Myelin water fraction estimation using small-tip fast recovery MRI

Reproducible Research Practices in Magnetic Resonance Neuroimaging: A Review Informed by Advanced Language Models

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.

Magnetic resonance fingerprinting-based myelin water fraction mapping for the assessment of white matter maturation and integrity in typical development and leukodystrophies

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.

A model-based MR parameter mapping network robust to substantial variations in acquisition settings

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.

Cramér-Rao Bound Optimized Subspace Reconstruction in Quantitative MRI

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.

P2T2: A physically-primed deep-neural-network approach for robust T2 distribution estimation from quantitative T2-weighted MRI

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.