1
|
Stuprich CM, Loh M, Nemerth JT, Nagel AM, Uder M, Laun FB. Velocity-compensated intravoxel incoherent motion of the human calf muscle. Magn Reson Med 2024; 92:543-555. [PMID: 38688865 DOI: 10.1002/mrm.30059] [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: 10/19/2023] [Revised: 01/15/2024] [Accepted: 02/03/2024] [Indexed: 05/02/2024]
Abstract
PURPOSE To determine whether intravoxel incoherent motion (IVIM) describes the blood perfusion in muscles better, assuming pseudo diffusion (Bihan Model 1) or ballistic motion (Bihan Model 2). METHODS IVIM parameters were measured in 18 healthy subjects with three different diffusion gradient time profiles (bipolar with two diffusion times and one with velocity compensation) and 17 b-values (0-600 s/mm2) at rest and after muscle activation. The diffusion coefficient, perfusion fraction, and pseudo-diffusion coefficient were estimated with a segmented fit in the gastrocnemius medialis (GM) and tibialis anterior (TA) muscles. RESULTS Velocity-compensated gradients resulted in a decreased perfusion fraction (6.9% ± 1.4% vs. 4.4% ± 1.3% in the GM after activation) and pseudo-diffusion coefficient (0.069 ± 0.046 mm2/s vs. 0.014 ± 0.006 in the GM after activation) compared to the bipolar gradients with the longer diffusion encoding time. Increased diffusion coefficients, perfusion fractions, and pseudo-diffusion coefficients were observed in the GM after activation for all gradient profiles. However, the increase was significantly smaller for the velocity-compensated gradients. A diffusion time dependence was found for the pseudo-diffusion coefficient in the activated muscle. CONCLUSION Velocity-compensated diffusion gradients significantly suppress the IVIM effect in the calf muscle, indicating that the ballistic limit is mostly reached, which is supported by the time dependence of the pseudo-diffusion coefficient.
Collapse
Affiliation(s)
- Christoph M Stuprich
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Martin Loh
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Johannes T Nemerth
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Armin M Nagel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Frederik B Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| |
Collapse
|
2
|
Cho KIK, Zhang F, Penzel N, Seitz-Holland J, Tang Y, Zhang T, Xu L, Li H, Keshavan M, Whitfield-Gabrieli S, Niznikiewicz M, Stone WS, Wang J, Shenton ME, Pasternak O. Excessive interstitial free-water in cortical gray matter preceding accelerated volume changes in individuals at clinical high risk for psychosis. Mol Psychiatry 2024:10.1038/s41380-024-02597-3. [PMID: 38830974 DOI: 10.1038/s41380-024-02597-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 05/01/2024] [Accepted: 05/03/2024] [Indexed: 06/05/2024]
Abstract
Recent studies show that accelerated cortical gray matter (GM) volume reduction seen in anatomical MRI can help distinguish between individuals at clinical high risk (CHR) for psychosis who will develop psychosis and those who will not. This reduction is suggested to represent atypical developmental or degenerative changes accompanying an accumulation of microstructural changes, such as decreased spine density and dendritic arborization. Detecting the microstructural sources of these changes before they accumulate into volume loss is crucial. Our study aimed to detect these microstructural GM alterations using diffusion MRI (dMRI). We tested for baseline and longitudinal group differences in anatomical and dMRI data from 160 individuals at CHR and 96 healthy controls (HC) acquired in a single imaging site. Of the CHR individuals, 33 developed psychosis (CHR-P), while 127 did not (CHR-NP). Among all participants, longitudinal data was available for 45 HCs, 17 CHR-P, and 66 CHR-NP. Eight cortical lobes were examined for GM volume and GM microstructure. A novel dMRI measure, interstitial free water (iFW), was used to quantify GM microstructure by eliminating cerebrospinal fluid contribution. Additionally, we assessed whether these measures differentiated the CHR-P from the CHR-NP. In addition, for completeness, we also investigated changes in cortical thickness and in white matter (WM) microstructure. At baseline the CHR group had significantly higher iFW than HC in the prefrontal, temporal, parietal, and occipital lobes, while volume was reduced only in the temporal lobe. Neither iFW nor volume differentiated between the CHR-P and CHR-NP groups at baseline. However, in many brain areas, the CHR-P group demonstrated significantly accelerated changes (iFW increase and volume reduction) with time than the CHR-NP group. Cortical thickness provided similar results as volume, and there were no significant changes in WM microstructure. Our results demonstrate that microstructural GM changes in individuals at CHR have a wider extent than volumetric changes or microstructural WM changes, and they predate the acceleration of brain changes that occur around psychosis onset. Microstructural GM changes, as reflected by the increased iFW, are thus an early pathology at the prodromal stage of psychosis that may be useful for a better mechanistic understanding of psychosis development.
Collapse
Affiliation(s)
- Kang Ik K Cho
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nora Penzel
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Johanna Seitz-Holland
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lihua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Huijun Li
- Department of Psychology, Florida A&M University, Tallahassee, FL, USA
| | - Matcheri Keshavan
- The Massachusetts Mental Health Center, Public Psychiatry Division, Beth Israel Deaconess Medical Center, and Harvard Medical School, Boston, MA, USA
| | - Susan Whitfield-Gabrieli
- Department of Psychology, Northeastern University, Boston, MA, USA
- The McGovern Institute for Brain Research and the Poitras Center for Affective Disorders Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Margaret Niznikiewicz
- The Department of Psychiatry, Veterans Affairs Boston Healthcare System, Brockton Division, Brockton, MA, USA
| | - William S Stone
- The Massachusetts Mental Health Center, Public Psychiatry Division, Beth Israel Deaconess Medical Center, and Harvard Medical School, Boston, MA, USA
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China.
| | - Martha E Shenton
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
3
|
Zong F, Wang L, Liu H, Xue B, Bai R, Liu Y. A genetic optimisation and iterative reconstruction framework for sparse multi-dimensional diffusion-relaxation correlation MRI. Comput Biol Med 2024; 175:108508. [PMID: 38678941 DOI: 10.1016/j.compbiomed.2024.108508] [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: 11/22/2023] [Revised: 04/11/2024] [Accepted: 04/21/2024] [Indexed: 05/01/2024]
Abstract
Multi-dimensional diffusion-relaxation correlation (DRC) magnetic resonance imaging (MRI) techniques have recently been developed to investigate tissue microstructures. Sub-voxel tissue heterogeneity is resolved from the local correlation distributions of relaxation time and molecular diffusivity. However, the implementation of these techniques considerably increases the total acquisition time, and simply reducing the scan time may be at the expense of detailed structural resolution. To overcome these limitations, an optimised framework was proposed for acquiring microstructural maps of the human brain on a clinically feasible timescale. First, the acquisition parameters of the multi-dimensional DRC MRI method were sparsely optimised using a genetic algorithm with a fitness function according to the spectral resolution of the correlation map, hardware requirements, and total scan time. Next, the acquired DRC MRI data were processed using a proposed numerical algorithm based on the dynamic inverse Laplace transform (ILT). Prior knowledge from one-dimensional data was then utilised in the iterative procedure to improve the spectral resolution. Finally, the proposed framework was validated using Monte Carlo simulations and experimental data acquired from healthy participants on an MRI scanner. The results demonstrated that the suggested approach is feasible for offering high-resolution DRC maps that correspond to distinct microstructures with a limited amount of optimised acquisition data from two-dimensional DRC sampling space. By significantly reducing scan time while retaining structural resolution, this approach may enable multi-dimensional DRC MRI to be more widely used for quantitative evaluation in biological and medical settings.
Collapse
Affiliation(s)
- Fangrong Zong
- School of Artificial Intelligence, Beijing University of Post and Telecommunication, Beijing, 100876, China.
| | - Lixian Wang
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Huabing Liu
- Beijing Limecho Technology Co., Ltd., Beijing, 102200, China
| | - Bing Xue
- School of Engineering and Computer Science, Victoria University of Wellington, Victoria, 6140, New Zealand
| | - Ruiliang Bai
- Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, 310020, China; MOE Frontier Science Center for Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, 310030, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Post and Telecommunication, Beijing, 100876, China.
| |
Collapse
|
4
|
Bäuchle TA, Stuprich CM, Loh M, Nagel AM, Uder M, Laun FB. Influence of Magnetic Field Strength on Intravoxel Incoherent Motion Parameters in Diffusion MRI of the Calf. Tomography 2024; 10:773-788. [PMID: 38787019 PMCID: PMC11126135 DOI: 10.3390/tomography10050059] [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: 03/11/2024] [Revised: 04/26/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024] Open
Abstract
Background: The purpose of this study was to investigate the dependence of Intravoxel Incoherent Motion (IVIM) parameters measured in the human calf on B0. Methods: Diffusion-weighted image data of eight healthy volunteers were acquired using five b-values (0-600 s/mm2) at rest and after muscle activation at 0.55 and 7 T. The musculus gastrocnemius mediale (GM, activated) was assessed. The perfusion fraction f and diffusion coefficient D were determined using segmented fits. The dependence on field strength was assessed using Student's t-test for paired samples and the Wilcoxon signed-rank test. A biophysical model built on the three non-exchanging compartments of muscle, venous blood, and arterial blood was used to interpret the data using literature relaxation times. Results: The measured perfusion fraction of the GM was significantly lower at 7 T, both for the baseline measurement and after muscle activation. For 0.55 and 7 T, the mean f values were 7.59% and 3.63% at rest, and 14.03% and 6.92% after activation, respectively. The biophysical model estimations for the mean proton-density-weighted perfusion fraction were 3.37% and 6.50% for the non-activated and activated states, respectively. Conclusions: B0 may have a significant effect on the measured IVIM parameters. The blood relaxation times suggest that 7 T IVIM may be arterial-weighted whereas 0.55 T IVIM may exhibit an approximately equal weighting of arterial and venous blood.
Collapse
Affiliation(s)
- Tamara Alice Bäuchle
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Christoph Martin Stuprich
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Martin Loh
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Armin Michael Nagel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Frederik Bernd Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| |
Collapse
|
5
|
Planchuelo-Gómez Á, Descoteaux M, Larochelle H, Hutter J, Jones DK, Tax CMW. Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning. Med Image Anal 2024; 94:103134. [PMID: 38471339 DOI: 10.1016/j.media.2024.103134] [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: 05/12/2023] [Revised: 02/26/2024] [Accepted: 03/04/2024] [Indexed: 03/14/2024]
Abstract
Diffusion-relaxation MRI aims to extract quantitative measures that characterise microstructural tissue properties such as orientation, size, and shape, but long acquisition times are typically required. This work proposes a physics-informed learning framework to extract an optimal subset of diffusion-relaxation MRI measurements for enabling shorter acquisition times, predict non-measured signals, and estimate quantitative parameters. In vivo and synthetic brain 5D-Diffusion-T1-T2∗-weighted MRI data obtained from five healthy subjects were used for training and validation, and from a sixth participant for testing. One fully data-driven and two physics-informed machine learning methods were implemented and compared to two manual selection procedures and Cramér-Rao lower bound optimisation. The physics-informed approaches could identify measurement-subsets that yielded more consistently accurate parameter estimates in simulations than other approaches, with similar signal prediction error. Five-fold shorter protocols yielded error distributions of estimated quantitative parameters with very small effect sizes compared to estimates from the full protocol. Selected subsets commonly included a denser sampling of the shortest and longest inversion time, lowest echo time, and high b-value. The proposed framework combining machine learning and MRI physics offers a promising approach to develop shorter imaging protocols without compromising the quality of parameter estimates and signal predictions.
Collapse
Affiliation(s)
- Álvaro Planchuelo-Gómez
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom; Imaging Processing Laboratory, Universidad de Valladolid, Valladolid, Spain
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | | | - Jana Hutter
- Centre for Medical Engineering, Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Chantal M W Tax
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom.
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Malyarenko D, Ono S, Lynch TJE, Swanson SD. Technical note: hydrogel-based mimics of prostate cancer with matched relaxation, diffusion and kurtosis for validating multi-parametric MRI. Med Phys 2024; 51:3590-3596. [PMID: 38128027 PMCID: PMC11138133 DOI: 10.1002/mp.16908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/16/2023] [Accepted: 12/10/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Protocol standardization and optimization for clinical translation of emerging quantitative multiparametric (mp)MRI biomarkers of high-risk prostate cancer requires imaging references that mimic realistic tissue value combinations for bias assessment in derived relaxation and diffusion parameters. PURPOSE This work aimed to develop a novel class of hydrogel-based synthetic materials with simultaneously controlled quantitative relaxation, diffusion, and kurtosis parameters that mimic in vivo prostate value combinations in the same spatial compartment and allow stable assemblies of adjacent structures. METHODS A set of materials with tunable T2, diffusion, and kurtosis were assembled to create quantitative biomimetic (mp)MRI references. T2 was controlled with variable agarose concentration, monoexponential diffusion by polyvinylpyrrolidone (PVP), and kurtosis by addition of lamellar vesicles. The materials were mechanically stabilized by UV cross-linked polyacrylamide gels (PAG) to allow biomimetic morphologies. The reference T2 were measured on a 3T scanner using multi-echo CPMG, and diffusion kurtosis-with multi-b DWI. RESULTS Agarose concentration controls T2 values which are nominally independent of PVP or vesicle concentration. For agarose PVP hydrogels, monoexponential diffusion values are a function of PVP concentration and independent of agarose concentration. Compared to free vesicles, for agarose-PAG combined with vesicles, diffusion was predominantly controlled by vesicles and PAG, while kurtosis was affected by agarose and vesicle concentration. Both hydrogel classes achieved image voxel parameter values (T2, Da, Ka) for relaxation (T2: 65-255 ms), apparent diffusion (Da: 0.8-1.7 μm2/ms), and kurtosis (Ka: 0.5-1.25) within the target literature ranges for normal prostate zones and cancer lesions. Relaxation and diffusion parameters remained stable for over 6 months for layered material assemblies. CONCLUSION A stable biomimetic mpMR reference based on hydrogels has been developed with a range of multi-compartment diffusion and relaxation parameter combinations observed in cancerous and healthy prostate tissue.
Collapse
Affiliation(s)
- Dariya Malyarenko
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Shigeto Ono
- Computerized Imaging Reference Systems (Sun Nuclear), Mirion Technologies Inc., Norfolk, VA 23513, USA
| | - Ted J. E. Lynch
- Computerized Imaging Reference Systems (Sun Nuclear), Mirion Technologies Inc., Norfolk, VA 23513, USA
| | - Scott D. Swanson
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| |
Collapse
|
8
|
Sun W, Xu D, Li H, Li S, Bao Q, Song X, Topgaard D, Xu H. Quantifying H&E staining results, grading and predicting IDH mutation status of gliomas using hybrid multi-dimensional MRI. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01154-x. [PMID: 38578520 DOI: 10.1007/s10334-024-01154-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/26/2024] [Accepted: 02/29/2024] [Indexed: 04/06/2024]
Abstract
OBJECTIVE To assess the performance of hybrid multi-dimensional magnetic resonance imaging (HM-MRI) in quantifying hematoxylin and eosin (H&E) staining results, grading and predicting isocitrate dehydrogenase (IDH) mutation status of gliomas. MATERIALS AND METHODS Included were 71 glioma patients (mean age, 50.17 ± 13.38 years; 35 men). HM-MRI images were collected at five different echo times (80-200 ms) with seven b-values (0-3000 s/mm2). A modified three-compartment model with very-slow, slow and fast diffusion components was applied to calculate HM-MRI metrics, including fractions, diffusion coefficients and T2 values of each component. Pearson correlation analysis was performed between HM-MRI derived fractions and H&E staining derived percentages. HM-MRI metrics were compared between high-grade and low-grade gliomas, and between IDH-wild and IDH-mutant gliomas. Using receiver operational characteristic (ROC) analysis, the diagnostic performance of HM-MRI in grading and genotyping was compared with mono-exponential models. RESULTS HM-MRI metrics FDvery-slow and FDslow demonstrated a significant correlation with the H&E staining results (p < .05). Besides, FDvery-slow showed the highest area under ROC curve (AUC = 0.854) for grading, while Dslow showed the highest AUC (0.845) for genotyping. Furthermore, a combination of HM-MRI metrics FDvery-slow and T2Dslow improved the diagnostic performance for grading (AUC = 0.876). DISCUSSION HM-MRI can aid in non-invasive diagnosis of gliomas.
Collapse
Affiliation(s)
- Wenbo Sun
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, People's Republic of China
| | - Dan Xu
- Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, People's Republic of China
| | - Huan Li
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, People's Republic of China
| | - Sirui Li
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, People's Republic of China
| | - Qingjia Bao
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071, Hubei, People's Republic of China
| | - Xiaopeng Song
- Central Research Institute, United-Imaging Healthcare, Shanghai, China
| | - Daniel Topgaard
- Department of Chemistry, Lund University, P.O.B. 124, 221 00, Lund, Sweden.
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, People's Republic of China.
| |
Collapse
|
9
|
Dong Z, Reese TG, Lee HH, Huang SY, Polimeni JR, Wald LL, Wang F. Romer-EPTI: rotating-view motion-robust super-resolution EPTI for SNR-efficient distortion-free in-vivo mesoscale dMRI and microstructure imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.26.577343. [PMID: 38352481 PMCID: PMC10862730 DOI: 10.1101/2024.01.26.577343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Purpose To overcome the major challenges in dMRI acquisition, including low SNR, distortion/blurring, and motion vulnerability. Methods A novel Romer-EPTI technique is developed to provide distortion-free dMRI with significant SNR gain, high motion-robustness, sharp spatial resolution, and simultaneous multi-TE imaging. It introduces a ROtating-view Motion-robust supEr-Resolution technique (Romer) combined with a distortion/blurring-free EPTI encoding. Romer enhances SNR by a simultaneous multi-thick-slice acquisition with rotating-view encoding, while providing high motion-robustness through a motion-aware super-resolution reconstruction, which also incorporates slice-profile and real-value diffusion, to resolve high-isotropic-resolution volumes. The in-plane encoding is performed using distortion/blurring-free EPTI, which further improves effective spatial resolution and motion robustness by preventing not only T2/T2*-blurring but also additional blurring resulting from combining encoded volumes with inconsistent geometries caused by dynamic distortions. Self-navigation was incorporated to enable efficient phase correction. Additional developments include strategies to address slab-boundary artifacts, achieve minimal TE for SNR gain at 7T, and achieve high robustness to strong phase variations at high b-values. Results Using Romer-EPTI, we demonstrate distortion-free whole-brain mesoscale in-vivo dMRI at both 3T (500-μm-iso) and 7T (485-μm-iso) for the first time, with high SNR efficiency (e.g., 25 × ), and high image quality free from distortion and slab-boundary artifacts with minimal blurring. Motion experiments demonstrate Romer-EPTI's high motion-robustness and ability to recover sharp images in the presence of motion. Romer-EPTI also demonstrates significant SNR gain and robustness in high b-value (b=5000s/mm2) and time-dependent dMRI. Conclusion Romer-EPTI significantly improves SNR, motion-robustness, and image quality, providing a highly efficient acquisition for high-resolution dMRI and microstructure imaging.
Collapse
Affiliation(s)
- Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Timothy G. Reese
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Susie Y. Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | - Jonathan R. Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | - Lawrence L. Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
10
|
Dong Z, Wald LL, Polimeni JR, Wang F. Single-shot Echo Planar Time-resolved Imaging for multi-echo functional MRI and distortion-free diffusion imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.24.577002. [PMID: 38328081 PMCID: PMC10849706 DOI: 10.1101/2024.01.24.577002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Purpose To develop EPTI, a multi-shot distortion-free multi-echo imaging technique, into a single-shot acquisition to achieve improved robustness to motion and physiological noise, increased temporal resolution, and high SNR efficiency for dynamic imaging applications. Methods A new spatiotemporal encoding was developed to achieve single-shot EPTI by enhancing spatiotemporal correlation in k-t space. The proposed single-shot encoding improves reconstruction conditioning and sampling efficiency, with additional optimization under various accelerations to achieve optimized performance. To achieve high SNR efficiency, continuous readout with minimized deadtime was employed that begins immediately after excitation and extends for an SNR-optimized length. Moreover, k-t partial Fourier and simultaneous multi-slice acquisition were integrated to further accelerate the acquisition and achieve high spatial and temporal resolution. Results We demonstrated that ss-EPTI achieves higher tSNR efficiency than multi-shot EPTI, and provides distortion-free imaging with densely-sampled multi-echo images at resolutions ~1.25-3 mm at 3T and 7T-with high SNR efficiency and with comparable temporal resolutions to ss-EPI. The ability of ss-EPTI to eliminate dynamic distortions common in EPI also further improves temporal stability. For fMRI, ss-EPTI also provides early-TE images (e.g., 2.9ms) to recover signal-intensity and functional-sensitivity dropout in challenging regions. The multi-echo images provide TE-dependent information about functional fluctuations, successfully distinguishing noise-components from BOLD signals and further improving tSNR. For diffusion MRI, ss-EPTI provides high-quality distortion-free diffusion images and multi-echo diffusion metrics. Conclusion ss-EPTI provides distortion-free imaging with high image quality, rich multi-echo information, and enhanced efficiency within comparable temporal resolution to ss-EPI, offering a robust and efficient acquisition for dynamic imaging.
Collapse
Affiliation(s)
- Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Lawrence L. Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | - Jonathan R. Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
11
|
Luo Y, Zhu M, Wei X, Xu J, Pan S, Liu G, Song Y, Hu W, Dai Y, Wu G. Investigation of clear cell renal cell carcinoma grades using diffusion-relaxation correlation spectroscopic imaging with optimized spatial-spectrum analysis. Br J Radiol 2024; 97:135-141. [PMID: 38263829 PMCID: PMC11008501 DOI: 10.1093/bjr/tqad003] [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: 06/12/2023] [Revised: 09/28/2023] [Accepted: 10/10/2023] [Indexed: 01/25/2024] Open
Abstract
OBJECTIVES To differentiate high-grade from low-grade clear cell renal cell carcinoma (ccRCC) using diffusion-relaxation correlation spectroscopic imaging (DR-CSI) spectra in an equal separating analysis. METHODS Eighty patients with 86 pathologically confirmed ccRCCs who underwent DR-CSI were enrolled. Two radiologists delineated the region of interest. The spectrum was derived based on DR-CSI and was further segmented into multiple equal subregions from 2*2 to 9*9. The agreement between the 2 radiologists was assessed by the intraclass correlation coefficient (ICC). Logistic regression was used to establish the regression model for differentiation, and 5-fold cross-validation was used to evaluate its accuracy. McNemar's test was used to compare the diagnostic performance between equipartition models and the traditional parameters, including the apparent diffusion coefficient (ADC) and T2 value. RESULTS The inter-reader agreement decreased as the divisions in the equipartition model increased (overall ICC ranged from 0.859 to 0.920). The accuracy increased from the 2*2 to 9*9 equipartition model (0.68 for 2*2, 0.69 for 3*3 and 4*4, 0.70 for 5*5, 0.71 for 6*6, 0.78 for 7*7, and 0.75 for 8*8 and 9*9). The equipartition models with divisions >7*7 were significantly better than ADC and T2 (vs ADC: P = .002-.008; vs T2: P = .001-.004). CONCLUSIONS The equipartition method has the potential to analyse the DR-CSI spectrum and discriminate between low-grade and high-grade ccRCC. ADVANCES IN KNOWLEDGE The evaluation of DR-CSI relies on prior knowledge, and how to assess the spectrum derived from DR-CSI without prior knowledge has not been well studied.
Collapse
Affiliation(s)
- Yuansheng Luo
- Department of Radiology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Mengying Zhu
- Department of Radiology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaobin Wei
- Department of Radiology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Jianrong Xu
- Department of Radiology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Shihang Pan
- Department of Radiology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Guiqin Liu
- Department of Radiology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers Ltd., 200129 Shanghai, China
| | - Wentao Hu
- Department of Radiology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yongming Dai
- School of Biomedical Engineering, Shanghai Tech University, 201210 Shanghai, China
| | - Guangyu Wu
- Department of Radiology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
| |
Collapse
|
12
|
Neves Silva S, Aviles Verdera J, Tomi‐Tricot R, Neji R, Uus A, Grigorescu I, Wilkinson T, Ozenne V, Lewin A, Story L, De Vita E, Rutherford M, Pushparajah K, Hajnal J, Hutter J. Real-time fetal brain tracking for functional fetal MRI. Magn Reson Med 2023; 90:2306-2320. [PMID: 37465882 PMCID: PMC10952752 DOI: 10.1002/mrm.29803] [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: 03/28/2023] [Revised: 07/03/2023] [Accepted: 07/03/2023] [Indexed: 07/20/2023]
Abstract
PURPOSE To improve motion robustness of functional fetal MRI scans by developing an intrinsic real-time motion correction method. MRI provides an ideal tool to characterize fetal brain development and growth. It is, however, a relatively slow imaging technique and therefore extremely susceptible to subject motion, particularly in functional MRI experiments acquiring multiple Echo-Planar-Imaging-based repetitions, for example, diffusion MRI or blood-oxygen-level-dependency MRI. METHODS A 3D UNet was trained on 125 fetal datasets to track the fetal brain position in each repetition of the scan in real time. This tracking, inserted into a Gadgetron pipeline on a clinical scanner, allows updating the position of the field of view in a modified echo-planar imaging sequence. The method was evaluated in real-time in controlled-motion phantom experiments and ten fetal MR studies (17 + 4-34 + 3 gestational weeks) at 3T. The localization network was additionally tested retrospectively on 29 low-field (0.55T) datasets. RESULTS Our method achieved real-time fetal head tracking and prospective correction of the acquisition geometry. Localization performance achieved Dice scores of 84.4% and 82.3%, respectively for both the unseen 1.5T/3T and 0.55T fetal data, with values higher for cephalic fetuses and increasing with gestational age. CONCLUSIONS Our technique was able to follow the fetal brain even for fetuses under 18 weeks GA in real-time at 3T and was successfully applied "offline" to new cohorts on 0.55T. Next, it will be deployed to other modalities such as fetal diffusion MRI and to cohorts of pregnant participants diagnosed with pregnancy complications, for example, pre-eclampsia and congenital heart disease.
Collapse
Affiliation(s)
- Sara Neves Silva
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Jordina Aviles Verdera
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Raphael Tomi‐Tricot
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- MR Research CollaborationsSiemens Healthcare LimitedCamberleyUK
| | - Radhouene Neji
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- MR Research CollaborationsSiemens Healthcare LimitedCamberleyUK
| | - Alena Uus
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Irina Grigorescu
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Thomas Wilkinson
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Valery Ozenne
- CNRS, CRMSB, UMR 5536, IHU LirycUniversité de BordeauxBordeauxFrance
| | - Alexander Lewin
- Institute of Neuroscience and Medicine 11, INM‐11Forschungszentrum JülichJülichGermany
- RWTHAachen UniversityAachenGermany
| | - Lisa Story
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Department of Women & Children's HealthKing's College LondonLondonUK
| | - Enrico De Vita
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- MRI Physics GroupGreat Ormond Street HospitalLondonUK
| | - Mary Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Kuberan Pushparajah
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Jo Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| |
Collapse
|
13
|
Fokkinga E, Hernandez-Tamames JA, Ianus A, Nilsson M, Tax CMW, Perez-Lopez R, Grussu F. Advanced Diffusion-Weighted MRI for Cancer Microstructure Assessment in Body Imaging, and Its Relationship With Histology. J Magn Reson Imaging 2023. [PMID: 38032021 DOI: 10.1002/jmri.29144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 10/30/2023] [Accepted: 10/31/2023] [Indexed: 12/01/2023] Open
Abstract
Diffusion-weighted magnetic resonance imaging (DW-MRI) aims to disentangle multiple biological signal sources in each imaging voxel, enabling the computation of innovative maps of tissue microstructure. DW-MRI model development has been dominated by brain applications. More recently, advanced methods with high fidelity to histology are gaining momentum in other contexts, for example, in oncological applications of body imaging, where new biomarkers are urgently needed. The objective of this article is to review the state-of-the-art of DW-MRI in body imaging (ie, not including the nervous system) in oncology, and to analyze its value as compared to reference colocalized histology measurements, given that demonstrating the histological validity of any new DW-MRI method is essential. In this article, we review the current landscape of DW-MRI techniques that extend standard apparent diffusion coefficient (ADC), describing their acquisition protocols, signal models, fitting settings, microstructural parameters, and relationship with histology. Preclinical, clinical, and in/ex vivo studies were included. The most used techniques were intravoxel incoherent motion (IVIM; 36.3% of used techniques), diffusion kurtosis imaging (DKI; 16.7%), vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT; 13.3%), and imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED; 11.7%). Another notable category of techniques relates to innovative b-tensor diffusion encoding or joint diffusion-relaxometry. The reviewed approaches provide histologically meaningful indices of cancer microstructure (eg, vascularization/cellularity) which, while not necessarily accurate numerically, may still provide useful sensitivity to microscopic pathological processes. Future work of the community should focus on improving the inter-/intra-scanner robustness, and on assessing histological validity in broader contexts. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Ella Fokkinga
- Biomedical Engineering, Track Medical Physics, Delft University of Technology, Delft, The Netherlands
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Juan A Hernandez-Tamames
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Markus Nilsson
- Department of Diagnostic Radiology, Clinical Sciences Lund, Lund, Sweden
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Center (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Francesco Grussu
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| |
Collapse
|
14
|
Lampinen B, Szczepankiewicz F, Lätt J, Knutsson L, Mårtensson J, Björkman-Burtscher IM, van Westen D, Sundgren PC, Ståhlberg F, Nilsson M. Probing brain tissue microstructure with MRI: principles, challenges, and the role of multidimensional diffusion-relaxation encoding. Neuroimage 2023; 282:120338. [PMID: 37598814 DOI: 10.1016/j.neuroimage.2023.120338] [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: 02/02/2023] [Revised: 06/30/2023] [Accepted: 08/17/2023] [Indexed: 08/22/2023] Open
Abstract
Diffusion MRI uses the random displacement of water molecules to sensitize the signal to brain microstructure and to properties such as the density and shape of cells. Microstructure modeling techniques aim to estimate these properties from acquired data by separating the signal between virtual tissue 'compartments' such as the intra-neurite and the extra-cellular space. A key challenge is that the diffusion MRI signal is relatively featureless compared with the complexity of brain tissue. Another challenge is that the tissue microstructure is wildly different within the gray and white matter of the brain. In this review, we use results from multidimensional diffusion encoding techniques to discuss these challenges and their tentative solutions. Multidimensional encoding increases the information content of the data by varying not only the b-value and the encoding direction but also additional experimental parameters such as the shape of the b-tensor and the echo time. Three main insights have emerged from such encoding. First, multidimensional data contradict common model assumptions on diffusion and T2 relaxation, and illustrates how the use of these assumptions cause erroneous interpretations in both healthy brain and pathology. Second, many model assumptions can be dispensed with if data are acquired with multidimensional encoding. The necessary data can be easily acquired in vivo using protocols optimized to minimize Cramér-Rao lower bounds. Third, microscopic diffusion anisotropy reflects the presence of axons but not dendrites. This insight stands in contrast to current 'neurite models' of brain tissue, which assume that axons in white matter and dendrites in gray matter feature highly similar diffusion. Nevertheless, as an axon-based contrast, microscopic anisotropy can differentiate gray and white matter when myelin alterations confound conventional MRI contrasts.
Collapse
Affiliation(s)
- Björn Lampinen
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden.
| | | | - Jimmy Lätt
- Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden
| | - Linda Knutsson
- Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Johan Mårtensson
- Clinical Sciences Lund, Logopedics, Phoniatrics and Audiology, Lund University, Lund, Sweden
| | - Isabella M Björkman-Burtscher
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Danielle van Westen
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden
| | - Pia C Sundgren
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden; Lund University BioImaging Centre (LBIC), Lund University, Lund, Sweden
| | - Freddy Ståhlberg
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Markus Nilsson
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden
| |
Collapse
|
15
|
Johnson JT, 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. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.10.561702. [PMID: 37987005 PMCID: PMC10659440 DOI: 10.1101/2023.10.10.561702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
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 , in addition to the diffusion tensor, D , and relaxation, R 1 , R 2 , correlations. A D ( ω ) - 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 their D ( ω ) - 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.
Collapse
Affiliation(s)
- Jessica T.E. Johnson
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| | - M. Okan Irfanoglu
- Quantitative Medical Imaging Section, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
| | - Eppu Manninen
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Thomas J. Ross
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
| | - Frederik B. Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Jan Martin
- Department of Chemistry, Lund University, Lund, Sweden
| | | | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| |
Collapse
|
16
|
Kundu S, Barsoum S, Ariza J, Nolan AL, Latimer CS, Keene CD, Basser PJ, Benjamini D. Mapping the individual human cortex using multidimensional MRI and unsupervised learning. Brain Commun 2023; 5:fcad258. [PMID: 37953850 PMCID: PMC10638106 DOI: 10.1093/braincomms/fcad258] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/31/2023] [Accepted: 10/05/2023] [Indexed: 11/14/2023] Open
Abstract
Human evolution has seen the development of higher-order cognitive and social capabilities in conjunction with the unique laminar cytoarchitecture of the human cortex. Moreover, early-life cortical maldevelopment has been associated with various neurodevelopmental diseases. Despite these connections, there is currently no noninvasive technique available for imaging the detailed cortical laminar structure. This study aims to address this scientific and clinical gap by introducing an approach for imaging human cortical lamina. This method combines diffusion-relaxation multidimensional MRI with a tailored unsupervised machine learning approach that introduces enhanced microstructural sensitivity. This new imaging method simultaneously encodes the microstructure, the local chemical composition and importantly their correlation within complex and heterogenous tissue. To validate our approach, we compared the intra-cortical layers obtained using our ex vivo MRI-based method with those derived from Nissl staining of postmortem human brain specimens. The integration of unsupervised learning with diffusion-relaxation correlation MRI generated maps that demonstrate sensitivity to areal differences in cytoarchitectonic features observed in histology. Significantly, our observations revealed layer-specific diffusion-relaxation signatures, showing reductions in both relaxation times and diffusivities at the deeper cortical levels. These findings suggest a radial decrease in myelin content and changes in cell size and anisotropy, reflecting variations in both cytoarchitecture and myeloarchitecture. Additionally, we demonstrated that 1D relaxation and high-order diffusion MRI scalar indices, even when aggregated and used jointly in a multimodal fashion, cannot disentangle the cortical layers. Looking ahead, our technique holds the potential to open new avenues of research in human neurodevelopment and the vast array of disorders caused by disruptions in neurodevelopment.
Collapse
Affiliation(s)
- Shinjini Kundu
- Department of Radiology, The Johns Hopkins Hospital, Baltimore, MD 21287, USA
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20892, USA
| | - Stephanie Barsoum
- Multiscale Imaging and Integrative Biophysics Unit, Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - Jeanelle Ariza
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Amber L Nolan
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Caitlin S Latimer
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Peter J Basser
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20892, USA
| | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics Unit, Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| |
Collapse
|
17
|
Gangolli M, Pajevic S, Kim JH, Hutchinson EB, Benjamini D, Basser PJ. Correspondence of mean apparent propagator MRI metrics with phosphorylated tau and astrogliosis in chronic traumatic encephalopathy. Brain Commun 2023; 5:fcad253. [PMID: 37901038 PMCID: PMC10600571 DOI: 10.1093/braincomms/fcad253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 08/03/2023] [Accepted: 10/03/2023] [Indexed: 10/31/2023] Open
Abstract
Chronic traumatic encephalopathy is a neurodegenerative disease that is diagnosed and staged based on the localization and extent of phosphorylated tau pathology. Although its identification remains the primary diagnostic criteria to distinguish chronic traumatic encephalopathy from other tauopathies, the hyperphosphorylated tau that accumulates in neurofibrillary tangles in cortical grey matter and perivascular regions is often accompanied by concomitant pathology such as astrogliosis. Mean apparent propagator MRI is a clinically feasible diffusion MRI method that is suitable to characterize microstructure of complex biological media efficiently and comprehensively. We performed quantitative correlations between propagator metrics and underlying phosphorylated tau and astroglial pathology in a cross-sectional study of 10 ex vivo human tissue specimens with 'high chronic traumatic encephalopathy' at 0.25 mm isotropic voxels. Linear mixed effects analysis of regions of interest showed significant relationships of phosphorylated tau with propagator-estimated non-Gaussianity in cortical grey matter (P = 0.002) and of astrogliosis with propagator anisotropy in superficial cortical white matter (P = 0.0009). The positive correlation between phosphorylated tau and non-Gaussianity was found to be modest but significant (R2 = 0.44, P = 6.0 × 10-5) using linear regression. We developed an unsupervised clustering algorithm with non-Gaussianity and propagator anisotropy as inputs, which was able to identify voxels in superficial cortical white matter that corresponded to astrocytes that were accumulated at the grey-white matter interface. Our results suggest that mean apparent propagator MRI at high spatial resolution provides a means to not only identify phosphorylated tau pathology but also detect regions with astrocytic pathology and may therefore prove diagnostically valuable in the evaluation of concomitant pathology in cortical tissue with complex microstructure.
Collapse
Affiliation(s)
- Mihika Gangolli
- Center for Neuroscience and Regenerative Medicine, Bethesda, MD 20817, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Sinisa Pajevic
- Section on Critical Brain Dynamics, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA
| | - Joong Hee Kim
- Center for Neuroscience and Regenerative Medicine, Bethesda, MD 20817, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Elizabeth B Hutchinson
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ 20892, USA
| | - Dan Benjamini
- Center for Neuroscience and Regenerative Medicine, Bethesda, MD 20817, USA
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA
| | - Peter J Basser
- Center for Neuroscience and Regenerative Medicine, Bethesda, MD 20817, USA
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA
| |
Collapse
|
18
|
Aviles Verdera J, Story L, Hall M, Finck T, Egloff A, Seed PT, Malik SJ, Rutherford MA, Hajnal JV, Tomi-Tricot R, Hutter J. Reliability and Feasibility of Low-Field-Strength Fetal MRI at 0.55 T during Pregnancy. Radiology 2023; 309:e223050. [PMID: 37847139 PMCID: PMC10623193 DOI: 10.1148/radiol.223050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 08/20/2023] [Accepted: 09/06/2023] [Indexed: 10/18/2023]
Abstract
Background The benefits of using low-field-strength fetal MRI to evaluate antenatal development include reduced image artifacts, increased comfort, larger bore size, and potentially reduced costs, but studies about fetal low-field-strength MRI are lacking. Purpose To evaluate the reliability and feasibility of low-field-strength fetal MRI to assess anatomic and functional measures in pregnant participants using a commercially available 0.55-T MRI scanner and a comprehensive 20-minute protocol. Materials and Methods This prospective study was performed at a large teaching hospital (St Thomas' Hospital; London, England) from May to November 2022 in healthy pregnant participants and participants with pregnancy-related abnormalities using a commercially available 0.55-T MRI scanner. A 20-minute protocol was acquired including anatomic T2-weighted fast-spin-echo, quantitative T2*, and diffusion sequences. Key measures like biparietal diameter, transcerebellar diameter, lung volume, and cervical length were evaluated by two radiologists and an MRI-experienced obstetrician. Functional organ-specific mean values were given. Comparison was performed with existing published values and higher-field MRI using linear regression, interobserver correlation, and Bland-Altman plots. Results A total of 79 fetal MRI examinations were performed (mean gestational age, 29.4 weeks ± 5.5 [SD] [age range, 17.6-39.3 weeks]; maternal age, 34.4 years ± 5.3 [age range, 18.4-45.5 years]) in 47 healthy pregnant participants (control participants) and in 32 participants with pregnancy-related abnormalities. The key anatomic two-dimensional measures for the 47 healthy participants agreed with large cross-sectional 1.5-T and 3-T control studies. The interobserver correlations for the biparietal diameter in the first 40 consecutive scans were 0.96 (95% CI: 0.7, 0.99; P = .002) for abnormalities and 0.93 (95% CI: 0.86, 0.97; P < .001) for control participants. Functional features, including placental and brain T2* and placental apparent diffusion coefficient values, strongly correlated with gestational age (mean placental T2* in the control participants: 5.2 msec of decay per week; R2 = 0.66; mean T2* at 30 weeks, 176.6 msec; P < .001). Conclusion The 20-minute low-field-strength fetal MRI examination protocol was capable of producing reliable structural and functional measures of the fetus and placenta in pregnancy. Clinical trial registration no. REC 21/LO/0742 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Gowland in this issue.
Collapse
Affiliation(s)
- Jordina Aviles Verdera
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| | - Lisa Story
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| | - Megan Hall
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| | - Tom Finck
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| | - Alexia Egloff
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| | - Paul T. Seed
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| | - Shaihan J. Malik
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| | - Mary A. Rutherford
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| | - Joseph V. Hajnal
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| | - Raphaël Tomi-Tricot
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| | - Jana Hutter
- From the Centre for the Developing Brain, School of Biomedical
Engineering & Imaging Sciences, King's College London, 1st Floor
South Wing, St Thomas’ Hospital, Westminster Bridge Road SE1 7EH London,
United Kingdom (J.A.V., L.S., M.H., P.T.S., S.J.M., M.A.R., J.V.H, J.H.); Centre
for Medical Biomedical Engineering Department, School of Biomedical Engineering
and Imaging Sciences, King's College London, London, UK (J.A.V., L.S.,
A.E., S.J.M., M.A.R., J.V.H., J.H.); Women's Health, GSTT, London, UK
(L.S., M.H., T.F., P.T.S.); Technical University Munich, Munich, Germany (T.F.);
MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK (R.T.T.);
and Radiological Institute, University Hospital Erlangen, Erlangen, Germany
(J.H.)
| |
Collapse
|
19
|
Kang IC, Pasternak O, Zhang F, Penzel N, Seitz-Holland J, Tang Y, Zhang T, Xu L, Li H, Keshavan M, Whitfield-Gabrielli S, Niznikiewicz M, Stone W, Wang J, Shenton M. Microstructural Cortical Gray Matter Changes Preceding Accelerated Volume Changes in Individuals at Clinical High Risk for Psychosis. RESEARCH SQUARE 2023:rs.3.rs-3179575. [PMID: 37841868 PMCID: PMC10571628 DOI: 10.21203/rs.3.rs-3179575/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Recent studies show that accelerated cortical gray matter (GM) volume reduction seen in anatomical MRI can help distinguish between individuals at clinical high risk (CHR) for psychosis who will develop psychosis and those who will not. This reduction is thought to result from an accumulation of microstructural changes, such as decreased spine density and dendritic arborization. Detecting the microstructural sources of these changes before they accumulate is crucial, as volume reduction likely indicates an underlying neurodegenerative process. Our study aimed to detect these microstructural GM alterations using diffusion MRI (dMRI). We tested for baseline and longitudinal group differences in anatomical and dMRI data from 160 individuals at CHR and 96 healthy controls (HC) acquired in a single imaging site. Eight cortical lobes were examined for GM volume and GM microstructure. A novel dMRI measure, interstitial free water (iFW), was used to quantify GM microstructure by eliminating cerebrospinal fluid contribution. Additionally, we assessed whether these measures differentiated the 33 individuals at CHR who developed psychosis (CHR-P) from the 127 individuals at CHR who did not (CHR-NP). At baseline the CHR group had significantly higher iFW than HC in the prefrontal, temporal, parietal, and occipital lobes, while volume was reduced only in the temporal lobe. Neither iFW nor volume differentiated between the CHR-P and CHR-NP groups at baseline. However, in most brain areas, the CHR-P group demonstrated significantly accelerated iFW increase and volume reduction with time than the CHR-NP group. Our results demonstrate that microstructural GM changes in individuals at CHR have a wider extent than volumetric changes and they predate the acceleration of brain changes that occur around psychosis onset. Microstructural GM changes are thus an early pathology at the prodromal stage of psychosis that may be useful for early detection and a better mechanistic understanding of psychosis development.
Collapse
Affiliation(s)
| | | | | | | | - Johanna Seitz-Holland
- Brigham and Women's Hospital and Massachusetts General Hospital, Harvard Medical School
| | - Yingying Tang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine
| | - Tianhong Zhang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, PR China
| | | | | | | | | | | | | | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine
| | | |
Collapse
|
20
|
Wu D, Turnbill V, Lee HH, Wang X, Ba R, Walczak P, Martin LJ, Fieremans E, Novikov DS, Northington FJ, Zhang J. In vivo Mapping of Cellular Resolution Neuropathology in Brain Ischemia by Diffusion MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.08.552374. [PMID: 37609182 PMCID: PMC10441332 DOI: 10.1101/2023.08.08.552374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Non-invasive mapping of cellular pathology can provide critical diagnostic and prognostic information. Recent developments in diffusion MRI have produced new tools for examining tissue microstructure at a level well below the imaging resolution. Here, we report the use of diffusion time ( t )-dependent diffusion kurtosis imaging ( t DKI) to simultaneously assess the morphology and transmembrane permeability of cells and their processes in the context of pathological changes in hypoxic-ischemic brain (HI) injury. Through Monte Carlo simulations and cell culture organoid imaging, we demonstrate feasibility in measuring effective size and permeability changes based on the peak and tail of t DKI curves. In a mouse model of HI, in vivo imaging at 11.7T detects a marked shift of the t DKI peak to longer t in brain edema, suggesting swelling and beading associated with the astrocytic processes and neuronal neurites. Furthermore, we observed a faster decrease of the t DKI tail in injured brain regions, reflecting increased membrane permeability that was associated with upregulated water exchange upon astrocyte activation at acute stage as well as necrosis with disrupted membrane integrity at subacute stage. Such information, unavailable with conventional diffusion MRI at a single t, can predict salvageable tissues. For a proof-of-concept, t DKI at 3T on an ischemic stroke patient suggested increased membrane permeability in the stroke region. This work therefore demonstrates the potential of t DKI for in vivo detection of the pathological changes in microstructural morphology and transmembrane permeability after ischemic injury using a clinically translatable protocol.
Collapse
|
21
|
Slator PJ, Verdera JA, Tomi-Tricot R, Hajnal JV, Alexander DC, Hutter J. Low-Field Combined Diffusion-Relaxation MRI for Mapping Placenta Structure and Function. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.06.23290983. [PMID: 37333076 PMCID: PMC10274995 DOI: 10.1101/2023.06.06.23290983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Purpose Demonstrating quantitative multi-parametric mapping in the placenta with combined T 2 ∗ -diffusion MRI at low-field (0.55T). Methods We present 57 placental MRI scans performed on a commercially available 0.55T scanner. We acquired the images using a combined T 2 ∗ -diffusion technique scan that simultaneously acquires multiple diffusion preparations and echo times. We processed the data to produce quantitative T 2 ∗ and diffusivity maps using a combined T 2 ∗ -ADC model. We compared the derived quantitative parameters across gestation in healthy controls and a cohort of clinical cases. Results Quantitative parameter maps closely resemble those from previous experiments at higher field strength, with similar trends in T 2 ∗ and ADC against gestational age observed. Conclusion Combined T 2 ∗ -diffusion placental MRI is reliably achievable at 0.55T. The advantages of lower field strength - such as cost, ease of deployment, increased accessibility and patient comfort due to the wider bore, and increased T 2 ∗ for larger dynamic ranges - can support the widespread roll out of placental MRI as an adjunct to ultrasound during pregnancy.
Collapse
Affiliation(s)
- Paddy J Slator
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | - Jordina Aviles Verdera
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Raphael Tomi-Tricot
- MR Research Collaborations, Siemens Healthcare Limited, Camberley, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Daniel C Alexander
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| |
Collapse
|
22
|
Luo P, Hu W, Xu R, Wang Y, Li X, Jiang L, Chang S, Wu D, Li G, Dai Y. Enabling early detection of knee osteoarthritis using diffusion-relaxation correlation spectrum imaging. Clin Radiol 2023:S0009-9260(23)00224-6. [PMID: 37336674 DOI: 10.1016/j.crad.2023.05.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/11/2023] [Accepted: 05/23/2023] [Indexed: 06/21/2023]
Abstract
AIM To present a technique that enables detection of early stage OA of the knee using diffusion-relaxation correlation spectrum imaging (DR-CSI). MATERIALS AND METHODS Fifty-five early osteoarthritis patients (OA, Kellgren-Lawrence [KL] score 1 to 2; mean age, 56.4 years) and 49 healthy volunteers (mean age, 56.7 years) were underwent magnetic resonance imaging (MRI) with T2-mapping and DR-CSI techniques. Maps of mean apparent diffusion coefficient (ADC), T2 relaxation time and volume fraction Vi for DR-CSI compartment i (A, B, C, D) sensitivity, specificity, and positive and negative likelihood ratio (PLR, NLR) were assessed to determine the diagnostic accuracy for detection of early-stage degeneration of knee articular cartilage. The structural abnormalities of articular cartilage were evaluated using modified Whole-Organ MR Imaging Scores (WORMS). RESULTS All intra- and interobserver agreements for DR-CSI compartment volume fractions and modified WORMS of cartilage were excellent. Early OA versus the controls had higher VC, lower VA and VB (p<0.001), but comparable VD (p>0.05). VA, VB and VC had a moderate association with WORMS. No significant correlation was identified between VD and WORMS. VC had better ability than VA,VB, VD, T2 and ADC to discriminate early OA patients from healthy controls (area under the curve, 0.898). Sensitivity, specificity, PLR, and NLR of VC with a cut-off value of 29.9% were 81.8% (95% confidence interval [CI], 69.1-90.9%), 95.9% (86-99.5%), 20.05% (5.13-78.34%), and 0.19% (0.11-0.33%). CONCLUSIONS DR-CSI compartment volume fractions may be sensitive indicators for detecting early-stage degeneration in knee articular cartilage.
Collapse
Affiliation(s)
- P Luo
- Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - W Hu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - R Xu
- Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Y Wang
- Department of Gastroenterology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - X Li
- Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - L Jiang
- Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - S Chang
- Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - D Wu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronics Science, East China Normal University, Shanghai 200062, China
| | - G Li
- Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China.
| | - Y Dai
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China.
| |
Collapse
|
23
|
Bouhrara M, Avram AV, Kiely M, Trivedi A, Benjamini D. Adult lifespan maturation and degeneration patterns in gray and white matter: A mean apparent propagator (MAP) MRI study. Neurobiol Aging 2023; 124:104-116. [PMID: 36641369 PMCID: PMC9985137 DOI: 10.1016/j.neurobiolaging.2022.12.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/24/2022] [Accepted: 12/27/2022] [Indexed: 01/02/2023]
Abstract
The relationship between brain microstructure and aging has been the subject of intense study, with diffusion MRI perhaps the most effective modality for elucidating these associations. Here, we used the mean apparent propagator (MAP)-MRI framework, which is suitable to characterize complex microstructure, to investigate age-related cerebral differences in a cohort of cognitively unimpaired participants and compared the results to those derived using diffusion tensor imaging. We studied MAP-MRI metrics, among them the non-Gaussianity (NG) and propagator anisotropy (PA), and established an opposing pattern in white matter of higher NG alongside lower PA among older adults, likely indicative of axonal degradation. In gray matter, however, these two indices were consistent with one another, and exhibited regional pattern heterogeneity compared to other microstructural parameters, which could indicate fewer neuronal projections across cortical layers along with an increased glial concentration. In addition, we report regional variations in the magnitude of age-related microstructural differences consistent with the posterior-anterior shift in aging paradigm. These results encourage further investigations in cognitive impairments and neurodegeneration.
Collapse
Affiliation(s)
- Mustapha Bouhrara
- Magnetic Resonance Physics of Aging and Dementia Unit, National Institute on Aging, NIH, Baltimore, MD 21224, USA.
| | - Alexandru V. Avram
- Section on Quantitative Imaging and Tissue Sciences,Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, USA,Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Matthew Kiely
- Magnetic Resonance Physics of Aging and Dementia Unit, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - Aparna Trivedi
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD 21224, USA.
| |
Collapse
|
24
|
Pang Y. Orientation dependent proton transverse relaxation in the human brain white matter: The magic angle effect on a cylindrical helix. Magn Reson Imaging 2023; 100:73-83. [PMID: 36965837 DOI: 10.1016/j.mri.2023.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 03/15/2023] [Accepted: 03/21/2023] [Indexed: 03/27/2023]
Abstract
PURPOSE To overcome some limitations of previous proton orientation-dependent transverse relaxation formalisms in human brain white matter (WM) by a generalized magic angle effect function. METHODS A cylindrical helix model was developed embracing anisotropic rotational and translational diffusion of restricted molecules in WM, with the former characterized by an axially symmetric system. Transverse relaxation rates R2 and R2∗ were divided into isotropic R2i and anisotropic parts, R2a ∗ f(α,Φ - ε0), with α denoting an open angle and ε0 an orientation (Φ) offset from DTI-derived primary diffusivity direction. The proposed framework (Fit A) was compared to prior models without ε0 on previously published water and methylene proton transverse relaxation rates from developing, healthy, and pathological WM at 3 T. Goodness of fit was represented by root-mean-square error (RMSE). F-test and linear correlation were used with statistical significance set to P ≤ 0.05. RESULTS Fit A significantly (P < 0.01) outperformed prior models as demonstrated by reduced RMSEs, e.g., 0.349 vs. 0.724 in myelin water. Fitted ε0 was in good agreement with calculated ε0 from directional diffusivities. Compared with those from healthy adult, the fitted R2i, R2a, and α from neonates were substantially reduced but ε0 increased, consistent with developing myelination. Significant positive (R2i) and negative (α and R2a) correlations were found with aging (demyelination) in elderly. CONCLUSION The developed framework can better characterize orientation dependences from a wide range of proton transverse relaxation measurements in the human brain WM, thus shedding new light on myelin microstructural alterations at the molecular level.
Collapse
Affiliation(s)
- Yuxi Pang
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., UH B2 RM A205F, Ann Arbor, MI 48109-5030, USA.
| |
Collapse
|
25
|
Sun K, Dan G, Zhong Z, Zhou XJ. Multi-readout DWI with a reduced FOV for studying the coupling between diffusion and T 2 * relaxation in the prostate. Magn Reson Med 2023; 90:250-258. [PMID: 36932652 DOI: 10.1002/mrm.29636] [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: 10/13/2022] [Revised: 02/16/2023] [Accepted: 02/23/2023] [Indexed: 03/19/2023]
Abstract
PURPOSE To develop a DWI sequence with multiple readout echo-trains in a single shot (multi-readout DWI) over a reduced FOV, and to demonstrate its ability to achieve high data acquisition efficiency in the study of coupling between diffusion and relaxation in the human prostate. METHODS The proposed multi-readout DWI sequence plays out multiple EPI readout echo-trains after a Stejskal-Tanner diffusion preparation module. Each EPI readout echo-train corresponded to a distinct effective TE. To maintain a high spatial resolution with a relatively short echo-train for each readout, a 2D RF pulse was used to limit the FOV. Experiments were performed on the prostate of six healthy subjects to acquire a set of images with three b values (0, 500, and 1000 s/mm2 ) and three TEs (63.0, 78.8, and 94.6 ms), producing three ADC maps at different TEs and three T 2 * $$ {T}_2^{\ast } $$ maps at different b values. RESULTS Multi-readout DWI enabled a threefold acceleration without compromising the spatial resolution when compared with a conventional single-readout sequence. Images with three b values and three TEs were obtained in 3 min 40 s with an adequate SNR (≥ 26.9). The ADC values (1.45 ± 0.13, 1.52 ± 0.14, and 1.58 ± 0.15 μm 2 / ms $$ {\upmu \mathrm{m}}^2/\mathrm{ms} $$ ; P < 0.01) exhibited an increasing trend as TEs increased (63.0 ms, 78.8 ms, and 94.6 ms), whereas T 2 * $$ {T}_2^{\ast } $$ values (74.78 ± 13.21, 63.21 ± 7.84, and 56.61 ± 5.05 ms; P < 0.01) decreases as the b values increased (0, 500, and 1000 s/mm2 ). CONCLUSION The multi-readout DWI sequence over a reduced FOV provides a time-efficient technique to study the coupling between diffusion and relaxation times.
Collapse
Affiliation(s)
- Kaibao Sun
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Guangyu Dan
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA.,Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Zheng Zhong
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA.,Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Xiaohong Joe Zhou
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA.,Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois, USA.,Departments of Radiology and Neurosurgery, University of Illinois College of Medicine at Chicago, Chicago, Illinois, USA
| |
Collapse
|
26
|
Ferizi U, Müller-Oehring EM, Peterson ET, Pohl KM. The distortions of the free water model for diffusion MRI data when assuming single compartment relaxometry and proton density. Phys Med Biol 2023; 68:10.1088/1361-6560/acb30b. [PMID: 36638532 PMCID: PMC10100575 DOI: 10.1088/1361-6560/acb30b] [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: 10/22/2022] [Accepted: 01/13/2023] [Indexed: 01/15/2023]
Abstract
Objective.To document the bias of thesimplifiedfree water model of diffusion MRI (dMRI) signal vis-à-vis aspecificmodel which, in addition to diffusion, incorporates compartment-specific proton density (PD), T1 recovery during repetition time (TR), and T2 decay during echo time (TE).Approach.Both models assume that volume fractionfof the total signal in any voxel arises from the free water compartment (fw) such as cerebrospinal fluid or edema, and the remainder (1-f) from hindered water (hw) which is constrained by cellular structures such as white matter (WM). Thespecificandsimplifiedmodels are compared on a synthetic dataset, using a range of PD, T1 and T2 values. We then fit the models to anin vivohealthy brain dMRI dataset. For bothsyntheticandin vivodata we use experimentally feasible TR, TE, signal-to-noise ratio (SNR) and physiologically plausible diffusion profiles.Main results.From the simulations we see that the difference between the estimatedsimplified fandspecific fis largest for mid-range ground-truthf, and it increases as SNR increases. The estimation of volume fractionfis sensitive to the choice of model,simplifiedorspecific, but the estimated diffusion parameters are robust to small perturbations in the simulation.Specific fis more accurate and precise thansimplified f. In the white matter (WM) regions of thein vivoimages,specific fis lower thansimplified f.Significance.In dMRI models for free water, accounting for compartment specific PD, T1 and T2, in addition to diffusion, improves the estimation of model parameters. This extra model specification attenuates the estimation bias of compartmental volume fraction without affecting the estimation of other diffusion parameters.
Collapse
Affiliation(s)
- Uran Ferizi
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Eva M Müller-Oehring
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Eric T Peterson
- Center for Health Sciences, SRI International, Menlo Park, CA, United States of America
| | - Kilian M Pohl
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States of America
- Center for Health Sciences, SRI International, Menlo Park, CA, United States of America
| |
Collapse
|
27
|
Palombo M, Valindria V, Singh S, Chiou E, Giganti F, Pye H, Whitaker HC, Atkinson D, Punwani S, Alexander DC, Panagiotaki E. Joint estimation of relaxation and diffusion tissue parameters for prostate cancer with relaxation-VERDICT MRI. Sci Rep 2023; 13:2999. [PMID: 36810476 PMCID: PMC9943845 DOI: 10.1038/s41598-023-30182-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 02/17/2023] [Indexed: 02/23/2023] Open
Abstract
This work presents a biophysical model of diffusion and relaxation MRI for prostate called relaxation vascular, extracellular and restricted diffusion for cytometry in tumours (rVERDICT). The model includes compartment-specific relaxation effects providing T1/T2 estimates and microstructural parameters unbiased by relaxation properties of the tissue. 44 men with suspected prostate cancer (PCa) underwent multiparametric MRI (mp-MRI) and VERDICT-MRI followed by targeted biopsy. We estimate joint diffusion and relaxation prostate tissue parameters with rVERDICT using deep neural networks for fast fitting. We tested the feasibility of rVERDICT estimates for Gleason grade discrimination and compared with classic VERDICT and the apparent diffusion coefficient (ADC) from mp-MRI. The rVERDICT intracellular volume fraction fic discriminated between Gleason 3 + 3 and 3 + 4 (p = 0.003) and Gleason 3 + 4 and ≥ 4 + 3 (p = 0.040), outperforming classic VERDICT and the ADC from mp-MRI. To evaluate the relaxation estimates we compare against independent multi-TE acquisitions, showing that the rVERDICT T2 values are not significantly different from those estimated with the independent multi-TE acquisition (p > 0.05). Also, rVERDICT parameters exhibited high repeatability when rescanning five patients (R2 = 0.79-0.98; CV = 1-7%; ICC = 92-98%). The rVERDICT model allows for accurate, fast and repeatable estimation of diffusion and relaxation properties of PCa sensitive enough to discriminate Gleason grades 3 + 3, 3 + 4 and ≥ 4 + 3.
Collapse
Affiliation(s)
- Marco Palombo
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK.
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK.
| | - Vanya Valindria
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Saurabh Singh
- Centre for Medical Imaging, University College London, London, UK
| | - Eleni Chiou
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Francesco Giganti
- Division of Surgery and Interventional Science, University College London, London, UK
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
| | - Hayley Pye
- Molecular Diagnostics and Therapeutics Group, Division of Surgery & Interventional Science, University College London, London, UK
| | - Hayley C Whitaker
- Molecular Diagnostics and Therapeutics Group, Division of Surgery & Interventional Science, University College London, London, UK
| | - David Atkinson
- Centre for Medical Imaging, University College London, London, UK
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Eleftheria Panagiotaki
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| |
Collapse
|
28
|
Jara H, Sakai O, Farrher E, Oros-Peusquens AM, Shah NJ, Alsop DC, Keenan KE. Primary Multiparametric Quantitative Brain MRI: State-of-the-Art Relaxometric and Proton Density Mapping Techniques. Radiology 2022; 305:5-18. [PMID: 36040334 PMCID: PMC9524578 DOI: 10.1148/radiol.211519] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 05/01/2022] [Accepted: 05/24/2022] [Indexed: 11/11/2022]
Abstract
This review on brain multiparametric quantitative MRI (MP-qMRI) focuses on the primary subset of quantitative MRI (qMRI) parameters that represent the mobile ("free") and bound ("motion-restricted") proton pools. Such primary parameters are the proton densities, relaxation times, and magnetization transfer parameters. Diffusion qMRI is also included because of its wide implementation in complete clinical MP-qMRI application. MP-qMRI advances were reviewed over the past 2 decades, with substantial progress observed toward accelerating image acquisition and increasing mapping accuracy. Areas that need further investigation and refinement are identified as follows: (a) the biologic underpinnings of qMRI parameter values and their changes with age and/or disease and (b) the theoretical limitations implicitly built into most qMRI mapping algorithms that do not distinguish between the different spatial scales of voxels versus spin packets, the central physical object of the Bloch theory. With rapidly improving image processing techniques and continuous advances in computer hardware, MP-qMRI has the potential for implementation in a wide range of clinical applications. Currently, three emerging MP-qMRI applications are synthetic MRI, macrostructural qMRI, and microstructural tissue modeling.
Collapse
Affiliation(s)
- Hernán Jara
- From the Department of Radiology, Boston University, 670 Albany St,
Boston, Mass 02118 (H.J., O.S.); Institute of Neuroscience and Medicine-4,
Forschungszentrum Jülich, Jülich, Germany (E.F., A.M.O.P.,
N.J.S.); Department of Radiology, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, Mass (D.C.A.); and Physical Measurement Laboratory,
National Institute of Standards and Technology, Boulder, Colo (K.E.K.)
| | - Osamu Sakai
- From the Department of Radiology, Boston University, 670 Albany St,
Boston, Mass 02118 (H.J., O.S.); Institute of Neuroscience and Medicine-4,
Forschungszentrum Jülich, Jülich, Germany (E.F., A.M.O.P.,
N.J.S.); Department of Radiology, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, Mass (D.C.A.); and Physical Measurement Laboratory,
National Institute of Standards and Technology, Boulder, Colo (K.E.K.)
| | - Ezequiel Farrher
- From the Department of Radiology, Boston University, 670 Albany St,
Boston, Mass 02118 (H.J., O.S.); Institute of Neuroscience and Medicine-4,
Forschungszentrum Jülich, Jülich, Germany (E.F., A.M.O.P.,
N.J.S.); Department of Radiology, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, Mass (D.C.A.); and Physical Measurement Laboratory,
National Institute of Standards and Technology, Boulder, Colo (K.E.K.)
| | - Ana-Maria Oros-Peusquens
- From the Department of Radiology, Boston University, 670 Albany St,
Boston, Mass 02118 (H.J., O.S.); Institute of Neuroscience and Medicine-4,
Forschungszentrum Jülich, Jülich, Germany (E.F., A.M.O.P.,
N.J.S.); Department of Radiology, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, Mass (D.C.A.); and Physical Measurement Laboratory,
National Institute of Standards and Technology, Boulder, Colo (K.E.K.)
| | - N. Jon Shah
- From the Department of Radiology, Boston University, 670 Albany St,
Boston, Mass 02118 (H.J., O.S.); Institute of Neuroscience and Medicine-4,
Forschungszentrum Jülich, Jülich, Germany (E.F., A.M.O.P.,
N.J.S.); Department of Radiology, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, Mass (D.C.A.); and Physical Measurement Laboratory,
National Institute of Standards and Technology, Boulder, Colo (K.E.K.)
| | - David C. Alsop
- From the Department of Radiology, Boston University, 670 Albany St,
Boston, Mass 02118 (H.J., O.S.); Institute of Neuroscience and Medicine-4,
Forschungszentrum Jülich, Jülich, Germany (E.F., A.M.O.P.,
N.J.S.); Department of Radiology, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, Mass (D.C.A.); and Physical Measurement Laboratory,
National Institute of Standards and Technology, Boulder, Colo (K.E.K.)
| | - Kathryn E. Keenan
- From the Department of Radiology, Boston University, 670 Albany St,
Boston, Mass 02118 (H.J., O.S.); Institute of Neuroscience and Medicine-4,
Forschungszentrum Jülich, Jülich, Germany (E.F., A.M.O.P.,
N.J.S.); Department of Radiology, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, Mass (D.C.A.); and Physical Measurement Laboratory,
National Institute of Standards and Technology, Boulder, Colo (K.E.K.)
| |
Collapse
|
29
|
Hubbard Cristinacce PL, Keaveney S, Aboagye EO, Hall MG, Little RA, O'Connor JPB, Parker GJM, Waterton JC, Winfield JM, Jauregui-Osoro M. Clinical translation of quantitative magnetic resonance imaging biomarkers - An overview and gap analysis of current practice. Phys Med 2022; 101:165-182. [PMID: 36055125 DOI: 10.1016/j.ejmp.2022.08.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 08/05/2022] [Accepted: 08/17/2022] [Indexed: 10/14/2022] Open
Abstract
PURPOSE This overview of the current landscape of quantitative magnetic resonance imaging biomarkers (qMR IBs) aims to support the standardisation of academic IBs to assist their translation to clinical practice. METHODS We used three complementary approaches to investigate qMR IB use and quality management practices within the UK: 1) a literature search of qMR and quality management terms during 2011-2015 and 2016-2020; 2) a database search for clinical research studies using qMR IBs during 2016-2020; and 3) a survey to ascertain the current availability and quality management practices for clinical MRI scanners and associated equipment at research institutions across the UK. RESULTS The analysis showed increased use of all qMR methods between the periods 2011-2015 and 2016-2020 and diffusion-tensor MRI and volumetry to be popular methods. However, the "translation ratio" of journal articles to clinical research studies was higher for qMR methods that have evidence of clinical translation via a commercial route, such as fat fraction and T2 mapping. The number of journal articles citing quality management terms doubled between the periods 2011-2015 and 2016-2020; although, its proportion relative to all journal articles only increased by 3.0%. The survey suggested that quality assurance (QA) and quality control (QC) of data acquisition procedures are under-reported in the literature and that QA/QC of acquired data/data analysis are under-developed and lack consistency between institutions. CONCLUSIONS We summarise current attempts to standardise and translate qMR IBs, and conclude by outlining the ideal quality management practices and providing a gap analysis between current practice and a metrological standard.
Collapse
Affiliation(s)
| | - Sam Keaveney
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey SM2 5PT, UK; Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Eric O Aboagye
- Department of Surgery & Cancer, Division of Cancer, Imperial College London, W12 0NN London, UK
| | - Matt G Hall
- National Physical Laboratory, Hampton Road, Teddington TW11 0LW, UK
| | - Ross A Little
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PT, UK
| | - James P B O'Connor
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PT, UK; Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Geoff J M Parker
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, 90 High Holborn, London WC1V 6LJ, UK; Bioxydyn Ltd, Manchester M15 6SZ, UK
| | - John C Waterton
- Bioxydyn Ltd, Manchester M15 6SZ, UK; Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester M13 9PT, UK
| | - Jessica M Winfield
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey SM2 5PT, UK; Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Maite Jauregui-Osoro
- Department of Surgery & Cancer, Division of Cancer, Imperial College London, W12 0NN London, UK
| |
Collapse
|
30
|
Benjamini D, Priemer DS, Perl DP, Brody DL, Basser PJ. Mapping astrogliosis in the individual human brain using multidimensional MRI. Brain 2022; 146:1212-1226. [PMID: 35953450 PMCID: PMC9976979 DOI: 10.1093/brain/awac298] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 06/13/2022] [Accepted: 08/06/2022] [Indexed: 11/14/2022] Open
Abstract
There are currently no non-invasive imaging methods available for astrogliosis assessment or mapping in the central nervous system despite its essential role in the response to many disease states, such as infarcts, neurodegenerative conditions, traumatic brain injury and infection. Multidimensional MRI is an increasingly employed imaging modality that maximizes the amount of encoded chemical and microstructural information by probing relaxation (T1 and T2) and diffusion mechanisms simultaneously. Here, we harness the exquisite sensitivity of this imagining modality to derive a signature of astrogliosis and disentangle it from normative brain at the individual level using machine learning. We investigated ex vivo cerebral cortical tissue specimens derived from seven subjects who sustained blast-induced injuries, which resulted in scar-border forming astrogliosis without being accompanied by other types of neuropathological abnormality, and from seven control brain donors. By performing a combined post-mortem radiology and histopathology correlation study we found that astrogliosis induces microstructural and chemical changes that are robustly detected with multidimensional MRI, and which can be attributed to astrogliosis because no axonal damage, demyelination or tauopathy were histologically observed in any of the cases in the study. Importantly, we showed that no one-dimensional T1, T2 or diffusion MRI measurement can disentangle the microscopic alterations caused by this neuropathology. Based on these findings, we developed a within-subject anomaly detection procedure that generates MRI-based astrogliosis biomarker maps ex vivo, which were significantly and strongly correlated with co-registered histological images of increased glial fibrillary acidic protein deposition (r = 0.856, P < 0.0001; r = 0.789, P < 0.0001; r = 0.793, P < 0.0001, for diffusion-T2, diffusion-T1 and T1-T2 multidimensional data sets, respectively). Our findings elucidate the underpinning of MRI signal response from astrogliosis, and the demonstrated high spatial sensitivity and specificity in detecting reactive astrocytes at the individual level, and if reproduced in vivo, will significantly impact neuroimaging studies of injury, disease, repair and aging, in which astrogliosis has so far been an invisible process radiologically.
Collapse
Affiliation(s)
- Dan Benjamini
- Correspondence to: Dan Benjamini, PhD National Institutes of Health (NIH), 251 Bayview Blvd., Baltimore, MD 21224, USA E-mail:
| | - David S Priemer
- Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University, Bethesda, MD 20814, USA,The Department of Defense/Uniformed Services, University Brain Tissue Repository, Bethesda, MD 20814, USA,The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD 20817, USA
| | - Daniel P Perl
- Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University, Bethesda, MD 20814, USA,The Department of Defense/Uniformed Services, University Brain Tissue Repository, Bethesda, MD 20814, USA
| | - David L Brody
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA,Department of Neurology, F. Edward Hébert School of Medicine, Uniformed Services University, Bethesda, MD 20814, USA,Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD 20892, USA
| | - Peter J Basser
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20891, USA,Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
| |
Collapse
|
31
|
Bouhrara M, Hutter J, Benjamini D. Editorial: Capturing Biological Complexity and Heterogeneity Using Multidimensional MRI. FRONTIERS IN PHYSICS 2022; 10:950928. [PMID: 38031630 PMCID: PMC10686314 DOI: 10.3389/fphy.2022.950928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Affiliation(s)
- Mustapha Bouhrara
- Magnetic Resonance Physics of Aging and Dementia Unit, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States
| | - Jana Hutter
- Centre for the Developing Brain, King’s College London, London, United Kingdom
- Biomedical Engineering Department, King’s College London, London, United Kingdom
| | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States
| |
Collapse
|
32
|
Bogusz F, Pieciak T, Afzali M, Pizzolato M. Diffusion-relaxation scattered MR signal representation in a multi-parametric sequence. Magn Reson Imaging 2022; 91:52-61. [PMID: 35561868 DOI: 10.1016/j.mri.2022.05.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 05/03/2022] [Accepted: 05/05/2022] [Indexed: 11/26/2022]
Abstract
This work focuses on obtaining a more general diffusion magnetic resonance imaging (MRI) signal representation that accounts for a longitudinal T1 and transverse T2⋆ relaxations while at the same time integrating directional diffusion in the context of scattered multi-parametric acquisitions, where only a few diffusion gradient directions and b-values are available for each pair of echo and inversion times. The method is based on the three-dimensional simple harmonic oscillator-based reconstruction and estimation (SHORE) representation of the diffusion signal, which enables the estimation of the orientation distribution function and the retrieval of various quantitative indices such as the generalized fractional anisotropy or the return-to-the-origin probability while simultaneously resolving for T1 and T2⋆ relaxation times. Our technique, the Relax-SHORE, has been tested on both in silico and in vivo diffusion-relaxation scattered MR data. The results show that Relax-SHORE is accurate in the context of scattered acquisitions while guaranteeing flexibility in the diffusion signal representation from multi-parametric sequences.
Collapse
Affiliation(s)
- Fabian Bogusz
- AGH University of Science and Technology, Kraków, Poland.
| | - Tomasz Pieciak
- AGH University of Science and Technology, Kraków, Poland; LPI, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Maryam Afzali
- Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM), Leeds, United Kingdom; Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Marco Pizzolato
- Department of applied mathematics and computer science, Technical University of Denmark, Kongens Lyngby, Denmark; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
| |
Collapse
|
33
|
Brabec J, Durmo F, Szczepankiewicz F, Brynolfsson P, Lampinen B, Rydelius A, Knutsson L, Westin CF, Sundgren PC, Nilsson M. Separating Glioma Hyperintensities From White Matter by Diffusion-Weighted Imaging With Spherical Tensor Encoding. Front Neurosci 2022; 16:842242. [PMID: 35527815 PMCID: PMC9069143 DOI: 10.3389/fnins.2022.842242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 03/01/2022] [Indexed: 11/13/2022] Open
Abstract
Background Tumor-related hyperintensities in high b-value diffusion-weighted imaging (DWI) are radiologically important in the workup of gliomas. However, the white matter may also appear as hyperintense, which may conflate interpretation. Purpose To investigate whether DWI with spherical b-tensor encoding (STE) can be used to suppress white matter and enhance the conspicuity of glioma hyperintensities unrelated to white matter. Materials and Methods Twenty-five patients with a glioma tumor and at least one pathology-related hyperintensity on DWI underwent conventional MRI at 3 T. The DWI was performed both with linear and spherical tensor encoding (LTE-DWI and STE-DWI). The LTE-DWI here refers to the DWI obtained with conventional diffusion encoding and averaged across diffusion-encoding directions. Retrospectively, the differences in contrast between LTE-DWI and STE-DWI, obtained at a b-value of 2,000 s/mm2, were evaluated by comparing hyperintensities and contralateral normal-appearing white matter (NAWM) both visually and quantitatively in terms of the signal intensity ratio (SIR) and contrast-to-noise ratio efficiency (CNReff). Results The spherical tensor encoding DWI was more effective than LTE-DWI at suppressing signals from white matter and improved conspicuity of pathology-related hyperintensities. The median SIR improved in all cases and on average by 28%. The median (interquartile range) SIR was 1.9 (1.6 – 2.1) for STE and 1.4 (1.3 – 1.7) for LTE, with a significant difference of 0.4 (0.3 –0.5) (p < 10–4, paired U-test). In 40% of the patients, the SIR was above 2 for STE-DWI, but with LTE-DWI, the SIR was below 2 for all patients. The CNReff of STE-DWI was significantly higher than of LTE-DWI: 2.5 (2 – 3.5) vs. 2.3 (1.7 – 3.1), with a significant difference of 0.4 (−0.1 –0.6) (p < 10–3, paired U-test). The STE improved CNReff in 70% of the cases. We illustrate the benefits of STE-DWI in three patients, where STE-DWI may facilitate an improved radiological description of tumor-related hyperintensity, including one case that could have been missed out if only LTE-DWI was inspected. Conclusion The contrast mechanism of high b-value STE-DWI results in a stronger suppression of white matter than conventional LTE-DWI, and may, therefore, be more sensitive and specific for assessment of glioma tumors and DWI-hyperintensities.
Collapse
Affiliation(s)
- Jan Brabec
- Medical Radiation Physics, Lund University, Lund, Sweden
- *Correspondence: Jan Brabec,
| | - Faris Durmo
- Diagnostic Radiology, Lund University, Lund, Sweden
| | - Filip Szczepankiewicz
- Diagnostic Radiology, Lund University, Lund, Sweden
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Patrik Brynolfsson
- Division of Medical Radiation Physics, Department of Translational Medicine, Lund University, Lund, Sweden
| | - Björn Lampinen
- Medical Radiation Physics, Lund University, Lund, Sweden
| | - Anna Rydelius
- Department of Neurology, Lund University, Lund, Sweden
| | - Linda Knutsson
- Medical Radiation Physics, Lund University, Lund, Sweden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Pia C. Sundgren
- Diagnostic Radiology, Lund University, Lund, Sweden
- Lund University Bioimaging Center, Lund University, Lund, Sweden
- Department of Imaging and Physiology, Skåne University Hospital, Lund University, Lund, Sweden
| | | |
Collapse
|
34
|
Improved diffusion parameter estimation by incorporating T 2 relaxation properties into the DKI-FWE model. Neuroimage 2022; 256:119219. [PMID: 35447354 DOI: 10.1016/j.neuroimage.2022.119219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 04/08/2022] [Accepted: 04/14/2022] [Indexed: 11/23/2022] Open
Abstract
The free water elimination (FWE) model and its kurtosis variant (DKI-FWE) can separate tissue and free water signal contributions, thus providing tissue-specific diffusional information. However, a downside of these models is that the associated parameter estimation problem is ill-conditioned, necessitating the use of advanced estimation techniques that can potentially bias the parameter estimates. In this work, we propose the T2-DKI-FWE model that exploits the T2 relaxation properties of both compartments, thereby better conditioning the parameter estimation problem and providing, at the same time, an additional potential biomarker (the T2 of tissue). In our approach, the T2 of tissue is estimated as an unknown parameter, whereas the T2 of free water is assumed known a priori and fixed to a literature value (1573 ms). First, the error propagation of an erroneous assumption on the T2 of free water is studied. Next, the improved conditioning of T2-DKI-FWE compared to DKI-FWE is illustrated using the Cramér-Rao lower bound matrix. Finally, the performance of the T2-DKI-FWE model is compared to that of the DKI-FWE and T2-DKI models on both simulated and real datasets. The error due to a biased approximation of the T2 of free water was found to be relatively small in various diffusion metrics and for a broad range of erroneous assumptions on its underlying ground truth value. Compared to DKI-FWE, using the T2-DKI-FWE model is beneficial for the identifiability of the model parameters. Our results suggest that the T2-DKI-FWE model can achieve precise and accurate diffusion parameter estimates, through effective reduction of free water partial volume effects and by using a standard nonlinear least squares approach. In conclusion, incorporating T2 relaxation properties into the DKI-FWE model improves the conditioning of the model fitting, while only requiring an acquisition scheme with at least two different echo times.
Collapse
|
35
|
Dong Z, Wang F, Wald L, Setsompop K. SNR
‐efficient distortion‐free diffusion relaxometry imaging using accelerated echo‐train shifted echo‐planar time‐resolving imaging (
ACE‐EPTI
). Magn Reson Med 2022; 88:164-179. [DOI: 10.1002/mrm.29198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 01/23/2022] [Accepted: 01/24/2022] [Indexed: 11/08/2022]
Affiliation(s)
- Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown Massachusetts USA
- Department of Electrical Engineering and Computer Science MIT Cambridge Massachusetts USA
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown Massachusetts USA
- Harvard‐MIT Health Sciences and Technology, MIT Cambridge Massachusetts USA
| | - Lawrence Wald
- Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown Massachusetts USA
- Harvard‐MIT Health Sciences and Technology, MIT Cambridge Massachusetts USA
| | - Kawin Setsompop
- Department of Radiology Stanford University Stanford California USA
- Department of Electrical Engineering Stanford University Stanford California USA
| |
Collapse
|
36
|
Ji Y, Hoge WS, Gagoski B, Westin CF, Rathi Y, Ning L. Accelerating joint relaxation-diffusion MRI by integrating time division multiplexing and simultaneous multi-slice (TDM-SMS) strategies. Magn Reson Med 2022; 87:2697-2709. [PMID: 35092081 DOI: 10.1002/mrm.29160] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 12/01/2021] [Accepted: 12/27/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE To accelerate the acquisition of relaxation-diffusion imaging by integrating time-division multiplexing (TDM) with simultaneous multi-slice (SMS) for EPI and evaluate imaging quality and diffusion measures. METHODS The time-division multiplexing (TDM) technique and SMS method were integrated to achieve a high slice-acceleration (e.g., 6×) factor for acquiring relaxation-diffusion MRI. Two variants of the sequence, referred to as TDM3e-SMS and TDM2s-SMS, were developed to simultaneously acquire slice groups with three distinct TEs and two slice groups with the same TE, respectively. Both sequences were evaluated on a 3T scanner with in vivo human brains and compared with standard single-band (SB) -EPI and SMS-EPI using diffusion measures and tractography results. RESULTS Experimental results showed that the TDM3e-SMS sequence with total slice acceleration of 6 (multiplexing factor (MP) = 3 × multi-band factor (MB) = 2) provided similar image intensity and microstructure measures compared to standard SMS-EPI with MB = 2, and yielded less bias in intensity compared to standard SMS-EPI with MB = 4. The three sequences showed a similar positive correlation between TE and mean kurtosis (MK) and a negative correlation between TE and mean diffusivity (MD) in white matter. Multi-fiber tractography also shows consistency of results in TE-dependent measures between different sequences. The TDM2s-SMS sequence (MP = 2, MB = 2) also provided imaging measures similar to standard SMS-EPI sequences (MB = 2) for single-TE diffusion imaging. CONCLUSIONS The TDM-SMS sequence can provide additional 2× to 3× acceleration to SMS without degrading imaging quality. With the significant reduction in scan time, TDM-SMS makes joint relaxation-diffusion MRI a feasible technique in neuroimaging research to investigate new markers of brain disorders.
Collapse
Affiliation(s)
- Yang Ji
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - W Scott Hoge
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Borjan Gagoski
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Carl-Fredrik Westin
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Lipeng Ning
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
37
|
Silletta EV, Velasco MI, Monti GA, Acosta RH. Comparison of experimental times in T 1-D and D-T 2 correlation experiments in single-sided NMR. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 334:107112. [PMID: 34864390 DOI: 10.1016/j.jmr.2021.107112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 11/16/2021] [Accepted: 11/22/2021] [Indexed: 06/13/2023]
Abstract
Diffusion-relaxation correlation experiments in nuclear magnetic resonance are a powerful technique for the characterization of fluid dynamics in confined geometries or soft matter, in which relaxation may be either spin-spin (T2) or spin-lattice (T1). The general approach is to acquire a set of bidimensional data in which diffusion is codified by the evolution of the magnetization with either Hahn or stimulated echoes (STE) in the presence of a constant magnetic field gradient. While T2 is codified by a Carr-Purcell-Meiboom-Gil (CPMG) sequence, T1 is either encoded by saturation or inversion-recovery methods. In this work, we analyse the measurement time of diffusion-relaxation times in single-sided NMR and show that T1-D acquisition is always shorter than D-T2. Depending on the hardware characteristics, this time reduction can be up to an order of magnitude. We present analytical calculations and examples in model porous media saturated with water and in a dairy product.
Collapse
Affiliation(s)
- Emilia V Silletta
- Universidad Nacional de Córdoba, Facultad de Matemática, Astronomía, Física y Computación, Córdoba, Argentina; CONICET, Instituto de Física Enrique Gaviola (IFEG), Córdoba, Argentina
| | - Manuel I Velasco
- Universidad Nacional de Córdoba, Facultad de Matemática, Astronomía, Física y Computación, Córdoba, Argentina; CONICET, Instituto de Física Enrique Gaviola (IFEG), Córdoba, Argentina
| | - Gustavo A Monti
- Universidad Nacional de Córdoba, Facultad de Matemática, Astronomía, Física y Computación, Córdoba, Argentina; CONICET, Instituto de Física Enrique Gaviola (IFEG), Córdoba, Argentina
| | - Rodolfo H Acosta
- Universidad Nacional de Córdoba, Facultad de Matemática, Astronomía, Física y Computación, Córdoba, Argentina; CONICET, Instituto de Física Enrique Gaviola (IFEG), Córdoba, Argentina.
| |
Collapse
|
38
|
Slator PJ, Palombo M, Miller KL, Westin C, Laun F, Kim D, Haldar JP, Benjamini D, Lemberskiy G, de Almeida Martins JP, Hutter J. Combined diffusion-relaxometry microstructure imaging: Current status and future prospects. Magn Reson Med 2021; 86:2987-3011. [PMID: 34411331 PMCID: PMC8568657 DOI: 10.1002/mrm.28963] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 06/25/2021] [Accepted: 07/20/2021] [Indexed: 12/15/2022]
Abstract
Microstructure imaging seeks to noninvasively measure and map microscopic tissue features by pairing mathematical modeling with tailored MRI protocols. This article reviews an emerging paradigm that has the potential to provide a more detailed assessment of tissue microstructure-combined diffusion-relaxometry imaging. Combined diffusion-relaxometry acquisitions vary multiple MR contrast encodings-such as b-value, gradient direction, inversion time, and echo time-in a multidimensional acquisition space. When paired with suitable analysis techniques, this enables quantification of correlations and coupling between multiple MR parameters-such as diffusivity, T 1 , T 2 , and T 2 ∗ . This opens the possibility of disentangling multiple tissue compartments (within voxels) that are indistinguishable with single-contrast scans, enabling a new generation of microstructural maps with improved biological sensitivity and specificity.
Collapse
Affiliation(s)
- Paddy J. Slator
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Marco Palombo
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Carl‐Fredrik Westin
- Department of RadiologyBrigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA
| | - Frederik Laun
- Institute of RadiologyUniversity Hospital ErlangenFriedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Daeun Kim
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
- Signal and Image Processing InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Justin P. Haldar
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
- Signal and Image Processing InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Dan Benjamini
- The Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBethesdaMDUSA
- The Center for Neuroscience and Regenerative MedicineUniformed Service University of the Health SciencesBethesdaMDUSA
| | | | - Joao P. de Almeida Martins
- Division of Physical Chemistry, Department of ChemistryLund UniversityLundSweden
- Department of Radiology and Nuclear MedicineSt. Olav’s University HospitalTrondheimNorway
| | - Jana Hutter
- Centre for Biomedical EngineeringSchool of Biomedical Engineering and ImagingKing’s College LondonLondonUK
- Centre for the Developing BrainSchool of Biomedical Engineering and ImagingKing’s College LondonLondonUK
| |
Collapse
|
39
|
Benjamini D, Bouhrara M, Komlosh ME, Iacono D, Perl DP, Brody DL, Basser PJ. Multidimensional MRI for Characterization of Subtle Axonal Injury Accelerated Using an Adaptive Nonlocal Multispectral Filter. FRONTIERS IN PHYSICS 2021; 9:737374. [PMID: 37408700 PMCID: PMC10321473 DOI: 10.3389/fphy.2021.737374] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/07/2023]
Abstract
Multidimensional MRI is an emerging approach that simultaneously encodes water relaxation (T1 and T2) and mobility (diffusion) and replaces voxel-averaged values with subvoxel distributions of those MR properties. While conventional (i.e., voxel-averaged) MRI methods cannot adequately quantify the microscopic heterogeneity of biological tissue, using subvoxel information allows to selectively map a specific T1-T2-diffusion spectral range that corresponds to a group of tissue elements. The major obstacle to the adoption of rich, multidimensional MRI protocols for diagnostic or monitoring purposes is the prolonged scan time. Our main goal in the present study is to evaluate the performance of a nonlocal estimation of multispectral magnitudes (NESMA) filter on reduced datasets to limit the total acquisition time required for reliable multidimensional MRI characterization of the brain. Here we focused and reprocessed results from a recent study that identified potential imaging biomarkers of axonal injury pathology from the joint analysis of multidimensional MRI, in particular voxelwise T1-T2 and diffusion-T2 spectra in human Corpus Callosum, and histopathological data. We tested the performance of NESMA and its effect on the accuracy of the injury biomarker maps, relative to the co-registered histological reference. Noise reduction improved the accuracy of the resulting injury biomarker maps, while permitting data reduction of 35.7 and 59.6% from the full dataset for T1-T2 and diffusion-T2 cases, respectively. As successful clinical proof-of-concept applications of multidimensional MRI are continuously being introduced, reliable and robust noise removal and consequent acquisition acceleration would advance the field towards clinically-feasible diagnostic multidimensional MRI protocols.
Collapse
Affiliation(s)
- Dan Benjamini
- Section on Quantitative Imaging and Tissue Sciences, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States
- The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, United States
| | - Mustapha Bouhrara
- Magnetic Resonance Physics of Aging and Dementia Unit, National Institute of Aging, National Institutes of Health, Baltimore, MD, United States
| | - Michal E. Komlosh
- Section on Quantitative Imaging and Tissue Sciences, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States
- The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, United States
| | - Diego Iacono
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States
- The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, United States
- Brain Tissue Repository and Neuropathology Program, Uniformed Services University (USU), Bethesda, MD, United States
- Department of Neurology, F. Edward Hébert School of Medicine, Uniformed Services University, Bethesda, MD, United States
- Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University, Bethesda, MD, United States
- Department of Anatomy, Physiology and Genetics, F. Edward Hébert School of Medicine, Uniformed Services University, Bethesda, MD, United States
- Neurodegeneration Disorders Clinic, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Daniel P. Perl
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States
- Brain Tissue Repository and Neuropathology Program, Uniformed Services University (USU), Bethesda, MD, United States
- Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University, Bethesda, MD, United States
| | - David L. Brody
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Peter J. Basser
- Section on Quantitative Imaging and Tissue Sciences, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States
| |
Collapse
|