Super-Resolution in Computer Tomography (CT)

Supervisor(s): Prof. Yonina Eldar, Shahar Tsiper
Abstract: X-Ray Computer Tomography, known as CT, is one of the main tools Doctors use today to examine patients. In every main hospital, there are CT machines that work 24/7 to provide Doctors with in-depth views of the human body. CT Scans enable them to save lives on a daily basis. Today, 3rd generation CTs can scan over 100 patients a day, but their radiation dose is relatively high. For instance, an abdomen CT scan radiation dose is equivalent to almost a 1000 chest Xray scans.It is highly desirable to reduce the radiation dose, while preserving the resolution and details of the scans. This is where we come in. In this project the students will first study the basics of computer aided tomography, advanced mathematical imaging tools and cutting edge techniques in digital signal processing. The students will then research, design and implement a novel reconstruction algorithm for CT scans, which could allow faster scan times and reduced radiation dosage. The basis for this algorithm will implement principles from novel fields such as “Compressed Sensing” and “Super Resolution by Dictionary Learning”.
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