4.3 Article

Clinical image quality perception and its relation to NECR measurements in PETD

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EJNMMI PHYSICS
卷 1, 期 -, 页码 -

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SPRINGER HEIDELBERG
DOI: 10.1186/s40658-014-0103-y

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NECR; Image quality; FDG; Weight; BMI

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Background: The purpose of this study is to describe a clinical relation of noise equivalent count rate (NECR) - an objective measurement of positron emission tomography (PET) systems - measured in a large number of patients, to clinical image quality of PET and their relation to 18F-fluoro-2-deoxyglucose (FDG) activity and patient's weight. Methods: A total of 71 consecutive patients were evaluated in this retrospective study. All data was automatically analysed using Matlab to estimate the noise equivalent count rate. Then, image quality was evaluated according to two subjective scores: the IQ local score was a 3-point scale assigned to each bed position in all patients and the IQ global score was a 10-point scale assigned after evaluating the coronal whole-body PET. Patient data was also analysed concerning weight, body mass index, FDG dose at the start of acquisition (DAcq), presence of bowel uptake and presence of FDG-positive pathologic lesions. Two additional parameters were defined for each patient: the ratio between DAcq and patient weight (RDW) and the ratio between DAcq and patient BMI (RDBMI). Results: Clinically perceived image quality in PET has a significant positive correlation with NECR measured in patients, RDW, RDBMI and presence of pathologic lesions. Clinical image quality furthermore has significant negative correlation with weight, body mass index (BMI) and presence of bowel uptake. Thresholds of RDW and RDBMI in which clinical IQ is good to excellent in more than 90% of the patients were 2.6 and 8.0, respectively. Conclusions: Clinically perceived image quality in PET systems is positively and significantly related to NECR measured in patients. An optimal threshold for the RDW and RDBMI was defined in which clinical IQ is good to excellent in more than 90% of patients. With this data, it is possible to extrapolate technical as well as clinical image quality to other PET system and to predict clinical image perception.

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