UCLA

Magnetic resonance imaging (MRI) is a non-invasive imaging technique that can produce high spatial resolution 3D images, especially for non-bony parts or soft tissues. Higher imaging resolution is usually preferred, to detect small lesions or irregularities in the imaging subject. This dissertation presents two projects that aims for high-resolution MRI. In the first project, we designed a single-loop miniature flexible coil that can be surgically positioned millimeters from the pituitary gland, enabling high-SNR pituitary MRI. We investigated the spatial distributions of the image SNR of the miniature coil, via both numerical simulation and phantom experiments. We also explored the feasibility of increased SNR within the pituitary gland based on simulated surgical placements. Compared to the commercial head coil, our miniature coil achieved up to a 19-fold SNR improvement within the region of interest, and the simulation and phantom experiment reached a good agreement, with an error of 1.1% � 0.8%. High resolution MRI scans further demonstrated the visual improvement of the miniature coil against the commercial head coil. The cross-validation of the simulation and the phantom experiment showed the potential of using the numerical simulation model to accelerate the coil design prototyping and iteration and to optimize coil design in the future. The clinical application study describes a transnasally-placed 2-cm flexible coil to improve the resolution of pituitary imaging. The coil is compatible with 95% of patients, can be successfully placed in contact with the sella in cadaver studies, shows no temperature changes in phantom studies during scanning, and improves the SNR of the pituitary by an order of 17. This study provides feasibility data for the promise of application to the clinical setting to improve the detection of small ACTH-secreting pituitary tumors when clinical pituitary MRI fails. In the second project, we propose a novel slice-profile transformation super-resolution (SPTSR) framework with deep generative learning for through-plane super-resolution (SR) of multi-slice 2D TSE imaging. The deep generative networks were trained by synthesized low-resolution training input via slice-profile downsampling (SP-DS), and the trained networks inferred on the slice profile convolved (SP-conv) testing input for 5.5x through-plane SR. The network output was further slice-profile deconvolved (SP-deconv) to achieve an isotropic super-resolution. Compared to the state-of-the-art SMORE SR method, where the networks trained by conventional downsampling, our SPTSR framework demonstrated the best overall image quality from 50 testing cases, evaluated by two abdominal radiologists. The quantitative analysis cross-validated the expert reader study results. 3D simulation experiments confirmed the quantitative improvement of the proposed SPTSR and the effectiveness of the SP-deconv step, compared to 3D ground-truths. Ablation studies were conducted on the individual contributions of SP-DS and SP-conv, networks structure, training dataset size, and different slice profiles.