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The aim of the study was to use a previously proposed mask region–based convolutional neural network (Mask R-CNN) for automatic abnormal liver density detection and segmentation based on hepatocellular carcinoma (HCC) computed tomography (CT) datasets from a radiological perspective. Training and testing datasets were acquired retrospectively from two hospitals of Taiwan. The training dataset contained 10,130 images of liver tumor densities of 11,258 regions of interest (ROIs). The positive testing dataset contained 1,833 images of liver tumor densities with 1,874 ROIs, and negative testing data comprised 20,283 images without abnormal densities in liver parenchyma. The Mask R-CNN was used to generate a medical model, and areas under the curve, true positive rates, false positive rates, and Dice coefficients were evaluated. For abnormal liver CT density detection, in each image, we identified the mean area under the curve, true positive rate, and false positive rate, which were 0.9490, 91.99%, and 13.68%, respectively. For segmentation ability, the highest mean Dice coefficient obtained was 0.8041. This study trained a Mask R-CNN on various HCC images to construct a medical model that serves as an auxiliary tool for alerting radiologists to abnormal CT density in liver scans; this model can simultaneously detect liver lesions and perform automatic instance segmentation.

We aimed to evaluate whether the hyperemic myocardial blood flow (MBF) can be estimated using cadmium zinc telluride (CZT)-based single-photon emission computed tomography (SPECT) cameras with a single, rapid rest/stress dynamic scan. Dynamic contrast-enhanced (DCE) cardiac magnetic resonance imaging (MRI) was used as a reference modality for flow measurement.The proposed protocol included both the rest and stress acquisitions within a 24-min scan. Patients were first injected with 99mTc-Sestamibi at the resting state. Sixty minutes after the first injection, the subject was positioned via scintigraphy, after which the list-mode data acquisition was initiated and continued for 24 minutes. Five minutes after data acquisition was initiated, a stressed state was induced via dipyridamole infusion, after which a second dose of 99mTc-Sestamibi was injected. Dynamic SPECT images were reconstructed for all subjects, who also underwent T1-weighted cardiac DCE-MRI performed on days other than those of the SPECT studies. MBF values were estimated for the rest and stress MRI studies, and for the stress portion of the SPECT study. The SPECT-measured hyperemic MBF was compared with the MR-measured hyperemic MBF and coronary flow reserve (CFR), based on the regions of interest.A total of 30 subjects were included in this study. The hyperemic MBF estimated from SPECT showed a strong correlation with the MR-measured hyperemic MBF (r2 = 0.76) and a modest correlation with the MR-measured CFR (r2 = 0.56). Using MR-measured CFR

PURPOSE:We compared attenuation correction of PET images with helical CT (PET/HCT) and respiration-averaged CT (PET/ACT) in patients with non-small-cell lung cancer (NSCLC) with the goal of investigating the impact of respiration-averaged CT on 18F FDG PET texture parameters. MATERIALS AND METHODS:A total of 56 patients were enrolled. Tumors were segmented on pretreatment PET images using the adaptive threshold. Twelve different texture parameters were computed: standard uptake value (SUV) entropy, uniformity, entropy, dissimilarity, homogeneity, coarseness, busyness, contrast, complexity, grey-level nonuniformity, zone-size nonuniformity, and high grey-level large zone emphasis. Comparisons of PET/HCT and PET/ACT were performed using Wilcoxon signed-rank tests, intraclass correlation coefficients, and Bland-Altman analysis. Receiver operating characteristic (ROC) curves as well as univariate and multivariate Cox regression analyses were used to identify the parameters significantly associated with disease-specific survival (DSS). A fixed threshold at 45% of the maximum SUV (T45) was used for validation. RESULTS:SUV maximum and total lesion glycolysis (TLG) were significantly higher in PET/ACT. However, texture parameters obtained with PET/ACT and PET/HCT showed a high degree of agreement. The lowest levels of variation between the two modalities were observed for SUV entropy (9.7%) and entropy (9.8%). SUV entropy, entropy, and coarseness from both PET/ACT and PET/HCT were significantly associated with DSS. Validation analyses using T45 confirmed the usefulness of SUV entropy and entropy in both PET/HCT and PET/ACT for the prediction of DSS, but only coarseness from PET/ACT achieved the statistical significance threshold. CONCLUSIONS:Our results indicate that 1) texture parameters from PET/ACT are clinically useful in the prediction of survival in NSCLC patients and 2) SUV entropy and entropy are robust to attenuation correction methods.