Engineering Commons^™

Organ Segmentation Of Pediatric Computed Tomography (Ct) With Generative Adversarial Networks, Chi Nok Enoch Kan

Master's Theses (2009 -)

Accurately segmenting organs in abdominal computed tomography (CT) is crucial for many clinical applications such as organ-specific dose estimation. With the recent emergence of deep learning techniques for computer vision, many powerful frameworks are proposed for organ segmentation in abdominal CT images. A major problem with these state-of-the-art methods is that they depend on large amounts of training data to achieve high segmentation accuracy. Pediatric abdominal CTs are particularly hard to obtain since these children are much more sensitive to ionizing radiation than adults. It is extremely challenging to train automatic segmentation algorithms on pediatric CT volumes. To address these …

Reducing Radiation Dose To The Female Breast During Conventional And Dedicated Breast Computed Tomography, Franco Rupcich

Dissertations (1934 -)

The purpose of this study was to quantify the effectiveness of techniques intended to reduce dose to the breast during CT coronary angiography (CTCA) scans with respect to task-based image quality, and to evaluate the effectiveness of optimal energy weighting in improving contrast-to-noise ratio (CNR), and thus the potential for reducing breast dose, during energy-resolved dedicated breast CT.

A database quantifying organ dose for several radiosensitive organs irradiated during CTCA, including the breast, was generated using Monte Carlo simulations. This database facilitates estimation of organ-specific dose deposited during CTCA protocols using arbitrary x-ray spectra or tube-current modulation schemes without the …