Reducing the echo time of a whole-body MRI scanner makes it possible to image collagen, an important structural protein found in bones and tendons.

We previously identified six clusters of people at different risks of type 2 diabetes and/or comorbidities, of which cluster 3 (β-cell deficient) and 5 (older age, higher BMI, severe insulin resistance) had a high risk of progression to diabetes. We have now investigated whether cluster 3 and 5 individuals differed from those of the other clusters in changes in insulin sensitivity, insulin secretion, and the development of type 2 diabetes during a long-term reduction of body weight. A total of 190 participants completed a 24-month lifestyle intervention in the Tübingen Lifestyle Intervention Program (TULIP) and were followed up for 8.7 ± 1.6 years. Sixty participants had a weight loss ≥3% (mean reduction of 8%) at the long-term follow-up. Of them, cluster 5 participants (n = 17) had a larger increase of adjusted fasting glycemia compared with the cluster group 1,2,4,6 (n = 33) and cluster 3 (n = 10) and a larger increase of adjusted 2-h glucose levels compared with cluster 3 (all P < 0.05). In cluster 5, a larger decrease of adjusted insulin secretion compared with cluster 3 (P = 0.01) and cluster group 1,2,4,6 (P = 0.05) was observed. Forty-one percent of cluster 5 participants (0% in cluster group 1,2,4,6 and 10% in cluster 3) developed type 2 diabetes. In conclusion, despite a sustained and large amount of weight loss, diabetes risk cluster 5 participants had deterioration of glycemia and insulin secretion and a high risk of type 2 diabetes. If this result can be replicated in a prospective study, people of this cluster would need targeted prevention strategies. ARTICLE HIGHLIGHTS:There may be heterogeneity in the response to a lifestyle intervention to prevent type 2 diabetes. This study investigated whether participants of Tübingen Lifestyle Intervention Program (TULIP) type 2 diabetes risk clusters 3 and 5, who have a very high risk of diabetes, benefit from long-term weight loss following a 2-year lifestyle intervention. Diabetes risk cluster 5 participants had an impaired response regarding improvement of glycemia and insulin secretion and a high risk of developing type 2 diabetes, despite a long-term (9-year) mean weight loss of 8%. Alternative or intensified interventions should be considered for people in Tübingen type 2 diabetes risk cluster 5.

Hybrid MR-Linac devices allow the acquisition of functional MR images during a regular radiotherapy treatment workflow, which could improve target delineation and response assessment. In this work, we investigate the feasibility of acquiring arterial spin labeling (ASL) perfusion images on a 0.35 T MR-Linac. A signal model for the flow-sensitive alternating inversion recovery (FAIR) balanced steady-state free precession (bSSFP) ASL sequence was introduced and an optimized set of sequence parameters for 0.35 T was determined. The signal dependence on the sequence parameters using the MR-Linac was examined in the brain of two healthy volunteers. Repeatability of cerebral blood flow (CBF) measurements with an adjusted MR-Linac protocol were compared to ASL images from a clinical 3 T scanner in six healthy volunteers that were scanned twice on each device. The signal model described the observed measurements well, except for the dependence on inversion time. CBF values in gray and white matter in the six volunteers were systematically higher than at the 3 T scanner, but they had a similar percentage repeatability coefficient of 16–17

Background/Objective: This study aimed to develop and validate an automated deep learning-based model for 3D segmentation and quantification of the psoas major and gluteus muscles at 3T MRI in a large cohort study and to analyze the distribution of findings as well as gender-, age-, and BMI-related differences. Methods: The study population consisted of 27,805 participants from the MR imaging subgroup of the population-based, longitudinal German National Cohort study. A deep learning segmentation model was trained, tested, and implemented to automatically quantify psoas major maximum cross-sectional area (CSApsoas) and gluteus volume (Vgluteus) on T1-weighted 3D VIBE DIXON sequences. Associations with gender, age, and BMI were assessed by linear regression. Results: The segmentation model demonstrated a high performance, with mean Dice coefficients of 0.92 for the psoas and 0.95 for the gluteus. Males showed higher total CSApsoas (males: 37.92 ± 5.80 cm2; females: 24.47 ± 3.65 cm2) and higher total Vgluteus (males: 3.384 ± 0.528 L; females: 2.386 ± 0.408 L) compared to females. Younger participants aged <30 years showed the highest CSApsoas, whereas participants aged 30–59 years showed the highest Vgluteus. Participants with higher BMI > 25 kg/m2 showed higher muscle CSA and volumes compared to subjects with lower BMI < 25 kg/m2. Vgluteus showed a strong correlation to body weight in both females and males. Conclusions: Deep learning-based models provide accurate 3D segmentation and quantification of skeletal muscle compartments from MR images in large cohort studies, thus offering a feasible method for skeletal muscle evaluation. The morphometric size characteristics of the psoas and gluteus muscles are dependent on gender and BMI. Deep learning enables accurate 3D segmentation and quantification of skeletal muscle in large MR imaging cohorts, providing a feasible tool for muscle evaluation. The morphometric characteristics of psoas and gluteus muscles are dependent on gender and BMI.

Skeletal muscle composition may provide novel insights into diabetes risk. To assess the predictive value of gluteus, psoas, and thigh muscle features, we segmented these muscle groups from MRI scans of 28,077 participants in the German National Cohort and derived fat fractions, volumes, and cross-sectional areas. Combined with metadata variables, an overall set of 208 features was used to predict HbA 1 c levels and diabetes diagnosis status. We trained XGBoost models on the full feature set to avoid human bias in feature selection and quantified contributions using Shapley Additive Explanations, which capture both main effects and interactions between features. To reduce variance from model uncertainty, training and analysis were repeated 50 times and averaged. Our results show that gluteus fat fraction is among the strongest predictors of diabetes, on par with established risk factors such as waist circumference and visceral or subcutaneous adipose tissue.

Abstract Background For radiotherapy of head and neck cancer (HNC) magnetic resonance imaging (MRI) plays a pivotal role due to its high soft tissue contrast. Moreover, it offers the potential to acquire functional information through diffusion weighted imaging (DWI) with the potential to personalize treatment. The aim of this study was to acquire repetitive DWI during the course of online adaptive radiotherapy on an 1.5 T MR-linear accelerator (MR-Linac) for HNC patients and to investigate temporal changes of apparent diffusion coefficient (ADC) values of the tumor and subvolume levels. Methods 27 patients treated with curative RT on the 1.5 T MR-Linac with at least weekly DWI in treatment position were included into this prospective analysis and divided in four risk groups (HPV-status and localisation). Tumor and lymph node volumes (GTV-P/GTV-N) were delineated on b = 500 s/mm2 images while ADC maps were calculated using b = 150/200 and 500 s/mm2 images. Absolute and relative temporal changes of mean ADC values, tumor volumes and a high-risk subvolume (HRS) defined by low ADC tumor voxels (600 < ADC < 900 × 10−6 mm2/s) were analyzed. Relative changes of mean ADC values, tumor volumes and HRS were statistically tested using Wilcoxon-signed-rank test. Results Median pretreatment ADC value for all patients resulted in 1167 × 10−6 mm2/s for GTV-P and 1002 × 10−6 mm2/s for GTV-N while absolute pretreatment tumor volume yielded 9.1 cm3 for GTV-P and 6.0 cm3 for GTV-N, respectively. Pretreatment HRS volumes were 1.5 cm3 for GTV-P and 1.3 cm3 for GTV-P and GTV-N. Median ADC values increase during 35 fractions of RT was 49% for GTV-P and 24% for GTV-N during RT. Median tumor volume decrease was 68% and 52% for GTV-P and GTV-N with a median HRS decrease of 93% and 87%. Significant differences from 0 for mean ADC were observed starting from week 1, for tumor volumes from week 2 for GTV-P and week 1 for GTV-N and for HRS in week 1 for GTV-P and week 2 for GTV-N. Conclusion Longitudinal DWI acquisition in HNC is feasible on a MR-Linac during the course of online adaptive MR-guided radiotherapy. Changes in ADC and volumes can be assessed, but future work needs to explore the potential for biologically guided treatment individualization. Trial registration: NCT04172753, actual study start: 09.05.2018.

BACKGROUND AND AIMS:While low plasma butyrylcholinesterase (BChE) is a well-established marker of reduced liver synthesis capacity, the clinical significance of elevated BChE is unclear. In small studies, high BChE has long been suspected in hepatic steatosis and metabolic syndrome. We aimed to clarify the relation between BChE, liver fat and glucose metabolism in deeply phenotyped cohorts. METHODS:Plasma BChE activity was measured in 844 humans (554 women) of the cross-sectional Tübingen Diabetes Family Study, with a wide BMI range (17.7-55.1 kg/m2). It was furthermore measured before and after two independent lifestyle intervention studies in 215 and 116 participants. Liver fat was quantified with 1H-MR-spectroscopy, and metabolism was assessed by oral glucose tolerance tests. RESULTS:BChE was positively associated with liver fat, independent of sex, age and BMI. BChE was higher in participants with metabolic syndrome. BChE was positively associated with fasting and 2-h glycaemia, independent of sex, age and BMI. BChE was negatively associated with insulin sensitivity, independent of sex, age, BMI and liver fat. The reduction of liver fat and improvement in insulin sensitivity during lifestyle interventions are associated with the reduction in BChE, independent of body weight loss. CONCLUSIONS:Higher plasma BChE activity is linked to liver fat accumulation, as well as impaired glucose tolerance and insulin resistance, independent of liver fat. This suggests that BChE could be a marker for processes in hepatocytes that contribute to impaired glucose metabolism. Further investigations are needed to clarify the mechanistic contribution and potential diagnostic value of elevated BChE in hepatic steatosis and metabolic diseases.

Introduction and Objective: Lifestyle intervention (LI) is recommended in people with prediabetes. High 1-hour post-load plasma glucose (1h-PG) predicts T2D risk earlier than current criteria. We hypothesized that high 1h-PG represents an intermediate state between normal glucose regulation (NGR) and impaired glucose regulation (IGR) and that LI is more effective in high 1h-PG. Methods: 318 people from the Tübingen Lifestyle Intervention Program with NGR, IGR [fasting PG≥ 5.6 mmol/L and <7.0 mmol/l or 2-h PG ≥ 7.8 mmol/L and < 11.1 mmol/l]) or high 1h-PG (NGR and 1h-PG ≥ 8.6 mmol/L)) underwent LI (monthly dietary counseling to achieve ≥ 5% weight loss; increased physical activity) for 9 months. Results: At baseline (Table), insulin sensitivity (IS) and beta cell function (BCF) declined progressively from NGR (n=106), to the high1h-PG (n=93) and IGR (n=119) groups. Numerically, liver fat content and visceral adipose tissue volume (VAT) increased from NGT to high 1h-PG and to IGR. Risk of T2D during 12-years follow-up was reduced by 80% (37 - 96 %, p = 0.005) in the high 1h-PG group compared to the IGR group. The odds of remission to NGR were two-fold higher in the high 1h-PG group compared to the IGR group (2.18 [1.13 - 4.28], p = 0.021). Conclusion: High 1h-PG is an intermediate pathophysiological state between NGR and IGR. LI reduces ectopic fat deposition, improves IS and BCF and reduces the risk of incident T2D more in high 1h-PG than in IGR. A. Sandforth: None. R. Jumpertz von Schwartzenberg: None. L. Sandforth: None. S. Katzenstein: None. H. Preissl: None. J. Machann: None. F. Schick: None. A. Fritsche: Advisory Panel; Abbott. Speaker's Bureau; AstraZeneca. N. Stefan: Speaker's Bureau; AstraZeneca, Boehringer-Ingelheim. Consultant; Lilly Diabetes. Speaker's Bureau; Lilly Diabetes. Consultant; Pfizer Inc. Speaker's Bureau; Sanofi. Research Support; Sanofi. Speaker's Bureau; Novo Nordisk, GlaxoSmithKline plc. Consultant; GlaxoSmithKline plc. M. Bergman: None. A.L. Birkenfeld: None. German Federal Ministry for Education and Research (01GI0925) via the German Center for Diabetes Research (DZD e.V.)

Background:Radiology is at the center of the digital transformation of the healthcare system. As a highly digital field, radiology is well-suited for the early implementation and critical evaluation of innovative technologies, such as artificial intelligence (AI). This review aims to comprehensively and distinctly present the opportunities and challenges of digital transformation in radiology, focusing on clinical applications, research, and promoting young talents. Materials and Methods:This narrative review is based on selective evaluation of relevant scientific literature and publications from the last 10 years. Relevant German- and English-language articles on the digital transformation of radiology were considered, particularly those addressing digital infrastructure, artificial intelligence, ethical and regulatory frameworks, and education and training. Results and Conclusion:Digitalization offers significant opportunities for radiology. In addition to advancing imaging procedures and automating image analysis with AI, digitalization optimizes workflows, enables personalized diagnostics, and fosters new care models, such as teleradiology. However, there are also key challenges: Data protection issues, a lack of standardization, insufficient validation, and regulatory hurdles are hindering its widespread implementation in hospitals. To future-proof radiology, it is essential to promote young talent and incorporate digital skills in the curriculum. Key Points:· Due to its digital structure, radiology is particularly well-suited to integrating new medical technologies.. · Some AI-powered applications have been adopted in everyday clinical practice but they require further validation.. · A key task for the future is systematically training prospective radiologists in digital skills.. Citation Format:· Hoffmann E, Bannas P, Bayerl N et al. Digital Transformation and Artificial Intelligence in Radiology: Challenges and Opportunities for Clinical Practice, Research, and the Next Generation. Rofo 2025; DOI 10.1055/a-2741-9717.

Clinical practice guidelines recommend defined weight loss goals for the prevention of type 2 diabetes (T2D) in those individuals with increased risk, such as prediabetes. However, achieving prediabetes remission, that is, reaching normal glucose regulation according to American Diabetes Association criteria, is more efficient in preventing T2D than solely reaching weight loss goals. Here we present a post hoc analysis of the large, multicenter, randomized, controlled Prediabetes Lifestyle Intervention Study (PLIS), demonstrating that prediabetes remission is achievable without weight loss or even weight gain, and that it also protects against incident T2D. The underlying mechanisms include improved insulin sensitivity, β-cell function and increments in β-cell-GLP-1 sensitivity. Weight gain was similar in those achieving prediabetes remission (responders) compared with nonresponders; however, adipose tissue was differentially redistributed in responders and nonresponders when compared against each other-while nonresponders increased visceral adipose tissue mass, responders increased adipose tissue in subcutaneous depots. The findings were reproduced in the US Diabetes Prevention Program. These data uncover essential pathways for prediabetes remission without weight loss and emphasize the need to include glycemic targets in current clinical practice guidelines to improve T2D prevention.

Objective Multiparametric MRI is a promising technique for noninvasive structural and functional imaging of the kidneys that is gaining increasing importance in clinical research. Still, there are no standardized recommendations for analyzing the acquired images and there is a need to further evaluate the accuracy and repeatability of currently recommended MRI parameters. The aim of the study was to evaluate the test-retest repeatability of functional renal MRI parameters using different image analysis strategies. Methods Ten healthy volunteers were examined twice with a multiparametric renal MRI protocol including arterial spin labeling (ASL), diffusion-weighted imaging (DWI) with intravoxel incoherent motion (IVIM), blood-oxygen-dependent (BOLD) imaging, T1 and T2 mapping, and volumetry with an interval of one week. The quantitative results of both kidneys were determined by manual organ segmentation, ROI analysis, and automatic segmentation based on the nnUNet framework. Test-retest repeatability of each parameter was computed using the within-subject coefficient of variance (wCV) and the intraclass coefficient (ICC). Segmentation accuracy and inter-reader agreement were evaluated using the dice score. Results Structural tissue parameters (T1, T2) showed wCV (%) between 4 and 11 and an ICC between 0.2 and 0.8. Functional parameters (ASL, BOLD and DWI) showed wCV (%) between 3 and 38 and an ICC between 0.0 and 0.7. The highest variances between test-retest scans were observed in perfusion measurements with ASL and IVIM (wCV: 17-37%). Quantitative analysis of the cortex and medulla showed a better repeatability when acquired using manual segmentation compared to ROI-based image analysis. Comparable repeatability was achieved with manual and automatic segmentation of the total kidney. Conclusion Reasonable repeatability was achieved for all MR parameters. Structural MR parameters showed better repeatability compared to functional parameters. ROI-based image analysis showed overall lower repeatability compared to manual segmentation. Comparable repeatability to manual segmentation as well as acceptable segmentation accuracy could be achieved with automatic segmentation.

PURPOSE:The temporal course of spontaneous mechanical activities of musculature (SMAMs) is investigated using a novel multiple-point diffusion-weighted stimulated echo imaging sequence (MP-DW-STE) with adapted spatial and temporal resolution. For this purpose, different sequence settings and measurement parameters are applied. METHODS:A single-shot MP-DW-STE imaging sequence with multiple signal rephasing by small flip angle RF pulses was developed to acquire image series during spontaneous muscle contractions with duration of several hundred milliseconds. Measurements were conducted on an incoherent motion phantom and in the calf muscles of eight healthy volunteers. The number and cross-sectional area of SMAM visualizations as well as the contractile behavior in terms of onset and duration of visible SMAMs was analyzed. RESULTS:Measurements on the incoherent motion phantom confirmed the ability of the proposed technique to characterize dynamic incoherent motion as signal voids in series of images with a high temporal resolution of approximately 40 ms. All human subjects showed SMAMs with a median duration of 120 ms, therefore, visible in several diffusion-weighted images in a row. Contraction time of SMAMs was in the range of 80 to 120 ms for the soleus muscle. The MP-DW-STE sequence settings have shown to significantly influence the mean frequency and cross-sectional area of SMAMs. CONCLUSION:MP-DW-STE imaging allows for time-resolved recording of spontaneous muscular contractions and provides new insights into their dynamic course. This new feature can be used for better characterization of physiological SMAMs in healthy subjects and pathological SMAMs in patients suffering from neuromuscular diseases.

Natural or synthetic scaffolds are essential for developing three-dimensional (3D) cell culture models, as they provide structural stability and accurately replicate the cellular microenvironment. When integrated into optimized setups, scaffold-supported cellular aggregates, such as spheroids, can be non-destructively characterized and monitored using 3T Magnetic Resonance Imaging (MRI). However, a significant technical limitation is the presence of MR artifacts generated by scaffolds, which can severely obscure the visualization of the embedded spheroids. This study systematically evaluated the suitability of various scaffolds and matrices (including Matrigel®, fibrin glue, and several hydrogels) for MRI and MR spectroscopy (MRS). The materials were investigated both native and seeded with chondrosarcoma cells (SW1353). Our findings revealed considerable variability in MR compatibility across different materials. Specifically, fibrin glue proved unsuitable for MR applications due to substantial artifact generation that interfered with the visualization of cellular components. Furthermore, the results emphasize the importance of the observation period, as material degradation processes can introduce confounding factors in longitudinal MR studies. The choice of scaffold material is paramount for the successful analysis of 3D cell models via MRI. Careful selection is required, as the materials’ properties and temporal stability directly impact the interpretability of the acquired data.

Over the past decade, significant progress has been made in the utilization of three-dimensional cell cultures in the form of spheroids as a bridge between in vitro and in vivo models. This is contributed by natural cell-cell interactions that occur within spheroids, leading to the subsequent development of extracellular matrix. The assessment of cell spheroids with conventional microscopy is destructive, requiring sectioning that damages their micro-structures. To address these issues, we developed and propose a non-invasive approach using magnetic resonance imaging (MRI). Despite its limited spatial resolution, this method adeptly reveals information about the composition and vitality of stem cell and cancer spheroids and their micro-environment in a non-invasive manner. This work reports on the development of an MRI-compatible setup for culturing cell spheroids, tailored for use with standard 3 T whole-body MRI systems. Systematic studies with different cell types show the potential of the proposed approach for simultaneous actuation and visualization of cell spheroids, with potential across a broad spectrum of applications.

OBJECTIVES:High prevalence of visceral obesity and its associated complications underscore the importance of accurately quantifying visceral adipose tissue (VAT) depots. While whole-body MRI offers comprehensive insights into adipose tissue distribution, it is resource-intensive. Alternatively, evaluation of defined single slices provides an efficient approach for estimation of total VAT volume. This study investigates the influence of sex-, age-, and BMI on VAT distribution along the craniocaudal axis and total VAT volume obtained from single slice versus volumetric assessment in 3D MRI and aims to identify age-independent locations for accurate estimation of VAT volume from single slice assessment. MATERIALS AND METHODS:This secondary analysis of the prospective population-based German National Cohort (NAKO) included 3D VIBE Dixon MRI from 11,191 participants (screened between May 2014 and December 2016). VAT and spine segmentations were automatically generated using fat-selective images. Standardized craniocaudal VAT profiles were generated. Axial percentage of total VAT was used for identification of reference locations for volume estimation of VAT from a single slice. RESULTS:Data from 11,036 participants (mean age, 52 ± 11 years, 5681 men) were analyzed. Craniocaudal VAT distribution differed qualitatively between men/women and with respect to age/BMI. Age-independent single slice VAT estimates demonstrated strong correlations with reference VAT volumes. Anatomical locations for accurate VAT estimation varied with sex/BMI. CONCLUSIONS:The selection of reference locations should be different depending on BMI groups, with a preference for caudal shifts in location with increasing BMI. For women with obesity (BMI >30 kg/m2), the L1 level emerges as the optimal reference location.

The use of destructive biochemical assays and the preparation of histologic samples are routinely employed to monitor development and viability of 3D cell aggregates. Magnetic resonance imaging (MRI) offers a non-destructive, high-resolution alternative to histological analysis, enabling longitudinal assessment of cellular dynamics while preserving sample integrity. Here, we present a protocol for non-invasive MR imaging of cell spheroid clusters by creating an adequate imaging environment. We describe steps for spheroid formation, casting of the imaging tube, cell cultivation, and data evaluation. For complete details on the use and execution of this protocol, please refer to Wißmann et al.1.