UCLA

Prostate Cancer (PCa) remains the second most common cause of cancer-related death in men in the U.S. Multi-parametric (mp) MRI is playing an increasingly important role for the localization, detection, and risk stratification of PCa. However, prostate mp-MRI still misses PCa in up to 45% of men and faces challenges in distinguishing clinically significant PCa from indolent PCa. Therefore, MRI technology must be improved to enhance diagnostic performance for PCa. This thesis aimed to improve prostate MRI by addressing two challenges. First, the diffusion-weighted imaging (DWI) component of mp-MRI often suffers from artifacts such as distortion and low signal-to-noise ratio (SNR), which can lead to low diagnostic image quality. Second, prostate microstructure features are key determinants for histopathological assessment of cancer aggressiveness; however, current MRI techniques have limitations in capturing this information. To address the first challenge, in Aim 1, we translated and evaluated an eddy current-nulled convex optimized diffusion encoding (ENCODE) based prostate DWI technique that achieves short echo time (TE) to maintain SNR while reducing prostate geometric distortion from eddy currents and susceptibility effects. Further, in Aim 2, we developed a combined TE-minimized ENCODE diffusion encoding acquisition with a random matrix theory-based denoising reconstruction technique to improve the SNR and robustness of high-resolution (in-plane: 1.0x1.0 mm2) prostate DWI and apparent diffusion coefficient mapping. To address the second challenge, in Aim 3, we performed a first proof-of-concept ex vivo evaluation and validation of the diffusion-relaxation correlation spectrum imaging (DR-CSI) technique at 3T for quantifying microscopic tissue compartments (epithelium, stroma, and lumen) in PCa using whole-mount digital histopathology as the reference standard. Further, in Aim 4, we explored and evaluated sequential backward selection analysis for the acceleration of DR-CSI through subsampling of the diffusion-relaxation contrast encoding space while maintaining the accuracy of prostate microstructure mapping in PCa.