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Accurate assessment of knee alignment and leg length discrepancy is currently measured manually from standing long-leg radiographs (LLR), a process that is both time consuming and poorly reproducible. The aim was to assess the performance of a commercial available AI software by comparing its outputs with manually performed measurements. The AI was trained on over 15,000 radiographs to measure various clinical angles and lengths from LLRs. We performed a retrospective single-center analysis on 295 LLRs obtained between 2015 and 2020 from male and female patients over 18 years. AI and expert measurements were performed independently. Kellgren-Lawrence score and reading time were assessed. All measurements were compared and non-inferiority, mean-absolute-deviation (sMAD), and intra-class-correlation (ICC) were calculated. A total of 295 LLRs from 284 patients (mean age, 65 years (18; 90); 97 (34.2%) men) were analyzed. The AI model produces outputs on 98.0% of the LLRs. Manually annotations were considered as 100% accurate. For each measurement, its divergence was calculated, resulting in an overall accuracy of 89.2% when comparing the AI outputs to the manually measured. AI vs. mean observer revealed an sMAD between 0.39 and 2.19° for angles and 1.45–5.00 mm for lengths. AI showed good reliability in all lengths and angles (ICC ≥ 0.87). Non-inferiority comparing AI to the mean observer revealed an equivalence-index (γ) between 0.54 and 3.03° for angles and − 0.70–1.95 mm for lengths. On average, AI was 130 s faster than clinicians. Automated measurements of knee alignment and length measurements produced with an AI tool result in reproducible, accurate measures with a time savings compared to manually acquired measurements.

Background Acetabular labral tears are managed with suture anchors providing good clinical outcomes. Knotless anchors are easier to use and have a quicker insertion time compared to knotted anchors. The purpose of this study was to compare the biomechanical behavior of two different anchor designs (knotted vs. knotless) in ultimate load testing in correlation with bone density in the acetabular rim. Methods Eighteen knotted Bio-FASTak and seventeen knotless PushLock anchors (both: Arthrex Inc., Naples, FL, USA) were inserted in the rims of two human acetabula, with known bone density distribution. The anchors were subjected to load-to-failure tests. Anchors were additionally tested in solid polyurethane foam with defined densities. Findings The Bio-FASTak group showed higher survival rates at 1, 2, and 3 mm displacement and was able to withstand significantly higher loads at 3 mm displacement (p = 0.031). There was no statistically significant difference in stiffness (p = 0.087), yield- (p = 0.190), and ultimate load (p = 0.222) between the two groups. Only the PushLock group showed correlation between bone volume over total volume (BV/TV) and stiffness (R = 0.750, p = 0.086) and between BV/TV and yield load (R = 0.838, p = 0.037). Experiments on solid polyurethane foam confirmed the correlation between the mechanical properties and tissue density for the same anchor. Interpretation PushLock shows similar biomechanical properties to the Bio-FASTak, but eliminates knot tying and potentially abrasive knots. In addition, biomechanical properties of the PushLock are governed by local bone density.

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The aim of this study was to assess the reproducibility of standard, Dixon-based attenuation correction (MR-AC) in PET/MR imaging. A further aim was to estimate a patient-specific lean body mass (LBM) from these MR-AC data. Methods: Ten subjects were positioned in a fully integrated PET/MR system, and 3 consecutive multibed acquisitions of the standard MR-AC image data were acquired. For each subject and MR-AC map, the following compartmental volumes were calculated: total body, soft tissue (ST), fat, lung, and intermediate tissue (IT). Intrasubject differences in the total body and subcompartmental volumes (ST, fat, lung, and IT) were assessed by means of coefficients of variation (CVs) calculated across the 3 consecutive measurements and, again, across these measurements but excluding those affected by major artifacts. All subjects underwent a body composition measurement using air displacement plethysmography (ADP) that was used to calculate a reference LBMADP. A second LBM estimate was derived from available MR-AC data using a formula incorporating the respective tissue volumes and densities as well as the subject-specific body weights. A third LBM estimate was obtained from a sex-specific formula (LBMFormula). Pearson correlation was calculated for LBMADP, LBMMR-AC, and LBMFormula. Further, linear regression analysis was performed on LBMMR-AC and LBMADP.Results: The mean CV for all 30 scans was 2.1 ± 1.9% (TB). When missing tissue artifacts were excluded, the CV was reduced to 0.3 ± 0.2%. The mean CVs for the subcompartments before and after exclusion of artifacts were 0.9 ± 1.1% and 0.7 ± 0.7% for the ST, 2.9 ± 4.1% and 1.3 ± 1.0% for fat, and 3.6 ± 3.9% and 1.3 ± 0.7% for the IT, respectively. Correlation was highest for LBMMR-AC and LBMADP (r = 0.99). Linear regression of data excluding artifacts resulted in a scaling factor of 1.06 for LBMMR-AC. Conclusion: LBMMR-AC is shown to correlate well with standard LBM measurements and thus offers routine LBM-based SUV quantification in PET/MR. However, MR-AC images must be controlled for systematic artifacts, including missing tissue and tissue swaps. Efforts to minimize these artifacts could help improve the reproducibility of MR-AC.

OBJECTIVES: Intra-individual spatial overlap analysis of tumor volumes assessed by MRI, the amino acid PET tracer [18F]-FET and the nucleoside PET tracer [18F]-FLT in high-grade gliomas (HGG). METHODS: MRI, [18F]-FET and [18F]-FLT PET data sets were retrospectively analyzed in 23 HGG patients. Morphologic tumor volumes on MRI (post-contrast T1 (cT1) and T2 images) were calculated using a semi-automatic image segmentation method. Metabolic tumor volumes for [18F]-FET and [18F]-FLT PETs were determined by image segmentation using a threshold-based volume of interest analysis. After co-registration with MRI the morphologic and metabolic tumor volumes were compared on an intra-individual basis in order to estimate spatial overlaps using the Spearman's rank correlation coefficient and the Mann-Whitney U test. RESULTS: [18F]-FLT uptake was negative in tumors with no or only moderate contrast enhancement on MRI, detecting only 21 of 23 (91%) HGG. In addition, [18F]-FLT uptake was mainly restricted to cT1 tumor areas on MRI and [18F]-FLT volumes strongly correlated with cT1 volumes (r = 0.841, p

Objective. We questioned whether the position of the dynamic reference frame (DRF) influences the application accuracy in electromagnetically navigated cranial procedures. A carrier for an electromagnetic DRF was developed, which could be fixed at the posterior edge of the vomer near the center of the head. This nasopharyngeal DRF was compared with a standard DRF fixed to the surface of the forehead. Methods. Image coordinates and real-world coordinates were co-registered and the total target error (TTE) was measured in the frontal and the lateral skull base of formalin fixed human head. At each anatomical site, 10 targets served for TTE determinations and 5 different fiducial combinations were used for registration. Results. With the nasopharyngeal DRF, lower TTE values (2.8 ± 1.4 mm; mean ± SD) were observed when compared with the forehead DRF (3.7 ± 2.8 mm; P = .004). TTEs of both anatomical sites investigated were significantly lower when using the nasopharyngeal DRF (frontal skull base 3.4 vs 2.1 mm, P = .005 and lateral skull base 3.9 vs 3.5 mm, P = .013) than with the standard forehead mounted one. Conclusion. Positioning the DRF in the center of the head significantly improved the application accuracy of targets in the skull base with electromagnetic navigation by 25%.

We present a tile-based approach for producing clinically relevant probability maps of prostatic carcinoma in histological sections from radical prostatectomy. Our methodology incorporates ensemble learning for feature selection and classification on expert-annotated images. Random forest feature selection performed over varying training sets provides a subset of generalized CIEL*a*b* co-occurrence texture features, while sample selection strategies with minimal constraints reduce training data requirements to achieve reliable results. Ensembles of classifiers are built using expert-annotated tiles from training images, and scores for the probability of cancer presence are calculated from the responses of each classifier in the ensemble. Spatial filtering of tile-based texture features prior to classification results in increased heat-map coherence as well as AUC values of 95% using ensembles of either random forests or support vector machines. Our approach is designed for adaptation to different imaging modalities, image features, and histological decision domains.