Abstract
Detection of subtle lesions on computed tomography (CT) images is a difficult task for radiologists, because subtle lesions such as small lung nodules tend to be low contrast, and a large number of CT images must be interpreted in a limited time. 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 shapes of new lesions and the interval changes in existing abnormalities) on medical images by removing most of the normal structures. For detection of lesions in chest radiographs, the temporal subtraction technique has been applied successfully to clinical cases. In this paper, we propose a new method for removing the artifacts in temporal subtraction images obtained from chest multiple detectors CT (MDCT) images by using a smoothing of shift vectors. With our method, more accurate correspondence of normal structures between current and previous images is determined. The new method was applied for 6 clinical cases of MDCT images. Preliminary results indicated that interval changes on the subtraction images were enhanced considerably with reduction of misregistration artifacts.
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References
Kakeda, S., Nakamura, K., Kamada, K., Watanebe, H., Nakata, H., Katsuragawa, S., Doi, K.: Improved detection of lung nodules by using a temporal subtraction technique. Radiology 224(1), 145–151 (2002)
Kano, A., Doi, K., MacMahon, H., Hassell, D.D., Giger, M.L.: Digital image subtraction of temporally sequential chest images for detection of interval change. Med. Phys. 21(3), 445–461 (1994)
Ishida, T., Ashizawa, K., Engelmann, R., Katsuragawa, S., MacMahon, H., Doi, K.: Application of temporal subtraction for detection of interval changes on chest radiographs: Improvement of subtraction images using automated initial image matching. J. Digital Imag. 12(2), 77–86 (1999)
Ishida, T., Katsuragawa, S., Nakamura, K., MacMahon, H., Doi, K.: Iterative image warping technique for temporal subtraction of sequential chest radiographs to detect interval change. Med. Phys. 26(7), 1320–1329 (1999)
Katsuragawa, S., Tagashira, H., Li, Q., MacMahon, H., Doi, K.: Comparison of temporal subtraction images obtained with manual and automated methods of digital chest radiography. J. Digital Imag. 12(4), 166–172 (1999)
Difazio, M.C., MacMahon, H., Xu, X.W., Tsai, P., Shiraishi, J., Aramato III, S.G., Doi, K.: Digital chest radiography: Effect of temporal subtraction images on detection accuracy. Radiology 202(2), 447–452 (1997)
Li, Q., Katsuragawa, S., Doi, K.: Improved contralateral subtraction images by use of elastic matching technique. Med. Phys. 27(8), 1934–1943 (2000)
Ishida, T., Katsuragawa, S., Abe, H., Ashizawa, K., Doi, K.: Development of 3D CT temporal Subtraction based on nonlinear 3D image warping Technique. In: Proc. The 91st Radiological Society of North America (RSNA), Chicago, USA, vol. 111 (2005)
Itai, Y., Kim, H., Ishikawa, S., Ishikawa, S., Ishida, T., Kawashita, I., Awai, K., Li, Q., Doi, K.: 3D elastic matching for temporal subtraction employing thorax MDCT image. In: Proc. of the World Congress on Medical Physics and Biomedical Engineering, pp. 2181–2191 (2006)
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© 2008 Springer-Verlag Berlin Heidelberg
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Itai, Y. et al. (2008). An Image Warping Method for Temporal Subtraction Images Employing Smoothing of Shift Vectors on MDCT Images. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69158-7_102
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DOI: https://doi.org/10.1007/978-3-540-69158-7_102
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69154-9
Online ISBN: 978-3-540-69158-7
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