To investigate the overall attitude of medical students towards radiology, their perceptions of the university curriculum, the clinical relevance, and the prospects within the specialty, as well as identify reasons for and against choosing a radiologist's career. An 18-question survey was distributed among 21 universities in Germany, Switzerland, and Austria in January 2024. It was returned by 1184 medical students (753 female, 427 male, 2 diverse, 2 others) within one month. The survey encompassed sociodemographic data, questions regarding the curriculum, the perceived clinical relevance of radiology, as well as job attractiveness, students' expectations regarding salary and work-life balance, the role of artificial intelligence (AI), and students' outlook on the future. Seven hundred and sixteen (60%) participants expressed a desire for increased exposure to radiology within the medical curriculum and 1177 (99%) acknowledged the clinical importance of radiology. However, 755 (64%) students definitively ruled out pursuing a career as a radiologist, citing limited patient interaction as the primary deterrent (n = 725). Notably, 85% of participants considered the potential salary and work-life balance in radiology as favorable compared to other medical disciplines. Furthermore, 396 students (33%) were discouraged by contemporary AI developments from pursuing a career in radiology. While medical students recognize the clinical importance of radiology, the majority does not consider becoming radiologists. The findings suggest a need for educational initiatives to address misconceptions and enhance the attractiveness of radiology as a viable career option for medical graduates. Question The majority of medical students do not consider pursuing a career as radiologists though they recognize the clinical relevance and importance. Findings Students hesitate to pursue it as a career due to the perceived lack of patient interaction and concerns regarding the potential impact of AI. Clinical relevance Radiologists' workload is increasing globally. However, the available workforce has remained relatively stable. Thus, recruiting future doctors into the specialty is extremely important. Therefore, insights into medical students' contemporary perceptions of radiology are valuable in addressing reservations and misconceptions.

Background Since the introduction of TotalSegmentator CT, there has been demand for a similar robust automated MRI segmentation tool that can be applied across all MRI sequences and anatomic structures. Purpose To develop and evaluate an automated MRI segmentation model for robust segmentation of major anatomic structures independent of MRI sequence. Materials and Methods In this retrospective study, an nnU-Net model (TotalSegmentator MRI) was trained on MRI and CT scans to segment 80 anatomic structures relevant for use cases such as organ volumetry, disease characterization, surgical planning, and opportunistic screening. Images were randomly sampled from routine clinical studies to represent real-world examples. Dice scores were calculated between the predicted segmentations and expert radiologist segmentations to evaluate model performance on an internal test set and two external test sets and against two publicly available models and TotalSegmentator CT. The Wilcoxon signed rank test was used to compare model performance. The proposed model was applied to a separate internal dataset containing abdominal MRI scans to investigate age-dependent volume changes. Results A total of 1143 scans (616 MRI, 527 CT; median patient age, 61 years [IQR, 50-72 years]) were split into a training set (n = 1088; CT and MRI) and an internal test set (n = 55; MRI only). The two external test sets (AMOS and CHAOS) contained 20 MRI scans each, and the aging-study dataset contained 8672 abdominal MRI scans (median patient age, 59 years [IQR, 45-70 years]). The proposed model had a Dice score of 0.839 for the 80 anatomic structures in the internal test set and outperformed two other models (Dice score of 0.862 vs 0.759 for 40 anatomic structures and 0.838 vs 0.560 for 13 anatomic structures; P < .001 for both). On the TotalSegmentator CT test set (89 CT scans), the performance of the proposed model almost matched that of TotalSegmentator CT (Dice score, 0.966 vs 0.970; P < .001). The aging study demonstrated a strong correlation between age and organ volume (eg, age and liver volume: ρ = -0.096; P < .0001). Conclusion The proposed open-source, easy-to-use model allows for automatic, robust segmentation of 80 structures, extending the capabilities of TotalSegmentator to MRI scans from any MRI sequence. The ready-to-use online tool is available at https://totalsegmentator.com; the model, at https://github.com/wasserth/TotalSegmentator; and the dataset, at http://zenodo.org/records/14710732. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Kitamura in this issue.

AIM:Spine fractures are a frequent and relevant diagnosis, but systematic documentation is time-consuming and sometimes overlooked. A deep learning pipeline for opportunistic fracture detection in computed tomography (CT) spine images of varying field-of-views is introduced. MATERIALS AND METHODS:This retrospective study builds on 452 CTs of the lumbar/thoracolumbar spine. Patients were included based on the evidence of ≥1 vertebral body fracture and excluded in case of history of spinal surgery or pathologic fractures. The collective was split into training/validation (405) and test (47) sets. An open-source spine dataset was used to train a preliminary segmentation model, which was applied on the training set. The resulting segmentation was post-processed to remove posterior vertebral structures and if needed, manually refined by a radiologist. Using the refined version as new training data, a final segmentation nnU-net was trained. Sagittal slices from each vertebra were labelled individually with regard to fracture evidence. Slices without fracture were used as negative class. Twenty seven thousand nineteen slices (20,396 negative, 6,623 positive) trained a classification algorithm using resnet18. Two senior readers independently assessed fractures in the test set to obtain a consensual ground truth. The segmentation-classification pipeline was applied to the test set and compared with the ground truth. RESULTS:The segmentation model correctly segmented 330/339 (97%) vertebrae. Considering every segmented vertebra, the classifier detected fractures with 88% sensitivity, 95% specificity, and 93% accuracy. CONCLUSION:A deep learning pipeline was built and shown to accurately detect fractures on CT images. The final models as well as our code material are available at https://github.com/usb-radiology/VertebraeFx.

PURPOSE:To quantitatively and qualitatively compare the magnitude of metal total hip arthroplasty-induced imaging artifacts in vivo between 1.5T and 0.55T MRI. MATERIALS AND METHODS:15 patients (69 ± 14 years) with THA, prospectively underwent 0.55T and 1.5T MRI of the hip. Two fellowship-trained musculoskeletal radiologists qualitatively rated artifact magnitude on T1-weighted and fluid-sensitive sequences using a 5-point Likert scale (0 = severe - 5 = no artifact). Quantitative artifact magnitude was assessed by a third fellowship-trained musculoskeletal radiologist, by measuring the maximum diameter of the THA-induced signal void (mm) anteroposteriorly d(ap) and mediolaterally d(ml) at the level of the acetabulum, proximal, mid and distal stem, including the prosthesis. Additionally, the area of signal void was noted (mm2). Statistical differences between Likert scores were evaluated using the t-test or the Wilcoxon signed-rank test (P-values <.05 = significant). RESULTS:Qualitative artifact magnitude was on average rated as moderate to small on 0.55T and as large to moderate on 1.5T by both readers. Artifacts were rated less severe on 0.55T compared with 1.5T on T1-weighted (3.7 ± 0.7 vs. 2.4 ± 1.1, p=0.004) and fluid-sensitive sequences (3.1 ± 0.5 vs. 2.2 ± 1.1, p=0.18). Overall image quality did not show any statistically significant differences between 0.55T and 1.5T MRI (each p≥0.18). Metal artifacts' areas and diameters were smaller on 0.55T when compared with 1.5T MRI for all sequences (each p>0.016). CONCLUSIONS:Total hip arthroplasty-induced metal artifacts are perceived as less severe at new-generation 0.55T when compared with conventional 1.5T MRI with no difference in overall image quality.

Objectives: To evaluate an optimized deep leaning-based image post-processing technique in lumbar spine MRI at 0.55 T in terms of image quality and image acquisition time. Materials and methods: Lumbar spine imaging was conducted on 18 patients using a 0.55 T MRI scanner, employing conventional (CDLR) and advanced (ADLR) deep learning-based post-processing techniques. Two musculoskeletal radiologists visually evaluated the images using a 5-point Likert scale to assess image quality and resolution. Quantitative assessment in terms of signal intensities (SI) and contrast ratios was performed by region of interest measurements in different body-tissues (vertebral bone, intervertebral disc, spinal cord, cerebrospinal fluid and autochthonous back muscles) to investigate differences between CDLR and ADLR sequences. Results: The images processed with the advanced technique (ADLR) were rated superior to the conventional technique (CDLR) in terms of signal/contrast, resolution, and assessability of the spinal canal and neural foramen. The interrater agreement was moderate for signal/contrast (ICC = 0.68) and good for resolution (ICC = 0.77), but moderate for spinal canal and neuroforaminal assessability (ICC = 0.55). Quantitative assessment showed a higher contrast ratio for fluid-sensitive sequences in the ADLR images. The use of ADLR reduced image acquisition time by 44.4%, from 14:22 min to 07:59 min. Conclusions: Advanced deep learning-based image reconstruction algorithms improve the visually perceived image quality in lumbar spine imaging at 0.55 T while simultaneously allowing to substantially decrease image acquisition times. Clinical relevance: Advanced deep learning-based image post-processing techniques (ADLR) in lumbar spine MRI at 0.55 T significantly improves image quality while reducing image acquisition time.

Rationale and Objectives: To compare image quality and metal artifact severity at 0.55 T and 1.5 T MRI in patients with spinal implants following posterior fusion surgery. Materials and Methods: 50 consecutive patients (mean age: 69 +/- 12 years) who underwent 0.55 T and 1.5 T MRI following posterior fusion surgery of the lumbar or thoracolumbar spine were included. Examinations used metal artifact reduction protocols from clinical routine. Images were rated by two fellowship-trained musculoskeletal radiologists for image quality, ability to assess the spinal canal and the neural foramina, and artifact severity on 5-point Likert scales. Additionally, differences in artifact severity and visibility of near-metal anatomy among implant sizes (1-level vs. 2-level vs. > 2-levels) were evaluated. Results: Signal/contrast (mean: 4.0 +/- 0.3 [0.55 T] vs. 4.4 +/- 0.6 [1.5 T]; p < .001) and resolution (3.8 +/- 0.5 vs. 4.2 +/- 0.7; p < .001) were rated lower at 0.55 T. The ability to assess the spinal canal (4.4 +/- 0.5 vs. 4.2 +/- 0.9; p = .69) and the neural foramina (3.8 +/- 0.5 vs. 3.8 +/- 0.9; p = .19) were however rated equally good with excellent interrater agreement (range: 0.84-0.94). Susceptibility artifacts were rated milder at 0.55 T (1.8 +/- 0.5 vs. 3.0 +/- 0.6; p < .001). For implant size-based subgroups, the visibility of near-metal anatomy decreased with implant length at 1.5 T, but remained unchanged at 0.55 T. In consequence, the spinal canal and neural foramina could be better assessed at 0.55 T in patients with multi-level implants (4.4 +/- 0.5 vs. 3.6 +/- 1.1; p < .001). Conclusion: Metal artifacts of spinal implants are substantially less pronounced at 0.55 T MRI. When examining patients with multi-level posterior fusion, this translates into a superior ability to assess near-metal anatomy, where 1.5 T MRI reaches diagnostic limitations.

Objectives The aim of this study was to compare deep learning reconstructed (DLR) 0.55 T magnetic resonance imaging (MRI) quality, identification, and grading of structural anomalies and reader confidence levels with conventional 3 T knee MRI in patients with knee pain following trauma. Materials and Methods This prospective study of 26 symptomatic patients (5 women) includes 52 paired DLR 0.55 T and conventional 3 T MRI examinations obtained in 1 setting. A novel, commercially available DLR algorithm was employed for 0.55 T image reconstruction. Four board-certified radiologists reviewed all images independently and graded image quality, noted structural anomalies and their respective reporting confidence levels for the presence or absence, as well as grading of bone, cartilage, meniscus, ligament, and tendon lesions. Image quality and reader confidence levels were compared (P < 0.05, significant), and MRI findings were correlated between 0.55 T and 3 T MRI using Cohen kappa (κ). Results In reader's consensus, good image quality was found for DLR 0.55 T MRI and 3 T MRI (3.8 vs 4.1/5 points, P = 0.06). There was near-perfect agreement between 0.55 T DLR and 3 T MRI regarding the identification of structural anomalies for all readers (each κ ≥ 0.80). Substantial to near-perfection agreement between 0.55 T and 3 T MRI was reported for grading of cartilage (κ = 0.65–0.86) and meniscus lesions (κ = 0.71–1.0). High confidence levels were found for all readers for DLR 0.55 T and 3 T MRI, with 3 readers showing higher confidence levels for reporting cartilage lesions on 3 T MRI. Conclusions In conclusion, new-generation 0.55 T DLR MRI provides good image quality, comparable to conventional 3 T MRI, and allows for reliable identification of internal derangement of the knee with high reader confidence.

PURPOSE:To evaluate the effect of lower field strength on quantitative apparent-diffusion-coefficient (ADC) values, contrast of the T2-weighted MR images and the performance of an AI-based segmentation. MATERIALS AND METHODS:25 screening clients (61.6 ± 7.5 years) from a study on a 3 T scanner were included and underwent a second examination on a 0.55 T scanner. Axial T2 weighted and diffusion-weighted images (DWI) sequences were acquired. An AI-based segmentation was performed. Based on this, the segmentation, volumetry, ADC values and the ratio of central gland (CG) and peripheral zone (PZ) in T2 weighting were compared by using correlations coefficient (Pearson), Bland-Altman plots and a non-inferior test with a paired t-test and a margin of ± 20% (lower and upper boundary). RESULTS:Volumetric assessment (peripheral zone//central gland) showed no significant (p = 0.13//0.38) difference between 3 T (mean volume: 14.81 (12.53-17.09)//23.07 (15.06-31.08)mL) and 0.55 T (mean volume of 14.29 (12.03-16.54; p = 0.13)//22.77 (14.41-31.12)mL). The deviation of the 0.55 T ADC values from the 3 T values was -10.14% (-16.09% to -4.18%) for the PZ and -4.68% (-10.12-0.76%) for the CG. Therefore, all confidence intervals remained within a margin of + /- 20% and thus demonstrated significant non-inferiority. CONCLUSION:Biparametric prostate imaging is feasible at 0.55 T: ADC values vary within a common inter-scanner range compared to a 3 T and no difference can be observed regarding contrast ratio between peripheral zone and central gland in T2 weighted images, volumetry and AI-based segmentation compared to 3 T.

Purpose To compare parameters of left ventricular (LV) and right ventricular (RV) volume and function between a commercially available 0.55-T low-field-strength cardiac cine MRI scanner and a 1.5-T scanner. Materials and Methods In this prospective study, healthy volunteers (May 2022 to July 2022) underwent same-day cine imaging using both scanners (0.55 T, 1.5 T). Volumetric and functional parameters were assessed by two experts. After analyzing the results of a blinded crossover reader study of the healthy volunteers, 20 participants with clinically indicated cardiac MRI were prospectively included (November 2022 to February 2023). In a second blinded expert reading, parameters from clinical 1.5-T scans in these participants were compared with those same-day 0.55-T scans. Results are displayed as Bland-Altman plots. Results Eleven healthy volunteers (mean age: 33 years [95% CI: 27, 40]; four of 11 [36%] female, seven of 11 [64%] male) were included. Very strong mean correlation was observed (r = 0.98 [95% CI: 0.97, 0.98]). Average deviation between MRI systems was 1.6% (95% CI: 0.3, 2.9) for both readers. Twenty participants with clinically indicated cardiac MRI were included (mean age: 55 years [95% CI: 48, 62], six of 20 [30%] female, 14 of 20 [70%] male). Mean correlation was very strong (r = 0.98 [95% CI: 0.97, 0.98]). LV and RV parameters demonstrated an average deviation of 1.1% (95% CI: 0.1, 2.1) between MRI systems. Conclusion Cardiac cine MRI at 0.55 T yielded comparable results for quantitative biventricular volumetric and functional parameters compared with routine imaging at 1.5 T, if acquisition time is doubled. Keywords: Cardiac, Comparative Studies, Heart, Cardiovascular MRI, Cine, Myocardium Supplemental material is available for this article. ©RSNA, 2024.

This study evaluates the advantages in diagnostic accuracy, confidence, and reading time of additional dual-energy CT-derived reconstructions for assessing acute bowel ischemia. This retrospective study includes 25 patients with surgically proven acute bowel ischemia and 25 gender- and age-matched controls who underwent biphasic abdominal dual-energy CT. Two fellowship-trained abdominal radiologists and two residents evaluated all cases with and without additional dual-energy CT-derived iodine maps and virtual non-contrast images. Diagnostic confidence was rated on a 10-point Likert scale. Reading time was recorded. The inter-reader agreement was assessed using Fleiss’ kappa. Sensitivity and specificity were compared using McNemar’s test, reader confidence, and reading times with the Wilcoxon signed-rank test. For conventional images alone, the inter-reader agreement was moderate (κ = 0.58), with a sensitivity of 77

Purpose: To assess the accuracy of 0.55 T MRI in diagnosing periprosthetic joint infection (PJI) in patients with symptomatic total hip arthroplasty (THA). Material and methods: 0.55 T MRI of patients with THA PJI (Group A) and noninfected THA (Group B), including aseptic loosening (Group C, subgroup of B) performed between May 2021 and July 2023 were analysed retrospectively. Two musculoskeletal fellowship-trained radiologists independently identified MRI bone and soft tissue changes including: marrow oedema, periosteal reaction, osteolysis, joint effusion, capsule oedema and thickening, fluid collections, muscle oedema, bursitis, inguinal adenopathy, and muscle tears. The diagnostic performance of MRI discriminators of PJI was evaluated using Fisher 's exact test (p < 0.05) and interrater reliability was determined. 61 MRI scans from 60 THA patients (34 female, median age 68, range 41-93 years) in Group A (n = 9; female 4; median age 69, range 56-82 years), B (n = 51; 30; 67.5, 41-93 years), and C (10; 6; 67; 41-82 years) were included. Results: Capsule oedema (sensitivity 89 %, specificity 92 %,), intramuscular oedema (89 %, 82 %) and joint effusion (89 %, 73 %) were the best performing discriminators for PJI diagnosis (p < 0.001), when viewed individually and had combined 70 % sensitivity and 100 % specificity for PJI diagnosis in parallel testing. For the differentiation between PJI and aseptic loosening, intramuscular oedema (89 %, 80 %) and capsule oedema (89 %, 80 %) were significant discriminators (p < 0.001) with combined 64 % sensitivity and 96 % specificity for PJI. Conclusions: New generation 0.55 T MRI may aid in the detection of PJI in symptomatic patients. Oedema of the joint capsule, adjacent muscles as well as joint effusion were indicative of the presence of PJI.

The purpose of this study is to assess segmentation reproducibility of artificial intelligence-based algorithm, TotalSegmentator, across 34 anatomical structures using multiphasic abdominal CT scans comparing unenhanced, arterial, and portal venous phases in the same patients. A total of 1252 multiphasic abdominal CT scans acquired at our institution between January 1, 2012, and December 31, 2022, were retrospectively included. TotalSegmentator was used to derive volumetric measurements of 34 abdominal organs and structures from the total of 3756 CT series. Reproducibility was evaluated across three contrast phases per CT and compared to two human readers and an independent nnU-Net trained on the BTCV dataset. Relative deviation in segmented volumes and absolute volume deviations (AVD) were reported. Volume deviation within 5

RATIONALE AND OBJECTIVES:To assess the potential of 0.55T low-field MRI system in lumbar spine imaging with and without the use of additional advanced postprocessing techniques. MATERIALS AND METHODS:The lumbar spine of 14 volunteers (32.9 ± 3.6 years) was imaged both at 0.55T and 1.5T using sequences from clinical routine. On the 0.55T scanner system, additional sequences with simultaneous multi-slice acquisition and artificial intelligence-based postprocessing techniques were acquired. Image quality of all 28 examinations was assessed by three musculoskeletal radiologists with respect to signal/contrast, resolution, and assessability of the spinal canal and neuroforamina using a 5-point Likert scale (1 = non-diagnostic to 5 = perfect quality). Interrater agreement was evaluated with the Intraclass Correlation Coefficient and the Mann-Whitney U test (significance level: p < 0.05). RESULTS:Image quality at 0.55T was rated lower on the 5-point Likert scale compared to 1.5T regarding signal/contrast (mean: 4.16 ± 0.29 vs. 4.54 ± 0.29; p < 0.001), resolution (4.07 ± 0.31 vs. 4.49 ± 0.30; p < 0.001), assessability of the spinal canal (4.28 ± 0.13 vs. 4.73 ± 0.26; p < 0.001) and the neuroforamina (4.14 ± 0.28 vs. 4.70 ± 0.27; p < 0.001). Image quality for the AI-processed sagittal T1 TSE and T2 TSE at 0.55T was also rated slightly lower, but still good to perfect with a concomitant reduction in measurement time. Interrater agreement was good to excellent (range: 0.60-0.91). CONCLUSION:While lumbar spine image quality at 0.55T is perceived inferior to imaging at 1.5T by musculoskeletal radiologists, good overall examination quality was observed with high interrater agreement. Advanced postprocessing techniques may accelerate intrinsically longer acquisition times at 0.55T.

Background Diffuse parenchymal liver diseases are contributing substantially to global morbidity and represent major causes of deaths worldwide. The aim of our study is to assess whether established hepatic fat and iron quantitation and relaxometry-based quantification of hepatocyte-specific contrast material as surrogate for liver function estimation allows to evaluate liver fibrosis. Methods Retrospective consecutive study. Seventy-two healthy patients (mean age: 53 years) without known liver disease, 21 patients with temporary elevated liver enzymes (mean: 65 years) and 109 patients with biopsy proven liver fibrosis or cirrhosis (mean: 61 years), who underwent liver magnetic resonance imaging (MRI) with a hepatocyte-specific contrast agent [gadoxetate disodium, gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA), 0.25 mmol/mL Primovist, Bayer AG, Leverkusen, Germany] at 1.5 T (n=133) and at 3 T (n=69), were included. Fibrosis was classified using the histopathological meta-analysis of histological data in viral hepatitis (METAVIR) and the clinical Child-Pugh scores. Gd-concentration were quantified using T1 map-based calculations. Gd-concentration mapping was performed by using a Look-Locker approach prior to and 912±159 s after intravenous administration of hepatocyte specific contrast agent. Additionally, parenchymal fat fraction, R2*, bilirubin, gender and age were defined as predicting factors. Diagnostic accuracy was calculated in a monoparametric (linear regression, predictor: Gd-concentration) and multiparametric model (predictors: age, bilirubin level, iron overload, liver fat fraction, Gd concentration in the left and right liver lobe). Results Mean Gd-concentration in the liver parenchyma was significantly higher for healthy patients ([Gd] =0.51 µmol/L) than for those with liver fibrosis or cirrhosis ([Gd] =0.31 µmol/L; P<0.0001) and with acute liver disease ([Gd] =0.28 µmol/L), though there were no significant differences for the latter two groups. There was a significant moderate negative correlation for the mean Gd-concentration and the METAVIR score (ρ=−0.44, P<0.0001) as well as for the Child-Pugh stage (ρ=−0.35, P<0.0001). There was a significant strong correlation between the bilirubin concentration and the Gd-concentration (ρ=−0.61, P<0.0001). The diagnostic accuracy for the discrimination of healthy patients and patients with known fibrosis or cirrhosis was 0.74 (0.71/0.60 sensitivity/specificity) in a monoparametric and 0.76 (0.85/0.61 sensitivity/specificity) in a machine learning based multiparametric model. Conclusions T1 mapping-based quantification of hepatic Gd-EOB-DTPA concentrations performed in a multiparametric model shows promising diagnostic accuracy for the detection of fibrotic changes. Liver biopsy might be replaced by imaging examinations.

You have accessJournal of UrologyCME1 Apr 2023MP66-03 LEARNING CURVE OF ROBOTIC-ASSISTED TRANSPERINEAL MRI/ULTRASOUND FUSION-GUIDED PROSTATE BIOPSY Vitkor Alargkof Anagnostou, Christian Engesser, Pawel Trotsenko, Hanns-Christian Breit, David Winkel, Helge Seifert, and Christian Wetterauer Vitkor Alargkof AnagnostouVitkor Alargkof Anagnostou More articles by this author , Christian EngesserChristian Engesser More articles by this author , Pawel TrotsenkoPawel Trotsenko More articles by this author , Hanns-Christian BreitHanns-Christian Breit More articles by this author , David WinkelDavid Winkel More articles by this author , Helge SeifertHelge Seifert More articles by this author , and Christian WetterauerChristian Wetterauer More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003329.03AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Robotic-assisted fusion prostate biopsy platforms promise precision, while offering user-friendliness, short procedure times and low complication rates. To date, there is no study investigating the learning curve (LC) of robotic-assisted transperineal MRI/Ultrasound fusion-guided prostate biopsy (RA-TP-FBx). A LC is essential to facilitate efficient training, maintain institutional biopsy quality and assess potential for widespread use. Herein, we are the first to assess the learning curve of RA-TP-FBx using the iSR’obot MonaLisa device (Biobot). METHODS: We prospectively analyzed consecutive RA-TP-FBx performed at the University Hospital Basel from January to October 2022. Cases were randomly assigned to the urology chief resident, a first-year biopsy-naive resident and an expert in RA-TP-FBx senior consultant. All resident cases were supervised by the expert. We used multiple parameters to define the LC: 1) efficiency – measured by time for completion of individual procedure steps, 2) accuracy – defined as detection rate of prostate cancer (PCa) and clinically significant PCa (csPCa) stratified by PI-RADS score, 3) operator and supervisor performance evaluation – measured by entrustable professional activities (EPA) questionnaires, 4) workload assessment – measured by the NASA task load index and 5) complication rates. RESULTS: We collectively performed 75 procedures. Procedure time reduction is shown in Graphic 1. The major difference in procedure time between residents and expert stemmed from probe positioning and biopsy time. PCa was detected in 62% and csPCa in 44%. csPCA detection for PI-RADS 4 was 46%. PCa detection rate for PI-RADS 4 lesions was similar between operators and over time. 13 biopsies were necessary before stable independent completion of all procedure steps. The most difficult skill for residents was ultrasound probe positioning. A transition point for workload reduction was observed at 13 biopsies. Complications rates were similar among operators and over time. CONCLUSIONS: Urology residents can quickly acquire skills to independently perform RA-TP-FBx regardless of previous biopsy experience. RA-TP-FBx is a procedure with low workload stress. Safety and accuracy are ensured during the LC when supervised by an expert. Source of Funding: Department of Surgery, University Hospital Basel © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e932 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Vitkor Alargkof Anagnostou More articles by this author Christian Engesser More articles by this author Pawel Trotsenko More articles by this author Hanns-Christian Breit More articles by this author David Winkel More articles by this author Helge Seifert More articles by this author Christian Wetterauer More articles by this author Expand All Advertisement PDF downloadLoading ...

Purpose:To present a deep learning segmentation model that can automatically and robustly segment all major anatomic structures on body CT images.Materials and Methods:In this retrospective study, 1204 CT examinations (from 2012, 2016, and 2020) were used to segment 104 anatomic structures (27 organs, 59 bones, 10 muscles, and eight vessels) relevant for use cases such as organ volumetry, disease characterization, and surgical or radiation therapy planning. The CT images were randomly sampled from routine clinical studies and thus represent a real-world dataset (different ages, abnormalities, scanners, body parts, sequences, and sites). The authors trained an nnU-Net segmentation algorithm on this dataset and calculated Dice similarity coefficients to evaluate the model's performance. The trained algorithm was applied to a second dataset of 4004 whole-body CT examinations to investigate age-dependent volume and attenuation changes.Results:The proposed model showed a high Dice score (0.943) on the test set, which included a wide range of clinical data with major abnormalities. The model significantly outperformed another publicly available segmentation model on a separate dataset (Dice score, 0.932 vs 0.871; P < .001). The aging study demonstrated significant correlations between age and volume and mean attenuation for a variety of organ groups (eg, age and aortic volume [rs = 0.64; P < .001]; age and mean attenuation of the autochthonous dorsal musculature [rs = -0.74; P < .001]).Conclusion:The developed model enables robust and accurate segmentation of 104 anatomic structures. The annotated dataset (https://doi.org/10.5281/zenodo.6802613) and toolkit (https://www.github.com/wasserth/TotalSegmentator) are publicly available.Keywords: CT, Segmentation, Neural Networks Supplemental material is available for this article. © RSNA, 2023See also commentary by Sebro and Mongan in this issue.

Abstract Background To investigate hip implant-related metal artifacts on a 0.55-T system compared with 1.5-T and 3-T systems. Methods Total hip arthroplasty made of three different alloys were evaluated in a water phantom at 0.55, 1.5, and 3 T using routine protocols. Visually assessment (VA) was performed by three readers using a Likert scale from 0 (no artifacts) to 6 (extremely severe artifacts). Quantitative assessment (QA) was performed using the coefficient of variation (CoV) and the fraction of voxels within a threshold of the mean signal intensity compared to an automatically defined region of interest (FVwT). Agreement was evaluated using intra/inter-class correlation coefficient (ICC). Results Interreader agreement of VA was strong-to-moderate (ICC 0.74−0.82). At all field strengths (0.55-T/1.5-T/3-T), artifacts were assigned a lower score for titanium (Ti) alloys (2.44/2.9/2.7) than for stainless steel (Fe-Cr) (4.1/3.9/5.1) and cobalt-chromium (Co-Cr) alloys (4.1/4.1/5.2) (p < 0.001 for both). Artifacts were lower for 0.55-T and 1.5-T than for 3-T systems, for all implants (p ≤ 0.049). A strong VA-to-QA correlation was found (r = 0.81; p < 0.001); CoV was lower for Ti alloys than for Fe-Cr and Co-Cr alloys at all field strengths. The FVwT showed a negative correlation with VA (-0.68 < r < -0.84; p < 0.001). Conclusions Artifact intensity was lowest for Ti alloys at 0.55 T. For other alloys, it was similar at 0.55 T and 1.5 T, higher at 3 T. Despite an inferior gradient system and a larger bore width, the 0.55-T system showed the same artifact intensity of the 1.5-T system.

OBJECTIVES:The aim of this study was to compare the detection rate of and reader confidence in 0.55 T knee magnetic resonance imaging (MRI) findings with 3 T knee MRI in patients with acute trauma and knee pain. MATERIALS AND METHODS:In this prospective study, 0.55 T and 3 T knee MRI of 25 symptomatic patients (11 women; median age, 38 years) with suspected internal derangement of the knee was obtained in 1 setting. On the 0.55 T system, a commercially available deep learning image reconstruction algorithm was used (Deep Resolve Gain and Deep Resolve Sharp; Siemens Healthineers), which was not available on the 3 T system. Two board-certified radiologists reviewed all images independently and graded image quality parameters, noted MRI findings and their respective reporting confidence level for the presence or absence, as well as graded the bone, cartilage, meniscus, ligament, and tendon lesions. Image quality and reader confidence levels were compared ( P < 0.05 = significant), and clinical findings were correlated between 0.55 T and 3 T MRI by calculation of the intraclass correlation coefficient (ICC). RESULTS:Image quality was rated higher at 3 T compared with 0.55 T studies (each P ≤ 0.017). Agreement between 0.55 T and 3 T MRI for the detection and grading of bone marrow edema and fractures, ligament and tendon lesions, high-grade meniscus and cartilage lesions, Baker cysts, and joint effusions was perfect for both readers. Overall identification and grading of cartilage and meniscal lesions showed good agreement between high- and low-field MRI (each ICC > 0.76), with lower agreement for low-grade cartilage (ICC = 0.77) and meniscus lesions (ICC = 0.49). There was no difference in readers' confidence levels for reporting lesions of bone, ligaments, tendons, Baker cysts, and joint effusions between 0.55 T and 3 T (each P > 0.157). Reader reporting confidence was higher for cartilage and meniscal lesions at 3 T (each P < 0.041). CONCLUSIONS:New-generation 0.55 T knee MRI, with deep learning-aided image reconstruction, allows for reliable detection and grading of joint lesions in symptomatic patients, but it showed limited accuracy and reader confidence for low-grade cartilage and meniscal lesions in comparison with 3 T MRI.

Purpose: As osteoporosis is still underdiagnosed by clinicians and radiologists, the aim of the present study was to assess the performance of an Artificial intelligence (AI)-based Convolutional Neuronal Network (CNN)-Algorithm for the detection of low bone density on routine non-contrast chest CT in comparison to clinical reports using DEXA scans as reference.Method: This retrospective cross-sectional study included patients who underwent non-contrast chest CT and DEXA between April 2018 and June 2018 (n = 109, 19 men, mean age: 67.7 years). CT studies were evaluated for thoracic vertebral bone pathologies using a CNN-Algorithm, which calculates the attenuation profile of the spine. The content of the radiological reports was evaluated for the description of osteoporosis or osteopenia. DEXA was used as the reference standard. To estimate correlation the Spearman test was used and the com-parison of the different groups was performed using the Wilcoxon rank sum test. Diagnostic was evaluated by performing a receiver operating characteristic curve analysis.Results: The DEXA examination revealed normal bone density in 42 patients, while 49 patients had osteopenia and 7 osteoporosis. There was a statistically significant correlation between the mean CNN-based attenuation of the thoracic spine and the bone density measured on the DEXA in the hip (r = 0.51, p < 0.001) and lumbar spine (r = 0.34, p = 0.01). The mean attenuation was significantly higher in patients with normal bone density (172 +/- 44.5 HU) compared to those with osteopenia or osteoporosis (125.2 +/- 33.8 HU), (p < 0.0001). Diagnostic performance in distinguishing normal from abnormal bone density was higher using the CNN-based vertebral attenuation (accuracy 0.75, sensitivity: 0.93, specificity: 0.61) compared to clinical reports (accuracy 0.51, sensitivity: 0.14, specificity: 0.53).Conclusion: CNN-based evaluation of bone density may provide additional value over standard clinical reports for the detection of osteopenia and osteoporosis in patients undergoing routine non-contrast chest CT scans. This additional value could improve identification of fracture risk and subsequent treatment.