Radiology Commons^™

A Deep Learning U-Net For Detecting And Segmenting Liver Tumors, Vidhya Cardozo

Theses and Dissertations

Visualization of liver tumors on simulation CT scans is challenging even with contrast-enhancement, due to the sensitivity of the contrast enhancement to the timing of the CT acquisition. Image registration to magnetic resonance imaging (MRI) can be helpful for delineation, but differences in patient position, liver shape and volume, and the lack of anatomical landmarks between the two image sets makes the task difficult. This study develops a U-Net based neural network for automated liver and tumor segmentation for purposes of radiotherapy treatment planning. Non-contrast simulation based abdominal CT axial scans of 52 patients with primary liver tumors were utilized. …

Lead X-Ray Vests: Pros And Cons In Dental Radiography And Patient Education On Radiographic Technology, Kayla D. Oakley, Emily Deker

Dental Hygiene Student Scholarship

Problem: There is a greater need to emphasize and educate patients and providers about radiologic advancements and patient exposure. The tradition of placing lead x-ray vests on patients for all radiographs is no longer a necessity. Without the explanation of why lead aprons are no longer needed, unnecessary steps for the provider and potential harm to the patient could be caused.

Methods: This review of literature was assembled by analyzing data from primary and secondary sources through online databases. The most recent research on the risks and benefits of the use of lead x-ray vests towards patients in …