Development of a Voxel-Matching Technique for Substantial Reduction of Subtraction Artifacts in Temporal Subtraction Images Obtained from Thoracic MDCT

Abstract

A temporal subtraction image, which is obtained by subtraction of a previous image from a current one, can be used for enhancing interval changes (such as formation of new lesions and changes in existing abnormalities) on medical images by removing most of the normal structures. However, subtraction artifacts are commonly included in temporal subtraction images obtained from thoracic computed tomography and thus tend to reduce its effectiveness in the detection of pulmonary nodules. In this study, we developed a new method for substantially removing the artifacts on temporal subtraction images of lungs obtained from multiple-detector computed tomography (MDCT) by using a voxel-matching technique. Our new method was examined on 20 clinical cases with MDCT images. With this technique, the voxel value in a warped (or nonwarped) 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. With the voxel-matching technique, the correspondence not only between the structures but also between the voxel values in the current and the previous images is determined. To evaluate the usefulness of the voxel-matching technique for removal of subtraction artifacts, the magnitude of artifacts remaining in the temporal subtraction images was examined by use of the full width at half maximum and the sum of a histogram of voxel values, which may indicate the average contrast and the total amount, respectively, of subtraction artifacts. With our new method, subtraction artifacts due to normal structures such as blood vessels were substantially removed on temporal subtraction images. This computerized method can enhance lung nodules on chest MDCT images without disturbing misregistration artifacts.

Appendix: A Method of Temporal Subtraction on 3D Thoracic Images

A. Overall scheme of the temporal subtraction method

For a temporal subtraction method, registration between the current and the previous image is the most important task because the image quality on the subtraction image would be degraded due to some artifacts caused by incorrect registration. In our temporal subtraction method, the registration is achieved by global image matching, local image matching, and 3-D nonlinear image-warping techniques as illustrated in Figure 6. First, the voxel size of the previous and current images is normalized by using a linear interpolation technique. Then, we employ a global matching technique to correct for the global displacement caused by variation in patient positioning. For more accurate registration, a local matching technique based on 3-D elastic matching is applied to obtain a shift vector for each voxel, which represent the extent of warping of the previous image relative to the current image. The previous image is then warped by use of shift vectors nonlinearly. In addition, the voxels of the warped previous image are matched to those of the current image by using the voxel-matching technique developed in this study. Finally, the matched-voxel warped previous image is subtracted from the current image, thus providing a temporal subtraction image.

Fig 6

Illustration of the overall scheme of a 3-D temporal subtraction method.

B. Global matching by use of a 2-D template matching technique

A global shift vector, which can correct for global temporal displacement caused by patient positioning, is determined on each current slice image. In this matching process, 2-D template matching based on a 2-D cross-correlation method is employed.8 First, blurred images are obtained from the previous and the current images by use of a Gaussian filter (kernel size 15 × 15), and we reduce the matrix size from 512 × 512 to 128 × 128 in the x–y plane for reducing the computation time and for matching only of large structures. In each current slice image, a rectangular region including the lung area is selected as a template image. We move the template image on the previous slice image in order to determine the global shift vector which is obtained from the template location with the maximum of the 2-D cross-correlation value, which indicates the similarity between the current and the previous slice images.

C. Local matching by use of a 3-D elastic matching technique

In order to achieve a high accuracy in matching the current and the previous image, we employ the local matching technique to determine local shift vectors. First, a number of the template and the search area volumes of interest (VOIs) are automatically located within the lung regions in the current and previous images, respectively. The matrix sizes of the template and the search area VOIs are 32 × 32 × 16 and 64 × 64 × 32, respectively. The distances between the adjacent VOIs are 16 pixels in the x–y plane and 8 pixels in the z direction. Then a 3-D cross-correlation value for each VOI pair is calculated with translation (producing shift vector) of the template VOI in the search area VOI. The local shift vector for each VOI pair is determined when the 3-D cross-correlation value becomes the maximum.9

The shift vector which is used for image warping is obtained by a combination of the global shift vector and the local shift vector determined previously. However, the orientation and the amplitude of the shift vector tend to suddenly change in comparison with those of the adjacent shift vectors due to noise in MDCT images. To overcome this problem, Itai et al.10 employ a 3-D elastic matching method for smoothing shift vectors.

With a 2-D elastic matching method, it is possible to obtain the shift vector, which preserves a high cross-correlation value and high consistency over the other shift vectors, as Li et al.13 have mentioned. We employed a 3-D elastic matching technique to deal with the shift vector in 3-D space. In the elastic matching method, the smoothed shift vector can be obtained by minimizing of a cost function that is a weighted sum of an internal and an external energy. The internal energy is given by the squared sum of the first- and second-order derivative values of the shift vectors. The smoother the shift vectors, the smaller the internal energy. On the other hand, the external energy is equal to the negative value of the 3-D cross-correlation value which is obtained with the VOI pair. A shift vector with a large correlation value can provide a small external energy. Therefore, with this elastic matching method, the smoothed shift vector can be obtained by taking into account not only the similarity between the current and the previous images but also the consistency of the shift vectors. With the smoothed shift vectors obtained, the shift vectors in all voxels in the previous image are determined by use of a tri-interpolation method.