SPECT-CT image fusion technique for diagnosis of head-neck cancer

Kang Ping Lin, Wei Jen Yao

Research output: Contribution to journalConference articlepeer-review

7 Citations (Scopus)

Abstract

A SPECT-CT image fusion technique is developed to provide more precise anatomical location and extension of basal skull involvement of nasopharyngeal carcinoma (NPC) detected by TC-99m MDP bone scan, and to achieve a better treatment planning. In this technique, both the SPECT images and the head-&-neck CT images were appropriately separated to two sections, the head (upper) part images and the neck (lower) part images. A multimodality image registration procedure was used to individually align the head/neck section of the SPECT images to the head/neck section of the CT images respectively. The procedure requires: generation of simulated SPECT images using the bone component in the CT images, and then alignment of the measured SPECT images to the simulated ones that are based on the CT images. In order to benefit from the advantages of CT and SPECT images, the two aligned images have been fused together. The fused images allows user to precisely delineate the interests of region (ROIs) shown to have involvement of NPC which is difficult to be indicated if one imaging modality is used. Moreover, the fused image is also useful for accurate therapeutic planning in radiation oncology and for consistent evaluation of the results between the pre- and post-treatments.

Original languageEnglish
Pages (from-to)377-378
Number of pages2
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume17
Issue number1
Publication statusPublished - 1995 Dec 1
EventProceedings of the 1995 IEEE Engineering in Medicine and Biology 17th Annual Conference and 21st Canadian Medical and Biological Engineering Conference. Part 2 (of 2) - Montreal, Can
Duration: 1995 Sept 201995 Sept 23

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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