Empirical transmit field bias correction of T1w/T2w myelin maps

T1-weighted divided by T2-weighted (T1w/T2w) myelin maps were initially developed for neuroanatomical analyses such as identifying cortical areas, but they are increasingly used in statistical comparisons across individuals and groups with other variables of interest. Existing T1w/T2w myelin maps contain radiofrequency transmit field (B1+) biases, which may be correlated with these variables of interest, leading to potentially spurious results. Here we propose two empirical methods for correcting these transmit field biases using either explicit measures of the transmit field or alternatively a \u27pseudo-transmit\u27 approach that is highly correlated with the transmit field at 3T. We find that the resulting corrected T1w/T2w myelin maps are both better neuroanatomical measures (e.g., for use in cross-species comparisons), and more appropriate for statistical comparisons of relative T1w/T2w differences across individuals and groups (e.g., sex, age, or body-mass-index) within a consistently acquired study at 3T. We recommend that investigators who use the T1w/T2w approach for mapping cortical myelin use these B1+ transmit field corrected myelin maps going forward

Excitation and readout Designs for high field spectroscopic imaging

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 86-90).In this thesis we state and demonstrate solutions to three engineering problems that arise in magnetic resonance imaging RF excitation with parallel transmission (pTx) and magnetic resonance spectroscopic imaging (MRSI). Recent work in parallel RF excitation in MRI has been demonstrated to offer dramatically improved flexibility for manipulation of magnetization preparation for imaging than is feasible with conventional single-channel transmission. We address two design problems that need to be solved before this emerging technology can be deployed in the clinical and research domain of human imaging at high field. First, we demonstrate a method for rapid and robust acquisition of the non-uniform fields of RF excitation due to arrays that are commonly used in pTx at high field. Our method achieves high-fidelity single-slice excitation and reception field mapping in 20 seconds, and we propose ways to extend this to multi-slice mapping in two minutes for twenty slices. A fundamental constraint to the application of pTx is the management of the deposition of power in human tissue, quantified by the specific absorption rate (SAR). The complex behavior of the spatial distribution of SAR in transmission arrays poses problems not encountered in conventional single-channel systems, and we propose a pTx design method to incorporate local SAR constraints within computation times that accommodate pTx pulse design during MRI acquisition of human subjects. Our approach builds on recent work to capture local SAR distribution with much lower computational complexity than a brute-force evaluation, and we demonstrate that this approach can reduce peak local SAR by 20~40% for commonly applied pTx design targets. This thesis focuses on the design of excitation methods for high field system (7T parallel transmit (pTx) system) and fast readout and post-processing methods to reduce the lipid contamination to the brain. The contributions include fast B1+ mapping and pTx RF pulse design with the local SAR constraints for excitation. Regarding the readout method we developed a real time filter design, variable density spiral trajectory, and iterative non-linear reconstruction technique that reduce the lipid contamination. The proposed excitation methods were demonstrated using a 7T pTx system and the readout methods were implemented in a 3T system. Our third contribution addresses a recurring problem in MRSI of the brain, namely strong contaminating artifacts in low signal-to-noise ratio brain metabolite maps due to subcutaneous, high-concentration lipid sources. We demonstrate two methods to address this problems, one during the acquisition stage where a spatial filter is designed based on spatial priors acquired from the subject being scanned, and the second is a post-processing method that applies the brain and lipid source prior for further artifact minimization. These methods are demonstrated to achieve 20~4OdB enhancement of lipid suppression in brain MRSI of human subjects.by Joonsung Lee.Ph.D

Magnetic Resonance Parameter Mapping using Self-supervised Deep Learning with Model Reinforcement

This paper proposes a novel self-supervised learning method, RELAX-MORE, for quantitative MRI (qMRI) reconstruction. The proposed method uses an optimization algorithm to unroll a model-based qMRI reconstruction into a deep learning framework, enabling the generation of highly accurate and robust MR parameter maps at imaging acceleration. Unlike conventional deep learning methods requiring a large amount of training data, RELAX-MORE is a subject-specific method that can be trained on single-subject data through self-supervised learning, making it accessible and practically applicable to many qMRI studies. Using the quantitative $T_1$ mapping as an example at different brain, knee and phantom experiments, the proposed method demonstrates excellent performance in reconstructing MR parameters, correcting imaging artifacts, removing noises, and recovering image features at imperfect imaging conditions. Compared with other state-of-the-art conventional and deep learning methods, RELAX-MORE significantly improves efficiency, accuracy, robustness, and generalizability for rapid MR parameter mapping. This work demonstrates the feasibility of a new self-supervised learning method for rapid MR parameter mapping, with great potential to enhance the clinical translation of qMRI