matter (WM) breakdown and, consequentially, age-related cognitive and behavioral deficits. Recent diffusion tensor imaging studies have examined the relationship of such factors with WM microstructure. This review summarizes the evidence regarding the relationship between WM microstructure and recognized modifiable factors, including hearing loss, hypertension, diabetes, obesity, smoking, depressive symptoms, physical (in) activity, and social isolation, as well as sleep disturbances, diet, cognitive training, and meditation. Current cross-sectional evidence suggests a clear link between loss of WM integrity (lower fractional anisotropy and higher mean diffusivity) and hypertension, obesity, diabetes, and smoking; a relationship that seems to hold for hearing loss, social isolation, depressive symptoms, and sleep disturbances. Physical activity, cognitive training, diet, and meditation, on the other hand, may protect WM with aging. Preliminary evidence from cross-sectional studies of treated risk factors suggests that modification of factors could slow down negative effects on WM microstructure. Careful intervention studies are needed for this literature to contribute to public health initiatives going forward.
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Affiliation(s)
- Thomas M Wassenaar
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroscience, FMRIB Centre, University of Oxford, John Radcliffe Hospital, UK
| | - Kristine Yaffe
- Departments of Psychiatry, Neurology, and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Ysbrand D van der Werf
- Department of Anatomy and Neurosciences, VU University Medical Center, MC, Amsterdam, the Netherlands
| | - Claire E Sexton
- Department of Neurology, Global Brain Health Institute, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA; Department of Psychiatry, Wellcome Centre for Integrative Neuroscience, Oxford Centre for Human Brain Activity, University of Oxford, John Radcliffe Hospital, UK.
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49
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Prohl AK, Scherrer B, Tomas-Fernandez X, Filip-Dhima R, Kapur K, Velasco-Annis C, Clancy S, Carmody E, Dean M, Valle M, Prabhu SP, Peters JM, Bebin EM, Krueger DA, Northrup H, Wu JY, Sahin M, Warfield SK. Reproducibility of Structural and Diffusion Tensor Imaging in the TACERN Multi-Center Study. Front Integr Neurosci 2019; 13:24. [PMID: 31417372 PMCID: PMC6650594 DOI: 10.3389/fnint.2019.00024] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 06/24/2019] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Multi-site MRI studies are often necessary for recruiting sufficiently sized samples when studying rare conditions. However, they require pooling data from multiple scanners into a single data set, and therefore it is critical to evaluate the variability of quantitative MRI measures within and across scanners used in multi-site studies. The aim of this study was to evaluate the reproducibility of structural and diffusion weighted (DW) MRI measurements acquired on seven scanners at five medical centers as part of the Tuberous Sclerosis Complex Autism Center of Excellence Research Network (TACERN) multisite study. METHODS The American College of Radiology (ACR) phantom was imaged monthly to measure reproducibility of signal intensity and uniformity within and across seven 3T scanners from General Electric, Philips, and Siemens vendors. One healthy adult male volunteer was imaged repeatedly on all seven scanners under the TACERN structural and DW protocol (5 b = 0 s/mm2 and 30 b = 1000 s/mm2) over a period of 5 years (age 22-27 years). Reproducibility of inter- and intra-scanner brain segmentation volumes and diffusion tensor imaging metrics fractional anisotropy (FA) and mean diffusivity (MD) within white matter regions was quantified with coefficient of variation. RESULTS The American College of Radiology Phantom signal intensity and uniformity were similar across scanners and changed little over time, with a mean intra-scanner coefficient of variation of 3.6 and 1.8%, respectively. The mean inter- and intra-scanner coefficients of variation of brain structure volumes derived from T1-weighted (T1w) images of the human phantom were 3.3 and 1.1%, respectively. The mean inter- and intra-scanner coefficients of variation of FA in white matter regions were 4.5 and 2.5%, while the mean inter- and intra-scanner coefficients of variation of MD in white matter regions were 5.4 and 1.5%. CONCLUSION Our results suggest that volumetric and diffusion tensor imaging (DTI) measurements are highly reproducible between and within scanners and provide typical variation amplitudes that can be used as references to interpret future findings in the TACERN network.
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Affiliation(s)
- Anna K. Prohl
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Benoit Scherrer
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Xavier Tomas-Fernandez
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Rajna Filip-Dhima
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Kush Kapur
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Clemente Velasco-Annis
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Sean Clancy
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Erin Carmody
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Meghan Dean
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Molly Valle
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Sanjay P. Prabhu
- Division of Neuroradiology, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Jurriaan M. Peters
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - E. Martina Bebin
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Darcy A. Krueger
- Department of Neurology and Rehabilitation Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - Hope Northrup
- Department of Pediatrics, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Joyce Y. Wu
- Division of Pediatric Neurology, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Mustafa Sahin
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
- F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Simon K. Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Harvard University, Boston, MA, United States
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50
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Davis AD, Hassel S, Arnott SR, Harris J, Lam RW, Milev R, Rotzinger S, Zamyadi M, Frey BN, Minuzzi L, Strother SC, MacQueen GM, Kennedy SH, Hall GB. White Matter Indices of Medication Response in Major Depression: A Diffusion Tensor Imaging Study. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 4:913-924. [PMID: 31471185 DOI: 10.1016/j.bpsc.2019.05.016] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 05/28/2019] [Indexed: 01/17/2023]
Abstract
BACKGROUND While response to antidepressants in major depressive disorder is difficult to predict, characterizing the organization and integrity of white matter in the brain with diffusion tensor imaging (DTI) may provide the means to distinguish between antidepressant responders and nonresponders. METHODS DTI data were collected at 6 sites (Canadian Biomarker Integration Network in Depression-1 [CAN-BIND-1 study]) from 200 (127 women) depressed and 112 (71 women) healthy participants at 3 time points: at baseline, 2 weeks, and 8 weeks following initiation of selective serotonin reuptake inhibitor treatment. Therapeutic response was established by a 50% reduction of symptoms at 8 weeks. Analysis on responders, nonresponders, and control subjects yielded 4 scalar metrics: fractional anisotropy and mean, axial, and radial diffusivity. Region-of-interest analysis was carried out on 40 white matter regions using a skeletonization approach. Mixed-effects regression was incorporated to test temporal trends. RESULTS The data acquired at baseline showed that axial diffusivity in the external capsule, which overlaps the superior longitudinal fasciculus, was significantly associated with medication response. Regression analysis revealed further baseline differences of responders compared with nonresponders in the cingulum regions, sagittal stratum, and corona radiata. Additional group differences relative to control subjects were seen in the internal capsule, posterior thalamic radiation, and uncinate fasciculus. Most effect sizes were moderate (near 0.5), with a maximum of 0.76 in the cingulum-hippocampus region. No temporal changes in DTI metrics were observed over the 8-week study period. CONCLUSIONS Several DTI measures of altered white matter specifically distinguished medication responders and nonresponders at baseline and show promise for predicting treatment response in depression.
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Affiliation(s)
- Andrew D Davis
- Department of Psychology, Neuroscience & Behavior, McMaster University, Hamilton, Ontario, Canada; Imaging Research Center, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
| | - Stefanie Hassel
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Stephen R Arnott
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario, Canada
| | - Jacqueline Harris
- Department of Computer Science, University of Alberta, Edmonton, Alberta, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Roumen Milev
- Department of Psychology, Queen's University, Kingston, Ontario, Canada; Department of Psychiatry, Queen's University and Providence Care Hospital, Kingston, Ontario, Canada
| | - Susan Rotzinger
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, St. Michael's Hospital, Toronto, Ontario, Canada; Department of Psychiatry, Krembil Research Centre, University Health Network, Toronto, Ontario, Canada; Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Mojdeh Zamyadi
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario, Canada
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada; Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
| | - Luciano Minuzzi
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada; Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Glenda M MacQueen
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Sidney H Kennedy
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, St. Michael's Hospital, Toronto, Ontario, Canada; Department of Psychiatry, Krembil Research Centre, University Health Network, Toronto, Ontario, Canada; Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Geoffrey B Hall
- Department of Psychology, Neuroscience & Behavior, McMaster University, Hamilton, Ontario, Canada; Imaging Research Center, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada.
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