A temporal subtraction image, which is obtained by subtraction of a previous image from a current one, can be used for
enhancing interval changes on medical images by removing most of normal structures. One of the important problems in
temporal subtraction is that subtraction images commonly include artifacts created by slight differences in the size, shape,
and/or location of anatomical structures. In this paper, we developed a new registration method with voxel-matching
technique for substantially removing the subtraction artifacts on the temporal subtraction image obtained from multiple-detector
computed tomography (MDCT). With this technique, the voxel value in a warped (or non-warped) previous
image is replaced by a voxel value within a kernel, such as a small cube centered at a given location, which would be
closest (identical or nearly equal) to the voxel value in the corresponding location in the current image. Our new method
was examined on 16 clinical cases with MDCT images. Preliminary results indicated that interval changes on the
subtraction images were enhanced considerably, with a substantial reduction of misregistration artifacts. The temporal
subtraction images obtained by use of the voxel-matching technique would be very useful for radiologists in the
detection of interval changes on MDCT images.
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.
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