Project Description:
Chest computed tomography (CT) exams are among some of the most frequently performed, high-yield, diagnostic imaging exams globally. However, traditional scanning methods often struggle with efficiency and reliability in producing high-quality imaging results. Consequently, multiple acquisitions are necessary in delivering proper results, especially due to inept tissue differentiation by conventional CT scanning processes. To address these issues, our Capstone team designed a machine-learning based photon-counting computed tomography pipeline. Our pipeline delivers superior image resolution as well as enhanced material decomposition at a fraction of a traditional CT exam radiation dose.
