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Purpose: Increased angiogenesis after myocardial infarction is considered an important favorable prognostic parameter. The αvβ3 integrin is a key mediator of cell-cell and cell-matrix interactions and an important molecular target for imaging of neovasculature and repair processes after MI. Thus, imaging of αvβ3 expression might provide a novel biomarker for assessment of myocardial angiogenesis as a prognostic marker of left ventricular remodeling after MI. Currently, there is limited data available regarding the association of myocardial blood flow and αvβ3 integrin expression after myocardial infarction in humans. Methods: Twelve patients were examined 31 ± 14 days after MI with PET/CT using [18F]Galacto-RGD and [13N]NH3 and with cardiac MRI including late enhancement on the same day. Normal myocardium (remote) and areas of infarction (lesion) were identified on the [18F]Galacto-RGD PET/CT images by correlation with [13N]NH3 PET and cardiac MRI. Lesion/liver-, lesion/blood-, and lesion/remote ratios were calculated. Blood flow and [18F]Galacto-RGD uptake were quantified and correlated for each myocardial segment (AHA 17-segment model). Results: In 5 patients, increased [18F]Galacto-RGD uptake was notable within or adjacent to the infarction areas with a lesion/remote ratio of 46% (26–83%; lesion/blood 1.15 ± 0.06; lesion/liver 0.61 ± 0.18). [18F]Galacto-RGD uptake correlated significantly with infarct size (R = 0.73; p = 0.016). Moreover, it correlated significantly with restricted blood flow for all myocardial segments (R = − 0.39; p < 0.0001) and even stronger in severely hypoperfused areas (R = − 0.75; p < 0.0001). Conclusion: [18F]Galacto-RGD PET/CT allows the visualization and quantification of myocardial αvβ3 expression as a key player in angiogenesis in a subset of patients after MI. αvβ3 expression was more pronounced in patients with larger infarcts and was generally more intense but not restricted to areas with more impaired blood flow, proving that tracer uptake was largely independent of unspecific perfusion effects. Based on these promising results, larger prospective studies are warranted to evaluate the potential of αvβ3 imaging for assessment of myocardial angiogenesis and prediction of ventricular remodeling. [ABSTRACT FROM AUTHOR]

This article presents a review of data in literature that deal with the characterization of coronary atherosclerosis by magnetic resonance imaging. It discusses the pathophysiology of atherosclerosis and transition of subclinical to clinical disease. The technical challenges of cardiovascular magnetic resonance of coronary atherosclerosis are described.

Abstract: Objective: Molecular magnetic resonance imaging (MRI) has emerged as a promising non-invasive modality to characterize atherosclerotic vessel wall changes on a morphological and molecular level. Intraplaque and endothelial fibrin has recently been recognized to play an important role in the progression of atherosclerosis. This study aimed to investigate the feasibility of intraplaque and endothelial fibrin detection using a fibrin-targeted contrast-agent, FTCA (EPIX Pharmaceuticals, Lexington, MA), in a mouse model of atherosclerosis. Methods: Male apolipoproteinE-knockout mice (ApoE−/−) were fed a high fat diet (HFD) for one to three months. MRI of the brachiocephalic artery was performed prior to and 90min after the administration of FTCA (n =8 per group). Contrast to noise ratios (CNR) and longitudinal relaxation rates (R1) of plaques were determined and compared to ex vivo fibrin density measurements on immunohistological sections stained with a fibrin-specific antibody and gadolinium concentrations measured by inductively coupled mass spectroscopy (ICP-MS). Results: Molecular MRI after FTCA administration demonstrated a significant increase (p <0.05) in contrast agent uptake in brachiocephalic artery plaques. In vivo CNR measurements were in good agreement with ex vivo fibrin density measurements on immunohistochemistry (y =2.4x +11.3, R 2 =0.82) and ICP-MS (y =0.95x +7.1, R 2 =0.70). Late stage atherosclerotic plaques displayed the strongest increase in CNR, R1, ex vivo fibrin staining and gadolinium concentration (p <0.05). Conclusion: This study demonstrated the feasibility of intraplaque and endothelial fibrin imaging using FTCA. Direct in vivo fibrin detection and quantification could be useful for characterization and staging of coronary and carotid atherosclerotic lesions, which may aid diagnosis and intervention. [Copyright &y& Elsevier]

Background: Cardiovascular magnetic resonance (CMR) is the current gold standard for the assessment of left ventricular (LV) function. Repeated breath-holds are needed for standard multi-slice 2D cine steady-state free precession sequences (M2D-SSFP). Accelerated single breath-hold techniques suffer from low contrast between blood pool and myocardium. In this study an intravascular contrast agent was prospectively compared to an extravascular contrast agent for the assessment of LV function using a single-breath-hold 3D-whole-heart cine SSFP sequence (3D-SSFP). Methods: LV function was assessed in fourteen patients on a 1.5 T MR-scanner (Philips Healthcare) using 32-channel coil technology. Patients were investigated twice using a 3D-SSFP sequence (acquisition time 18-25 s) after Gadopentetate dimeglumine (GdD, day 1) and Gadofosveset trisodium (GdT, day 2) administration. Image acquisition was accelerated using sensitivity encoding in both phase encoding directions (4xSENSE). CNR and BMC were both measured between blood and myocardium. The CNR incorporated noise measurements, while the BMC represented the coeffiancy between the signal from blood and myocardium [1]. Contrast to noise ratio (CNR), blood to myocardium contrast (BMC), image quality, LV functional parameters and intra-/interobserver variability were compared. A M2D-SSFP sequence was used as a reference standard on both days. Results: All 3D-SSFP sequences were successfully acquired within one breath-hold after GdD and GdT administration. CNR and BMC were significantly (p < 0.05) higher using GdT compared to GdD, resulting in an improved endocardial definition. Using 3D-SSFP with GdT, Bland-Altman plots showed a smaller bias (95% confidence interval LVEF: 9.0 vs. 23.7) and regression analysis showed a stronger correlation to the reference standard (R2 = 0.92 vs. R2 = 0.71), compared to 3D-SSFP with GdD. Conclusions: A single-breath-hold 3D-whole-heart cine SSFP sequence in combination with 32-channel technology and an intravascular contrast agent allows for the accurate and fast assessment of LV function. Trial registration: The study was approved by the local research ethics committee (Study No. 07/Q0704/2) and was registered with the Medicines and Healthcare Products Regulatory Agency (MHRA Study No. 28482/0002/001-0001, EudraCTnumber 2006-007042). [ABSTRACT FROM AUTHOR]

Atherosclerosis and its consequences remain the main cause of mortality in industrialized and developing nations. Plaque burden and progression have been shown to be independent predictors for future cardiac events by intravascular ultrasound. Routine prospective imaging is hampered by the invasive nature of intravascular ultrasound. A noninvasive technique would therefore be more suitable for screening of atherosclerosis in large populations. Here we introduce an elastin-specific magnetic resonance contrast agent (ESMA) for noninvasive quantification of plaque burden in a mouse model of atherosclerosis. The strong signal provided by ESMA allows for imaging with high spatial resolution, resulting in accurate assessment of plaque burden. Additionally, plaque characterization by quantifying intraplaque elastin content using signal intensity measurements is possible. Changes in elastin content and the high abundance of elastin during plaque development, in combination with the imaging properties of ESMA, provide potential for noninvasive assessment of plaque burden by molecular magnetic resonance imaging (MRI). [ABSTRACT FROM AUTHOR]

Objectives: MR imaging-based proton density fat fraction (PDFF) and T2* imaging has shown to be useful for the evaluation of degenerative changes in the spine. Therefore, the aim of this study was to investigate the influence of myelotoxic chemotherapy on the PDFF and T2* of the thoracolumbar spine in comparison to changes in bone mineral density (BMD). Methods: In this study, 19 patients were included who had received myelotoxic chemotherapy (MC) and had received a MR imaging scan of the thoracolumbar vertebrates before and after the MC. Every patient was matched for age, sex, and time between the MRI scans to two controls without MC. All patients underwent 3-T MR imaging including the thoracolumbar spine comprising chemical shift encoding-based water-fat imaging to extract PDFF and T2* maps. Moreover, trabecular BMD values were determined before and after chemotherapy. Longitudinal changes in PDFF and T2* were evaluated and compared to changes in BMD. Results: Absolute mean differences of PDFF values between scans before and after MC were at 8.7% (p = 0.01) and at −0.5% (p = 0.57) in the control group, resulting in significantly higher changes in PDFF in patients with MC (p = 0.008). BMD and T2* values neither showed significant changes in patients with nor in those without myelotoxic chemotherapy (p = 0.15 and p = 0.47). There was an inverse, yet non-significant correlation between changes in PDFF and BMD found in patients with myelotoxic chemotherapy (r = −0.41, p = 0.12). Conclusion: Therefore, PDFF could be a useful non-invasive biomarker in order to detect changes in the bone marrow in patients receiving myelotoxic therapy. Clinical relevance statement: Using PDFF as a non-invasive biomarker for early bone marrow changes in oncologic patients undergoing myelotoxic treatment may help enable more targeted countermeasures at commencing states of bone marrow degradation and reduce risks of possible fragility fractures. Key Points: Quantifying changes in bone marrow fat fraction, as well as T2* caused by myelotoxic pharmaceuticals using proton density fat fraction, is feasible. Proton density fat fraction could potentially be established as a non-invasive biomarker for early bone marrow changes in oncologic patients undergoing myelotoxic treatment. [ABSTRACT FROM AUTHOR]

Objectives: Differentiation between high-grade glioma (HGG) and post-treatment-related effects (PTRE) is challenging, but advanced imaging techniques were shown to provide benefit. We aim to investigate microstructure characteristics of metabolic compartments identified from amino acid PET and to evaluate the diagnostic potential of this multimodal and integrative O-(2-18F-fluoroethyl)-l-tyrosine-(FET)-PET and fast diffusion kurtosis imaging (DKI) approach for the detection of recurrence and IDH genotyping. Methods: Fifty-nine participants with neuropathologically confirmed recurrent HGG (n = 39) or PTRE (n = 20) were investigated using static 18F-FET PET and a fast-DKI variant. PET and advanced diffusion metrics of metabolically defined (80–100% and 60–75% areas of 18F-FET uptake) compartments were assessed. Comparative analysis was performed using Mann–Whitney U tests with Holm-Šídák multiple-comparison test and Wilcoxon signed-rank test. Receiver operating characteristic (ROC) curves, regression, and Spearman's correlation analysis were used for statistical evaluations. Results: Compared to PTRE, recurrent HGG presented increased 18F-FET uptake and diffusivity (MD60), but lower (relative) mean kurtosis tensor (rMKT60) and fractional anisotropy (FA60) (respectively p <.05). Diffusion metrics determined from the metabolic periphery showed improved diagnostic performance — most pronounced for FA60 (AUC = 0.86, p <.001), which presented similar benefit to 18F-FET PET (AUC = 0.86, p <.001) and was negatively correlated with amino acid uptake (rs = − 0.46, p <.001). When PET and DKI metrics were evaluated in a multimodal biparametric approach, TBRmax + FA60 showed highest diagnostic accuracy (AUC = 0.93, p <.001), which improved the detection of relapse compared to PET alone (difference in AUC = 0.069, p =.04). FA60 and MD60 distinguished the IDH genotype in the post-treatment setting. Conclusion: Detection of glioma recurrence benefits from a multimodal and integrative PET/DKI approach, which presented significant diagnostic advantage to the assessment based on PET alone. Clinical relevance statement: A multimodal and integrative 18F-FET PET/fast-DKI approach for the non-invasive microstructural characterization of metabolic compartments provided improved diagnostic capability for differentiation between recurrent glioma and post-treatment-related changes, suggesting a role for the diagnostic workup of patients in post-treatment settings. Key Points: • Multimodal PET/MRI with integrative analysis of 18F-FET PET and fast-DKI presents clinical benefit for the assessment of CNS cancer, particularly for the detection of recurrent high-grade glioma. • Microstructure markers of the metabolic periphery yielded biologically pertinent estimates characterising the tumour microenvironment, and, thereby, presented improved diagnostic accuracy with similar accuracy to amino acid PET. • Combined 18F-FET PET/fast-DKI achieved the best diagnostic performance for detection of high-grade glioma relapse with significant benefit to the assessment based on PET alone. [ABSTRACT FROM AUTHOR]

An abstract of the article "Molecular assessment of aortic aneurysm wall integrity using an elastin-specific MR imaging probe," by Marcus R Makowski and colleagues is presented.

Objective: Conventional magnetic resonance imaging (MRI) uses T1-weighted and short-tau inversion recovery (STIR) sequences to characterize bone marrow in axial spondyloarthritis. However, quantification is restricted to estimating the extent of lesions because signal intensities are highly variable both within individuals and across patients and MRI scanners. This study evaluates the performance of quantitative T1 mapping for distinguishing different types of bone marrow lesions of the sacroiliac joints. Materials and methods: In this prospective study, 62 patients underwent computed tomography (CT) and MRI of the sacroiliac joints including T1, STIR, and T1 mapping. Bone marrow lesions were characterized by three readers and assigned to one of four groups: sclerosis, osteitis, fat lesions, and mixed marrow lesions. Relaxation times on T1 maps were compared using generalized estimating equations and receiver operating characteristics (ROC) analysis. Results: A total of 119 lesions were selected (sclerosis: 38, osteitis: 27, fat lesions: 40; mixed lesions: 14). T1 maps showed highly significant differences between the lesions with the lowest values for sclerosis (1516±220 ms), followed by osteitis (1909±75 ms), and fat lesions (2391±200 ms); p<0.001. T1 mapping differentiated lesions with areas under the ROC curve of 99% (sclerosis vs. osteitis) and 100% (other comparisons). Conclusion: T1 mapping allows accurate characterization of sclerosis, osteitis, and fat lesions at the sacroiliac joint but only for homogeneous, non-mixed lesions. Thus, further sequence development is needed before implementation in clinical routine. [ABSTRACT FROM AUTHOR]

An abstract of the conference paper "First pass vasodilator-stress myocardial perfusion CMR in mice on a whole-body 3Tesla scanner: validation against microspheres," by Roy Jogiya, and colleagues is presented.

Objective: To evaluate the performance of dynamic contrast-enhanced CT (DCE-CT) in detecting and quantitatively assessing perfusion parameters in patients with arthritis of the hand compared with dynamic contrast-enhanced MRI (DCE-MRI) as a standard of reference. Materials and methods: In this IRB-approved randomized prospective single-centre study, 36 consecutive patients with suspected rheumatoid arthritis underwent DCE-CT (320-row, tube voltage 80 kVp, tube current 8.25 mAs) and DCE-MRI (1.5 T) of the hand. Perfusion maps were calculated separately for mean transit time (MTT), time to peak (TTP), relative blood volume (rBV), and relative blood flow (rBF) using four different decomposition techniques. Region of interest (ROI) analysis was performed in metacarpophalangeal joints II–V and in the wrist. Pairs of perfusion parameters in DCE-CT and DCE-MRI were compared using a two-tailed t test for paired samples and interpreted for effect size (Cohen's d). According to the Rheumatoid Arthritis Magnetic Resonance Imaging Score (RAMRIS) scoring results, differentiation of synovitis-positive and synovitis-negative joints with both modalities was assessed with the independent t test. Results: The two modalities yielded similar perfusion parameters. Identified differences had small effects (d 0.01–0.4). DCE-CT additionally differentiates inflamed and noninflamed joints based on rBF and rBV but tends to underestimate these parameters in severe inflammation. The total dose-length product (DLP) was 48 mGy*cm with an estimated effective dose of 0.038 mSv. Conclusion: DCE-CT is a promising imaging technique in arthritis. In patients with a contraindication to MRI or when MRI is not available, DCE-CT is a suitable alternative to detect and assess arthritis. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate and compare the diagnostic performance of CT-like images based on a 3D T1-weighted spoiled gradient-echo sequence (T1 GRE), an ultra-short echo time sequence (UTE), and a 3D T1-weighted spoiled multi-echo gradient-echo sequence (FRACTURE) with conventional CT in patients with suspected osseous shoulder pathologies. Materials and methods: Patients with suspected traumatic dislocation of the shoulder (n = 46, mean age 40 ± 14.5 years, 19 women) were prospectively recruited and received 3-T MR imaging including 3D T1 GRE, UTE, and 3D FRACTURE sequences. CT was performed in patients with acute fractures and served as standard of reference (n = 25). Agreement of morphological features between the modalities was analyzed including the glenoid bone loss, Hill-Sachs interval, glenoid track, and the anterior straight-line length. Agreement between the modalities was assessed using Bland-Altman plots, Student's t-test, and Pearson's correlation coefficient. Inter- and intrareader assessment was evaluated with weighted Cohen's κ and intraclass correlation coefficient. Results: All osseous pathologies were detected accurately on all three CT-like sequences (n = 25, κ = 1.00). No significant difference in the percentage of glenoid bone loss was found between CT (mean ± standard deviation, 20.3% ± 8.0) and CT-like MR images (FRACTURE 20.6% ± 7.9, T1 GRE 20.4% ± 7.6, UTE 20.3% ± 7.7, p > 0.05). When comparing the different measurements on CT-like images, measurements performed using the UTE images correlated best with CT. Conclusion: Assessment of bony Bankart lesions and other osseous pathologies was feasible and accurate using CT-like images based on 3-T MRI compared with conventional CT. Compared to the T1 GRE and FRACTURE sequence, the UTE measurements correlated best with CT. Clinical relevance statement: In an acute trauma setting, CT-like images based on a T1 GRE, UTE, or FRACTURE sequence might be a useful alternative to conventional CT scan sparing associated costs as well as radiation exposure. Key Points: • No significant differences were found for the assessment of the glenoid bone loss when comparing measurements of CT-like MR images with measurements of conventional CT images. • Compared to the T1 GRE and FRACTURE sequence, the UTE measurements correlated best with CT whereas the FRACTURE sequence appeared to be the most robust regarding motion artifacts. • The T1 GRE sequence had the highest resolution with high bone contrast and detailed delineation of even small fractures but was more susceptible to motion artifacts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objectives: To examine the effect of high-b-value computed diffusion-weighted imaging (cDWI) on solid lesion detection and classification in pancreatic intraductal papillary mucinous neoplasm (IPMN), using endoscopic ultrasound (EUS) and histopathology as a standard of reference. Methods: Eighty-two patients with known or suspected IPMN were retrospectively enrolled. Computed high-b-value images at b = 1000 s/mm2 were calculated from standard (b = 0, 50, 300, and 600 s/mm2) DWI images for conventional full field-of-view (fFOV, 3 × 3 × 4 mm3 voxel size) DWI. A subset of 39 patients received additional high-resolution reduced-field-of-view (rFOV, 2.5 × 2.5 × 3 mm3 voxel size) DWI. In this cohort, rFOV cDWI was compared against fFOV cDWI additionally. Two experienced radiologists evaluated (Likert scale 1–4) image quality (overall image quality, lesion detection and delineation, fluid suppression within the lesion). In addition, quantitative image parameters (apparent signal-to-noise ratio (aSNR), apparent contrast-to-noise ratio (aCNR), contrast ratio (CR)) were assessed. Diagnostic confidence regarding the presence/absence of diffusion-restricted solid nodules was assessed in an additional reader study. Results: High-b-value cDWI at b = 1000 s/mm2 outperformed acquired DWI at b = 600 s/mm2 regarding lesion detection, fluid suppression, aCNR, CR, and lesion classification (p = <.001–.002). Comparing cDWI from fFOV and rFOV revealed higher image quality in high-resolution rFOV-DWI compared to conventional fFOV-DWI (p ≤.001–.018). High-b-value cDWI images were rated non-inferior to directly acquired high-b-value DWI images (p =.095–.655). Conclusions: High-b-value cDWI may improve the detection and classification of solid lesions in IPMN. Combining high-resolution imaging and high-b-value cDWI may further increase diagnostic precision. Clinical relevance statement: This study shows the potential of computed high-resolution high-sensitivity diffusion-weighted magnetic resonance imaging for solid lesion detection in pancreatic intraductal papillary mucinous neoplasia (IPMN). The technique may enable early cancer detection in patients under surveillance. Key Points: • Computed high-b-value diffusion-weighted imaging (cDWI) may improve the detection and classification of intraductal papillary mucinous neoplasms (IPMN) of the pancreas. • cDWI calculated from high-resolution imaging increases diagnostic precision compared to cDWI calculated from conventional-resolution imaging. • cDWI has the potential to strengthen the role of MRI for screening and surveillance of IPMN, particularly in view of the rising incidence of IPMNs combined with now more conservative therapeutic approaches. [ABSTRACT FROM AUTHOR]

Objectives: To detect and evaluate early signs of apical periodontitis using MRI based on a 3D short-tau-inversion-recovery (STIR) sequence compared to conventional panoramic radiography (OPT) and periapical radiographs in patients with apical periodontitis. Materials and methods: Patients with clinical evidence of periodontal disease were enrolled prospectively and received OPT as well as MRI of the viscerocranium including a 3D-STIR sequence. The MRI sequences were assessed for the occurrence and extent of bone changes associated with apical periodontitis including bone edema, periradicular cysts, and dental granulomas. OPTs and intraoral periapical radiographs, if available, were assessed for corresponding periapical radiolucencies using the periapical index (PAI). Results: In total, 232 teeth of 37 patients (mean age 62±13.9 years, 18 women) were assessed. In 69 cases reactive bone edema was detected on MRI with corresponding radiolucency according to OPT. In 105 cases edema was detected without corresponding radiolucency on OPT. The overall extent of edema measured on MRI was significantly larger compared to the radiolucency on OPT (mean: STIR 2.4±1.4 mm, dental radiograph 1.3±1.2 mm, OPT 0.8±1.1 mm, P=0.01). The overall PAI score was significantly higher on MRI compared to OPT (mean PAI: STIR 1.9±0.7, dental radiograph 1.3±0.5, OPT 1.2±0.7, P=0.02). Conclusion: Early detection and assessment of bone changes of apical periodontitis using MRI was feasible while the extent of bone edema measured on MRI exceeded the radiolucencies measured on OPT. Clinical relevance: In clinical routine, dental MRI might be useful for early detection and assessment of apical periodontitis before irreversible bone loss is detected on conventional panoramic and intraoral periapical radiographs. [ABSTRACT FROM AUTHOR]

Purpose : To develop a CT-based radiomic signature to predict biochemical recurrence (BCR) in prostate cancer patients after sRT guided by positron-emission tomography targeting prostate-specific membrane antigen (PSMA-PET). Material and methods: Consecutive patients, who underwent 68Ga-PSMA11-PET/CT-guided sRT from three high-volume centers in Germany, were included in this retrospective multicenter study. Patients had PET-positive local recurrences and were treated with intensity-modulated sRT. Radiomic features were extracted from volumes of interests on CT guided by focal PSMA-PET uptakes. After preprocessing, clinical, radiomics, and combined clinical-radiomic models were developed combining different feature reduction techniques and Cox proportional hazard models within a nested cross validation approach. Results: Among 99 patients, median interval until BCR was the radiomic models outperformed clinical models and combined clinical-radiomic models for prediction of BCR with a C-index of 0.71 compared to 0.53 and 0.63 in the test sets, respectively. In contrast to the other models, the radiomic model achieved significantly improved patient stratification in Kaplan-Meier analysis. The radiomic and clinical-radiomic model achieved a significantly better time-dependent net reclassification improvement index (0.392 and 0.762, respectively) compared to the clinical model. Decision curve analysis demonstrated a clinical net benefit for both models. Mean intensity was the most predictive radiomic feature. Conclusion: This is the first study to develop a PSMA-PET-guided CT-based radiomic model to predict BCR after sRT. The radiomic models outperformed clinical models and might contribute to guide personalized treatment decisions. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the diagnostic performance of an automated reconstruction algorithm combining MR imaging acquired using compressed SENSE (CS) with deep learning (DL) in order to reconstruct denoised high-quality images from undersampled MR images in patients with shoulder pain. Methods: Prospectively, thirty-eight patients (14 women, mean age 40.0 ± 15.2 years) with shoulder pain underwent morphological MRI using a pseudo-random, density-weighted k-space scheme with an acceleration factor of 2.5 using CS only. An automated DL-based algorithm (CS DL) was used to create reconstructions of the same k-space data as used for CS reconstructions. Images were analyzed by two radiologists and assessed for pathologies, image quality, and visibility of anatomical landmarks using a 4-point Likert scale. Results: Overall agreement for the detection of pathologies between the CS DL reconstructions and CS images was substantial to almost perfect (κ 0.95 (95% confidence interval 0.82–1.00)). Image quality and the visibility of the rotator cuff, articular cartilage, and axillary recess were overall rated significantly higher for CS DL images compared to CS (p < 0.03). Contrast-to-noise ratios were significantly higher for cartilage/fluid (CS DL 198 ± 24.3, CS 130 ± 32.2, p = 0.02) and ligament/fluid (CS DL 184 ± 17.3, CS 141 ± 23.5, p = 0.03) and SNR values were significantly higher for ligaments and muscle of the CS DL reconstructions (p < 0.04). Conclusion: Evaluation of shoulder pathologies was feasible using a DL-based algorithm for MRI reconstruction and denoising. In clinical routine, CS DL may be beneficial in particular for reducing image noise and may be useful for the detection and better discrimination of discrete pathologies. Summary statement : Assessment of shoulder pathologies was feasible with improved image quality as well as higher SNR using a compressed sensing deep learning–based framework for image reconstructions and denoising. Key Points: • Automated deep learning–based reconstructions showed a significant increase in signal-to-noise ratio and contrast-to-noise ratio (p < 0.04) with only a slight increase of reconstruction time of 40 s compared to CS. • All pathologies were accurately detected with no loss of diagnostic information or prolongation of the scan time. • Significant improvements of the image quality as well as the visibility of the rotator cuff, articular cartilage, and axillary recess were detected. [ABSTRACT FROM AUTHOR]

Simple Summary: Differentiating atypical lipomatous tumors from lipomas on MR images is a challenging task due to similar imaging characteristics. Given these challenges, it would be highly beneficial to develop a reliable diagnostic tool, thereby minimizing the need for invasive diagnostic procedures. Therefore, the aim of this study was to develop and validate radiogenomic machine-learning models to predict the MDM2 gene amplification status in order to differentiate between ALTs and lipomas on preoperative MR images. The best machine-learning model was based on radiomic features from multiple MR sequences using a LASSO algorithm and showed a high discriminatory power to predict the MDM2 gene amplification. Due to the varying settings in which patients with lipomatous tumors present, this model may enhance the clinical diagnostic workup. Background: The aim of this study was to develop and validate radiogenomic models to predict the MDM2 gene amplification status and differentiate between ALTs and lipomas on preoperative MR images. Methods: MR images were obtained in 257 patients diagnosed with ALTs (n = 65) or lipomas (n = 192) using histology and the MDM2 gene analysis as a reference standard. The protocols included T2-, T1-, and fat-suppressed contrast-enhanced T1-weighted sequences. Additionally, 50 patients were obtained from a different hospital for external testing. Radiomic features were selected using mRMR. Using repeated nested cross-validation, the machine-learning models were trained on radiomic features and demographic information. For comparison, the external test set was evaluated by three radiology residents and one attending radiologist. Results: A LASSO classifier trained on radiomic features from all sequences performed best, with an AUC of 0.88, 70% sensitivity, 81% specificity, and 76% accuracy. In comparison, the radiology residents achieved 60–70% accuracy, 55–80% sensitivity, and 63–77% specificity, while the attending radiologist achieved 90% accuracy, 96% sensitivity, and 87% specificity. Conclusion: A radiogenomic model combining features from multiple MR sequences showed the best performance in predicting the MDM2 gene amplification status. The model showed a higher accuracy compared to the radiology residents, though lower compared to the attending radiologist. [ABSTRACT FROM AUTHOR]

Objectives: To assess and compare the diagnostic performance of CT-like images based on a three- dimensional (3D) T1-weighted spoiled gradient-echo sequence (3D T1 GRE) with CT in patients with acute traumatic fractures of the mandible. Materials and methods: Subjects with acute mandibular fractures diagnosed on conventional CT were prospectively recruited and received an additional 3 T MRI with a CT-like 3D T1 GRE sequence. The images were assessed by two radiologists with regard to fracture localization, degree of dislocation, and number of fragments. Bone to soft tissue contrast, diagnostic confidence, artifacts, and overall image quality were rated using a five-point Likert-scale. Agreement of measurements was assessed using an independent t-test. Results: Fourteen subjects and 22 fracture sites were included (26 ± 3.9 years; 4 females, 10 males). All traumatic fractures were accurately detected on CT-like MRI (n = 22, κ 1.00 (95% CI 1.00–1.00)). There was no statistically significant difference in the assessment of the fracture dislocation (axial mean difference (MD) 0.06 mm, p = 0.93, coronal MD, 0.08 mm, p = 0.89 and sagittal MD, 0.04 mm, p = 0.96). The agreement for the fracture classification as well as the inter- and intra-rater agreement was excellent (range κ 0.92–0.98 (95% CI 0.96–0.99)). Conclusion: Assessment of mandibular fractures was feasible and accurate using CT-like MRI based on a 3D T1 GRE sequence and is comparable to conventional CT. Clinical relevance: For the assessment of acute mandibular fractures, CT-like MRI might become a useful alternative to CT in order to reduce radiation exposure particularly in young patients. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the perception of different types of AI-based assistance and the interaction of radiologists with the algorithm's predictions and certainty measures. Methods: In this retrospective observer study, four radiologists were asked to classify Breast Imaging-Reporting and Data System 4 (BI-RADS4) lesions (n = 101 benign, n = 99 malignant). The effect of different types of AI-based assistance (occlusion-based interpretability map, classification, and certainty) on the radiologists' performance (sensitivity, specificity, questionnaire) were measured. The influence of the Big Five personality traits was analyzed using the Pearson correlation. Results: Diagnostic accuracy was significantly improved by AI-based assistance (an increase of 2.8% ± 2.3%, 95 %-CI 1.5 to 4.0 %, p = 0.045) and trust in the algorithm was generated primarily by the certainty of the prediction (100% of participants). Different human-AI interactions were observed ranging from nearly no interaction to humanization of the algorithm. High scores in neuroticism were correlated with higher persuasibility (Pearson's r = 0.98, p = 0.02), while higher consciousness and change of accuracy showed an inverse correlation (Pearson's r = −0.96, p = 0.04). Conclusion: Trust in the algorithm's performance was mostly dependent on the certainty of the predictions in combination with a plausible heatmap. Human-AI interaction varied widely and was influenced by personality traits. Key Points: • AI-based assistance significantly improved the diagnostic accuracy of radiologists in classifying BI-RADS 4 mammography lesions. • Trust in the algorithm's performance was mostly dependent on the certainty of the prediction in combination with a reasonable heatmap. • Personality traits seem to influence human-AI collaboration. Radiologists with specific personality traits were more likely to change their classification according to the algorithm's prediction than others. [ABSTRACT FROM AUTHOR]

Background: To assess the ability of low-dose dual-energy computed tomography (ld-DECT) virtual non-calcium (VNCa) images for detecting bone marrow pathologies of the sacroiliac joints (SIJs) in patients with axial spondyloarthritis (axSpA). Material and Methods: Sixty-eight patients with suspected or proven axSpA underwent ld-DECT and MRI of the SIJ. VNCa images were reconstructed from DECT data and scored for the presence of osteitis and fatty bone marrow deposition by two readers with different experience (beginner and expert). Diagnostic accuracy and correlation (Kohen's k) with magnetic resonance imaging (MRI) as the reference standard were calculated for the overall and for each reader separately. Furthermore, quantitative analysis was performed using region-of-interest (ROI) analysis. Results: Twenty-eight patients were classified as positive for osteitis, 31 for fatty bone marrow deposition. DECT's sensitivity (SE) and specificity (SP) were 73.3% and 44.4% for osteitis and 75% and 67.3% for fatty bone lesions, respectively. The expert reader achieved higher diagnostic accuracy for both osteitis (SE 93.33%; SP: 51.85%) and fatty bone marrow deposition (SE: 65%; SP: 77.55%) than the beginner (SE: 26.67%; SP: 70.37% for osteitis; SE: 60%; SP: 44.9% for fatty bone marrow deposition). Overall correlation with MRI was moderate (r = 0.25, p = 0.04) for osteitis and fatty bone marrow deposition (r = 0.25, p = 0.04). Fatty bone marrow attenuation in VNCa images (mean: −129.58 HU; ±103.61 HU) differed from normal bone marrow (mean: 118.84 HU, ±99.91 HU; p < 0.01) and from osteitis (mean: 172 HU, ±81.02 HU; p < 0.01) while osteitis did not differ significantly from normal bone marrow (p = 0.27). Conclusion: In our study, low-dose DECT failed to detect osteitis or fatty lesions in patients with suspected axSpA. Thus, we conclude that higher radiation might be needed for DECT−based bone marrow analysis. [ABSTRACT FROM AUTHOR]

Purpose: Brachytherapy is a mandatory component of primary radiochemotherapy in cervical cancer. The dose can be applied with a traditional intracavitary approach (IC alone) or with multiple catheter brachytherapy to optimize dose distribution in an individual concept. We therefore evaluated whether the utilization of a tandem–ring applicator plus additional intracavitary applicators (add IC) provides an advantage over the traditional IC alone approach, as this method is less time consuming and less invasive compared to a combined intracavitary/interstitial brachytherapy. Methods: Twenty three procedures of intracavitary brachytherapy for cervical cancer with additional intracavitary applicators performed in seven patients treated between 2016 and 2018 in our institution were included in this study. Plans were optimized for D90 HR-CTV with and without the utilization of the additional applicators and compared by statistical analysis. Results: D90 for HR-CTV was 5.71 Gy (±1.17 Gy) for fractions optimized with add IC approach and 5.29 Gy (±1.24 Gy) for fractions without additional applicators (p < 0.01). This translates to a calculated mean EQD2 HR-CTV D90 of 80.72 Gy (±8.34 Gy) compared to 77.84 Gy (±8.49 Gy) after external beam therapy and four fractions of brachytherapy for add IC and IC alone, respectively (p < 0.01). The predictive value of improved coverage of HR-CTV in the first fraction was high. Conclusion: In a subgroup of cases, the addition of intracavitary Heyman capsules can be an alternative to interstitial brachytherapy to improve the plan quality compared to standard IC alone brachytherapy. The benefit from the addition of applicators in the first fraction is predictive for the following fractions. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the performance of single-echo Dixon water-fat imaging and computed tomography (CT)–like imaging based on a single ultrashort echo time (sUTE) MR sequence for imaging of vertebral fractures as well as degenerative bone changes of the spine in comparison to conventional CT and MR sequences. Methods: Thirty patients with suspected acute vertebral fractures were examined using a 3-T MRI, including an sUTE sequence as well as short-tau inversion recovery (STIR) and T1-weighted sequences. During postprocessing, water-fat separation was performed by solving the smoothness-constrained inverse water-fat problem based on a single-complex UTE image. By removing the unwanted low-frequency phase terms, additional MR-based susceptibility-weighted-like (SW-like) images with CT-like contrast were created. Two radiologists evaluated semi-quantitative and quantitative features of fractures and degenerative changes independently and separately on CT and MR images. Results: In total, all 58 fractures were accurately detected of whom 24 were correctly classified as acute fractures with an edema detected on the water-fat-separated UTE images, using STIR and T1w sequences as standard of reference. For the morphological assessment of fractures and degenerative changes, the overall agreement between SW-like images and CT was substantial to excellent (e.g., Genant: κ 0.90 (95% confidence interval 0.54–1.00); AO/Magerl: κ 0.75 (95% confidence interval 0.43–1.00)). Overall inter-reader agreement for water-fat-separated UTE images and SW-like images was substantial to almost perfect. Conclusion: Detection and assessment of vertebral fractures and degenerative bone changes of the spine were feasible and accurate using water-fat-separated images as well as SW-like images, both derived from the same sUTE-Dixon sequence. Key Points: • The detection of acute vertebral fractures was feasible using water-fat-separated images and CT-like images reconstructed from one sUTE sequence. • Assessment of the vertebral fractures using SW-like images with CT-like contrast was found to be comparable to conventional CT. • sUTE imaging of the spine can help reduce examination times and radiation exposure. [ABSTRACT FROM AUTHOR]

Constant interactions between tumor cells and the extracellular matrix (ECM) influence the progression of prostate cancer (PCa). One of the key components of the ECM are collagen fibers, since they are responsible for the tissue stiffness, growth, adhesion, proliferation, migration, invasion/metastasis, cell signaling, and immune recruitment of tumor cells. To explore this molecular marker in the content of PCa, we investigated two different tumor volumes (500 mm3 and 1000 mm3) of a xenograft mouse model of PCa with molecular magnetic resonance imaging (MRI) using a collagen-specific probe. For in vivo MRI evaluation, T1-weighted sequences before and after probe administration were analyzed. No significant signal difference between the two tumor volumes could be found. However, we detected a significant difference between the signal intensity of the peripheral tumor area and the central area of the tumor, at both 500 mm3 (p < 0.01, n = 16) and at 1000 mm3 (p < 0.01, n = 16). The results of our histologic analyses confirmed the in vivo studies: There was no significant difference in the amount of collagen between the two tumor volumes (p > 0.05), but within the tumor, higher collagen expression was observed in the peripheral area compared with the central area of the tumor. Laser ablation with inductively coupled plasma mass spectrometry further confirmed these results. The 1000 mm3 tumors contained 2.8 ± 1.0% collagen and the 500 mm3 tumors contained 3.2 ± 1.2% (n = 16). There was a strong correlation between the in vivo MRI data and the ex vivo histological data (y = −0.068x + 1.1; R2 = 0.74) (n = 16). The results of elemental analysis by inductively coupled plasma mass spectrometry supported the MRI data (y = 3.82x + 0.56; R2 = 0.79; n = 7). MRI with the collagen-specific probe in PCa enables differentiation between different tumor areas. This may help to differentiate tumor from healthy tissue, potentially identifying tumor areas with a specific tumor biology. [ABSTRACT FROM AUTHOR]

Purpose: Pancreatic ductal adenocarcinoma (PDAC) is a molecularly heterogeneous tumor entity with no clinically established imaging biomarkers. We hypothesize that tumor morphology and physiology, including vascularity and perfusion, show variations that can be detected by differences in contrast agent (CA) accumulation measured non-invasively. This work seeks to establish imaging biomarkers for tumor stratification and therapy response monitoring in PDAC, based on this hypothesis. Methods and materials: Regional CA accumulation in PDAC was correlated with tumor vascularization, stroma content, and tumor cellularity in murine and human subjects. Changes in CA distribution in response to gemcitabine (GEM) were monitored longitudinally with computed tomography (CT) Hounsfield Units ratio (HUr) of tumor to the aorta or with magnetic resonance imaging (MRI) ΔR1 area under the curve at 60 s tumor-to-muscle ratio (AUC60r). Tissue analyses were performed on co-registered samples, including endothelial cell proliferation and cisplatin tissue deposition as a surrogate of chemotherapy delivery. Results: Tumor cell poor, stroma-rich regions exhibited high CA accumulation both in human (meanHUr 0.64 vs. 0.34, p < 0.001) and mouse PDAC (meanAUC60r 2.0 vs. 1.1, p < 0.001). Compared to the baseline, in vivo CA accumulation decreased specifically in response to GEM treatment in a subset of human (HUr −18%) and mouse (AUC60r −36%) tumors. Ex vivo analyses of mPDAC showed reduced cisplatin delivery (GEM: 0.92 ± 0.5 mg/g, vs. vehicle: 3.1 ± 1.5 mg/g, p = 0.004) and diminished endothelial cell proliferation (GEM: 22.3% vs. vehicle: 30.9%, p = 0.002) upon GEM administration. Conclusion: In PDAC, CA accumulation, which is related to tumor vascularization and perfusion, inversely correlates with tumor cellularity. The standard of care GEM treatment results in decreased CA accumulation, which impedes drug delivery. Further investigation is warranted into potentially detrimental effects of GEM in combinatorial therapy regimens. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Simple Summary: Since prostate MRI is increasingly applied and yet limited by long acquisition times, we prospectively investigated the performance of a novel reconstruction algorithm combining compressed sensing, parallel imaging and deep learning (C-SENSE AI) in patients with histologically proven prostate cancer. Highly accelerated T2w images were compared to clinical standard-of-reference T2w images. C-SENSE AI enabled a 58% acceleration in T2w imaging of the prostate while obtaining significantly better image quality and tumor detection. C-SENSE AI seems particularly interesting in view of the need for accelerated prostate MRI (e.g., in screening protocols) with preserved high image quality. Background: To assess the performance of prospectively accelerated and deep learning (DL) reconstructed T2-weighted (T2w) imaging in volunteers and patients with histologically proven prostate cancer (PCa). Methods: Prospectively undersampled T2w datasets were acquired with acceleration factors of 1.7 (reference), 3.4 and 4.8 in 10 healthy volunteers and 23 patients with histologically proven PCa. Image reconstructions using compressed SENSE (C-SENSE) and a combination of C-SENSE and DL-based artificial intelligence (C-SENSE AI) were analyzed. Qualitative image comparison was performed using a 6-point Likert scale (overall image quality, noise, motion artifacts, lesion detection, diagnostic certainty); the T2 and PI-RADS scores were compared between the two reconstructions. Additionally, quantitative image parameters were assessed (apparent SNR, apparent CNR, lesion size, line profiles). Results: All C-SENSE AI-reconstructed images received a significantly higher qualitative rating compared to the C-SENSE standard images. Analysis of the quantitative parameters supported this finding, with significantly higher aSNR and aCNR. The line profiles demonstrated a significantly steeper signal change at the border of the prostatic lesion and the adjacent normal tissue in the C-SENSE AI-reconstructed images, whereas the T2 and PI-RADS scores as well as the lesion size did not differ. Conclusion: In this prospective study, we demonstrated the clinical feasibility of a novel C-SENSE AI reconstruction enabling a 58% acceleration in T2w imaging of the prostate while obtaining significantly better image quality. [ABSTRACT FROM AUTHOR]

Water-fat separation is a non-linear non-convex parameter estimation problem in magnetic resonance imaging typically solved using spatial constraints. However, there is still limited knowledge on how to separate in vivo three chemical species in the presence of magnetic field inhomogeneities. The proposed method uses multiple graph-cuts in a hierarchical multi-resolution framework to perform robust chemical species separation in the breast for subjects with and without silicone implants. Experimental results show that the proposed method can decrease the computational time for water-fat separation and perform accurate water-fat-silicone separation with only a limited number of acquired echo images at 3 T. The silicone-separated images have an improved spatial resolution and image contrast compared to conventional scans used for regular monitoring of the silicone implant’s integrity. [ABSTRACT FROM AUTHOR]

The separation of mixtures of substances into their individual components plays an important role in many areas of science. In medical imaging, one method is the established analysis using dual-energy computed tomography. However, when analyzing mixtures consisting of more than three individual basis materials, a physical limit is reached that no longer allows this standard analysis. In addition, the X-ray attenuation coefficients of chemically complicated basis materials may not be known and also cannot be determined by other or previous analyses. To address these issues, we developed a novel theoretical approach and algorithm and tested it on samples prepared in the laboratory as well as on ex-vivo medical samples. This method allowed both five-material decomposition and determination or optimization of the X-ray attenuation coefficients of the sample base materials via optimizations of objective functions. After implementation, this new multimodal method was successfully tested on self-mixed samples consisting of the aqueous base solutions iomeprol, eosin Y disodiumsalt, sodium chloride, and pure water. As a first proof of concept of this technique for detailed material decomposition in medicine we analyzed exact percentage composition of ex vivo clots from patients with acute ischemic stroke, using histological analysis as a reference standard. [ABSTRACT FROM AUTHOR]

Background: Proton-density fat fraction (PDFF) and T2* of the vertebrae, as well as the cross-sectional area (CSA) of the paraspinal musculature (PSM), have been suggested as biomarkers for bone fragility. The aim of this study was to longitudinally assess changes in PDFF, T2* and CSA of the PSM over 6 months in patients with and without osteoporosis. Methods: Opportunistic bone mineral density (BMD) measurements (BMD < 120 mg/cm3) were obtained from a CT acquired during the clinical routine work up in osteoporotic/osteopenic patients (n = 29, mean age 72.37 ± 10.12 years, 16 women). These patients were frequency-matched for age and sex to subjects with normal BMD values (n = 29). All study patients underwent 3T MR imaging at baseline and 6-month follow up, including spoiled gradient echo sequences for chemical shift encoding-based water-fat separation, from which T2* and PDFF values of the lumbar spine and the PSM were obtained. Moreover, the CSA of the PSM was assessed longitudinally. Changes in T2*, PDFF and CSA over 6 months were calculated for the vertebrae and PSM and associations with baseline BMD values were assessed. Results: The change in CSA of the PSM over 6 months was significantly lower in the osteoporotic/osteopenic group (−91.5 ± 311.7 mm2), compared to the non-osteoporotic group, in which the CSA increased (29.9 ± 164.0 mm2, p = 0.03). In a further analysis, patients with higher vertebral PDFF at baseline showed a significantly stronger increase in vertebral T2*, compared to those patients with lower vertebral PDFF at baseline (0.9 ± 1.6 ms vs. 0.0 ± 1.8 ms, p = 0.04). Moreover, patients with higher PSM PDFF at baseline showed a significantly stronger increase in vertebral T2*, compared to those patients with lower PSM PDFF at baseline (0.9 ± 2.0 ms vs. 0.0 ± 1.3 ms, p = 0.03). Conclusion: The PSM CSA decreased significantly longitudinally in patients with osteoporosis/osteopenia, compared to those without. Additionally, higher vertebral and PSM PDFF at baseline were associated with stronger changes in vertebral bone marrow T2*. Therefore, longitudinal PDFF and T2* mapping may be useful quantitative radiation-free tools for the assessment and prediction of muscle and bone health in patients with suspected osteoporosis/osteopenia. [ABSTRACT FROM AUTHOR]

Background: Shoulder dislocations represent common injuries and are often combined with rotator cuff tears and potentially damage to the biceps pulley. Purpose: To assess the occurrence and type of biceps pulley lesions in patients after traumatic anterior shoulder dislocation using 3T MRI. Methods: Thirty-three consecutive patients were enrolled between June 2021 and March 2022 (14 women, mean age 48.0 ± 19 years). All patients underwent MR imaging at 3 T within one week. Images were analyzed for the presence and type of pulley tears, subluxation/dislocation of the LHBT, rotator cuff lesions, joint effusion, labral lesions, and osseous defects. Results: Seventeen patients (52%) with traumatic anterior shoulder dislocation demonstrated biceps pulley lesions. Of those, eleven patients (33%) showed a combined tear of the sGHL and CHL. All seventeen patients with lesions of the biceps pulley showed associated partial tearing of the rotator cuff, whereas three patients showed an additional subluxation of the LHBT. Patients with pulley lesions after dislocations were significantly older than those without (mean age 52 ± 12 years vs. 44 ± 14 years, p = 0.023). Conclusion: Our results suggest an increased awareness for lesions of the biceps pulley in acute traumatic shoulder dislocation, particularly in patients over 45 years. [ABSTRACT FROM AUTHOR]

Background and purpose: Treatment of oral squamous cell carcinoma (OSCC) is based on clinical exam, biopsy, and a precise imaging-based TNM-evaluation. A high sensitivity and specificity for magnetic resonance imaging (MRI) and F-18 FDG PET/CT are reported for N-staging. Nevertheless, staging of oral squamous cell carcinoma is most often based on computed tomography (CT) scans. This study aims to evaluate cost-effectiveness of MRI and PET/CT compared to standard of care imaging in initial staging of OSCC within the US Healthcare System. Methods: A decision model was constructed using quality-adjusted life years (QALYs) and overall costs of different imaging strategies including a CT of the head, neck, and the thorax, MRI of the neck with CT of the thorax, and whole body F-18 FDG PET/CT using Markov transition simulations for different disease states. Input parameters were derived from literature and willingness to pay (WTP) was set to US $100,000/QALY. Deterministic sensitivity analysis of diagnostic parameters and costs was performed. Monte Carlo modeling was used for probabilistic sensitivity analysis. Results: In the base-case scenario, total costs were at US $239,628 for CT, US $240,001 for MRI, and US $239,131 for F-18 FDG PET/CT whereas the model yielded an effectiveness of 5.29 QALYs for CT, 5.30 QALYs for MRI, and 5.32 QALYs for F-18 FDG PET/CT respectively. F-18 FDG PET/CT was the most cost-effective strategy over MRI as well as CT, and MRI was the cost-effective strategy over CT. Deterministic and probabilistic sensitivity analysis showed high robustness of the model with incremental cost effectiveness ratio remaining below US $100,000/QALY for a wide range of variability of input parameters. Conclusion: F-18 FDG PET/CT is the most cost-effective strategy in the initial N-staging of OSCC when compared to MRI and CT. Despite less routine use, both whole body PET/CT and MRI are cost-effective modalities in the N-staging of OSCC. Based on these findings, the implementation of PET/CT for initial staging could be suggested to help reduce costs while increasing effectiveness in OSCC. [ABSTRACT FROM AUTHOR]

Background: Fibrin deposition is a fundamental pathophysiological event in the inflammatory component of various CNS disorders, such as multiple sclerosis (MS) and Alzheimer's disease. Beyond its traditional role in coagulation, fibrin elicits immunoinflammatory changes with oxidative stress response and activation of CNS-resident/peripheral immune cells contributing to CNS injury. Purpose: To investigate if CNS fibrin deposition can be determined using molecular MRI, and to assess its capacity as a non-invasive imaging biomarker that corresponds to inflammatory response and barrier impairment. Materials and methods: Specificity and efficacy of a peptide-conjugated Gd-based molecular MRI probe (EP2104-R) to visualise and quantify CNS fibrin deposition were evaluated. Probe efficacy to specifically target CNS fibrin deposition in murine adoptive-transfer experimental autoimmune encephalomyelitis (EAE), a pre-clinical model for MS (n = 12), was assessed. Findings were validated using immunohistochemistry and laser ablation inductively coupled plasma mass spectrometry. Deposition of fibrin in neuroinflammatory conditions was investigated and its diagnostic capacity for disease staging and monitoring as well as quantification of immunoinflammatory response was determined. Results were compared using t-tests (two groups) or one-way ANOVA with multiple comparisons test. Linear regression was used to model the relationship between variables. Results: For the first time (to our knowledge), CNS fibrin deposition was visualised and quantified in vivo using molecular imaging. Signal enhancement was apparent in EAE lesions even 12-h after administration of EP2104-R due to targeted binding (M ± SD, 1.07 ± 0.10 (baseline) vs. 0.73 ± 0.09 (EP2104-R), p =.008), which could be inhibited with an MRI-silent analogue (M ± SD, 0.60 ± 0.14 (EP2104-R) vs. 0.96 ± 0.13 (EP2104-La), p =.006). CNS fibrin deposition corresponded to immunoinflammatory activity (R2 = 0.85, p <.001) and disability (R2 = 0.81, p <.001) in a model for MS, which suggests a clinical role for staging and monitoring. Additionally, EP2104-R showed substantially higher SNR (M ± SD, 6.6 ± 1 (EP2104-R) vs. 2.7 ± 0.4 (gadobutrol), p =.004) than clinically used contrast media, which increases sensitivity for lesion detection. Conclusions: Molecular imaging of CNS fibrin deposition provides an imaging biomarker for inflammatory CNS pathology, which corresponds to pathophysiological ECM remodelling and disease activity, and yields high signal-to-noise ratio, which can improve diagnostic neuroimaging across several neurological diseases with variable degrees of barrier impairment. [ABSTRACT FROM AUTHOR]

The differentiation between the atypical cartilaginous tumor (ACT) and the enchondromas is crucial as ACTs require a curettage and clinical as well as imaging follow-ups, whereas in the majority of cases enchondromas require neither a treatment nor follow-ups. Differentiating enchondromas from ACTs radiologically remains challenging. Therefore, this study evaluated imaging criteria in a combination of computed tomography (CT) and magnetic resonance (MR) imaging for the differentiation between enchondromas and ACTs in long bones. A total of 82 patients who presented consecutively at our institution with either an ACT (23, age 52.7 ±18.8 years; 14 women) or an enchondroma (59, age 46.0 ± 11.1 years; 37 women) over a period of 10 years, who had undergone preoperative MR and CT imaging and subsequent biopsy or/and surgical removal, were included in this study. A histopathological diagnosis was available in all cases. Two experienced radiologists evaluated several imaging criteria on CT and MR images. Likelihood of an ACT was significantly increased if either edema within the bone (p = 0.049), within the adjacent soft tissue (p = 0.006) or continuous growth pattern (p = 0.077) were present or if the fat entrapment (p = 0.027) was absent on MR images. Analyzing imaging features on CT, the likelihood of the diagnosis of an ACT was significantly increased if endosteal scalloping >2/3 (p < 0.001), cortical penetration (p < 0.001) and expansion of bone (p = 0.002) were present and if matrix calcifications were observed in less than 1/3 of the tumor (p = 0.013). All other imaging criteria evaluated showed no significant influence on likelihood of ACT or enchondroma (p > 0.05). In conclusion, both CT and MR imaging show suggestive signs which can help to adequately differentiate enchondromas from ACTs in long bones and therefore can improve diagnostics and consequently patient management. Nevertheless, these features are rare and a combination of CT and MR imaging features did not improve the diagnostic performance substantially. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate machine learning models to distinguish between benign and malignant bone lesions and compare the performance to radiologists. Methods: In 880 patients (age 33.1 ± 19.4 years, 395 women) diagnosed with malignant (n = 213, 24.2%) or benign (n = 667, 75.8%) primary bone tumors, preoperative radiographs were obtained, and the diagnosis was established using histopathology. Data was split 70%/15%/15% for training, validation, and internal testing. Additionally, 96 patients from another institution were obtained for external testing. Machine learning models were developed and validated using radiomic features and demographic information. The performance of each model was evaluated on the test sets for accuracy, area under the curve (AUC) from receiver operating characteristics, sensitivity, and specificity. For comparison, the external test set was evaluated by two radiology residents and two radiologists who specialized in musculoskeletal tumor imaging. Results: The best machine learning model was based on an artificial neural network (ANN) combining both radiomic and demographic information achieving 80% and 75% accuracy at 75% and 90% sensitivity with 0.79 and 0.90 AUC on the internal and external test set, respectively. In comparison, the radiology residents achieved 71% and 65% accuracy at 61% and 35% sensitivity while the radiologists specialized in musculoskeletal tumor imaging achieved an 84% and 83% accuracy at 90% and 81% sensitivity, respectively. Conclusions: An ANN combining radiomic features and demographic information showed the best performance in distinguishing between benign and malignant bone lesions. The model showed lower accuracy compared to specialized radiologists, while accuracy was higher or similar compared to residents. Key Points: • The developed machine learning model could differentiate benign from malignant bone tumors using radiography with an AUC of 0.90 on the external test set. • Machine learning models that used radiomic features or demographic information alone performed worse than those that used both radiomic features and demographic information as input, highlighting the importance of building comprehensive machine learning models. • An artificial neural network that combined both radiomic and demographic information achieved the best performance and its performance was compared to radiology readers on an external test set. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With rising breast augmentations worldwide, there is an increasing clinical need for an early and accurate detection of implant complications.To compare the quality of chemical shift encoding-based (CSE) water-fat-silicone separation compared to double inversion recovery (DIR) silicone-only imaging in breast magnetic resonance imaging (MRI).This retrospective, single-center study included women with silicone implants subjected to 3-T MRI between January 2021 and March 2022. MRI included (i) two-dimensional silicone-only T2-weighted turbo spin echo DIR acquisition and (ii) three-dimensional CSE imaging based on multi-echo gradient-echo sequence enabling water-, fat-, and silicone-image separation. Images were evaluated and compared by three independent radiologists using a clinically established rating including differentiability of the silicone implant, visibility and contouring of the adjacent fibrous capsule, and accuracy of intralesional folds in a ranking of 1–5. The apparent contrast-to-noise (aCNR) was calculated.In 71 women, the average quality of water-fat-silicone images from CSE imaging was assessed as “good” (assessment 4 ± 0.9). In 68 (96%) patients, CSE imaging achieved a concise delineation of the silicone implant and precise visualization of the fibrous capsule that was not distinguishable in DIR imaging. Implant ruptures were more easily detected in CSE imaging. The aCNR was higher in CSE compared to DIR imaging (18.43 ± 9.8 vs. 14.73 ± 2.5; P = 0.002).Intrinsically co-registered water-fat-silicone-separated CSE-based images enable a reliable assessment of silicone implants. The simultaneously improved differentiability of the implant and fibrous capsule may provide clinicians with a valuable tool for an accurate evaluation of implant integrity and early detection of potential complications. [ABSTRACT FROM AUTHOR]

Simple Summary: Magnetic resonance imaging (MRI) is a non-invasive method and can be used to diagnose prostate cancer (PCa). Due to their high biological safety, iron oxide nanoparticles are becoming increasingly important as contrast agents for MRI. Macrophages are able to take up these iron particles, which leads to a loss of signal in T2- and T2*-weighted images during MRI. Macrophages play an important role in the development and progression of prostate cancer. In this article, ferumoxytol is visualized at two different PCa volumes on MRI in a xenograft mouse model. Ferumoxytol is a superparamagnetic iron oxide probe and was used here as a contrast agent. The in vivo data were correlated with histological data. When using ferumoxytol, we found that small tumors took up more ferumoxytol than larger tumor volumes. These results were obtained in vivo as well as ex vivo. Prostate cancer (PCa) is one of the most common cancers in men. For detection and diagnosis of PCa, non-invasive methods, including magnetic resonance imaging (MRI), can reduce the risk potential of surgical intervention. To explore the molecular characteristics of the tumor, we investigated the applicability of ferumoxytol in PCa in a xenograft mouse model in two different tumor volumes, 500 mm3 and 1000 mm3. Macrophages play a key role in tumor progression, and they are able to internalize iron-oxide particles, such as ferumoxytol. When evaluating T2*-weighted sequences on MRI, a significant decrease of signal intensity between pre- and post-contrast images for each tumor volume (n = 14; p < 0.001) was measured. We, furthermore, observed a higher signal loss for a tumor volume of 500 mm3 than for 1000 mm3. These findings were confirmed by histological examinations and laser ablation inductively coupled plasma-mass spectrometry. The 500 mm3 tumors had 1.5% iron content (n = 14; σ = 1.1), while the 1000 mm3 tumors contained only 0.4% iron (n = 14; σ = 0.2). In vivo MRI data demonstrated a correlation with the ex vivo data (R2 = 0.75). The results of elemental analysis by inductively coupled plasma-mass spectrometry correlated strongly with the MRI data (R2 = 0.83) (n = 4). Due to its long retention time in the blood, biodegradability, and low toxicity to patients, ferumoxytol has great potential as a contrast agent for visualization PCa. [ABSTRACT FROM AUTHOR]

To prospectively evaluate an L1 regularized iterative SENSE reconstruction (L1-R SENSE) to eliminate band-like artifacts frequently seen with parallel imaging (SENSE) at high acceleration factors in high resolution diffusion weighted magnetic resonance imaging of the pancreas. Fourteen patients with pancreatic ductal adenocarcinoma (PDAC) underwent respiratory triggered DWI ss-EPI at a resolution of 2.5 × 2.5 × 3 mm3 with uniform undersampling in the phase encoding direction (AP axis) with an acceleration factor of 4. Data were reconstructed using the standard SENSE reconstruction routine of the vendor and an iterative SENSE reconstruction employing L1 regularization after a wavelet sparsifying transformation (L1-R SENSE). Retrospective reconstruction of the data with a lower number of averages was performed using both reconstruction methods. Two radiologists independently assessed noise artifacts, anatomical details and image quality (IQ) subjectively with a 4-point scale. Apparent diffusion coefficient (ADC) and covariance (CV) of ADC estimated from images reconstructed at a different number of averages for PDAC and the normal pancreas were assessed. L1-R SENSE resulted in higher IQ and less noise artifacts than SENSE. Anatomical details were significantly higher for SENSE in one reader. Mean ADC of PDAC and normal pancreas were significantly higher for L1-R SENSE than SENSE. L1-R SENSE revealed lower CV of ADC for normal pancreas compared to SENSE, whereas no difference was noted for PDAC. Compared with traditional SENSE reconstruction, L1-R SENSE effectively reduces band-like noise and improves the robustness of the ADC estimation from acquisitions using single-shot DW-EPI of the pancreas. • Parallel imaging is used for DWI of the pancreas but frequently suffers from band-like noise artifacts at high acceleration factors. • L1-regularized SENSE is based on the CS framework concepts and can effectively eliminate noise artifacts using the same scan parameters. • Implementation of L1-regularized SENSE improves image quality and robustness of ADC values in DWI of the pancreas. [ABSTRACT FROM AUTHOR]

Objective: Pancreatic cancer is portrayed to become the second leading cause of cancer-related death within the next years. Potentially complicating surgical resection emphasizes the importance of an accurate TNM classification. In particular, the failure to detect features for non-resectability has profound consequences on patient outcomes and economic costs due to incorrect indication for resection. In the detection of liver metastases, contrast-enhanced MRI showed high sensitivity and specificity; however, the cost-effectiveness compared to the standard of care imaging remains unclear. The aim of this study was to analyze whether additional MRI of the liver is a cost-effective approach compared to routinely acquired contrast-enhanced computed tomography (CE-CT) in the initial staging of pancreatic cancer. Methods: A decision model based on Markov simulation was developed to estimate the quality-adjusted life-years (QALYs) and lifetime costs of the diagnostic modalities. Model input parameters were assessed based on evidence from recent literature. The willingness-to-pay (WTP) was set to $100,000/QALY. To evaluate model uncertainty, deterministic and probabilistic sensitivity analyses were performed. Results: In the base-case analysis, the model yielded a total cost of $185,597 and an effectiveness of 2.347 QALYs for CE-MR/CT and $187,601 and 2.337 QALYs for CE-CT respectively. With a net monetary benefit (NMB) of $49,133, CE-MR/CT is shown to be dominant over CE-CT with a NMB of $46,117. Deterministic and probabilistic survival analysis showed model robustness for varying input parameters. Conclusion: Based on our results, combined CE-MR/CT can be regarded as a cost-effective imaging strategy for the staging of pancreatic cancer. Key Points: • Additional MRI of the liver for initial staging of pancreatic cancer results in lower total costs and higher effectiveness. • The economic model showed high robustness for varying input parameters. [ABSTRACT FROM AUTHOR]

Background: To evaluate the diagnostic value of MR-derived CT-like images and simulated radiographs compared with conventional radiographs in patients with suspected shoulder pathology.Methods: 3 T MRI of the shoulder including a 3D T1-weighted gradient echo sequence was performed in 25 patients (mean age 52.4 ± 18 years, 13 women) with suspected shoulder pathology. Subsequently a cone-beam forward projection algorithm was used to obtain intensity-inverted CT-like images and simulated radiographs. Two radiologists evaluated the simulated images separately and independently using the conventional radiographs as the standard of reference, including measurements of the image quality, acromiohumeral distance, critical shoulder angle, degenerative joint changes and the acromial type. Additionally, the CT-like MR images were evaluated for glenoid defects, subcortical cysts and calcifications. Agreement between the MR-derived simulated radiographs and conventional radiographs was calculated using Cohen's Kappa.Results: Measurements on simulated radiographs and conventional radiographs overall showed a substantial to almost perfect inter- and intra-rater agreement (κ = 0.69-1.00 and κ = 0.65-0.85, respectively). Image quality of the simulated radiographs was rated good to excellent (1.6 ± 0.7 and 1.8 ± 0.6, respectively) by the radiologists. A substantial agreement was found regarding diagnostically relevant features, assessed on Y- and anteroposterior projections (κ = 0.84 and κ = 0.69 for the measurement of the CSA; κ = 0.95 and κ = 0.60 for the measurement of the AHD; κ = 0.77 and κ = 0.77 for grading of the Samilson-Prieto classification; κ = 0.83 and κ = 0.67 for the grading of the Bigliani classification, respectively).Conclusion: In this proof-of-concept study, clinically relevant features of the shoulder joint were assessed reliably using MR-derived CT-like images and simulated radiographs with an image quality equivalent to conventional radiographs. MR-derived CT-like images and simulated radiographs may provide useful diagnostic information while reducing the amount of radiation exposure. [ABSTRACT FROM AUTHOR]

• medBERT.de : German BERT model tailored for medical domain expertise. • Trained using 4.7 million diverse medical documents for optimal results. • Achieved new state-of-the-art on eight medical NLP benchmarks. • Efficient tokenization has minor role, focus on domain-specific training. • Public release of pre-trained model weights and new benchmarks for research. This paper presents medBERT.de , a pre-trained German BERT model specifically designed for the German medical domain. The model has been trained on a large corpus of 4.7 Million German medical documents and has been shown to achieve new state-of-the-art performance on eight different medical benchmarks covering a wide range of disciplines and medical document types. In addition to evaluating the overall performance of the model, this paper also conducts a more in-depth analysis of its capabilities. We investigate the impact of data deduplication on the model's performance, as well as the potential benefits of using more efficient tokenization methods. Our results indicate that domain-specific models such as medBERT.de are particularly useful for longer texts, and that deduplication of training data does not necessarily lead to improved performance. Furthermore, we found that efficient tokenization plays only a minor role in improving model performance, and attribute most of the improved performance to the large amount of training data. To encourage further research, the pre-trained model weights and new benchmarks based on radiological data are made publicly available for use by the scientific community. [ABSTRACT FROM AUTHOR]

Objective: This study analyzes the potential cost-effectiveness of integrating an artificial intelligence (AI)–assisted system into the differentiation of incidental renal lesions as benign or malignant on MR images during follow-up.For estimation of quality-adjusted life years (QALYs) and lifetime costs, a decision model was created, including the MRI strategy and MRI + AI strategy. Model input parameters were derived from recent literature. Willingness to pay (WTP) was set to $100,000/QALY. Costs of $0 for the AI were assumed in the base-case scenario. Model uncertainty and costs of the AI system were assessed using deterministic and probabilistic sensitivity analysis.Average total costs were at $8054 for the MRI strategy and $7939 for additional use of an AI-based algorithm. The model yielded a cumulative effectiveness of 8.76 QALYs for the MRI strategy and of 8.77 for the MRI + AI strategy. The economically dominant strategy was MRI + AI. Deterministic and probabilistic sensitivity analysis showed high robustness of the model with the incremental cost-effectiveness ratio (ICER), which represents the incremental cost associated with one additional QALY gained, remaining below the WTP for variation of the input parameters. If increasing costs for the algorithm, the ICER of $0/QALY was exceeded at $115, and the defined WTP was exceeded at $667 for the use of the AI.This analysis, rooted in assumptions, suggests that the additional use of an AI-based algorithm may be a potentially cost-effective alternative in the differentiation of incidental renal lesions using MRI and needs to be confirmed in the future.These results hint at AI’s the potential impact on diagnosing renal masses. While the current study urges careful interpretation, ongoing research is essential to confirm and seamlessly integrate AI into clinical practice, ensuring its efficacy in routine diagnostics.• This is a model-based study using data from literature where AI has been applied in the diagnostic workup of incidental renal lesions.• MRI + AI has the potential to be a cost-effective alternative in the differentiation of incidental renal lesions.• The additional use of AI can reduce costs in the diagnostic workup of incidental renal lesions.Materials and methods: This study analyzes the potential cost-effectiveness of integrating an artificial intelligence (AI)–assisted system into the differentiation of incidental renal lesions as benign or malignant on MR images during follow-up.For estimation of quality-adjusted life years (QALYs) and lifetime costs, a decision model was created, including the MRI strategy and MRI + AI strategy. Model input parameters were derived from recent literature. Willingness to pay (WTP) was set to $100,000/QALY. Costs of $0 for the AI were assumed in the base-case scenario. Model uncertainty and costs of the AI system were assessed using deterministic and probabilistic sensitivity analysis.Average total costs were at $8054 for the MRI strategy and $7939 for additional use of an AI-based algorithm. The model yielded a cumulative effectiveness of 8.76 QALYs for the MRI strategy and of 8.77 for the MRI + AI strategy. The economically dominant strategy was MRI + AI. Deterministic and probabilistic sensitivity analysis showed high robustness of the model with the incremental cost-effectiveness ratio (ICER), which represents the incremental cost associated with one additional QALY gained, remaining below the WTP for variation of the input parameters. If increasing costs for the algorithm, the ICER of $0/QALY was exceeded at $115, and the defined WTP was exceeded at $667 for the use of the AI.This analysis, rooted in assumptions, suggests that the additional use of an AI-based algorithm may be a potentially cost-effective alternative in the differentiation of incidental renal lesions using MRI and needs to be confirmed in the future.These results hint at AI’s the potential impact on diagnosing renal masses. While the current study urges careful interpretation, ongoing research is essential to confirm and seamlessly integrate AI into clinical practice, ensuring its efficacy in routine diagnostics.• This is a model-based study using data from literature where AI has been applied in the diagnostic workup of incidental renal lesions.• MRI + AI has the potential to be a cost-effective alternative in the differentiation of incidental renal lesions.• The additional use of AI can reduce costs in the diagnostic workup of incidental renal lesions.Results: This study analyzes the potential cost-effectiveness of integrating an artificial intelligence (AI)–assisted system into the differentiation of incidental renal lesions as benign or malignant on MR images during follow-up.For estimation of quality-adjusted life years (QALYs) and lifetime costs, a decision model was created, including the MRI strategy and MRI + AI strategy. Model input parameters were derived from recent literature. Willingness to pay (WTP) was set to $100,000/QALY. Costs of $0 for the AI were assumed in the base-case scenario. Model uncertainty and costs of the AI system were assessed using deterministic and probabilistic sensitivity analysis.Average total costs were at $8054 for the MRI strategy and $7939 for additional use of an AI-based algorithm. The model yielded a cumulative effectiveness of 8.76 QALYs for the MRI strategy and of 8.77 for the MRI + AI strategy. The economically dominant strategy was MRI + AI. Deterministic and probabilistic sensitivity analysis showed high robustness of the model with the incremental cost-effectiveness ratio (ICER), which represents the incremental cost associated with one additional QALY gained, remaining below the WTP for variation of the input parameters. If increasing costs for the algorithm, the ICER of $0/QALY was exceeded at $115, and the defined WTP was exceeded at $667 for the use of the AI.This analysis, rooted in assumptions, suggests that the additional use of an AI-based algorithm may be a potentially cost-effective alternative in the differentiation of incidental renal lesions using MRI and needs to be confirmed in the future.These results hint at AI’s the potential impact on diagnosing renal masses. While the current study urges careful interpretation, ongoing research is essential to confirm and seamlessly integrate AI into clinical practice, ensuring its efficacy in routine diagnostics.• This is a model-based study using data from literature where AI has been applied in the diagnostic workup of incidental renal lesions.• MRI + AI has the potential to be a cost-effective alternative in the differentiation of incidental renal lesions.• The additional use of AI can reduce costs in the diagnostic workup of incidental renal lesions.Conclusions: This study analyzes the potential cost-effectiveness of integrating an artificial intelligence (AI)–assisted system into the differentiation of incidental renal lesions as benign or malignant on MR images during follow-up.For estimation of quality-adjusted life years (QALYs) and lifetime costs, a decision model was created, including the MRI strategy and MRI + AI strategy. Model input parameters were derived from recent literature. Willingness to pay (WTP) was set to $100,000/QALY. Costs of $0 for the AI were assumed in the base-case scenario. Model uncertainty and costs of the AI system were assessed using deterministic and probabilistic sensitivity analysis.Average total costs were at $8054 for the MRI strategy and $7939 for additional use of an AI-based algorithm. The model yielded a cumulative effectiveness of 8.76 QALYs for the MRI strategy and of 8.77 for the MRI + AI strategy. The economically dominant strategy was MRI + AI. Deterministic and probabilistic sensitivity analysis showed high robustness of the model with the incremental cost-effectiveness ratio (ICER), which represents the incremental cost associated with one additional QALY gained, remaining below the WTP for variation of the input parameters. If increasing costs for the algorithm, the ICER of $0/QALY was exceeded at $115, and the defined WTP was exceeded at $667 for the use of the AI.This analysis, rooted in assumptions, suggests that the additional use of an AI-based algorithm may be a potentially cost-effective alternative in the differentiation of incidental renal lesions using MRI and needs to be confirmed in the future.These results hint at AI’s the potential impact on diagnosing renal masses. While the current study urges careful interpretation, ongoing research is essential to confirm and seamlessly integrate AI into clinical practice, ensuring its efficacy in routine diagnostics.• This is a model-based study using data from literature where AI has been applied in the diagnostic workup of incidental renal lesions.• MRI + AI has the potential to be a cost-effective alternative in the differentiation of incidental renal lesions.• The additional use of AI can reduce costs in the diagnostic workup of incidental renal lesions.Clinical relevance statement: This study analyzes the potential cost-effectiveness of integrating an artificial intelligence (AI)–assisted system into the differentiation of incidental renal lesions as benign or malignant on MR images during follow-up.For estimation of quality-adjusted life years (QALYs) and lifetime costs, a decision model was created, including the MRI strategy and MRI + AI strategy. Model input parameters were derived from recent literature. Willingness to pay (WTP) was set to $100,000/QALY. Costs of $0 for the AI were assumed in the base-case scenario. Model uncertainty and costs of the AI system were assessed using deterministic and probabilistic sensitivity analysis.Average total costs were at $8054 for the MRI strategy and $7939 for additional use of an AI-based algorithm. The model yielded a cumulative effectiveness of 8.76 QALYs for the MRI strategy and of 8.77 for the MRI + AI strategy. The economically dominant strategy was MRI + AI. Deterministic and probabilistic sensitivity analysis showed high robustness of the model with the incremental cost-effectiveness ratio (ICER), which represents the incremental cost associated with one additional QALY gained, remaining below the WTP for variation of the input parameters. If increasing costs for the algorithm, the ICER of $0/QALY was exceeded at $115, and the defined WTP was exceeded at $667 for the use of the AI.This analysis, rooted in assumptions, suggests that the additional use of an AI-based algorithm may be a potentially cost-effective alternative in the differentiation of incidental renal lesions using MRI and needs to be confirmed in the future.These results hint at AI’s the potential impact on diagnosing renal masses. While the current study urges careful interpretation, ongoing research is essential to confirm and seamlessly integrate AI into clinical practice, ensuring its efficacy in routine diagnostics.• This is a model-based study using data from literature where AI has been applied in the diagnostic workup of incidental renal lesions.• MRI + AI has the potential to be a cost-effective alternative in the differentiation of incidental renal lesions.• The additional use of AI can reduce costs in the diagnostic workup of incidental renal lesions.Key Points: This study analyzes the potential cost-effectiveness of integrating an artificial intelligence (AI)–assisted system into the differentiation of incidental renal lesions as benign or malignant on MR images during follow-up.For estimation of quality-adjusted life years (QALYs) and lifetime costs, a decision model was created, including the MRI strategy and MRI + AI strategy. Model input parameters were derived from recent literature. Willingness to pay (WTP) was set to $100,000/QALY. Costs of $0 for the AI were assumed in the base-case scenario. Model uncertainty and costs of the AI system were assessed using deterministic and probabilistic sensitivity analysis.Average total costs were at $8054 for the MRI strategy and $7939 for additional use of an AI-based algorithm. The model yielded a cumulative effectiveness of 8.76 QALYs for the MRI strategy and of 8.77 for the MRI + AI strategy. The economically dominant strategy was MRI + AI. Deterministic and probabilistic sensitivity analysis showed high robustness of the model with the incremental cost-effectiveness ratio (ICER), which represents the incremental cost associated with one additional QALY gained, remaining below the WTP for variation of the input parameters. If increasing costs for the algorithm, the ICER of $0/QALY was exceeded at $115, and the defined WTP was exceeded at $667 for the use of the AI.This analysis, rooted in assumptions, suggests that the additional use of an AI-based algorithm may be a potentially cost-effective alternative in the differentiation of incidental renal lesions using MRI and needs to be confirmed in the future.These results hint at AI’s the potential impact on diagnosing renal masses. While the current study urges careful interpretation, ongoing research is essential to confirm and seamlessly integrate AI into clinical practice, ensuring its efficacy in routine diagnostics.• This is a model-based study using data from literature where AI has been applied in the diagnostic workup of incidental renal lesions.• MRI + AI has the potential to be a cost-effective alternative in the differentiation of incidental renal lesions.• The additional use of AI can reduce costs in the diagnostic workup of incidental renal lesions. [ABSTRACT FROM AUTHOR]

To evaluate the diagnostic performance of CT-like MR images reconstructed with an algorithm combining compressed sense (CS) with deep learning (DL) in patients with suspected osseous shoulder injury compared to conventional CS-reconstructed images. Thirty-two patients (12 women, mean age 46 ± 14.9 years) with suspected traumatic shoulder injury were prospectively enrolled into the study. All patients received MR imaging of the shoulder, including a CT-like 3D T1-weighted gradient-echo (T1 GRE) sequence and in case of suspected fracture a conventional CT. An automated DL-based algorithm, combining CS and DL (CS DL) was used to reconstruct images of the same k-space data as used for CS reconstructions. Two musculoskeletal radiologists assessed the images for osseous pathologies, image quality and visibility of anatomical landmarks using a 5-point Likert scale. Moreover, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Compared to CT, all acute fractures (n = 23) and osseous pathologies were detected accurately on the CS only and CS DL images with almost perfect agreement between the CS DL and CS only images (κ 0.95 (95 %confidence interval 0.82–1.00). Image quality as well as the visibility of the fracture lines, bone fragments and glenoid borders were overall rated significantly higher for the CS DL reconstructions than the CS only images (CS DL range 3.7–4.9 and CS only range 3.2–3.8, P = 0.01–0.04). Significantly higher SNR and CNR values were observed for the CS DL reconstructions (P = 0.02–0.03). Evaluation of traumatic shoulder pathologies is feasible using a DL-based algorithm for reconstruction of high-resolution CT-like MR imaging. [ABSTRACT FROM AUTHOR]

One possible complication after mechanical thrombectomy is hemorrhage. In conventional CT it is often difficult to differ between extravasation of iodinated contrast medium and blood. This differentiation, however, is essential for treatments with anticoagulants and antiplatelets. To evaluate dual-layer spectral Computed Tomography (DLSCT) for the differentiation between intracranial hemorrhage and iodinated contrast medium in ischemic stroke patients after mechanical thrombectomy. First, in vitro experiments were performed. Then, head CT images of 47 patients after mechanical thrombectomy were analyzed. Virtual non-contrast (VNC) images and iodine density maps (IDM) were calculated and evaluated. Region of interests (ROIs) analyses were performed. Sensitivity and specificity as well as ROC curves were calculated. IDM and VNC images enabled clear differentiation between blood and iodine and reliable quantification of different iodine concentrations in vitro. A total of 23 hyperdense areas were detected in 13 patients, classified as hemorrhage (n = 7), iodinated contrast medium (n = 4) and a mixture of both (n = 12). Sensitivity and specificity for the detection of blood was 100%. DLSCT enables differentiation between intracranial hemorrhage and iodinated contrast medium in patients after mechanical thrombectomy and might improve diagnostic imaging in post-interventional stroke patients. • DLSCT delivers simultaneously information about anatomy and material decomposition per default. • DLSCT enables differentiation between blood and iodine in ischemic stroke patients after mechanical thrombectomy. • DLSCT enables reliable quantification of iodine in vitro. • DLSCT might improve diagnostics in neuroradiology especially in patients after mechanical thrombectomy. [ABSTRACT FROM AUTHOR]

Purpose: Rectal cancer is one of the most frequent causes of cancer-related morbidity and mortality in the world. Correct identification of the TNM state in primary staging of rectal cancer has critical implications on patient management. Initial evaluations revealed a high sensitivity and specificity for whole-body PET/MRI in the detection of metastases allowing for metastasis-directed therapy regimens. Nevertheless, its cost-effectiveness compared with that of standard-of-care imaging (SCI) using pelvic MRI + chest and abdominopelvic CT is yet to be investigated. Therefore, the aim of this study was to analyze the cost-effectiveness of whole-body 18F FDG PET/MRI as an alternative imaging method to standard diagnostic workup for initial staging of rectal cancer. Methods: For estimation of quality-adjusted life years (QALYs) and lifetime costs of diagnostic modalities, a decision model including whole-body 18F FDG PET/MRI with a hepatocyte-specific contrast agent and pelvic MRI + chest and abdominopelvic CT was created based on Markov simulations. For obtaining model input parameters, review of recent literature was performed. Willingness to pay (WTP) was set to $100,000/QALY. Deterministic sensitivity analysis of diagnostic parameters and costs was applied, and probabilistic sensitivity was determined using Monte Carlo modeling. Results: In the base-case scenario, the strategy whole-body 18F FDG PET/MRI resulted in total costs of $52,186 whereas total costs of SCI were at $51,672. Whole-body 18F FDG PET/MRI resulted in an expected effectiveness of 3.542 QALYs versus 3.535 QALYs for SCI. This resulted in an incremental cost-effectiveness ratio of $70,291 per QALY for PET/MRI. Thus, from an economic point of view, whole-body 18F FDG PET/MRI was identified as an adequate diagnostic alternative to SCI with high robustness of results to variation of input parameters. Conclusion: Based on the results of the analysis, use of whole-body 18F FDG PET/MRI was identified as a feasible diagnostic strategy for initial staging of rectal cancer from a cost-effectiveness perspective. [ABSTRACT FROM AUTHOR]

We present a method to generate synthetic thorax radiographs with realistic nodules from CT scans, and a perfect ground truth knowledge. We evaluated the detection performance of nine radiologists and two convolutional neural networks in a reader study. Nodules were artificially inserted into the lung of a CT volume and synthetic radiographs were obtained by forward-projecting the volume. Hence, our framework allowed for a detailed evaluation of CAD systems' and radiologists' performance due to the availability of accurate ground-truth labels for nodules from synthetic data. Radiographs for network training (U-Net and RetinaNet) were generated from 855 CT scans of a public dataset. For the reader study, 201 radiographs were generated from 21 nodule-free CT scans with altering nodule positions, sizes and nodule counts of inserted nodules. Average true positive detections by nine radiologists were 248.8 nodules, 51.7 false positive predicted nodules and 121.2 false negative predicted nodules. The best performing CAD system achieved 268 true positives, 66 false positives and 102 false negatives. Corresponding weighted alternative free response operating characteristic figure-of-merits (wAFROC FOM) for the radiologists range from 0.54 to 0.87 compared to a value of 0.81 (CI 0.75–0.87) for the best performing CNN. The CNN did not perform significantly better against the combined average of the 9 readers (p = 0.49). Paramediastinal nodules accounted for most false positive and false negative detections by readers, which can be explained by the presence of more tissue in this area. [ABSTRACT FROM AUTHOR]

This study aimed to improve the accuracy of CT for detection of COVID-19-associated pneumonia and to identify patient subgroups who might benefit most from CT imaging. A total of 269 patients who underwent CT for suspected COVID-19 were included in this retrospective analysis. COVID-19 was confirmed by reverse-transcription-polymerase-chain-reaction. Basic demographics (age and sex) and initial vital parameters (O 2 -saturation, respiratory rate, and body temperature) were recorded. Generalized mixed models were used to calculate the accuracy of vital parameters for detection of COVID-19 and to evaluate the diagnostic accuracy of CT. A clinical score based on vital parameters, age, and sex was established to estimate the pretest probability of COVID-19 and used to define low, intermediate, and high risk groups. A p -value of <0.05 was considered statistically significant. The sole use of vital parameters for the prediction of COVID-19 was inferior to CT. After correction for confounders, such as age and sex, CT showed a sensitivity of 0.86, specificity of 0.78, and positive predictive value of 0.36. In the subgroup analysis based on pretest probability, positive predictive value and sensitivity increased to 0.53 and 0.89 in the high-risk group, while specificity was reduced to 0.68. In the low-risk group, sensitivity and positive predictive value decreased to 0.76 and 0.33 with a specificity of 0.83. The negative predictive value remained high (0.94 and 0.97) in both groups. The accuracy of CT for the detection of COVID-19 might be increased by selecting patients with a high-pretest probability of COVID-19. • A simple score to estimate the pre-test probability of COVID-19 can be calculated using vital parameters, basic vital parameters. • Accuracy of CT for the detection of COVID-19 might be increased by selecting patients with a high-pretest probability. • In patients with mild symptoms, CT examinations are unlikel unlikely to be effective with a very low positive predictive value. [ABSTRACT FROM AUTHOR]

Objective: Determination of coronary artery calcium scoring (CACS) in non-contrast computed tomography (CT) images has been shown to be an important prognostic factor in coronary artery disease (CAD). The objective of this study was to evaluate the accuracy of CACS from virtual non-contrast (VNC) imaging generated from spectral data in comparison to standard (true) non-contrast (TNC) imaging in a representative patient cohort with clinically approved software. Methods: One hundred three patients referred to coronary CTA with suspicion of CAD were investigated on a dual-layer spectral detector CT (SDCT) scanner. CACS was calculated from both TNC and VNC images by software certified for medical use. Patients with a CACS of 0 were excluded from analysis. Results: The mean age of the study population was 61 ± 11 years with 48 male patients (67%). Inter-quartile range of clinical CACS was 22–282. Correlation of measured CACS from true- and VNC images was high (0.95); p < 0.001. The slope was 3.83, indicating an underestimation of VNC CACS compared to TNC CACS by that factor. Visual analysis of the Bland-Altman plot of CACS showed good accordance with both methods after correction of VNC CACS by the abovementioned factor. Conclusions: In clinical diagnostics of CAD, the determination of CACS is feasible using VNC images generated from spectral data obtained on a dual-layer spectral detector CT. When multiplied by a correction factor, results were in good agreement with the standard technique. This could enable radiation dose reductions by obviating the need for native scans typically used for CACS. Key Points: • Calcium scoring is feasible from contrast-enhanced CT images using a dual-layer spectral detector CT scanner. • When multiplied by a correction factor, calcium scoring from virtual non-contrast images shows good agreement with the standard technique. • Omitting native scans for calcium scoring could enable radiation dose reduction. [ABSTRACT FROM AUTHOR]

Purpose: In this prospective exploratory study, we evaluated the feasibility of [18F]fluorodeoxyglucose ([18F]FDG) PET/MRI-based chemotherapy response prediction in pancreatic ductal adenocarcinoma at two weeks upon therapy onset. Material and methods: In a mixed cohort, seventeen patients treated with chemotherapy in neoadjuvant or palliative intent were enrolled. All patients were imaged by [18F]FDG PET/MRI before and two weeks after onset of chemotherapy. Response per RECIST1.1 was then assessed at 3 months [18F]FDG PET/MRI-derived parameters (MTV50%, TLG50%, MTV2.5, TLG2.5, SUVmax, SUVpeak, ADCmax, ADCmean and ADCmin) were assessed, using multiple t-test, Man–Whitney-U test and Fisher's exact test for binary features. Results: At 72 ± 43 days, twelve patients were classified as responders and five patients as non-responders. An increase in ∆MTV50% and ∆ADC (≥ 20% and 15%, respectively) and a decrease in ∆TLG50% (≤ 20%) at 2 weeks after chemotherapy onset enabled prediction of responders and non-responders, respectively. Parameter combinations (∆TLG50% and ∆ADCmax or ∆MTV50% and ∆ADCmax) further improved discrimination. Conclusion: Multiparametric [18F]FDG PET/MRI-derived parameters, in particular indicators of a change in tumor glycolysis and cellularity, may enable very early chemotherapy response prediction. Further prospective studies in larger patient cohorts are recommended to their clinical impact. [ABSTRACT FROM AUTHOR]