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, T1 , 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 T1 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 T1 maps and repeatable T1, T2, and T1 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 T1 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 T1 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 T1 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 T1 maps of 20 cardiac phases with high temporal resolution (15ms) in a single, continuous acquisition of 1.5min per slice. Multitasking T1 and T1 measurements in the heart were comparable with gold standard techniques.

Current clinical magnetic resonance (MR) acquisitions primarily rely on qualitative or 'weighted' images and diagnosis is made by subjective assessment of regional signal intensity (hyperintense or hypointense). However, MR signal for the same material can vary due to different scanners and different protocols, which hinders objective evaluation of disease severity. In contrast, quantitative MRI provides objective information for tissue characterization, offering enhanced inter-session and inter-site reproducibility. It enables improved pathology detection and disease monitoring and has better sensitivity to mild or diffuse tissue alterations compared to qualitative imaging. The combination of multiple biomarkers provides more comprehensive information and shows great promise for risk assessment and early detection.Despite these advantages, the clinical application of multiparametric MRI has been limited due to the prolonged scan times for acquiring different biomarkers, motion artifacts, and misregistration between parametric maps. MR Multitasking presents a promising approach for motion-resolved, multi-parametric mapping. However, it has yet to exploit the multi-echo information (magnitude and phase) for T2*, susceptibility, and fat fraction mapping, which necessitates further technical development. This includes flow compensation for more accurate susceptibility mapping, achieving adequate temporal resolution for motion tracking, and improving imaging efficiency for multi-echo readouts. In addition, MR Multitasking demands further improvement in quantitative performance (precision and repeatability) and scan time for practical applications. The dissertation will be focused on technical developments of MR Multitasking to enable comprehensive tissue characterization and to improve quantitative performance. The first objective is to develop a technique for three-dimensional, whole-brain simultaneous T1, T2, T2*, and susceptibility mapping. The proposed method is evaluated on phantoms and human subjects. The second objective involves further technical development to achieve free-breathing, non-ECG, simultaneous myocardial T1, T2, T2*, and FF mapping in a 2.5-min scan. Lastly, a novel reconstruction approach is introduced to improve precision and repeatability and shorten scan time. The approach is evaluated with numerical simulations and healthy subjects. The dissertation represents a step toward motion-resolved, comprehensive tissue characterization within a clinically feasible scan time and without the need for extra physiological monitoring. It lays the groundwork for future clinical use of quantitative multiparametric MRI.

In cardiovascular disease, which is the most common cause of death in the world, early diagnosis is crucial for disease outcome. Diagnosis of cardiovascular disease can be challenging, though. Quantification of myocardial T1 and T2 relaxation times with MRI has demonstrated to be a promising method for characterizing myocardial tissue, but long measurement times have hampered clinical use. The overall aim of this doctoral thesis was to develop, validate and, in patient studies, evaluate a very fast three-dimensional method for simultaneous quantification of myocardial T1 and T2 relaxation times with whole coverage of the left ventricle. The 3D-QALAS method is presented in Paper I of this thesis. It is a method that simultaneous measures both T1 and T2 relaxation times in a three-dimensional volume of the heart. The method requires 15 heartbeats, to produce 13 short-axis slices of the left ventricle with voxelwise information of both T1 and T2 relaxation times. The 3D-QALAS method was validated in phantoms and in 10 healthy volunteers by comparing the method with reference methods and demonstrated good accuracy and robustness both in-vitro and in-vivo. In Paper II, the 3D-QALAS method was carefully validated in-vivo by investigating accuracy and precision in 10 healthy volunteers, while the clinical feasibility of the method was investigated in 23 patients with various cardiac pathologies. Repeated independent and dependent scans together with the intra-scan repeatability, demonstrated all a very good precision for the 3D-QALAS method in healthy volunteers. In Paper III and IV, the 3D-QALAS method was applied and evaluated in patient cohorts where the heart muscle alters over time. In Paper III, patients with severe aortic stenosis underwent MRI examinations with 3D-QALAS before, 3 months after and 12 months after aortic valve surgery. Changes in T1 and T2 were observed, which might be used as markers of myocardial changes with respect to edema and fibrosis, which may develop due to increased workload over a long period of time. In study IV, 3D-QALAS was used to investigate 10 breast cancer patients treated with radiation therapy prior to treatment, 2-3 weeks into treatment, and one and 6 months after completion of treatment, to investigate any changes in T1 and T2 and further if they can be correlated to unwanted irradiation of the heart during radiation therapy.

Magnetic resonance multitasking (MR Multitasking) is a multi-dimensional imaging framework that was developed recently. With low-rank tensor modeling, signal correlation among images at different time dimensions are exploited in MR Multitasking to resolve motion, accelerate image acquisition, and enhance image quality. Though initially developed for cardiovascular imaging, it has also been extended to many other applications, such as whole-brain multi-parametric mapping, free-breathing abdominal dynamic contrast enhanced imaging, etc. The primary focus of this dissertation is to improve two important MR tissue characterization techniques using MR Multitasking: (1) Electrocardiogram (ECG)-less myocardial T1 and extracellular volume fraction (ECV) mapping in small animals at 9.4 T, and (2) Fast 3D chemical exchange saturation transfer (CEST) imaging for human studies at 3.0 T. ECV quantification with cardiovascular magnetic resonance T1 mapping is a powerful tool for the characterization of focal or diffuse myocardial fibrosis. However, it is technically challenging to acquire high-quality T1 and ECV maps in small animals for preclinical research because of high heart rates and high respiration rates. An ECG-less, free-breathing ECV mapping method using MR Multitasking was developed on a 9.4 T small animal MR system. The feasibility of characterizing diffuse myocardial fibrosis was tested in a rat heart failure model with preserved ejection fraction (HFpEF). A 25-min exam, including two 4-min T1 Multitasking scans before and after gadolinium injection, were performed on each rat. It allows a cardiac temporal resolution of 20 ms for a heart rate of ~300 bpm. Elevated ECV found in the HFpEF group is consistent with previous human studies and well correlated with histological data. This technique has the potential to be a viable imaging tool for myocardial tissue characterization in small animal models. CEST imaging is a non-contrast MRI technique that indirectly detects exchangeable protons in the water pool. It is achieved by performing frequency selective saturation at those protons before acquiring water signal readout. CEST MRI provides a novel contrast mechanism to image important physiological information, such as pH and metabolite concentration. However, long scan time is still a crucial problem in many CEST imaging applications, which makes it difficult to translate current CEST techniques into clinical practice. A novel 3D steady-state CEST method using MR Multitasking was developed in the brain at 3.0 T. This allows the Z-spectrum of 55 frequency offsets to be acquired with whole-brain coverage at 1.7 x 1.7 x 3.0 mm3 spatial resolution in 5.5 min. Quantitative CEST maps from multi-pool fitting showed consistent image quality across the volume. Motion handling in moving organs is another challenge for practical CEST imaging. For instance, breath-holding is currently needed in liver CEST imaging to reduce motion artifacts, which limits not only spatial resolution, but also scan volume coverage. Following the whole-brain CEST protocol, a respiration-resolved 3D abdominal CEST imaging technique using MR Multitasking was developed, which enables whole-liver coverage with free-breathing acquisition. CEST images of 55 frequency offsets with entire-liver coverage and 2.0 x 2.0 x 6.0 mm3 spatial resolution were generated within 9 min. Both APTw and glycoCEST signals showed high sensitivity between post-fasting and post-meal acquisitions.

Dynamic contrast enhanced (DCE) MRI plays a central role in the diagnosis, characterization, and treatment monitoring of various diseases. It can provide essential functional information of the tissues, including the perfusion and permeability properties. Besides, the changes in functional properties is recognized to precede the morphological alterations, which can potentially provide an avenue for early diagnosis and treatment evaluation. However, current practice of DCE MRI continues to face demanding technical challenges. First, there is direct conflict in the requirements for adequate anatomical coverage and high spatial and temporal resolution for tissues characterization. Existing techniques usually compromise one or more aspects, leading to suboptimal protocol. Second, dynamic T1 mapping for the quantification of CA concentration is hard to realize. Approximation in this step can introduce error in the derivation of DCE parameters. Third, imaging of some organs, such as heart or abdominal organs, is subject to artifacts caused by physiological motion, which can significantly degrade image quality or increase the scan time. Forth, the growing concern towards Gd deposition within body parts makes it controversial to use contrast agent. The primary goal of the work in the dissertation is to address the aforementioned limitations by developing a novel quantitative DCE MRI technique using MR Multitasking framework to achieve: 1) motion-resolved imaging with free-breathing acquisition and bulk-motion correction, 2) high spatial-temporal resolution with 3D anatomical coverage for simultaneous perfusion and permeability quantification, 3) dynamic T1 mapping for accurate quantification of CA concentration and potentials for reducing GBCA dose. All the technical advancements aims to improve the detection, staging, characterization, and treatment monitoring for multiple organs and diseases. There are three specific aims to achieve the ultimate goal. First, the Multitasking DCE technique was developed and tested in the study of carotid atherosclerosis. It enables 3D coverage of entire carotid vasculature with high spatial resolution (0.7 mm isotropic), high temporal resolution (600 ms), and dynamic T1 mapping for direct quantification of CA concentration. Bulk motion detection and removal scheme were also implemented for the improvement of image quality. In vivo studies have shown that the proposed technique can achieve accurate T1 quantification and robustness to motion. The PK parameters were repeatable in vivo and showed significant difference between carotid atherosclerosis and normal carotid vessel wall. Second, 6-dimensional (6D) Multitasking DCE technique was developed for the characterization of PDAC. It achieves respiratory-motion-resolved, high-temporal-resolution T1 quantification of the entire abdomen in a 10-min free-breathing scan. Sixteen healthy volunteers and 14 patients with pathologically confirmed PDAC were recruited for the in vivo study. The results demonstrated that the quantitative PK parameters using Multitasking DCE were repeatable in vivo and showed significant differences between normal pancreas, tumor and non-tumoral regions in PDAC patients. In addition, 8 patients with confirmed CP were recruited. The PK parameters representing blood flow and vascular properties showed significant difference between normal pancreas, PDAC, and CP. Third, the Multitasking DCE technique using a low dose at 0.02 mmol/kg (LD-MT-DCE) was developed for breast imaging. It produced co-registered high-spatial-resolution (0.9 mm 0.9 mm 1.14 mm) dynamic T1 maps at 1.4-second temporal resolution with whole-breast coverage in the 10-min scan. The dose was chosen to be 0.02 mmol/kg, 20% of the standard dose, based on a numerical simulation conducted to evaluate the dose effect on the accuracy and precision of the PK parameters quantified using LD-MT-DCE. Twenty healthy volunteers and 7 patients with breast cancer were recruited for the in vivo study. The results demonstrated that LD-MT-DCE were repeatable, showed excellent image quality and equivalent diagnosis compared with standard-dose clinical DCE. The estimated PK parameters were capable of differentiating between normal breast tissue, and benign and malignant tumors.

H.P. HIGER 1 In the seventeenth century people dreamed about a machine to get rid of evil spirits and obsessions, which were thought to be the main source of mis fortune and disease. I am not going to question this approach, because in a way it sounds reasonable. They dreamed of a machine that would display im ages from the inner world of men which could be easily identified and named. Somehow these are the roots of MR imaging. Of course, we now view disease from a different point of view but our objectives remain the same, namely to make diseases visible and to try to characterize them in order to cure them. This was the reason for setting up a symposium on tissue characterization. About 300 years later the clinical introduction of MRI has great potential for making this dream come true, and I hope that this symposium has con stituted another step toward its realization. When Damadian published his article in 1971 about differences in T1 relaxation times between healthy and pathological tissues, this was a milestone in tissue characterization. His results initiated intensive research in to MR imaging and tissue parameters. Actually his encouraging discovery was not only the first but also the last for a long time in the field of MR tissue characterization.

Among medical imaging modalities, magnetic resonance imaging (MRI) stands out for its excellent soft-tissue contrast, anatomical detail, and high sensitivity for disease detection. However, as proven by the continuous and vast effort to develop new MRI techniques, limitations and open challenges remain. The primary source of contrast in MRI images are the various relaxation parameters associated with the nuclear magnetic resonance (NMR) phenomena upon which MRI is based. Although it is possible to quantify these relaxation parameters (qMRI) they are rarely used in the clinic, and radiological interpretation of images is primarily based upon images that are relaxation time weighted. The clinical adoption of qMRI is mainly limited by the long acquisition times required to quantify each relaxation parameter as well as questions around their accuracy and reliability. More specifically, the main limitations of qMRI methods have been the difficulty in dealing with the high inter-parameter correlations and a high sensitivity to MRI system imperfections. Recently, new methods for rapid qMRI have been proposed. The multi-parametric models at the heart of these techniques have the main advantage of accounting for the correlations between the parameters of interest as well as system imperfections. This holistic view on the MR signal makes it possible to regress many individual parameters at once, potentially with a higher accuracy. Novel, accurate techniques promise a fast estimation of relevant MRI quantities, including but not limited to longitudinal (T1) and transverse (T2) relaxation times. Among these emerging methods, MR Fingerprinting (MRF), synthetic MR (syMRI or MAGIC), and T1‒T2 Shuffling are making their way into the clinical world at a very fast pace. However, the main underlying assumptions and algorithms used are sometimes different from those found in the conventional MRI literature, and can be elusive at times. In this book, we take the opportunity to study and describe the main assumptions, theoretical background, and methods that are the basis of these emerging techniques. Quantitative transient state imaging provides an incredible, transformative opportunity for MRI. There is huge potential to further extend the physics, in conjunction with the underlying physiology, toward a better theoretical description of the underlying models, their application, and evaluation to improve the assessment of disease and treatment efficacy.