A Fast Accuracy Crystal Identification Method Based on Fuzzy C-Means (FCM) Clustering Algorithm for MicroPET

A high resolution detector is being developed for our small animal position emission tomography (MicroPET). The detector unit consist of 8x8 crystal blocks, coupled to four photomultiplier tubes (PMTs). Each scintillation event is mapped in a two dimensional (2-D) position through the relative ratio...

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Bibliographic Details
Published in:2008 International Conference on BioMedical Engineering and Informatics Vol. 1; pp. 779 - 782
Main Authors: Xiaowen Kang, Xishan Sun, Shi Wang, Yaqiang Liu, Yan Xia, Rong Zhou, Zhaoxia Wu, Yongjie Jin
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2008
Subjects:
Animals
Clustering algorithms
crystal identification
Crystal Look-up table (CLT)
Event detection
Fuzzy C-Means (FCM) Clustering Algorithm
Humans
Photomultipliers
positron emission tomography (PET)
Signal resolution
Solid scintillation detectors
Table lookup
Tomography
Two dimensional displays
ISBN:9780769531182, 0769531180
ISSN:1948-2914
Online Access:Get full text
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Summary:A high resolution detector is being developed for our small animal position emission tomography (MicroPET). The detector unit consist of 8x8 crystal blocks, coupled to four photomultiplier tubes (PMTs). Each scintillation event is mapped in a two dimensional (2-D) position through the relative ratio of the output signals of the PMTs. Crystal Look-up table (CLT) used in ThuMicroPET scanner defines the matching relation between signal position of a detected event to a corresponding detector pixel location. It has a direct impact on imaging quality and brings significant influence to the gantry overall performance. However, the currently used method involves a lot of human interaction for CLT corrections, and cannot be implemented as a general process due to its complexity. This paper introduces a fast accuracy method based on Fuzzy C-Means (FCM) Clustering Algorithm for crystal identification. In the FCM, a cluster center and a fuzzy partition matrix of individual events in the 2-D position are defined. By iteratively updating the cluster centers and the membership grades for each event, we can move the cluster center to the right location in a short time, based on minimizing objective function that represents the distance from any given events to a cluster center weighted by its membership grade. The preliminary result shows that FCM can be used effectively in CLT construction, which significantly reduces the time, and brings excellent accuracy than we expected.
ISBN:9780769531182
0769531180
ISSN:1948-2914
DOI:10.1109/BMEI.2008.351