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Genç B, Aslan K, Atay MH, Akan H. Evaluation of microstructural changes in the brain in transfusion dependent thalassemia patients with advanced magnetic resonance imaging techniques. Neuroradiology 2024:10.1007/s00234-024-03414-y. [PMID: 38975995 DOI: 10.1007/s00234-024-03414-y] [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: 03/06/2024] [Accepted: 06/25/2024] [Indexed: 07/09/2024]
Abstract
PURPOSE Transfusion-dependent thalassemia (TDT) is associated with iron accumulation in the body and an increased tendency for thrombosis. With the increased life expectancy in these patients, the detection of neurocognitive complications has gained importance. This study investigates the microstructural changes in TDT patients using advanced diffusion MRI techniques and their relationship with laboratory parameters. METHODS The study included 14 TDT patients and 14 control subjects. Tract-based spatial statistics (TBSS) were used to examine differences in DTI parameters such as fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) in thalassemia patients using multi-shell DWI images. The mean kurtosis (MK) difference was investigated using diffusion kurtosis imaging. Fiber density (FD), fiber cross-section (FC), and fiber density and cross-section (FDC) differences were examined using fixel-based analysis. In the patient group, correlative tractography was used to investigate the relationship between DTI parameters and platelet (PLT) and ferritin levels. RESULTS Increase in RD and MD was observed, particularly in the white matter tracts of the corona radiata in patient group. Additionally, an increase in AD was detected in a limited area. Correlative tractography in thalasemia patients showed a positive correlation between increases in RD, MD, and AD with PLT and ferritin. Fixel-based analysis demonstrated a dispersed distribution in white matter fibers, with a more pronounced decrease in FD, FC, and FDC in the internal capsule. CONCLUSION There is widespread involvement in the white matter and fiber tracts in thalassemia patients, which is highly correlated with thrombotic parameters.
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Affiliation(s)
- Barış Genç
- Department of Radiology, Ondokuz Mayıs University Faculty of Medicine, Samsun, Turkey.
| | - Kerim Aslan
- Department of Radiology, Ondokuz Mayıs University Faculty of Medicine, Samsun, Turkey
| | - Memiş Hilmi Atay
- Department of Hematology, Ondokuz Mayıs University Faculty of Medicine, Samsun, Turkey
| | - Hüseyin Akan
- Department of Radiology, Ondokuz Mayıs University Faculty of Medicine, Samsun, Turkey
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Julian A, Ruthotto L. PyHySCO: GPU-enabled susceptibility artifact distortion correction in seconds. Front Neurosci 2024; 18:1406821. [PMID: 38863882 PMCID: PMC11165994 DOI: 10.3389/fnins.2024.1406821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 04/25/2024] [Indexed: 06/13/2024] Open
Abstract
Over the past decade, reversed gradient polarity (RGP) methods have become a popular approach for correcting susceptibility artifacts in echo-planar imaging (EPI). Although several post-processing tools for RGP are available, their implementations do not fully leverage recent hardware, algorithmic, and computational advances, leading to correction times of several minutes per image volume. To enable 3D RGP correction in seconds, we introduce PyTorch Hyperelastic Susceptibility Correction (PyHySCO), a user-friendly EPI distortion correction tool implemented in PyTorch that enables multi-threading and efficient use of graphics processing units (GPUs). PyHySCO uses a time-tested physical distortion model and mathematical formulation and is, therefore, reliable without training. An algorithmic improvement in PyHySCO is its use of the one-dimensional distortion correction method by Chang and Fitzpatrick to initialize the non-linear optimization. PyHySCO is published under the GNU public license and can be used from the command line or its Python interface. Our extensive numerical validation using 3T and 7T data from the Human Connectome Project suggests that PyHySCO can achieve accuracy comparable to that of leading RGP tools at a fraction of the cost. We also validate the new initialization scheme, compare different optimization algorithms, and test the algorithm on different hardware and arithmetic precisions.
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Affiliation(s)
- Abigail Julian
- Department of Computer Science, Emory University, Atlanta, GA, United States
| | - Lars Ruthotto
- Department of Computer Science, Emory University, Atlanta, GA, United States
- Department of Mathematics, Emory University, Atlanta, GA, United States
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3
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Pardo J, Montal V, Campabadal A, Oltra J, Uribe C, Roura I, Bargalló N, Martí MJ, Compta Y, Iranzo A, Fortea J, Junqué C, Segura B. Cortical Macro- and Microstructural Changes in Parkinson's Disease with Probable Rapid Eye Movement Sleep Behavior Disorder. Mov Disord 2024; 39:814-824. [PMID: 38456361 DOI: 10.1002/mds.29761] [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: 09/18/2023] [Revised: 01/17/2024] [Accepted: 02/16/2024] [Indexed: 03/09/2024] Open
Abstract
BACKGROUND Evidence regarding cortical atrophy patterns in Parkinson's disease (PD) with probable rapid eye movement sleep behavior disorder (RBD) (PD-pRBD) remains scarce. Cortical mean diffusivity (cMD), as a novel imaging biomarker highly sensitive to detecting cortical microstructural changes in different neurodegenerative diseases, has not been investigated in PD-pRBD yet. OBJECTIVES The aim was to investigate cMD as a sensitive measure to identify subtle cortical microstructural changes in PD-pRBD and its relationship with cortical thickness (CTh). METHODS Twenty-two PD-pRBD, 31 PD without probable RBD (PD-nonpRBD), and 28 healthy controls (HC) were assessed using 3D T1-weighted and diffusion-weighted magnetic resonance imaging on a 3-T scanner and neuropsychological testing. Measures of cortical brain changes were obtained through cMD and CTh. Two-class group comparisons of a general linear model were performed (P < 0.05). Cohen's d effect size for both approaches was computed. RESULTS PD-pRBD patients showed higher cMD than PD-nonpRBD patients in the left superior temporal, superior frontal, and precentral gyri, precuneus cortex, as well as in the right middle frontal and postcentral gyri and paracentral lobule (d > 0.8), whereas CTh did not detect significant differences. PD-pRBD patients also showed increased bilateral posterior cMD in comparison with HCs (d > 0.8). These results partially overlapped with CTh results (0.5 < d < 0.8). PD-nonpRBD patients showed no differences in cMD when compared with HCs but showed cortical thinning in the left fusiform gyrus and lateral occipital cortex bilaterally (d > 0.5). CONCLUSIONS cMD may be more sensitive than CTh displaying significant cortico-structural differences between PD subgroups, indicating this imaging biomarker's utility in studying early cortical changes in PD. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Grants
- PID2020-114640GB-I00/AEI/10.13039/501100011033 Ministerio de Economía y Competitividad
- H2020-SC1-BHC-2018-2020/GA 965422 European Union's Horizon 2020, "MES-CoBraD"
- FI18/00275 Instituto de Salud Carlos III
- IIBSP-DOW-2020-151 Departament de Salut, Generalitat de Catalunya, Fundación Tatiana Pérez de Guzmán el Bueno
- PRE2018-086675 Ministerio de Ciencia, Innovación y Universidades
- PI20/01473 Fondo de Investigaciones Sanitario, Carlos III Health Institute
- SGR 2021SGR00801 Generalitat de Catalunya
- 1R01AG056850-01A1 CIBERNED Program 1, National Institutes of Health (NIH) grants
- 3RF1AG056850-01S1 CIBERNED Program 1, National Institutes of Health (NIH) grants
- AG056850 CIBERNED Program 1, National Institutes of Health (NIH) grants
- R01AG061566 CIBERNED Program 1, National Institutes of Health (NIH) grants
- R21AG056974 CIBERNED Program 1, National Institutes of Health (NIH) grants
- 888692 H2020 Marie Skłodowska-Curie Actions
- LCF/BQ/DR22/11950012 'la Caixa' Foundation
- PRE2021-099689 Ministerio de Ciencia e Innovación
- CEX2021-001159-M María de Maeztu Unit of Excellence (Institute of Neurosciences, University of Barcelona), Ministry of Science and Innovation
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Affiliation(s)
- Jèssica Pardo
- Medical Psychology Unit, Department of Medicine, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Victor Montal
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Life Sciences, Barcelona Supercomputing Center, Barcelona, Spain
| | - Anna Campabadal
- Medical Psychology Unit, Department of Medicine, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Neurology Service, Consorci Corporació Sanitària Parc Taulí de Sabadell, Barcelona, Spain
| | - Javier Oltra
- Medical Psychology Unit, Department of Medicine, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Carme Uribe
- Medical Psychology Unit, Department of Medicine, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Brain Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada
| | - Ignacio Roura
- Medical Psychology Unit, Department of Medicine, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Núria Bargalló
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Imaging Diagnostic Center (CDI), Hospital Clínic Universitari de Barcelona, Barcelona, Spain
| | - Maria J Martí
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Barcelona, Spain
- Parkinson's Disease and Movement Disorders Unit, Hospital Clínic Universitari de Barcelona, UBNeuro Institute of Neurosciences, University of Barcelona, Barcelona, Spain
| | - Yaroslau Compta
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Barcelona, Spain
- Parkinson's Disease and Movement Disorders Unit, Hospital Clínic Universitari de Barcelona, UBNeuro Institute of Neurosciences, University of Barcelona, Barcelona, Spain
| | - Alex Iranzo
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Barcelona, Spain
- Sleep Disorders Center, Neurology Service, Hospital Clínic Universitari de Barcelona, University of Barcelona, Barcelona, Spain
| | - Juan Fortea
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Barcelona, Spain
- Barcelona Down Medical Center, Fundació Catalana de Síndrome de Down, Barcelona, Spain
| | - Carme Junqué
- Medical Psychology Unit, Department of Medicine, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Barcelona, Spain
| | - Bàrbara Segura
- Medical Psychology Unit, Department of Medicine, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Barcelona, Spain
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Tuerxun R, Kamagata K, Saito Y, Andica C, Takabayashi K, Uchida W, Yoshida S, Kikuta J, Tabata H, Naito H, Someya Y, Kaga H, Miyata M, Akashi T, Wada A, Taoka T, Naganawa S, Tamura Y, Watada H, Kawamori R, Aoki S. Assessing interstitial fluid dynamics in type 2 diabetes mellitus and prediabetes cases through diffusion tensor imaging analysis along the perivascular space. Front Aging Neurosci 2024; 16:1362457. [PMID: 38515515 PMCID: PMC10954820 DOI: 10.3389/fnagi.2024.1362457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 02/19/2024] [Indexed: 03/23/2024] Open
Abstract
Background and purpose Glymphatic system in type 2 diabetes mellitus (T2DM) but not in the prodrome, prediabetes (Pre-DM) was investigated using diffusion tensor image analysis along the perivascular space (DTI-ALPS). Association between glymphatic system and insulin resistance of prominent characteristic in T2DM and Pre-DM between is yet elucidated. Therefore, this study delves into the interstitial fluid dynamics using the DTI-ALPS in both Pre-DM and T2DM and association with insulin resistance. Materials and methods In our cross-sectional study, we assessed 70 elderly individuals from the Bunkyo Health Study, which included 22 with Pre-DM, 18 with T2DM, and 33 healthy controls with normal glucose metabolism (NGM). We utilized the general linear model (GLM) to evaluate the ALPS index based on DTI-ALPS across these groups, considering variables like sex, age, intracranial volume, years of education, anamnesis of hypertension and hyperlipidemia, and the total Fazekas scale. Furthermore, we have explored the relationship between the ALPS index and insulin resistance, as measured by the homeostasis model assessment of insulin resistance (HOMA-IR) using GLM and the same set of covariates. Results In the T2DM group, the ALPS index demonstrated a reduction compared with the NGM group [family-wise error (FWE)-corrected p < 0.001; Cohen's d = -1.32]. Similarly, the Pre-DM group had a lower ALPS index than the NGM group (FWE-corrected p < 0.001; Cohen's d = -1.04). However, there was no significant disparity between the T2DM and Pre-DM groups (FWE-corrected p = 1.00; Cohen's d = -0.63). A negative correlation was observed between the ALPS index and HOMA-IR in the combined T2DM and Pre-DM groups (partial correlation coefficient r = -0.35, p < 0.005). Conclusion The ALPS index significantly decreased in both the pre-DM and T2DM groups and showed a correlated with insulin resistance. This indicated that changes in interstitial fluid dynamics are associated with insulin resistance.
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Affiliation(s)
- Rukeye Tuerxun
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuya Saito
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Christina Andica
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Faculty of Health Data Science, Juntendo University, Chiba, Japan
| | - Kaito Takabayashi
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Seina Yoshida
- 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
| | - Junko Kikuta
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Hiroki Tabata
- Sportology Center, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Hitoshi Naito
- Department of Metabolism and Endocrinology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuki Someya
- Sportology Center, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Graduate School of Health and Sports Science, Juntendo University, Chiba, Japan
| | - Hideyoshi Kaga
- Department of Metabolism and Endocrinology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Mari Miyata
- Department of Functional Brain Imaging, National Institutes for Quantum and Radiological Science and Technology, Chiba, 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
| | - Toshiaki Taoka
- Department of Innovative Biomedical Visualization, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yoshifumi Tamura
- Sportology Center, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Metabolism and Endocrinology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Hirotaka Watada
- Sportology Center, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Metabolism and Endocrinology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Ryuzo Kawamori
- Sportology Center, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Metabolism and Endocrinology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
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5
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Zaid Alkilani A, Çukur T, Saritas EU. FD-Net: An unsupervised deep forward-distortion model for susceptibility artifact correction in EPI. Magn Reson Med 2024; 91:280-296. [PMID: 37811681 DOI: 10.1002/mrm.29851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 07/18/2023] [Accepted: 08/15/2023] [Indexed: 10/10/2023]
Abstract
PURPOSE To introduce an unsupervised deep-learning method for fast and effective correction of susceptibility artifacts in reversed phase-encode (PE) image pairs acquired with echo planar imaging (EPI). METHODS Recent learning-based correction approaches in EPI estimate a displacement field, unwarp the reversed-PE image pair with the estimated field, and average the unwarped pair to yield a corrected image. Unsupervised learning in these unwarping-based methods is commonly attained via a similarity constraint between the unwarped images in reversed-PE directions, neglecting consistency to the acquired EPI images. This work introduces a novel unsupervised deep Forward-Distortion Network (FD-Net) that predicts both the susceptibility-induced displacement field and the underlying anatomically correct image. Unlike previous methods, FD-Net enforces the forward-distortions of the correct image in both PE directions to be consistent with the acquired reversed-PE image pair. FD-Net further leverages a multiresolution architecture to maintain high local and global performance. RESULTS FD-Net performs competitively with a gold-standard reference method (TOPUP) in image quality, while enabling a leap in computational efficiency. Furthermore, FD-Net outperforms recent unwarping-based methods for unsupervised correction in terms of both image and field quality. CONCLUSION The unsupervised FD-Net method introduces a deep forward-distortion approach to enable fast, high-fidelity correction of susceptibility artifacts in EPI by maintaining consistency to measured data. Therefore, it holds great promise for improving the anatomical accuracy of EPI imaging.
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Affiliation(s)
- Abdallah Zaid Alkilani
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey
- National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey
| | - Tolga Çukur
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey
- National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey
- Neuroscience Graduate Program, Bilkent University, Ankara, Turkey
| | - Emine Ulku Saritas
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey
- National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey
- Neuroscience Graduate Program, Bilkent University, Ankara, Turkey
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6
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Belato FA, Mello B, Coates CJ, Halanych KM, Brown FD, Morandini AC, de Moraes Leme J, Trindade RIF, Costa-Paiva EM. Divergence time estimates for the hypoxia-inducible factor-1 alpha (HIF1α) reveal an ancient emergence of animals in low-oxygen environments. GEOBIOLOGY 2024; 22:e12577. [PMID: 37750460 DOI: 10.1111/gbi.12577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 07/13/2023] [Accepted: 09/07/2023] [Indexed: 09/27/2023]
Abstract
Unveiling the tempo and mode of animal evolution is necessary to understand the links between environmental changes and biological innovation. Although the earliest unambiguous metazoan fossils date to the late Ediacaran period, molecular clock estimates agree that the last common ancestor (LCA) of all extant animals emerged ~850 Ma, in the Tonian period, before the oldest evidence for widespread ocean oxygenation at ~635-560 Ma in the Ediacaran period. Metazoans are aerobic organisms, that is, they are dependent on oxygen to survive. In low-oxygen conditions, most animals have an evolutionarily conserved pathway for maintaining oxygen homeostasis that triggers physiological changes in gene expression via the hypoxia-inducible factor (HIFa). However, here we confirm the absence of the characteristic HIFa protein domain responsible for the oxygen sensing of HIFa in sponges and ctenophores, indicating the LCA of metazoans lacked the functional protein domain as well, and so could have maintained their transcription levels unaltered under the very low-oxygen concentrations of their environments. Using Bayesian relaxed molecular clock dating, we inferred that the ancestral gene lineage responsible for HIFa arose in the Mesoproterozoic Era, ~1273 Ma (Credibility Interval 957-1621 Ma), consistent with the idea that important genetic machinery associated with animals evolved much earlier than the LCA of animals. Our data suggest at least two duplication events in the evolutionary history of HIFa, which generated three vertebrate paralogs, products of the two successive whole-genome duplications that occurred in the vertebrate LCA. Overall, our results support the hypothesis of a pre-Tonian emergence of metazoans under low-oxygen conditions, and an increase in oxygen response elements during animal evolution.
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Affiliation(s)
- Flavia A Belato
- Institute of Biosciences, Department of Zoology, University of Sao Paulo, São Paulo - SP, Brazil
| | - Beatriz Mello
- Biology Institute, Genetics Department, Federal University of Rio de Janeiro, Rio de Janeiro - RJ, Brazil
| | - Christopher J Coates
- Zoology, Ryan Institute, School of Natural Sciences, University of Galway, Galway, Ireland
| | - Kenneth M Halanych
- Center for Marine Science, University of North Carolina Wilmington, Wilmington, North Carolina, USA
| | - Federico D Brown
- Institute of Biosciences, Department of Zoology, University of Sao Paulo, São Paulo - SP, Brazil
| | - André C Morandini
- Institute of Biosciences, Department of Zoology, University of Sao Paulo, São Paulo - SP, Brazil
| | | | - Ricardo I F Trindade
- Institute of Astronomy, Geophysics and Atmospheric Sciences, University of Sao Paulo, São Paulo - SP, Brazil
| | - Elisa Maria Costa-Paiva
- Institute of Biosciences, Department of Zoology, University of Sao Paulo, São Paulo - SP, Brazil
- Institute of Astronomy, Geophysics and Atmospheric Sciences, University of Sao Paulo, São Paulo - SP, Brazil
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7
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Yao ZF, Hsieh S, Yang MH. Exercise habits and mental health: Exploring the significance of multimodal imaging markers. PROGRESS IN BRAIN RESEARCH 2023; 286:179-209. [PMID: 38876575 DOI: 10.1016/bs.pbr.2023.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2024]
Abstract
Engaging in regular physical activity and establishing exercise habits is known to have multifaceted benefits extending beyond physical health to cognitive and mental well-being. This study explores the intricate relationship between exercise habits, brain imaging markers, and mental health outcomes. While extensive evidence supports the positive impact of exercise on cognitive functions and mental health, recent advancements in multimodal imaging techniques provide a new dimension to this exploration. By using a cross-sectional multimodal brain-behavior statistic in participants with different exercise habits, we aim to unveil the intricate mechanisms underlying exercise's influence on cognition and mental health, including the status of depression, anxiety, and quality of life. This integration of exercise science and imaging promises to substantiate cognitive benefits on mental health and uncover functional and structural changes underpinning these effects. This study embarks on a journey to explore the significance of multimodal imaging metrics (i.e., structural and functional metrics) in deciphering the intricate interplay between exercise habits and mental health, enhancing the comprehension of how exercise profoundly shapes psychological well-being. Our analysis of group comparisons uncovered a strong association between regular exercise habits and improved mental well-being, encompassing factors such as depression, anxiety levels, and overall life satisfaction. Additionally, individuals who engaged in exercise displayed enhanced brain metrics across different modalities. These metrics encompassed greater gray matter volume within the left frontal regions and hippocampus, improved white matter integrity in the frontal-occipital fasciculus, as well as more robust functional network configurations in the anterior segments of the default mode network. The interplay between exercise habits, brain adaptations, and mental health outcomes underscores the pivotal role of an active lifestyle in nurturing a resilient and high-functioning brain, thus paving the way for tailored interventions and improved well-being.
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Affiliation(s)
- Zai-Fu Yao
- College of Education, National Tsing Hua University, Hsinchu City, Taiwan; Research Center for Education and Mind Sciences, National Tsing Hua University, Hsinchu City, Taiwan; Basic Psychology Group, Department of Educational Psychology and Counseling, National Tsing Hua University, Hsinchu City, Taiwan; Department of Kinesiology, National Tsing Hua University, Hsinchu City, Taiwan.
| | - Shulan Hsieh
- Cognitive Electrophysiology Laboratory, Control, Aging, Sleep, and Emotion (CASE), National Cheng Kung University, Tainan City, Taiwan; Department of Psychology, National Cheng Kung University, Tainan City, Taiwan; Institute of Allied Health Sciences, National Cheng Kung University, Tainan City, Taiwan; Department of Public Health, National Cheng Kung University, Tainan City, Taiwan.
| | - Meng-Heng Yang
- Cognitive Electrophysiology Laboratory, Control, Aging, Sleep, and Emotion (CASE), National Cheng Kung University, Tainan City, Taiwan
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8
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Van AN, Montez DF, Laumann TO, Suljic V, Madison T, Baden NJ, Ramirez-Perez N, Scheidter KM, Monk JS, Whiting FI, Adeyemo B, Chauvin RJ, Krimmel SR, Metoki A, Rajesh A, Roland JL, Salo T, Wang A, Weldon KB, Sotiras A, Shimony JS, Kay BP, Nelson SM, Tervo-Clemmens B, Marek SA, Vizioli L, Yacoub E, Satterthwaite TD, Gordon EM, Fair DA, Tisdall MD, Dosenbach NU. Framewise multi-echo distortion correction for superior functional MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.28.568744. [PMID: 38077010 PMCID: PMC10705259 DOI: 10.1101/2023.11.28.568744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Functional MRI (fMRI) data are severely distorted by magnetic field (B0) inhomogeneities which currently must be corrected using separately acquired field map data. However, changes in the head position of a scanning participant across fMRI frames can cause changes in the B0 field, preventing accurate correction of geometric distortions. Additionally, field maps can be corrupted by movement during their acquisition, preventing distortion correction altogether. In this study, we use phase information from multi-echo (ME) fMRI data to dynamically sample distortion due to fluctuating B0 field inhomogeneity across frames by acquiring multiple echoes during a single EPI readout. Our distortion correction approach, MEDIC (Multi-Echo DIstortion Correction), accurately estimates B0 related distortions for each frame of multi-echo fMRI data. Here, we demonstrate that MEDIC's framewise distortion correction produces improved alignment to anatomy and decreases the impact of head motion on resting-state functional connectivity (RSFC) maps, in higher motion data, when compared to the prior gold standard approach (i.e., TOPUP). Enhanced framewise distortion correction with MEDIC, without the requirement for field map collection, furthers the advantage of multi-echo over single-echo fMRI.
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Affiliation(s)
- Andrew N. Van
- Department of Biomedical Engineering, Washington University in St. Louis, MO 63130
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - David F. Montez
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
| | - Timothy O. Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
| | - Vahdeta Suljic
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Thomas Madison
- Institute of Child Development, University of Minnesota Medical School, Minneapolis, MN 55455
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Noah J. Baden
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | | | - Kristen M. Scheidter
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Julia S. Monk
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Forrest I. Whiting
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Roselyne J. Chauvin
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Samuel R. Krimmel
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Athanasia Metoki
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Aishwarya Rajesh
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Jarod L. Roland
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO 63110
| | - Taylor Salo
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104
| | - Anxu Wang
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Division of Computation and Data Science, Washington University School of Medicine, St. Louis, MO 63110
| | - Kimberly B. Weldon
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine, St. Louis, MO 63130
| | - Joshua S. Shimony
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Benjamin P. Kay
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Steven M. Nelson
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Brenden Tervo-Clemmens
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
- Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Scott A. Marek
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Luca Vizioli
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Theodore D. Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104
| | - Evan M. Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Damien A. Fair
- Institute of Child Development, University of Minnesota Medical School, Minneapolis, MN 55455
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Nico U.F. Dosenbach
- Department of Biomedical Engineering, Washington University in St. Louis, MO 63130
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110
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9
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Saito Y, Kamagata K, Andica C, Uchida W, Takabayashi K, Yoshida S, Nakaya M, Tanaka Y, Kamio S, Sato K, Nishizawa M, Akashi T, Shimoji K, Wada A, Aoki S. Glymphatic system impairment in corticobasal syndrome: diffusion tensor image analysis along the perivascular space (DTI-ALPS). Jpn J Radiol 2023; 41:1226-1235. [PMID: 37273112 DOI: 10.1007/s11604-023-01454-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 05/19/2023] [Indexed: 06/06/2023]
Abstract
PURPOSE This study aimed to evaluate the along the perivascular space (ALPS) index based on the diffusion tensor image ALPS (DTI-ALPS) in corticobasal degeneration with corticobasal syndrome (CBD-CBS) and investigate its correlation with motor and cognitive functions. MATERIALS AND METHODS The data of 21 patients with CBD-CBS and 17 healthy controls (HCs) were obtained from the 4-Repeat Tauopathy Neuroimaging Initiative and the Frontotemporal Lobar Degeneration Neuroimaging Initiative databases. Diffusion magnetic resonance imaging was performed using a 3-Tesla MRI scanner. The ALPS index based on DTI-ALPS was automatically calculated after preprocessing. The ALPS index was compared between the CBD-CBS and HC groups via a general linear model analysis, with covariates such as age, sex, years of education, and intracranial volume (ICV). Furthermore, to confirm the relation between the ALPS index and the motor and cognitive score in CBD-CBS, the partial Spearman's rank correlation coefficient was calculated with covariates such as age, sex, years of education, and ICV. A p value of < 0.05 was considered as statistically significant in all statistical analyses. RESULTS The ALPS index of CBD-CBS was significantly lower than that of HC (Cohen's d = - 1.53, p < 0.005). Moreover, the ALPS index had a significant positive correlation with the mini mental state evaluation score (rs = 0.65, p < 0.005) and a significant negative correlation with the unified Parkinson's Disease Rating Scale III score (rs = - 0.75, p < 0.001). CONCLUSION The ALPS index of patients with CBD-CBS, which is significantly lower than that of HCs, is significantly associated with motor and cognitive functions.
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Affiliation(s)
- Yuya Saito
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan.
| | - Christina Andica
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
- Faculty of Health Data Science, Juntendo University, Chiba, Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
| | - Kaito Takabayashi
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
| | - Seina Yoshida
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
- Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Moto Nakaya
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
- Department of Radiology, The University of Tokyo, Tokyo, Japan
| | - Yuya Tanaka
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
- Department of Radiology, The University of Tokyo, Tokyo, Japan
| | - Satoru Kamio
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
- Department of Radiology, The University of Tokyo, Tokyo, Japan
| | - Kanako Sato
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
| | - Mitsuo Nishizawa
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
- Faculty of Health Data Science, Juntendo University, Chiba, Japan
| | - Toshiaki Akashi
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
| | - Keigo Shimoji
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
- Faculty of Health Data Science, Juntendo University, Chiba, Japan
| | - Akihiko Wada
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
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10
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Robinson SD, Bachrata B, Eckstein K, Bollmann S, Bollmann S, Hodono S, Cloos M, Tourell M, Jin J, O'Brien K, Reutens DC, Trattnig S, Enzinger C, Barth M. Improved dynamic distortion correction for fMRI using single-echo EPI and a readout-reversed first image (REFILL). Hum Brain Mapp 2023; 44:5095-5112. [PMID: 37548414 PMCID: PMC10502646 DOI: 10.1002/hbm.26440] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 06/01/2023] [Accepted: 07/12/2023] [Indexed: 08/08/2023] Open
Abstract
The boundaries between tissues with different magnetic susceptibilities generate inhomogeneities in the main magnetic field which change over time due to motion, respiration and system instabilities. The dynamically changing field can be measured from the phase of the fMRI data and corrected. However, methods for doing so need multi-echo data, time-consuming reference scans and/or involve error-prone processing steps, such as phase unwrapping, which are difficult to implement robustly on the MRI host. The improved dynamic distortion correction method we propose is based on the phase of the single-echo EPI data acquired for fMRI, phase offsets calculated from a triple-echo, bipolar reference scan of circa 3-10 s duration using a method which avoids the need for phase unwrapping and an additional correction derived from one EPI volume in which the readout direction is reversed. This Reverse-Encoded First Image and Low resoLution reference scan (REFILL) approach is shown to accurately measure B0 as it changes due to shim, motion and respiration, even with large dynamic changes to the field at 7 T, where it led to a > 20% increase in time-series signal to noise ratio compared to data corrected with the classic static approach. fMRI results from REFILL-corrected data were free of stimulus-correlated distortion artefacts seen when data were corrected with static field mapping. The method is insensitive to shim changes and eddy current differences between the reference scan and the fMRI time series, and employs calculation steps that are simple and robust, allowing most data processing to be performed in real time on the scanner image reconstruction computer. These improvements make it feasible to routinely perform dynamic distortion correction in fMRI.
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Affiliation(s)
- Simon Daniel Robinson
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
- Department of NeurologyMedical University of GrazGrazAustria
- High Field MR Centre, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
- Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal ImagingViennaAustria
| | - Beata Bachrata
- High Field MR Centre, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
- Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal ImagingViennaAustria
- Department of Medical EngineeringCarinthia University of Applied SciencesKlagenfurtAustria
| | - Korbinian Eckstein
- High Field MR Centre, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
| | - Saskia Bollmann
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
| | - Steffen Bollmann
- School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia
| | - Shota Hodono
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
- ARC Training Centre for Innovation in Biomedical Imaging Technology (CIBIT)The University of QueenslandBrisbaneAustralia
| | - Martijn Cloos
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
- ARC Training Centre for Innovation in Biomedical Imaging Technology (CIBIT)The University of QueenslandBrisbaneAustralia
| | - Monique Tourell
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
- Siemens Healthcare Pty Ltd.BrisbaneAustralia
| | - Jin Jin
- Siemens Healthcare Pty Ltd.BrisbaneAustralia
| | | | - David C. Reutens
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
- ARC Training Centre for Innovation in Biomedical Imaging Technology (CIBIT)The University of QueenslandBrisbaneAustralia
| | - Siegfried Trattnig
- High Field MR Centre, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
| | | | - Markus Barth
- Centre of Advanced ImagingUniversity of QueenslandBrisbaneAustralia
- School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia
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11
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Saito Y, Kamagata K, Andica C, Taoka T, Tuerxun R, Uchida W, Takabayashi K, Owaki M, Yoshida S, Yamazaki K, Naganawa S, Aoki S. Multisite harmonization of diffusion tensor image analysis along the perivascular space using the COMBined Association Test. Jpn J Radiol 2023; 41:1072-1083. [PMID: 37093548 PMCID: PMC10543582 DOI: 10.1007/s11604-023-01432-z] [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: 12/27/2022] [Accepted: 04/06/2023] [Indexed: 04/25/2023]
Abstract
PURPOSE This multisite study aimed to use the COMBined Association Test (COMBAT), a harmonization technique that uses regression of covariates with an empirical Bayesian framework, to harmonize diffusion tensor image analysis along the perivascular space (DTI-ALPS) variations caused by scanner, site, and protocol differences. MATERIALS AND METHODS This study included multisite diffusion magnetic resonance imaging (dMRI) data of 45 patients with Alzheimer's disease (AD) and 82 cognitively normal (CN) participants from the AD neuroimaging initiative database. The dMRI data were obtained with two b values (0 and 1000 s/mm2) from 27 institutions and three different 3-Tesla MRI scanners (two vendors). The ALPS index was calculated from multisite dMRI data, and COMBAT was used to harmonize the factors causing site variations. Welch's t test was used, Cohen's d was calculated to compare the difference in the ALPS index between AD and CN before and after harmonization, and Pearson's correlation coefficient was calculated to assess the relationships between the ALPS index and the cognitive score, [18F] fluorodeoxyglucose (FDG)-positron emission tomography (PET), and [18F] florbetapir (AV45)-PET standardized uptake value ratios (SUVRs). RESULTS COMBAT harmonized scanner differences and increased Cohen's d of the left and right ALPS indexes between AD and CN from 0.288 to 0.438 and 0.328 to 0.480, respectively. The ALPS indexes were significantly different between AD and CN after harmonization (P < 0.05) but not before it. Moreover, Pearson's correlation coefficients between the ALPS index and cognitive score, FDG-PET, and AV45-PET SUVRs were higher after harmonization than before it. CONCLUSION This study demonstrates the application of COMBAT harmonization to eliminate between-scanner, site, and protocol variations in the ALPS index calculated from DTI-ALPS using dMRI and possibly facilitate the use of the ALPS index in multi-center studies.
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Affiliation(s)
- Yuya Saito
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan.
| | - Christina Andica
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
- Faculty of Health Data Science, Juntendo University, Chiba, Japan
| | - Toshiaki Taoka
- Department of Innovative Biomedical Visualization (iBMV), Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Rukeye Tuerxun
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
| | - Kaito Takabayashi
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
| | - Mana Owaki
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
- Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Seina Yoshida
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
- Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Keigo Yamazaki
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
- Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyoku, Tokyo, 113-8421, Japan
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12
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Saito Y, Kamagata K, Andica C, Uchida W, Takabayashi K, Yoshida S, Nakaya M, Tanaka Y, Kamiyo S, Sato K, Nishizawa M, Akashi T, Shimoji K, Wada A, Aoki S. Reproducibility of automated calculation technique for diffusion tensor image analysis along the perivascular space. Jpn J Radiol 2023; 41:947-954. [PMID: 37162692 DOI: 10.1007/s11604-023-01415-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 03/16/2023] [Indexed: 05/11/2023]
Abstract
PURPOSE The method of diffusion tensor image analysis along the perivascular space (DTI-ALPS) was gathering attention to evaluate the brain's glymphatic function or interstitial fluid dynamics. However, to the best knowledge, no study was conducted on the reproducibility of these automated methods for ALPS index values. Therefore, the current study evaluated the ALPS index reproducibility based on DTI-ALPS using two major automated calculation techniques in scan and rescan of the same subject on the same day. MATERIALS AND METHODS This study included 23 participants, including 2 with Alzheimer's disease, 15 with mild cognitive impairment, and 6 with cognitive normals. Scan and rescan data of diffusion magnetic resonance images were obtained, as well as automatically index for ALPS (ALPS index) and ALPS index maintaining tensor vector orientation information (vALPS index) with region of interest on the template fractional anisotropy map calculated by FSL software.These ALPS indices were compared in terms of scan and rescan reproducibility. RESULTS The absolute difference in ALPS-index values between scan and rescan was larger in the ALPS index than in the vALPS index by approximately 0.6% as the relative difference. Cohen's d for the left and right ALPS indices between methods were 0.121 and 0.159, respectively. CONCLUSION The vALPS index based on DTI-ALPS maintaining tensor vector orientation information has higher reproducibility than the ALPS index. This result encourages a multisite study on the ALPS index with a large sample size and helps detect a subtle pathological change in the ALPS index.
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Affiliation(s)
- Yuya Saito
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
| | - Christina Andica
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Faculty of Health Data Science, Juntendo University, Chiba, Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kaito Takabayashi
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Seina Yoshida
- 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
| | - Moto Nakaya
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Radiology, University of Tokyo, Tokyo, Japan
| | - Yuya Tanaka
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Radiology, University of Tokyo, Tokyo, Japan
| | - Satoru Kamiyo
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Radiology, University of Tokyo, Tokyo, Japan
| | - Kanako Sato
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Mitsuo Nishizawa
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Toshiaki Akashi
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Keigo Shimoji
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Akihiko Wada
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
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13
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Domingos C, Fouto AR, Nunes RG, Ruiz-Tagle A, Esteves I, Silva NA, Vilela P, Gil-Gouveia R, Figueiredo P. Impact of susceptibility-induced distortion correction on perfusion imaging by pCASL with a segmented 3D GRASE readout. Magn Reson Imaging 2023:S0730-725X(23)00104-2. [PMID: 37343905 DOI: 10.1016/j.mri.2023.06.010] [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: 02/07/2023] [Revised: 05/18/2023] [Accepted: 06/17/2023] [Indexed: 06/23/2023]
Abstract
PURPOSE The consensus for the clinical implementation of arterial spin labeling (ASL) perfusion imaging recommends a segmented 3D Gradient and Spin-Echo (GRASE) readout for optimal signal-to-noise-ratio(SNR). The correction of the associated susceptibility-induced geometric distortions has been shown to improve diagnostic precision, but its impact on ASL data has not been systematically assessed and it is not consistently part of pre-processing pipelines. Here, we investigate the effects of susceptibility-induced distortion correction on perfusion imaging by pseudo-continuous ASL (pCASL) with a segmented 3D GRASE readout. METHODS Data acquired from 28 women using pCASL with 3D GRASE at 3T was analyzed using three pre-processing options: without distortion correction, with distortion correction, and with spatial smoothing (without distortion correction) matched to control for blurring effects induced by distortion correction. Maps of temporal SNR (tSNR) and relative perfusion were analyzed in eight regions-of-interest (ROIs) across the brain. RESULTS Distortion correction significantly affected tSNR and relative perfusion across the brain. Increases in tSNR were like those produced by matched spatial smoothing in most ROIs, indicating that they were likely due to blurring effects. However, that was not the case in the frontal and temporal lobes, where we also found increased relative perfusion with distortion correction even compared with matched spatial smoothing. These effects were found in both controls and patients, with no interactions with the participant group. CONCLUSION Correction of Susceptibility-induced distortions significantly impacts ASL perfusion imaging using a segmented 3D GRASE readout, and this step should therefore be considered in ASL pre-processing pipelines. This is of special importance in clinical studies, reporting perfusion across ROIs defined on relatively undistorted images and when conducting group analyses requiring the alignment of images across different subjects.
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Affiliation(s)
- Catarina Domingos
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal; Agência Regional para o Desenvolvimento da Investigação, Tecnologia e Inovação, Funchal, Portugal.
| | - Ana R Fouto
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Rita G Nunes
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Amparo Ruiz-Tagle
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Inês Esteves
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | | | - Pedro Vilela
- Neurology Department, Hospital da Luz, Lisbon, Portugal
| | - Raquel Gil-Gouveia
- Neurology Department, Hospital da Luz, Lisbon, Portugal.; Center for Interdisciplinary Research in Health, Universidade Católica Portuguesa, Lisbon, Portugal
| | - Patrícia Figueiredo
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
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14
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Montez DF, Van AN, Miller RL, Seider NA, Marek S, Zheng A, Newbold DJ, Scheidter K, Feczko E, Perrone AJ, Miranda-Dominguez O, Earl EA, Kay BP, Jha AK, Sotiras A, Laumann TO, Greene DJ, Gordon EM, Tisdall MD, van der Kouwe A, Fair DA, Dosenbach NUF. Using synthetic MR images for distortion correction. Dev Cogn Neurosci 2023; 60:101234. [PMID: 37023632 PMCID: PMC10106483 DOI: 10.1016/j.dcn.2023.101234] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 03/07/2023] [Accepted: 03/16/2023] [Indexed: 04/07/2023] Open
Abstract
Functional MRI (fMRI) data acquired using echo-planar imaging (EPI) are highly distorted by magnetic field inhomogeneities. Distortion and differences in image contrast between EPI and T1-weighted and T2-weighted (T1w/T2w) images makes their alignment a challenge. Typically, field map data are used to correct EPI distortions. Alignments achieved with field maps can vary greatly and depends on the quality of field map data. However, many public datasets lack field map data entirely. Additionally, reliable field map data is often difficult to acquire in high-motion pediatric or developmental cohorts. To address this, we developed Synth, a software package for distortion correction and cross-modal image registration that does not require field map data. Synth combines information from T1w and T2w anatomical images to construct an idealized undistorted synthetic image with similar contrast properties to EPI data. This synthetic image acts as an effective reference for individual-specific distortion correction. Using pediatric (ABCD: Adolescent Brain Cognitive Development) and adult (MSC: Midnight Scan Club; HCP: Human Connectome Project) data, we demonstrate that Synth performs comparably to field map distortion correction approaches, and often outperforms them. Field map-less distortion correction with Synth allows accurate and precise registration of fMRI data with missing or corrupted field map information.
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Affiliation(s)
- David F Montez
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America.
| | - Andrew N Van
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Ryland L Miller
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Nicole A Seider
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Scott Marek
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Annie Zheng
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Dillan J Newbold
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Neurology, New York University Langone Medical Center, New York, NY 10016, United States of America
| | - Kristen Scheidter
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455, United States of America; Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455, United States of America
| | - Anders J Perrone
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455, United States of America; Department of Psychiatry, Oregon Health and Science University, Portland, OR 97239, United States of America
| | - Oscar Miranda-Dominguez
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455, United States of America; Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455, United States of America
| | - Eric A Earl
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455, United States of America; Department of Psychiatry, Oregon Health and Science University, Portland, OR 97239, United States of America
| | - Benjamin P Kay
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Abhinav K Jha
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Institute for Informatics, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Deanna J Greene
- Department of Cognitive Science, University of California, San Diego, La Jolla CA 92093, United States of America
| | - Evan M Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - M Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Andre van der Kouwe
- Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, United States of America; Department of Radiology, Harvard Medical School, Boston, MA 02115, United States of America
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455, United States of America; Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455, United States of America; Institute of Child Development, University of Minnesota Medical School, Minneapolis, MN 55455, United States of America
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, United States of America
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15
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Hsu CCH, Chong ST, Kung YC, Kuo KT, Huang CC, Lin CP. Integrated diffusion image operator (iDIO): A pipeline for automated configuration and processing of diffusion MRI data. Hum Brain Mapp 2023; 44:2669-2683. [PMID: 36807461 PMCID: PMC10089090 DOI: 10.1002/hbm.26239] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 01/17/2023] [Accepted: 02/09/2023] [Indexed: 02/23/2023] Open
Abstract
The preprocessing of diffusion magnetic resonance imaging (dMRI) data involve numerous steps, including the corrections for head motion, susceptibility distortion, low signal-to-noise ratio, and signal drifting. Researchers or clinical practitioners often need to configure different preprocessing steps depending on disparate image acquisition schemes, which increases the technical threshold for dMRI analysis for nonexpert users. This could cause disparities in data processing approaches and thus hinder the comparability between studies. To make the dMRI data processing steps transparent and adapt to various dMRI acquisition schemes for researchers, we propose a semi-automated pipeline tool for dMRI named integrated diffusion image operator or iDIO. This pipeline integrates features from a wide range of advanced dMRI software tools and targets at providing a one-click solution for dMRI data analysis, via adaptive configuration for a set of suggested processing steps based on the image header of the input data. Additionally, the pipeline provides options for post-processing, such as estimation of diffusion tensor metrics and whole-brain tractography-based connectomes reconstruction using common brain atlases. The iDIO pipeline also outputs an easy-to-interpret quality control report to facilitate users to assess the data quality. To keep the transparency of data processing, the execution log and all the intermediate images produced in the iDIO's workflow are accessible. The goal of iDIO is to reduce the barriers for clinical or nonspecialist users to adopt the state-of-art dMRI processing steps.
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Affiliation(s)
- Chih-Chin Heather Hsu
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Shin Tai Chong
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yi-Chia Kung
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Kuan-Tsen Kuo
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China.,Shanghai Changning Mental Health Center, Shanghai, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Medical Device Innovation and Translation Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
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16
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Middlebrooks EH, Popple RA, Greco E, Okromelidze L, Walker HC, Lakhani DA, Anderson AR, Thomas EM, Deshpande HD, McCullough BA, Stover NP, Sung VW, Nicholas AP, Standaert DG, Yacoubian T, Dean MN, Roper JA, Grewal SS, Holland MT, Bentley JN, Guthrie BL, Bredel M. Connectomic Basis for Tremor Control in Stereotactic Radiosurgical Thalamotomy. AJNR Am J Neuroradiol 2023; 44:157-164. [PMID: 36702499 PMCID: PMC9891328 DOI: 10.3174/ajnr.a7778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 12/30/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND AND PURPOSE Given the increased use of stereotactic radiosurgical thalamotomy and other ablative therapies for tremor, new biomarkers are needed to improve outcomes. Using resting-state fMRI and MR tractography, we hypothesized that a "connectome fingerprint" can predict tremor outcomes and potentially serve as a targeting biomarker for stereotactic radiosurgical thalamotomy. MATERIALS AND METHODS We evaluated 27 patients who underwent unilateral stereotactic radiosurgical thalamotomy for essential tremor or tremor-predominant Parkinson disease. Percentage postoperative improvement in the contralateral limb Fahn-Tolosa-Marin Clinical Tremor Rating Scale (TRS) was the primary end point. Connectome-style resting-state fMRI and MR tractography were performed before stereotactic radiosurgery. Using the final lesion volume as a seed, "connectivity fingerprints" representing ideal connectivity maps were generated as whole-brain R-maps using a voxelwise nonparametric Spearman correlation. A leave-one-out cross-validation was performed using the generated R-maps. RESULTS The mean improvement in the contralateral tremor score was 55.1% (SD, 38.9%) at a mean follow-up of 10.0 (SD, 5.0) months. Structural connectivity correlated with contralateral TRS improvement (r = 0.52; P = .006) and explained 27.0% of the variance in outcome. Functional connectivity correlated with contralateral TRS improvement (r = 0.50; P = .008) and explained 25.0% of the variance in outcome. Nodes most correlated with tremor improvement corresponded to areas of known network dysfunction in tremor, including the cerebello-thalamo-cortical pathway and the primary and extrastriate visual cortices. CONCLUSIONS Stereotactic radiosurgical targets with a distinct connectivity profile predict improvement in tremor after treatment. Such connectomic fingerprints show promise for developing patient-specific biomarkers to guide therapy with stereotactic radiosurgical thalamotomy.
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Affiliation(s)
- E H Middlebrooks
- From the Departments of Radiology (E.H.M., E.G., L.O., D.A.L.)
- Neurosurgery (E.H.M., S.S.G.), Mayo Clinic, Jacksonville, Florida
| | - R A Popple
- Departments of Radiation Oncology (R.A.P., A.R.A., E.M.T., M.B.)
| | - E Greco
- From the Departments of Radiology (E.H.M., E.G., L.O., D.A.L.)
| | - L Okromelidze
- From the Departments of Radiology (E.H.M., E.G., L.O., D.A.L.)
| | - H C Walker
- Neurology (H.C.W., B.A.M., N.P.S., V.W.S., A.P.N., D.G.S., T.Y., M.N.D.)
| | - D A Lakhani
- From the Departments of Radiology (E.H.M., E.G., L.O., D.A.L.)
- Department of Radiology (D.A.L.), West Virginia University, Morgantown, West Virginia
| | - A R Anderson
- Departments of Radiation Oncology (R.A.P., A.R.A., E.M.T., M.B.)
| | - E M Thomas
- Departments of Radiation Oncology (R.A.P., A.R.A., E.M.T., M.B.)
- Department of Radiation Oncology (E.M.T.), Ohio State University, Columbus, Ohio
| | | | - B A McCullough
- Neurology (H.C.W., B.A.M., N.P.S., V.W.S., A.P.N., D.G.S., T.Y., M.N.D.)
| | - N P Stover
- Neurology (H.C.W., B.A.M., N.P.S., V.W.S., A.P.N., D.G.S., T.Y., M.N.D.)
| | - V W Sung
- Neurology (H.C.W., B.A.M., N.P.S., V.W.S., A.P.N., D.G.S., T.Y., M.N.D.)
| | - A P Nicholas
- Neurology (H.C.W., B.A.M., N.P.S., V.W.S., A.P.N., D.G.S., T.Y., M.N.D.)
| | - D G Standaert
- Neurology (H.C.W., B.A.M., N.P.S., V.W.S., A.P.N., D.G.S., T.Y., M.N.D.)
| | - T Yacoubian
- Neurology (H.C.W., B.A.M., N.P.S., V.W.S., A.P.N., D.G.S., T.Y., M.N.D.)
| | - M N Dean
- Neurology (H.C.W., B.A.M., N.P.S., V.W.S., A.P.N., D.G.S., T.Y., M.N.D.)
| | - J A Roper
- School of Kinesiology (J.A.R.), Auburn University, Auburn, Alabama
| | - S S Grewal
- Neurosurgery (E.H.M., S.S.G.), Mayo Clinic, Jacksonville, Florida
| | - M T Holland
- Neurosurgery (M.T.H., J.N.B., B.L.G.), University of Alabama at Birmingham, Birmingham, Alabama
| | - J N Bentley
- Neurosurgery (M.T.H., J.N.B., B.L.G.), University of Alabama at Birmingham, Birmingham, Alabama
| | - B L Guthrie
- Neurosurgery (M.T.H., J.N.B., B.L.G.), University of Alabama at Birmingham, Birmingham, Alabama
| | - M Bredel
- Departments of Radiation Oncology (R.A.P., A.R.A., E.M.T., M.B.)
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17
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SEMAC + VAT for Suppression of Artifacts Induced by Dental-Implant-Supported Restorations in Magnetic Resonance Imaging. J Clin Med 2023; 12:jcm12031117. [PMID: 36769765 PMCID: PMC9917855 DOI: 10.3390/jcm12031117] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/29/2023] [Accepted: 01/30/2023] [Indexed: 02/04/2023] Open
Abstract
The purpose of this study was to assess the feasibility of SEMAC + VAT to reduce artifacts induced by dental implant-supported restorations, such as its impact on the image quality. Dental-implant supported restorations were installed in a dry mandible. Magnetic resonance scans were acquired on a 3-Tesla MRI system. Artifact suppression (SEMAC + VAT) was applied with different intensity modes (weak, moderate, strong). Artifacts assessment was performed by measuring the mandible volume increase in MRI images prior (reference dataset) and after installation of dental implant-supported prosthesis. Image quality was assessed by two examiners using a five-point scale. Inter-examiner concordance and correlation analysis was performed with Cronbach's alpha and Spearman's test with a significance level at p = 0.05. Mandible volume increased by 60.23% when no artifact suppression method was used. By applying SEMAC + VAT, the volume increase ranged from 17.13% (strong mode) to 32.77% (weak mode). Visualization of mandibular bone was positively correlated with SEMAC intensity degree. SEMAC + VAT reduced MRI artifacts caused by dental-implant supported restorations. A stronger suppression mode improved visualization of mandibular bone in detriment of the scanning time.
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18
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Chandio BQ, Olivetti E, Romero-Bascones D, Harezlak J, Garyfallidis E. BundleWarp, streamline-based nonlinear registration of white matter tracts. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.04.522802. [PMID: 36711974 PMCID: PMC9881938 DOI: 10.1101/2023.01.04.522802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Nonlinear registration plays a central role in most neuroimage analysis methods and pipelines, such as in tractography-based individual and group-level analysis methods. However, nonlinear registration is a non-trivial task, especially when dealing with tractography data that digitally represent the underlying anatomy of the brain's white matter. Furthermore, such process often changes the structure of the data, causing artifacts that can suppress the underlying anatomical and structural details. In this paper, we introduce BundleWarp, a novel and robust streamline-based nonlinear registration method for the registration of white matter tracts. BundleWarp intelligently warps two bundles while preserving the bundles' crucial topological features. BundleWarp has two main steps. The first step involves the solution of an assignment problem that matches corresponding streamlines from the two bundles (iterLAP step). The second step introduces streamline-specific point-based deformations while keeping the topology of the bundle intact (mlCPD step). We provide comparisons against streamline-based linear registration and image-based nonlinear registration methods. BundleWarp quantitatively and qualitatively outperforms both, and we show that BundleWarp can deform and, at the same time, preserve important characteristics of the original anatomical shape of the bundles. Results are shown on 1,728 pairs of bundle registrations across 27 different bundle types. In addition, we present an application of BundleWarp for quantifying bundle shape differences using the generated deformation fields.
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Affiliation(s)
- Bramsh Qamar Chandio
- Department of Intelligent Systems Engineering, Indiana University Bloomington, USA
| | | | - David Romero-Bascones
- Biomedical Engineering Department, Faculty of Engineering (MU-ENG), Mondragon Unibertsitatea, Spain
- Moorfields Eye Hospital NHS Foundation Trust, UK
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, Indiana University Bloomington, USA
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19
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Yuan Y, Qiu T, Chong ST, Hsu SPC, Chu YH, Hsu YC, Xu G, Ko YT, Kuo KT, Yang Z, Zhu W, Lin CP, Song J. Automatic bundle-specific white matter fiber tracking tool using diffusion tensor imaging data: A pilot trial in the application of language-related glioma resection. Front Oncol 2023; 13:1089923. [PMID: 37035157 PMCID: PMC10080097 DOI: 10.3389/fonc.2023.1089923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 03/03/2023] [Indexed: 04/11/2023] Open
Abstract
Cerebral neoplasms like gliomas may cause intracranial pressure increasing, neural tract deviation, infiltration, or destruction in peritumoral areas, leading to neuro-functional deficits. Novel tracking technology, such as DTI, can objectively reveal and visualize three-dimensional white matter trajectories; in combination with intraoperative navigation, it can help achieve maximum resection whilst minimizing neurological deficit. Since the reconstruction of DTI raw data largely relies on the technical engineering and anatomical experience of the operator; it is time-consuming and prone to operator-induced bias. Here, we develop new user-friendly software to automatically segment and reconstruct functionally active areas to facilitate precise surgery. In this pilot trial, we used an in-house developed software (DiffusionGo) specially designed for neurosurgeons, which integrated a reliable diffusion-weighted image (DWI) preprocessing pipeline that embedded several functionalities from software packages of FSL, MRtrix3, and ANTs. The preprocessing pipeline is as follows: 1. DWI denoising, 2. Gibbs-ringing removing, 3. Susceptibility distortion correction (process if opposite polarity data were acquired), 4. Eddy current and motion correction, and 5. Bias correction. Then, this fully automatic multiple assigned criteria algorithms for fiber tracking were used to achieve easy modeling and assist precision surgery. We demonstrated the application with three language-related cases in three different centers, including a left frontal, a left temporal, and a left frontal-temporal glioma, to achieve a favorable surgical outcome with language function preservation or recovery. The DTI tracking result using DiffusionGo showed robust consistency with direct cortical stimulation (DCS) finding. We believe that this fully automatic processing pipeline provides the neurosurgeon with a solution that may reduce time costs and operating errors and improve care quality and surgical procedure quality across different neurosurgical centers.
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Affiliation(s)
- Yifan Yuan
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
- Research Units of New Technologies of Micro-Endoscopy Combination in Skull Base Surgery, Chinese Academy of Medical Sciences (CAMS), Shanghai, China
| | - Tianming Qiu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
- Research Units of New Technologies of Micro-Endoscopy Combination in Skull Base Surgery, Chinese Academy of Medical Sciences (CAMS), Shanghai, China
| | - Shin Tai Chong
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Sanford Pin-Chuan Hsu
- Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Ying-Hua Chu
- Magnetic Resonance (MR) Collaboration, Siemens Healthineers Ltd., Shanghai, China
| | - Yi-Cheng Hsu
- Magnetic Resonance (MR) Collaboration, Siemens Healthineers Ltd., Shanghai, China
| | - Geng Xu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
- Research Units of New Technologies of Micro-Endoscopy Combination in Skull Base Surgery, Chinese Academy of Medical Sciences (CAMS), Shanghai, China
| | - Yu-Ting Ko
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Kuan-Tsen Kuo
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Zixiao Yang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
- Research Units of New Technologies of Micro-Endoscopy Combination in Skull Base Surgery, Chinese Academy of Medical Sciences (CAMS), Shanghai, China
| | - Wei Zhu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
- Research Units of New Technologies of Micro-Endoscopy Combination in Skull Base Surgery, Chinese Academy of Medical Sciences (CAMS), Shanghai, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- *Correspondence: Ching-Po Lin, ; Jianping Song,
| | - Jianping Song
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
- Research Units of New Technologies of Micro-Endoscopy Combination in Skull Base Surgery, Chinese Academy of Medical Sciences (CAMS), Shanghai, China
- Department of Neurosurgery, National Regional Medical Center, Fudan University Huashan Hospital, Fuzhou, Fujian, China
- *Correspondence: Ching-Po Lin, ; Jianping Song,
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20
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Visualization of human optic nerve by diffusion tensor mapping and degree of neuropathy. PLoS One 2022; 17:e0278987. [PMID: 36508429 PMCID: PMC9744320 DOI: 10.1371/journal.pone.0278987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 11/28/2022] [Indexed: 12/14/2022] Open
Abstract
Diffusion-weighted magnetic resonance imaging of the human optic nerve and tract is technically difficult because of its small size, the inherent strong signal generated by the surrounding fat and the cerebrospinal fluid, and due to eddy current-induced distortions and subject movement artifacts. The effects of the bone canal through which the optic nerve passes, and the proximity of blood vessels, muscles and tendons are generally unknown. Also, the limited technical capabilities of the scanners and the minimization of acquisition times result in poor quality diffusion-weighted images. It is challenging for current tractography methods to accurately track optic pathway fibers that correspond to known anatomy. Despite these technical limitations and low image resolution, here we show how to visualize the optic nerve and tract and quantify nerve atrophy. Our visualization method based on the analysis of the diffusion tensor shows marked differences between a healthy male subject and a male subject with progressive optic nerve neuropathy. These differences coincide with diffusion scalar metrics and are not visible on standard morphological images. A quantification of the degree of optic nerve atrophy in a systematic way is provided and it is tested on 9 subjects from the Human Connectome Project.
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21
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Kumazawa S, Yoshiura T. Estimation of undistorted images in brain echo-planar images with distortions using the conjugate gradient method with anatomical regularization. Med Phys 2022; 49:7531-7544. [PMID: 35901497 PMCID: PMC10086945 DOI: 10.1002/mp.15881] [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: 11/01/2021] [Revised: 05/27/2022] [Accepted: 07/07/2022] [Indexed: 12/27/2022] Open
Abstract
PURPOSE Although echo-planar imaging (EPI) is widely used for diffusion magnetic resonance (MR) imaging, EPI images suffer from susceptibility-induced geometric distortions. We herein propose a new estimation method for undistorted EPI images using anatomical T1 -weighted images (T1 WIs) based on the physics of MR imaging. METHODS Our proposed method estimates the undistorted EPI image in the image domain while estimating the magnetic field inhomogeneity map using the conjugate gradient method with anatomical regularization. Our method synthesizes the distorted image to match the measured EPI image containing geometric distortions by alternately updating the undistorted EPI image and the magnetic field inhomogeneity map. We evaluated our proposed method and compared it with a nonrigid registration-based distortion correction method using simulated data and using real data. In the evaluation of the estimation of the magnetic field inhomogeneity map, we used the normalized root-mean-squared error (NRMSE) between the estimated results and the ground truth. In the evaluation of the estimation of undistorted images, we used mutual information (MI) between the undistorted EPI image and the anatomical T1 WI. RESULTS Using the simulated data, the means and standard deviations of the NRMSE values in the nonrigid registration-based method and proposed method were 1.29 ± 0.63 and 0.64 ± 0.30, respectively. The MI values in the proposed method were larger than those in the nonrigid registration-based method in all evaluated slices. For the real data, the proposed method improved the distortion, and the MI values in the proposed method were larger than those in the nonrigid registration-based method. In the estimation of the magnetic field inhomogeneity map, the NRMSE values in our method were smaller than those in the nonrigid registration-based method. CONCLUSIONS We demonstrated that our proposed method can estimate the regions with compressed distortions that are not well represented by the nonrigid registration-based methods. The results suggest that the proposed method could be useful in analyses combining EPI images with T1 WIs.
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Affiliation(s)
- Seiji Kumazawa
- Department of Radiological Technology, Faculty of Health Sciences, Hokkaido University of Science, Sapporo, Hokkaido, Japan
| | - Takashi Yoshiura
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Kyushu, Japan
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22
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Jang YH, Kim H, Lee JY, Ahn JH, Chung AW, Lee HJ. Altered development of structural MRI connectome hubs at near-term age in very and moderately preterm infants. Cereb Cortex 2022; 33:5507-5523. [PMID: 36408630 DOI: 10.1093/cercor/bhac438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 10/04/2022] [Accepted: 10/05/2022] [Indexed: 11/22/2022] Open
Abstract
Abstract
Preterm infants may exhibit altered developmental patterns of the brain structural network by endogenous and exogenous stimuli, which are quantifiable through hub and modular network topologies that develop in the third trimester. Although preterm brain networks can compensate for white matter microstructural abnormalities of core connections, less is known about how the network developmental characteristics of preterm infants differ from those of full-term infants. We identified 13 hubs and 4 modules and revealed subtle differences in edgewise connectivity and local network properties between 134 preterm and 76 full-term infants, identifying specific developmental patterns of the brain structural network in preterm infants. The modules of preterm infants showed an imbalanced composition. The edgewise connectivity in preterm infants showed significantly decreased long- and short-range connections and local network properties in the dorsal superior frontal gyrus. In contrast, the fusiform gyrus and several nonhub regions showed significantly increased wiring of short-range connections and local network properties. Our results suggested that decreased local network in the frontal lobe and excessive development in the occipital lobe may contribute to the understanding of brain developmental deviances in preterm infants.
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Affiliation(s)
- Yong Hun Jang
- Hanyang University Graduate School of Biomedical Science and Engineering Department of Translational Medicine, , Seoul 04763 , Republic of Korea
| | - Hyuna Kim
- Hanyang University Graduate School of Biomedical Science and Engineering Department of Translational Medicine, , Seoul 04763 , Republic of Korea
| | - Joo Young Lee
- Hanyang University Graduate School of Biomedical Science and Engineering Department of Translational Medicine, , Seoul 04763 , Republic of Korea
| | - Ja-Hye Ahn
- Hanyang University College of Medicine Department of Pediatrics, Hanyang University Hospital, , Seoul 04763 , Republic of Korea
| | - Ai Wern Chung
- Harvard Medical School Fetal Neonatal-Neuroimaging and Developmental Science Center, Boston Children’s Hospital, , Boston, MA 02115 , USA
- Harvard Medical School Department of Pediatrics, Boston Children’s Hospital, , Boston, MA 02115 , USA
| | - Hyun Ju Lee
- Hanyang University College of Medicine Department of Pediatrics, Hanyang University Hospital, , Seoul 04763 , Republic of Korea
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Enguix V, Kenley J, Luck D, Cohen-Adad J, Lodygensky GA. NeoRS: A Neonatal Resting State fMRI Data Preprocessing Pipeline. Front Neuroinform 2022; 16:843114. [PMID: 35784189 PMCID: PMC9247272 DOI: 10.3389/fninf.2022.843114] [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: 12/24/2021] [Accepted: 05/27/2022] [Indexed: 11/20/2022] Open
Abstract
Resting state functional MRI (rsfMRI) has been shown to be a promising tool to study intrinsic brain functional connectivity and assess its integrity in cerebral development. In neonates, where functional MRI is limited to very few paradigms, rsfMRI was shown to be a relevant tool to explore regional interactions of brain networks. However, to identify the resting state networks, data needs to be carefully processed to reduce artifacts compromising the interpretation of results. Because of the non-collaborative nature of the neonates, the differences in brain size and the reversed contrast compared to adults due to myelination, neonates can’t be processed with the existing adult pipelines, as they are not adapted. Therefore, we developed NeoRS, a rsfMRI pipeline for neonates. The pipeline relies on popular neuroimaging tools (FSL, AFNI, and SPM) and is optimized for the neonatal brain. The main processing steps include image registration to an atlas, skull stripping, tissue segmentation, slice timing and head motion correction and regression of confounds which compromise functional data interpretation. To address the specificity of neonatal brain imaging, particular attention was given to registration including neonatal atlas type and parameters, such as brain size variations, and contrast differences compared to adults. Furthermore, head motion was scrutinized, and motion management optimized, as it is a major issue when processing neonatal rsfMRI data. The pipeline includes quality control using visual assessment checkpoints. To assess the effectiveness of NeoRS processing steps we used the neonatal data from the Baby Connectome Project dataset including a total of 10 neonates. NeoRS was designed to work on both multi-band and single-band acquisitions and is applicable on smaller datasets. NeoRS also includes popular functional connectivity analysis features such as seed-to-seed or seed-to-voxel correlations. Language, default mode, dorsal attention, visual, ventral attention, motor and fronto-parietal networks were evaluated. Topology found the different analyzed networks were in agreement with previously published studies in the neonate. NeoRS is coded in Matlab and allows parallel computing to reduce computational times; it is open-source and available on GitHub (https://github.com/venguix/NeoRS). NeoRS allows robust image processing of the neonatal rsfMRI data that can be readily customized to different datasets.
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Affiliation(s)
- Vicente Enguix
- Department of Pediatrics, CHU Sainte-Justine, University of Montreal, Montreal, QC, Canada
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Canadian Neonatal Brain Platform, Montreal, QC, Canada
- *Correspondence: Vicente Enguix,
| | - Jeanette Kenley
- Washington University School of Medicine, St. Louis, MO, United States
| | - David Luck
- Department of Pediatrics, CHU Sainte-Justine, University of Montreal, Montreal, QC, Canada
- Canadian Neonatal Brain Platform, Montreal, QC, Canada
| | - Julien Cohen-Adad
- Department of Pediatrics, CHU Sainte-Justine, University of Montreal, Montreal, QC, Canada
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Functional Neuroimaging Unit, CRIUGM, University of Montreal, Montreal, QC, Canada
- Mila – Quebec AI Institute, Montreal, QC, Canada
| | - Gregory Anton Lodygensky
- Department of Pediatrics, CHU Sainte-Justine, University of Montreal, Montreal, QC, Canada
- Canadian Neonatal Brain Platform, Montreal, QC, Canada
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24
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Qiao Y, Shi Y. Unsupervised Deep Learning for FOD-Based Susceptibility Distortion Correction in Diffusion MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:1165-1175. [PMID: 34882551 PMCID: PMC9177803 DOI: 10.1109/tmi.2021.3134496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Susceptibility induced distortion is a major artifact that affects the diffusion MRI (dMRI) data analysis. In the Human Connectome Project (HCP), the state-of-the-art method adopted to correct this kind of distortion is to exploit the displacement field from the B0 image in the reversed phase encoding images. However, both the traditional and learning-based approaches have limitations in achieving high correction accuracy in certain brain regions, such as brainstem. By utilizing the fiber orientation distribution (FOD) computed from the dMRI, we propose a novel deep learning framework named DistoRtion Correction Net (DrC-Net), which consists of the U-Net to capture the latent information from the 4D FOD images and the spatial transformer network to propagate the displacement field and back propagate the losses between the deformed FOD images. The experiments are performed on two datasets acquired with different phase encoding (PE) directions including the HCP and the Human Connectome Low Vision (HCLV) dataset. Compared to two traditional methods topup and FODReg and two deep learning methods S-Net and flow-net, the proposed method achieves significant improvements in terms of the mean squared difference (MSD) of fractional anisotropy (FA) images and minimum angular difference between two PEs in white matter and also brainstem regions. In the meantime, the proposed DrC-Net takes only several seconds to predict a displacement field, which is much faster than the FODReg method.
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25
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Keuken MC, Liebrand LC, Bazin PL, Alkemade A, van Berendonk N, Groot JM, Isherwood SJS, Kemp S, Lute N, Mulder MJ, Trutti AC, Caan MWA, Forstmann BU. A high-resolution multi-shell 3T diffusion magnetic resonance imaging dataset as part of the Amsterdam Ultra-high field adult lifespan database (AHEAD). Data Brief 2022; 42:108086. [PMID: 35372652 PMCID: PMC8971326 DOI: 10.1016/j.dib.2022.108086] [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: 10/28/2021] [Revised: 03/07/2022] [Accepted: 03/16/2022] [Indexed: 11/18/2022] Open
Abstract
In order to further our understanding of brain function and the underlying networks, more advanced diffusion weighted magnetic resonance imaging (DWI MRI) data are essential. Here we present freely available high-resolution multi-shell multi-directional 3 Tesla (T) DWI MRI data as part of the ‘Amsterdam Ultra-high field adult lifespan database’ (AHEAD). The 3T DWI AHEAD dataset include 1.28mm isotropic whole brain DWI data of 49 healthy adult participants between 18 and 90 years old. The acquired data include DWIs at three non-zero b-values (48 directions, b-value 700 s/mm2; 56 directions, b-value 1000 s/mm2; 64 directions, b-value 1600 s/mm2) including a total of twelve volumes with a b-value of 0 s/mm2 (b0 volumes). In addition, eight b0 volumes with a reversed phase encoding direction were acquired to correct for distortions. To facilitate future use, the DWI data have been denoised, corrected for eddy currents, susceptibility-induced off-resonance field distortions, bias fields, and are skull stripped.
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Affiliation(s)
- Max C Keuken
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, The Netherlands
| | - Luka C Liebrand
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Pierre-Louis Bazin
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, The Netherlands.,Departments of Neurophysics and Neurology, Max Planck Institute for Human, Cognitive and Brain Sciences, Leipzig, Germany
| | - Anneke Alkemade
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, The Netherlands
| | - Nikita van Berendonk
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, The Netherlands
| | - Josephine M Groot
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, The Netherlands.,Department of Psychology, UiT-The Arctic University of Norway, Tromsø, Norway
| | - Scott J S Isherwood
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, The Netherlands
| | - Sarah Kemp
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, The Netherlands.,Sensorimotor Neuroscience and Ageing Research Lab, School of Psychological Sciences, College of Health and Medicine, University of Tasmania, Hobart, Australia
| | - Nicky Lute
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, The Netherlands
| | - Martijn J Mulder
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
| | - Anne C Trutti
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, The Netherlands.,Cognitive Psychology Unit and Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Matthan W A Caan
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Birte U Forstmann
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, The Netherlands
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26
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Courtney KE, Sorg S, Baca R, Doran N, Jacobson A, Liu TT, Jacobus J. The Effects of Nicotine and Cannabis Co-Use During Late Adolescence on White Matter Fiber Tract Microstructure. J Stud Alcohol Drugs 2022; 83:287-295. [PMID: 35254252 PMCID: PMC8909919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023] Open
Abstract
OBJECTIVE Co-use of cannabis and nicotine and tobacco products (NTPs) in adolescence/young adulthood is common and associated with worse outcomes than the use of either substance in isolation. Despite this, little is known about the unique contributions of co-use to neurostructural microstructure during this neurodevelopmentally important period. This study sought to investigate the interactive effects of nicotine and cannabis co-use on white matter fiber tract microstructure in emerging adulthood. METHOD A total of 111 late adolescent (16-22 years old) nicotine (NTP; n = 55, all past-year cannabis users) and non-nicotine users (non-NTP; n = 56, 61% reporting cannabis use in the past year) completed demographic and clinical interviews and a neuroimaging session comprising anatomical and diffusion-weighted imaging scans. Group connectometry analysis identified white matter tracts significantly associated with the interaction between nicotine group and past-year cannabis use according to generalized fractional anisotropy (GFA). RESULTS Nicotine Group × Cannabis Use interactions were observed in the right and left cingulum and left fornix tracts (false discovery rate = 0.053), where greater cannabis use was associated with increased GFA in the cingulum and left fornix, but only when co-used with nicotine. CONCLUSIONS This report represents the first group connectometry analysis in late adolescent/young adult cannabis and/or NTP users. Results suggest that co-use of cannabis and NTPs results in a structurally distinct white matter phenotype as compared with cannabis use only, although to what extent this may change over time with more chronic nicotine and cannabis use remains to be examined in future work.
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Affiliation(s)
- Kelly E. Courtney
- Department of Psychiatry, University of California, San Diego, La Jolla, California
| | - Scott Sorg
- Department of Psychiatry, University of California, San Diego, La Jolla, California,Veterans Affairs San Diego Healthcare System, La Jolla, California
| | - Rachel Baca
- Department of Psychiatry, University of California, San Diego, La Jolla, California
| | - Neal Doran
- Department of Psychiatry, University of California, San Diego, La Jolla, California,Veterans Affairs San Diego Healthcare System, La Jolla, California
| | - Aaron Jacobson
- Department of Radiology, University of California San Diego, La Jolla, California
| | - Thomas T. Liu
- Department of Radiology, University of California San Diego, La Jolla, California
| | - Joanna Jacobus
- Department of Psychiatry, University of California, San Diego, La Jolla, California,Correspondence may be sent to Joanna Jacobus at the Department of Psychiatry, University of California, San Diego, 9500 Gilman Drive, MC 0405, La Jolla, CA 92093, or via email at:
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27
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Courtney KE, Sorg S, Baca R, Doran N, Jacobson A, Liu TT, Jacobus J. The Effects of Nicotine and Cannabis Co-Use During Late Adolescence on White Matter Fiber Tract Microstructure. J Stud Alcohol Drugs 2022; 83:287-295. [PMID: 35254252 PMCID: PMC8909919 DOI: 10.15288/jsad.2022.83.287] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 08/24/2021] [Indexed: 08/10/2023] Open
Abstract
OBJECTIVE Co-use of cannabis and nicotine and tobacco products (NTPs) in adolescence/young adulthood is common and associated with worse outcomes than the use of either substance in isolation. Despite this, little is known about the unique contributions of co-use to neurostructural microstructure during this neurodevelopmentally important period. This study sought to investigate the interactive effects of nicotine and cannabis co-use on white matter fiber tract microstructure in emerging adulthood. METHOD A total of 111 late adolescent (16-22 years old) nicotine (NTP; n = 55, all past-year cannabis users) and non-nicotine users (non-NTP; n = 56, 61% reporting cannabis use in the past year) completed demographic and clinical interviews and a neuroimaging session comprising anatomical and diffusion-weighted imaging scans. Group connectometry analysis identified white matter tracts significantly associated with the interaction between nicotine group and past-year cannabis use according to generalized fractional anisotropy (GFA). RESULTS Nicotine Group × Cannabis Use interactions were observed in the right and left cingulum and left fornix tracts (false discovery rate = 0.053), where greater cannabis use was associated with increased GFA in the cingulum and left fornix, but only when co-used with nicotine. CONCLUSIONS This report represents the first group connectometry analysis in late adolescent/young adult cannabis and/or NTP users. Results suggest that co-use of cannabis and NTPs results in a structurally distinct white matter phenotype as compared with cannabis use only, although to what extent this may change over time with more chronic nicotine and cannabis use remains to be examined in future work.
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Affiliation(s)
- Kelly E. Courtney
- Department of Psychiatry, University of California, San Diego, La Jolla, California
| | - Scott Sorg
- Department of Psychiatry, University of California, San Diego, La Jolla, California
- Veterans Affairs San Diego Healthcare System, La Jolla, California
| | - Rachel Baca
- Department of Psychiatry, University of California, San Diego, La Jolla, California
| | - Neal Doran
- Department of Psychiatry, University of California, San Diego, La Jolla, California
- Veterans Affairs San Diego Healthcare System, La Jolla, California
| | - Aaron Jacobson
- Department of Radiology, University of California San Diego, La Jolla, California
| | - Thomas T. Liu
- Department of Radiology, University of California San Diego, La Jolla, California
| | - Joanna Jacobus
- Department of Psychiatry, University of California, San Diego, La Jolla, California
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28
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Coll-Font J, Chen S, Eder R, Fang Y, Han QJ, van den Boomen M, Sosnovik DE, Mekkaoui C, Nguyen CT. Manifold-based respiratory phase estimation enables motion and distortion correction of free-breathing cardiac diffusion tensor MRI. Magn Reson Med 2022; 87:474-487. [PMID: 34390021 PMCID: PMC8616783 DOI: 10.1002/mrm.28972] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 07/22/2021] [Accepted: 07/25/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE For in vivo cardiac DTI, breathing motion and B0 field inhomogeneities produce misalignment and geometric distortion in diffusion-weighted (DW) images acquired with conventional single-shot EPI. We propose using a dimensionality reduction method to retrospectively estimate the respiratory phase of DW images and facilitate both distortion correction (DisCo) and motion compensation. METHODS Free-breathing electrocardiogram-triggered whole left-ventricular cardiac DTI using a second-order motion-compensated spin echo EPI sequence and alternating directionality of phase encoding blips was performed on 11 healthy volunteers. The respiratory phase of each DW image was estimated after projecting the DW images into a 2D space with Laplacian eigenmaps. DisCo and motion compensation were applied to the respiratory sorted DW images. The results were compared against conventional breath-held T2 half-Fourier single shot turbo spin echo. Cardiac DTI parameters including fractional anisotropy, mean diffusivity, and helix angle transmurality were compared with and without DisCo. RESULTS The left-ventricular geometries after DisCo and motion compensation resulted in significantly improved alignment of DW images with T2 reference. DisCo reduced the distance between the left-ventricular contours by 13.2% ± 19.2%, P < .05 (2.0 ± 0.4 for DisCo and 2.4 ± 0.5 mm for uncorrected). DisCo DTI parameter maps yielded no significant differences (mean diffusivity: 1.55 ± 0.13 × 10-3 mm2 /s and 1.53 ± 0.13 × 10-3 mm2 /s, P = .09; fractional anisotropy: 0.375 ± 0.041 and 0.379 ± 0.045, P = .11; helix angle transmurality: 1.00% ± 0.10°/% and 0.99% ± 0.12°/%, P = .44), although the orientation of individual tensors differed. CONCLUSION Retrospective respiratory phase estimation with LE-based DisCo and motion compensation in free-breathing cardiac DTI resulting in significantly reduced geometric distortion and improved alignment within and across slices.
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Affiliation(s)
- Jaume Coll-Font
- Cardiovascular Research Center, Massachusetts General Hospital, Boston (MA), USA,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston (MA), USA,Harvard Medical School, Boston (MA), USA
| | - Shi Chen
- Cardiovascular Research Center, Massachusetts General Hospital, Boston (MA), USA
| | - Robert Eder
- Cardiovascular Research Center, Massachusetts General Hospital, Boston (MA), USA
| | - Yiling Fang
- Cardiovascular Research Center, Massachusetts General Hospital, Boston (MA), USA,Institute of Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, (MA), USA
| | - Qiao Joyce Han
- Cardiovascular Research Center, Massachusetts General Hospital, Boston (MA), USA,Harvard Medical School, Boston (MA), USA
| | - Maaike van den Boomen
- Cardiovascular Research Center, Massachusetts General Hospital, Boston (MA), USA,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston (MA), USA,Harvard Medical School, Boston (MA), USA,Department of Radiology, University Medical Center Groningen, Groningen, Netherlands
| | - David E. Sosnovik
- Cardiovascular Research Center, Massachusetts General Hospital, Boston (MA), USA,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston (MA), USA,Harvard Medical School, Boston (MA), USA
| | - Choukri Mekkaoui
- Cardiovascular Research Center, Massachusetts General Hospital, Boston (MA), USA,Harvard Medical School, Boston (MA), USA
| | - Christopher T. Nguyen
- Cardiovascular Research Center, Massachusetts General Hospital, Boston (MA), USA,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston (MA), USA,Harvard Medical School, Boston (MA), USA
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29
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What's New and What's Next in Diffusion MRI Preprocessing. Neuroimage 2021; 249:118830. [PMID: 34965454 PMCID: PMC9379864 DOI: 10.1016/j.neuroimage.2021.118830] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/26/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on “what’s new” since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on “Mapping the Connectome” in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on “what’s next” in dMRI preprocessing.
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30
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Zhang J, Liu S, Dai E, Ye X, Shi D, Wu Y, Lu J, Guo H. Slab boundary artifact correction in multislab imaging using convolutional-neural-network-enabled inversion for slab profile encoding. Magn Reson Med 2021; 87:1546-1560. [PMID: 34655095 DOI: 10.1002/mrm.29047] [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: 05/21/2021] [Revised: 09/23/2021] [Accepted: 09/25/2021] [Indexed: 11/06/2022]
Abstract
PURPOSE This study aims to propose a novel algorithm for slab boundary artifact correction in both single-band multislab imaging and simultaneous multislab (SMSlab) imaging. THEORY AND METHODS In image domain, the formation of slab boundary artifacts can be regarded as modulating the artifact-free images using the slab profiles and introducing aliasing along the slice direction. Slab boundary artifact correction is the inverse problem of this process. An iterative algorithm based on convolutional neural networks (CNNs) is proposed to solve the problem, termed CNN-enabled inversion for slab profile encoding (CPEN). Diffusion-weighted SMSlab images and reference images without slab boundary artifacts were acquired in 7 healthy subjects for training. Images of 5 healthy subjects were acquired for testing, including single-band multislab and SMSlab images with 1.3-mm or 1-mm isotropic resolution. CNN-enabled inversion for slab profile encoding was compared with a previously reported method (i.e., nonlinear inversion for slab profile encoding [NPEN]). RESULTS CNN-enabled inversion for slab profile encoding reduces the slab boundary artifacts in both single-band multislab and SMSlab images. It also suppresses the slab boundary artifacts in the diffusion metric maps. Compared with NPEN, CPEN shows fewer residual artifacts in different acquisition protocols and more significant improvements in quantitative assessment, and it also accelerates the computation by more than 35 times. CONCLUSION CNN-enabled inversion for slab profile encoding can reduce the slab boundary artifacts in multislab acquisitions. It shows better slab boundary artifact correction capacity, higher robustness, and computation efficiency when compared with NPEN. It has the potential to improve the accuracy of multislab acquisitions in high-resolution DWI and functional MRI.
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Affiliation(s)
- Jieying Zhang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China
| | - Simin Liu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China
| | - Erpeng Dai
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China.,Department of Radiology, Stanford University, Stanford, California, USA
| | - Xinyu Ye
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China
| | - Diwei Shi
- Center for Nano and Micro Mechanics, Tsinghua University, Beijing, People's Republic of China
| | - Yuhsuan Wu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China
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31
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Chong ST, Liu X, Kao HW, Lin CYE, Hsu CCH, Kung YC, Kuo KT, Huang CC, Lo CYZ, Li Y, Zhao G, Lin CP. Exploring Peritumoral Neural Tracts by Using Neurite Orientation Dispersion and Density Imaging. Front Neurosci 2021; 15:702353. [PMID: 34646116 PMCID: PMC8502884 DOI: 10.3389/fnins.2021.702353] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 08/17/2021] [Indexed: 12/12/2022] Open
Abstract
Diffusion Tensor Imaging (DTI) tractography has been widely used in brain tumor surgery to ensure thorough resection and minimize functional damage. However, due to enhanced anisotropic uncertainty in the area with peritumoral edema, diffusion tractography is generally not practicable leading to high false-negative results in neural tracking. In this study, we evaluated the usefulness of the neurite orientation dispersion and density imaging (NODDI) derived tractography for investigating structural heterogeneity of the brain in patients with brain tumor. A total of 24 patients with brain tumors, characterized by peritumoral edema, and 10 healthy counterparts were recruited from 2014 to 2021. All participants underwent magnetic resonance imaging. Moreover, we used the images obtained from the healthy participants for calibrating the orientation dispersion threshold for NODDI-derived corticospinal tract (CST) reconstruction. Compared to DTI, NODDI-derived tractography has a great potential to improve the reconstruction of fiber tracking through regions of vasogenic edema. The regions with edematous CST in NODDI-derived tractography demonstrated a significant decrease in the intracellular volume fraction (VFic, p < 0.000) and an increase in the isotropic volume fraction (VFiso, p < 0.014). Notably, the percentage of the involved volume of the concealed CST and lesion-to-tract distance could reflect the motor function of the patients. After the tumor resection, four patients with 1–5 years follow-up were showed subsidence of the vasogenic edema and normal CST on DTI tractography. NODDI-derived tractography revealed tracts within the edematous area and could assist neurosurgeons to locate the neural tracts that are otherwise not visualized by conventional DTI tractography.
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Affiliation(s)
- Shin Tai Chong
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Xinrui Liu
- Department of Neurosurgery, First Hospital of Jilin University, Changchun, China
| | - Hung-Wen Kao
- Department of Medical Imaging, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan.,Department of Radiology, School of Medicine, Tzu Chi University, Hualien, Taiwan
| | | | - Chih-Chin Heather Hsu
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.,Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yi-Chia Kung
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Kuan-Tsen Kuo
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Chu-Chung Huang
- School of Psychology and Cognitive Science, Institute of Cognitive Neuroscience, East China Normal University, Shanghai, China
| | - Chun-Yi Zac Lo
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yunqian Li
- Department of Neurosurgery, First Hospital of Jilin University, Changchun, China
| | - Gang Zhao
- Department of Neurosurgery, First Hospital of Jilin University, Changchun, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.,Brain Research Center, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
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32
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Magnetic Resonance Simulation in Education: Quantitative Evaluation of an Actual Classroom Experience. SENSORS 2021; 21:s21186011. [PMID: 34577231 PMCID: PMC8468339 DOI: 10.3390/s21186011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 08/30/2021] [Accepted: 09/04/2021] [Indexed: 11/17/2022]
Abstract
Magnetic resonance is an imaging modality that implies a high complexity for radiographers. Despite some simulators having been developed for training purposes, we are not aware of any attempt to quantitatively measure their educational performance. The present study gives an answer to the question: Does an MRI simulator built on specific functional and non-functional requirements help radiographers learn MRI theoretical and practical concepts better than traditional educational method based on lectures? Our study was carried out in a single day by a total of 60 students of a main hospital in Madrid, Spain. The experiment followed a randomized pre-test post-test design with a control group that used a traditional educational method, and an experimental group that used our simulator. Knowledge level was assessed by means of an instrument with evidence of validity in its format and content, while its reliability was analyzed after the experiment. Statistical differences between both groups were measured. Significant statistical differences were found in favor of the participants who used the simulator for both the post-test score and the gain (difference between post-test and pre-test scores). The effect size turned out to be significant as well. In this work we evaluated a magnetic resonance simulation paradigm as a tool to help in the training of radiographers. The study shows that a simulator built on specific design requirements is a valuable complement to traditional education procedures, backed up with significant quantitative results.
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33
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Middlebrooks EH, Okromelidze L, Carter RE, Jain A, Lin C, Westerhold E, Peña AB, Quiñones-Hinojosa A, Uitti RJ, Grewal SS. Directed stimulation of the dentato-rubro-thalamic tract for deep brain stimulation in essential tremor: a blinded clinical trial. Neuroradiol J 2021; 35:203-212. [PMID: 34340623 DOI: 10.1177/19714009211036689] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVE Observational studies utilising diffusion tractography have suggested a common mechanism for tremor alleviation in deep brain stimulation for essential tremor: the decussating portion of the dentato-rubro-thalamic tract. We hypothesised that directional stimulation of the dentato-rubro-thalamic tract would result in greater tremor improvement compared to sham programming, as well as comparable improvement as more tedious standard-of-care programming. METHODS A prospective, blinded crossover trial was performed to assess the feasibility, safety and outcomes of programming based solely on dentato-rubro-thalamic tract anatomy. Using magnetic resonance imaging diffusion-tractography, the dentato-rubro-thalamic tract was identified and a connectivity-based treatment setting was derived by modelling a volume of tissue activated using directional current steering oriented towards the dentato-rubro-thalamic tract centre. A sham setting was created at approximately 180° opposite the connectivity-based treatment. Standard-of-care programming at 3 months was compared to connectivity-based treatment and sham settings that were blinded to the programmer. The primary outcome measure was percentage improvement in the Fahn-Tolosa-Marín tremor rating score compared to the preoperative baseline. RESULTS Among the six patients, tremor rating scores differed significantly among the three experimental conditions (P=0.030). The mean tremor rating score improvement was greater with the connectivity-based treatment settings (64.6% ± 14.3%) than with sham (44.8% ± 18.6%; P=0.031) and standard-of-care programming (50.7% ± 19.2%; P=0.062). The distance between the centre of the dentato-rubro-thalamic tract and the volume of tissue activated inversely correlated with the percentage improvement in the tremor rating score (R2=0.24; P=0.04). No significant adverse events were encountered. CONCLUSIONS Using a blinded, crossover trial design, we have shown the technical feasibility, safety and potential efficacy of connectivity-based stimulation settings in deep brain stimulation for treatment of essential tremor.
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Affiliation(s)
- Erik H Middlebrooks
- Department of Radiology, Mayo Clinic, USA.,Department of Neurosurgery, Mayo Clinic, USA
| | | | | | | | - Chen Lin
- Department of Radiology, Mayo Clinic, USA
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Autio JA, Zhu Q, Li X, Glasser MF, Schwiedrzik CM, Fair DA, Zimmermann J, Yacoub E, Menon RS, Van Essen DC, Hayashi T, Russ B, Vanduffel W. Minimal specifications for non-human primate MRI: Challenges in standardizing and harmonizing data collection. Neuroimage 2021; 236:118082. [PMID: 33882349 PMCID: PMC8594288 DOI: 10.1016/j.neuroimage.2021.118082] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 02/16/2021] [Accepted: 04/07/2021] [Indexed: 02/07/2023] Open
Abstract
Recent methodological advances in MRI have enabled substantial growth in neuroimaging studies of non-human primates (NHPs), while open data-sharing through the PRIME-DE initiative has increased the availability of NHP MRI data and the need for robust multi-subject multi-center analyses. Streamlined acquisition and analysis protocols would accelerate and improve these efforts. However, consensus on minimal standards for data acquisition protocols and analysis pipelines for NHP imaging remains to be established, particularly for multi-center studies. Here, we draw parallels between NHP and human neuroimaging and provide minimal guidelines for harmonizing and standardizing data acquisition. We advocate robust translation of widely used open-access toolkits that are well established for analyzing human data. We also encourage the use of validated, automated pre-processing tools for analyzing NHP data sets. These guidelines aim to refine methodological and analytical strategies for small and large-scale NHP neuroimaging data. This will improve reproducibility of results, and accelerate the convergence between NHP and human neuroimaging strategies which will ultimately benefit fundamental and translational brain science.
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Affiliation(s)
- Joonas A Autio
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
| | - Qi Zhu
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven 3000, Belgium; Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France
| | - Xiaolian Li
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven 3000, Belgium
| | - Matthew F Glasser
- Departments of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Departments of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
| | - Caspar M Schwiedrzik
- Neural Circuits and Cognition Lab, European Neuroscience Institute Göttingen - A Joint Initiative of the University Medical Center Göttingen and the Max Planck Society, Grisebachstraße 5, 37077 Göttingen, Germany; Perception and Plasticity Group, German Primate Center - Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany
| | - Damien A Fair
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Jan Zimmermann
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Ravi S Menon
- Centre for Functional and Metabolic Mapping, Western University, London, ON, Canada
| | - David C Van Essen
- Departments of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
| | - Takuya Hayashi
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Brian Russ
- Department of Psychiatry, New York University Langone, New York City, New York, USA; Center for the Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York, USA; Department of Neuroscience, Icahn School of Medicine, Mount Sinai, New York City, New York, USA
| | - Wim Vanduffel
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven Medical School, Leuven 3000, Belgium; Leuven Brain Institute, KU Leuven, Leuven 3000, Belgium; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, Boston, MA 02144, USA
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Lahti K, Parkkola R, Jääsaari P, Haataja L, Saunavaara V. The impact of susceptibility correction on diffusion metrics in adolescents. Pediatr Radiol 2021; 51:1471-1480. [PMID: 33893847 PMCID: PMC8266789 DOI: 10.1007/s00247-021-05000-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 09/16/2020] [Accepted: 02/03/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND Diffusion tensor imaging is a widely used imaging method of brain white matter, but it is prone to imaging artifacts. The data corrections can affect the measured values. OBJECTIVE To explore the impact of susceptibility correction on diffusion metrics. MATERIALS AND METHODS A cohort of 27 healthy adolescents (18 boys, 9 girls, mean age 12.7 years) underwent 3-T MRI, and we collected two diffusion data sets (anterior-posterior). The data were processed both with and without susceptibility artifact correction. We derived fractional anisotropy, mean diffusivity and histogram data of fiber length distribution from both the corrected and uncorrected data, which were collected from the corpus callosum, corticospinal tract and cingulum bilaterally. RESULTS Fractional anisotropy and mean diffusivity values significantly differed when comparing the pathways in all measured tracts. The fractional anisotropy values were lower and the mean diffusivity values higher in the susceptibility-corrected data than in the uncorrected data. We found a significant difference in total tract length in the corpus callosum and the corticospinal tract. CONCLUSION This study indicates that susceptibility correction has a significant effect on measured fractional anisotropy, and on mean diffusivity values and tract lengths. To receive reliable and comparable results, the correction should be used systematically.
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Affiliation(s)
- Katri Lahti
- Department of Pediatric Neurology, University of Turku and Turku University Hospital, P.O. Box 52, 20521, Turku, Finland.
- Department of Adolescent Psychiatry, Turku University Hospital, Turku, Finland.
| | - Riitta Parkkola
- Department of Radiology, University of Turku and Turku University Hospital, Turku, Finland
| | - Päivi Jääsaari
- Department of Oral and Maxillofacial Diseases, Turku University Hospital, Turku, Finland
| | - Leena Haataja
- Children's Hospital, and Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Virva Saunavaara
- Department of Medical Physics, Turku University Hospital, Turku, Finland
- Turku PET Centre, Turku University Hospital, Turku, Finland
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Barakovic M, Girard G, Schiavi S, Romascano D, Descoteaux M, Granziera C, Jones DK, Innocenti GM, Thiran JP, Daducci A. Bundle-Specific Axon Diameter Index as a New Contrast to Differentiate White Matter Tracts. Front Neurosci 2021; 15:646034. [PMID: 34211362 PMCID: PMC8239216 DOI: 10.3389/fnins.2021.646034] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 05/17/2021] [Indexed: 12/30/2022] Open
Abstract
In the central nervous system of primates, several pathways are characterized by different spectra of axon diameters. In vivo methods, based on diffusion-weighted magnetic resonance imaging, can provide axon diameter index estimates non-invasively. However, such methods report voxel-wise estimates, which vary from voxel-to-voxel for the same white matter bundle due to partial volume contributions from other pathways having different microstructure properties. Here, we propose a novel microstructure-informed tractography approach, COMMITAxSize, to resolve axon diameter index estimates at the streamline level, thus making the estimates invariant along trajectories. Compared to previously proposed voxel-wise methods, our formulation allows the estimation of a distinct axon diameter index value for each streamline, directly, furnishing a complementary measure to the existing calculation of the mean value along the bundle. We demonstrate the favourable performance of our approach comparing our estimates with existing histologically-derived measurements performed in the corpus callosum and the posterior limb of the internal capsule. Overall, our method provides a more robust estimation of the axon diameter index of pathways by jointly estimating the microstructure properties of the tissue and the macroscopic organisation of the white matter connectivity.
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Affiliation(s)
- Muhamed Barakovic
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Gabriel Girard
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- CIBM Center for BioMedical Imaging, Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Simona Schiavi
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Computer Science, University of Verona, Verona, Italy
| | - David Romascano
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, Australia
| | - Giorgio M. Innocenti
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Brain and Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Lab 5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- CIBM Center for BioMedical Imaging, Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
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Behler A, Kassubek J, Müller HP. Age-Related Alterations in DTI Metrics in the Human Brain-Consequences for Age Correction. Front Aging Neurosci 2021; 13:682109. [PMID: 34211389 PMCID: PMC8239142 DOI: 10.3389/fnagi.2021.682109] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 05/12/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Over the life span, the diffusion metrics in brain MRI show different, partly nonlinear changes. These age-dependent changes also seem to exhibit regional differences with respect to the brain anatomy. The age correction of a study cohort's diffusion metrics might thus require consideration of age-related factors. Methods: Diffusion tensor imaging data sets were acquired from 219 healthy participants at ages between 19 and 81 years. Fractional anisotropy (FA), mean diffusivity (MD), and axial and radial diffusivity (AD and RD, respectively) maps were analyzed by a tract of interest-based fiber tracking approach. To describe diffusion metrics as a function of the participant age, linear splines were used to perform curve fitting in 21 specific tract systems covering different functional areas and diffusion directions. Results: In the majority of tracts, an interpolation with a change of alteration rate during adult life described the diffusion properties more accurately than a linear model. Consequently, the diffusion properties remained relatively stable until a decrease (of FA) or increase (of MD, AD, and RD) started at a region-specific time point, whereas a uniform change of diffusion properties was observed only in a few tracts. Single tracts, e.g., located in the cerebellum, remained nearly unaltered throughout the ages between 19 and 81 years. Conclusions: Age corrections of diffusion properties should not be applied to all white matter regions and all age spans in the same way. Therefore, we propose three different approaches for age correction based on fiber tracking techniques, i.e., no correction for areas that do not experience age-related changes and two variants of an age correction depending on the age range of the cohort and the tracts considered.
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Affiliation(s)
- Anna Behler
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Jan Kassubek
- Department of Neurology, University of Ulm, Ulm, Germany
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Liu S, Xiong Y, Dai E, Zhang J, Guo H. Improving distortion correction for isotropic high-resolution 3D diffusion MRI by optimizing Jacobian modulation. Magn Reson Med 2021; 86:2780-2794. [PMID: 34121222 DOI: 10.1002/mrm.28884] [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: 02/03/2021] [Revised: 05/14/2021] [Accepted: 05/15/2021] [Indexed: 11/07/2022]
Abstract
PURPOSE To improve distortion correction for isotropic high-resolution whole-brain 3D diffusion MRI when in a time-saving acquisition scenario. THEORY AND METHODS Data were acquired using simultaneous multi-slab (SMSlab) acquisitions, with a b = 0 image pair encoded by reversed polarity gradients (RPG) for phase encoding (PE) and diffusion weighted images encoded by a single PE direction. Eddy current-induced distortions were corrected first. During the following susceptibility distortion correction, image deformation was first corrected by the field map estimated from the b = 0 image pair. Intensity variation was subsequently corrected by Jacobian modulation. Two Jacobian modulation methods were compared. They calculated the Jacobian modulation map from the field map, or from the deformation corrected b = 0 image pair, termed as JField and JRPG , respectively. A modified version of the JRPG method, with proper smoothing, was further proposed for improved correction performance, termed as JRPG-smooth . RESULTS Compared to JField modulation, less remaining distortions are observed when using the JRPG and JRPG-smooth methods, especially in areas with large B0 field inhomogeneity. The original JRPG method causes signal-to-noise ratio (SNR) deficiency problem, which manifests as degraded SNR of the diffusion weighted images, while the JRPG-smooth method maintains the original image SNR. Less estimation errors of diffusion metrics are observed when using the JRPG-smooth method. CONCLUSION This study improves the distortion correction for isotropic high-resolution whole-brain 3D diffusion MRI by optimizing Jacobian modulation. The optimized method outperforms the conventional JField method regarding intensity variation correction and accuracy of diffusion metrics estimation, and outperforms the original JRPG method regarding SNR performance.
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Affiliation(s)
- Simin Liu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Yuhui Xiong
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Erpeng Dai
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Jieying Zhang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
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Tracking white-matter brain modifications in chronic non-bothersome acoustic trauma tinnitus. NEUROIMAGE-CLINICAL 2021; 31:102696. [PMID: 34029920 PMCID: PMC8163994 DOI: 10.1016/j.nicl.2021.102696] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 05/05/2021] [Accepted: 05/06/2021] [Indexed: 11/30/2022]
Abstract
Tractography was compared between two groups of tinnitus and control participants. Diffusion was modeled with ss3t-CSD allowing apparent fiber density (AFD) calculation. 27 bundles of interest were chosen for their link to the auditory and limbic systems. AFD was significantly increased in the tinnitus group in the right frontal isthmus. AFD in the acoustic radiations was not significantly different between the groups.
Subjective tinnitus is a symptom characterized by the perception of sound with no external acoustic source, most often accompanied by co-morbidities. To date, the specific role of white matter abnormalities related to tinnitus reaches no consensus in the literature. The goal of this study was to explore the structural connectivity related to tinnitus percept per se, thus focusing on a specific population presenting chronic non-bothersome tinnitus of similar etiology (noise induced) without co-morbidities. We acquired diffusion-weighted images with high angular resolution in a homogeneous group of mildly impacted tinnitus participants (n = 19) and their matched controls (n = 19). We focused the study on two subsets of fiber bundles of interest: on one hand, we extracted the acoustic radiation and further included any intersecting fiber bundles; on the other hand, we explored the tracts related to the limbic system. We modeled the diffusion signal using constrained spherical deconvolution. We conducted a deep-learning based tractography segmentation and mapped Apparent Fiber Density (AFD) on the bundles of interest. C, as well as Fractional Anisotropy (FA) and FOD peak amplitude for comparison. Between group statistical comparison was performed along the 27 tracts of interest controlling for confounding hearing loss, tinnitus severity, and duration since onset. We tested a potential correlation with hearing loss, tinnitus duration and tinnitus handicap score along these tracts. In the tinnitus group, we observed increased AFD related to chronic tinnitus percept after acoustic trauma in two main white matter regions. First, in the right hemisphere, in the isthmus between inferior temporal and inferior frontal cortices, in the uncinate fasciculus (UF), and in the inferior fronto-occipital bundle (IFO). Second, in the left hemisphere, underneath the superior parietal region in the thalamo parietal tract and parieto-occipital pontine tract. Between-group differences in the acoustic radiations were not significant with AFD but were with FA. Furthermore, significant correlations with hearing loss were found in the left hemisphere in the inferior longitudinal fasciculus and in the fronto-pontine tract. No additional correlation was found with tinnitus duration nor with tinnitus handicap, as reflected by THI scores. The regions that displayed tinnitus related increased AFD also displayed increased FA. The isthmus of the UF and IFO in the right hemisphere appear to be involved with a number of neuropsychiatric and traumatic disorders confirming the involvement of the limbic system even in chronic non-bothersome tinnitus subjects, potentially suggesting a common pathway between these pathologies. White matter changes underneath the superior parietal cortex found here in tinnitus participants supports the implication of an auditory-somatosensory pathway in tinnitus perception.
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Song R, Glass JO, Reddick WE. Modified Diffusion Tensor Image Processing Pipeline for Archived Studies of Patients With Leukoencephalopathy. J Magn Reson Imaging 2021; 54:997-1008. [PMID: 33856092 DOI: 10.1002/jmri.27636] [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: 09/09/2020] [Revised: 03/26/2021] [Accepted: 03/30/2021] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND In archived diffusion tensor imaging (DTI) studies, a reversed-phase encoding (PE) scan required to correct the distortion in single-shot echo-planar imaging (EPI) may not have been acquired. Furthermore, DTI tractography is adversely affected by incorrect white matter segmentation due to leukoencephalopathy (LE). All these issues need to be addressed. PURPOSE To propose and evaluate a modified DTI processing pipeline with DIstortion COrrection using pseudo T2 -weighted images (DICOT) to overcome limitations in existing acquisition protocols. STUDY TYPE Retrospective feasibility. SUBJECTS DICOT was assessed in simulated data and 84 acute lymphoblastic leukemia (ALL) patients with reversed PE acquired. The pipeline was then tested in 522 scans from 261 ALL patients without a reversed PE acquired. FIELD STRENGTH/SEQUENCE A 3 T; diffusion-weighted EPI; 3D magnetization prepared rapid acquisition gradient echo (MPRAGE). STATISTICAL TESTS Repeated measures analysis of variance and Tukey post hoc tests were performed to compare fractional anisotropy (FA) values obtained by different methods. ASSESSMENT FA and corresponding absolute error maps were obtained using TOPUP, DICOT, INVERSION (Inverse contrast Normalization for VERy Simple registratION) and NO CORR (no correction). Each method was assessed by comparing to TOPUP. The pipeline in the ALL patients was evaluated based on the failure rate of the distortion correction using the global correlation values. RESULTS Using DICOT reduced the mean absolute errors by an average of 32% in FA in simulation datasets. In 84 patients, the error reductions were approximately 15% in FA with DICOT, while it was 5% with INVERSION. No significant differences between the TOPUP and DICOT were observed in FA with P = 0.090/0.894(AP/PA). Only 15 of 516 examinations requiring any additional manual intervention. CONCLUSION This modified pipeline produced better results than the INVERSION. Furthermore, robust performance was demonstrated in archived patient scans acquired without an inverse PE necessary for TOPUP correction. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Ruitian Song
- Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - John O Glass
- Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Wilburn E Reddick
- Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis, Tennessee, USA
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Jenabi M, Young RJ, Moreno R, Gene M, Cho N, Otazo R, Holodny AI, Peck KK. Multiband diffusion tensor imaging for presurgical mapping of motor and language pathways in patients with brain tumors. J Neuroimaging 2021; 31:784-795. [PMID: 33817896 DOI: 10.1111/jon.12859] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 03/04/2021] [Accepted: 03/05/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND AND PURPOSE Assessment of the essential white matter fibers of arcuate fasciculus and corticospinal tract (CST), required for preoperative planning in brain tumor patients, relies on the reliability of diffusion tensor imaging (DTI). The recent development of multiband DTI (mb-DTI) based on simultaneous multislice excitation could maintain the overall quality of tractography while not exceeding standard clinical care time. To address this potential, we performed quantitative analyses to evaluate tractography results of arcuate fasciculus and CST acquired by mb-DTI in brain tumor patients. METHODS We retrospectively analyzed 44 patients with brain lesions who underwent presurgical single-shot DTI (s-DTI) and mb-DTI. We measured DTI parameters: fractional anisotropy (FA) and mean diffusivity (MD [mm2 s-1 ]) in whole brain and tumor regions; and the tractography parameters: fiber FA, MD (mm2 s-1 ), volume (mm3 ), and length (mm) in the whole brain, arcuate fasciculus, and CST. Additionally, three neuroradiologists performed a blinded visual assessment comparing s-DTI with mb-DTI. RESULTS The mb-DTI showed higher mean FA and lower MD (r > .95, p < .002) in whole brain and tumor regions of interest; slightly higher fiber FA, volume, and length; and slightly lower fiber MD in whole brain, arcuate fasciculus, and CST than in s-DTI. These differences were significant for fiber FA in all tracts; length (mm) in arcuate fasciculus; and fiber MD (mm2 s-1 ) and volume (mm3 ) in all patients with tumor involved in the arcuate fasciculus, CST, and whole brain tracts (p = .001). Visual assessment demonstrated that both techniques produced visually similar tracts. CONCLUSIONS This study demonstrated the clinical potential and significant advantages of preoperative mb-DTI in brain tumor patients.
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Affiliation(s)
- Mehrnaz Jenabi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Robert J Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Raquel Moreno
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Madeleine Gene
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Nicholas Cho
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Andrei I Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.,Department of Neuroscience, Weill-Cornell Graduate School of the Medical Sciences, New York, New York, USA
| | - Kyung K Peck
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Abreu R, Duarte JV. Quantitative Assessment of the Impact of Geometric Distortions and Their Correction on fMRI Data Analyses. Front Neurosci 2021; 15:642808. [PMID: 33767610 PMCID: PMC7985341 DOI: 10.3389/fnins.2021.642808] [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: 12/16/2020] [Accepted: 02/15/2021] [Indexed: 11/13/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) data is typically collected with gradient-echo echo-planar imaging (GE-EPI) sequences, which are particularly prone to the susceptibility artifact as a result of B0 field inhomogeneity. The component derived from in-plane spin dephasing induces pixel intensity variations and, more critically, geometric distortions. Despite the physical mechanisms underlying the susceptibility artifact being well established, a systematic investigation on the impact of the associated geometric distortions, and the direct comparison of different approaches to tackle them, on fMRI data analyses is missing. Here, we compared two different distortion correction approaches, by acquiring additional: (1) EPI data with reversed phase encoding direction (TOPUP), and (2) standard (and undistorted) GE data at two different echo times (GRE). We first characterized the geometric distortions and the correction approaches based on the estimated ΔB0 field offset and voxel shift maps, and then conducted three types of analyses on the distorted and corrected fMRI data: (1) registration into structural data, (2) identification of resting-state networks (RSNs), and (3) mapping of task-related brain regions of interest. GRE estimated the largest voxel shifts and more positively impacted the quality of the analyses, in terms of the (significantly lower) cost function of the registration, the (higher) spatial overlap between the RSNs and appropriate templates, and the (significantly higher) sensitivity of the task-related mapping based on the Z-score values of the associated activation maps, although also evident when considering TOPUP. fMRI data should thus be corrected for geometric distortions, with the choice of the approach having a modest, albeit positive, impact on the fMRI analyses.
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Affiliation(s)
- Rodolfo Abreu
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - João Valente Duarte
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal.,Faculty of Medicine, University of Coimbra, Coimbra, Portugal
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Courtney KE, Baca R, Doran N, Jacobson A, Liu TT, Jacobus J. The effects of nicotine and cannabis co-use during adolescence and young adulthood on white matter cerebral blood flow estimates. Psychopharmacology (Berl) 2020; 237:3615-3624. [PMID: 32803367 PMCID: PMC7686080 DOI: 10.1007/s00213-020-05640-7] [Citation(s) in RCA: 8] [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: 05/07/2020] [Accepted: 08/10/2020] [Indexed: 12/15/2022]
Abstract
RATIONALE Co-use of cannabis and nicotine is common among adolescents/young adults and is associated with poorer psychological and physical outcomes, compared with single substance use. Little is known about the impact of co-use on the developing brain. OBJECTIVES Preliminary investigation of the effects of nicotine on white matter (WM) cerebral blood flow (CBF) in adolescents/young adults and its potential moderation by cannabis use. METHODS Adolescent/young adult (16-22 years old) nicotine and tobacco product users (NTP; N = 37) and non-nicotine users (non-NTP; N = 26) underwent a neuroimaging session comprised of anatomical, optimized pseudo-continuous arterial spin labeling, and diffusion tensor imaging scans. Groups were compared on whole-brain WM CBF estimates and their relation to past-year cannabis use. Follow-up analyses assessed correlations between identified CBF clusters and corresponding fractional anisotropy (FA) values. RESULTS Group by cannabis effects were observed in five clusters (voxel-wise alpha < 0.001, cluster-wise alpha < 0.05; ≥ 11 contiguous voxels): non-NTP exhibited positive correlations between CBF and cannabis use in all clusters, whereas no significant relationships were observed for NTP. Greater CBF extracted from one cluster (including portions of right superior longitudinal fasciculus) was associated with reduced FA for non-NTP group only. CONCLUSIONS This is the first investigation of WM health as indexed by CBF, and its association with FA, in adolescents/young adults with nicotine and/or cannabis use. Results suggest that cannabis use by itself may be related to increased CBF in WM fiber tracts demonstrating poorer structural intergrity, yet the occurrence of even infrequent NTP use (greater than once per month) appears to diminish this relationship.
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Affiliation(s)
- Kelly E Courtney
- Department of Psychiatry, University of California San Diego, 9500 Gilman Drive, MC 0405, La Jolla, CA, 92093, USA
| | - Rachel Baca
- Department of Psychiatry, University of California San Diego, 9500 Gilman Drive, MC 0405, La Jolla, CA, 92093, USA
| | - Neal Doran
- Department of Psychiatry, University of California San Diego, 9500 Gilman Drive, MC 0405, La Jolla, CA, 92093, USA
- Veterans Affairs San Diego Healthcare System, La Jolla, CA, USA
| | - Aaron Jacobson
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Thomas T Liu
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Joanna Jacobus
- Department of Psychiatry, University of California San Diego, 9500 Gilman Drive, MC 0405, La Jolla, CA, 92093, USA.
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44
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Patzig F, Mildner T, Schlumm T, Müller R, Möller HE. Deconvolution-based distortion correction of EPI using analytic single-voxel point-spread functions. Magn Reson Med 2020; 85:2445-2461. [PMID: 33220010 DOI: 10.1002/mrm.28591] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 10/19/2020] [Accepted: 10/19/2020] [Indexed: 11/11/2022]
Abstract
PURPOSE To develop a postprocessing algorithm that corrects geometric distortions due to spatial variations of the static magnetic field amplitude, B0 , and effects from relaxation during signal acquisition in EPI. THEORY AND METHODS An analytic, complex point-spread function is deduced for k-space trajectories of EPI variants and applied to corresponding acquisitions in a resolution phantom and in human volunteers at 3 T. With the analytic point-spread function and experimental maps of B0 (and, optionally, the effective transverse relaxation time, T 2 * ) as input, a point-spread function matrix operator is devised for distortion correction by a Thikonov-regularized deconvolution in image space. The point-spread function operator provides additional information for an appropriate correction of the signal intensity distribution. A previous image combination algorithm for acquisitions with opposite phase blip polarities is adapted to the proposed method to recover destructively interfering signal contributions. RESULTS Applications of the proposed deconvolution-based distortion correction ("DecoDisCo") algorithm demonstrate excellent distortion corrections and superior performance regarding the recovery of an undistorted intensity distribution in comparison to a multifrequency reconstruction. Examples include full and partial Fourier standard EPI scans as well as double-shot center-out trajectories. Compared with other distortion-correction approaches, DecoDisCo permits additional deblurring to obtain sharper images in cases of significant T 2 * effects. CONCLUSION Robust distortion corrections in EPI acquisitions are feasible with high quality by regularized deconvolution with an analytic point-spread function. The general algorithm, which is publicly released on GitHub, can be straightforwardly adapted for specific EPI variants or other acquisition schemes.
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Affiliation(s)
- Franz Patzig
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Toralf Mildner
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Torsten Schlumm
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Roland Müller
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Harald E Möller
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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45
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Hur J, Smith JF, DeYoung KA, Anderson AS, Kuang J, Kim HC, Tillman RM, Kuhn M, Fox AS, Shackman AJ. Anxiety and the Neurobiology of Temporally Uncertain Threat Anticipation. J Neurosci 2020; 40:7949-7964. [PMID: 32958570 PMCID: PMC7548695 DOI: 10.1523/jneurosci.0704-20.2020] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 07/31/2020] [Accepted: 08/05/2020] [Indexed: 01/18/2023] Open
Abstract
When extreme, anxiety-a state of distress and arousal prototypically evoked by uncertain danger-can be debilitating. Uncertain anticipation is a shared feature of situations that elicit signs and symptoms of anxiety across psychiatric disorders, species, and assays. Despite the profound significance of anxiety for human health and wellbeing, the neurobiology of uncertain-threat anticipation remains unsettled. Leveraging a paradigm adapted from animal research and optimized for fMRI signal decomposition, we examined the neural circuits engaged during the anticipation of temporally uncertain and certain threat in 99 men and women. Results revealed that the neural systems recruited by uncertain and certain threat anticipation are anatomically colocalized in frontocortical regions, extended amygdala, and periaqueductal gray. Comparison of the threat conditions demonstrated that this circuitry can be fractionated, with frontocortical regions showing relatively stronger engagement during the anticipation of uncertain threat, and the extended amygdala showing the reverse pattern. Although there is widespread agreement that the bed nucleus of the stria terminalis and dorsal amygdala-the two major subdivisions of the extended amygdala-play a critical role in orchestrating adaptive responses to potential danger, their precise contributions to human anxiety have remained contentious. Follow-up analyses demonstrated that these regions show statistically indistinguishable responses to temporally uncertain and certain threat anticipation. These observations provide a framework for conceptualizing anxiety and fear, for understanding the functional neuroanatomy of threat anticipation in humans, and for accelerating the development of more effective intervention strategies for pathological anxiety.SIGNIFICANCE STATEMENT Anxiety-an emotion prototypically associated with the anticipation of uncertain harm-has profound significance for public health, yet the underlying neurobiology remains unclear. Leveraging a novel neuroimaging paradigm in a relatively large sample, we identify a core circuit responsive to both uncertain and certain threat anticipation, and show that this circuitry can be fractionated into subdivisions with a bias for one kind of threat or the other. The extended amygdala occupies center stage in neuropsychiatric models of anxiety, but its functional architecture has remained contentious. Here we demonstrate that its major subdivisions show statistically indistinguishable responses to temporally uncertain and certain threat. Collectively, these observations indicate the need to revise how we think about the neurobiology of anxiety and fear.
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Affiliation(s)
- Juyoen Hur
- Department of Psychology, Yonsei University, Seoul, 03722, Republic of Korea
| | | | | | - Allegra S Anderson
- Department of Psychological Sciences, Vanderbilt University, Nashville, Tennessee 37240
| | - Jinyi Kuang
- Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Hyung Cho Kim
- Departments of Psychology
- Neuroscience and Cognitive Science Program
| | | | - Manuel Kuhn
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Harvard Medical School, Belmont, Massachusetts 02478
| | - Andrew S Fox
- Department of Psychology
- California National Primate Research Center, University of California, Davis, California 95616
| | - Alexander J Shackman
- Departments of Psychology
- Neuroscience and Cognitive Science Program
- Maryland Neuroimaging Center, University of Maryland, College Park, Maryland 20742
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46
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Treceño-Fernández D, Calabia-Del-Campo J, Bote-Lorenzo ML, Gómez-Sánchez E, Luis-García RD, Alberola-López C. Integration of an intelligent tutoring system in a magnetic resonance simulator for education: Technical feasibility and user experience. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 195:105634. [PMID: 32645627 DOI: 10.1016/j.cmpb.2020.105634] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 06/23/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE In this paper we propose to include an intelligent tutoring system (ITS) within a magnetic resonance (MR) simulator that has been developed in house. With this, we intend to measure the impact, in terms of user experience, of including an ITS in our simulator. METHODS We thoroughly describe the integration procedure and we have tested the benefits of this integration by means of two actual educational experiences, with one of them using the simulator as a standalone tool, and the other with the joint use of simulator+ITS. The experiences have consisted of two online courses with a number of students around 180 in both of them, where measurements of usability, perceived utility and likelihood to recommend were collected. RESULTS We have observed that the three measurements improved noticeably in the second course with respect to the first one; specifically, overall usability improved by 22.3%, perceived utility by an average of 55.1% and likelihood to recommend by 13.7%. In addition, quantitative measurements are complemented with comments in free text format directly provided by the students. Results show evidence on the benefits of integrating an ITS in terms of quantitative user experience, as well as qualitative comparative comments directly by students of both courses. CONCLUSIONS This is the first time that an ITS is used within the scope of MR simulation for training purposes. Benefits of integrating an ITS within an MR simulator have been evaluated in terms of user experience, with satisfactory comparative results.
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Schilling KG, Blaber J, Hansen C, Cai L, Rogers B, Anderson AW, Smith S, Kanakaraj P, Rex T, Resnick SM, Shafer AT, Cutting LE, Woodward N, Zald D, Landman BA. Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps. PLoS One 2020; 15:e0236418. [PMID: 32735601 PMCID: PMC7394453 DOI: 10.1371/journal.pone.0236418] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 07/06/2020] [Indexed: 02/04/2023] Open
Abstract
Diffusion magnetic resonance images may suffer from geometric distortions due to susceptibility induced off resonance fields, which cause geometric mismatch with anatomical images and ultimately affect subsequent quantification of microstructural or connectivity indices. State-of-the art diffusion distortion correction methods typically require data acquired with reverse phase encoding directions, resulting in varying magnitudes and orientations of distortion, which allow estimation of an undistorted volume. Alternatively, additional field maps acquisitions can be used along with sequence information to determine warping fields. However, not all imaging protocols include these additional scans and cannot take advantage of state-of-the art distortion correction. To avoid additional acquisitions, structural MRI (undistorted scans) can be used as registration targets for intensity driven correction. In this study, we aim to (1) enable susceptibility distortion correction with historical and/or limited diffusion datasets that do not include specific sequences for distortion correction and (2) avoid the computationally intensive registration procedure typically required for distortion correction using structural scans. To achieve these aims, we use deep learning (3D U-nets) to synthesize an undistorted b0 image that matches geometry of structural T1w images and intensity contrasts from diffusion images. Importantly, the training dataset is heterogenous, consisting of varying acquisitions of both structural and diffusion. We apply our approach to a withheld test set and show that distortions are successfully corrected after processing. We quantitatively evaluate the proposed distortion correction and intensity-based registration against state-of-the-art distortion correction (FSL topup). The results illustrate that the proposed pipeline results in b0 images that are geometrically similar to non-distorted structural images, and more closely match state-of-the-art correction with additional acquisitions. In addition, we show generalizability of the proposed approach to datasets that were not in the original training / validation / testing datasets. These datasets included varying populations, contrasts, resolutions, and magnitudes and orientations of distortion and show efficacious distortion correction. The method is available as a Singularity container, source code, and an executable trained model to facilitate evaluation.
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Affiliation(s)
- Kurt G. Schilling
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America
| | - Justin Blaber
- Electrical Engineering, Vanderbilt University, Nashville, TN, United States of America
| | - Colin Hansen
- Computer Science, Vanderbilt University, Nashville, TN, United States of America
| | - Leon Cai
- Biomedical Engineering, Vanderbilt University, Nashville, TN, United States of America
| | - Baxter Rogers
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America
| | - Adam W. Anderson
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America
- Computer Science, Vanderbilt University, Nashville, TN, United States of America
| | - Seth Smith
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America
- Computer Science, Vanderbilt University, Nashville, TN, United States of America
| | - Praitayini Kanakaraj
- Electrical Engineering, Vanderbilt University, Nashville, TN, United States of America
| | - Tonia Rex
- Vanderbilt Eye Institute, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Andrea T. Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Laurie E. Cutting
- Special Education, Vanderbilt University, Nashville, TN, United States of America
| | - Neil Woodward
- Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - David Zald
- Neuroscience, Vanderbilt University, Nashville, TN, United States of America
| | - Bennett A. Landman
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America
- Electrical Engineering, Vanderbilt University, Nashville, TN, United States of America
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Wright DK, Gardner AJ, Wojtowicz M, Iverson GL, O'Brien TJ, Shultz SR, Stanwell P. White Matter Abnormalities in Retired Professional Rugby League Players with a History of Concussion. J Neurotrauma 2020; 38:983-988. [PMID: 32245344 DOI: 10.1089/neu.2019.6886] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
The topic of potential long-term neurological consequences from having multiple concussions during a career in collision sports is controversial. We sought to investigate white matter microstructure using diffusion tensor imaging (DTI) in retired professional Australian National Rugby League (NRL) players (n = 11) with a history of multiple self-reported concussions compared with age- and education-matched controls (n = 13) who have had no history of brain trauma. Diffusion-weighted images were acquired with a Siemens 3T scanner. All participants completed a clinical interview. There were no significant differences between groups on measures of depression, anxiety, stress, or post-concussion symptoms; however, NRL players scored significantly higher on the alcohol use disorder identification test (AUDIT). Voxelwise analyses of DTI measures were performed using tract-based spatial statistics (TBSS) with age and AUDIT scores included as covariates. TBSS revealed significantly reduced fractional anisotropy (FA), and increased radial diffusivity (RD), axial diffusivity (AD), and trace (TR) in white matter regions of recently retired NRL players compared with controls. FA was significantly reduced in the right superior longitudinal fasciculus and right corticospinal tract while TR, RD, and AD were increased in these regions, as well as the corpus callosum, forceps major, right uncinate fasciculus, and left corticospinal tract. In summary, DTI in a small cohort of recently retired professional NRL players with a history of multiple concussions showed differences in white matter microstructure compared with age- and education-matched controls with no history of brain trauma.
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Affiliation(s)
- David K Wright
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Andrew J Gardner
- Hunter New England Local Health District Sports Concussion Program, New Lambton Heights, New South Wales, Australia
| | | | - Grant L Iverson
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, Massachusetts, USA.,Spaulding Rehabilitation Hospital and Spaulding Research Institute, Boston, Massachusetts, USA.,MassGeneral Hospital for ChildrenTM Sport Concussion Program Foundation, and Massachusetts General Hospital Home Base Program, Boston, Massachusetts, USA
| | - Terence J O'Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Sandy R Shultz
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Peter Stanwell
- School of Health Sciences, Faculty of Health, University of Newcastle, Callaghan, New South Wales, Australia
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Deep flow-net for EPI distortion estimation. Neuroimage 2020; 217:116886. [PMID: 32389728 DOI: 10.1016/j.neuroimage.2020.116886] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 04/23/2020] [Accepted: 04/24/2020] [Indexed: 11/23/2022] Open
Abstract
INTRODUCTION Geometric distortions along the phase encoding direction caused by off-resonant spins are a major issue in EPI based functional and diffusion imaging. The widely used blip up/down approach estimates the underlying distortion field from a pair of images with inverted phase encoding direction. Typically, iterative methods are used to find a solution to the ill-posed problem of finding the displacement field that maps up/down acquisitions onto each other. Here, we explore the use of a deep convolutional network to estimate the displacement map from a pair of input images. METHODS We trained a deep convolutional U-net architecture that was previously used to estimate optic flow between moving images to learn to predict the distortion map from an input pair of distorted EPI acquisitions. During the training step, the network minimizes a loss function (similarity metric) that is calculated from corrected input image pairs. This approach does not require the explicit knowledge of the ground truth distortion map, which is difficult to get for real life data. RESULTS We used data from a total of Ntrain = 22 healthy subjects to train our network. A separate dataset of Ntest = 12 patients including some with abnormal findings and unseen acquisition modes, e.g. LR-encoding, coronal orientation) was reserved for testing and evaluation purposes. We compared our results to FSL's topup function with default parameters that served as the gold standard. We found that our approach results in a correction accuracy that is virtually identical to the optimum found by an iterative search, but with reduced computational time. CONCLUSION By using a deep convolutional network, we can reduce the processing time to a few seconds per volume, which is significantly faster than iterative approaches like FSL's topup which takes around 10min on the same machine (but using only 1 CPU). This facilitates the use of a blip up/down scheme for all diffusion-weighted acquisitions and potential real-time EPI distortion correction without sacrificing accuracy.
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50
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Gu X, Eklund A. Evaluation of Six Phase Encoding Based Susceptibility Distortion Correction Methods for Diffusion MRI. Front Neuroinform 2019; 13:76. [PMID: 31866847 PMCID: PMC6906182 DOI: 10.3389/fninf.2019.00076] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 11/21/2019] [Indexed: 11/23/2022] Open
Abstract
Purpose: Susceptibility distortions impact diffusion MRI data analysis and is typically corrected during preprocessing. Correction strategies involve three classes of methods: registration to a structural image, the use of a fieldmap, or the use of images acquired with opposing phase encoding directions. It has been demonstrated that phase encoding based methods outperform the other two classes, but unfortunately, the choice of which phase encoding based method to use is still an open question due to the absence of any systematic comparisons. Methods: In this paper we quantitatively evaluated six popular phase encoding based methods for correcting susceptibility distortions in diffusion MRI data. We employed a framework that allows for the simulation of realistic diffusion MRI data with susceptibility distortions. We evaluated the ability for methods to correct distortions by comparing the corrected data with the ground truth. Four diffusion tensor metrics (FA, MD, eigenvalues and eigenvectors) were calculated from the corrected data and compared with the ground truth. We also validated two popular indirect metrics using both simulated data and real data. The two indirect metrics are the difference between the corrected LR and AP data, and the FA standard deviation over the corrected LR, RL, AP, and PA data. Results: We found that DR-BUDDI and TOPUP offered the most accurate and robust correction compared to the other four methods using both direct and indirect evaluation metrics. EPIC and HySCO performed well in correcting b0 images but produced poor corrections for diffusion weighted volumes, and also they produced large errors for the four diffusion tensor metrics. We also demonstrate that the indirect metric (the difference between corrected LR and AP data) gives a different ordering of correction quality than the direct metric. Conclusion: We suggest researchers to use DR-BUDDI or TOPUP for susceptibility distortion correction. The two indirect metrics (the difference between corrected LR and AP data, and the FA standard deviation) should be interpreted together as a measure of distortion correction quality. The performance ranking of the various tools inferred from direct and indirect metrics differs slightly. However, across all tools, the results of direct and indirect metrics are highly correlated indicating that the analysis of indirect metrics may provide a good proxy of the performance of a correction tool if assessment using direct metrics is not feasible.
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Affiliation(s)
- Xuan Gu
- Division of Medical Informatics, Department of Biomedical Engineering, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Anders Eklund
- Division of Medical Informatics, Department of Biomedical Engineering, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.,Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, Linköping, Sweden
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