Background Multiparametric magnetic resonance imaging (MRI) of the prostate plays a central role in the diagnosis of patients with suspected prostate cancer. The increasing distribution and application of the guideline for the standardization of image acquisition, evaluation, and reporting (Prostate Imaging – Reporting and Data System, PI-RADS), which was updated in 2019 to version 2.1, contributes to the success of the technique. Materials and Methods The most important updates of PI-RADS version 2.1 presented in 2019 compared to the previous version PI-RADS 2.0 are highlighted and interpreted with regard to their clinical implications. Results PI-RADS version 2.1 aims to simplify the application of the scoring scheme without changing the basic concept of dominant sequences (DWI in the peripheral zone, T2 in the transition zone). Of particular importance are the increasing role of diffusion-weighted imaging in the transition zone, the now mandatory high b-value of at least 1400 s/mm², and new information on the assessment of the central zone and the anterior fibromuscular stroma. Conclusion PI-RADS version 2.1 published in 2019 addresses a number of changes to the previous version, including both the examination technique and image interpretation. Prospective clinical studies have yet to prove the extent to which the goals of reducing interreader variability and increasing the detection rate in the transition zone will be achieved. Key Points: Citation Format Received: 04 March 2020 Accepted: 10 November 2020 Publication Date: 21 December 2020 (online) © 2020. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany

With its standardized MRI datasets of the entire spine, the German National Cohort (GNC) has the potential to deliver standardized biometric reference values for intervertebral discs (VD), vertebral bodies (VB) and spinal canal (SC). To handle such large-scale big data, artificial intelligence (AI) tools are needed. In this manuscript, we will present an AI software tool to analyze spine MRI and generate normative standard values. 330 representative GNC MRI datasets were randomly selected in equal distribution regarding parameters of age, sex and height. By using a 3D U-Net, an AI algorithm was trained, validated and tested. Finally, the machine learning algorithm explored the full dataset (n = 10,215). VB, VD and SC were successfully segmented and analyzed by using an AI-based algorithm. A software tool was developed to analyze spine-MRI and provide age, sex, and height-matched comparative biometric data. Using an AI algorithm, the reliable segmentation of MRI datasets of the entire spine from the GNC was possible and achieved an excellent agreement with manually segmented datasets. With the analysis of the total GNC MRI dataset with almost 30,000 subjects, it will be possible to generate real normative standard values in the future.

Background: Traumatic vertebral fractures present a significant diagnostic challenge in emergency settings. Magnetic resonance imaging (MRI) excels in detecting bone marrow edema but faces practical limitations in acute trauma care. This prospective study evaluates the diagnostic accuracy of Dual-Energy Computed Tomography (DECT) in detecting traumatic vertebral bone marrow edema within a Level I trauma center environment. Methods: Between May 2020 and July 2023, 291 DECT examinations were performed on adult patients presenting with suspected or confirmed spinal injury. From these, 233 (80.1%) met quality criteria for analysis. A subgroup of 47 patients underwent additional MRI as reference standard, with 44 (93.6%) providing diagnostically evaluable images. Two board-certified radiologists independently assessed vertebral bone marrow edema presence in blinded, randomized evaluations using both modalities. Diagnostic parameters, examination times, radiation exposure, and cost-efficiency were analyzed. Results: DECT demonstrated an overall sensitivity of 82.9% and specificity of 96.6% for detecting vertebral bone marrow edema compared to MRI. The thoracolumbar junction showed highest sensitivity (91.7% for L3). DECT examination time was 7.2 minutes (including post-processing) versus 12 minutes for MRI, meaning MRI required 66.7% more time than DECT. DECT radiation exposure showed a mean dose-length product increase of only 3% compared to conventional CT. Body mass index showed no significant influence on DECT interpretability (p=0.196) or diagnostic accuracy except in isolated segments (L3, T11). Cost-benefit analysis revealed potential savings of 49.1% (€104.40) per spinal segment with DECT-based diagnostic pathways. Conclusion: DECT offers high diagnostic accuracy for detecting traumatic vertebral bone marrow edema with substantial time and cost advantages compared to MRI. The technique demonstrates particular value in acute trauma settings, while acknowledging limitations from artifacts (19.9% of cases) primarily caused by medical devices. These findings support implementing DECT as an efficient alternative to MRI in spinal trauma diagnostics.

Background/Objectives. Automated lateral ventricle volumetry is increasingly used in population-based neuroimaging, but correlation between methods does not establish agreement of absolute volumes. We quantified agreement and calibration between FreeSurfer and a visually quality-controlled FSL/FAST–ALVIN lateral ventricle workflow within the Study of Health in Pomerania (SHIP). Methods. This cross-sectional agreement-and-calibration study included 2988 SHIP participants with visually accepted FSL/FAST–ALVIN total lateral ventricle volumes; paired FreeSurfer data were available for 1913 participants. FSL/FAST–ALVIN was treated as the study reference scale rather than biological ground truth. Agreement was assessed using Pearson and Spearman correlations, Bland–Altman analysis, log-ratio agreement, Lin’s concordance correlation coefficient, and a two-way mixed-effects single-measure absolute agreement intraclass correlation coefficient. Directional calibration models predicted FSL/FAST–ALVIN volume from FreeSurfer volume and were internally validated using 2000 bootstrap resamples. Results. In the paired sample, volumes were almost perfectly associated (Pearson r = 0.9978; Spearman ρ = 0.9974), but FreeSurfer yielded systematically lower values (mean FreeSurfer-minus-FSL bias, −3.02 mL; 95% limits of agreement, −4.52 to −1.53 mL; geometric mean FreeSurfer/FSL ratio, 0.844). Lin’s concordance coefficient and the absolute agreement ICC were both 0.9598. Calibration was strong but workflow-specific: FSL/FAST–ALVIN volume = 2.611 + 1.0210 × FreeSurfer volume (R2 = 0.9955; optimism-corrected RMSE = 0.732 mL). Conclusions. FreeSurfer and visually quality-controlled FSL/FAST–ALVIN preserved participant ranking extremely well but were not directly interchangeable as absolute measurements. Cross-workflow comparisons require explicit method reporting, formal agreement analysis, and calibration to the intended measurement scale; the equation should not be used as a universal conversion formula outside comparable acquisition, segmentation, QC and software settings.