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Proceedings Paper

Optimized 3D stitching algorithm for whole body SPECT based on transition error minimization (TEM)
Author(s): Xinhua Cao; Xiaoyin Xu; Stephan Voss
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Paper Abstract

Standard Single Photon Emission Computed Tomography (SPECT) has a limited field of view (FOV) and cannot provide a 3D image of an entire long whole body SPECT. To produce a 3D whole body SPECT image, two to five overlapped SPECT FOVs from head to foot are acquired and assembled using image stitching. Most commercial software from medical imaging manufacturers applies a direct mid-slice stitching method to avoid blurring or ghosting from 3D image blending. Due to intensity changes across the middle slice of overlapped images, direct mid-slice stitching often produces visible seams in the coronal and sagittal views and maximal intensity projection (MIP). In this study, we proposed an optimized algorithm to reduce the visibility of stitching edges. The new algorithm computed, based on transition error minimization (TEM), a 3D stitching interface between two overlapped 3D SPECT images. To test the suggested algorithm, four studies of 2-FOV whole body SPECT were used and included two different reconstruction methods (filtered back projection (FBP) and ordered subset expectation maximization (OSEM)) as well as two different radiopharmaceuticals (Tc-99m MDP for bone metastases and I-131 MIBG for neuroblastoma tumors). Relative transition errors of stitched whole body SPECT using mid-slice stitching and the TEM-based algorithm were measured for objective evaluation. Preliminary experiments showed that the new algorithm reduced the visibility of the stitching interface in the coronal, sagittal, and MIP views. Average relative transition errors were reduced from 56.7% of mid-slice stitching to 11.7% of TEM-based stitching. The proposed algorithm also avoids blurring artifacts by preserving the noise properties of the original SPECT images.

Paper Details

Date Published: 24 February 2017
PDF: 10 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013321 (24 February 2017); doi: 10.1117/12.2254096
Show Author Affiliations
Xinhua Cao, Boston Children's Hospital (United States)
Harvard Medical School (United States)
Xiaoyin Xu, Brigham and Women's Hospital (United States)
Harvard Medical School (United States)
Stephan Voss, Boston Children's Hospital (United States)
Harvard Medical School (United States)


Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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