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Registration-based image enhancement improves multi-atlas segmentation of the thalamic nuclei and hippocampal subfields.
Bao S, Bermudez C, Huo Y, Parvathaneni P, Rodriguez W, Resnick SM, D'Haese PF, McHugo M, Heckers S, Dawant BM, Lyu I, Landman BA
(2019) Magn Reson Imaging 59: 143-152
MeSH Terms: Algorithms, Brain Mapping, Hippocampus, Humans, Image Enhancement, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Temporal Lobe, Thalamic Nuclei
Show Abstract · Added March 26, 2019
Magnetic resonance imaging (MRI) is an important tool for analysis of deep brain grey matter structures. However, analysis of these structures is limited due to low intensity contrast typically found in whole brain imaging protocols. Herein, we propose a big data registration-enhancement (BDRE) technique to augment the contrast of deep brain structures using an efficient large-scale non-rigid registration strategy. Direct validation is problematic given a lack of ground truth data. Rather, we validate the usefulness and impact of BDRE for multi-atlas (MA) segmentation on two sets of structures of clinical interest: the thalamic nuclei and hippocampal subfields. The experimental design compares algorithms using T1-weighted 3 T MRI for both structures (and additional 7 T MRI for the thalamic nuclei) with an algorithm using BDRE. As baseline comparisons, a recent denoising (DN) technique and a super-resolution (SR) method are used to preprocess the original 3 T MRI. The performance of each MA segmentation is evaluated by the Dice similarity coefficient (DSC). BDRE significantly improves mean segmentation accuracy over all methods tested for both thalamic nuclei (3 T imaging: 9.1%; 7 T imaging: 15.6%; DN: 6.9%; SR: 16.2%) and hippocampal subfields (3 T T1 only: 8.7%; DN: 8.4%; SR: 8.6%). We also present DSC performance for each thalamic nucleus and hippocampal subfield and show that BDRE can help MA segmentation for individual thalamic nuclei and hippocampal subfields. This work will enable large-scale analysis of clinically relevant deep brain structures from commonly acquired T1 images.
Copyright © 2019 Elsevier Inc. All rights reserved.
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9 MeSH Terms
Evaluation of principal component analysis image denoising on multi-exponential MRI relaxometry.
Does MD, Olesen JL, Harkins KD, Serradas-Duarte T, Gochberg DF, Jespersen SN, Shemesh N
(2019) Magn Reson Med 81: 3503-3514
MeSH Terms: Algorithms, Animals, Brain, Computer Simulation, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Mice, Principal Component Analysis
Show Abstract · Added March 5, 2020
PURPOSE - Multi-exponential relaxometry is a powerful tool for characterizing tissue, but generally requires high image signal-to-noise ratio (SNR). This work evaluates the use of principal-component-analysis (PCA) denoising to mitigate these SNR demands and improve the precision of relaxometry measures.
METHODS - PCA denoising was evaluated using both simulated and experimental MRI data. Bi-exponential transverse relaxation signals were simulated for a wide range of acquisition and sample parameters, and experimental data were acquired from three excised and fixed mouse brains. In both cases, standard relaxometry analysis was performed on both original and denoised image data, and resulting estimated signal parameters were compared.
RESULTS - Denoising reduced the root-mean-square-error of parameters estimated from multi-exponential relaxometry by factors of ≈3×, for typical acquisition and sample parameters. Denoised images and subsequent parameter maps showed little or no signs of spatial artifact or loss of resolution.
CONCLUSION - Experimental studies and simulations demonstrate that PCA denoising of MRI relaxometry data is an effective method of improving parameter precision without sacrificing image resolution. This simple yet important processing step thus paves the way for broader applicability of multi-exponential MRI relaxometry.
© 2019 International Society for Magnetic Resonance in Medicine.
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8 MeSH Terms
Low-rank plus sparse compressed sensing for accelerated proton resonance frequency shift MR temperature imaging.
Cao Z, Gore JC, Grissom WA
(2019) Magn Reson Med 81: 3555-3566
MeSH Terms: Ablation Techniques, Animals, Brain, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Magnetic Resonance Imaging, Interventional, Models, Biological, Phantoms, Imaging, Thalamus, Thermography
Show Abstract · Added March 26, 2019
PURPOSE - To improve multichannel compressed sensing (CS) reconstruction for MR proton resonance frequency (PRF) shift thermography, with application to MRI-induced RF heating evaluation and MR guided high intensity focused ultrasound (MRgFUS) temperature monitoring.
METHODS - A new compressed sensing reconstruction is proposed that enforces joint low rank and sparsity of complex difference domain PRF data between post heating and baseline images. Validations were performed on 4 retrospectively undersampled dynamic data sets in PRF applications, by comparing the proposed method to a previously described L and total variation- (TV-) based CS approach that also operates on complex difference domain data, and to a conventional low rank plus sparse (L+S) separation-based CS reconstruction applied to the original domain data.
RESULTS - In all 4 retrospective validations, the proposed reconstruction method outperformed the conventional L+S and L +TV CS reconstruction methods with a 3.6× acceleration ratio in terms of temperature accuracy with respect to fully sampled data. For RF heating evaluation, the proposed method achieved RMS error of 12%, compared to 19% for the L+S method and 17% for the L +TV method. For in vivo MRgFUS thalamotomy, the peak temperature reconstruction errors were 19%, 31%, and 35%, respectively.
CONCLUSION - The complex difference-based low rank and sparse model enhances compressibility for dynamic PRF temperature imaging applications. The proposed multichannel CS reconstruction method enables high acceleration factors for PRF applications including RF heating evaluation and MRgFUS sonication.
© 2019 International Society for Magnetic Resonance in Medicine.
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11 MeSH Terms
White matter differences between essential tremor and Parkinson disease.
Juttukonda MR, Franco G, Englot DJ, Lin YC, Petersen KJ, Trujillo P, Hedera P, Landman BA, Kang H, Donahue MJ, Konrad PE, Dawant BM, Claassen DO
(2019) Neurology 92: e30-e39
MeSH Terms: Aged, Anisotropy, Cohort Studies, Diffusion Tensor Imaging, Essential Tremor, Female, Humans, Image Processing, Computer-Assisted, Leukoencephalopathies, Logistic Models, Male, Middle Aged, Parkinson Disease
Show Abstract · Added June 22, 2019
OBJECTIVE - To assess white matter integrity in patients with essential tremor (ET) and Parkinson disease (PD) with moderate to severe motor impairment.
METHODS - Sedated participants with ET (n = 57) or PD (n = 99) underwent diffusion tensor imaging (DTI) and fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity values were computed. White matter tracts were defined using 3 well-described atlases. To determine candidate white matter regions that differ between ET and PD groups, a bootstrapping analysis was applied using the least absolute shrinkage and selection operator. Linear regression was applied to assess magnitude and direction of differences in DTI metrics between ET and PD populations in the candidate regions.
RESULTS - Fractional anisotropy values that differentiate ET from PD localize primarily to thalamic and visual-related pathways, while diffusivity differences localized to the cerebellar peduncles. Patients with ET exhibited lower fractional anisotropy values than patients with PD in the lateral geniculate body ( < 0.01), sagittal stratum ( = 0.01), forceps major ( = 0.02), pontine crossing tract ( = 0.03), and retrolenticular internal capsule ( = 0.04). Patients with ET exhibited greater radial diffusivity values than patients with PD in the superior cerebellar peduncle ( < 0.01), middle cerebellar peduncle ( = 0.05), and inferior cerebellar peduncle ( = 0.05).
CONCLUSIONS - Regionally, distinctive white matter microstructural values in patients with ET localize to the cerebellar peduncles and thalamo-cortical visual pathways. These findings complement recent functional imaging studies in ET but also extend our understanding of putative physiologic features that account for distinctions between ET and PD.
© 2018 American Academy of Neurology.
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13 MeSH Terms
Characterization and correlation of signal drift in diffusion weighted MRI.
Hansen CB, Nath V, Hainline AE, Schilling KG, Parvathaneni P, Bayrak RG, Blaber JA, Irfanoglu O, Pierpaoli C, Anderson AW, Rogers BP, Landman BA
(2019) Magn Reson Imaging 57: 133-142
MeSH Terms: Brain, Diffusion Magnetic Resonance Imaging, Humans, Image Processing, Computer-Assisted, Phantoms, Imaging, Time Factors
Show Abstract · Added March 18, 2020
Diffusion weighted MRI (DWMRI) and the myriad of analysis approaches (from tensors to spherical harmonics and brain tractography to body multi-compartment models) depend on accurate quantification of the apparent diffusion coefficient (ADC). Signal drift during imaging (e.g., due to b0 drift associated with heating) can cause systematic non-linearities that manifest as ADC changes if not corrected. Herein, we present a case study on two phantoms on one scanner. Different scan protocols exhibit different degrees of drift during similar scans and may be sensitive to the order of scans within an exam. Vos et al. recently reviewed the effects of signal drift in DWMRI acquisitions and proposed a temporal model for correction. We propose a novel spatial-temporal model to correct for higher order aspects of the signal drift and derive a statistically robust variant. We evaluate the Vos model and propose a method using two phantoms that mimic the ADC of the relevant brain tissue (0.36-2.2 × 10-3 mm/s) on a single 3 T scanner. The phantoms are (1) a spherical isotropic sphere consisting of a single concentration of polyvinylpyrrolidone (PVP) and (2) an ice-water phantom with 13 vials of varying PVP concentrations. To characterize the impact of interspersed minimally weighted volumes ("b0's"), image volumes with b-value equal to 0.1 s/mm are interspersed every 8, 16, 32, 48, and 96 diffusion weighted volumes in different trials. Signal drift is found to have spatially varying effects that are not accounted for with temporal-only models. The novel model captures drift more accurately (i.e., reduces the overall change per-voxel over the course of a scan) and results in more consistent ADC metrics.
Copyright © 2018 Elsevier Inc. All rights reserved.
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MeSH Terms
Regionally specific volume deficits along the hippocampal long axis in early and chronic psychosis.
McHugo M, Talati P, Woodward ND, Armstrong K, Blackford JU, Heckers S
(2018) Neuroimage Clin 20: 1106-1114
MeSH Terms: Adult, Aged, Bipolar Disorder, Dentate Gyrus, Early Diagnosis, Female, Hippocampus, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Psychotic Disorders
Show Abstract · Added March 26, 2019
Previous studies in psychosis patients have shown hippocampal volume deficits across anterior and posterior regions or across subfields, but subfield specific changes in volume along the hippocampal long axis have not been examined. Here, we tested the hypothesis that volume changes exist across the hippocampus in chronic psychosis but only the anterior CA region is affected in early psychosis patients. We analyzed structural MRI data from 179 patients with a non-affective psychotic disorder (94 chronic psychosis; 85 early psychosis) and 167 heathy individuals demographically matched to the chronic and early psychosis samples respectively (82 matched to chronic patients; 85 matched to early patients). We measured hippocampal volumes using Freesurfer 6-derived automated segmentation of both anterior and posterior regions and the CA, dentate gyrus, and subiculum subfields. We found a hippocampal volume deficit in both anterior and posterior regions in chronic psychosis, but this deficit was limited to the anterior hippocampus in early psychosis patients. This volume change was more pronounced in the anterior CA subfield of early psychosis patients than in the dentate gyrus or subiculum. Our findings support existing models of psychosis implicating initial CA dysfunction with later progression to other hippocampal regions and suggest that the anterior hippocampus may be an important target for early interventions.
Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
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MeSH Terms
Trajectory optimized NUFFT: Faster non-Cartesian MRI reconstruction through prior knowledge and parallel architectures.
Smith DS, Sengupta S, Smith SA, Brian Welch E
(2019) Magn Reson Med 81: 2064-2071
MeSH Terms: Algorithms, Deglutition, Esophagus, Fourier Analysis, Humans, Hypopharynx, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Mouth, Optic Nerve, Phantoms, Imaging, Programming Languages, Reproducibility of Results, Software, Whole Body Imaging
Show Abstract · Added April 10, 2019
PURPOSE - The non-uniform fast Fourier transform (NUFFT) involves interpolation of non-uniformly sampled Fourier data onto a Cartesian grid, an interpolation that is slowed by complex, non-local data access patterns. A faster NUFFT would increase the clinical relevance of the plethora of advanced non-Cartesian acquisition methods.
METHODS - Here we customize the NUFFT procedure for a radial trajectory and GPU architecture to eliminate the bottlenecks encountered when allowing for arbitrary trajectories and hardware. We call the result TRON, for TRajectory Optimized NUFFT. We benchmark the speed and accuracy TRON on a Shepp-Logan phantom and on whole-body continuous golden-angle radial MRI.
RESULTS - TRON was 6-30× faster than the closest competitor, depending on test data set, and was the most accurate code tested.
CONCLUSIONS - Specialization of the NUFFT algorithm for a particular trajectory yielded significant speed gains. TRON can be easily extended to other trajectories, such as spiral and PROPELLER. TRON can be downloaded at http://github.com/davidssmith/TRON.
© 2018 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.
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16 MeSH Terms
Limits to anatomical accuracy of diffusion tractography using modern approaches.
Schilling KG, Nath V, Hansen C, Parvathaneni P, Blaber J, Gao Y, Neher P, Aydogan DB, Shi Y, Ocampo-Pineda M, Schiavi S, Daducci A, Girard G, Barakovic M, Rafael-Patino J, Romascano D, Rensonnet G, Pizzolato M, Bates A, Fischi E, Thiran JP, Canales-Rodríguez EJ, Huang C, Zhu H, Zhong L, Cabeen R, Toga AW, Rheault F, Theaud G, Houde JC, Sidhu J, Chamberland M, Westin CF, Dyrby TB, Verma R, Rathi Y, Irfanoglu MO, Thomas C, Pierpaoli C, Descoteaux M, Anderson AW, Landman BA
(2019) Neuroimage 185: 1-11
MeSH Terms: Brain, Brain Mapping, Diffusion Tensor Imaging, Humans, Image Processing, Computer-Assisted, Neural Pathways
Show Abstract · Added March 26, 2019
Diffusion MRI fiber tractography is widely used to probe the structural connectivity of the brain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the 3-D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge at the ISBI 2018 conference. We made available three unique independent tractography validation datasets - a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography's inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.
Copyright © 2018 Elsevier Inc. All rights reserved.
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6 MeSH Terms
Functional tractography of white matter by high angular resolution functional-correlation imaging (HARFI).
Schilling KG, Gao Y, Li M, Wu TL, Blaber J, Landman BA, Anderson AW, Ding Z, Gore JC
(2019) Magn Reson Med 81: 2011-2024
MeSH Terms: Algorithms, Anisotropy, Contrast Media, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Healthy Volunteers, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, White Matter
Show Abstract · Added March 26, 2019
PURPOSE - Functional magnetic resonance imaging with BOLD contrast is widely used for detecting brain activity in the cortex. Recently, several studies have described anisotropic correlations of resting-state BOLD signals between voxels in white matter (WM). These local WM correlations have been modeled as functional-correlation tensors, are largely consistent with underlying WM fiber orientations derived from diffusion MRI, and appear to change during functional activity. However, functional-correlation tensors have several limitations. The use of only nearest-neighbor voxels makes functional-correlation tensors sensitive to noise. Furthermore, adjacent voxels tend to have higher correlations than diagonal voxels, resulting in orientation-related biases. Finally, the tensor model restricts functional correlations to an ellipsoidal bipolar-symmetric shape, and precludes the ability to detect complex functional orientation distributions (FODs).
METHODS - We introduce high-angular-resolution functional-correlation imaging (HARFI) to address these limitations. In the same way that high-angular-resolution diffusion imaging (HARDI) techniques provide more information than diffusion tensors, we show that the HARFI model is capable of characterizing complex FODs expected to be present in WM.
RESULTS - We demonstrate that the unique radial and angular sampling strategy eliminates orientation biases present in tensor models. We further show that HARFI FODs are able to reconstruct known WM pathways. Finally, we show that HARFI allows asymmetric "bending" and "fanning" distributions, and propose asymmetric and functional indices which may increase fiber tracking specificity, or highlight boundaries between functional regions.
CONCLUSIONS - The results suggest the HARFI model could be a robust, new way to evaluate anisotropic BOLD signal changes in WM.
© 2018 International Society for Magnetic Resonance in Medicine.
0 Communities
1 Members
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10 MeSH Terms
Anatomical accuracy of standard-practice tractography algorithms in the motor system - A histological validation in the squirrel monkey brain.
Schilling KG, Gao Y, Stepniewska I, Janve V, Landman BA, Anderson AW
(2019) Magn Reson Imaging 55: 7-25
MeSH Terms: Algorithms, Animals, Brain, Brain Mapping, Diffusion Tensor Imaging, Image Processing, Computer-Assisted, Models, Anatomic, Motor Cortex, Probability, Reproducibility of Results, Saimiri, Sensitivity and Specificity, Software, White Matter
Show Abstract · Added March 26, 2019
For two decades diffusion fiber tractography has been used to probe both the spatial extent of white matter pathways and the region to region connectivity of the brain. In both cases, anatomical accuracy of tractography is critical for sound scientific conclusions. Here we assess and validate the algorithms and tractography implementations that have been most widely used - often because of ease of use, algorithm simplicity, or availability offered in open source software. Comparing forty tractography results to a ground truth defined by histological tracers in the primary motor cortex on the same squirrel monkey brains, we assess tract fidelity on the scale of voxels as well as over larger spatial domains or regional connectivity. No algorithms are successful in all metrics, and, in fact, some implementations fail to reconstruct large portions of pathways or identify major points of connectivity. The accuracy is most dependent on reconstruction method and tracking algorithm, as well as the seed region and how this region is utilized. We also note a tremendous variability in the results, even though the same MR images act as inputs to all algorithms. In addition, anatomical accuracy is significantly decreased at increased distances from the seed. An analysis of the spatial errors in tractography reveals that many techniques have trouble properly leaving the gray matter, and many only reveal connectivity to adjacent regions of interest. These results show that the most commonly implemented algorithms have several shortcomings and limitations, and choices in implementations lead to very different results. This study should provide guidance for algorithm choices based on study requirements for sensitivity, specificity, or the need to identify particular connections, and should serve as a heuristic for future developments in tractography.
Copyright © 2018 Elsevier Inc. All rights reserved.
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14 MeSH Terms