Background. We aim to assess the spill-in effect and the benefit in quantitative accuracy for [ 18 F]-NaF PET/CT imaging of abdominal aortic aneurysms (AAA) using the background correction (BC) technique. Methods. Seventy-two datasets of patients diagnosed with AAA were reconstructed with ordered subset expectation maximization algorithm incorporating point spread function (PSF). Spill-in effect was investigated for the entire aneurysm (AAA), and part of the aneurysm excluding the region close to the bone (AAA exc). Quantifications of PSF and PSF1BC images using different thresholds (% of max. SUV in target regions-of-interest) to derive target-to-background (TBR) values (TBR max , TBR 90 , TBR 70 and TBR 50) were compared at 3 and 10 iterations. Results. TBR differences were observed between AAA and AAA exc due to spill-in effect from the bone into the aneurysm. TBR max showed the highest sensitivity to the spill-in effect while TBR 50 showed the least. The spill-in effect was reduced at 10 iterations compared to 3 iterations, but at the expense of reduced contrast-to-noise ratio (CNR). TBR 50 yielded the best trade-off between increased CNR and reduced spill-in effect. PSF1BC method reduced TBR sensitivity to spill-in effect, especially at 3 iterations, compared to PSF (P-value £ 0.05). Conclusion. TBR 50 is robust metric for reduced spill-in and increased CNR. (J Nucl Cardiol 2020) Key Words: Abdominal aortic aneurysm AE spill-in effect AE background correction AE target-tobackground ratio Electronic supplementary material The online version of this article (
BackgroundThis study aimed to explore the radiomic features from PET images to detect active cardiac sarcoidosis (CS).MethodsForty sarcoid patients and twenty-nine controls were scanned using FDG PET-CMR. Five feature classes were compared between the groups. From the PET images alone, two different segmentations were drawn. For segmentation A, a region of interest (ROI) was manually delineated for the patients' myocardium hot regions with standardized uptake value (SUV) higher than 2.5 and the controls' normal myocardium region. A second ROI was drawn in the entire left ventricular myocardium for both study groups, segmentation B. The conventional metrics and radiomic features were then extracted for each ROI. Mann-Whitney U-test and a logistic regression classifier were used to compare the individual features of the study groups.ResultsFor segmentation A, the SUVmin had the highest area under the curve (AUC) and greatest accuracy among the conventional metrics. However, for both segmentations, the AUC and accuracy of the TBRmax were relatively high, >0.85. Twenty-two (from segmentation A) and thirty-five (from segmentation B) of 75 radiomic features fulfilled the criteria: P-value < 0.00061 (after Bonferroni correction), AUC >0.5, and accuracy >0.7. Principal Component Analysis (PCA) was conducted, with five components leading to cumulative variance higher than 90%. Ten machine learning classifiers were then tested and trained. Most of them had AUCs and accuracies ≥0.8. For segmentation A, the AUCs and accuracies of all classifiers are >0.9, but k-neighbors and neural network classifiers were the highest (=1). For segmentation B, there are four classifiers with AUCs and accuracies ≥0.8. However, the gaussian process classifier indicated the highest AUC and accuracy (0.9 and 0.8, respectively).ConclusionsRadiomic analysis of the specific PET data was not proven to be necessary for the detection of CS. However, building an automated procedure will help to accelerate the analysis and potentially lead to more reproducible findings across different scanners and imaging centers and consequently improve standardization procedures that are important for clinical trials and development of more robust diagnostic protocols.
Background: The aim of this study is to explore the utility of cardiac magnetic resonance (CMR) imaging of radiomic features to distinguish active and inactive cardiac sarcoidosis (CS). Methods: Subjects were classified into active cardiac sarcoidosis (CSactive) and inactive cardiac sarcoidosis (CSinactive) based on PET-CMR imaging. CSactive was classified as featuring patchy [18F]fluorodeoxyglucose ([18F]FDG) uptake on PET and presence of late gadolinium enhancement (LGE) on CMR, while CSinactive was classified as featuring no [18F]FDG uptake in the presence of LGE on CMR. Among those screened, thirty CSactive and thirty-one CSinactive patients met these criteria. A total of 94 radiomic features were subsequently extracted using PyRadiomics. The values of individual features were compared between CSactive and CSinactive using the Mann–Whitney U test. Subsequently, machine learning (ML) approaches were tested. ML was applied to two sub-sets of radiomic features (signatures A and B) that were selected by logistic regression and PCA, respectively. Results: Univariate analysis of individual features showed no significant differences. Of all features, gray level co-occurrence matrix (GLCM) joint entropy had a good area under the curve (AUC) and accuracy with the smallest confidence interval, suggesting it may be a good target for further investigation. Some ML classifiers achieved reasonable discrimination between CSactive and CSinactive patients. With signature A, support vector machine and k-neighbors showed good performance with AUC (0.77 and 0.73) and accuracy (0.67 and 0.72), respectively. With signature B, decision tree demonstrated AUC and accuracy around 0.7; Conclusion: CMR radiomic analysis in CS provides promising results to distinguish patients with active and inactive disease.
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