Fall 2016
Project Title:

Super-Resolution in Computer Tomography (CT) – Part 2

Project ID:



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 X-ray 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.

The students are now after research, design and implementation for a novel reconstruction algorithm for CT scans, which could allow faster scan times and reduced radiation dosage. The basis for this algorithm will relies on principles from novel fields such as "Compressed Sensing" and "Super Resolution by Dictionary Learning".

During Part 2 the students will attempt novel ideas in order to improve the Dictionary Learning algorithm, and implement it on real data acquired from Rambam hospital. The main effort will focus on isolating artifacts related from foreign metal objects that penetrated the human body.

• A collaboration with Rambam Hospital will take place.





Gal Sadeh & Dmitry Lavrov


Fall 2016

INTERIA Web Design & Development