Doctor of Philosophy, Case Western Reserve University, 2022, Biomedical Engineering

Tracking the dynamics of magnetic-resonance (MR) contrast agents in magnetic resonance imaging (MRI) studies allows non-invasive assessment of dynamic tissue properties including perfusion, permeability, and metabolism. The concentration of MR contrast agents based on paramagnetic ions can be determined from the changes in the spin-lattice (T1) and spin-spin (T2) relaxation time of water proton (1H) MR signal. However, conventional T1 and T2 mapping methods may not provide adequate temporal resolution to track the tracer dynamics, and the measurements can be prone to inhomogeneities in the main (B0) and transmit (B1+) magnetic fields. On the other side, MR contrast agents based on MR detectable isotopes, such as oxygen-17 (17O), can be tracked by hetero-nuclear MR techniques with the signal intensity directly proportional to the tracer concentration. However, the low MR sensitivity of hetero-nuclei leads to limited spatial and temporal resolution even at high B0 fields. In this work, novel quantitative 1H- and 17O-MRI methods were developed to track the dynamics of paramagnetic ions and 17O-water, respectively, to study tissue perfusion in small laboratory animals at high fields. This thesis focuses on demonstrating that the non-Cartesian encoded 1H- and 17O-MRI approaches allow delineation of perfusion profile with improved spatial and temporal resolution. Three projects are described in this thesis. First, a 2D magnetic-resonance-fingerprinting (MRF) framework was developed for rapid, simultaneous T1 and T2 quantification in mouse at 7 T. The method was demonstrated in dynamic contrast-enhanced MRI studies performed on a preclinical tumor model. Second, the MRF framework was extended to 3D for whole-brain coverage at high spatial resolution. The method was demonstrated in macaque brain covering large field-of-view (FOV) with inhomogeneous B1+ field. Finally, a dynamic 17O-MRI method using golden-means-based 3D radial acquisition combined with adaptiv (open full item for complete abstract)

Committee: Xin Yu (Advisor); Dan Ma (Committee Chair); Chris Flask (Committee Member); Yunmei Wang (Committee Member); Charlie Androjna (Committee Member) Subjects: Biomedical Engineering

Doctor of Philosophy, Case Western Reserve University, 2015, Biomedical Engineering

Tissue perfusion is an important metric that can provide valuable information for disease diagnosis, treatment planning, and treatment follow-up. MRI can quantify perfusion both with and without Gd-based contrast agents, and can provide spatially-localized perfusion maps. However, these techniques are rarely used in the clinical environment. The works in this thesis focused on providing novel data acquisition and/or reconstruction techniques to overcome limitations in DCE MRI and ASL in order to provide clinically-viable perfusion exams. There are three main projects that will be described in this thesis. First, a simultaneous 3D magnetic resonance angiography and perfusion (MRAP) exam is proposed and is demonstrated in the distal lower extremities. Second, a 3D non-Cartesian parallel imaging method is described and used to achieve a low-dose, 3D, high spatiotemporal resolution renal DCE MRI exam that is acquired without breath-holding. Finally, a novel approach to ASL is proposed using the Magnetic Resonance Fingerprinting framework to simultaneously quantify perfusion, transit time, and tissue T1 in a single, efficient acquisition.

Committee: Vikas Gulani M.D., Ph.D. (Advisor); David Wilson Ph.D. (Committee Chair); Mark Griswold Ph.D. (Committee Member); Anant Madabhushi Ph.D. (Committee Member); Michael Martens Ph.D. (Committee Member) Subjects: Biomedical Engineering

Doctor of Philosophy, The Ohio State University, 2008, Electrical and Computer Engineering

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is considered to have great potential in cancer diagnosis and monitoring. During the DCE-MRI procedure, repeated MRI scans are used to monitor contrast agent movement through the vascular system and into tissue. By observing the vascular permeability characteristics, radiologists can detect and classify malignant tissues. When used for diagnostic purposes, the DCE-MRI procedure often requires manual detection, classification, and marking of tumor tissues. This process can be time consuming and fatiguing especially when multiple DCE-MRI procedures must be processed to monitor the progress of a cancer therapy. Manual analysis also suffers from inter- and intra-observer variations which can lead to lesion segmentation inconsistencies. The goal of this dissertation research is to design and develop a tool to aid radiologists, researchers, and clinicians in the detection, segmentation, and analysis of malignant lesions from DCE-MRI datasets. The diagnostic tool presented in this research is model independent, speeds analysis, and provides more consistent segmentations. The approach of the project is to apply statistical 4-D image texture analysis features along with a classifier (such as a neural network) to analyze DCE-MRI datasets. Performance of the computer aided diagnosis (CAD) tool for this project is demonstrated with breast and prostate DCE-MRI data. Training methodology is reported so that extension to other types of cancers and anatomical regions is made possible. Results from the computer assisted diagnostic tool are compared with manual analysis performed by radiologists. The specific research aims of this dissertation are: a) provide a tool for quantitative and quick DCE-MRI analysis by providing radiologists a segmentation (for semi-automatic or automatic application), b) quantify inter- and intra-observer variations that occur during manual lesion segmentation and compare performance with comp (open full item for complete abstract)

Committee: Bradley Clymer PhD (Advisor); Ashok Krishnamurthy PhD (Committee Member); Tahsin Kurc PhD (Committee Member) Subjects: Artificial Intelligence; Bioinformatics; Biomedical Research; Computer Science; Electrical Engineering; Radiology

Doctor of Philosophy, The Ohio State University, 2006, Biophysics

Benign prostatic hyperplasia (BPH) is a highly prevalent disease in older men and occurs in more than 50% of men aged 60 to 70 years. BPH results in prostate enlargement with bladder outflow obstruction. Treatment with a 5α-reductase inhibitor such as finasteride induces apoptosis in epithelial cells and leads to the reduction of prostate volume. However, pharmacologic treatment is not uniformly effective in shrinking the prostate and in relieving symptoms, so the ability to predict how each patient will benefit best from varying pharmacotherapy is a question of great medical and economic importance. Finasteride is also used as a prophylaxis of BPH-associated hematuria and to reduce blood loss at surgical resection of the prostate. The important questions to be addressed include what is the optimum dose and how long should the patients be treated. An effective non-invasive tool may be helpful to solve these questions by monitoring the changes in prostatic blood flow. Twenty-four male beagles with benign prostatic hyperplasia were enrolled in a drug trial and imaged at five time points by magnetic resonance imaging (MRI). The capabilities of different MRI-based methodologies for measuring prostate volume were evaluated from anatomical MR images. The possibility of using pharmacokinetic parameters as a predictor of MRI prostate volume changes were evaluated and the use of DCE-MRI as a biologic marker of in-vivo changes in microcirculation in prostatic suburethral region was assessed. The segmented MRI prostate volume significantly correlated with post necropsy volume. The changes in prostate volume at the end of the treatment exhibited a significant linear correlation to the initial parenchymal Maximum Enhancement Ratio (MER) (p < 0.02) in the finasteride group. After completion of the therapeutic regiment, Tmax on prostatic suburethral area was significantly longer in the finasteride group compared to controls (p < 0.01), and the pharmacokinetic parameters amplitude (open full item for complete abstract)

Committee: Michael Knopp (Advisor) Subjects: Health Sciences, Radiology

Doctor of Philosophy, Case Western Reserve University, 2011, Biomedical Engineering

Ca2+ cycling between the cellular and subcellular compartments plays an important role in regulating cardiac contraction. Disturbance in Ca2+ handling occurs in heart failure and is closely related to abnormal contractile performance. The influx of extracellular Ca2+ through L-type calcium channel is the trigger and a key player in the Ca2+ cycling process. However, there are limited ways to measure it in vivo. Recently, manganese (Mn2+)-enhanced MRI (MEMRI) has been proposed as a promising probe to assess Ca2+ uptake because Mn2+ also enters the cell through the Ca2+ channels. However, quantitative analysis and substantial validation are still lacking, which has limited the application of MEMRI as an in vivo method for quantitative delineation of the Ca2+ influx rate. In the current thesis project, a quantitative MEMRI method was developed and validated using small animal models. The sensitivity to subtle alterations in Ca2+ influx rate was demonstrated in a qualitative MEMRI study using a genetically manipulated mouse model that manifested slightly altered L-type Ca2+ channel activity. To provide quantitative estimation of Mn2+ dynamics, fast T1 mapping techniques were developed based on the direct linear relationship between Mn2+ concentration and proton R1. An ECG-triggered saturation recovery Look-Locker (SRLL) method and a model-based compressed sensing method was developed and validated, respectively. When these two methods were combined, rapid T1 mapping (< 80s) of both myocardium and blood were achieved at high spatial resolution (234x469 μm2). Subsequently, a kinetic model was developed to determine Ca2+ influx rate from the quantitative MEMRI measurements. The robustness and accuracy of estimated Ca2+ influx rate was validated using perfusion MEMRI datasets with L-type Ca2+ channel activity well controlled by buffer ingredients. In conclusion, the accomplishment of this project provides a robust MEMRI method for in vivo quantification of L-type Ca2+ (open full item for complete abstract)

Committee: Xin Yu (Committee Chair); Chris Flask (Committee Member); Mark Griswold (Committee Member); David Rosenbaum (Committee Member); David Wilson (Committee Member) Subjects: Biomedical Engineering; Biomedical Research; Medical Imaging; Radiation; Radiology

Doctor of Philosophy, The Ohio State University, 2013, Biophysics

It is estimated by the American Cancer Society that there will be about 72,570 newly diagnosed cases of bladder cancer (about 54,610 in men and 17,960 in women) and about 15,210 (about 10,820 in men and 4,390 in women) deaths from bladder cancer in 2013. Early detection and accurate staging of bladder cancer are crucial to stratify the treatment plan and ensure the best patient prognosis. In the treatment of bladder cancer patients with chemotherapy, it is important to predict a patient as a chemotherapeutic responder or non-responder to both maximize the patient benefit from chemotherapy and avoid unnecessary delay of cystectomy. However, these clinical needs in bladder cancer management are still unmet in clinical tests, cystoscopy, and bladder imaging. This study is aimed at evaluating the abilities of 3T dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DWI) to improve the detection and staging and to enable the prediction of chemotherapeutic response in bladder cancer. A total of fifty-three patients were enrolled in the study. Different inclusion criteria were established for three different types of data assessment: (1) Improving bladder cancer detection with DCE-MRI; (2) early prediction of chemotherapeutic response in bladder cancer with DCE-MRI; and (3) quantitative assessment of T staging of bladder cancer with DWI. Thirty-six patients were included in the analysis of DCE-MRI for bladder cancer detection. The results demonstrated that the maps of DCE-MRI pharmacokinetic parameters can better visualize small malignant tumors and the tumors within bladder wall thickenings to improve the detection of bladder cancer compared to conventional T2-weighted MRI alone. Twenty-five patients were included in the analysis of DCE-MRI for early prediction of chemotherapeutic response in bladder cancer. Using k-means clustering of DCE-MRI pharmacokinetic parameters, each bladder tumor was divided into three clusters of different microcirculation ch (open full item for complete abstract)

Committee: Michael Knopp (Advisor); Guang Jia (Committee Member); Michael Tweedle (Committee Member); Edward Martin (Committee Member) Subjects: Biophysics