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Dual energy computed tomography (DECT) allows the quantification of specific materials such as iodine contrast agent in human body tissue, potentially providing additional diagnostic data. Yet full diagnostic value can only be achieved if physiological normal values for iodine concentrations are known. We retrospectively evaluated abdominal DECT scans of 105 patients with healthy liver between March and August 2018 (age 17 to 86 years, 43 female and 62 male). The iodine concentrations within ROIs of the liver parenchyma as well as of the abdominal aorta and main portal vein were obtained. We evaluated the absolute iodine concentration and blood-normalized iodine concentrations relating the measured iodine concentration of the liver parenchyma to those of the supplying vessels. The influence of age and gender on the iodine uptake was assessed. The absolute iodine concentration was significantly different for the male and female cohort, but the difference was eliminated by the blood-normalized values. The average blood-normalized iodine concentrations were 2.107 mg/ml (+/- 0.322 mg/ml), 2.125 mg/ml (+/- 0.426 mg/ml) and 2.103 mg/ml (+/- 0.317 mg/ml) for the portal vein normalized, aorta normalized and mixed blood normalized iodine concentrations, respectively. A significant negative correlation between the patients’ age and the iodine concentration was detected only for the blood-normalized values. A physiological range for iodine concentration in portal venous phase contrast enhanced DECT images can be defined for absolute and blood-normalized values. Deviations of blood-normalized iodine concentration values might be a robust biomarker for diagnostic evaluation. Patient age but not the gender influences the blood-normalized iodine concentrations in healthy liver parenchyma.

Generative models, such as DALL-E 2 (OpenAI), could represent promising future tools for image generation, augmentation, and manipulation for artificial intelligence research in radiology, provided that these models have sufficient medical domain knowledge. Herein, we show that DALL-E 2 has learned relevant representations of x-ray images, with promising capabilities in terms of zero-shot text-to-image generation of new images, the continuation of an image beyond its original boundaries, and the removal of elements; however, its capabilities for the generation of images with pathological abnormalities (eg, tumors, fractures, and inflammation) or computed tomography, magnetic resonance imaging, or ultrasound images are still limited. The use of generative models for augmenting and generating radiological data thus seems feasible, even if the further fine-tuning and adaptation of these models to their respective domains are required first.

Objectives To evaluate a compressed sensing artificial intelligence framework (CSAI) to accelerate MRI acquisition of the ankle. Methods Thirty patients were scanned at 3T. Axial T2-w, coronal T1-w, and coronal/sagittal intermediate-w scans with fat saturation were acquired using compressed sensing only (12:44 min, CS), CSAI with an acceleration factor of 4.6–5.3 (6:45 min, CSAI2x), and CSAI with an acceleration factor of 6.9–7.7 (4:46 min, CSAI3x). Moreover, a high-resolution axial T2-w scan was obtained using CSAI with a similar scan duration compared to CS. Depiction and presence of abnormalities were graded. Signal-to-noise and contrast-to-noise were calculated. Wilcoxon signed-rank test and Cohen’s kappa were used to compare CSAI with CS sequences. Results The correlation was perfect between CS and CSAI2x ( κ = 1.0) and excellent for CS and CSAI3x ( κ = 0.86–1.0). No significant differences were found for the depiction of structures between CS and CSAI2x and the same abnormalities were detected in both protocols. For CSAI3x the depiction was graded lower ( p ≤ 0.001), though most abnormalities were also detected. For CSAI2x contrast-to-noise fluid/muscle was higher compared to CS ( p ≤ 0.05), while no differences were found for other tissues. Signal-to-noise and contrast-to-noise were higher for CSAI3x compared to CS ( p ≤ 0.05). The high - resolution axial T2-w sequence specifically improved the depiction of tendons and the tibial nerve ( p ≤ 0.005). Conclusions Acquisition times can be reduced by 47% using CSAI compared to CS without decreasing diagnostic image quality. Reducing acquisition times by 63% is feasible but should be reserved for specific patients. The depiction of specific structures is improved using a high-resolution axial T2-w CSAI scan. Key Points • Prospective study showed that CSAI enables reduction in acquisition times by 47% without decreasing diagnostic image quality. • Reducing acquisition times by 63% still produces images with an acceptable diagnostic accuracy but should be reserved for specific patients. • CSAI may be implemented to scan at a higher resolution compared to standard CS images without increasing acquisition times.

Objectives To evaluate the performance of 3D T1w spoiled gradient-echo (T1SGRE) and ultra-short echo time (UTE) MRI sequences for the detection and assessment of vertebral fractures and degenerative bone changes compared with conventional CT. Methods Fractures ( n = 44) and degenerative changes ( n = 60 spinal segments) were evaluated in 30 patients (65 ± 14 years, 18 women) on CT and 3-T MRI, including CT-like images derived from T1SGRE and UTE. Two radiologists evaluated morphological features on both modalities: Genant and AO/Magerl classifications, anterior/posterior vertebral height, fracture age; disc height, neuroforaminal diameter, grades of spondylolisthesis, osteophytes, sclerosis, and facet joint degeneration. Diagnostic accuracy and agreement between MRI and CT and between radiologists were assessed using crosstabs, weighted κ, and intraclass correlation coefficients. Image quality was graded on a Likert scale. Results For fracture detection, sensitivity, specificity, and accuracy were 0.95, 0.98, and 0.97 for T1SGRE and 0.91, 0.96, and 0.95 for UTE. Agreement between T1SGRE and CT was substantial to excellent (e.g., Genant: κ , 0.92 [95% confidence interval, 0.83–1.00]; AO/Magerl: κ , 0.90 [0.76–1.00]; osteophytes: κ , 0.91 [0.82–1.00]; sclerosis: κ , 0.68 [0.48–0.88]; spondylolisthesis: ICCs, 0.99 [0.99–1.00]). Agreement between UTE and CT was lower, ranging from moderate (e.g., sclerosis: κ , 0.43 [0.26–0.60]) to excellent (spondylolisthesis: ICC, 0.99 [0.99–1.00]). Inter-reader agreement was substantial to excellent (0.52–1.00), respectively, for all parameters. Median image quality of T1SGRE was rated significantly higher than that of UTE ( p < 0.001). Conclusions Morphologic assessment of bone pathologies of the spine using MRI was feasible and comparable to CT, with T1SGRE being more robust than UTE. Key Points • Vertebral fractures and degenerative bone changes can be assessed on CT-like MR images, with 3D T1w spoiled gradient-echo–based images showing a high diagnostic accuracy and agreement with CT. • This could enable MRI to precisely assess bone morphology, and 3D T1SGRE MRI sequences may substitute additional spinal CT examinations in the future. • Image quality and robustness of T1SGRE sequences are higher than those of UTE MRI for the assessment of bone structures.

This study demonstrates that GPT-4V outperforms GPT-4 across radiology subspecialties in analyzing 207 cases with 1312 images from the Radiological Society of North America Case Collection.This study demonstrates that GPT-4V outperforms GPT-4 across radiology subspecialties in analyzing 207 cases with 1312 images from the Radiological Society of North America Case Collection.

Background Radiographs of the sacroiliac joints are commonly used for the diagnosis and classification of axial spondyloarthritis. The aim of this study was to develop and validate an artificial neural network for the detection of definite radiographic sacroiliitis as a manifestation of axial spondyloarthritis (axSpA). Methods Conventional radiographs of the sacroiliac joints obtained in two independent studies of patients with axSpA were used. The first cohort comprised 1553 radiographs and was split into training ( n  = 1324) and validation ( n  = 229) sets. The second cohort comprised 458 radiographs and was used as an independent test dataset. All radiographs were assessed in a central reading session, and the final decision on the presence or absence of definite radiographic sacroiliitis was used as a reference. The performance of the neural network was evaluated by calculating areas under the receiver operating characteristic curves (AUCs) as well as sensitivity and specificity. Cohen’s kappa and the absolute agreement were used to assess the agreement between the neural network and the human readers. Results The neural network achieved an excellent performance in the detection of definite radiographic sacroiliitis with an AUC of 0.97 and 0.94 for the validation and test datasets, respectively. Sensitivity and specificity for the cut-off weighting both measurements equally were 88% and 95% for the validation and 92% and 81% for the test set. The Cohen’s kappa between the neural network and the reference judgements were 0.79 and 0.72 for the validation and test sets with an absolute agreement of 90% and 88%, respectively. Conclusion Deep artificial neural networks enable the accurate detection of definite radiographic sacroiliitis relevant for the diagnosis and classification of axSpA.

Magnetic particle imaging (MPI) is a non-invasive, non-ionizing imaging technique for the visualization and quantification of magnetic nanoparticles (MNPs). The technique is especially suitable for cell imaging as it offers zero background contribution from the surrounding tissue, high sensitivity, and good spatial and temporal resolutions. Previous studies have demonstrated that the dynamic magnetic behaviour of MNPs changes during cellular binding and internalization. In this study, we demonstrate how this information is encoded in the MPI imaging signal. Through MPI imaging we are able to discriminate between free and cell-bound MNPs in reconstructed images. This technique was used to image and quantify the changes that occur in-vitro when free MNPs come into contact with cells and undergo cellular-uptake over time. The quantitative MPI results were verified by colorimetric measurements of the iron content. The results showed a mean relative difference between the MPI results and the reference method of 23.8% for the quantification of cell-bound MNPs. With this technique, the uptake of MNPs in cells can be imaged and quantified directly from the first MNP cell contact, providing information on the dynamics of cellular uptake.

When dealing with a newly emerging disease such as COVID-19, the impact of patient- and disease-specific factors (e.g., body weight or known co-morbidities) on the immediate course of the disease is largely unknown. An accurate prediction of the most likely individual disease progression can improve the planning of limited resources and finding the optimal treatment for patients. In the case of COVID-19, the need for intensive care unit (ICU) admission of pneumonia patients can often only be determined on short notice by acute indicators such as vital signs (e.g., breathing rate, blood oxygen levels), whereas statistical analysis and decision support systems that integrate all of the available data could enable an earlier prognosis. To this end, we propose a holistic, multimodal graph-based approach combining imaging and non-imaging information. Specifically, we introduce a multimodal similarity metric to build a population graph that shows a clustering of patients. For each patient in the graph, we extract radiomic features from a segmentation network that also serves as a latent image feature encoder. Together with clinical patient data like vital signs, demographics, and lab results, these modalities are combined into a multimodal representation of each patient. This feature extraction is trained end-to-end with an image-based Graph Attention Network to process the population graph and predict the COVID-19 patient outcomes: admission to ICU, need for ventilation, and mortality. To combine multiple modalities, radiomic features are extracted from chest CTs using a segmentation neural network. Results on a dataset collected in Klinikum rechts der Isar in Munich, Germany and the publicly available iCTCF dataset show that our approach outperforms single modality and non-graph baselines. Moreover, our clustering and graph attention increases understanding of the patient relationships within the population graph and provides insight into the network’s decision-making process.

Non-alcoholic fatty liver disease (NAFLD) was shown to also occur in lean and underweight patients. So far, the prevalence of NAFLD in underweight individuals with and without inflammatory bowel disease (IBD) is insufficiently enlightened. In this cross-sectional age, gender and disease-matched case-control study, underweight patients (BMI<18.5 kg/m2) with inflammatory bowel disease (IBD), who underwent abdominal MRI at 1.5 T/3 T with fat-saturated fast-spin-echo imaging from 10/2005-07/2018 were analysed (control-to-case-ratio 1:1, n = 130). All patients were additionally investigated for duration, history of surgery, medical treatment, laboratory values, liver and spleen diameters. On MRI, liver fat was quantified by two observers based on the relative signal loss on T2-weighted fast spin-echo MR images with fat saturation compared to images without fat saturation. The prevalence of NAFLD/liver steatosis, defined as a measured intrahepatic fat content of at least 5%, was significantly higher in underweight IBD patients than in normal weight patients (87.6% versus 21.5%, p<0.001). Compared to the cases, the liver fat content of the controls was reduced by -0.19 units on average (-19%; 95%Cl: -0.20; -0.14). Similar results were obtained for the subgroup of non-IBD individuals (n = 12; -0.25 units on average (-25%); 95%Cl: -0.35; -0.14). Patients with extremely low body weight (BMI <17.5 kg/m2) showed the highest liver fat content (+0.15 units on average (+15%) compared to underweight patients with a BMI of 17.5-18.5 kg/m2 (p<0.05)). Furthermore, underweight patients showed slightly increased liver enzymes and liver diameters. There were no indications of significant differences in disease duration, type of medications or surgery between cases and controls and also, there were no significant differences between observers or field strengths (p>0.05). The prevalence of liver steatosis was higher among underweight IBD and non-IBD patients compared to normal weight controls. Also, underweight patients showed slightly increased liver enzymes and liver diameters, hinting at initial metabolic disturbances.

We proposed a generic template-derived approach for (semi-) automated brain extraction in animal MRI studies and evaluated our implementation with different animal models (macaque, marmoset, rodent) and MRI protocols (T1, T2). While conventional MR-neuroimaging studies perform brain extraction as an initial step priming subsequent image-registration from subject to template, our proposed approach propagates an anatomical template to (whole-head) individual subjects in reverse order, which is challenging due to the surrounding extracranial tissue, greater differences in contrast pattern and larger areas with field inhomogeneity. As a novel approach, the herein introduced brain extraction algorithm derives whole-brain segmentation using rigid and non-rigid deformation based on unbiased anatomical atlas building with a priori estimates from study-cohort and an initial approximate brain extraction. We evaluated our proposed method in comparison to several other technical approaches including “Marker based watershed scalper”, “Brain-Extraction-Tool”, “3dSkullStrip”, “Primatologist-Toolbox”, “Rapid Automatic Tissue Segmentation” and “Robust automatic rodent brain extraction using 3D pulse-coupled neural networks” with manual skull-stripping as reference standard. ABX demonstrated best performance with accurate (≥92%) and consistent results throughout datasets and across species, age and MRI protocols. ABX was made available to the public with documentation, templates and sample material ( https://www.github.com/jlohmeier/atlasBREX ).