Investigating the Intrinsic and Extrinsic Drivers of Primate Heterothermy

Seasonal heterothermy—an orchestrated set of extreme physiological responses—is directly responsible for the over-winter survival of many mammalian groups living in seasonal environments. Historically, it was thought that the use of seasonal heterothermy (i.e. daily torpor and hibernation) was restricted to cold-adapted species; it is now known that such thermoregulatory strategies are used by more species than previously appreciated, including many tropical species. The dwarf and mouse lemurs (family Cheirogaleidae) are among the few primates known to use seasonal heterothermy to avoid Madagascar’s harsh and unpredictable environments. These primates provide an ideal study system for investigating a common mechanism of mammalian seasonal heterothermy. The overarching theme of this dissertation is to understand both the intrinsic and extrinsic drivers of heterothermy in three species of the family Cheirogaleidae. By using transcriptome sequencing to characterize gene expression in both captive and natural settings, we identify unique patterns of differential gene expression that are correlated with extreme changes in physiology in two species of dwarf lemurs: C. medius under captive conditions at the Duke Lemur Center and C. crossleyi studied under field conditions in Madagascar. Genes that are differentially expressed appear to be critical for maintaining the health of these animals when they undergo prolonged periods of metabolic depression concurrent with the hibernation phenotype. Further, a comparative analysis of previously studied mammalian heterotherms identifies shared genetic mechanisms underlying the hibernation phenotype across the phylogeny of mammals. Lastly, conducting a diet manipulation study with a captive colony of mouse lemurs (Microcebus murinus) at the Duke Lemur Center, we investigated the degree to which dietary effects influence torpor patterns. We find that tropical primate heterotherms may be exempt from the traditional paradigms governing cold-adapted heterothermy, having evolved different dietary strategies to tolerate circadian changes in body temperature.

Automated Assessment of Image Quality and Dose Attributes in Clinical CT Images

Computed tomography (CT) is a valuable technology to the healthcare enterprise as evidenced by the more than 70 million CT exams performed every year. As a result, CT has become the largest contributor to population doses amongst all medical imaging modalities that utilize man-made ionizing radiation. Acknowledging the fact that ionizing radiation poses a health risk, there exists the need to strike a balance between diagnostic benefit and radiation dose. Thus, to ensure that CT scanners are optimally used in the clinic, an understanding and characterization of image quality and radiation dose are essential.

The state-of-the-art in both image quality characterization and radiation dose estimation in CT are dependent on phantom based measurements reflective of systems and protocols. For image quality characterization, measurements are performed on inserts imbedded in static phantoms and the results are ascribed to clinical CT images. However, the key objective for image quality assessment should be its quantification in clinical images; that is the only characterization of image quality that clinically matters as it is most directly related to the actual quality of clinical images. Moreover, for dose estimation, phantom based dose metrics, such as CT dose index (CTDI) and size specific dose estimates (SSDE), are measured by the scanner and referenced as an indicator for radiation exposure. However, CTDI and SSDE are surrogates for dose, rather than dose per-se.

Currently there are several software packages that track the CTDI and SSDE associated with individual CT examinations. This is primarily the result of two causes. The first is due to bureaucracies and governments pressuring clinics and hospitals to monitor the radiation exposure to individuals in our society. The second is due to the personal concerns of patients who are curious about the health risks associated with the ionizing radiation exposure they receive as a result of their diagnostic procedures.

An idea that resonates with clinical imaging physicists is that patients come to the clinic to acquire quality images so they can receive a proper diagnosis, not to be exposed to ionizing radiation. Thus, while it is important to monitor the dose to patients undergoing CT examinations, it is equally, if not more important to monitor the image quality of the clinical images generated by the CT scanners throughout the hospital.

The purposes of the work presented in this thesis are threefold: (1) to develop and validate a fully automated technique to measure spatial resolution in clinical CT images, (2) to develop and validate a fully automated technique to measure image contrast in clinical CT images, and (3) to develop a fully automated technique to estimate radiation dose (not surrogates for dose) from a variety of clinical CT protocols.