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Chatterjee A, Dwivedi DK. MRI-based virtual pathology of the prostate. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01163-w. [PMID: 38856839 DOI: 10.1007/s10334-024-01163-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 06/11/2024]
Abstract
Prostate cancer poses significant diagnostic challenges, with conventional methods like prostate-specific antigen (PSA) screening and transrectal ultrasound (TRUS)-guided biopsies often leading to overdiagnosis or miss clinically significant cancers. Multiparametric MRI (mpMRI) has emerged as a more reliable tool. However, it is limited by high inter-observer variability and radiologists missing up to 30% of clinically significant cancers. This article summarizes a few of these recent advancements in quantitative MRI techniques that look at the "Virtual Pathology" of the prostate with an aim to enhance prostate cancer detection and characterization. These techniques include T2 relaxation-based techniques such as luminal water imaging, diffusion based such as vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) and restriction spectrum imaging or combined relaxation-diffusion techniques such as hybrid multi-dimensional MRI (HM-MRI), time-dependent diffusion imaging, and diffusion-relaxation correlation spectrum imaging. These methods provide detailed insights into underlying prostate microstructure and tissue composition and have shown improved diagnostic accuracy over conventional MRI. These innovative MRI methods hold potential for augmenting mpMRI, reducing variability in diagnosis, and paving the way for MRI as a 'virtual histology' tool in prostate cancer diagnosis. However, they require further validation in larger multi-center clinical settings and rigorous in-depth radiological-pathology correlation are needed for broader implementation.
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Affiliation(s)
- Aritrick Chatterjee
- Department of Radiology, University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, IL, 60637, USA.
- Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, IL, USA.
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Johnson JTE, Irfanoglu MO, Manninen E, Ross TJ, Yang Y, Laun FB, Martin J, Topgaard D, Benjamini D. In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI. Hum Brain Mapp 2024; 45:e26697. [PMID: 38726888 PMCID: PMC11082920 DOI: 10.1002/hbm.26697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 03/28/2024] [Accepted: 04/12/2024] [Indexed: 05/13/2024] Open
Abstract
Diffusion MRI with free gradient waveforms, combined with simultaneous relaxation encoding, referred to as multidimensional MRI (MD-MRI), offers microstructural specificity in complex biological tissue. This approach delivers intravoxel information about the microstructure, local chemical composition, and importantly, how these properties are coupled within heterogeneous tissue containing multiple microenvironments. Recent theoretical advances incorporated diffusion time dependency and integrated MD-MRI with concepts from oscillating gradients. This framework probes the diffusion frequency,ω $$ \omega $$ , in addition to the diffusion tensor,D $$ \mathbf{D} $$ , and relaxation,R 1 $$ {R}_1 $$ ,R 2 $$ {R}_2 $$ , correlations. AD ω - R 1 - R 2 $$ \mathbf{D}\left(\omega \right)-{R}_1-{R}_2 $$ clinical imaging protocol was then introduced, with limited brain coverage and 3 mm3 voxel size, which hinder brain segmentation and future cohort studies. In this study, we introduce an efficient, sparse in vivo MD-MRI acquisition protocol providing whole brain coverage at 2 mm3 voxel size. We demonstrate its feasibility and robustness using a well-defined phantom and repeated scans of five healthy individuals. Additionally, we test different denoising strategies to address the sparse nature of this protocol, and show that efficient MD-MRI encoding design demands a nuanced denoising approach. The MD-MRI framework provides rich information that allows resolving the diffusion frequency dependence into intravoxel components based on theirD ω - R 1 - R 2 $$ \mathbf{D}\left(\omega \right)-{R}_1-{R}_2 $$ distribution, enabling the creation of microstructure-specific maps in the human brain. Our results encourage the broader adoption and use of this new imaging approach for characterizing healthy and pathological tissues.
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Affiliation(s)
- Jessica T. E. Johnson
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIHBaltimoreMarylandUSA
| | - M. Okan Irfanoglu
- Quantitative Medical Imaging Section, National Institute of Biomedical Imaging and Bioengineering, National Institutes of HealthBethesdaMarylandUSA
| | - Eppu Manninen
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIHBaltimoreMarylandUSA
| | - Thomas J. Ross
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of HealthBaltimoreMarylandUSA
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of HealthBaltimoreMarylandUSA
| | - Frederik B. Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Jan Martin
- Department of ChemistryLund UniversityLundSweden
| | | | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIHBaltimoreMarylandUSA
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Wang N, Maharjan S, Tsai AP, Lin PB, Qi Y, Wallace A, Jewett M, Liu F, Landreth GE, Oblak AL. Integrating multimodality magnetic resonance imaging to the Allen Mouse Brain Common Coordinate Framework. NMR IN BIOMEDICINE 2023; 36:e4887. [PMID: 36454009 PMCID: PMC10106385 DOI: 10.1002/nbm.4887] [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] [Received: 02/16/2022] [Revised: 11/28/2022] [Accepted: 11/30/2022] [Indexed: 05/07/2023]
Abstract
High-resolution magnetic resonance imaging (MRI) affords unique image contrasts to nondestructively probe the tissue microstructure; validation of MRI findings with conventional histology is essential to better understand the MRI contrasts. However, the dramatic difference in the spatial resolution and image contrast of these two techniques impedes accurate comparison between MRI metrics and traditional histology. To better validate various MRI metrics, we acquired whole mouse brain multigradient recalled-echo and multishell diffusion MRI datasets at 25-μm isotropic resolution. The recently developed Allen Mouse Brain Common Coordinate Framework (CCFv3) provides opportunities to integrate multimodal and multiscale datasets of the whole mouse brain in a common three-dimensional (3D) space. The T2*, quantitative susceptibility mapping, diffusion tensor imaging, and neurite orientation dispersion and density imaging parameters were compared with both serial two-photon tomography images and 3D Nissl staining images in the CCFv3 at the same spatial resolution. The correlation between MRI and Nissl staining strongly depends on different metrics and different regions of the brain. Integrating different imaging modalities to the same space may substantially improve our understanding of the complexity of the brain at different scales.
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Affiliation(s)
- Nian Wang
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana, USA
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, Indiana, USA
| | - Surendra Maharjan
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana, USA
| | - Andy P. Tsai
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, Indiana, USA
| | - Peter B. Lin
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, Indiana, USA
| | - Yi Qi
- Center for In Vivo Microscopy, Department of Radiology, Duke University, Durham, North Carolina, USA
| | - Abigail Wallace
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana, USA
| | - Megan Jewett
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana, USA
| | - Fang Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Gary E. Landreth
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, Indiana, USA
| | - Adrian L. Oblak
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana, USA
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, Indiana, USA
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Dwivedi DK, Jagannathan NR. Emerging MR methods for improved diagnosis of prostate cancer by multiparametric MRI. MAGMA (NEW YORK, N.Y.) 2022; 35:587-608. [PMID: 35867236 DOI: 10.1007/s10334-022-01031-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 06/28/2022] [Accepted: 07/08/2022] [Indexed: 06/15/2023]
Abstract
Current challenges of using serum prostate-specific antigen (PSA) level-based screening, such as the increased false positive rate, inability to detect clinically significant prostate cancer (PCa) with random biopsy, multifocality in PCa, and the molecular heterogeneity of PCa, can be addressed by integrating advanced multiparametric MR imaging (mpMRI) approaches into the diagnostic workup of PCa. The standard method for diagnosing PCa is a transrectal ultrasonography (TRUS)-guided systematic prostate biopsy, but it suffers from sampling errors and frequently fails to detect clinically significant PCa. mpMRI not only increases the detection of clinically significant PCa, but it also helps to reduce unnecessary biopsies because of its high negative predictive value. Furthermore, non-Cartesian image acquisition and compressed sensing have resulted in faster MR acquisition with improved signal-to-noise ratio, which can be used in quantitative MRI methods such as dynamic contrast-enhanced (DCE)-MRI. With the growing emphasis on the role of pre-biopsy mpMRI in the evaluation of PCa, there is an increased demand for innovative MRI methods that can improve PCa grading, detect clinically significant PCa, and biopsy guidance. To meet these demands, in addition to routine T1-weighted, T2-weighted, DCE-MRI, diffusion MRI, and MR spectroscopy, several new MR methods such as restriction spectrum imaging, vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) method, hybrid multi-dimensional MRI, luminal water imaging, and MR fingerprinting have been developed for a better characterization of the disease. Further, with the increasing interest in combining MR data with clinical and genomic data, there is a growing interest in utilizing radiomics and radiogenomics approaches. These big data can also be utilized in the development of computer-aided diagnostic tools, including automatic segmentation and the detection of clinically significant PCa using machine learning methods.
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Affiliation(s)
- Durgesh Kumar Dwivedi
- Department of Radiodiagnosis, King George Medical University, Lucknow, UP, 226 003, India.
| | - Naranamangalam R Jagannathan
- Department of Radiology, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam, TN, 603 103, India.
- Department of Radiology, Sri Ramachandra Institute of Higher Education and Research, Chennai, TN, 600 116, India.
- Department of Electrical Engineering, Indian Institute Technology Madras, Chennai, TN, 600 036, India.
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Syed Nasser N, Rajan S, Venugopal VK, Lasič S, Mahajan V, Mahajan H. A review on investigation of the basic contrast mechanism underlying multidimensional diffusion MRI in assessment of neurological disorders. J Clin Neurosci 2022; 102:26-35. [PMID: 35696817 DOI: 10.1016/j.jocn.2022.05.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 05/20/2022] [Accepted: 05/30/2022] [Indexed: 12/26/2022]
Abstract
INTRODUCTION Multidimensional diffusion MRI (MDD MRI) is a novel diffusion technique that uses advanced gradient waveforms for microstructural tissue characterization to provide information about average rate, anisotropy and orientation of the diffusion and to disentangle the signal fraction from specific cell types i.e., elongated cells, isotropic cells and free water. AIM To review the diagnostic potential of MDD MRI in the clinical setting for microstructural tissue characterization in patients with neurological disorders to aid in patient care and treatment. METHOD A scoping review on the clinical applications of MDD MRI was conducted from original articles published in PubMed and Scopus from 2015 to 2021 using the keywords "Multidimensional diffusion MRI" OR "diffusion tensor distribution" OR "Tensor-Valued Diffusion" OR "b-tensor encoding" OR "microscopic diffusion anisotropy" OR "microscopic anisotropy" OR "microscopic fractional anisotropy" OR "double diffusion encoding" OR "triple diffusion encoding" OR "double pulsed field gradients" OR "double wave vector" OR "correlation tensor imaging" AND "brain" OR "axons". RESULTS Initially 145 articles were screened and after applying inclusion and exclusion criteria, nine articles were included in the final analysis. In most of these studies, microscopic diffusion anisotropy within the lesion showed deviation from the normal-appearing tissue. CONCLUSION Multidimensional diffusion MRI can provide better quantification and visualization of tissue microstructure than conventional diffusion MRI and can be used in the clinical setting for diagnosis of neurological disorders.
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Affiliation(s)
| | - Sriram Rajan
- Department of Radiology, Mahajan Imaging, New Delhi, India
| | | | | | | | - Harsh Mahajan
- CARPL.ai, New Delhi, India; Department of Radiology, Mahajan Imaging, New Delhi, India
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Afzali M, Nilsson M, Palombo M, Jones DK. SPHERIOUSLY? The challenges of estimating sphere radius non-invasively in the human brain from diffusion MRI. Neuroimage 2021; 237:118183. [PMID: 34020013 PMCID: PMC8285594 DOI: 10.1016/j.neuroimage.2021.118183] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 04/25/2021] [Accepted: 05/16/2021] [Indexed: 11/16/2022] Open
Abstract
The Soma and Neurite Density Imaging (SANDI) three-compartment model was recently proposed to disentangle cylindrical and spherical geometries, attributed to neurite and soma compartments, respectively, in brain tissue. There are some recent advances in diffusion-weighted MRI signal encoding and analysis (including the use of multiple so-called 'b-tensor' encodings and analysing the signal in the frequency-domain) that have not yet been applied in the context of SANDI. In this work, using: (i) ultra-strong gradients; (ii) a combination of linear, planar, and spherical b-tensor encodings; and (iii) analysing the signal in the frequency domain, three main challenges to robust estimation of sphere size were identified: First, the Rician noise floor in magnitude-reconstructed data biases estimates of sphere properties in a non-uniform fashion. It may cause overestimation or underestimation of the spherical compartment size and density. This can be partly ameliorated by accounting for the noise floor in the estimation routine. Second, even when using the strongest diffusion-encoding gradient strengths available for human MRI, there is an empirical lower bound on the spherical signal fraction and radius that can be detected and estimated robustly. For the experimental setup used here, the lower bound on the sphere signal fraction was approximately 10%. We employed two different ways of establishing the lower bound for spherical radius estimates in white matter. The first, examining power-law relationships between the DW-signal and diffusion weighting in empirical data, yielded a lower bound of 7μm, while the second, pure Monte Carlo simulations, yielded a lower limit of 3μm and in this low radii domain, there is little differentiation in signal attenuation. Third, if there is sensitivity to the transverse intra-cellular diffusivity in cylindrical structures, e.g., axons and cellular projections, then trying to disentangle two diffusion-time-dependencies using one experimental parameter (i.e., change in frequency-content of the encoding waveform) makes spherical radii estimates particularly challenging. We conclude that due to the aforementioned challenges spherical radii estimates may be biased when the corresponding sphere signal fraction is low, which must be considered.
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Affiliation(s)
- Maryam Afzali
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
| | - Markus Nilsson
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden.
| | - Marco Palombo
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
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Reymbaut A, Critchley J, Durighel G, Sprenger T, Sughrue M, Bryskhe K, Topgaard D. Toward nonparametric diffusion- T1 characterization of crossing fibers in the human brain. Magn Reson Med 2021; 85:2815-2827. [PMID: 33301195 PMCID: PMC7898694 DOI: 10.1002/mrm.28604] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 10/26/2020] [Accepted: 10/27/2020] [Indexed: 12/24/2022]
Abstract
PURPOSE To estimate T 1 for each distinct fiber population within voxels containing multiple brain tissue types. METHODS A diffusion- T 1 correlation experiment was carried out in an in vivo human brain using tensor-valued diffusion encoding and multiple repetition times. The acquired data were inverted using a Monte Carlo algorithm that retrieves nonparametric distributions P ( D , R 1 ) of diffusion tensors and longitudinal relaxation rates R 1 = 1 / T 1 . Orientation distribution functions (ODFs) of the highly anisotropic components of P ( D , R 1 ) were defined to visualize orientation-specific diffusion-relaxation properties. Finally, Monte Carlo density-peak clustering (MC-DPC) was performed to quantify fiber-specific features and investigate microstructural differences between white matter fiber bundles. RESULTS Parameter maps corresponding to P ( D , R 1 ) 's statistical descriptors were obtained, exhibiting the expected R 1 contrast between brain tissue types. Our ODFs recovered local orientations consistent with the known anatomy and indicated differences in R 1 between major crossing fiber bundles. These differences, confirmed by MC-DPC, were in qualitative agreement with previous model-based works but seem biased by the limitations of our current experimental setup. CONCLUSIONS Our Monte Carlo framework enables the nonparametric estimation of fiber-specific diffusion- T 1 features, thereby showing potential for characterizing developmental or pathological changes in T 1 within a given fiber bundle, and for investigating interbundle T 1 differences.
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Affiliation(s)
- Alexis Reymbaut
- Department of Physical ChemistryLund UniversityLundSweden
- Random Walk Imaging ABLundSweden
| | | | | | - Tim Sprenger
- Karolinska InstituteStockholmSweden
- GE HealthcareStockholmSweden
| | | | | | - Daniel Topgaard
- Department of Physical ChemistryLund UniversityLundSweden
- Random Walk Imaging ABLundSweden
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Detecting microstructural white matter abnormalities of frontal pathways in children with ADHD using advanced diffusion models. Brain Imaging Behav 2021; 14:981-997. [PMID: 31041662 DOI: 10.1007/s11682-019-00108-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Studies using diffusion tensor imaging (DTI) have documented alterations in the attention and executive system in children and adolescents with attention-deficit/hyperactivity disorder (ADHD). While abnormalities in the frontal lobe have also been reported, the associated white matter fiber bundles have not been investigated comprehensively due to the complexity in tracing them through fiber crossings. Furthermore, most studies have used a non-specific DTI model to understand white matter abnormalities. We present results from a first study that uses a multi-shell diffusion MRI (dMRI) data set coupled with an advanced multi-fiber tractography algorithm to probe microstructural measures related to axonal/cellular density and volume of fronto-striato-thalamic pathways in children with ADHD (N = 30) and healthy controls (N = 28). Head motion was firstly examined as a priority in order to assure that no group difference existed. We investigated 45 different white matter fiber bundles in the brain. After correcting for multiple comparisons, we found lower axonal/cellular packing density and volume in ADHD children in 8 of the 45 fiber bundles, primarily in the right hemisphere as follows: 1) Superior longitudinal fasciculus-II (SLF-II) (right), 2) Thalamus to precentral gyrus (right), 3) Thalamus to superior-frontal gyrus (right), 4) Caudate to medial orbitofrontal gyrus (right), 5) Caudate to precentral gyrus (right), 6) Thalamus to paracentral gyrus (left), 7) Caudate to caudal middlefrontal gyrus (left), and 8) Cingulum (bilateral). Our results demonstrate reduced axonal/cellular density and volume in certain frontal lobe white matter fiber tracts, which sub-serve the attention function and executive control systems. Further, our work shows specific microstructural abnormalities in the striato-thalamo-cortical connections, which have not been previously reported in children with ADHD.
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Puri A, Shakya S, Kumar S. An enhanced multi-fiber reconstruction technique using adaptive gradient directions coupled with MoNCW model in diffusion MRI. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2021; 325:106931. [PMID: 33684888 DOI: 10.1016/j.jmr.2021.106931] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 01/04/2021] [Accepted: 01/29/2021] [Indexed: 06/12/2023]
Abstract
In this paper, we introduced a novel approach for generating unit gradient vectors named as adaptive gradient directions (AGD) for reconstructing single and decussating (crossing or kissing) white matter fibers in brain. The present study is focusing on reconstruction process of brain's white matter fibers but not dealing with data acquisition where scanning is performed. The gradient vectors used in the state-of-art methodologies for reconstruction are uniformly distributed vectors on a unit sphere but AGD, in contrary, are non-uniformly distributed points on a unit sphere. These points are uniformly distributed in some pattern on the surface of a unit sphere. For reconstruction, we have coupled the proposed AGD approach with mixture of non-central Wishart (MoNCW) model. We uphold the proposed approach with different simulations including synthetic as well as real data experiments. Resistivity to different Rician noise levels (σ=0.02-0.1) is demonstrated in simulated data for single as well as two and three decussating fibers. Our approach of using AGD dissipates the limitations that are encountered by the state-of-art technique of uniformly distributed points over the surface of unit sphere and outperforms showing significant reduction in angular errors.
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Affiliation(s)
- Ashishi Puri
- Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee 247667, India.
| | - Snehlata Shakya
- Department of Clinical Physiology, Skane University Hospital, Lund University, Sweden.
| | - Sanjeev Kumar
- Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee 247667, India.
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Probing tissue microstructure by diffusion skewness tensor imaging. Sci Rep 2021; 11:135. [PMID: 33420140 PMCID: PMC7794496 DOI: 10.1038/s41598-020-79748-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 11/30/2020] [Indexed: 01/29/2023] Open
Abstract
Probing the cellular structure of in vivo biological tissue is a fundamental problem in biomedical imaging and medical science. This work introduces an approach for analyzing diffusion magnetic resonance imaging data acquired by the novel tensor-valued encoding technique for characterizing tissue microstructure. Our approach first uses a signal model to estimate the variance and skewness of the distribution of apparent diffusion tensors modeling the underlying tissue. Then several novel imaging indices, such as weighted microscopic anisotropy and microscopic skewness, are derived to characterize different ensembles of diffusion processes that are indistinguishable by existing techniques. The contributions of this work also include a theoretical proof that shows that, to estimate the skewness of a diffusion tensor distribution, the encoding protocol needs to include full-rank tensor diffusion encoding. This proof provides a guideline for the application of this technique. The properties of the proposed indices are illustrated using both synthetic data and in vivo data acquired from a human brain.
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Huynh KM, Xu T, Wu Y, Wang X, Chen G, Wu H, Thung KH, Lin W, Shen D, Yap PT. Probing Tissue Microarchitecture of the Baby Brain via Spherical Mean Spectrum Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:3607-3618. [PMID: 32746109 PMCID: PMC7688284 DOI: 10.1109/tmi.2020.3001175] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
During the first years of life, the human brain undergoes dynamic spatially-heterogeneous changes, invo- lving differentiation of neuronal types, dendritic arbori- zation, axonal ingrowth, outgrowth and retraction, synaptogenesis, and myelination. To better quantify these changes, this article presents a method for probing tissue microarchitecture by characterizing water diffusion in a spectrum of length scales, factoring out the effects of intra-voxel orientation heterogeneity. Our method is based on the spherical means of the diffusion signal, computed over gradient directions for a set of diffusion weightings (i.e., b -values). We decompose the spherical mean profile at each voxel into a spherical mean spectrum (SMS), which essentially encodes the fractions of spin packets undergoing fine- to coarse-scale diffusion proce- sses, characterizing restricted and hindered diffusion stemming respectively from intra- and extra-cellular water compartments. From the SMS, multiple orientation distribution invariant indices can be computed, allowing for example the quantification of neurite density, microscopic fractional anisotropy ( μ FA), per-axon axial/radial diffusivity, and free/restricted isotropic diffusivity. We show that these indices can be computed for the developing brain for greater sensitivity and specificity to development related changes in tissue microstructure. Also, we demonstrate that our method, called spherical mean spectrum imaging (SMSI), is fast, accurate, and can overcome the biases associated with other state-of-the-art microstructure models.
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Lampinen B, Zampeli A, Björkman-Burtscher IM, Szczepankiewicz F, Källén K, Compagno Strandberg M, Nilsson M. Tensor-valued diffusion MRI differentiates cortex and white matter in malformations of cortical development associated with epilepsy. Epilepsia 2020; 61:1701-1713. [PMID: 32667688 PMCID: PMC7963222 DOI: 10.1111/epi.16605] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 05/21/2020] [Accepted: 06/15/2020] [Indexed: 12/24/2022]
Abstract
Objective: Delineation of malformations of cortical development (MCD) is central in presurgical evaluation of drug-resistant epilepsy. Delineation using magnetic resonance imaging (MRI) can be ambiguous, however, because the conventional T1- and T2-weighted contrasts depend strongly on myelin for differentiation of cortical tissue and white matter. Variations in myelin content within both cortex and white matter may cause MCD findings on MRI to change size, become undetectable, or disagree with histopathology. The novel tensor-valued diffusion MRI (dMRI) technique maps microscopic diffusion anisotropy, which is sensitive to axons rather than myelin. This work investigated whether tensor-valued dMRI may improve differentiation of cortex and white matter in the delineation of MCD. Methods: Tensor-valued dMRI was performed on a 7 T MRI scanner in 13 MCD patients (age = 32 ± 13 years) featuring periventricular heterotopia, subcortical heterotopia, focal cortical dysplasia, and polymicrogyria. Data analysis yielded maps of microscopic anisotropy that were compared with T1-weighted and T2-fluid-attenuated inversion recovery images and with the fractional anisotropy from diffusion tensor imaging. Results: Maps of microscopic anisotropy revealed large white matter-like regions within MCD that were uniformly cortex-like in the conventional MRI contrasts. These regions were seen particularly in the deep white matter parts of subcortical heterotopias and near the gray-white boundaries of focal cortical dysplasias and polymicrogyrias. Significance: By being sensitive to axons rather than myelin, mapping of microscopic anisotropy may yield a more robust differentiation of cortex and white matter and improve MCD delineation in presurgical evaluation of epilepsy.
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Affiliation(s)
- Björn Lampinen
- Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Ariadne Zampeli
- Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
| | | | - Filip Szczepankiewicz
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden.,Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Kristina Källén
- Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden.,Clinical Sciences Lund, Department of Clinical Sciences Helsingborg, Lund University, Lund, Sweden
| | | | - Markus Nilsson
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden
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Cottaar M, Szczepankiewicz F, Bastiani M, Hernandez-Fernandez M, Sotiropoulos SN, Nilsson M, Jbabdi S. Improved fibre dispersion estimation using b-tensor encoding. Neuroimage 2020; 215:116832. [PMID: 32283273 DOI: 10.1016/j.neuroimage.2020.116832] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 03/17/2020] [Accepted: 04/06/2020] [Indexed: 12/19/2022] Open
Abstract
Measuring fibre dispersion in white matter with diffusion magnetic resonance imaging (MRI) is limited by an inherent degeneracy between fibre dispersion and microscopic diffusion anisotropy (i.e., the diffusion anisotropy expected for a single fibre orientation). This means that estimates of fibre dispersion rely on strong assumptions, such as constant microscopic anisotropy throughout the white matter or specific biophysical models. Here we present a simple approach for resolving this degeneracy using measurements that combine linear (conventional) and spherical tensor diffusion encoding. To test the accuracy of the fibre dispersion when our microstructural model is only an approximation of the true tissue structure, we simulate multi-compartment data and fit this with a single-compartment model. For such overly simplistic tissue assumptions, we show that the bias in fibre dispersion is greatly reduced (~5x) for single-shell linear and spherical tensor encoding data compared with single-shell or multi-shell conventional data. In in-vivo data we find a consistent estimate of fibre dispersion as we reduce the b-value from 3 to 1.5 ms/μm2, increase the repetition time, increase the echo time, or increase the diffusion time. We conclude that the addition of spherical tensor encoded data to conventional linear tensor encoding data greatly reduces the sensitivity of the estimated fibre dispersion to the model assumptions of the tissue microstructure.
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Affiliation(s)
- Michiel Cottaar
- Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional Magnetic Resonance, Imaging of the Brain (FMRIB), University of Oxford, UK.
| | - Filip Szczepankiewicz
- Harvard Medical School, Boston, MA, USA; Radiology, Brigham and Women's Hospital, Boston, MA, USA; Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Matteo Bastiani
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; NIHR Biomedical Research Centre, University of Nottingham, UK; Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional Magnetic Resonance, Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Moises Hernandez-Fernandez
- Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional Magnetic Resonance, Imaging of the Brain (FMRIB), University of Oxford, UK; NVIDIA, Santa Clara, CA, USA
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; NIHR Biomedical Research Centre, University of Nottingham, UK; Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional Magnetic Resonance, Imaging of the Brain (FMRIB), University of Oxford, UK
| | | | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional Magnetic Resonance, Imaging of the Brain (FMRIB), University of Oxford, UK
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14
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Lampinen B, Szczepankiewicz F, Mårtensson J, van Westen D, Hansson O, Westin CF, Nilsson M. Towards unconstrained compartment modeling in white matter using diffusion-relaxation MRI with tensor-valued diffusion encoding. Magn Reson Med 2020; 84:1605-1623. [PMID: 32141131 DOI: 10.1002/mrm.28216] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 01/27/2020] [Accepted: 01/28/2020] [Indexed: 01/05/2023]
Abstract
PURPOSE To optimize diffusion-relaxation MRI with tensor-valued diffusion encoding for precise estimation of compartment-specific fractions, diffusivities, and T2 values within a two-compartment model of white matter, and to explore the approach in vivo. METHODS Sampling protocols featuring different b-values (b), b-tensor shapes (bΔ ), and echo times (TE) were optimized using Cramér-Rao lower bounds (CRLB). Whole-brain data were acquired in children, adults, and elderly with white matter lesions. Compartment fractions, diffusivities, and T2 values were estimated in a model featuring two microstructural compartments represented by a "stick" and a "zeppelin." RESULTS Precise parameter estimates were enabled by sampling protocols featuring seven or more "shells" with unique b/bΔ /TE-combinations. Acquisition times were approximately 15 minutes. In white matter of adults, the "stick" compartment had a fraction of approximately 0.5 and, compared with the "zeppelin" compartment, featured lower isotropic diffusivities (0.6 vs. 1.3 μm2 /ms) but higher T2 values (85 vs. 65 ms). Children featured lower "stick" fractions (0.4). White matter lesions exhibited high "zeppelin" isotropic diffusivities (1.7 μm2 /ms) and T2 values (150 ms). CONCLUSIONS Diffusion-relaxation MRI with tensor-valued diffusion encoding expands the set of microstructure parameters that can be precisely estimated and therefore increases their specificity to biological quantities.
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Affiliation(s)
- Björn Lampinen
- Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Filip Szczepankiewicz
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden.,Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Johan Mårtensson
- Clinical Sciences Lund, Department of Logopedics, Phoniatrics and Audiology, Lund University, Lund, Sweden
| | | | - Oskar Hansson
- Clinical Sciences Malmö, Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Carl-Fredrik Westin
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Markus Nilsson
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
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15
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Szczepankiewicz F, Westin CF, Nilsson M. Maxwell-compensated design of asymmetric gradient waveforms for tensor-valued diffusion encoding. Magn Reson Med 2019; 82:1424-1437. [PMID: 31148245 PMCID: PMC6626569 DOI: 10.1002/mrm.27828] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 05/03/2019] [Accepted: 05/06/2019] [Indexed: 01/13/2023]
Abstract
PURPOSE Diffusion encoding with asymmetric gradient waveforms is appealing because the asymmetry provides superior efficiency. However, concomitant gradients may cause a residual gradient moment at the end of the waveform, which can cause significant signal error and image artifacts. The purpose of this study was to develop an asymmetric waveform designs for tensor-valued diffusion encoding that is not sensitive to concomitant gradients. METHODS The "Maxwell index" was proposed as a scalar invariant to capture the effect of concomitant gradients. Optimization of "Maxwell-compensated" waveforms was performed in which this index was constrained. Resulting waveforms were compared to waveforms from literature, in terms of the measured and predicted impact of concomitant gradients, by numerical analysis as well as experiments in a phantom and in a healthy human brain. RESULTS Maxwell-compensated waveforms with Maxwell indices below 100 (mT/m)2 ms showed negligible signal bias in both numerical analysis and experiments. By contrast, several waveforms from literature showed gross signal bias under the same conditions, leading to a signal bias that was large enough to markedly affect parameter maps. Experimental results were accurately predicted by theory. CONCLUSION Constraining the Maxwell index in the optimization of asymmetric gradient waveforms yields efficient diffusion encoding that negates the effects of concomitant fields while enabling arbitrary shapes of the b-tensor. This waveform design is especially useful in combination with strong gradients, long encoding times, thick slices, simultaneous multi-slice acquisition, and large FOVs.
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Affiliation(s)
- Filip Szczepankiewicz
- Radiology, Brigham and Women’s Hospital, Boston, MA, US
- Harvard Medical School, Boston, MA, US
| | - Carl-Fredrik Westin
- Radiology, Brigham and Women’s Hospital, Boston, MA, US
- Harvard Medical School, Boston, MA, US
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16
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Nilsson M, Szczepankiewicz F, Brabec J, Taylor M, Westin CF, Golby A, van Westen D, Sundgren PC. Tensor-valued diffusion MRI in under 3 minutes: an initial survey of microscopic anisotropy and tissue heterogeneity in intracranial tumors. Magn Reson Med 2019; 83:608-620. [PMID: 31517401 PMCID: PMC6900060 DOI: 10.1002/mrm.27959] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 07/05/2019] [Accepted: 07/30/2019] [Indexed: 12/23/2022]
Abstract
PURPOSE To evaluate the feasibility of a 3-minutes protocol for assessment of the microscopic anisotropy and tissue heterogeneity based on tensor-valued diffusion MRI in a wide range of intracranial tumors. METHODS B-tensor encoding was performed in 42 patients with intracranial tumors (gliomas, meningiomas, adenomas, and metastases). Microscopic anisotropy and tissue heterogeneity were evaluated by estimating the anisotropic kurtosis (MKA ) and isotropic kurtosis (MKI ), respectively. An extensive imaging protocol was compared with a 3-minutes protocol. RESULTS The fast imaging protocol yielded parameters with characteristics in terms of bias and precision similar to the full protocol. Glioblastomas had lower microscopic anisotropy than meningiomas (MKA = 0.29 ± 0.06 vs. 0.45 ± 0.08, P = 0.003). Metastases had higher tissue heterogeneity (MKI = 0.57 ± 0.07) than both the glioblastomas (0.44 ± 0.06, P < 0.001) and meningiomas (0.46 ± 0.06, P = 0.03). CONCLUSION Evaluation of the microscopic anisotropy and tissue heterogeneity in intracranial tumor patients is feasible in clinically relevant times frames.
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Affiliation(s)
- Markus Nilsson
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
| | | | - Jan Brabec
- Department of Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Marie Taylor
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
| | | | - Alexandra Golby
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Danielle van Westen
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
| | - Pia C Sundgren
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden.,Lund University Bioimaging Center (LBIC), Lund University, Lund, Sweden
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17
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van Zijl P, Knutsson L. In vivo magnetic resonance imaging and spectroscopy. Technological advances and opportunities for applications continue to abound. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2019; 306:55-65. [PMID: 31377150 PMCID: PMC6703925 DOI: 10.1016/j.jmr.2019.07.034] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Revised: 06/19/2019] [Accepted: 07/08/2019] [Indexed: 05/07/2023]
Abstract
Over the past decades, the field of in vivo magnetic resonance (MR) has built up an impressive repertoire of data acquisition and analysis technologies for anatomical, functional, physiological, and molecular imaging, the description of which requires many book volumes. As such it is impossible for a few authors to have an authoritative overview of the field and for a brief article to be inclusive. We will therefore focus mainly on data acquisition and attempt to give some insight into the principles underlying current advanced methods in the field and the potential for further innovation. In our view, the foreseeable future is expected to show continued rapid progress, for instance in imaging of microscopic tissue properties in vivo, assessment of functional and anatomical connectivity, higher resolution physiologic and metabolic imaging, and even imaging of receptor binding. In addition, acquisition speed and information content will continue to increase due to the continuous development of approaches for parallel imaging (including simultaneous multi-slice imaging), compressed sensing, and MRI fingerprinting. Finally, artificial intelligence approaches are becoming more realistic and will have a tremendous effect on both acquisition and analysis strategies. Together, these developments will continue to provide opportunity for scientific discovery and, in combination with large data sets from other fields such as genomics, allow the ultimate realization of precision medicine in the clinic.
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Affiliation(s)
- Peter van Zijl
- Department of Radiology, Johns Hopkins University, F.M. Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
| | - Linda Knutsson
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
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18
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Coelho S, Pozo JM, Jespersen SN, Jones DK, Frangi AF. Resolving degeneracy in diffusion MRI biophysical model parameter estimation using double diffusion encoding. Magn Reson Med 2019; 82:395-410. [PMID: 30865319 PMCID: PMC6593681 DOI: 10.1002/mrm.27714] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 01/25/2019] [Accepted: 02/05/2019] [Indexed: 12/12/2022]
Abstract
PURPOSE Biophysical tissue models are increasingly used in the interpretation of diffusion MRI (dMRI) data, with the potential to provide specific biomarkers of brain microstructural changes. However, it has been shown recently that, in the general Standard Model, parameter estimation from dMRI data is ill-conditioned even when very high b-values are applied. We analyze this issue for the Neurite Orientation Dispersion and Density Imaging with Diffusivity Assessment (NODDIDA) model and demonstrate that its extension from single diffusion encoding (SDE) to double diffusion encoding (DDE) resolves the ill-posedness for intermediate diffusion weightings, producing an increase in accuracy and precision of the parameter estimation. METHODS We analyze theoretically the cumulant expansion up to fourth order in b of SDE and DDE signals. Additionally, we perform in silico experiments to compare SDE and DDE capabilities under similar noise conditions. RESULTS We prove analytically that DDE provides invariant information non-accessible from SDE, which makes the NODDIDA parameter estimation injective. The in silico experiments show that DDE reduces the bias and mean square error of the estimation along the whole feasible region of 5D model parameter space. CONCLUSIONS DDE adds additional information for estimating the model parameters, unexplored by SDE. We show, as an example, that this is sufficient to solve the previously reported degeneracies in the NODDIDA model parameter estimation.
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Affiliation(s)
- Santiago Coelho
- Centre for Computational Imaging & Simulation Technologies in Biomedicine (CISTIB) and Leeds Institute for Cardiac and Metabolic Medicine (LICAMM), School of Computing & School of MedicineUniversity of LeedsLeedsUnited Kingdom
- CISTIB, Electronic and Electrical Engineering DepartmentThe University of SheffieldSheffieldUnited Kingdom
| | - Jose M. Pozo
- Centre for Computational Imaging & Simulation Technologies in Biomedicine (CISTIB) and Leeds Institute for Cardiac and Metabolic Medicine (LICAMM), School of Computing & School of MedicineUniversity of LeedsLeedsUnited Kingdom
- CISTIB, Electronic and Electrical Engineering DepartmentThe University of SheffieldSheffieldUnited Kingdom
| | - Sune N. Jespersen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical MedicineAarhus UniversityAarhusDenmark
- Department of Physics and AstronomyAarhus UniversityAarhusDenmark
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC)Cardiff UniversityCardiffUnited Kingdom
- School of PsychologyAustralian Catholic UniversityMelbourneAustralia
| | - Alejandro F. Frangi
- Centre for Computational Imaging & Simulation Technologies in Biomedicine (CISTIB) and Leeds Institute for Cardiac and Metabolic Medicine (LICAMM), School of Computing & School of MedicineUniversity of LeedsLeedsUnited Kingdom
- CISTIB, Electronic and Electrical Engineering DepartmentThe University of SheffieldSheffieldUnited Kingdom
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19
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Lundell H, Nilsson M, Dyrby TB, Parker GJM, Cristinacce PLH, Zhou FL, Topgaard D, Lasič S. Multidimensional diffusion MRI with spectrally modulated gradients reveals unprecedented microstructural detail. Sci Rep 2019; 9:9026. [PMID: 31227745 PMCID: PMC6588609 DOI: 10.1038/s41598-019-45235-7] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Accepted: 06/04/2019] [Indexed: 12/11/2022] Open
Abstract
Characterization of porous media is essential in a wide range of biomedical and industrial applications. Microstructural features can be probed non-invasively by diffusion magnetic resonance imaging (dMRI). However, diffusion encoding in conventional dMRI may yield similar signatures for very different microstructures, which represents a significant limitation for disentangling individual microstructural features in heterogeneous materials. To solve this problem, we propose an augmented multidimensional diffusion encoding (MDE) framework, which unlocks a novel encoding dimension to assess time-dependent diffusion specific to structures with different microscopic anisotropies. Our approach relies on spectral analysis of complex but experimentally efficient MDE waveforms. Two independent contrasts to differentiate features such as cell shape and size can be generated directly by signal subtraction from only three types of measurements. Analytical calculations and simulations support our experimental observations. Proof-of-concept experiments were applied on samples with known and distinctly different microstructures. We further demonstrate substantially different contrasts in different tissue types of a post mortem brain. Our simultaneous assessment of restriction size and shape may be instrumental in studies of a wide range of porous materials, enable new insights into the microstructure of biological tissues or be of great value in diagnostics.
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Affiliation(s)
- H Lundell
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark.
| | - M Nilsson
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
| | - T B Dyrby
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - G J M Parker
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, The University of Manchester, Manchester, M13 9PT, United Kingdom
- Bioxydyn Limited, Manchester, United Kingdom
| | - P L Hubbard Cristinacce
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, The University of Manchester, Manchester, M13 9PT, United Kingdom
| | - F-L Zhou
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, The University of Manchester, Manchester, M13 9PT, United Kingdom
| | - D Topgaard
- Division of Physical Chemistry, Department of Chemistry, Lund University, Lund, Sweden
| | - S Lasič
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
- Random Walk Imaging AB, Lund, Sweden
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20
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Topgaard D. Diffusion tensor distribution imaging. NMR IN BIOMEDICINE 2019; 32:e4066. [PMID: 30730586 PMCID: PMC6593682 DOI: 10.1002/nbm.4066] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Revised: 11/28/2018] [Accepted: 12/19/2018] [Indexed: 05/30/2023]
Abstract
Conventional diffusion MRI yields voxel-averaged parameters that suffer from ambiguities for heterogeneous anisotropic materials such as brain tissue. Using principles from solid-state NMR spectroscopy, we have previously introduced the shape of the diffusion encoding tensor as a separate acquisition dimension that disentangles isotropic and anisotropic contributions to the observed diffusivities, thereby allowing for unconstrained data inversion into diffusion tensor distributions with "size," "shape," and orientation dimensions. Here we combine our recent non-parametric data inversion algorithm and data acquisition protocol with an imaging pulse sequence to demonstrate spatial mapping of diffusion tensor distributions using a previously developed composite phantom with multiple isotropic and anisotropic components. We propose a compact format for visualizing two-dimensional arrays of the distributions, new scalar parameters quantifying intra-voxel heterogeneity, and a binning procedure giving maps of all relevant parameters for each of the components resolved in the multidimensional distribution space.
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Affiliation(s)
- Daniel Topgaard
- Physical Chemistry, Department of ChemistryLund UniversityLundSweden
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21
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Herberthson M, Yolcu C, Knutsson H, Westin CF, Özarslan E. Orientationally-averaged diffusion-attenuated magnetic resonance signal for locally-anisotropic diffusion. Sci Rep 2019; 9:4899. [PMID: 30894611 PMCID: PMC6426978 DOI: 10.1038/s41598-019-41317-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 03/06/2019] [Indexed: 11/19/2022] Open
Abstract
Diffusion-attenuated MR signal for heterogeneous media has been represented as a sum of signals from anisotropic Gaussian sub-domains to the extent that this approximation is permissible. Any effect of macroscopic (global or ensemble) anisotropy in the signal can be removed by averaging the signal values obtained by differently oriented experimental schemes. The resulting average signal is identical to what one would get if the micro-domains are isotropically (e.g., randomly) distributed with respect to orientation, which is the case for “powdered” specimens. We provide exact expressions for the orientationally-averaged signal obtained via general gradient waveforms when the microdomains are characterized by a general diffusion tensor possibly featuring three distinct eigenvalues. This extends earlier results which covered only axisymmetric diffusion as well as measurement tensors. Our results are expected to be useful in not only multidimensional diffusion MR but also solid-state NMR spectroscopy due to the mathematical similarities in the two fields.
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Affiliation(s)
| | - Cem Yolcu
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Hans Knutsson
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Carl-Fredrik Westin
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden.,Laboratory for Mathematics in Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Evren Özarslan
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
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22
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Yang G, McNab JA. Eddy current nulled constrained optimization of isotropic diffusion encoding gradient waveforms. Magn Reson Med 2019; 81:1818-1832. [PMID: 30368913 PMCID: PMC6347544 DOI: 10.1002/mrm.27539] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Revised: 08/02/2018] [Accepted: 08/29/2018] [Indexed: 12/15/2022]
Abstract
PURPOSE Isotropic diffusion encoding efficiently encodes additional microstructural information in combination with conventional linear diffusion encoding. However, the gradient-intensive isotropic diffusion waveforms generate significant eddy currents, which cause image distortions. The purpose of this study is to present a method for designing isotropic diffusion encoding waveforms with intrinsic eddy current nulling. METHODS Eddy current nulled gradient waveforms were designed using a constrained optimization waveform for a 3T GE Premier MRI system. Encoding waveforms were designed for a variety of eddy current null times and sequence timings to evaluate the achievable b-value. Waveforms were also tested with both eddy current nulling and concomitant field compensation. Distortion reduction was tested in both phantoms and the in vivo human brain. RESULTS The feasibility of isotropic diffusion encoding with intrinsic correction of eddy current distortion and signal bias from concomitant fields was demonstrated. The constrained optimization algorithm produced gradient waveforms with the specified eddy current null times. The reduction in the achievable diffusion weighting was dependent on the number of eddy current null times. A reduction in the eddy current-induced image distortions was observed in both phantoms and in vivo human subjects. CONCLUSION The proposed framework allows the design of isotropic diffusion-encoding sequences with reduced image distortion.
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Affiliation(s)
- Grant Yang
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
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23
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Nilsson M, Larsson J, Lundberg D, Szczepankiewicz F, Witzel T, Westin C, Bryskhe K, Topgaard D. Liquid crystal phantom for validation of microscopic diffusion anisotropy measurements on clinical MRI systems. Magn Reson Med 2018; 79:1817-1828. [PMID: 28686785 PMCID: PMC5756689 DOI: 10.1002/mrm.26814] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Revised: 05/21/2017] [Accepted: 06/08/2017] [Indexed: 01/05/2023]
Abstract
PURPOSE To develop a phantom for validating MRI pulse sequences and data processing methods to quantify microscopic diffusion anisotropy in the human brain. METHODS Using a liquid crystal consisting of water, detergent, and hydrocarbon, we designed a 0.5-L spherical phantom showing the theoretically highest possible degree of microscopic anisotropy. Data were acquired on the Connectome scanner using echo-planar imaging signal readout and diffusion encoding with axisymmetric b-tensors of varying magnitude, anisotropy, and orientation. The mean diffusivity, fractional anisotropy (FA), and microscopic FA (µFA) parameters were estimated. RESULTS The phantom was observed to have values of mean diffusivity similar to brain tissue, and relaxation times compatible with echo-planar imaging echo times on the order of 100 ms. The estimated values of µFA were at the theoretical maximum of 1.0, whereas the values of FA spanned the interval from 0.0 to 0.8 as a result of varying orientational order of the anisotropic domains within each voxel. CONCLUSIONS The proposed phantom can be manufactured by mixing three widely available chemicals in volumes comparable to a human head. The acquired data are in excellent agreement with theoretical predictions, showing that the phantom is ideal for validating methods for measuring microscopic diffusion anisotropy on clinical MRI systems. Magn Reson Med 79:1817-1828, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
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Affiliation(s)
- Markus Nilsson
- Diagnostic Radiology, Department of Clinical SciencesLund UniversityLundSweden
| | - Johan Larsson
- Physical Chemistry, Department of ChemistryLund UniversityLundSweden
| | | | | | - Thomas Witzel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | | | | | - Daniel Topgaard
- Physical Chemistry, Department of ChemistryLund UniversityLundSweden
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24
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de Almeida Martins JP, Topgaard D. Multidimensional correlation of nuclear relaxation rates and diffusion tensors for model-free investigations of heterogeneous anisotropic porous materials. Sci Rep 2018; 8:2488. [PMID: 29410433 PMCID: PMC5802831 DOI: 10.1038/s41598-018-19826-9] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 01/08/2018] [Indexed: 11/25/2022] Open
Abstract
Despite their widespread use in non-invasive studies of porous materials, conventional MRI methods yield ambiguous results for microscopically heterogeneous materials such as brain tissue. While the forward link between microstructure and MRI observables is well understood, the inverse problem of separating the signal contributions from different microscopic pores is notoriously difficult. Here, we introduce an experimental protocol where heterogeneity is resolved by establishing 6D correlations between the individual values of isotropic diffusivity, diffusion anisotropy, orientation of the diffusion tensor, and relaxation rates of distinct populations. Such procedure renders the acquired signal highly specific to the sample's microstructure, and allows characterization of the underlying pore space without prior assumptions on the number and nature of distinct microscopic environments. The experimental feasibility of the suggested method is demonstrated on a sample designed to mimic the properties of nerve tissue. If matched to the constraints of whole body scanners, this protocol could allow for the unconstrained determination of the different types of tissue that compose the living human brain.
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Affiliation(s)
| | - Daniel Topgaard
- Division of Physical Chemistry, Department of Chemistry, Lund University, Lund, Sweden
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25
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Yang G, Tian Q, Leuze C, Wintermark M, McNab JA. Double diffusion encoding MRI for the clinic. Magn Reson Med 2017; 80:507-520. [PMID: 29266375 DOI: 10.1002/mrm.27043] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Revised: 11/17/2017] [Accepted: 11/18/2017] [Indexed: 12/13/2022]
Abstract
PURPOSE The purpose of this study is to develop double diffusion encoding (DDE) MRI methods for clinical use. Microscopic diffusion anisotropy measurements from DDE promise greater specificity to changes in tissue microstructure compared with conventional diffusion tensor imaging, but implementation of DDE sequences on whole-body MRI scanners is challenging because of the limited gradient strengths and lengthy acquisition times. METHODS A custom single-refocused DDE sequence was implemented on a 3T whole-body scanner. The DDE gradient orientation scheme and sequence parameters were optimized based on a Gaussian diffusion assumption. Using an optimized 5-min DDE acquisition, microscopic fractional anisotropy (μFA) maps were acquired for the first time in multiple sclerosis patients. RESULTS Based on simulations and in vivo human measurements, six parallel and six orthogonal diffusion gradient pairs were found to be the minimum number of diffusion gradient pairs necessary to produce a rotationally invariant measurement of μFA. Simulations showed that optimal precision and accuracy of μFA measurements were obtained using b-values between 1500 and 3000 s/mm2 . The μFA maps showed improved delineation of multiple sclerosis lesions compared with conventional fractional anisotropy and distinct contrast from T2 -weighted fluid attenuated inversion recovery and T1 -weighted imaging. CONCLUSION The μFA maps can be measured using DDE in a clinical setting and may provide new opportunities for characterizing multiple sclerosis lesions and other types of tissue degeneration. Magn Reson Med 80:507-520, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Grant Yang
- Department of Electrical Engineering, Stanford University, Stanford, California, USA.,Department of Radiology, Stanford University, Stanford, California, USA
| | - Qiyuan Tian
- Department of Electrical Engineering, Stanford University, Stanford, California, USA.,Department of Radiology, Stanford University, Stanford, California, USA
| | - Christoph Leuze
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Max Wintermark
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Jennifer A McNab
- Department of Radiology, Stanford University, Stanford, California, USA
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Westin CF, Knutsson H, Pasternak O, Szczepankiewicz F, Özarslan E, van Westen D, Mattisson C, Bogren M, O'Donnell LJ, Kubicki M, Topgaard D, Nilsson M. Q-space trajectory imaging for multidimensional diffusion MRI of the human brain. Neuroimage 2016; 135:345-62. [PMID: 26923372 PMCID: PMC4916005 DOI: 10.1016/j.neuroimage.2016.02.039] [Citation(s) in RCA: 196] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Revised: 12/29/2015] [Accepted: 02/12/2016] [Indexed: 12/28/2022] Open
Abstract
This work describes a new diffusion MR framework for imaging and modeling of microstructure that we call q-space trajectory imaging (QTI). The QTI framework consists of two parts: encoding and modeling. First we propose q-space trajectory encoding, which uses time-varying gradients to probe a trajectory in q-space, in contrast to traditional pulsed field gradient sequences that attempt to probe a point in q-space. Then we propose a microstructure model, the diffusion tensor distribution (DTD) model, which takes advantage of additional information provided by QTI to estimate a distributional model over diffusion tensors. We show that the QTI framework enables microstructure modeling that is not possible with the traditional pulsed gradient encoding as introduced by Stejskal and Tanner. In our analysis of QTI, we find that the well-known scalar b-value naturally extends to a tensor-valued entity, i.e., a diffusion measurement tensor, which we call the b-tensor. We show that b-tensors of rank 2 or 3 enable estimation of the mean and covariance of the DTD model in terms of a second order tensor (the diffusion tensor) and a fourth order tensor. The QTI framework has been designed to improve discrimination of the sizes, shapes, and orientations of diffusion microenvironments within tissue. We derive rotationally invariant scalar quantities describing intuitive microstructural features including size, shape, and orientation coherence measures. To demonstrate the feasibility of QTI on a clinical scanner, we performed a small pilot study comparing a group of five healthy controls with five patients with schizophrenia. The parameter maps derived from QTI were compared between the groups, and 9 out of the 14 parameters investigated showed differences between groups. The ability to measure and model the distribution of diffusion tensors, rather than a quantity that has already been averaged within a voxel, has the potential to provide a powerful paradigm for the study of complex tissue architecture.
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Affiliation(s)
- Carl-Fredrik Westin
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
| | - Hans Knutsson
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Ofer Pasternak
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Evren Özarslan
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Physics, Bogazici University, Istanbul, Turkey
| | | | | | - Mats Bogren
- Clinical Sciences, Psychiatry, Lund University, Lund, Sweden
| | | | - Marek Kubicki
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniel Topgaard
- Division of Physical Chemistry, Department of Chemistry, Lund University, Lund, Sweden
| | - Markus Nilsson
- Lund University Bioimaging Center, Lund University, Lund, Sweden
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Pasternak O, Kubicki M, Shenton ME. In vivo imaging of neuroinflammation in schizophrenia. Schizophr Res 2016; 173:200-212. [PMID: 26048294 PMCID: PMC4668243 DOI: 10.1016/j.schres.2015.05.034] [Citation(s) in RCA: 95] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2015] [Revised: 05/18/2015] [Accepted: 05/20/2015] [Indexed: 12/18/2022]
Abstract
In recent years evidence has accumulated to suggest that neuroinflammation might be an early pathology of schizophrenia that later leads to neurodegeneration, yet the exact role in the etiology, as well as the source of neuroinflammation, are still not known. The hypothesis of neuroinflammation involvement in schizophrenia is quickly gaining popularity, and thus it is imperative that we have reliable and reproducible tools and measures that are both sensitive, and, most importantly, specific to neuroinflammation. The development and use of appropriate human in vivo imaging methods can help in our understanding of the location and extent of neuroinflammation in different stages of the disorder, its natural time-course, and its relation to neurodegeneration. Thus far, there is little in vivo evidence derived from neuroimaging methods. This is likely the case because the methods that are specific and sensitive to neuroinflammation are relatively new or only just being developed. This paper provides a methodological review of both existing and emerging positron emission tomography and magnetic resonance imaging techniques that identify and characterize neuroinflammation. We describe \how these methods have been used in schizophrenia research. We also outline the shortcomings of existing methods, and we highlight promising future techniques that will likely improve state-of-the-art neuroimaging as a more refined approach for investigating neuroinflammation in schizophrenia.
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Affiliation(s)
- Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA; Department of Applied Mathematics, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Marek Kubicki
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Martha E Shenton
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA; VA Boston Healthcare System, Brockton, MA, USA
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28
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Yolcu C, Memiç M, Şimşek K, Westin CF, Özarslan E. NMR signal for particles diffusing under potentials: From path integrals and numerical methods to a model of diffusion anisotropy. Phys Rev E 2016; 93:052602. [PMID: 27300946 DOI: 10.1103/physreve.93.052602] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2015] [Indexed: 06/06/2023]
Abstract
We study the influence of diffusion on NMR experiments when the molecules undergo random motion under the influence of a force field and place special emphasis on parabolic (Hookean) potentials. To this end, the problem is studied using path integral methods. Explicit relationships are derived for commonly employed gradient waveforms involving pulsed and oscillating gradients. The Bloch-Torrey equation, describing the temporal evolution of magnetization, is modified by incorporating potentials. A general solution to this equation is obtained for the case of parabolic potential by adopting the multiple correlation function (MCF) formalism, which has been used in the past to quantify the effects of restricted diffusion. Both analytical and MCF results were found to be in agreement with random walk simulations. A multidimensional formulation of the problem is introduced that leads to a new characterization of diffusion anisotropy. Unlike the case of traditional methods that employ a diffusion tensor, anisotropy originates from the tensorial force constant, and bulk diffusivity is retained in the formulation. Our findings suggest that some features of the NMR signal that have traditionally been attributed to restricted diffusion are accommodated by the Hookean model. Under certain conditions, the formalism can be envisioned to provide a viable approximation to the mathematically more challenging restricted diffusion problems.
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Affiliation(s)
- Cem Yolcu
- Department of Physics, Boğaziçi University, Bebek, İstanbul 34342, Turkey
| | - Muhammet Memiç
- Department of Physics, Boğaziçi University, Bebek, İstanbul 34342, Turkey
| | - Kadir Şimşek
- Department of Physics, Boğaziçi University, Bebek, İstanbul 34342, Turkey
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02215, USA
| | - Evren Özarslan
- Department of Physics, Boğaziçi University, Bebek, İstanbul 34342, Turkey
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29
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de Almeida Martins JP, Topgaard D. Two-Dimensional Correlation of Isotropic and Directional Diffusion Using NMR. PHYSICAL REVIEW LETTERS 2016; 116:087601. [PMID: 26967442 DOI: 10.1103/physrevlett.116.087601] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2015] [Indexed: 05/12/2023]
Abstract
Diffusion nuclear magnetic resonance (NMR) is a powerful technique for studying porous media, but yields ambiguous results when the sample comprises multiple regions with different pore sizes, shapes, and orientations. Inspired by solid-state NMR techniques for correlating isotropic and anisotropic chemical shifts, we propose a diffusion NMR method to resolve said ambiguity. Numerical data inversion relies on sparse representation of the data in a basis of radial and axial diffusivities. Experiments are performed on a composite sample with a cell suspension and a liquid crystal.
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Affiliation(s)
| | - Daniel Topgaard
- Division of Physical Chemistry, Department of Chemistry, Lund University, 221 00 Lund, Sweden
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30
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Ianuş A, Alexander DC, Drobnjak I. Microstructure Imaging Sequence Simulation Toolbox. SIMULATION AND SYNTHESIS IN MEDICAL IMAGING 2016. [DOI: 10.1007/978-3-319-46630-9_4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Sjölund J, Szczepankiewicz F, Nilsson M, Topgaard D, Westin CF, Knutsson H. Constrained optimization of gradient waveforms for generalized diffusion encoding. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2015; 261:157-68. [PMID: 26583528 PMCID: PMC4752208 DOI: 10.1016/j.jmr.2015.10.012] [Citation(s) in RCA: 83] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2015] [Revised: 10/19/2015] [Accepted: 10/24/2015] [Indexed: 05/10/2023]
Abstract
Diffusion MRI is a useful probe of tissue microstructure. The conventional diffusion encoding sequence, the single pulsed field gradient, has recently been challenged as more general gradient waveforms have been introduced. Out of these, we focus on q-space trajectory imaging, which generalizes the scalar b-value to a tensor valued entity. To take full advantage of its capabilities, it is imperative to respect the constraints imposed by the hardware, while at the same time maximizing the diffusion encoding strength. We provide a tool that achieves this by solving a constrained optimization problem that accommodates constraints on maximum gradient amplitude, slew rate, coil heating and positioning of radio frequency pulses. The method's efficacy and flexibility is demonstrated both experimentally and by comparison with previous work on optimization of isotropic diffusion sequences.
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Affiliation(s)
- Jens Sjölund
- Elekta Instrument AB, Kungstensgatan 18, Box 7593, SE-103 93 Stockholm, Sweden; Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Sweden.
| | | | - Markus Nilsson
- Lund University Bioimaging Center, Lund University, Lund, Sweden
| | | | | | - Hans Knutsson
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Sweden
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Nilsson M, Szczepankiewicz F, van Westen D, Hansson O. Extrapolation-Based References Improve Motion and Eddy-Current Correction of High B-Value DWI Data: Application in Parkinson's Disease Dementia. PLoS One 2015; 10:e0141825. [PMID: 26528541 PMCID: PMC4631453 DOI: 10.1371/journal.pone.0141825] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2015] [Accepted: 10/13/2015] [Indexed: 11/26/2022] Open
Abstract
Purpose Conventional motion and eddy-current correction, where each diffusion-weighted volume is registered to a non diffusion-weighted reference, suffers from poor accuracy for high b-value data. An alternative approach is to extrapolate reference volumes from low b-value data. We aim to compare the performance of conventional and extrapolation-based correction of diffusional kurtosis imaging (DKI) data, and to demonstrate the impact of the correction approach on group comparison studies. Methods DKI was performed in patients with Parkinson’s disease dementia (PDD), and healthy age-matched controls, using b-values of up to 2750 s/mm2. The accuracy of conventional and extrapolation-based correction methods was investigated. Parameters from DTI and DKI were compared between patients and controls in the cingulum and the anterior thalamic projection tract. Results Conventional correction resulted in systematic registration errors for high b-value data. The extrapolation-based methods did not exhibit such errors, yielding more accurate tractography and up to 50% lower standard deviation in DKI metrics. Statistically significant differences were found between patients and controls when using the extrapolation-based motion correction that were not detected when using the conventional method. Conclusion We recommend that conventional motion and eddy-current correction should be abandoned for high b-value data in favour of more accurate methods using extrapolation-based references.
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Affiliation(s)
- Markus Nilsson
- Lund University Bioimaging Center, Lund University, Lund, Sweden
- * E-mail:
| | | | - Danielle van Westen
- Department of Radiology, Lund University, Lund, Sweden
- Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
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33
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Liu S, Cai W, Liu S, Zhang F, Fulham M, Feng D, Pujol S, Kikinis R. Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders. Brain Inform 2015; 2:167-180. [PMID: 27747507 PMCID: PMC4737664 DOI: 10.1007/s40708-015-0019-x] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2015] [Accepted: 08/08/2015] [Indexed: 12/20/2022] Open
Abstract
Multimodal neuroimaging is increasingly used in neuroscience research, as it overcomes the limitations of individual modalities. One of the most important applications of multimodal neuroimaging is the provision of vital diagnostic data for neuropsychiatric disorders. Multimodal neuroimaging computing enables the visualization and quantitative analysis of the alterations in brain structure and function, and has reshaped how neuroscience research is carried out. Research in this area is growing exponentially, and so it is an appropriate time to review the current and future development of this emerging area. Hence, in this paper, we review the recent advances in multimodal neuroimaging (MRI, PET) and electrophysiological (EEG, MEG) technologies, and their applications to the neuropsychiatric disorders. We also outline some future directions for multimodal neuroimaging where researchers will design more advanced methods and models for neuropsychiatric research.
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Affiliation(s)
- Sidong Liu
- School of IT, The University of Sydney, Sydney, Australia.
| | - Weidong Cai
- School of IT, The University of Sydney, Sydney, Australia
| | - Siqi Liu
- School of IT, The University of Sydney, Sydney, Australia
| | - Fan Zhang
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
| | - Michael Fulham
- Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, and the Sydney Medical School, The University of Sydney, Sydney, Australia
| | - Dagan Feng
- School of IT, The University of Sydney, Sydney, Australia
- Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Sonia Pujol
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
| | - Ron Kikinis
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
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34
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Özarslan E, Westin CF, Mareci TH. Characterizing magnetic resonance signal decay due to Gaussian diffusion: the path integral approach and a convenient computational method. CONCEPTS IN MAGNETIC RESONANCE. PART A, BRIDGING EDUCATION AND RESEARCH 2015; 44:203-213. [PMID: 27182208 PMCID: PMC4864615 DOI: 10.1002/cmr.a.21354] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
The influence of Gaussian diffusion on the magnetic resonance signal is determined by the apparent diffusion coefficient (ADC) and tensor (ADT) of the diffusing fluid as well as the gradient waveform applied to sensitize the signal to diffusion. Estimations of ADC and ADT from diffusion-weighted acquisitions necessitate computations of, respectively, the b-value and b-matrix associated with the employed pulse sequence. We establish the relationship between these quantities and the gradient waveform by expressing the problem as a path integral and explicitly evaluating it. Further, we show that these important quantities can be conveniently computed for any gradient waveform using a simple algorithm that requires a few lines of code. With this representation, our technique complements the multiple correlation function (MCF) method commonly used to compute the effects of restricted diffusion, and provides a consistent and convenient framework for studies that aim to infer the microstructural features of the specimen.
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Affiliation(s)
- Evren Özarslan
- Department of Physics, Bođaziçi University, Bebek, Ýstanbul, Turkey
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Corresponding author.
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Thomas H. Mareci
- Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, FL, USA
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Eriksson S, Lasič S, Nilsson M, Westin CF, Topgaard D. NMR diffusion-encoding with axial symmetry and variable anisotropy: Distinguishing between prolate and oblate microscopic diffusion tensors with unknown orientation distribution. J Chem Phys 2015; 142:104201. [PMID: 25770532 PMCID: PMC4359170 DOI: 10.1063/1.4913502] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2014] [Accepted: 02/12/2015] [Indexed: 11/14/2022] Open
Abstract
We introduce a nuclear magnetic resonance method for quantifying the shape of axially symmetric microscopic diffusion tensors in terms of a new diffusion anisotropy metric, DΔ, which has unique values for oblate, spherical, and prolate tensor shapes. The pulse sequence includes a series of equal-amplitude magnetic field gradient pulse pairs, the directions of which are tailored to give an axially symmetric diffusion-encoding tensor b with variable anisotropy bΔ. Averaging of data acquired for a range of orientations of the symmetry axis of the tensor b renders the method insensitive to the orientation distribution function of the microscopic diffusion tensors. Proof-of-principle experiments are performed on water in polydomain lyotropic liquid crystals with geometries that give rise to microscopic diffusion tensors with oblate, spherical, and prolate shapes. The method could be useful for characterizing the geometry of fluid-filled compartments in porous solids, soft matter, and biological tissues.
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Affiliation(s)
- Stefanie Eriksson
- Division of Physical Chemistry, Department of Chemistry, Lund University, Lund, Sweden
| | | | - Markus Nilsson
- Lund University Bioimaging Center, Lund University, Lund, Sweden
| | - Carl-Fredrik Westin
- Department of Radiology, BWH, Harvard Medical School, Boston, Massachusetts MA 02215, USA
| | - Daniel Topgaard
- Division of Physical Chemistry, Department of Chemistry, Lund University, Lund, Sweden
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36
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Szczepankiewicz F, Lasič S, van Westen D, Sundgren PC, Englund E, Westin CF, Ståhlberg F, Lätt J, Topgaard D, Nilsson M. Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: applications in healthy volunteers and in brain tumors. Neuroimage 2014; 104:241-52. [PMID: 25284306 DOI: 10.1016/j.neuroimage.2014.09.057] [Citation(s) in RCA: 179] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2014] [Revised: 08/28/2014] [Accepted: 09/25/2014] [Indexed: 12/11/2022] Open
Abstract
The anisotropy of water diffusion in brain tissue is affected by both disease and development. This change can be detected using diffusion MRI and is often quantified by the fractional anisotropy (FA) derived from diffusion tensor imaging (DTI). Although FA is sensitive to anisotropic cell structures, such as axons, it is also sensitive to their orientation dispersion. This is a major limitation to the use of FA as a biomarker for "tissue integrity", especially in regions of complex microarchitecture. In this work, we seek to circumvent this limitation by disentangling the effects of microscopic diffusion anisotropy from the orientation dispersion. The microscopic fractional anisotropy (μFA) and the order parameter (OP) were calculated from the contrast between signal prepared with directional and isotropic diffusion encoding, where the latter was achieved by magic angle spinning of the q-vector (qMAS). These parameters were quantified in healthy volunteers and in two patients; one patient with meningioma and one with glioblastoma. Finally, we used simulations to elucidate the relation between FA and μFA in various micro-architectures. Generally, μFA was high in the white matter and low in the gray matter. In the white matter, the largest differences between μFA and FA were found in crossing white matter and in interfaces between large white matter tracts, where μFA was high while FA was low. Both tumor types exhibited a low FA, in contrast to the μFA which was high in the meningioma and low in the glioblastoma, indicating that the meningioma contained disordered anisotropic structures, while the glioblastoma did not. This interpretation was confirmed by histological examination. We conclude that FA from DTI reflects both the amount of diffusion anisotropy and orientation dispersion. We suggest that the μFA and OP may complement FA by independently quantifying the microscopic anisotropy and the level of orientation coherence.
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Affiliation(s)
- Filip Szczepankiewicz
- Clinical Sciences, Lund, Department of Medical Radiation Physics, Lund University, Lund, Sweden.
| | | | - Danielle van Westen
- Diagnostic Radiology, Department of Clinical Sciences, Lund, Lund University, Lund, Sweden; Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Pia C Sundgren
- Diagnostic Radiology, Department of Clinical Sciences, Lund, Lund University, Lund, Sweden; Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Elisabet Englund
- Division of Oncology and Pathology, Department of Clinical Sciences, Lund, Lund University, Skåne University Hospital, Lund, Sweden
| | - Carl-Fredrik Westin
- Laboratory for Mathematics in Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Freddy Ståhlberg
- Clinical Sciences, Lund, Department of Medical Radiation Physics, Lund University, Lund, Sweden; Diagnostic Radiology, Department of Clinical Sciences, Lund, Lund University, Lund, Sweden; Lund University Bioimaging Center, Lund University, Lund, Sweden
| | - Jimmy Lätt
- Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Daniel Topgaard
- Division of Physical Chemistry, Department of Chemistry, Lund University, Lund, Sweden
| | - Markus Nilsson
- Lund University Bioimaging Center, Lund University, Lund, Sweden
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