ABSTRACT
OBJECTIVES: Accurately acquiring and assigning different contrast-enhanced phases in computed tomography (CT) is relevant for clinicians and for artificial intelligence orchestration to select the most appropriate series for analysis. However, this information is commonly extracted from the CT metadata, which is often wrong. This study aimed at developing an automatic pipeline for classifying intravenous (IV) contrast phases and additionally for identifying contrast media in the gastrointestinal tract (GIT). MATERIALS AND METHODS: This retrospective study used 1200 CT scans collected at the investigating institution between January 4, 2016 and September 12, 2022, and 240 CT scans from multiple centers from The Cancer Imaging Archive for external validation. The open-source segmentation algorithm TotalSegmentator was used to identify regions of interest (pulmonary artery, aorta, stomach, portal/splenic vein, liver, portal vein/hepatic veins, inferior vena cava, duodenum, small bowel, colon, left/right kidney, urinary bladder), and machine learning classifiers were trained with 5-fold cross-validation to classify IV contrast phases (noncontrast, pulmonary arterial, arterial, venous, and urographic) and GIT contrast enhancement. The performance of the ensembles was evaluated using the receiver operating characteristic area under the curve (AUC) and 95% confidence intervals (CIs). RESULTS: For the IV phase classification task, the following AUC scores were obtained for the internal test set: 99.59% [95% CI, 99.58-99.63] for the noncontrast phase, 99.50% [95% CI, 99.49-99.52] for the pulmonary-arterial phase, 99.13% [95% CI, 99.10-99.15] for the arterial phase, 99.8% [95% CI, 99.79-99.81] for the venous phase, and 99.7% [95% CI, 99.68-99.7] for the urographic phase. For the external dataset, a mean AUC of 97.33% [95% CI, 97.27-97.35] and 97.38% [95% CI, 97.34-97.41] was achieved for all contrast phases for the first and second annotators, respectively. Contrast media in the GIT could be identified with an AUC of 99.90% [95% CI, 99.89-99.9] in the internal dataset, whereas in the external dataset, an AUC of 99.73% [95% CI, 99.71-99.73] and 99.31% [95% CI, 99.27-99.33] was achieved with the first and second annotator, respectively. CONCLUSIONS: The integration of open-source segmentation networks and classifiers effectively classified contrast phases and identified GIT contrast enhancement using anatomical landmarks.
ABSTRACT
PURPOSE: Solitary fibrous tumor (SFT) of the orbit is a rare tumor that was first described in 1994. We aimed to investigate its imaging characteristics that may facilitate the differential diagnosis between SFT and other types of orbital tumors. MATERIAL AND METHODS: Magnetic resonance imaging (MRI) data of patients with immunohistochemically confirmed orbital SFT from 2002 to 2022 at a tertiary care center were retrospectively analyzed. Tumor location, size, morphological characteristics, and contrast enhancement features were evaluated. RESULTS: Of the 18 eligible patients 10 were female (56%) with a mean age of 52 years. Most of the SFTs were oval-shaped (67%) with a sharp margin (83%). The most frequent locations were the laterocranial quadrant (44%), the extraconal space (67%) and the dorsal half of the orbit (67%). A flow void phenomenon was observed in nearly all cases (94%). On the T1-weighted imaging, tumor signal intensity (SI) was significantly lower than that of the retrobulbar fat and appeared predominantly equivalent (82%) to the temporomesial brain cortex, while on T2-weighted imaging its SI remained equivalent (50%) or slightly hyperintense to that of brain cortex. More than half of the lesions showed a homogeneous contrast enhancement pattern with a median SI increase of 2.2-fold compared to baseline precontrast imaging. CONCLUSION: The SFT represents a rare orbital tumor with several characteristic imaging features. It was mostly oval-shaped with a sharp margin and frequently localized in the extraconal space and dorsal half of the orbit. Flow voids indicating hypervascularization were the most common findings.
ABSTRACT
OBJECTIVES: To reduce the dose of intravenous iodine-based contrast media (ICM) in CT through virtual contrast-enhanced images using generative adversarial networks. METHODS: Dual-energy CTs in the arterial phase of 85 patients were randomly split into an 80/20 train/test collective. Four different generative adversarial networks (GANs) based on image pairs, which comprised one image with virtually reduced ICM and the original full ICM CT slice, were trained, testing two input formats (2D and 2.5D) and two reduced ICM dose levels (-50% and -80%). The amount of intravenous ICM was reduced by creating virtual non-contrast series using dual-energy and adding the corresponding percentage of the iodine map. The evaluation was based on different scores (L1 loss, SSIM, PSNR, FID), which evaluate the image quality and similarity. Additionally, a visual Turing test (VTT) with three radiologists was used to assess the similarity and pathological consistency. RESULTS: The -80% models reach an SSIM of > 98%, PSNR of > 48, L1 of between 7.5 and 8, and an FID of between 1.6 and 1.7. In comparison, the -50% models reach a SSIM of > 99%, PSNR of > 51, L1 of between 6.0 and 6.1, and an FID between 0.8 and 0.95. For the crucial question of pathological consistency, only the 50% ICM reduction networks achieved 100% consistency, which is required for clinical use. CONCLUSIONS: The required amount of ICM for CT can be reduced by 50% while maintaining image quality and diagnostic accuracy using GANs. Further phantom studies and animal experiments are required to confirm these initial results. KEY POINTS: ⢠The amount of contrast media required for CT can be reduced by 50% using generative adversarial networks. ⢠Not only the image quality but especially the pathological consistency must be evaluated to assess safety. ⢠A too pronounced contrast media reduction could influence the pathological consistency in our collective at 80%.
