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de Souza DAR, Mathieu H, Deloulme JC, Barbier EL. Evaluation of kernel low-rank compressed sensing in preclinical diffusion magnetic resonance imaging. Front Neurosci 2023; 17:1172830. [PMID: 37332879 PMCID: PMC10272537 DOI: 10.3389/fnins.2023.1172830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 04/28/2023] [Indexed: 06/20/2023] Open
Abstract
Compressed sensing (CS) is widely used to accelerate clinical diffusion MRI acquisitions, but it is not widely used in preclinical settings yet. In this study, we optimized and compared several CS reconstruction methods for diffusion imaging. Different undersampling patterns and two reconstruction approaches were evaluated: conventional CS, based on Berkeley Advanced Reconstruction Toolbox (BART-CS) toolbox, and a new kernel low-rank (KLR)-CS, based on kernel principal component analysis and low-resolution-phase (LRP) maps. 3D CS acquisitions were performed at 9.4T using a 4-element cryocoil on mice (wild type and a MAP6 knockout). Comparison metrics were error and structural similarity index measure (SSIM) on fractional anisotropy (FA) and mean diffusivity (MD), as well as reconstructions of the anterior commissure and fornix. Acceleration factors (AF) up to 6 were considered. In the case of retrospective undersampling, the proposed KLR-CS outperformed BART-CS up to AF = 6 for FA and MD maps and tractography. For instance, for AF = 4, the maximum errors were, respectively, 8.0% for BART-CS and 4.9% for KLR-CS, considering both FA and MD in the corpus callosum. Regarding undersampled acquisitions, these maximum errors became, respectively, 10.5% for BART-CS and 7.0% for KLR-CS. This difference between simulations and acquisitions arose mainly from repetition noise, but also from differences in resonance frequency drift, signal-to-noise ratio, and in reconstruction noise. Despite this increased error, fully sampled and AF = 2 yielded comparable results for FA, MD and tractography, and AF = 4 showed minor faults. Altogether, KLR-CS based on LRP maps seems a robust approach to accelerate preclinical diffusion MRI and thereby limit the effect of the frequency drift.
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Affiliation(s)
| | - Hervé Mathieu
- Université Grenoble Alpes, INSERM, U1216, Grenoble Institut Neurosciences, Grenoble, France
- Université Grenoble Alpes, INSERM, US17, CNRS, UAR 3552, CHU Grenoble Alpes, Grenoble, France
| | | | - Emmanuel L. Barbier
- Université Grenoble Alpes, INSERM, U1216, Grenoble Institut Neurosciences, Grenoble, France
- Université Grenoble Alpes, INSERM, US17, CNRS, UAR 3552, CHU Grenoble Alpes, Grenoble, France
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2
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Arefin TM, Lee CH, Liang Z, Rallapalli H, Wadghiri YZ, Turnbull DH, Zhang J. Towards reliable reconstruction of the mouse brain corticothalamic connectivity using diffusion MRI. Neuroimage 2023; 273:120111. [PMID: 37060936 PMCID: PMC10149621 DOI: 10.1016/j.neuroimage.2023.120111] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/29/2023] [Accepted: 04/12/2023] [Indexed: 04/17/2023] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography has yielded intriguing insights into brain circuits and their relationship to behavior in response to gene mutations or neurological diseases across a number of species. Still, existing tractography approaches suffer from limited sensitivity and specificity, leading to uncertain interpretation of the reconstructed connections. Hence, in this study, we aimed to optimize the imaging and computational pipeline to achieve the best possible spatial overlaps between the tractography and tracer-based axonal projection maps within the mouse brain corticothalamic network. We developed a dMRI-based atlas of the mouse forebrain with structural labels imported from the Allen Mouse Brain Atlas (AMBA). Using the atlas and dMRI tractography, we first reconstructed detailed node-to-node mouse brain corticothalamic structural connectivity matrices using different imaging and tractography parameters. We then investigated the effects of each condition for accurate reconstruction of the corticothalamic projections by quantifying the similarities between the tractography and the tracer data from the Allen Mouse Brain Connectivity Atlas (AMBCA). Our results suggest that these parameters significantly affect tractography outcomes and our atlas can be used to investigate macroscopic structural connectivity in the mouse brain. Furthermore, tractography in mouse brain gray matter still face challenges and need improved imaging and tractography methods.
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Affiliation(s)
- Tanzil Mahmud Arefin
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States; Center for Neurotechnology in Mental Health Research, Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, United States
| | - Choong Heon Lee
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States
| | - Zifei Liang
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States
| | - Harikrishna Rallapalli
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States
| | - Youssef Z Wadghiri
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States
| | - Daniel H Turnbull
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States
| | - Jiangyang Zhang
- Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States.
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3
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Mohammadi M, Kaye EA, Alus O, Kee Y, Golia Pernicka JS, El Homsi M, Petkovska I, Otazo R. Accelerated Diffusion-Weighted MRI of Rectal Cancer Using a Residual Convolutional Network. Bioengineering (Basel) 2023; 10:bioengineering10030359. [PMID: 36978750 PMCID: PMC10045764 DOI: 10.3390/bioengineering10030359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/28/2023] [Accepted: 03/09/2023] [Indexed: 03/16/2023] Open
Abstract
This work presents a deep-learning-based denoising technique to accelerate the acquisition of high b-value diffusion-weighted MRI for rectal cancer. A denoising convolutional neural network (DCNN) with a combined L1–L2 loss function was developed to denoise high b-value diffusion-weighted MRI data acquired with fewer repetitions (NEX: number of excitations) using the low b-value image as an anatomical guide. DCNN was trained using 85 datasets acquired on patients with rectal cancer and tested on 20 different datasets with NEX = 1, 2, and 4, corresponding to acceleration factors of 16, 8, and 4, respectively. Image quality was assessed qualitatively by expert body radiologists. Reader 1 scored similar overall image quality between denoised images with NEX = 1 and NEX = 2, which were slightly lower than the reference. Reader 2 scored similar quality between NEX = 1 and the reference, while better quality for NEX = 2. Denoised images with fourfold acceleration (NEX = 4) received even higher scores than the reference, which is due in part to the effect of gas-related motion in the rectum, which affects longer acquisitions. The proposed deep learning denoising technique can enable eightfold acceleration with similar image quality (average image quality = 2.8 ± 0.5) and fourfold acceleration with higher image quality (3.0 ± 0.6) than the clinical standard (2.5 ± 0.8) for improved diagnosis of rectal cancer.
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Affiliation(s)
- Mohaddese Mohammadi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Elena A. Kaye
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Or Alus
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Youngwook Kee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | | | - Maria El Homsi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Iva Petkovska
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Correspondence:
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Accelerated Diffusion-Weighted MR Image Reconstruction Using Deep Neural Networks. J Digit Imaging 2023; 36:276-288. [PMID: 36333593 PMCID: PMC9984585 DOI: 10.1007/s10278-022-00709-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 09/20/2022] [Accepted: 09/22/2022] [Indexed: 11/06/2022] Open
Abstract
Under-sampling in diffusion-weighted imaging (DWI) decreases the scan time that helps to reduce off-resonance effects, geometric distortions, and susceptibility artifacts; however, it leads to under-sampling artifacts. In this paper, diffusion-weighted MR image (DWI-MR) reconstruction using deep learning (DWI U-Net) is proposed to recover artifact-free DW images from variable density highly under-sampled k-space data. Additionally, different optimizers, i.e., RMSProp, Adam, Adagrad, and Adadelta, have been investigated to choose the best optimizers for DWI U-Net. The reconstruction results are compared with the conventional Compressed Sensing (CS) reconstruction. The quality of the recovered images is assessed using mean artifact power (AP), mean root mean square error (RMSE), mean structural similarity index measure (SSIM), and mean apparent diffusion coefficient (ADC). The proposed method provides up to 61.1%, 60.0%, 30.4%, and 28.7% improvements in the mean AP value of the reconstructed images in our experiments with different optimizers, i.e., RMSProp, Adam, Adagrad, and Adadelta, respectively, as compared to the conventional CS at an acceleration factor of 6 (i.e., AF = 6). The results of DWI U-Net with the RMSProp, Adam, Adagrad, and Adadelta optimizers show 13.6%, 10.0%, 8.7%, and 8.74% improvements, respectively, in terms of mean SSIM with respect to the conventional CS at AF = 6. Also, the proposed technique shows 51.4%, 29.5%, 24.04%, and 18.0% improvements in terms of mean RMSE using the RMSProp, Adam, Adagrad, and Adadelta optimizers, respectively, with reference to the conventional CS at AF = 6. The results confirm that DWI U-Net performs better than the conventional CS reconstruction. Also, when comparing the different optimizers in DWI U-Net, RMSProp provides better results than the other optimizers.
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Xu T, Wu Y, Hong Y, Ahmad S, Huynh KM, Wang Z, Lin W, Chang WT, Yap PT. Rapid Diffusion Magnetic Resonance Imaging Using Slice-Interleaved Encoding. Med Image Anal 2022; 81:102548. [PMID: 35917693 PMCID: PMC9988327 DOI: 10.1016/j.media.2022.102548] [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: 07/26/2021] [Revised: 06/24/2022] [Accepted: 07/12/2022] [Indexed: 11/28/2022]
Abstract
In this paper, we present a robust reconstruction scheme for diffusion MRI (dMRI) data acquired using slice-interleaved diffusion encoding (SIDE). When combined with SIDE undersampling and simultaneous multi-slice (SMS) imaging, our reconstruction strategy is capable of significantly reducing the amount of data that needs to be acquired, enabling high-speed diffusion imaging for pediatric, elderly, and claustrophobic individuals. In contrast to the conventional approach of acquiring a full diffusion-weighted (DW) volume per diffusion wavevector, SIDE acquires in each repetition time (TR) a volume that consists of interleaved slice groups, each group corresponding to a different diffusion wavevector. This strategy allows SIDE to rapidly acquire data covering a large number of wavevectors within a short period of time. The proposed reconstruction method uses a diffusion spectrum model and multi-dimensional total variation to recover full DW images from DW volumes that are slice-undersampled due to unacquired SIDE volumes. We formulate an inverse problem that can be solved efficiently using the alternating direction method of multipliers (ADMM). Experiment results demonstrate that DW images can be reconstructed with high fidelity even when the acquisition is accelerated by 25 folds.
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Affiliation(s)
- Tiantian Xu
- Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Ye Wu
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA
| | - Yoonmi Hong
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA
| | - Sahar Ahmad
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA
| | - Khoi Minh Huynh
- Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Zhixing Wang
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA
| | - Weili Lin
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA
| | - Wei-Tang Chang
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA
| | - Pew-Thian Yap
- Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA.
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6
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Periquito JS, Gladytz T, Millward JM, Delgado PR, Cantow K, Grosenick D, Hummel L, Anger A, Zhao K, Seeliger E, Pohlmann A, Waiczies S, Niendorf T. Continuous diffusion spectrum computation for diffusion-weighted magnetic resonance imaging of the kidney tubule system. Quant Imaging Med Surg 2021; 11:3098-3119. [PMID: 34249638 DOI: 10.21037/qims-20-1360] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 03/08/2021] [Indexed: 12/24/2022]
Abstract
Background The use of rigid multi-exponential models (with a priori predefined numbers of components) is common practice for diffusion-weighted MRI (DWI) analysis of the kidney. This approach may not accurately reflect renal microstructure, as the data are forced to conform to the a priori assumptions of simplified models. This work examines the feasibility of less constrained, data-driven non-negative least squares (NNLS) continuum modelling for DWI of the kidney tubule system in simulations that include emulations of pathophysiological conditions. Methods Non-linear least squares (LS) fitting was used as reference for the simulations. For performance assessment, a threshold of 5% or 10% for the mean absolute percentage error (MAPE) of NNLS and LS results was used. As ground truth, a tri-exponential model using defined volume fractions and diffusion coefficients for each renal compartment (tubule system: Dtubules , ftubules ; renal tissue: Dtissue , ftissue ; renal blood: Dblood , fblood ;) was applied. The impact of: (I) signal-to-noise ratio (SNR) =40-1,000, (II) number of b-values (n=10-50), (III) diffusion weighting (b-rangesmall =0-800 up to b-rangelarge =0-2,180 s/mm2), and (IV) fixation of the diffusion coefficients Dtissue and Dblood was examined. NNLS was evaluated for baseline and pathophysiological conditions, namely increased tubular volume fraction (ITV) and renal fibrosis (10%: grade I, mild) and 30% (grade II, moderate). Results NNLS showed the same high degree of reliability as the non-linear LS. MAPE of the tubular volume fraction (ftubules ) decreased with increasing SNR. Increasing the number of b-values was beneficial for ftubules precision. Using the b-rangelarge led to a decrease in MAPE ftubules compared to b-rangesmall. The use of a medium b-value range of b=0-1,380 s/mm2 improved ftubules precision, and further bmax increases beyond this range yielded diminishing improvements. Fixing Dblood and Dtissue significantly reduced MAPE ftubules and provided near perfect distinction between baseline and ITV conditions. Without constraining the number of renal compartments in advance, NNLS was able to detect the (fourth) fibrotic compartment, to differentiate it from the other three diffusion components, and to distinguish between 10% vs. 30% fibrosis. Conclusions This work demonstrates the feasibility of NNLS modelling for DWI of the kidney tubule system and shows its potential for examining diffusion compartments associated with renal pathophysiology including ITV fraction and different degrees of fibrosis.
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Affiliation(s)
- Joāo S Periquito
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.,Institute of Physiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany.,Experimental and Clinical Research Center, a Joint Cooperation between the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Thomas Gladytz
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Jason M Millward
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Paula Ramos Delgado
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.,Experimental and Clinical Research Center, a Joint Cooperation between the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Kathleen Cantow
- Institute of Physiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Dirk Grosenick
- Physikalisch-Technische Bundesanstalt (PTB), Berlin, Germany
| | - Luis Hummel
- Institute of Physiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Ariane Anger
- Institute of Physiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Kaixuan Zhao
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Erdmann Seeliger
- Institute of Physiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Andreas Pohlmann
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Sonia Waiczies
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.,Experimental and Clinical Research Center, a Joint Cooperation between the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
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7
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Mapping the living mouse brain neural architecture: strain-specific patterns of brain structural and functional connectivity. Brain Struct Funct 2021; 226:647-669. [PMID: 33635426 DOI: 10.1007/s00429-020-02190-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 11/27/2020] [Indexed: 10/22/2022]
Abstract
Mapping brain structural and functional connectivity (FC) became an essential approach in neuroscience as network properties can underlie behavioral phenotypes. In mouse models, revealing strain-related patterns of brain wiring is crucial, since these animals are used to answer questions related to neurological or neuropsychiatric disorders. C57BL/6 and BALB/cJ strains are two of the primary "genetic backgrounds" for modeling brain disease and testing therapeutic approaches. However, extensive literature describes basal differences in the behavioral, neuroanatomical and neurochemical profiles of the two strains, which raises questions on whether the observed effects are pathology specific or depend on the genetic background of each strain. Here, we performed a systematic comparative exploration of brain structure and function of C57BL/6 and BALB/cJ mice using Magnetic Resonance Imaging (MRI). We combined deformation-based morphometry (DBM), diffusion MRI and high-resolution fiber mapping (hrFM) along with resting-state functional MRI (rs-fMRI) and demonstrated brain-wide differences in the morphology and "connectome" features of the two strains. Essential inter-strain differences were depicted regarding the size and the fiber density (FD) within frontal cortices, along cortico-striatal, thalamic and midbrain pathways as well as genu and splenium of corpus callosum. Structural dissimilarities were accompanied by specific FC patterns, emphasizing strain differences in frontal and basal forebrain functional networks as well as hubness characteristics. Rs-fMRI data further indicated differences of reward-aversion circuitry and default mode network (DMN) patterns. The inter-hemispherical FC showed flexibility and strain-specific adjustment of their patterns in agreement with the structural characteristics.
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8
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Menon RG, Zibetti MVW, Jain R, Ge Y, Regatte RR. Performance Comparison of Compressed Sensing Algorithms for Accelerating T 1ρ Mapping of Human Brain. J Magn Reson Imaging 2020; 53:1130-1139. [PMID: 33190362 DOI: 10.1002/jmri.27421] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 10/15/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND 3D-T1ρ mapping is useful to quantify various neurologic disorders, but data are currently time-consuming to acquire. PURPOSE To compare the performance of five compressed sensing (CS) algorithms-spatiotemporal finite differences (STFD), exponential dictionary (EXP), 3D-wavelet transform (WAV), low-rank (LOW) and low-rank plus sparse model with spatial finite differences (L + S SFD)-for 3D-T1ρ mapping of the human brain with acceleration factors (AFs) of 2, 5, and 10. STUDY TYPE Retrospective. SUBJECTS Eight healthy volunteers underwent T1ρ imaging of the whole brain. FIELD STRENGTH/SEQUENCE The sequence was fully sampled 3D Cartesian ultrafast gradient echo sequence with a customized T1ρ preparation module on a clinical 3T scanner. ASSESSMENT The fully sampled data was undersampled by factors of 2, 5, and 10 and reconstructed with the five CS algorithms. Image reconstruction quality was evaluated and compared to the SENSE reconstruction of the fully sampled data (reference) and T1ρ estimation errors were assessed as a function of AF. STATISTICAL TESTS Normalized root mean squared errors (nRMSE) and median normalized absolute deviation (MNAD) errors were calculated to compare image reconstruction errors and T1ρ estimation errors, respectively. Linear regression plots, Bland-Altman plots, and Pearson correlation coefficients (CC) are shown. RESULTS For image reconstruction quality, at AF = 2, EXP transforms had the lowest mRMSE (1.56%). At higher AF values, STFD performed better, with the smallest errors (3.16% at AF = 5, 4.32% at AF = 10). For whole-brain quantitative T1ρ mapping, at AF = 2, EXP performed best (MNAD error = 1.62%). At higher AF values (AF = 5, 10), the STFD technique had the least errors (2.96% at AF = 5, 4.24% at AF = 10) and the smallest variance from the reference T1ρ estimates. DATA CONCLUSION This study demonstrates the use of different CS algorithms that may be useful in reducing the scan time required to perform volumetric T1ρ mapping of the brain. LEVEL OF EVIDENCE 2. TECHNICAL EFFICACY STAGE 1.
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Affiliation(s)
- Rajiv G Menon
- Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, New York, USA
| | - Marcelo V W Zibetti
- Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, New York, USA
| | - Rajan Jain
- Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Department of Neurosurgery, New York University Grossman School of Medicine, New York, New York, USA
| | - Yulin Ge
- Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, New York, USA
| | - Ravinder R Regatte
- Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, New York, USA
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9
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Ly M, Foley L, Manivannan A, Hitchens TK, Richardson RM, Modo M. Mesoscale diffusion magnetic resonance imaging of the ex vivo human hippocampus. Hum Brain Mapp 2020; 41:4200-4218. [PMID: 32621364 PMCID: PMC7502840 DOI: 10.1002/hbm.25119] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 06/01/2020] [Accepted: 06/16/2020] [Indexed: 12/21/2022] Open
Abstract
Mesoscale diffusion magnetic resonance imaging (MRI) endeavors to bridge the gap between macroscopic white matter tractography and microscopic studies investigating the cytoarchitecture of human brain tissue. To ensure a robust measurement of diffusion at the mesoscale, acquisition parameters were arrayed to investigate their effects on scalar indices (mean, radial, axial diffusivity, and fractional anisotropy) and streamlines (i.e., graphical representation of axonal tracts) in hippocampal layers. A mesoscale resolution afforded segementation of the pyramidal cell layer (CA1-4), the dentate gyrus, as well as stratum moleculare, radiatum, and oriens. Using ex vivo samples, surgically excised from patients with intractable epilepsy (n = 3), we found that shorter diffusion times (23.7 ms) with a b-value of 4,000 s/mm2 were advantageous at the mesoscale, providing a compromise between mean diffusivity and fractional anisotropy measurements. Spatial resolution and sample orientation exerted a major effect on tractography, whereas the number of diffusion gradient encoding directions minimally affected scalar indices and streamline density. A sample temperature of 15°C provided a compromise between increasing signal-to-noise ratio and increasing the diffusion properties of the tissue. Optimization of the acquisition afforded a system's view of intra- and extra-hippocampal connections. Tractography reflected histological boundaries of hippocampal layers. Individual layer connectivity was visualized, as well as streamlines emanating from individual sub-fields. The perforant path, subiculum and angular bundle demonstrated extra-hippocampal connections. Histology of the samples confirmed individual cell layers corresponding to ROIs defined on MR images. We anticipate that this ex vivo mesoscale imaging will yield novel insights into human hippocampal connectivity.
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Affiliation(s)
- Maria Ly
- Department of RadiologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Lesley Foley
- Department of NeurobiologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | | | - T. Kevin Hitchens
- Department of NeurobiologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - R. Mark Richardson
- Department of Neurological SurgeryUniversity of PittsburghPittsburghPennsylvaniaUSA
- McGowan Institute for Regenerative MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
- Brain InstituteUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Michel Modo
- Department of RadiologyUniversity of PittsburghPittsburghPennsylvaniaUSA
- McGowan Institute for Regenerative MedicineUniversity of PittsburghPittsburghPennsylvaniaUSA
- Department of BioengineeringUniversity of PittsburghPittsburghPennsylvaniaUSA
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10
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Ke J, Foley LM, Hitchens TK, Richardson RM, Modo M. Ex vivo mesoscopic diffusion MRI correlates with seizure frequency in patients with uncontrolled mesial temporal lobe epilepsy. Hum Brain Mapp 2020; 41:4529-4548. [PMID: 32691978 PMCID: PMC7555080 DOI: 10.1002/hbm.25139] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 06/08/2020] [Accepted: 07/05/2020] [Indexed: 12/28/2022] Open
Abstract
The role of hippocampal connectivity in mesial temporal lobe epilepsy (mTLE) remains poorly understood. The use of ex vivo hippocampal samples excised from patients with mTLE affords mesoscale diffusion magnetic resonance imaging (MRI) to identify individual cell layers, such as the pyramidal (PCL) and granule cell layers (GCL), which are thought to be impacted by seizure activity. Diffusion tensor imaging (DTI) of control (n = 3) and mTLE (n = 7) hippocampi on an 11.7 T MRI scanner allowed us to reveal intra‐hippocampal connectivity and evaluate how epilepsy affected mean (MD), axial (AD), and radial diffusivity (RD), as well as fractional anisotropy (FA). Regional measurements indicated a volume loss in the PCL of the cornu ammonis (CA) 1 subfield in mTLE patients compared to controls, which provided anatomical context. Diffusion measurements, as well as streamline density, were generally higher in mTLE patients compared to controls, potentially reflecting differences due to tissue fixation. mTLE measurements were more variable than controls. This variability was associated with disease severity, as indicated by a strong correlation (r = 0.87) between FA in the stratum radiatum and the frequency of seizures in patients. MD and RD of the PCL in subfields CA3 and CA4 also correlated strongly with disease severity. No correlation of MR measures with disease duration was evident. These results reveal the potential of mesoscale diffusion MRI to examine layer‐specific diffusion changes and connectivity to determine how these relate to clinical measures. Improving the visualization of intra‐hippocampal connectivity will advance the development of novel hypotheses about seizure networks.
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Affiliation(s)
- Justin Ke
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Lesley M Foley
- Animal Imaging Center, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - T Kevin Hitchens
- Animal Imaging Center, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Neurobiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - R Mark Richardson
- Centre for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Neurological Surgery, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Michel Modo
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Centre for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,McGowan Institute for Regenerative Medicine, Pittsburgh, Pennsylvania, USA
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