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Proceedings Paper

3D temporal subtraction on multislice CT images using nonlinear warping technique
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Paper Abstract

The detection of very subtle lesions and/or lesions overlapped with vessels on CT images is a time consuming and difficult task for radiologists. In this study, we have developed a 3D temporal subtraction method to enhance interval changes between previous and current multislice CT images based on a nonlinear image warping technique. Our method provides a subtraction CT image which is obtained by subtraction of a previous CT image from a current CT image. Reduction of misregistration artifacts is important in the temporal subtraction method. Therefore, our computerized method includes global and local image matching techniques for accurate registration of current and previous CT images. For global image matching, we selected the corresponding previous section image for each current section image by using 2D cross-correlation between a blurred low-resolution current CT image and a blurred previous CT image. For local image matching, we applied the 3D template matching technique with translation and rotation of volumes of interests (VOIs) which were selected in the current and the previous CT images. The local shift vector for each VOI pair was determined when the cross-correlation value became the maximum in the 3D template matching. The local shift vectors at all voxels were determined by interpolation of shift vectors of VOIs, and then the previous CT image was nonlinearly warped according to the shift vector for each voxel. Finally, the warped previous CT image was subtracted from the current CT image. The 3D temporal subtraction method was applied to 19 clinical cases. The normal background structures such as vessels, ribs, and heart were removed without large misregistration artifacts. Thus, interval changes due to lung diseases were clearly enhanced as white shadows on subtraction CT images.

Paper Details

Date Published: 30 March 2007
PDF: 8 pages
Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65143I (30 March 2007); doi: 10.1117/12.709004
Show Author Affiliations
Takayuki Ishida, Hiroshima International Univ. (Japan)
Shigehiko Katsuragawa, Kumamoto Univ. (Japan)
Ikuo Kawashita, Hiroshima International Univ. (Japan)
Hyounseop Kim, Kyushu Institute of Technology (Japan)
Yoshinori Itai, Kyushu Institute of Technology (Japan)
Kazuo Awai, Kumamoto Univ. (Japan)
Qiang Li, The Univ. of Chicago (United States)
Kunio Doi, The Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 6514:
Medical Imaging 2007: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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