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Simultaneous Quantitative Multiparametric MRI for In Vivo Tissue Characterization using Magnetic Resonance Multitasking: Methodology and Clinical Experience

Abstract

In current clinical systems, magnetic resonance imaging scans for disease diagnosis and prognosis are dominated by qualitative contrast-weighted imaging. These qualitative MR images reveal regional differences in signal intensities between tissues with focal structural or functional abnormalities and tissues that are supposedly in healthy states, facilitating subjective determination for disease diagnosis. The administration of gadolinium-based contrast agents is prevalent in clinical MRI exams, which alternates the relaxation time of neighboring water protons and creates enhanced signal intensities from damaged tissues with high vascular density and thin vessel wall for better visualization. Nowadays, nearly 50% of the MRI studies were conducted with contrast agents. However, patients with renal insufficiency are at risk of developing nephrogenic system fibrosis if exposed to gadolinium-based contrast agents, and chronic toxic effects of possible gadolinium retention have been reported. In the meantime, qualitative contrast-weighted images have limited sensitivity to subtle alteration in tissue states, lack of biological specificity and multi-center reproducibility, and limited predictive values.

One promising alternative is quantitative multiparametric MRI, which contains various methods to quantify multiple parameters with interpretable physical units that are intrinsic to tissue properties. Most of these quantitative approaches do not involve the administration of contrast agents, therefore ensuring the safety of the application to a wide range of patients and reducing the costs of MRI. These quantitative parameters are highly reproducible, sensitive to subtle physiological tissue changes, and specific for disease pathologies. More importantly, each of these parameters reveal tissue properties in different aspects, having the potential to offer complementary information for comprehensive tissue characterization, and acting as biomarkers that are directly associated with diseases states. Despite the benefits to clinical studies, quantitative multiparametric MRI has yet to be widely adopted in routine clinical practices because of several major technical limitations including (i) long scan times that compromises image resolution and/or spatial coverage, (ii) motion artifacts, (iii) misaligned parametric maps due to separate acquisitions, and (iv) complicated clinical workflow. This dissertation aims to address some of these challenges by proposing a simultaneous quantitative multiparametric MRI approach with Magnetic Resonance Multitasking and focus on the quantification of T1, T2, T1ρ, and ADC, which serves as the start of the ultimate goal to provide a clinically translatable, multiparametric whole-body quantitative tissue characterization technique.

A novel approach to simultaneously quantifying T1, T2, and ADC in the brain was first developed using MR Multitasking in conjunction with a time-resolved phase correction strategy to compensate for the inter-shot phase inconsistencies introduced by physiological motion. It was implemented as a push-button, continuous acquisition that simplified the workflow. This technique was initially demonstrated in healthy subjects to efficiently produce distortion-free, co-registered T1, T2, and ADC maps with 3D brain coverage (100mm) in 9.3min. The resulting T1, T2, and ADC measurements in the brain were comparable to reference quantitative approaches. Abrupt motion was manually identified and removed to yield T1, T2, and ADC maps that were free from motion artifacts and with accurate quantitative measurements. Clinical feasibility was demonstrated on post-surgery glioblastoma patients.

A motion-resolved, simultaneous T1, T2, and T1ρ quantification technique was then developed using MR Multitasking in a push-button 9min acquisition. Rigid intra-scan head motion was captured and simultaneously resolved along with the relaxation processes. This technique was first validated in healthy subjects to produce high quality, whole-brain (140mm) T1, T2, and T1ρ maps and repeatable T1, T2, and T1ρ measurements that were in excellent agreement with gold standard methods. Motion-resolved, artifact-free maps were generated under either in-plane or through-plane motion, which provided a novel avenue for handling rigid motion in brain MRI. Synthetic contrast-weighted qualitative images comparable to clinical images were generated using the parameter maps, demonstrating the significant potential to replace conventional MRI scans with a single Multitasking scan for clinical purposes. This technique was applied in a pilot clinical setting to perform tissue characterization in relapsing-remitting multiple sclerosis patients. The combination of T1, T2, and T1ρ significantly improved the accuracy of the differentiation of multiple sclerosis patients from healthy controls, compared to either single parameter alone, indicating the clinical utility of T1, T2, and T1ρ as quantitative biomarkers.

Lastly, the above two quantitative techniques were extended to other body organs for a preliminary demonstration of potential applications, where we 1) simultaneously quantified T1, T2, and ADC in the breast with whole-breast coverage (160mm) in 8min, incorporating a B1+-compensated multiparametric fitting approach to address the notable B1+ inhomogeneity across the bilateral breast FOV, and to provide distortion-free, co-registered whole-breast T1, T2, and ADC maps with good in vivo repeatability; and 2) simultaneously quantified myocardial T1 and T1ρ in a single non-ECG, free-breathing acquisition, where cardiac motion and respiratory motion were retrospectively identified and simultaneously resolved to produce dynamic myocardial T1 and T1ρ maps of 20 cardiac phases with high temporal resolution (15ms) in a single, continuous acquisition of 1.5min per slice. Multitasking T1 and T1ρ measurements in the heart were comparable with gold standard techniques.

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