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Afrough A, Mokhtari R, Feilberg KL. Simple MATLAB and Python scripts for multi-exponential analysis. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2024; 62:698-711. [PMID: 38813596 DOI: 10.1002/mrc.5453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 03/21/2024] [Accepted: 04/22/2024] [Indexed: 05/31/2024]
Abstract
Multi-exponential decay is prevalent in magnetic resonance spectroscopy, relaxation, and imaging. This paper describes simple MATLAB and Python functions and scripts for regularized multi-exponential analysis methods for 1D and 2D data and example test problems and experiments. Regularized least-squares solutions provide production-quality outputs with robust stopping rules in ~5 and ~20 lines of code for 1D and 2D inversions, respectively. The software provides an open-architecture simple solution for transforming exponential decay data to the distribution of their decay lifetimes. Examples from magnetic resonance relaxation of a complex fluid, a Danish North Sea crude oil, and fluid mixtures in porous materials-brine/crude oil mixture in North Sea reservoir chalk-are presented. Developed codes may be incorporated in other software or directly used by other researchers, in magnetic resonance relaxation, diffusion, and imaging or other physical phenomena that require multi-exponential analysis.
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Affiliation(s)
- Armin Afrough
- Danish Offshore Technology Centre, Technical University of Denmark, Kongens Lyngby, Denmark
- Interdisciplinary Nanoscience Center, Aarhus University, Aarhus, Denmark
| | - Rasoul Mokhtari
- Danish Offshore Technology Centre, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Karen L Feilberg
- Danish Offshore Technology Centre, Technical University of Denmark, Kongens Lyngby, Denmark
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Hu W, Dai Y, Liu F, Yang T, Wang Y, Shen Y, Zhou W, Wu D, Gu L, Zhang M, Zhou Y. Assessing renal interstitial fibrosis using compartmental, non-compartmental, and model-free diffusion MRI approaches. Insights Imaging 2024; 15:156. [PMID: 38900336 PMCID: PMC11189852 DOI: 10.1186/s13244-024-01736-2] [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: 12/19/2023] [Accepted: 06/02/2024] [Indexed: 06/21/2024] Open
Abstract
OBJECTIVE To assess renal interstitial fibrosis (IF) using diffusion MRI approaches, and explore whether corticomedullary difference (CMD) of diffusion parameters, combination among MRI parameters, or combination with estimated glomerular filtration rate (eGFR) benefit IF evaluation. METHODS Forty-two patients with chronic kidney disease were included, undergoing MRI examinations. MRI parameters from apparent diffusion coefficient (ADC), intra-voxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and diffusion-relaxation correlated spectrum imaging (DR-CSI) were obtained both for renal cortex and medulla. CMD of these parameters was calculated. Pathological IF scores (1-3) were obtained by biopsy. Patients were divided into mild (IF = 1, n = 23) and moderate-severe fibrosis (IF = 2-3, n = 19) groups. Group comparisons for MRI parameters were performed. Diagnostic performances were assessed by the receiver operator's curve analysis for discriminating mild from moderate-severe IF patients. RESULTS Significant inter-group differences existed for cortical ADC, IVIM-D, IVIM-f, DKI-MD, DR-CSI VB, and DR-CSI VC. Significant inter-group differences existed in ΔADC, ΔMD, ΔVB, ΔVC, ΔQB, and ΔQC. Among the cortical MRI parameters, VB displayed the highest AUC = 0.849, while ADC, f, and MD also showed AUC > 0.8. After combining cortical value and CMD, the diagnostic performances of the MRI parameters were slightly improved except for IVIM-D. Combining VB with f brings the best performance (AUC = 0.903) among MRI bi-variant models. A combination of cortical VB, ΔADC, and eGFR brought obvious improvement in diagnostic performance (AUC 0.963 vs 0.879, specificity 0.826 vs 0.896, and sensitivity 1.000 vs 0.842) than eGFR alone. CONCLUSION Our study shows promising results for the assessment of renal IF using diffusion MRI approaches. CRITICAL RELEVANCE STATEMENT Our study explores the non-invasive assessment of renal IF, an independent and effective predictor of renal outcomes, by comparing and combining diffusion MRI approaches including compartmental, non-compartmental, and model-free approaches. KEY POINTS Significant difference exists for diffusion parameters between mild and moderate-severe IF. Generally, cortical parameters show better performance than corresponding CMD. Bi-variant model lifts the diagnostic performance for assessing IF.
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Affiliation(s)
- Wentao Hu
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yongming Dai
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Fang Liu
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianshu Yang
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yao Wang
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yiwei Shen
- Department of Nephrology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenyan Zhou
- Department of Nephrology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronics Science, East China Normal University, Shanghai, China
| | - Leyi Gu
- Department of Nephrology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Minfang Zhang
- Department of Nephrology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Yan Zhou
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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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] [Grants] [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.
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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.
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Dai Y, Zhu M, Hu W, Wu D, He S, Luo Y, Wei X, Zhou Y, Wu G, Hu P. To characterize small renal cell carcinoma using diffusion relaxation correlation spectroscopic imaging and apparent diffusion coefficient based histogram analysis: a preliminary study. LA RADIOLOGIA MEDICA 2024; 129:834-844. [PMID: 38662246 DOI: 10.1007/s11547-024-01819-6] [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: 07/23/2023] [Accepted: 04/16/2024] [Indexed: 04/26/2024]
Abstract
PURPOSE To study the capability of diffusion-relaxation correlation spectroscopic imaging (DR-CSI) on subtype classification and grade differentiation for small renal cell carcinoma (RCC). Histogram analysis for apparent diffusion coefficient (ADC) was studied for comparison. MATERIALS AND METHODS A total of 61 patients with small RCC (< 4 cm) were included in the retrospective study. MRI data were reviewed, including a multi-b (0-1500 s/mm2) multi-TE (51-200 ms) diffusion weighted imaging (DWI) sequence. Region of interest (ROI) was delineated manually on DWI to include solid tumor. For each patient, a D-T2 spectrum was fitted and segmented into 5 compartments, and the volume fractions VA, VB, VC, VD, VE were obtained. ADC mapping was calculated, and histogram parameters ADC 90th, 10th, median, standard deviation, skewness and kurtosis were obtained. All MRI metrices were compared between clear cell RCC (ccRCC) and non-ccRCC group, and between high-grade and low-grade group. Receiver operator curve analysis was used to assess the corresponding diagnostic performance. RESULTS Significantly higher ADC 90th, ADC 10th and ADC median, and significantly lower DR-CSI VB was found for ccRCC compared to non-ccRCC. Significantly lower ADC 90th, ADC median and significantly higher VB was found for high-grade RCC compared to low-grade. For identifying ccRCC from non-ccRCC, VB showed the highest area under curve (AUC, 0.861) and specificity (0.882). For differentiating high- from low-grade, ADC 90th showed the highest AUC (0.726) and specificity (0.786), while VB also displayed a moderate AUC (0.715). CONCLUSION DR-CSI may offer improved accuracy in subtype identification for small RCC, while do not show better performance for small RCC grading compared to ADC histogram.
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Affiliation(s)
- Yongming Dai
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices & Shanghai Clinical Research and Trial Center, ShanghaiTech University, Shanghai, China
| | - Mengying Zhu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Wentao Hu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Shenyun He
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yuansheng Luo
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaobin Wei
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yan Zhou
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Guangyu Wu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Peng Hu
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices & Shanghai Clinical Research and Trial Center, ShanghaiTech University, Shanghai, China.
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Johnson JTE, Irfanoglu MO, Manninen E, Ross TJ, Yang Y, Laun FB, Martin J, Topgaard D, Benjamini D. In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI. Hum Brain Mapp 2024; 45:e26697. [PMID: 38726888 PMCID: PMC11082920 DOI: 10.1002/hbm.26697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 03/28/2024] [Accepted: 04/12/2024] [Indexed: 05/13/2024] Open
Abstract
Diffusion MRI with free gradient waveforms, combined with simultaneous relaxation encoding, referred to as multidimensional MRI (MD-MRI), offers microstructural specificity in complex biological tissue. This approach delivers intravoxel information about the microstructure, local chemical composition, and importantly, how these properties are coupled within heterogeneous tissue containing multiple microenvironments. Recent theoretical advances incorporated diffusion time dependency and integrated MD-MRI with concepts from oscillating gradients. This framework probes the diffusion frequency,ω $$ \omega $$ , in addition to the diffusion tensor,D $$ \mathbf{D} $$ , and relaxation,R 1 $$ {R}_1 $$ ,R 2 $$ {R}_2 $$ , correlations. AD ω - R 1 - R 2 $$ \mathbf{D}\left(\omega \right)-{R}_1-{R}_2 $$ clinical imaging protocol was then introduced, with limited brain coverage and 3 mm3 voxel size, which hinder brain segmentation and future cohort studies. In this study, we introduce an efficient, sparse in vivo MD-MRI acquisition protocol providing whole brain coverage at 2 mm3 voxel size. We demonstrate its feasibility and robustness using a well-defined phantom and repeated scans of five healthy individuals. Additionally, we test different denoising strategies to address the sparse nature of this protocol, and show that efficient MD-MRI encoding design demands a nuanced denoising approach. The MD-MRI framework provides rich information that allows resolving the diffusion frequency dependence into intravoxel components based on theirD ω - R 1 - R 2 $$ \mathbf{D}\left(\omega \right)-{R}_1-{R}_2 $$ distribution, enabling the creation of microstructure-specific maps in the human brain. Our results encourage the broader adoption and use of this new imaging approach for characterizing healthy and pathological tissues.
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Affiliation(s)
- Jessica T. E. Johnson
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIHBaltimoreMarylandUSA
| | - M. Okan Irfanoglu
- Quantitative Medical Imaging Section, National Institute of Biomedical Imaging and Bioengineering, National Institutes of HealthBethesdaMarylandUSA
| | - Eppu Manninen
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIHBaltimoreMarylandUSA
| | - Thomas J. Ross
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of HealthBaltimoreMarylandUSA
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of HealthBaltimoreMarylandUSA
| | - Frederik B. Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Jan Martin
- Department of ChemistryLund UniversityLundSweden
| | | | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIHBaltimoreMarylandUSA
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Dai Y, Hu W, Wu G, Wu D, Zhu M, Luo Y, Wang J, Zhou Y, Hu P. Grading Clear Cell Renal Cell Carcinoma Grade Using Diffusion Relaxation Correlated MR Spectroscopic Imaging. J Magn Reson Imaging 2024; 59:699-710. [PMID: 37209407 DOI: 10.1002/jmri.28777] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 05/01/2023] [Accepted: 05/02/2023] [Indexed: 05/22/2023] Open
Abstract
BACKGROUND Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC, and accurate grading is crucial for prognosis and treatment selection. Biopsy is the reference standard for grading, but MRI methods can improve and complement the grading procedure. PURPOSE Assess the performance of diffusion relaxation correlation spectroscopic imaging (DR-CSI) in grading ccRCC. STUDY TYPE Prospective. SUBJECTS 79 patients (age: 58.1 +/- 11.5 years; 55 male) with ccRCC confirmed by histopathology (grade 1, 7; grade 2, 45; grade 3, 18; grade 4, 9) following surgery. FIELD STRENGTH/SEQUENCE 3.0 T MRI scanner. DR-CSI with a diffusion-weighted echo-planar imaging sequence and T2-mapping with a multi-echo spin echo sequence. ASSESSMENT DR-CSI results were analyzed for the solid tumor regions of interest using spectrum segmentation with five sub-region volume fraction metrics (VA , VB , VC , VD , and VE ). The regulations for spectrum segmentation were determined based on the D-T2 spectra of distinct macro-components. Tumor size, voxel-wise T2, and apparent diffusion coefficient (ADC) values were obtained. Histopathology assessed tumor grade (G1-G4) for each case. STATISTICAL TESTS One-way ANOVA or Kruskal-Wallis test, Spearman's correlation (coefficient, rho), multivariable logistic regression analysis, receiver operating characteristic curve analysis, and DeLong's test. Significance criteria: P < 0.05. RESULTS Significant differences were found in ADC, T2, DR-CSI VB , and VD among the ccRCC grades. Correlations were found for ccRCC grade to tumor size (rho = 0.419), age (rho = 0.253), VB (rho = 0.553) and VD (rho = -0.378). AUC of VB was slightly larger than ADC in distinguishing low-grade (G1-G2) from high-grade (G3-G4) ccRCC (0.801 vs. 0.762, P = 0.406) and G1 from G2 to G4 (0.796 vs. 0.647, P = 0.175), although not significant. Combining VB , VD , and VE had better diagnostic performance than combining ADC and T2 for differentiating G1 from G2-G4 (AUC: 0.814 vs 0.643). DATA CONCLUSION DR-CSI parameters are correlated with ccRCC grades, and may help to differentiate ccRCC grades. EVIDENCE LEVEL 2 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Yongming Dai
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Wentao Hu
- Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Guangyu Wu
- Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Mengying Zhu
- Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yuansheng Luo
- Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jieying Wang
- Clinical Research Center, Renji hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yan Zhou
- Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Peng Hu
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
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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.
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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
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Slator PJ, Cromb D, Jackson LH, Ho A, Counsell SJ, Story L, Chappell LC, Rutherford M, Hajnal JV, Hutter J, Alexander DC. Non-invasive mapping of human placenta microenvironments throughout pregnancy with diffusion-relaxation MRI. Placenta 2023; 144:29-37. [PMID: 37952367 DOI: 10.1016/j.placenta.2023.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 10/13/2023] [Accepted: 11/01/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION In-vivo measurements of placental structure and function have the potential to improve prediction, diagnosis, and treatment planning for a wide range of pregnancy complications, such as fetal growth restriction and pre-eclampsia, and hence inform clinical decision making, ultimately improving patient outcomes. MRI is emerging as a technique with increased sensitivity to placental structure and function compared to the current clinical standard, ultrasound. METHODS We demonstrate and evaluate a combined diffusion-relaxation MRI acquisition and analysis pipeline on a sizable cohort of 78 normal pregnancies with gestational ages ranging from 15 + 5 to 38 + 4 weeks. Our acquisition comprises a combined T2*-diffusion MRI acquisition sequence - which is simultaneously sensitive to oxygenation, microstructure and microcirculation. We analyse our scans with a data-driven unsupervised machine learning technique, InSpect, that parsimoniously identifies distinct components in the data. RESULTS We identify and map seven potential placental microenvironments and reveal detailed insights into multiple microstructural and microcirculatory features of the placenta, and assess their trends across gestation. DISCUSSION By demonstrating direct observation of micro-scale placental structure and function, and revealing clear trends across pregnancy, our work contributes towards the development of robust imaging biomarkers for pregnancy complications and the ultimate goal of a normative model of placental development.
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Affiliation(s)
- Paddy J Slator
- Cardiff University Brain Research Imaging Centre, School of Psychology, Maindy Road, Cardiff, CF24 4HQ, UK; School of Computer Science and Informatics, Cardiff University, Cardiff, UK; Centre for Medical Image Computing and Department of Computer Science, University College London, London, UK.
| | - Daniel Cromb
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Laurence H Jackson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Alison Ho
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Lisa Story
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Lucy C Chappell
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Mary Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, UK
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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.
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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
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10
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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.
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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
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11
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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.
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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
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12
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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.
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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
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13
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Su CQ, Wang BB, Tang WT, Tao C, Zhao P, Pan MH, Hong XN, Hu WT, Dai YM, Shi HB, Lu SS. Diffusion-relaxation correlation spectrum imaging for predicting tumor consistency and gross total resection in patients with pituitary adenomas: a preliminary study. Eur Radiol 2023; 33:6993-7002. [PMID: 37148353 DOI: 10.1007/s00330-023-09694-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 02/17/2023] [Accepted: 03/09/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVE To evaluate the ability of diffusion-relaxation correlation spectrum imaging (DR-CSI) to predict the consistency and extent of resection (EOR) of pituitary adenomas (PAs). METHODS Forty-four patients with PAs were prospectively enrolled. Tumor consistency was evaluated at surgery as either soft or hard, followed by histological assessment. In vivo DR-CSI was performed and spectra were segmented following to a peak-based strategy into four compartments, designated A (low ADC), B (mediate ADC, short T2), C (mediate ADC, long T2), and D (high ADC). The corresponding volume fractions ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]) along with the ADC and T2 values were calculated and assessed using univariable analysis for discrimination between hard and soft PAs. Predictors of EOR > 95% were analyzed using logistic regression model and receiver-operating-characteristic analysis. RESULTS Tumor consistency was classified as soft (n = 28) or hard (n = 16). Hard PAs presented higher [Formula: see text] (p = 0.001) and lower [Formula: see text] (p = 0.013) than soft PAs, while no significant difference was found in other parameters. [Formula: see text] significantly correlated with the level of collagen content (r = 0.448, p = 0.002). Knosp grade (odds ratio [OR], 0.299; 95% confidence interval [CI], 0.124-0.716; p = 0.007) and [Formula: see text] (OR, 0.834, per 1% increase; 95% CI, 0.731-0.951; p = 0.007) were independently associated with EOR > 95%. A prediction model based on these variables yielded an AUC of 0.934 (sensitivity, 90.9%; specificity, 90.9%), outperforming the Knosp grade alone (AUC, 0.785; p < 0.05). CONCLUSION DR-CSI may serve as a promising tool to predict the consistency and EOR of PAs. CLINICAL RELEVANCE STATEMENT DR-CSI provides an imaging dimension for characterizing tissue microstructure of PAs and may serve as a promising tool to predict the tumor consistency and extent of resection in patients with PAs. KEY POINTS • DR-CSI provides an imaging dimension for characterizing tissue microstructure of PAs by visualizing the volume fraction and corresponding spatial distribution of four compartments ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]). • [Formula: see text] correlated with the level of collagen content and may be the best DR-CSI parameter for discrimination between hard and soft PAs. • The combination of Knosp grade and [Formula: see text] achieved an AUC of 0.934 for predicting the total or near-total resection, outperforming the Knosp grade alone (AUC, 0.785).
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Affiliation(s)
- Chun-Qiu Su
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guang Zhou Road, Gulou District, Nanjing, 210029, Jiangsu Province, China
| | - Bin-Bin Wang
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu Province, China
| | - Wen-Tian Tang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guang Zhou Road, Gulou District, Nanjing, 210029, Jiangsu Province, China
| | - Chao Tao
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu Province, China
| | - Peng Zhao
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu Province, China
| | - Min-Hong Pan
- Department of Pathology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu Province, China
| | - Xun-Ning Hong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guang Zhou Road, Gulou District, Nanjing, 210029, Jiangsu Province, China
| | - Wen-Tao Hu
- Central Research Institute, MR Collaboration, United Imaging Healthcare, Shanghai, China
| | - Yong-Ming Dai
- Central Research Institute, MR Collaboration, United Imaging Healthcare, Shanghai, China
- School of Biomedical Engineering, Shanghai Tech University, Shanghai, China
| | - Hai-Bin Shi
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guang Zhou Road, Gulou District, Nanjing, 210029, Jiangsu Province, China.
| | - Shan-Shan Lu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guang Zhou Road, Gulou District, Nanjing, 210029, Jiangsu Province, China.
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14
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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.
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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
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15
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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.
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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.
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16
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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.
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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.
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17
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Girometti R, Bertolotto M. Editorial for “Exploration of Interstitial Fibrosis in Chronic Kidney Disease by Diffusion‐Relaxation Correlation Spectrum MR Imaging: A Preliminary Study”. J Magn Reson Imaging 2022. [DOI: 10.1002/jmri.28541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 11/14/2022] [Indexed: 11/27/2022] Open
Affiliation(s)
- Rossano Girometti
- Institute of Radiology, Department of Medicine (DAME) University of Udine, University Hospital S. Maria della Misericordia – Azienda Sanitaria Universitaria Friuli Centrale (ASU FC) Udine Italy
| | - Michele Bertolotto
- Radiology Unit, Department of Medical, Surgical and Health Sciences University of Trieste, University Hospital Cattinara ‐ Azienda Sanitaria Universitaria Giuliano Isontina (ASU GI) Trieste Italy
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18
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Liu F, Hu W, Sun Y, Shen Y, Zhou W, Dai Y, Gu L, Zhang M, Zhou Y. Exploration of Interstitial Fibrosis in Chronic Kidney Disease by Diffusion‐Relaxation Correlation Spectrum
MR
Imaging: A Preliminary Study. J Magn Reson Imaging 2022. [DOI: 10.1002/jmri.28535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 11/23/2022] Open
Affiliation(s)
- Fang Liu
- Department of Radiology Renji Hospital, School of Medicine, Shanghai Jiao Tong University Shanghai China
| | - Wentao Hu
- Central Research Institute, United Imaging Healthcare Shanghai China
| | - Yawen Sun
- Department of Radiology Renji Hospital, School of Medicine, Shanghai Jiao Tong University Shanghai China
| | - Yiwei Shen
- Department of Nephrology Renji Hospital, School of Medicine, Shanghai Jiao Tong University Shanghai China
| | - Wenyan Zhou
- Department of Nephrology Renji Hospital, School of Medicine, Shanghai Jiao Tong University Shanghai China
| | - Yongming Dai
- Central Research Institute, United Imaging Healthcare Shanghai China
| | - Leyi Gu
- Department of Nephrology Renji Hospital, School of Medicine, Shanghai Jiao Tong University Shanghai China
| | - Minfang Zhang
- Department of Nephrology Renji Hospital, School of Medicine, Shanghai Jiao Tong University Shanghai China
| | - Yan Zhou
- Department of Radiology Renji Hospital, School of Medicine, Shanghai Jiao Tong University Shanghai China
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19
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Rosenberg JT, Grant SC, Topgaard D. Nonparametric 5D D-R 2 distribution imaging with single-shot EPI at 21.1 T: Initial results for in vivo rat brain. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 341:107256. [PMID: 35753184 PMCID: PMC9339475 DOI: 10.1016/j.jmr.2022.107256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/27/2022] [Accepted: 06/10/2022] [Indexed: 06/15/2023]
Abstract
In vivo human diffusion MRI is by default performed using single-shot EPI with greater than 50-ms echo times and associated signal loss from transverse relaxation. The individual benefits of the current trends of increasing B0 to boost SNR and employing more advanced signal preparation schemes to improve the specificity for selected microstructural properties eventually may be cancelled by increased relaxation rates at high B0 and echo times with advanced encoding. Here, initial attempts to translate state-of-the-art diffusion-relaxation correlation methods from 3 T to 21.1 T are made to identify hurdles that need to be overcome to fulfill the promises of both high SNR and readily interpretable microstructural information.
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Affiliation(s)
- Jens T Rosenberg
- National High Magnetic Field Laboratory, Florida State University, Tallahassee FL, United States.
| | - Samuel C Grant
- National High Magnetic Field Laboratory, Florida State University, Tallahassee FL, United States; Chemical and Biomedical Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL, United States.
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20
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Stabilization of parameter estimates from multiexponential decay through extension into higher dimensions. Sci Rep 2022; 12:5773. [PMID: 35388008 PMCID: PMC8986819 DOI: 10.1038/s41598-022-08638-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 03/07/2022] [Indexed: 11/23/2022] Open
Abstract
Analysis of multiexponential decay has remained a topic of active research for over 200 years. This attests to the widespread importance of this problem and to the profound difficulties in characterizing the underlying monoexponential decays. Here, we demonstrate the fundamental improvement in stability and conditioning of this classic problem through extension to a second dimension; we present statistical analysis, Monte-Carlo simulations, and experimental magnetic resonance relaxometry data to support this remarkable fact. Our results are readily generalizable to higher dimensions and provide a potential means of circumventing conventional limits on multiexponential parameter estimation.
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21
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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.
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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.
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22
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Nonparametric D-R 1-R 2 distribution MRI of the living human brain. Neuroimage 2021; 245:118753. [PMID: 34852278 DOI: 10.1016/j.neuroimage.2021.118753] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/17/2021] [Accepted: 11/22/2021] [Indexed: 11/23/2022] Open
Abstract
Diffusion-relaxation correlation NMR can simultaneously characterize both the microstructure and the local chemical composition of complex samples that contain multiple populations of water. Recent developments on tensor-valued diffusion encoding and Monte Carlo inversion algorithms have made it possible to transfer diffusion-relaxation correlation NMR from small-bore scanners to clinical MRI systems. Initial studies on clinical MRI systems employed 5D D-R1 and D-R2 correlation to characterize healthy brain in vivo. However, these methods are subject to an inherent bias that originates from not including R2 or R1 in the analysis, respectively. This drawback can be remedied by extending the concept to 6D D-R1-R2 correlation. In this work, we present a sparse acquisition protocol that records all data necessary for in vivo 6D D-R1-R2 correlation MRI across 633 individual measurements within 25 min-a time frame comparable to previous lower-dimensional acquisition protocols. The data were processed with a Monte Carlo inversion algorithm to obtain nonparametric 6D D-R1-R2 distributions. We validated the reproducibility of the method in repeated measurements of healthy volunteers. For a post-therapy glioblastoma case featuring cysts, edema, and partially necrotic remains of tumor, we present representative single-voxel 6D distributions, parameter maps, and artificial contrasts over a wide range of diffusion-, R1-, and R2-weightings based on the rich information contained in the D-R1-R2 distributions.
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23
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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: 41] [Impact Index Per Article: 13.7] [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.
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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
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24
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Telkki VV, Urbańczyk M, Zhivonitko V. Ultrafast methods for relaxation and diffusion. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2021; 126-127:101-120. [PMID: 34852922 DOI: 10.1016/j.pnmrs.2021.07.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 07/07/2021] [Accepted: 07/08/2021] [Indexed: 06/13/2023]
Abstract
Relaxation and diffusion NMR measurements offer an approach to studying rotational and translational motion of molecules non-invasively, and they also provide chemical resolution complementary to NMR spectra. Multidimensional experiments enable the correlation of relaxation and diffusion parameters as well as the observation of molecular exchange phenomena through relaxation or diffusion contrast. This review describes how to accelerate multidimensional relaxation and diffusion measurements significantly through spatial encoding. This so-called ultrafast Laplace NMR approach shortens the experiment time to a fraction and makes even single-scan experiments possible. Single-scan experiments, in turn, significantly facilitate the use of nuclear spin hyperpolarization methods to boost sensitivity. The ultrafast Laplace NMR method is also applicable with low-field, mobile NMR instruments, and it can be exploited in many disciplines. For example, it has been used in studies of the dynamics of fluids in porous materials, identification of intra- and extracellular metabolites in cancer cells, and elucidation of aggregation phenomena in atmospheric surfactant solutions.
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Affiliation(s)
| | - Mateusz Urbańczyk
- NMR Research Unit, University of Oulu, P.O. Box 3000, FIN-90014, Finland; Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
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25
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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.
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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
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26
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Crețu A, Mattea C, Stapf S. Low-field and variable-field NMR relaxation studies of H2O and D2O molecular dynamics in articular cartilage. PLoS One 2021; 16:e0256177. [PMID: 34432832 PMCID: PMC8386884 DOI: 10.1371/journal.pone.0256177] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 07/30/2021] [Indexed: 11/19/2022] Open
Abstract
Osteoarthritis (OA) as the main degenerative disease of articular cartilage in joints is accompanied by structural and compositional changes in the tissue. Degeneration is a consequence of a reduction of the amount of macromolecules, the so-called proteoglycans, and of a corresponding increase in water content, both leading to structural weakening of cartilage. NMR investigations of cartilage generally address only the relaxation properties of water. In this study, two-dimensional (T1-T2) measurements of bovine articular cartilage samples were carried out for different stages of hydration, complemented by molecular exchange with D2O and treatment by trypsin which simulates degeneration by OA. Two signal components were identified in all measurements, characterized by very different T2 which suggests liquid-like and solid-like dynamics. These measurements allow the quantification of separate hydrogen components and their assignment to defined physical pools which had been discussed repeatedly in the literature, i.e. bulk-like water and a combination of protein hydrogens and strongly bound water. The first determination of 2H relaxation dispersion in comparison to 1H dispersion suggests intramolecular interactions as the dominating source for the pronounced magnetic field dependence of the longitudinal relaxation time T1.
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Affiliation(s)
- Andrea Crețu
- Fachgebiet Technische Physik II/Polymerphysik, Institute of Physics, Technische Universität Ilmenau, Germany
| | - Carlos Mattea
- Fachgebiet Technische Physik II/Polymerphysik, Institute of Physics, Technische Universität Ilmenau, Germany
| | - Siegfried Stapf
- Fachgebiet Technische Physik II/Polymerphysik, Institute of Physics, Technische Universität Ilmenau, Germany
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27
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Benjamini D, Iacono D, Komlosh ME, Perl DP, Brody DL, Basser PJ. Diffuse axonal injury has a characteristic multidimensional MRI signature in the human brain. Brain 2021; 144:800-816. [PMID: 33739417 DOI: 10.1093/brain/awaa447] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 09/11/2020] [Accepted: 10/11/2020] [Indexed: 02/01/2023] Open
Abstract
Axonal injury is a major contributor to the clinical symptomatology in patients with traumatic brain injury. Conventional neuroradiological tools, such as CT and MRI, are insensitive to diffuse axonal injury (DAI) caused by trauma. Diffusion tensor MRI parameters may change in DAI lesions; however, the nature of these changes is inconsistent. Multidimensional MRI is an emerging approach that combines T1, T2, and diffusion, and replaces voxel-averaged values with distributions, which allows selective isolation of specific potential abnormal components. By performing a combined post-mortem multidimensional MRI and histopathology study, we aimed to investigate T1-T2-diffusion changes linked to DAI and to define their histopathological correlates. Corpora callosa derived from eight subjects who had sustained traumatic brain injury, and three control brain donors underwent post-mortem ex vivo MRI at 7 T. Multidimensional, diffusion tensor, and quantitative T1 and T2 MRI data were acquired and processed. Following MRI acquisition, slices from the same tissue were tested for amyloid precursor protein (APP) immunoreactivity to define DAI severity. A robust image co-registration method was applied to accurately match MRI-derived parameters and histopathology, after which 12 regions of interest per tissue block were selected based on APP density, but blind to MRI. We identified abnormal multidimensional T1-T2, diffusion-T2, and diffusion-T1 components that are strongly associated with DAI and used them to generate axonal injury images. We found that compared to control white matter, mild and severe DAI lesions contained significantly larger abnormal T1-T2 component (P = 0.005 and P < 0.001, respectively), and significantly larger abnormal diffusion-T2 component (P = 0.005 and P < 0.001, respectively). Furthermore, within patients with traumatic brain injury the multidimensional MRI biomarkers differentiated normal-appearing white matter from mild and severe DAI lesions, with significantly larger abnormal T1-T2 and diffusion-T2 components (P = 0.003 and P < 0.001, respectively, for T1-T2; P = 0.022 and P < 0.001, respectively, for diffusion-T2). Conversely, none of the conventional quantitative MRI parameters were able to differentiate lesions and normal-appearing white matter. Lastly, we found that the abnormal T1-T2, diffusion-T1, and diffusion-T2 components and their axonal damage images were strongly correlated with quantitative APP staining (r = 0.876, P < 0.001; r = 0.727, P < 0.001; and r = 0.743, P < 0.001, respectively), while producing negligible intensities in grey matter and in normal-appearing white matter. These results suggest that multidimensional MRI may provide non-invasive biomarkers for detection of DAI, which is the pathological substrate for neurological disorders ranging from concussion to severe traumatic brain injury.
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Affiliation(s)
- Dan Benjamini
- Section on Quantitative Imaging and Tissue Sciences, The Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.,Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.,The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
| | - Diego Iacono
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.,The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA.,Department of Neurology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, MD, USA.,Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, MD, USA.,Neuroscience Graduate Program, Department of Anatomy, Physiology, and Genetics, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, MD, USA.,Motor Neuron Disorders Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Michal E Komlosh
- Section on Quantitative Imaging and Tissue Sciences, The Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.,Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.,The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
| | - Daniel P Perl
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.,Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, MD, USA
| | - David L Brody
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.,Department of Neurology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, MD, USA.,Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Peter J Basser
- Section on Quantitative Imaging and Tissue Sciences, The Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.,Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
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28
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Reymbaut A, Critchley J, Durighel G, Sprenger T, Sughrue M, Bryskhe K, Topgaard D. Toward nonparametric diffusion- T1 characterization of crossing fibers in the human brain. Magn Reson Med 2021; 85:2815-2827. [PMID: 33301195 PMCID: PMC7898694 DOI: 10.1002/mrm.28604] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 10/26/2020] [Accepted: 10/27/2020] [Indexed: 12/24/2022]
Abstract
PURPOSE To estimate T 1 for each distinct fiber population within voxels containing multiple brain tissue types. METHODS A diffusion- T 1 correlation experiment was carried out in an in vivo human brain using tensor-valued diffusion encoding and multiple repetition times. The acquired data were inverted using a Monte Carlo algorithm that retrieves nonparametric distributions P ( D , R 1 ) of diffusion tensors and longitudinal relaxation rates R 1 = 1 / T 1 . Orientation distribution functions (ODFs) of the highly anisotropic components of P ( D , R 1 ) were defined to visualize orientation-specific diffusion-relaxation properties. Finally, Monte Carlo density-peak clustering (MC-DPC) was performed to quantify fiber-specific features and investigate microstructural differences between white matter fiber bundles. RESULTS Parameter maps corresponding to P ( D , R 1 ) 's statistical descriptors were obtained, exhibiting the expected R 1 contrast between brain tissue types. Our ODFs recovered local orientations consistent with the known anatomy and indicated differences in R 1 between major crossing fiber bundles. These differences, confirmed by MC-DPC, were in qualitative agreement with previous model-based works but seem biased by the limitations of our current experimental setup. CONCLUSIONS Our Monte Carlo framework enables the nonparametric estimation of fiber-specific diffusion- T 1 features, thereby showing potential for characterizing developmental or pathological changes in T 1 within a given fiber bundle, and for investigating interbundle T 1 differences.
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Affiliation(s)
- Alexis Reymbaut
- Department of Physical ChemistryLund UniversityLundSweden
- Random Walk Imaging ABLundSweden
| | | | | | - Tim Sprenger
- Karolinska InstituteStockholmSweden
- GE HealthcareStockholmSweden
| | | | | | - Daniel Topgaard
- Department of Physical ChemistryLund UniversityLundSweden
- Random Walk Imaging ABLundSweden
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29
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Slator PJ, Hutter J, Marinescu RV, Palombo M, Jackson LH, Ho A, Chappell LC, Rutherford M, Hajnal JV, Alexander DC. Data-Driven multi-Contrast spectral microstructure imaging with InSpect: INtegrated SPECTral component estimation and mapping. Med Image Anal 2021; 71:102045. [PMID: 33934005 PMCID: PMC8543043 DOI: 10.1016/j.media.2021.102045] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 02/08/2021] [Accepted: 03/16/2021] [Indexed: 11/19/2022]
Abstract
Unsupervised learning technique for spectroscopic analysis of quantitative MRI. Shares information across voxels to improve estimation of multi-dimensional or single-dimensional spectra. Spectral maps are dramatically improved compared to existing approaches. Can potentially identify and map tissue environments; in placental diffusion-relaxometry MRI we demonstrate that it identifies components that correspond to distinct tissue types.
We introduce and demonstrate an unsupervised machine learning technique for spectroscopic analysis of quantitative MRI experiments. Our algorithm supports estimation of one-dimensional spectra from single-contrast data, and multidimensional correlation spectra from simultaneous multi-contrast data. These spectrum-based approaches allow model-free investigation of tissue properties, but require regularised inversion of a Laplace transform or Fredholm integral, which is an ill-posed calculation. Here we present a method that addresses this limitation in a data-driven way. The algorithm simultaneously estimates a canonical basis of spectral components and voxelwise maps of their weightings, thereby pooling information across whole images to regularise the ill-posed problem. We show in simulations that our algorithm substantially outperforms current voxelwise spectral approaches. We demonstrate the method on multi-contrast diffusion-relaxometry placental MRI scans, revealing anatomically-relevant sub-structures, and identifying dysfunctional placentas. Our algorithm vastly reduces the data required to reliably estimate spectra, opening up the possibility of quantitative MRI spectroscopy in a wide range of new applications. Our InSpect code is available at github.com/paddyslator/inspect.
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Affiliation(s)
- Paddy J Slator
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK.
| | - Jana Hutter
- Centre for the Developing Brain, Kings College London, London, UK; Biomedical Engineering Department, Kings College London, London, UK
| | - Razvan V Marinescu
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
| | - Marco Palombo
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
| | - Laurence H Jackson
- Centre for the Developing Brain, Kings College London, London, UK; Biomedical Engineering Department, Kings College London, London, UK
| | - Alison Ho
- Women's Health Department, King's College London, London, UK
| | - Lucy C Chappell
- Women's Health Department, King's College London, London, UK
| | - Mary Rutherford
- Centre for the Developing Brain, Kings College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, Kings College London, London, UK; Biomedical Engineering Department, Kings College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
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30
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Leppert IR, Andrews DA, Campbell JSW, Park DJ, Pike GB, Polimeni JR, Tardif CL. Efficient whole-brain tract-specific T 1 mapping at 3T with slice-shuffled inversion-recovery diffusion-weighted imaging. Magn Reson Med 2021; 86:738-753. [PMID: 33749017 DOI: 10.1002/mrm.28734] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 12/31/2020] [Accepted: 01/25/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE Most voxels in white matter contain multiple fiber populations with different orientations and levels of myelination. Conventional T1 mapping measures 1 T1 value per voxel, representing a weighted average of the multiple tract T1 times. Inversion-recovery diffusion-weighted imaging (IR-DWI) allows the T1 times of multiple tracts in a voxel to be disentangled, but the scan time is prohibitively long. Recently, slice-shuffled IR-DWI implementations have been proposed to significantly reduce scan time. In this work, we demonstrate that we can measure tract-specific T1 values in the whole brain using simultaneous multi-slice slice-shuffled IR-DWI at 3T. METHODS We perform simulations to evaluate the accuracy and precision of our crossing fiber IR-DWI signal model for various fiber parameters. The proposed sequence and signal model are tested in a phantom consisting of crossing asparagus pieces doped with gadolinium to vary T1 , and in 2 human subjects. RESULTS Our simulations show that tract-specific T1 times can be estimated within 5% of the nominal fiber T1 values. Tract-specific T1 values were resolved in subvoxel 2 fiber crossings in the asparagus phantom. Tract-specific T1 times were resolved in 2 different tract crossings in the human brain where myelination differences have previously been reported; the crossing of the cingulum and genu of the corpus callosum and the crossing of the corticospinal tract and pontine fibers. CONCLUSION Whole-brain tract-specific T1 mapping is feasible using slice-shuffled IR-DWI at 3T. This technique has the potential to improve the microstructural characterization of specific tracts implicated in neurodevelopment, aging, and demyelinating disorders.
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Affiliation(s)
- Ilana R Leppert
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada
| | - Daniel A Andrews
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada.,Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Jennifer S W Campbell
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada
| | - Daniel J Park
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - G Bruce Pike
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Department of Radiology and Department of Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Christine L Tardif
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada.,Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
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31
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Fair MJ, Liao C, Manhard MK, Setsompop K. Diffusion-PEPTIDE: Distortion- and blurring-free diffusion imaging with self-navigated motion-correction and relaxometry capabilities. Magn Reson Med 2020; 85:2417-2433. [PMID: 33314281 DOI: 10.1002/mrm.28579] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 10/11/2020] [Accepted: 10/12/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE To implement the time-resolved relaxometry PEPTIDE technique into a diffusion acquisition to provide self-navigated, distortion- and blurring-free diffusion imaging that is robust to motion, while simultaneously providing T2 and T 2 ∗ mapping. THEORY AND METHODS The PEPTIDE readout was implemented into a spin-echo diffusion acquisition, enabling reconstruction of a time-series of T2 - and T 2 ∗ -weighted images, free from conventional echo planar imaging (EPI) distortion and blurring, for each diffusion-encoding. Robustness of PEPTIDE to motion and shot-to-shot phase variation was examined through a deliberate motion-corrupted diffusion experiment. Two diffusion-relaxometry in vivo brain protocols were also examined: (1)1 × 1 × 3 mm3 across 32 diffusion directions in 20 min, (2)1.5 × 1.5 × 3.0 mm3 across 6 diffusion-weighted images in 3.4 min. T2 , T 2 ∗ , and diffusion parameter maps were calculated from these data. As initial exploration of the rich diffusion-relaxometry data content for use in multi-compartment modeling, PEPTIDE data were acquired of a gadolinium-doped asparagus phantom. These datasets contained two compartments with different relaxation parameters and different diffusion orientation properties, and T2 relaxation variations across these diffusion directions were explored. RESULTS Diffusion-PEPTIDE showed the capability to provide high quality diffusion images and T2 and T 2 ∗ maps from both protocols. The reconstructions were distortion-free, avoided potential resolution losses exceeding 100% in equivalent EPI acquisitions, and showed tolerance to nearly 30° of rotational motion. Expected variation in T2 values as a function of diffusion direction was observed in the two-compartment asparagus phantom (P < .01), demonstrating potential to explore diffusion-PEPTIDE data for multi-compartment modeling. CONCLUSIONS Diffusion-PEPTIDE provides highly robust diffusion and relaxometry data and offers potential for future applications in diffusion-relaxometry multi-compartment modeling.
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Affiliation(s)
- Merlin J Fair
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Mary Kate Manhard
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Kawin Setsompop
- 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
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