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Krzyżak AT, Lasek J, Schneider Z, Wnuk M, Bryll A, Popiela T, Słowik A. Diffusion tensor imaging metrics as natural markers of multiple sclerosis-induced brain disorders with a low Expanded Disability Status Scale score. Neuroimage 2024; 290:120567. [PMID: 38471597 DOI: 10.1016/j.neuroimage.2024.120567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 02/12/2024] [Accepted: 03/05/2024] [Indexed: 03/14/2024] Open
Abstract
Non-invasive and effective differentiation along with determining the degree of deviations compared to the healthy cohort is important in the case of various brain disorders, including multiple sclerosis (MS). Evaluation of the effectiveness of diffusion tensor metrics (DTM) in 3T DTI for recording MS-related deviations was performed using a time-acceptable MRI protocol with unique comprehensive detection of systematic errors related to spatial heterogeneity of magnetic field gradients. In a clinical study, DTMs were acquired in segmented regions of interest (ROIs) for 50 randomly selected healthy controls (HC) and 50 multiple sclerosis patients. Identical phantom imaging was performed for each clinical measurement to estimate and remove the influence of systematic errors using the b-matrix spatial distribution in the DTI (BSD-DTI) technique. In the absence of statistically significant differences due to age in healthy volunteers and patients with multiple sclerosis, the existence of significant differences between groups was proven using DTM. Moreover, a statistically significant impact of spatial systematic errors occurs for all ROIs and DTMs in the phantom and for approximately 90 % in the HC and MS groups. In the case of a single patient measurement, this appears for all the examined ROIs and DTMs. The obtained DTMs effectively discriminate healthy volunteers from multiple sclerosis patients with a low mean score on the Expanded Disability Status Scale. The magnitude of the group differences is typically significant, with an effect size of approximately 0.5, and similar in both the standard approach and after elimination of systematic errors. Differences were also observed between metrics obtained using these two approaches. Despite a small alterations in mean DTMs values for groups and ROIs (1-3 %), these differences were characterized by a huge effect (effect size ∼0.8 or more). These findings indicate the importance of determining the spatial distribution of systematic errors specific to each MR scanner and DTI acquisition protocol in order to assess their impact on DTM in the ROIs examined. This is crucial to establish accurate DTM values for both individual patients and mean values for a healthy population as a reference. This approach allows for an initial reliable diagnosis based on DTI metrics.
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Affiliation(s)
| | - Julia Lasek
- AGH University of Kraków, 30-059 Krakow, Poland
| | | | - Marcin Wnuk
- UJ CM: Department of Neurology, Jagiellonian University Medical College, University Hospital in Krakow, Krakow, Poland; University Hospital in Krakow, Krakow, Poland
| | - Amira Bryll
- UJ CM: Department of Neurology, Jagiellonian University Medical College, University Hospital in Krakow, Krakow, Poland
| | | | - Agnieszka Słowik
- UJ CM: Department of Neurology, Jagiellonian University Medical College, University Hospital in Krakow, Krakow, Poland
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2
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Xu H, Newlin NR, Kim ME, Gao C, Kanakaraj P, Krishnan AR, Remedios LW, Khairi NM, Pechman K, Archer D, Hohman TJ, Jefferson AL, Isgum I, Huo Y, Moyer D, Schilling KG, Landman BA. Evaluation of Mean Shift, ComBat, and CycleGAN for Harmonizing Brain Connectivity Matrices Across Sites. ARXIV 2024:arXiv:2401.06798v2. [PMID: 38344221 PMCID: PMC10854272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
Connectivity matrices derived from diffusion MRI (dMRI) provide an interpretable and generalizable way of understanding the human brain connectome. However, dMRI suffers from inter-site and between-scanner variation, which impedes analysis across datasets to improve robustness and reproducibility of results. To evaluate different harmonization approaches on connectivity matrices, we compared graph measures derived from these matrices before and after applying three harmonization techniques: mean shift, ComBat, and CycleGAN. The sample comprises 168 age-matched, sex-matched normal subjects from two studies: the Vanderbilt Memory and Aging Project (VMAP) and the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD). First, we plotted the graph measures and used coefficient of variation (CoV) and the Mann-Whitney U test to evaluate different methods' effectiveness in removing site effects on the matrices and the derived graph measures. ComBat effectively eliminated site effects for global efficiency and modularity and outperformed the other two methods. However, all methods exhibited poor performance when harmonizing average betweenness centrality. Second, we tested whether our harmonization methods preserved correlations between age and graph measures. All methods except for CycleGAN in one direction improved correlations between age and global efficiency and between age and modularity from insignificant to significant with p-values less than 0.05.
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Affiliation(s)
- Hanliang Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Nancy R Newlin
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Michael E Kim
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Chenyu Gao
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | | | - Aravind R Krishnan
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Lucas W Remedios
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Nazirah Mohd Khairi
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kimberly Pechman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Derek Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Angela L Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ivana Isgum
- Department of Biomedical Engineering and Physics & Radiology and Nuclear Medicine, University Medical Center Amsterdam, University of Amsterdam, Amsterdam, the Netherlands
| | - Yuankai Huo
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Daniel Moyer
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Kurt G Schilling
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
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3
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Kanakaraj P, Cai LY, Yao T, Rheault F, Rogers BP, Anderson A, Schilling KG, Landman BA. Efficient approximate signal reconstruction for correction of gradient nonlinearities in diffusion-weighted imaging. Magn Reson Imaging 2023; 102:20-25. [PMID: 36965836 PMCID: PMC10517071 DOI: 10.1016/j.mri.2023.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/20/2023] [Accepted: 03/21/2023] [Indexed: 03/27/2023]
Abstract
In diffusion weighted MRI (DW-MRI), hardware nonlinearities lead to spatial variations in the orientation and magnitude of diffusion weighting. While the correction of these spatial distortions has been well established for analyses of DW-MRI, the existing voxel-wise empirical correction for gradient nonlinearities requires reimplementation of existing models, as the resultant gradients vary by voxel. Herein, we propose a two-step signal approximation after voxel-wise correction of gradient nonlinearity effects in DW-MRI. The proposed technique (1) scales the diffusion signal and (2) resamples the gradient orientations. This results in uniform gradients across the corrected image and provides the key advantage of seamless integration into current diffusion workflows. We investigated the validity of our technique by fitting a multi-compartment neurite orientation dispersion and density imaging (NODDI) model to the empirical correction and proposed approximation in five subjects from the MASiVar pediatric dataset. We evaluated intra-cellular volume fraction (iVF), CSF volume fraction (cVF), and orientation dispersion index (ODI) from NODDI. The Cohen's d of iVF, cVF and ODI between the techniques was <0.2 indicating the proposed technique does not exhibit significant differences from the voxel-wise correction technique. Our two-step signal approximation is an efficient representation of the voxel-wise gradient table correction. Using this approximation, correction of gradient nonlinearities can be easily incorporated into existing diffusion preprocessing pipelines and is implemented in "PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images".
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Affiliation(s)
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Tianyuan Yao
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
| | - Francois Rheault
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Baxter P Rogers
- Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Adam Anderson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Services, Vanderbilt University Medical Center, Vanderbilt University Medical, Nashville, TN, USA.
| | - Kurt G Schilling
- Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Services, Vanderbilt University Medical Center, Vanderbilt University Medical, Nashville, TN, USA.
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4
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Kose R, Kose K, Fujimoto K, Okada T, Tamada D, Motosugi U. Nonlinear Gradient Field Mapping Using a Spherical Grid Phantom for 3 and 7 Tesla MR Imaging Systems Equipped with High-performance Gradient Coils. Magn Reson Med Sci 2023:tn.2023-0063. [PMID: 37690843 DOI: 10.2463/mrms.tn.2023-0063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023] Open
Abstract
Recent high-performance gradient coils are fabricated mainly at the expense of spatial linearity. In this study, we measured the spatial nonlinearity of the magnetic field generated by the gradient coils of two MRI systems with high-performance gradient coils. The nonlinearity of the gradient fields was measured using 3D gradient echo sequences and a spherical phantom with a built-in lattice structure. The spatial variation of the gradient field was approximated to the 3rd order polynomials. The coefficients of the polynomials were calculated using the steepest descent method. The geometric distortion of the acquired 3D MR images was corrected using the polynomials and compared with the 3D images corrected using the harmonic functions provided by the MRI venders. As a result, it was found that the nonlinearity correction formulae provided by the vendors were insufficient and needed to be verified or corrected using a geometric phantom such as used in this study.
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Affiliation(s)
| | | | - Koji Fujimoto
- Human Brain Research Center, Graduate School of Medicine, Kyoto University
| | - Tomohisa Okada
- Human Brain Research Center, Graduate School of Medicine, Kyoto University
| | - Daiki Tamada
- Department of Radiology, University of Yamanashi
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Kanakaraj P, Cai LY, Rheault F, Yehe FC, Rogers BP, Schilling KG, Landman BA. Mapping the impact of nonlinear gradient fields with noise on diffusion MRI. Magn Reson Imaging 2023; 98:124-131. [PMID: 36632947 DOI: 10.1016/j.mri.2023.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 01/06/2023] [Indexed: 01/11/2023]
Abstract
In diffusion MRI, gradient nonlinearities cause spatial variations in the magnitude and direction of diffusion gradients. Studies have shown artifacts from these distortions can results in biased diffusion tensor information and tractography. Here, we investigate the impact of gradient nonlinearity correction in the presence of noise. We introduced empirically derived gradient nonlinear fields at different signal-to-noise ratio (SNR) levels in two experiments: tensor field simulation and simulation of the brain. For each experiment, this work compares two techniques empirically: voxel-wise gradient table correction and approximate correction by scaling the signal directly. The impact was assessed through diffusion metrics including mean diffusivity (MD), fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and principal eigen vector (V1). The study shows (1) the correction of gradient nonlinearities will not lead to substantively incorrect estimation of diffusion metrics in a linear system, (2) gradient nonlinearity correction does not interact adversely with noise, (3) nonlinearity correction suppresses the impact of nonlinearities in typical SNR data, (4) for SNR below 30, the performance of both the gradient nonlinearity correction techniques were similar, and (5) larger impacts are seen in regions where the gradient nonlinearities are distinct. Thus, this study suggests that there were greater beneficial effects than adverse effects due to the correction of nonlinearities. Additionally, correction of nonlinearities is recommended when region of interests are in areas with pronounced nonlinearities.
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Affiliation(s)
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Francois Rheault
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Fang-Cheng Yehe
- Department of Neurological Surgery, University of Pittsburg, School of Medicine, Pittsburg, PA, USA.
| | - Baxter P Rogers
- Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Kurt G Schilling
- Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Services, Vanderbilt University Medical Center, Vanderbilt University Medical, Nashville, TN, USA.
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6
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Kanakaraj P, Rheault F, Cai LY, Newlin N, Yeh FC, Rogers BP, Schilling KG, Landman BA. Mapping the Impact of Approximate Gradient Nonlinearity Fields Correction on Tractography. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2023; 12464:1246427. [PMID: 37621418 PMCID: PMC10448744 DOI: 10.1117/12.2653884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
Nonlinear gradients impact diffusion weighted MRI by introducing spatial variation in estimated diffusion tensors. Recent studies have shown that increasing signal-to-noise ratios and the use of ultra-strong gradients may lead to clinically significant impacts on analyses due to these nonlinear gradients in microstructural measures. These effects can potentially bias tractography results and cause misinterpretation of data. Herein, we characterize the impact of an "approximate" gradient nonlinearity correction technique in tractography using empirically derived gradient nonlinear fields. This technique scales the diffusion signal by the change in magnitude due to the gradient nonlinearities, without concomitant correction of gradient direction errors. The impact of this correction on tractography is assessed through white matter bundle segmentation and connectomics via bundle-wise volume, fractional anisotropy, mean diffusivity, radial diffusivity, axial diffusivity, primary eigenvector, and length; as well as the modularity, global efficiency, and characteristic path length connectomics graph measures. We investigate the differences between (1) these measures directly and (2) the within session variability of these measures before and after approximate correction in 61 subjects from the MASiVar pediatric reproducibility dataset. We find approximate correction results is little to no differences on the population level, but large differences on the subject-specific level for both the measures directly and their within session variability. Thus, this study suggests though approximate correction of gradient nonlinearities may not change tractography findings on the population level, subject-specific interpretations may exhibit large fluctuations. A limitation is the lack of comparison with the empirical voxel-wise gradient table correction.
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Affiliation(s)
| | - Francois Rheault
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Nancy Newlin
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburg, School of Medicine, Pittsburg, PA, USA
| | - Baxter P Rogers
- Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kurt G Schilling
- Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology and Radiological Services, Vanderbilt University Medical Center, Vanderbilt University Medical, Nashville, TN, USA
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7
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Li W, Mouli SK. Editorial for “Diffusion Weighted Imaging of the Abdomen: Correction for Gradient Nonlinearity Bias in Apparent Diffusion Coefficient”. J Magn Reson Imaging 2022. [DOI: 10.1002/jmri.28531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 10/27/2022] [Indexed: 11/16/2022] Open
Affiliation(s)
- Weiguo Li
- Department of Radiology Northwestern University Chicago Illinois USA
- Department of Biomedical Engineering University of Illinois at Chicago Chicago Illinois USA
- Research Resource Center University of Illinois at Chicago Chicago Illinois USA
| | - Samdeep K. Mouli
- Department of Radiology Northwestern University Chicago Illinois USA
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8
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Kanakaraj P, Hansen CB, Rheault F, Cai LY, Ramadass K, Rogers BP, Schilling KG, Landman BA. Mapping the Impact of Non-Linear Gradient Fields on Diffusion MRI Tensor Estimation. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12032:1203203. [PMID: 36303581 PMCID: PMC9604130 DOI: 10.1117/12.2611900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Non-linear gradients impact diffusion weighted (DW) MRI by corrupting the experimental setup and lead to problems during image encoding including the effects in-plane distortion, in-plane shifts, intensity modulations and phase errors. Recent studies have been shown this may present significant complication in the interpretation of results and conclusion while studying tractography and tissue microstructure in data. To interpret the degree in consequences of gradient non-linearities between the desired and achieved gradients, we introduced empirically derived gradient nonlinear fields at different orientations and different tensor properties. The impact is assessed through diffusion tensor properties including mean diffusivity (MD), fractional anisotropy (FA) and principal eigen vector (PEV). The study shows lower FA are more susceptible to LR fields and LR fields with determinant <1 or >1 corrupt tensor more. The corruption can result in significantly different FA based on true-FA and LR field. Apparent MD decreases for negative determinant, on the other hand positive determinant shows the opposite effect. LR field have a larger impact on PEV when FA value is small. The results are dependent on the underlying orientation, non-linear field corruption can cause both increase and decrease of estimated FA, MD and PEV value. This work provides insight into characterizing the non-linear gradient error and aid in selecting correction techniques to address the inaccuracies in b-values.
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Affiliation(s)
| | - Colin B. Hansen
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Francois Rheault
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Leon Y. Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Baxter P. Rogers
- Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Radiology and Radiological Services, Vanderbilt University Medical Center, Vanderbilt University Medical, Nashville, TN, USA
| | - Kurt G. Schilling
- Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Radiology and Radiological Services, Vanderbilt University Medical Center, Vanderbilt University Medical, Nashville, TN, USA
| | - Bennett A. Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA,Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA,Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Radiology and Radiological Services, Vanderbilt University Medical Center, Vanderbilt University Medical, Nashville, TN, USA
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What's New and What's Next in Diffusion MRI Preprocessing. Neuroimage 2021; 249:118830. [PMID: 34965454 PMCID: PMC9379864 DOI: 10.1016/j.neuroimage.2021.118830] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/26/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on “what’s new” since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on “Mapping the Connectome” in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on “what’s next” in dMRI preprocessing.
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Mazur W, Urbańczyk-Zawadzka M, Banyś R, Obuchowicz R, Trystuła M, Krzyżak AT. Diffusion as a Natural Contrast in MR Imaging of Peripheral Artery Disease (PAD) Tissue Changes. A Case Study of the Clinical Application of DTI for a Patient with Chronic Calf Muscles Ischemia. Diagnostics (Basel) 2021; 11:diagnostics11010092. [PMID: 33429993 PMCID: PMC7827719 DOI: 10.3390/diagnostics11010092] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 01/05/2021] [Indexed: 02/06/2023] Open
Abstract
This paper reports a first application of diffusion tensor imaging with corrections by using the B-matrix spatial distribution method (BSD-DTI) for peripheral artery disease (PAD) detected in the changes of diffusion tensor parameters (DTPs). A 76-year-old male was diagnosed as having PAD, since he demonstrated in angiographic images of lower legs severe arterial stenosis and the presence of lateral and peripheral circulation and assigned to the double-blind RCT using mesenchymal stem cells (MSCs) or placebo for the regenerative treatment of implications of ischemic diseases. In order to indicate changes in diffusivity in calf muscles in comparison to a healthy control, a DTI methodology was developed. The main advantage of the applied protocol was decreased scanning time, which was achieved by reducing b-value and number of scans (to 1), while maintaining minimal number of diffusion gradient directions and high resolution. This was possible due to calibration via the BSD method, which reduced systematic errors and allowed quantitative analysis. In the course of PAD, diffusivities were elevated across the calf muscles in posterior compartment and lost their anisotropy. Different character was noticed for anterior compartment, in which diffusivities along and across muscles were decreased without a significant loss of anisotropy. After the intervention involving a series of injections, the improvement of DTPs and tractography was visible, but can be assigned neither to MSCs nor placebo before unblinding.
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Affiliation(s)
- Weronika Mazur
- Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, Mickiewicza Avenue 30, 30-059 Cracow, Poland;
- Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Mickiewicza Avenue 30, 30-059 Cracow, Poland
| | - Małgorzata Urbańczyk-Zawadzka
- Department of Radiology and Diagnostic Imaging, John Paul II Hospital, Prądnicka Street 80, 31-202 Cracow, Poland; (M.U.-Z.); (R.B.)
| | - Robert Banyś
- Department of Radiology and Diagnostic Imaging, John Paul II Hospital, Prądnicka Street 80, 31-202 Cracow, Poland; (M.U.-Z.); (R.B.)
| | - Rafał Obuchowicz
- Department of Diagnostic Imaging, Jagiellonian University Medical College, Jakubowskiego 2, 30-688 Cracow, Poland;
| | - Mariusz Trystuła
- Department of Vascular Surgery with Endovascular Procedures Subdivision, John Paul II Hospital, Prądnicka Street 80, 31-202 Cracow, Poland;
| | - Artur T. Krzyżak
- Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, Mickiewicza Avenue 30, 30-059 Cracow, Poland;
- Correspondence:
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