2020
DOI: 10.1371/journal.pone.0229560
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Towards guidelines to harmonize textural features in PET: Haralick textural features vary with image noise, but exposure-invariant domains enable comparable PET radiomics

Abstract: PurposeImage texture is increasingly used to discriminate tissues and lesions in PET/CT. For quantification or in computer-aided diagnosis, textural feature analysis must produce robust and comparable values. Because statistical feature values depend on image count statistics, we investigated in depth the stability of Haralick features values as functions of acquisition duration, and for common image resolutions and reconstructions. MethodsA homogeneous cylindrical phantom containing 9.6 kBq/ml Ge-68 was repea… Show more

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Cited by 17 publications
(12 citation statements)
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“…Shortening image acquisition durations broadens intensity distributions and should shift the supremum (of a set of values) found within a VOI towards higher values. Furthermore, low counts give rise to tail-heavy distributions in images reconstructed using OSEM and PSF-based algorithms [ 14 ], and the choice of reconstruction algorithm can have a significant effect on lesion quantification [ 13 ] Therefore, SUVmax, by definition the supremum, is expected to rise when lowering image count [ 14 , 15 ]. However, when considering all SUVmax values found in our study, no clear differences can be seen between images for different list-mode acquisitions.…”
Section: Discussionmentioning
confidence: 99%
“…Shortening image acquisition durations broadens intensity distributions and should shift the supremum (of a set of values) found within a VOI towards higher values. Furthermore, low counts give rise to tail-heavy distributions in images reconstructed using OSEM and PSF-based algorithms [ 14 ], and the choice of reconstruction algorithm can have a significant effect on lesion quantification [ 13 ] Therefore, SUVmax, by definition the supremum, is expected to rise when lowering image count [ 14 , 15 ]. However, when considering all SUVmax values found in our study, no clear differences can be seen between images for different list-mode acquisitions.…”
Section: Discussionmentioning
confidence: 99%
“…Lesions evaluated at different emulated scan times (obtained by binning the list mode data with different frame durations) for the LAFOV, and for the routine SAFOV acquisitions, were evaluated by comparison of lesion signal or integral measured activity: where A l is lesion activity concentration in Bq/ml (where activities were decay corrected to time of injection) and T LM is the acquisition duration in seconds (s), where scan acquisition duration is given by the time/bp). This lesion integral activity can be considered a measure of the count statistic or count density (counts/ml) [ 16 ]. A linear regression model was used for the LAFOV integral activity as a function of total acquisition duration, to calculate an equivalent scan time to yield equal integral lesion activity on the SAFOV, i.e.…”
Section: Methodsmentioning
confidence: 99%
“…1 Study flowchart showing patient recruitment, total patients included and excluded ml (where activities were decay corrected to time of injection) and T LM is the acquisition duration in seconds (s), where scan acquisition duration is given by the time/bp). This lesion integral activity can be considered a measure of the count statistic or count density (counts/ml) [16]. A linear regression model was used for the LAFOV integral activity as a function of total acquisition duration, to calculate an equivalent scan time to yield equal integral lesion activity on the SAFOV, i.e.…”
Section: Image Evaluationmentioning
confidence: 99%
“…Images were rated by both readers according to subjective image quality rated on a 5-point Likert scale: (1) very poor; (2) poor; (3) acceptable; (4) very good; (5) excellent. Lesion uptake was measured in terms of SUV peak [19], which has been shown to be a more reliable parameter irrespective of acquisition time than SUV max [20]. The background was defined as the SUV mean of a background volume of interest (VOI) placed in a reference region of healthy liver tissue, where SUV mean was shown to be the most reliable parameter for liver [11,19].…”
Section: Image Analysismentioning
confidence: 99%