TotalSegmentator MRI is an open-source, user-friendly tool that provides automatic, robust sequence-independent segmentations of major anatomic structures on MRI scans; the ready-to-use online tool is available at https://totalsegmentator.com .

A multitask deep learning model that provided classification of primary bone tumors on radiographs demonstrated performance that surpassed that of radiologic residents and was comparable with that ... Background An artificial intelligence model that assesses primary bone tumors on radiographs may assist in the diagnostic workflow. Purpose To develop a multitask deep learning (DL) model for simul...

Haploinsufficiency of the class I bHLH transcription factor TCF4 causes Pitt-Hopkins syndrome (PTHS), a severe neurodevelopmental disorder, while common variants in the TCF4 gene have been identified as susceptibility factors for schizophrenia. It remains largely unknown, which brain regions are dependent on TCF4 for their development and function. We systematically analyzed the expression pattern of TCF4 in the developing and adult mouse brain. We used immunofluorescent staining to identify candidate regions whose development and function depend on TCF4. In addition, we determined TCF4 expression in the developing rhesus monkey brain and in the developing and adult human brain through analysis of transcriptomic datasets and compared the expression pattern between species. Finally, we morphometrically and histologically analyzed selected brain structures in Tcf4-haploinsufficient mice and compared our morphometric findings to neuroanatomical findings in PTHS patients. TCF4 is broadly expressed in cortical and subcortical structures in the developing and adult mouse brain. The TCF4 expression pattern was highly similar between humans, rhesus monkeys, and mice. Moreover, Tcf4 haploinsufficiency in mice replicated structural brain anomalies observed in PTHS patients. Our data suggests that TCF4 is involved in the development and function of multiple brain regions and indicates that its regulation is evolutionary conserved. Moreover, our data validate Tcf4-haploinsufficient mice as a model to study the neurodevelopmental basis of PTHS.

Manually extracted imaging-based body composition measures from a single-slice area (A) have shown associations with clinical outcomes in patients with cardiometabolic disease and cancer. With advances in artificial intelligence, fully automated volumetric (V) segmentation approaches are now possible, but it is unknown whether these measures carry prognostic value to predict mortality in the general population. Here, we developed and tested a deep learning framework to automatically quantify volumetric body composition measures from whole-body magnetic resonance imaging (MRI) and investigated their prognostic value to predict mortality in a large Western population. The framework was developed using data from two large Western European population-based cohort studies, the UK Biobank (UKBB) and the German National Cohort (NAKO). Body composition was defined as (i) subcutaneous adipose tissue (SAT), (ii) visceral adipose tissue (VAT), (iii) skeletal muscle (SM), SM fat fraction (SMFF), and (iv) intramuscular adipose tissue (IMAT). The prognostic value of the body composition measures was assessed in the UKBB using Cox regression analysis. Additionally, we extracted body composition areas for every level of the thoracic and lumbar spine (i) to compare the proposed volumetric whole-body approach to the currently established single-slice area approach on the height of the L3 vertebra and (ii) to investigate the correlation between volumetric and single slice area body composition measures on the level of each vertebral body. In 36,317 UKBB participants (mean age 65.1 ± 7.8 years, age range 45-84 years; 51.7% female; 1.7% [634/36,471] all-cause deaths; median follow-up 4.8 years), Cox regression revealed an independent association between V_SM (adjusted hazard ratio [aHR]: 0.88, 95% confidence interval [CI] [0.81-0.91], p = 0.00023), V_SMFF (aHR: 1.06, 95% CI [1.02-1.10], p = 0.0043), and V_IMAT (aHR: 1.19, 95% CI [1.05-1.35], p = 0.0056) and mortality after adjustment for demographics (age, sex, BMI, race) and cardiometabolic risk factors (alcohol consumption, smoking status, hypertension, diabetes, history of cancer, blood serum markers). This association was attenuated when using traditional single-slice area measures. Highest correlation coefficients (R) between volumetric and single-slice area body composition measures were located at vertebra L5 for SAT (R = 0.820) and SMFF (R = 0.947), at L3 for VAT (R = 0.892), SM (R = 0.944), and at L4 for IMAT (R = 0.546) (all p < 0.0001). A similar pattern was found in 23,725 NAKO participants (mean age 53.9 ± 8.3 years, age range 40-75; 44.9% female). Automated volumetric body composition assessment from whole-body MRI predicted mortality in a large Western population beyond traditional clinical risk factors. Single slice areas were highly correlated with volumetric body composition measures but their association with mortality attenuated after multivariable adjustment. As volumetric body composition measures are increasingly accessible using automated techniques, identifying high-risk individuals may help to improve personalised prevention and lifestyle interventions. This project was conducted using data from the German National Cohort (NAKO) (www.nako.de). The NAKO is funded by the Federal Ministry of Education and Research (BMBF) [project funding reference numbers: 01ER1301A/B/C, 01ER1511D, and 01ER1801A/B/C/D], federal states of Germany and the Helmholtz Association, the participating universities and the institutes of the Leibniz Association. This research has been conducted using the UK Biobank Resource under Application Number 80337. MJ was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-518480401. VKR was funded by American Heart Association Career Development Award 935176 and National Heart, Lung, and Blood Institute-K01HL168231.

Purpose: Ankle injuries are frequent sports injuries. Despite optimizing treatment strategies during recent years, the percentage of chronification following an ankle sprain remains high. The purpose of this review article is, to highlight current epidemiological, clinical and novel advanced cross-sectional imaging trends that may help to evaluate ankle sprain injuries. Methods: Systematic PubMed literature research. Identification and review of studies (i) analyzing and describing ankle sprain and (ii) focusing on advanced cross-sectional imaging techniques at the ankle. Results: The ankle is one of the most frequently injured body parts in sports. During the COVID-19 pandemic, there was a change in sporting behavior and sports injuries. Ankle sprains account for about 16– 40% of the sports-related injuries. Novel cross-sectional imaging techniques, including Compressed Sensing MRI, 3D MRI, ankle MRI with traction or plantarflexion-supination, quantitative MRI, CT-like MRI, CT arthrography, weight-bearing cone beam CT, dual-energy CT, photon-counting CT, and projection-based metal artifact reduction CT may be introduced for detection and evaluation of specific pathologies after ankle injury. While simple ankle sprains are generally treated conservatively, unstable syndesmotic injuries may undergo stabilization using suture-button-fixation. Minced cartilage implantation is a novel cartilage repair technique for osteochondral defects at the ankle. Conclusion: Applications and advantages of different cross-sectional imaging techniques at the ankle are highlighted. In a personalized approach, optimal imaging techniques may be chosen that best detect and delineate structural ankle injuries in athletes.

Objective: To assess the association of lumbar bone marrow adipose tissue fat fraction (BMAT-FF) and paraspinal muscle proton density fat fraction (PDFF) and their interplay with intervertebral disc degeneration (IVDD). Methods: In this retrospective cross-sectional study based on a prospective population-based cohort, BMAT-FF and PDFF of asymptomatic individuals were calculated based on 3T-MRI dual-echo and multi-echo Dixon VIBE sequences. IVDD was assessed at motion segments L1 to L5 and dichotomized based on Pfirrmann grade ≥ 4 and/or presence of other severe degenerative changes or spinal abnormalities at least at one segment. Pearson’s correlation coefficients were calculated for BMAT-FF and PDFF. Univariable and multivariable logistic regression models for IVDD were calculated. Results: Among 335 participants (mean age: 56.2 ± 9.0 years, 43.3% female), the average BMI was 27.7 ± 4.5 kg/m² and the prevalence of IVDD was high (69.9%). BMAT-FF and PDFF were significantly correlated (r = 0.31–0.34; p < 0.001). The risk for IVDD increased with higher PDFF (OR = 1.45; CI 1.03, 2.04) and BMAT-FF (OR = 1.56; CI 1.16, 2.11). Pairwise combinations of PDFF and BMAT-FF quartiles revealed a lower risk for IVDD in individuals in the lowest BMAT-FF and PDFF quartile (OR = 0.21; CI 0.1, 0.48). Individuals in the highest BMAT-FF and PDFF quartile showed an increased risk for IVDD (OR = 5.12; CI 1.17, 22.34) Conclusion: Lumbar BMAT-FF and paraspinal muscle PDFF are correlated and represent both independent and additive risk factors for IVDD. Quantitative MRI measurements of paraspinal myosteatosis and vertebral bone marrow fatty infiltration may serve as imaging biomarkers to assess the individual risk for IVDD. Key Points: • Fat composition of the lumbar vertebral bone marrow is positively correlated with paraspinal skeletal muscle fat. • Higher fat-fractions of lumbar vertebral bone marrow and paraspinal muscle are both independent as well as additive risk factors for intervertebral disc degeneration. • Quantitative magnetic resonance imaging measurements of bone marrow and paraspinal muscle may serve as imaging biomarkers for intervertebral disc degeneration.