Motion correction algorithm of lung tumors for respiratory gated PET images

Respiratory gating in lung PET imaging to compensate for respiratory motion artifacts is a current research issue with broad potential impact on quantitation, diagnosis and clinical management of lung tumors. However, PET images collected at discrete bins can be significantly affected by noise as there are lower activity counts in each gated bin unless the total PET acquisition time is prolonged, so that gating methods should be combined with imaging-based motion correction and registration methods. The aim of this study was to develop and validate a fast and practical solution to the problem of respiratory motion for the detection and accurate quantitation of lung tumors in PET images. This included: (1) developing a computer-assisted algorithm for PET/CT images that automatically segments lung regions in CT images, identifies and localizes lung tumors of PET images; (2) developing and comparing different registration algorithms which processes all the information within the entire respiratory cycle and integrate all the tumor in different gated bins into a single reference bin. Four registration/integration algorithms: Centroid Based, Intensity Based, Rigid Body and Optical Flow registration were compared as well as two registration schemes: Direct Scheme and Successive Scheme. Validation was demonstrated by conducting experiments with the computerized 4D NCAT phantom and with a dynamic lung-chest phantom imaged using a GE PET/CT System. Iterations were conducted on different size simulated tumors and different noise levels. Static tumors without respiratory motion were used as gold standard; quantitative results were compared with respect to tumor activity concentration, cross-correlation coefficient, relative noise level and computation time. Comparing the results of the tumors before and after correction, the tumor activity values and tumor volumes were closer to the static tumors (gold standard). Higher correlation values and lower noise were also achieved after applying the correction algorithms. With this method the compromise between short PET scan time and reduced image noise can be achieved, while quantification and clinical analysis become fast and precise.

Motion Correction Algorithm of Lung Tumors for Respiratory Gated PET Images

Respiratory gating in lung PET imaging to compensate for respiratory motion artifacts is a current research issue with broad potential impact on quantitation, diagnosis and clinical management of lung tumors. However, PET images collected at discrete bins can be significantly affected by noise as there are lower activity counts in each gated bin unless the total PET acquisition time is prolonged, so that gating methods should be combined with imaging-based motion correction and registration methods. The aim of this study was to develop and validate a fast and practical solution to the problem of respiratory motion for the detection and accurate quantitation of lung tumors in PET images. This included: (1) developing a computer-assisted algorithm for PET/CT images that automatically segments lung regions in CT images, identifies and localizes lung tumors of PET images; (2) developing and comparing different registration algorithms which processes all the information within the entire respiratory cycle and integrate all the tumor in different gated bins into a single reference bin. Four registration/integration algorithms: Centroid Based, Intensity Based, Rigid Body and Optical Flow registration were compared as well as two registration schemes: Direct Scheme and Successive Scheme. Validation was demonstrated by conducting experiments with the computerized 4D NCAT phantom and with a dynamic lung-chest phantom imaged using a GE PET/CT System. Iterations were conducted on different size simulated tumors and different noise levels. Static tumors without respiratory motion were used as gold standard; quantitative results were compared with respect to tumor activity concentration, cross-correlation coefficient, relative noise level and computation time. Comparing the results of the tumors before and after correction, the tumor activity values and tumor volumes were closer to the static tumors (gold standard). Higher correlation values and lower noise were also achieved after applying the correction algorithms. With this method the compromise between short PET scan time and reduced image noise can be achieved, while quantification and clinical analysis become fast and precise.

The influence of body size on the ecology of coastal fish predators in the Bahamas

Body size is a fundamental structural characteristic of organisms, determining critical life history and physiological traits, and influencing population dynamics, community structure, and ecosystem function. For my dissertation, I focused on effects of body size on habitat use and diet of important coastal fish predators, as well as their influence on faunal communities in Bahamian wetlands. First, using acoustic telemetry and stable isotope analysis, I identified high variability in movement patterns and habitat use among individuals within a gray snapper (Lutjanus griseus) and schoolmaster snapper (L. apodus) population. This intrapopulation variation was not explained by body size, but by individual behavior in habitat use. Isotope values differed between individuals that moved further distances and individuals that stayed close to their home sites, suggesting movement differences were related to specific patterns of foraging behavior. Subsequently, while investigating diet of schoolmaster snapper over a two-year period using stomach content and stable isotope analyses, I also found intrapopulation diet variation, mostly explained by differences in size class, individual behavior and temporal variability. I then developed a hypothesis-testing framework examining intrapopulation niche variation between size classes using stable isotopes. This framework can serve as baseline to categorize taxonomic or functional groupings into specific niche shift scenarios, as well as to help elucidate underlying mechanisms causing niche shifts in certain size classes. Finally, I examined the effect of different-sized fish predators on epifaunal community structure in shallow seagrass beds using exclusion experiments at two spatial scales. Overall, I found that predator effects were rather weak, with predator size and spatial scale having no impact on the community. Yet, I also found some evidence of strong interactions on particular common snapper prey. As Bahamian wetlands are increasingly threatened by human activities (e.g., overexploitation, habitat degradation), an enhanced knowledge of the ecology of organisms inhabiting these systems is crucial for developing appropriate conservation and management strategies. My dissertation research contributed to this effort by providing critical information about the resource use of important Bahamian fish predators, as well as their effect on faunal seagrass communities.