Warped matching for digital subtraction of CT-angiography studies

Ali R. Bani-Hashemi; Arun Krishnan; Sumitro Samaddar

doi:10.1117/12.237945

16 April 1996 Warped matching for digital subtraction of CT-angiography studies

Ali R. Bani-Hashemi, Arun Krishnan, Sumitro Samaddar

Proceedings Volume 2710, Medical Imaging 1996: Image Processing; (1996) https://doi.org/10.1117/12.237945
Event: Medical Imaging 1996, 1996, Newport Beach, CA, United States

Abstract

Spiral CT-angiography (CTA) is a new, minimally invasive technique for vascular imaging. A key task in CTA is to suppress bone and other dense tissues, such as calcification, in order to visualize the vessels of interest. Common practice in CTA employs automatic or semiautomatic bone editing techniques. However, editing is not possible where the bone structures are complex, and particularly when they are close to vessels. In addition, it is quite unlikely that any automatic editing technique would succeed in removing calcification. An alternative approach is to extend conventional digital subtraction techniques (DSA) from two dimensions to three dimensions. The potential advantages of digital subtraction CTA are; (1) fully automatic bone removal, (2) elimination of calcification, and (3) reducing the concentration of contrast medium. This paper presents a 3D flexible registration technique that brings a pre-contrast (native) volume to register with a post-contrast (contrast) volume prior to subtraction. Two volumes are treated to be composed of blocks; each block in one data set is translated, rotated, and scaled in three dimensions to match the corresponding block of the other volume. In essence, flexible matching is realized by rigid body motion and scaling of a large number of blocks. The proposed algorithm has been successfully applied on a number of clinical studies.

Citation Download Citation

Ali R. Bani-Hashemi, Arun Krishnan, and Sumitro Samaddar "Warped matching for digital subtraction of CT-angiography studies", Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); https://doi.org/10.1117/12.237945

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