| Title |
Normal model construction for statistical image analysis of torso FDG-PET images based on anatomical standardization by CT images from FDG-PET/CT devices
|
|---|---|
| Published in |
International Journal of Computer Assisted Radiology and Surgery, February 2017
|
| DOI | 10.1007/s11548-017-1526-4 |
| Pubmed ID | |
| Authors |
Kenshiro Takeda, Takeshi Hara, Xiangrong Zhou, Tetsuro Katafuchi, Masaya Kato, Satoshi Ito, Keiichi Ishihara, Shinichiro Kumita, Hiroshi Fujita |
| Abstract |
A better understanding of the standardized uptake value (SUV) ranges of fludeoxyglucose positron emission tomography (FDG-PET) is crucial for radiologists. We have developed a statistical image analysis method for FDG-PET imaging of the torso, based on comparisons with normal data. The purpose of this study was to verify the accuracy of the normal model and usefulness of the statistical image analysis method by using typical cancer cases in the liver, lungs, and abdomen. Our study and the data collection (49 normal and 34 abnormal cases, in terms of PET/CT findings) were approved by the institutional review board. Our scheme consisted of the following steps: (1) normal model construction, (2) anatomical standardization of patient images, and (3) Z-score calculation to show the results of the statistical image analysis. To validate the Z-score index, we sampled 3603 and 1270 voxels in normal organs and abnormal regions, respectively, from the liver, lungs, and the abdomen. We then obtained the SUV and Z-score for each region. A receiver operating characteristics (ROC) analysis-based method was performed to evaluate the discrimination performances of the SUV and Z-score. The discrimination performances of the SUV and Z-score for the objective regions of interest (ROIs) were evaluated by the areas under the ROC curves (AUCs). As a result of the ROC analysis and statistical tests, all AUCs were found to be larger than 0.98. When the ROIs in the objective regions were combined, the mean AUCs of the Z-score and SUV were 0.99 and 0.98, respectively, the difference being statistically significant ([Formula: see text]). The results suggested the possibility of applying a quantitative image reading method for torso FDG-PET imaging. Furthermore, a combination of the SUV and Z-score may provide increased accuracy of the determination methods, such as computer-aided detection and diagnosis. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 14 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 4 | 29% |
| Student > Ph. D. Student | 3 | 21% |
| Student > Doctoral Student | 1 | 7% |
| Student > Bachelor | 1 | 7% |
| Lecturer | 1 | 7% |
| Other | 3 | 21% |
| Unknown | 1 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 7 | 50% |
| Computer Science | 2 | 14% |
| Linguistics | 1 | 7% |
| Nursing and Health Professions | 1 | 7% |
| Psychology | 1 | 7% |
| Other | 1 | 7% |
| Unknown | 1 | 7% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 January 2018.
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#18,584,192
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Outputs from International Journal of Computer Assisted Radiology and Surgery
#616
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#310,820
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Outputs of similar age from International Journal of Computer Assisted Radiology and Surgery
#5
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