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Karakuzu A, Boudreau M, Stikov N. Reproducible Research Practices in Magnetic Resonance Neuroimaging: A Review Informed by Advanced Language Models. Magn Reson Med Sci 2024; 23:252-267. [PMID: 38897936 PMCID: PMC11234949 DOI: 10.2463/mrms.rev.2023-0174] [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] [Indexed: 06/21/2024] Open
Abstract
MRI has progressed significantly with the introduction of advanced computational methods and novel imaging techniques, but their wider adoption hinges on their reproducibility. This concise review synthesizes reproducible research insights from recent MRI articles to examine the current state of reproducibility in neuroimaging, highlighting key trends and challenges. It also provides a custom generative pretrained transformer (GPT) model, designed specifically for aiding in an automated analysis and synthesis of information pertaining to the reproducibility insights associated with the articles at the core of this review.
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Affiliation(s)
- Agah Karakuzu
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Quebec, Canada
- Montréal Heart Institute, Montréal, Quebec, Canada
| | - Mathieu Boudreau
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Quebec, Canada
| | - Nikola Stikov
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Quebec, Canada
- Montréal Heart Institute, Montréal, Quebec, Canada
- Center for Advanced Interdisciplinary Research, Ss. Cyril and Methodius University, Skopje, North Macedonia
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2
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Lancione M, Cencini M, Scaffei E, Cipriano E, Buonincontri G, Schulte RF, Pirkl CM, Buchignani B, Pasquariello R, Canapicchi R, Battini R, Biagi L, Tosetti M. Magnetic resonance fingerprinting-based myelin water fraction mapping for the assessment of white matter maturation and integrity in typical development and leukodystrophies. NMR IN BIOMEDICINE 2024; 37:e5114. [PMID: 38390667 DOI: 10.1002/nbm.5114] [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: 08/29/2023] [Revised: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 02/24/2024]
Abstract
A quantitative biomarker for myelination, such as myelin water fraction (MWF), would boost the understanding of normative and pathological neurodevelopment, improving patients' diagnosis and follow-up. We quantified the fraction of a rapidly relaxing pool identified as MW using multicomponent three-dimensional (3D) magnetic resonance fingerprinting (MRF) to evaluate white matter (WM) maturation in typically developing (TD) children and alterations in leukodystrophies (LDs). We acquired DTI and 3D MRF-based R1, R2 and MWF data of 15 TD children and 17 LD patients (9 months-12.5 years old) at 1.5 T. We computed normative maturation curves in corpus callosum and corona radiata and performed WM tract profile analysis, comparing MWF with R1, R2 and fractional anisotropy (FA). Normative maturation curves demonstrated a steep increase for all tissue parameters in the first 3 years of age, followed by slower growth for MWF while R1, R2R2 and FA reached a plateau. Unlike FA, MWF values were similar for regions of interest (ROIs) with different degrees of axonal packing, suggesting independence from fiber bundle macro-organization and higher myelin specificity. Tract profile analysis indicated a specific spatial pattern of myelination in the major fiber bundles, consistent across subjects. LD were better distinguished from TD by MWF rather than FA, showing reduced MWF with respect to age-matched controls in both ROI-based and tract analysis. In conclusion, MRF-based MWF provides myelin-specific WM maturation curves and is sensitive to alteration due to LDs, suggesting its potential as a biomarker for WM disorders. As MRF allows fast simultaneous acquisition of relaxometry and MWF, it can represent a valuable diagnostic tool to study and follow up developmental WM disorders in children.
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Affiliation(s)
| | - Matteo Cencini
- Pisa Division, National Institute for Nuclear Physics (INFN), Pisa, Italy
| | | | - Emilio Cipriano
- IRCCS Stella Maris Foundation, Pisa, Italy
- Department of Physics, University of Pisa, Pisa, Italy
| | | | | | | | | | | | | | - Roberta Battini
- IRCCS Stella Maris Foundation, Pisa, Italy
- Department of Clinical and Experimental Medicine, Università di Pisa, Pisa, Italy
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Lu Q, Li J, Lian Z, Zhang X, Feng Q, Chen W, Ma J, Feng Y. A model-based MR parameter mapping network robust to substantial variations in acquisition settings. Med Image Anal 2024; 94:103148. [PMID: 38554550 DOI: 10.1016/j.media.2024.103148] [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: 10/20/2022] [Revised: 12/03/2023] [Accepted: 03/20/2024] [Indexed: 04/01/2024]
Abstract
Deep learning methods show great potential for the efficient and precise estimation of quantitative parameter maps from multiple magnetic resonance (MR) images. Current deep learning-based MR parameter mapping (MPM) methods are mostly trained and tested using data with specific acquisition settings. However, scan protocols usually vary with centers, scanners, and studies in practice. Thus, deep learning methods applicable to MPM with varying acquisition settings are highly required but still rarely investigated. In this work, we develop a model-based deep network termed MMPM-Net for robust MPM with varying acquisition settings. A deep learning-based denoiser is introduced to construct the regularization term in the nonlinear inversion problem of MPM. The alternating direction method of multipliers is used to solve the optimization problem and then unrolled to construct MMPM-Net. The variation in acquisition parameters can be addressed by the data fidelity component in MMPM-Net. Extensive experiments are performed on R2 mapping and R1 mapping datasets with substantial variations in acquisition settings, and the results demonstrate that the proposed MMPM-Net method outperforms other state-of-the-art MR parameter mapping methods both qualitatively and quantitatively.
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Affiliation(s)
- Qiqi Lu
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510000, China; Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510000, China; Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education & Guangdong-Hong Kong Joint Laboratory for Psychiatric Disorders, Southern Medical University, Guangzhou 510000, China; Department of Radiology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde, Foshan), Foshan 528000, China
| | - Jialong Li
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510000, China; Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510000, China; Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education & Guangdong-Hong Kong Joint Laboratory for Psychiatric Disorders, Southern Medical University, Guangzhou 510000, China; Department of Radiology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde, Foshan), Foshan 528000, China
| | - Zifeng Lian
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510000, China; Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510000, China; Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education & Guangdong-Hong Kong Joint Laboratory for Psychiatric Disorders, Southern Medical University, Guangzhou 510000, China; Department of Radiology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde, Foshan), Foshan 528000, China
| | - Xinyuan Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510000, China; Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510000, China
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510000, China; Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510000, China
| | - Wufan Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510000, China; Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510000, China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510000, China; Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510000, China
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510000, China; Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510000, China; Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education & Guangdong-Hong Kong Joint Laboratory for Psychiatric Disorders, Southern Medical University, Guangzhou 510000, China; Department of Radiology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde, Foshan), Foshan 528000, China.
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Mao A, Flassbeck S, Gultekin C, Assländer J. Cramér-Rao Bound Optimized Subspace Reconstruction in Quantitative MRI. ARXIV 2023:arXiv:2305.00326v2. [PMID: 37961734 PMCID: PMC10635289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
We extend the traditional framework for estimating subspace bases that maximize the preserved signal energy to additionally preserve the Cramér-Rao bound (CRB) of the biophysical parameters and, ultimately, improve accuracy and precision in the quantitative maps. To this end, we introduce an approximate compressed CRB based on orthogonalized versions of the signal's derivatives with respect to the model parameters. This approximation permits singular value decomposition (SVD)-based minimization of both the CRB and signal losses during compression. Compared to the traditional SVD approach, the proposed method better preserves the CRB across all biophysical parameters with negligible cost to the preserved signal energy, leading to reduced bias and variance of the parameter estimates in simulation. In vivo, improved accuracy and precision are observed in two quantitative neuroimaging applications, permitting the use of smaller basis sizes in subspace reconstruction and offering significant computational savings.
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Affiliation(s)
- Andrew Mao
- Center for Biomedical Imaging, NYU School of Medicine, New York, NY 10016
| | | | - Cem Gultekin
- Courant Institute of Mathematical Sciences, New York University, New York, NY 10012
| | - Jakob Assländer
- Center for Biomedical Imaging, NYU School of Medicine, New York, NY 10016
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Ben-Atya H, Freiman M. P 2T 2: A physically-primed deep-neural-network approach for robust T 2 distribution estimation from quantitative T 2-weighted MRI. Comput Med Imaging Graph 2023; 107:102240. [PMID: 37224742 DOI: 10.1016/j.compmedimag.2023.102240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 04/27/2023] [Accepted: 04/27/2023] [Indexed: 05/26/2023]
Abstract
Estimating T2 relaxation time distributions from multi-echo T2-weighted MRI (T2W) data can provide valuable biomarkers for assessing inflammation, demyelination, edema, and cartilage composition in various pathologies, including neurodegenerative disorders, osteoarthritis, and tumors. Deep neural network (DNN) based methods have been proposed to address the complex inverse problem of estimating T2 distributions from MRI data, but they are not yet robust enough for clinical data with low Signal-to-Noise ratio (SNR) and are highly sensitive to distribution shifts such as variations in echo-times (TE) used during acquisition. Consequently, their application is hindered in clinical practice and large-scale multi-institutional trials with heterogeneous acquisition protocols. We propose a physically-primed DNN approach, called P2T2, that incorporates the signal decay forward model in addition to the MRI signal into the DNN architecture to improve the accuracy and robustness of T2 distribution estimation. We evaluated our P2T2 model in comparison to both DNN-based methods and classical methods for T2 distribution estimation using 1D and 2D numerical simulations along with clinical data. Our model improved the baseline model's accuracy for low SNR levels (SNR<80) which are common in the clinical setting. Further, our model achieved a ∼35% improvement in robustness against distribution shifts in the acquisition process compared to previously proposed DNN models. Finally, Our P2T2 model produces the most detailed Myelin-Water fraction maps compared to baseline approaches when applied to real human MRI data. Our P2T2 model offers a reliable and precise means of estimating T2 distributions from MRI data and shows promise for use in large-scale multi-institutional trials with heterogeneous acquisition protocols. Our source code is available at: https://github.com/Hben-atya/P2T2-Robust-T2-estimation.git.
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Affiliation(s)
- Hadas Ben-Atya
- Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel.
| | - Moti Freiman
- Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
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Scope Crafts E, Lu H, Ye H, Wald LL, Zhao B. An efficient approach to optimal experimental design for magnetic resonance fingerprinting with B‐splines. Magn Reson Med 2022; 88:239-253. [DOI: 10.1002/mrm.29212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 01/30/2022] [Accepted: 02/08/2022] [Indexed: 11/05/2022]
Affiliation(s)
- Evan Scope Crafts
- Oden Institute for Computational Engineering and Sciences University of Texas at Austin Austin Texas USA
| | - Hengfa Lu
- Department of Biomedical Engineering University of Texas at Austin Austin Texas USA
| | - Huihui Ye
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering Zhejiang University Hangzhou Zhejiang China
- Center for Brain Imaging Science and Technology Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University Hangzhou Zhejiang China
| | - Lawrence L. Wald
- Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown Massachusetts USA
- Department of Radiology Harvard Medical School Boston Massachusetts USA
- Harvard‐MIT Division of Health Sciences and Technology Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Bo Zhao
- Oden Institute for Computational Engineering and Sciences University of Texas at Austin Austin Texas USA
- Department of Biomedical Engineering University of Texas at Austin Austin Texas USA
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