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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.

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.

Quantifying tumour heterogeneity from [18F]FDG-PET images promises benefits for treatment selection of cancer patients. Here, the calculation of texture parameters mandates an initial discretization step (binning) to reduce the number of intensity levels. Typically, three types of discrimination methods are used: lesion relative resampling (LRR) with fixed bin number, lesion absolute resampling (LAR) and absolute resampling (AR) with fixed bin widths. We investigated the effects of varying bin widths or bin number using 27 commonly cited local and regional texture indices (TIs) applied on lung tumour volumes. The data set were extracted from 58 lung cancer patients, with three different and robust tumour segmentation methods. In our cohort, the variations of the mean value as the function of the bin widths were similar for TIs calculated with LAR and AR quantification. The TI histograms calculated by LRR method showed distinct behaviour and its numerical values substantially effected by the selected bin number. The correlations of the AR and LAR based TIs demonstrated no principal differences between these methods. However, no correlation was found for the interrelationship between the TIs calculated by LRR and LAR (or AR) discretization method. Visual classification of the texture was also performed for each lesion. This classification analysis revealed that the parameters show statistically significant correlation with the visual score, if LAR or AR discretization method is considered, in contrast to LRR. Moreover, all the resulted tendencies were similar regardless the segmentation methods and the type of textural features involved in this work.

We describe a novel approach to treatment planning for focal brachytherapy utilizing a biologically based inverse optimization algorithm and biological imaging to target an ablative dose at known regions of significant tumour burden and a lower, therapeutic dose to low risk regions.

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.