ArticlePloS one2015
Quantitative Analysis of Torso FDG-PET Scans by Using Anatomical Standardization of Normal Cases from Thorough Physical Examinations.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Development and Initial Validation of a Statistical Image Analysis Framework for Quantitative Bone SPECT/CT in Patients with Prostate Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- Post-acquisition standardization of positron emission tomography images.Frontiers in nuclear medicine · 2023Article
- Anomaly detection in chestJapanese journal of radiology · 2022Article
- Normal model construction for statistical image analysis of torso FDG-PET images based on anatomical standardization by CT images from FDG-PET/CT devices.International journal of computer assisted radiology and surgery · 2017Article
- A Registration Method Based on Contour Point Cloud for 3D Whole-Body PET and CT Images.BioMed research international · 2017Article
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Authors and funding
6 authors.
Funding
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Abstract
Understanding of standardized uptake value (SUV) of 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography (FDG-PET) depends on the background accumulations of glucose because the SUV often varies the status of patients. The purpose of this study was to develop a new method for quantitative analysis of SUV of FDG-PET scan images. The method included an anatomical standardization and a statistical comparison with normal cases by using Z-score that are often used in SPM or 3D-SSP approach for brain function analysis. Our scheme consisted of two approaches, which included the construction of a normal model and the determination of the SUV scores as Z-score index for measuring the abnormality of an FDG-PET scan image. To construct the normal torso model, all of the normal images were registered into one shape, which indicated the normal range of SUV at all voxels. The image deformation process consisted of a whole body rigid registration of shoulder to bladder region and liver registration and a non-linear registration of body surface by using the thin-plate spline technique. In order to validate usefulness of our method, we segment suspicious regions on FDG-PET images manually, and obtained the Z-scores of the regions based on the corresponding voxels that stores the mean and the standard deviations from the normal model. We collected 243 (143 males and 100 females) normal cases to construct the normal model. We also extracted 432 abnormal spots from 63 abnormal cases (73 cancer lesions) to validate the Z-scores. The Z-scores of 417 out of 432 abnormal spots were higher than 2.0, which statistically indicated the severity of the spots. In conclusions, the Z-scores obtained by our computerized scheme with anatomical standardization of torso region would be useful for visualization and detection of subtle lesions on FDG-PET scan images even when the SUV may not clearly show an abnormality.
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