Quantitative MR T1 measurements with TOWERS: T-One with enhanced robustness and speed

T1 mapping can be beneficial for many applications in magnetic resonance imaging. Such applications include sequence optimization, clinical utility and tissue segmentation. However, the methods in the T1 mapping literature proposed to date either take a great deal of time to acquire or suffer from fundamental shortcomings. In addition, if significant motion occurs even once early in the scan, the operator needs to rerun the sequence, which is costly and time-consuming. Therefore, it is desirable to design a sequence that is not only fast, but also reliable to yield a good-quality T1 map, even in the presence of motion. In this study, we propose an EPI-based sequence with an efficient slice reordering scheme introduced relatively recently. The proposed sequence acquires saturation recovery samples that not only help improve estimation accuracy, but also serve as references for estimating motion parameters that will be used for mitigating the effects of motion. Furthermore, the reconstruction parameters are updated in the middle and at the end of the scan, and are used to retrospectively correct for motion. Phantom and in vivo experiments show the promise of the method.Doctor of Philosoph

Absolute Oxygenation Metabolism Measurements Using Magnetic Resonance Imaging

Cerebral oxygen metabolism plays a critical role in maintaining normal function of the brain. It is the primary energy source to sustain neuronal functions. Abnormalities in oxygen metabolism occur in various neuro-pathologic conditions such as ischemic stroke, cerebral trauma, cancer, Alzheimer’s disease and shock. Therefore, the ability to quantitatively measure tissue oxygenation and oxygen metabolism is essential to the understanding of pathophysiology and treatment of various diseases. The focus of this review is to provide an introduction of various blood oxygenation level dependent (BOLD) contrast methods for absolute measurements of tissue oxygenation, including both magnitude and phase image based approaches. The advantages and disadvantages of each method are discussed

Evaluating the use of rCBV as a tumor grade and treatment response classifier across NCI Quantitative Imaging Network sites: Part II of the DSC-MRI digital reference object (DRO) challenge

We have previously characterized the reproducibility of brain tumor relative cerebral blood volume (rCBV) using a dynamic susceptibility contrast magnetic resonance imaging digital reference object across 12 sites using a range of imaging protocols and software platforms. As expected, reproducibility was highest when imaging protocols and software were consistent, but decreased when they were variable. Our goal in this study was to determine the impact of rCBV reproducibility for tumor grade and treatment response classification. We found that varying imaging protocols and software platforms produced a range of optimal thresholds for both tumor grading and treatment response, but the performance of these thresholds was similar. These findings further underscore the importance of standardizing acquisition and analysis protocols across sites and software benchmarking

TOWERS: T-One with Enhanced Robustness and Speed: TOWERS

A new T1 mapping method is proposed that is accurate, rapid and robust to motion. Considering these features, the method is dubbed “T-One with Enhanced Robustness and Speed (TOWERS)”

Reperfusion Beyond 6 Hours Reduces Infarct Probability in Moderately Ischemic Brain Tissue

We aimed to examine perfusion changes between 3 and 6, and 6 and 24 hours after stroke onset and their impact on tissue outcome

Self-Supervised Deep Equilibrium Models for Inverse Problems with Theoretical Guarantees

Deep equilibrium models (DEQ) have emerged as a powerful alternative to deep unfolding (DU) for image reconstruction. DEQ models-implicit neural networks with effectively infinite number of layers-were shown to achieve state-of-the-art image reconstruction without the memory complexity associated with DU. While the performance of DEQ has been widely investigated, the existing work has primarily focused on the settings where groundtruth data is available for training. We present self-supervised deep equilibrium model (SelfDEQ) as the first self-supervised reconstruction framework for training model-based implicit networks from undersampled and noisy MRI measurements. Our theoretical results show that SelfDEQ can compensate for unbalanced sampling across multiple acquisitions and match the performance of fully supervised DEQ. Our numerical results on in-vivo MRI data show that SelfDEQ leads to state-of-the-art performance using only undersampled and noisy training data

Spatiotemporal Uptake Characteristics of [18]F-2-Fluoro-2-Deoxy-D-Glucose in a Rat Middle Cerebral Artery Occlusion Model

Alterations of cerebral glucose metabolism are well anticipated during cerebral ischemia. However, detailed spatiotemporal characteristics of disturbed cerebral glucose metabolism during acute ischemia remain largely elusive. This study aims to delineate spatiotemporal distributions of [18]F-2-fluoro-2-deoxy-D-glucose (FDG) uptake using positron emission tomography imaging, particularly at the peri-ischemic zone, and its correlation with tissue outcome