Gray matter involvement in MS has been demonstrated in numerous recent studies. However, because of the limited resolution of existing structural imaging techniques, widespread quantification of gray matter damage and the corresponding impact on cognition has been limited. Perfusion imaging has been shown to enable the quantitative study of gray matter function through the identification of blood flow and volume. We are interested in combining image processing and statistical analysis techniques to study the localization of perfusion in different areas of gray matter and correlate it with patients’ cognitive function. These techniques have direct clinical applicability, as they enable quantitative assessment of gray matter in MS and could, in turn, aid clinicians in evaluating patients’ clinical states and treatment effects. I have co-authored 7 publications in this area and continue to explore new image processing techniques to study perfusion images.
Multimodality imaging in general is a great asset for an in-depth analysis of structural and functional abnormalities. However, access to scanners that simultaneously acquire multiple modalities (eg, PET/CT) is limited. As a result, a large variation in image characteristics such as resolution and field of view exists when utilizing multiple modalities. These variations could directly or indirectly affect the corresponding analysis and might lead to excessive overhead for radiologists trying to gather information about the particular abnormality from a number of different modalities. This has motivated the development of multimodal image registration, fusion, and annotation techniques in order to maintain localization across different modalities and aid in a more objective and quantitative diagnosis. I also have a focused interest in multimodal image processing for brain, nerve, and spine applications and have co-authored over 30 publications/conference proceedings in this area. Throughout my work term as a scientist at GE Healthcare, I have also co-invented 3 patents on automated image annotation and segmentation.