Automated Change Detection of Serial MR Images Using Compressed Sensing

We aim to develop a new algorithm to detect changes in serial MR examinations.

Computer-based change detection system is an important tool to automatically

process abundant information produced by imaging systems and assist physicians to

identify clinically important changes in the images. Many existing methods are

computationally costly. The emerging mathematical theory of compressed sensing

(CS) allows exact reconstruction of a sparse signal from fewer samples. Generally, a

medical image does not have a noticeable quality change by discarding certain

information) and changes of the same patient over time are spatially sparse. We thus

utilize the CS techniques to develop novel change detection algorithms that reduce

computational cost and extract clinical-relevant information robust to the presence

of noise. Our algorithm is expected to assist radiologists to make early diagnosis and

be used as a training tool for young practitioners as well as to advance the

development of change detection technology in geological and other applications.

Faculty Supervisor:

Hongmei Zhu

Student:

Partner:

Hospital for Sick Children;AUG Signals Ltd

Discipline:

Mathematics

Sector:

Health and Related Sciences & Technology

University:

York University

Program:

Accelerate

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