Optimizing MRFâ ASL scan design for precise quantification of brain hemodynamics using neural network regression

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154517/1/mrm28051.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154517/2/mrm28051_am.pd

Recent advances in arterial spin labeling perfusion MRI in patients with vascular cognitive impairment

Cognitive impairment (CI) is a major health concern in aging populations. It impairs patients’ independent life and may progress to dementia. Vascular cognitive impairment (VCI) encompasses all cerebrovascular pathologies that contribute to cognitive impairment (CI). Moreover, the majority of CI subtypes involve various aspects of vascular dysfunction. Recent research highlights the critical role of reduced cerebral blood flow (CBF) in the progress of VCI, and the detection of altered CBF may help to detect or even predict the onset of VCI. Arterial spin labeling (ASL) is a non-invasive, non-ionizing perfusion MRI technique for assessing CBF qualitatively and quantitatively. Recent methodological advances enabling improved signal-to-noise ratio (SNR) and data acquisition have led to an increase in the use of ASL to assess CBF in VCI patients. Combined with other imaging modalities and biomarkers, ASL has great potential for identifying early VCI and guiding prediction and prevention strategies. This review focuses on recent advances in ASL-based perfusion MRI for identifying patients at high risk of VCI

Learning-based Algorithms for Inverse Problems in MR Image Reconstruction and Quantitative Perfusion Imaging

Medical imaging has become an integral part of the clinical pipeline through its widespread use in the diagnosis, prognosis and treatment planning of several diseases. Magnetic Resonance Imaging (MRI) is particularly useful because it is free from ionizing radiation and is able to provide excellent soft tissue contrast. However, MRI suffers from drawbacks like long scanning durations that increase the cost of imaging and render the acquired images vulnerable to artifacts like motion. In modalities like Arterial Spin Labeling (ASL), which is used for non-invasive and quantitative perfusion imaging, low signal-to-noise ratio and lack of precision in parameter estimates also present significant problems. In this thesis, we develop and present algorithms whose focus can be divided into two broad categories. First, we investigate the reconstruction of MR images from fewer measurements, using data-driven machine learning to fill in the gaps in acquisition, thereby reducing the scan duration. Specifically, we first combine a supervised and an unsupervised (blind) learned dictionary in a residual fashion as a spatial prior in MR image reconstruction, and then extend this framework to include deep supervised learning. The latter, called blind primed supervised (BLIPS) learning, proposes that there exists synergy between features learned using shallower dictionary-based methods or traditional prior-based image reconstruction and those learned using newer deep supervised learning-based approaches. We show that this synergy can be exploited to yield reconstructions that are approx. 0.5-1 dB better in PSNR (in avg. across undersampling patterns). We also observe that the BLIPS algorithm is more robust to a scarcity of available training data, yielding reconstructions that are approx. 0.8 dB better (in terms of avg. PSNR) compared to strict supervised learning reconstruction when training data is very limited. Secondly, we aim to provide more precise estimates for multiple physiological parameters and tissue properties from ASL scans by estimation-theory-based optimization of ASL scan design, and combination with MR Fingerprinting. For this purpose, we use the Cramer-Rao Lower Bound (CRLB) for optimizing the scan design, and deep learning for regression-based estimation. We also show that regardless of the estimator used, optimization improves the precision in parameter estimates, and enables us to increase the available ‘useful’ information obtained in a fixed scanning duration. Specifically, we successfully improve the theoretical precision of perfusion estimates by 4.6% compared to a scan design where the repetition times are randomly chosen (a popular choice in literature) thereby yielding a 35.2% improvement in the corresponding RMSE in our in-silico experiments. This improvement is also visually evident in our in-vivo studies on healthy human subjects.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169819/1/anishl_1.pd

Optimization, Validation, and Acceleration of Magnetic Resonance Vascular Fingerprinting to Measure Cerebrovascular Function

Vascular contributions to cognitive impairment and dementia (VCID) are the second leading cause of dementia and increasing in prevalence as lifespans increase. Clinical MRI traditionally relies on structural abnormalities to identify this vascular dysfunction but lacks microstructure and functional information that could be critical for early identification and assessment of disease. Cerebrovascular dysfunction is one of the only contributors to dementia that can currently be treated, and therefore, earlier identification and subsequent intervention could prevent irreversible structural changes that lead to cognitive decline.Magnetic resonance fingerprinting (MRF) is a novel approach to MRI acquisition and reconstruction using biophysical modeling in parallel to image acquisition for the simultaneous collection of quantitative, multiparametric brain maps. MRF can be adapted to specifically measure cerebrovascular parameters via MR vascular fingerprinting (MRvF), which produces quantitative cerebral blood volume (CBV), microvascular vessel radius (R), and tissue oxygen saturation (SO2) maps of the whole brain. This dissertation aims to advance MRvF for contrast-free, dynamic mapping of cerebrovascular function. First, we compare MRvF to another quantitative MRI method, quantitative blood oxygen level dependent (BOLD) imaging, and show consistency between the techniques, reliable oxygen extraction fraction (OEF) measurements, and expected changes in OEF in response to hypoxia and hyperoxia. Next, we describe a new MRvF pattern-matching algorithm developed for improved mapping without contrast agents, investigate the tradeoffs between SNR, sensitivity, and temporal resolution, and optimize an accelerated spin- and gradient-echo (SAGE) sequence for dynamic MRvF. We show adequate SNR with the SAGE sequence from just one repetition for robust whole-brain vascular parameter mapping every 4.5 seconds. Following this, we demonstrate a novel protocol in which this optimized SAGE sequence allows for dynamic and simultaneous acquisition of MRvF and BOLD measures. By combining this with a tailored hypercapnic (5% CO2) breathing paradigm, we show parameter consistency over time and regional changes in BOLD, CBV, R, and SO2 in response to this stimulus, enabling the calculation of cerebrovascular reactivity (CVR). Finally, we use this newly developed imaging paradigm to compare differences in MRvF-derived CVR measurements in healthy young and healthy old adults. We juxtapose these CVR results against more commonly utilized techniques of measuring CVR to compare and validate our MRvF metrics. Collectively, we demonstrate the development of dynamic MRvF in an ongoing effort toward new quantitative functional imaging biomarkers of cerebrovascular dysfunction with the potential to enable better understanding and earlier diagnoses of diseases like VCID