The purpose of this study was to evaluate the ability of ultra-high-resolution computed tomography (UHR-CT) to assess stapes and chorda tympani nerve anatomy using a deep learning (DLR), a model-based, and a hybrid iterative reconstruction algorithm compared to simulated conventional CT. CT acquisitions were performed with a Mercury 4.0 phantom. Images were acquired with a 1024 × 1024 matrix and a 0.25 mm slice thickness and reconstructed using DLR, model-based, and hybrid iterative reconstruction algorithms. To simulate conventional CT, images were also reconstructed with a 512 × 512 matrix and a 0.5 mm slice thickness. Spatial resolution, noise power spectrum, and objective high-contrast detectability were compared. Three radiologists evaluated the clinical acceptability of these algorithms by assessing the thickness and image quality of the stapes footplate and superstructure elements, as well as the image quality of the chorda tympani nerve bony and tympanic segments using a 5-point confidence scale on 13 temporal bone CT examinations reconstructed with the four algorithms. UHR-CT provided higher spatial resolution than simulated conventional CT at the penalty of higher noise. DLR and model-based iterative reconstruction provided better noise reduction than hybrid iterative reconstruction, and DLR had the highest detectability index, regardless of the dose level. All stapedial structure thicknesses were thinner using UHR-CT by comparison with conventional simulated CT (P < 0.009). DLR showed the best visualization scores compared to the other reconstruction algorithms (P < 0.032). UHR-CT with DLR results in less noise than UHR-CT with hybrid iterative reconstruction and significantly improves stapes and tympanic chorda tympani nerve depiction compared to simulated conventional CT and UHR-CT with iterative reconstruction.

The purpose of this study was to evaluate the achievable radiation dose reduction of an ultra-high resolution computed tomography (UHR-CT) scanner using deep learning reconstruction (DLR) while maintaining temporal bone image quality equal to or better than high-resolution CT (HR-CT). UHR-CT acquisitions were performed with variable tube voltages and currents at eight different dose levels (volumic CT dose index [CTDIvol] range: 4.6-79 mGy), 1024² matrix, and 0.25 mm slice thickness and reconstructed using DLR and hybrid iterative reconstruction (HIR) algorithms. HR-CT images were acquired using a standard protocol (120 kV/220 mAs; CTDI vol, 54.2 mGy, 512² matrix, and 0.5 mm slice thickness). Two radiologists rated the image quality of seven structures using a five point confidence scale on six cadaveric temporal bone CTs. A global image quality score was obtained for each CT protocol by summing the image quality scores of all structures. With DLR, UHR-CT at 120 kV/220 mAs (CTDIvol, 50.9 mGy) and 140 kV/220 mAs (CTDIvol, 79 mGy) received the highest global image quality scores (4.88 ± 0.32 [standard deviation (SD)] [range: 4-5] and 4.85 ± 0.35 [range: 4-5], respectively; P = 0.31), while HR-CT at 120 kV/220 mAs and UHR-CT at 120 kV/20 mAs received the lowest (i.e., 3.14 ± 0.75 [SD] [range: 2-5] and 2.97 ± 0.86 [SD] [range: 1-5], respectively; P = 0.14). All the DLR protocols had better image quality scores than HR-CT with HIR. UHR-CT with DLR can be performed with up to a tenfold reduction in radiation dose compared to HR-CT with HIR while maintaining or improving image quality.

Background Initial imaging work-up using radiography and CT arthrography sometimes can be insufficient to identify a scapholunate (SL) instability (SLI) in patients suspected of having SL ligament tears. Purpose To determine the diagnostic performance of four-dimensional (4D) CT in the identification of SLI and apply the findings to patients suspected of having SLI and with inconclusive findings on radiographs and CT arthrograms. Materials and Methods This prospective single-center study enrolled participants suspected of having SLI (recent trauma, dorsal pain, positive Watson test results, decreased grip strength) between March 2015 and March 2020. Participants with wrist fractures, substantial joint stiffness, or history of wrist surgery were excluded. Each participant underwent radiography, CT arthrography, and 4D CT on the same day. Participants were divided into three groups: those with no SLI, those with SLI, and those with inconclusive results. SL gap and radioscaphoid and lunocapitate angle were measured using semiautomatic quantitative analysis of 4D CT images by two independent readers. Receiver operating characteristic curves were used to evaluate the diagnostic performance of 4D CT. Thresholds were determined with the Youden index and were applied to the inconclusive group. Results Of the 150 included participants (mean age, 41 years ± 14 [SD]; 102 male, 48 female), there were 63 with no SLI, 48 with SLI, and 39 with inconclusive results. The maximum value and range of SL gap measurements on 4D CT scans showed high sensitivity (83% [40 of 48] and 90% [43 of 48], respectively) and high specificity (95% [59 of 62] and 81% [50 of 62], respectively) in the identification of SLI. At least one of these parameters was abnormal on 4D CT scans in 17 of 39 (44%) participants in the inconclusive group, and 10 of 17 (59%) participants had confirmed SLI. In the 22 participants in the inconclusive group with no indication of SLI at 4D CT, follow-up showed no evidence of SLI in 10 (45%) and enabled confirmation of SLI via arthroscopy in three (14%). Conclusion Scapholunate gap measurements on kinematic 4D CT scans enabled correct identification of SLI in 59% of participants with inconclusive results on conventional images. ClinicalTrials.gov registration no. NCT02401568 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Demehri and Ibad in this issue.

Purpose: This study aimed to determine the impact of carbidopa premedication on static, dynamic and radiomics parameters of ¹⁸F-FDOPA PET in brain tumors. Methods: The study included 54 patients, 18 of whom received carbidopa, who underwent ¹⁸F-FDOPA PET for newly diagnosed gliomas. SUV-derived, 105 radiomics features and TTP dynamic parameters were extracted from volumes of interest in healthy brains and tumors. Simulation of the effects of carbidopa on time-activity curves were generated. Results: All static and TTP dynamic parameters were significantly higher in healthy brain regions of premedicated patients (ΔSUV_mean = +53%, ΔTTP = +48%, p < 0.001). Furthermore, carbidopa impacted 81% of radiomics features, of which 92% correlated with SUV_mean (absolute correlation coefficient ≥ 0.4). In tumors, premedication with carbidopa was an independent predictor of SUV_mean (ΔSUV_mean = +52%, p < 0.001) and TTP (ΔTTP = +24%, p = 0.025). All parameters were no longer significantly modified by carbidopa premedication when using ratios to healthy brain. Simulated data confirmed that carbidopa leads to higher tumor TTP values, corrected by the ratios. Conclusion: In ¹⁸F-FDOPA PET, carbidopa induces similarly higher SUV and TTP dynamic parameters and similarly impacts SUV-dependent radiomics in healthy brain and tumor regions, which is compensated for by correcting for the tumor-to-healthy-brain ratio. This is a significant advantage for multicentric study harmonization.

A machine learning algorithm using radiologic features from structured reports accurately classified focal bone lesions; Bone Tumor Imaging Reporting and Data System 2.0 was proposed to categorize model outputs into clinically meaningful malignancy risk classes.