Dissertation Defense: Yifan Hong, "List Mode Reconstruction in X-ray CT and SPECT imaging"


11 a.m. to noon, Dec. 6, 2023



Tomographic imaging has become an indispensable tool in medical imaging, driving continuous advancements in pursuit of higher image quality, reduced scan times, lower radiation doses, and more recorded attributes. List mode data, which records all attributes in a list format, offers distinct advantages over binned data. This thesis explores the application of list mode data in two tomographic systems.  We present a list-mode filtered back projection (FBP) for single-photon emission tomography (SPECT), which allows continuous-to-continuous reconstruction from imaging space to object space. By simulating an SPECT imaging system, we further qualify the performance of the novel FBP with different image quality metrics including task-based image quality metrics. Additionally, we extended the application of list mode data to X-ray computed tomography (CT) system. To accommodate cone beam scan geometries, we develop a reconstruction method based on maximum likelihood estimation. We validate the algorithm’s effectiveness using both a 3D Shepp-Logan phantom and a 3D patient phantom. In the reconstruction process, graphics processing units (GPU) and Siddon’s algorithm were applied to accelerate the computational speed by over 100 times. To present comprehensive results, the results under different noise levels and the impact of applied filters are demonstrated.