3D-Volumetric Shunt Measurement for Detection of High-Risk Esophageal Varices in Liver Cirrhosis.

Background and Objectives: Esophageal varices (EV) and variceal hemorrhages are major causes of mortality in liver cirrhosis patients. Detecting EVs early is crucial for effective management. Computed tomography (CT) scans, commonly performed for various liver-related indications, provide an opportunity for non-invasive EV assessment. However, previous CT studies focused on variceal diameter, neglecting the three-dimensional (3D) nature of varices and shunt vessels. This study aims to evaluate the potential of 3D volumetric shunt-vessel measurements from routine CT scans for detecting high-risk esophageal varices in portal hypertension. Methods: 3D volumetric measurements of esophageal varices were conducted using routine CT scans and compared to endoscopic variceal grading. Receiver operating characteristic (ROC) analyses were performed to determine the optimal cutoff value for identifying high-risk varices based on shunt volume. The study included 142 patients who underwent both esophagogastroduodenoscopy (EGD) and contrast-enhanced CT within six months. Results: The study established a cutoff value for identifying high-risk varices. The CT measurements exhibited a significant correlation with endoscopic EV grading (correlation coefficient r = 0.417, p < 0.001). A CT cutoff value of 2060 mm3 for variceal volume showed a sensitivity of 72.1% and a specificity of 65.5% for detecting high-risk varices during endoscopy. Conclusions: This study demonstrates the feasibility of opportunistically measuring variceal volumes from routine CT scans. CT volumetry for assessing EVs may have prognostic value, especially in cirrhosis patients who are not suitable candidates for endoscopy.

Generative Pre-trained Transformer 4 makes cardiovascular magnetic resonance reports easy to understand.

BACKGROUND: Patients are increasingly using Generative Pre-trained Transformer 4 (GPT-4) to better understand their own radiology findings. PURPOSE: To evaluate the performance of GPT-4 in transforming cardiovascular magnetic resonance (CMR) reports into text that is comprehensible to medical laypersons. METHODS: ChatGPT with GPT-4 architecture was used to generate three different explained versions of 20 various CMR reports (n = 60) using the same prompt: "Explain the radiology report in a language understandable to a medical layperson". Two cardiovascular radiologists evaluated understandability, factual correctness, completeness of relevant findings, and lack of potential harm, while 13 medical laypersons evaluated the understandability of the original and the GPT-4 reports on a Likert scale (1 "strongly disagree", 5 "strongly agree"). Readability was measured using the Automated Readability Index (ARI). Linear mixed-effects models (values given as median [interquartile range]) and intraclass correlation coefficient (ICC) were used for statistical analysis. RESULTS: GPT-4 reports were generated on average in 52 s ± 13. GPT-4 reports achieved a lower ARI score (10 [9-12] vs 5 [4-6]; p < 0.001) and were subjectively easier to understand for laypersons than original reports (1 [1] vs 4 [4,5]; p < 0.001). Eighteen out of 20 (90%) standard CMR reports and 2/60 (3%) GPT-generated reports had an ARI score corresponding to the 8th grade level or higher. Radiologists' ratings of the GPT-4 reports reached high levels for correctness (5 [4, 5]), completeness (5 [5]), and lack of potential harm (5 [5]); with "strong agreement" for factual correctness in 94% (113/120) and completeness of relevant findings in 81% (97/120) of reports. Test-retest agreement for layperson understandability ratings between the three simplified reports generated from the same original report was substantial (ICC: 0.62; p < 0.001). Interrater agreement between radiologists was almost perfect for lack of potential harm (ICC: 0.93, p < 0.001) and moderate to substantial for completeness (ICC: 0.76, p < 0.001) and factual correctness (ICC: 0.55, p < 0.001). CONCLUSION: GPT-4 can reliably transform complex CMR reports into more understandable, layperson-friendly language while largely maintaining factual correctness and completeness, and can thus help convey patient-relevant radiology information in an easy-to-understand manner.

Deep learning denoising reconstruction for improved image quality in fetal cardiac cine MRI.

Purpose: This study aims to evaluate deep learning (DL) denoising reconstructions for image quality improvement of Doppler ultrasound (DUS)-gated fetal cardiac MRI in congenital heart disease (CHD). Methods: Twenty-five fetuses with CHD (mean gestational age: 35 ± 1 weeks) underwent fetal cardiac MRI at 3T. Cine imaging was acquired using a balanced steady-state free precession (bSSFP) sequence with Doppler ultrasound gating. Images were reconstructed using both compressed sensing (bSSFP CS) and a pre-trained convolutional neural network trained for DL denoising (bSSFP DL). Images were compared qualitatively based on a 5-point Likert scale (from 1 = non-diagnostic to 5 = excellent) and quantitatively by calculating the apparent signal-to-noise ratio (aSNR) and contrast-to-noise ratio (aCNR). Diagnostic confidence was assessed for the atria, ventricles, foramen ovale, valves, great vessels, aortic arch, and pulmonary veins. Results: Fetal cardiac cine MRI was successful in 23 fetuses (92%), with two studies excluded due to extensive fetal motion. The image quality of bSSFP DL cine reconstructions was rated superior to standard bSSFP CS cine images in terms of contrast [3 (interquartile range: 2-4) vs. 5 (4-5), P < 0.001] and endocardial edge definition [3 (2-4) vs. 4 (4-5), P < 0.001], while the extent of artifacts was found to be comparable [4 (3-4.75) vs. 4 (3-4), P = 0.40]. bSSFP DL images had higher aSNR and aCNR compared with the bSSFP CS images (aSNR: 13.4 ± 6.9 vs. 8.3 ± 3.6, P < 0.001; aCNR: 26.6 ± 15.8 vs. 14.4 ± 6.8, P < 0.001). Diagnostic confidence of the bSSFP DL images was superior for the evaluation of cardiovascular structures (e.g., atria and ventricles: P = 0.003). Conclusion: DL image denoising provides superior quality for DUS-gated fetal cardiac cine imaging of CHD compared to standard CS image reconstruction.

CT-derived Epicardial Adipose Tissue Inflammation Predicts Outcome in Patients Undergoing Transcatheter Aortic Valve Replacement.

PURPOSE: Inflammatory changes in epicardial (EAT) and pericardial adipose tissue (PAT) are associated with increased overall cardiovascular risk. Using routine, preinterventional cardiac CT data, we examined the predictive value of quantity and quality of EAT and PAT for outcome after transcatheter aortic valve replacement (TAVR). MATERIALS AND METHODS: Cardiac CT data of 1197 patients who underwent TAVR at the in-house heart center between 2011 and 2020 were retrospectively analyzed. The amount and density of EAT and PAT were quantified from single-slice CT images at the level of the aortic valve. Using established risk scores and known independent risk factors, a clinical benchmark model (BMI, Chronic kidney disease stage, EuroSCORE 2, STS Prom, year of intervention) for outcome prediction (2-year mortality) after TAVR was established. Subsequently, we tested whether the additional inclusion of area and density values of EAT and PAT in the clinical benchmark model improved prediction. For this purpose, the cohort was divided into a training (n=798) and a test cohort (n=399). RESULTS: Within the 2-year follow-up, 264 patients died. In the training cohort, particularly the addition of EAT density to the clinical benchmark model showed a significant association with outcome (hazard ratio 1.04, 95% CI: 1.01-1.07; P =0.013). In the test cohort, the outcome prediction of the clinical benchmark model was also significantly improved with the inclusion of EAT density (c-statistic: 0.589 vs. 0.628; P =0.026). CONCLUSIONS: EAT density as a surrogate marker of EAT inflammation was associated with 2-year mortality after TAVR and may improve outcome prediction independent of established risk parameters.

Optical Flow-Guided Cine MRI Segmentation With Learned Corrections.

In cardiac cine magnetic resonance imaging (MRI), the heart is repeatedly imaged at numerous time points during the cardiac cycle. Frequently, the temporal evolution of a certain region of interest such as the ventricles or the atria is highly relevant for clinical diagnosis. In this paper, we devise a novel approach that allows for an automatized propagation of an arbitrary region of interest (ROI) along the cardiac cycle from respective annotated ROIs provided by medical experts at two different points in time, most frequently at the end-systolic (ES) and the end-diastolic (ED) cardiac phases. At its core, a 3D TV- L1 -based optical flow algorithm computes the apparent motion of consecutive MRI images in forward and backward directions. Subsequently, the given terminal annotated masks are propagated by this bidirectional optical flow in 3D, which results, however, in improper initial estimates of the segmentation masks due to numerical inaccuracies. These initially propagated segmentation masks are then refined by a 3D U-Net-based convolutional neural network (CNN), which was trained to enforce consistency with the forward and backward warped masks using a novel loss function. Moreover, a penalization term in the loss function controls large deviations from the initial segmentation masks. This method is benchmarked both on a new dataset with annotated single ventricles containing patients with severe heart diseases and on a publicly available dataset with different annotated ROIs. We emphasize that our novel loss function enables fine-tuning the CNN on a single patient, thereby yielding state-of-the-art results along the complete cardiac cycle.

Deep learning-based assessment of CT markers of sarcopenia and myosteatosis for outcome assessment in patients with advanced pancreatic cancer after high-intensity focused ultrasound treatment.

OBJECTIVES: To evaluate the prognostic value of CT-based markers of sarcopenia and myosteatosis in comparison to the Eastern Cooperative Oncology Group (ECOG) score for survival of patients with advanced pancreatic cancer treated with high-intensity focused ultrasound (HIFU). MATERIALS AND METHODS: For 142 retrospective patients, the skeletal muscle index (SMI), skeletal muscle radiodensity (SMRD), fatty muscle fraction (FMF), and intermuscular fat fraction (IMFF) were determined on superior mesenteric artery level in pre-interventional CT. Each marker was tested for associations with sex, age, body mass index (BMI), and ECOG. The prognostic value of the markers was examined in Kaplan-Meier analyses with the log-rank test and in uni- and multivariable Cox proportional hazards (CPH) models. RESULTS: The following significant associations were observed: Male patients had higher BMI and SMI. Patients with lower ECOG had lower BMI and SMI. Patients with BMI lower than 21.8 kg/m2 (median) also showed lower SMI and IMFF. Patients younger than 63.3 years (median) were found to have higher SMRD, lower FMF, and lower IMFF. In the Kaplan-Meier analysis, significantly lower survival times were observed in patients with higher ECOG or lower SMI. Increased patient risk was observed for higher ECOG, lower BMI, and lower SMI in univariable CPH analyses for 1-, 2-, and 3-year survival. Multivariable CPH analysis for 1-year survival revealed increased patient risk for higher ECOG, lower SMI, lower IMFF, and higher FMF. In multivariable analysis for 2- and 3-year survival, only ECOG and FMF remained significant. CONCLUSION: CT-based markers of sarcopenia and myosteatosis show a prognostic value for assessment of survival in advanced pancreatic cancer patients undergoing HIFU therapy. CLINICAL RELEVANCE STATEMENT: The results indicate a greater role of myosteatosis for additional risk assessment beyond clinical scores, as only FMF was associated with long-term survival in multivariable CPH analyses along ECOG and also showed independence to ECOG in group analysis. KEY POINTS: • This study investigates the prognostic value of CT-based markers of sarcopenia and myosteatosis for patients with pancreatic cancer treated with high-intensity focused ultrasound. • Markers for sarcopenia and myosteatosis showed a prognostic value besides clinical assessment of the physical status by the Eastern Cooperative Oncology Group score. In contrast to muscle size measurements, the myosteatosis marker fatty muscle fraction demonstrated independence to the clinical score. • The results indicate that myosteatosis might play a greater role for additional patient risk assessments beyond clinical assessments of physical status.

Opportunistic CT-derived analysis of fat and muscle tissue composition predicts mortality in patients with cardiogenic shock.

Prognosis estimation in patients with cardiogenic shock (CS) is important to guide clinical decision making. Aim of this study was to investigate the predictive value of opportunistic CT-derived body composition analysis in CS patients. Amount and density of fat and muscle tissue of 152 CS patients were quantified from single-slice CT images at the level of the intervertebral disc space L3/L4. Multivariable Cox regression and Kaplan-Meier survival analyses were performed to evaluate the predictive value of opportunistically CT-derived body composition parameters on the primary endpoint of 30-day mortality. Within the 30-day follow-up, 90/152 (59.2%) patients died. On multivariable analyses, lactate (Hazard Ratio 1.10 [95% Confidence Interval 1.04-1.17]; p = 0.002) and patient age (HR 1.04 [95% CI 1.01-1.07], p = 0.017) as clinical prognosticators, as well as visceral adipose tissue (VAT) area (HR 1.004 [95% CI 1.002-1.007]; p = 0.001) and skeletal muscle (SM) area (HR 0.987 [95% CI 0.975-0.999]; p = 0.043) as imaging biomarkers remained as independent predictors of 30-day mortality. Kaplan-Meier survival analyses showed significantly increased 30-day mortality in patients with higher VAT area (p = 0.015) and lower SM area (p = 0.035). CT-derived VAT and SM area are independent predictors of dismal outcomes in CS patients and have the potential to emerge as new imaging biomarkers available from routine diagnostic CT.

Development of image-based decision support systems utilizing information extracted from radiological free-text report databases with text-based transformers.

OBJECTIVES: To investigate the potential and limitations of utilizing transformer-based report annotation for on-site development of image-based diagnostic decision support systems (DDSS). METHODS: The study included 88,353 chest X-rays from 19,581 intensive care unit (ICU) patients. To label the presence of six typical findings in 17,041 images, the corresponding free-text reports of the attending radiologists were assessed by medical research assistants ("gold labels"). Automatically generated "silver" labels were extracted for all reports by transformer models trained on gold labels. To investigate the benefit of such silver labels, the image-based models were trained using three approaches: with gold labels only (MG), with silver labels first, then with gold labels (MS/G), and with silver and gold labels together (MS+G). To investigate the influence of invested annotation effort, the experiments were repeated with different numbers (N) of gold-annotated reports for training the transformer and image-based models and tested on 2099 gold-annotated images. Significant differences in macro-averaged area under the receiver operating characteristic curve (AUC) were assessed by non-overlapping 95% confidence intervals. RESULTS: Utilizing transformer-based silver labels showed significantly higher macro-averaged AUC than training solely with gold labels (N = 1000: MG 67.8 [66.0-69.6], MS/G 77.9 [76.2-79.6]; N = 14,580: MG 74.5 [72.8-76.2], MS/G 80.9 [79.4-82.4]). Training with silver and gold labels together was beneficial using only 500 gold labels (MS+G 76.4 [74.7-78.0], MS/G 75.3 [73.5-77.0]). CONCLUSIONS: Transformer-based annotation has potential for unlocking free-text report databases for the development of image-based DDSS. However, on-site development of image-based DDSS could benefit from more sophisticated annotation pipelines including further information than a single radiological report. CLINICAL RELEVANCE STATEMENT: Leveraging clinical databases for on-site development of artificial intelligence (AI)-based diagnostic decision support systems by text-based transformers could promote the application of AI in clinical practice by circumventing highly regulated data exchanges with third parties. KEY POINTS: • The amount of data from a database that can be used to develop AI-assisted diagnostic decision systems is often limited by the need for time-consuming identification of pathologies by radiologists. • The transformer-based structuring of free-text radiological reports shows potential to unlock corresponding image databases for on-site development of image-based diagnostic decision support systems. • However, the quality of image annotations generated solely on the content of a single radiology report may be limited by potential inaccuracies and incompleteness of this report.

Direct deep learning-based survival prediction from pre-interventional CT prior to transcatheter aortic valve replacement.

PURPOSE: To investigate survival prediction in patients undergoing transcatheter aortic valve replacement (TAVR) using deep learning (DL) methods applied directly to pre-interventional CT images and to compare performance with survival models based on scalar markers of body composition. METHOD: This retrospective single-center study included 760 patients undergoing TAVR (mean age 81 ± 6 years; 389 female). As a baseline, a Cox proportional hazards model (CPHM) was trained to predict survival on sex, age, and the CT body composition markers fatty muscle fraction (FMF), skeletal muscle radiodensity (SMRD), and skeletal muscle area (SMA) derived from paraspinal muscle segmentation of a single slice at L3/L4 level. The convolutional neural network (CNN) encoder of the DL model for survival prediction was pre-trained in an autoencoder setting with and without a focus on paraspinal muscles. Finally, a combination of DL and CPHM was evaluated. Performance was assessed by C-index and area under the receiver operating curve (AUC) for 1-year and 2-year survival. All methods were trained with five-fold cross-validation and were evaluated on 152 hold-out test cases. RESULTS: The CNN for direct image-based survival prediction, pre-trained in a focussed autoencoder scenario, outperformed the baseline CPHM (CPHM: C-index = 0.608, 1Y-AUC = 0.606, 2Y-AUC = 0.594 vs. DL: C-index = 0.645, 1Y-AUC = 0.687, 2Y-AUC = 0.692). Combining DL and CPHM led to further improvement (C-index = 0.668, 1Y-AUC = 0.713, 2Y-AUC = 0.696). CONCLUSIONS: Direct DL-based survival prediction shows potential to improve image feature extraction compared to segmentation-based scalar markers of body composition for risk assessment in TAVR patients.

Deep Learning Super-Resolution Reconstruction for Fast and Motion-Robust T2-weighted Prostate MRI.

Background Deep learning (DL) reconstructions can enhance image quality while decreasing MRI acquisition time. However, DL reconstruction methods combined with compressed sensing for prostate MRI have not been well studied. Purpose To use an industry-developed DL algorithm to reconstruct low-resolution T2-weighted turbo spin-echo (TSE) prostate MRI scans and compare these with standard sequences. Materials and Methods In this prospective study, participants with suspected prostate cancer underwent prostate MRI with a Cartesian standard-resolution T2-weighted TSE sequence (T2C) and non-Cartesian standard-resolution T2-weighted TSE sequence (T2NC) between August and November 2022. Additionally, a low-resolution Cartesian DL-reconstructed T2-weighted TSE sequence (T2DL) with compressed sensing DL denoising and resolution upscaling reconstruction was acquired. Image sharpness was assessed qualitatively by two readers using a five-point Likert scale (from 1 = nondiagnostic to 5 = excellent) and quantitatively by calculating edge rise distance. The Friedman test and one-way analysis of variance with post hoc Bonferroni and Tukey tests, respectively, were used for group comparisons. Prostate Imaging Reporting and Data System (PI-RADS) score agreement between sequences was compared by using Cohen κ. Results This study included 109 male participants (mean age, 68 years ± 8 [SD]). Acquisition time of T2DL was 36% and 29% lower compared with that of T2C and T2NC (mean duration, 164 seconds ± 20 vs 257 seconds ± 32 and 230 seconds ± 28; P < .001 for both). T2DL showed improved image sharpness compared with standard sequences using both qualitative (median score, 5 [IQR, 4-5] vs 4 [IQR, 3-4] for T2C and 4 [IQR, 3-4] for T2NC; P < .001 for both) and quantitative (mean edge rise distance, 0.75 mm ± 0.39 vs 1.15 mm ± 0.68 for T2C and 0.98 mm ± 0.65 for T2NC; P < .001 and P = .01) methods. PI-RADS score agreement between T2NC and T2DL was excellent (κ range, 0.92-0.94 [95% CI: 0.87, 0.98]). Conclusion DL reconstruction of low-resolution T2-weighted TSE sequences enabled accelerated acquisition times and improved image quality compared with standard acquisitions while showing excellent agreement with conventional sequences for PI-RADS ratings. Clinical trial registration no. NCT05820113 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Turkbey in this issue.

Myosteatosis independently predicts transplant-free survival in patients with primary sclerosing cholangitis.

BACKGROUND: Primary Sclerosing Cholangitis (PSC) is a progressive cholestatic liver disease with liver transplantation (LT) as the only curative therapy. Some regions use body-weight-loss as standard-exception criteria for organ allocation but data on the impact of body composition on survival of patients with PSC is scarce. METHODS: Abdominal MRI of PSC patients were quantitatively analyzed for intramuscular fat fraction (IMFF) as surrogate of myosteatosis. Clinical and laboratory data were retrieved from patient records. Primary outcome was transplant-free survival (TFS). RESULTS: 116 PSC patients were included. Median age was 38 (18-71) years with 74 (64%) male patients. 15 (13%) patients had significant weigh loss. IMFF was significantly associated with survival. Multivariate regression analysis showed IMFF ≥ 15% as independent predictor for TFS (p = 0.032, HR 3.215 CI 1.104-9.366), but not significant weight loss (p = 0.618). CONCLUSION: IMFF is independently associated with TFS in patients with PSC and may identify patients with more urgent need for LT. NCT03584204.

Computed tomography derived cervical fat-free muscle fraction as an imaging-based outcome marker in patients with acute ischemic stroke: a pilot study.

BACKGROUND: Outcome assessment in stroke patients is essential for evidence-based stroke care planning. Computed tomography (CT) is the mainstay of diagnosis in acute stroke. This study aimed to investigate whether CT-derived cervical fat-free muscle fraction (FFMF) as a biomarker of muscle quality is associated with outcome parameters after acute ischemic stroke. METHODS: In this retrospective study, 66 patients (mean age: 76 ± 13 years, 30 female) with acute ischemic stroke in the anterior circulation who underwent CT, including CT-angiography, and endovascular mechanical thrombectomy of the middle cerebral artery between August 2016 and January 2020 were identified. Based on densitometric thresholds, cervical paraspinal muscles covered on CT-angiography were separated into areas of fatty and lean muscle and FFMF was calculated. The study cohort was binarized based on median FFMF (cutoff value: < 71.6%) to compare clinical variables and outcome data between two groups. Unpaired t test and Mann-Whitney U test were used for statistical analysis. RESULTS: National Institute of Health Stroke Scale (NIHSS) (12.2 ± 4.4 vs. 13.6 ± 4.5, P = 0.297) and modified Rankin scale (mRS) (4.3 ± 0.9 vs. 4.4 ± 0.9, P = 0.475) at admission, and pre-stroke mRS (1 ± 1.3 vs. 0.9 ± 1.4, P = 0.489) were similar between groups with high and low FFMF. NIHSS and mRS at discharge were significantly better in patients with high FFMF compared to patients with low FFMF (NIHSS: 4.5 ± 4.4 vs. 9.5 ± 6.7; P = 0.004 and mRS: 2.9 ± 2.1 vs.3.9 ± 1.8; P = 0.049). 90-day mRS was significantly better in patients with high FFMF compared to patients with low FFMF (3.3 ± 2.2 vs. 4.3 ± 1.9, P = 0.045). CONCLUSION: Cervical FFMF obtained from routine clinical CT might be a new imaging-based muscle quality biomarker for outcome prediction in stroke patients.

Deep learning enables automated MRI-based estimation of uterine volume also in patients with uterine fibroids undergoing high-intensity focused ultrasound therapy.

BACKGROUND: High-intensity focused ultrasound (HIFU) is used for the treatment of symptomatic leiomyomas. We aim to automate uterine volumetry for tracking changes after therapy with a 3D deep learning approach. METHODS: A 3D nnU-Net model in the default setting and in a modified version including convolutional block attention modules (CBAMs) was developed on 3D T2-weighted MRI scans. Uterine segmentation was performed in 44 patients with routine pelvic MRI (standard group) and 56 patients with uterine fibroids undergoing ultrasound-guided HIFU therapy (HIFU group). Here, preHIFU scans (n = 56), postHIFU imaging maximum one day after HIFU (n = 54), and the last available follow-up examination (n = 53, days after HIFU: 420 ± 377) were included. The training was performed on 80% of the data with fivefold cross-validation. The remaining data were used as a hold-out test set. Ground truth was generated by a board-certified radiologist and a radiology resident. For the assessment of inter-reader agreement, all preHIFU examinations were segmented independently by both. RESULTS: High segmentation performance was already observed for the default 3D nnU-Net (mean Dice score = 0.95 ± 0.05) on the validation sets. Since the CBAM nnU-Net showed no significant benefit, the less complex default model was applied to the hold-out test set, which resulted in accurate uterus segmentation (Dice scores: standard group 0.92 ± 0.07; HIFU group 0.96 ± 0.02), which was comparable to the agreement between the two readers. CONCLUSIONS: This study presents a method for automatic uterus segmentation which allows a fast and consistent assessment of uterine volume. Therefore, this method could be used in the clinical setting for objective assessment of therapeutic response to HIFU therapy.

Three Tesla magnetic resonance imaging detects oxalate osteopathy in patients with primary hyperoxaluria type I.

BACKGROUND: With declining kidney function and therefore increasing plasma oxalate, patients with primary hyperoxaluria type I (PHI) are at risk to systemically deposit calcium-oxalate crystals. This systemic oxalosis may occur even at early stages of chronic kidney failure (CKD) but is difficult to detect with non-invasive imaging procedures. METHODS: We tested if magnetic resonance imaging (MRI) is sensitive to detect oxalate deposition in bone. A 3 Tesla MRI of the left knee/tibial metaphysis was performed in 46 patients with PHI and in 12 healthy controls. In addition to the investigator's interpretation, signal intensities (SI) within a region of interest (ROI, transverse images below the level of the physis in the proximal tibial metaphysis) were measured pixelwise, and statistical parameters of their distribution were calculated. In addition, 52 parameters of texture analysis were evaluated. Plasma oxalate and CKD status were correlated to MRI findings. MRI was then implemented in routine practice. RESULTS: Independent interpretation by investigators was consistent in most cases and clearly differentiated patients from controls. Statistically significant differences were seen between patients and controls (p < 0.05). No correlation/relation between the MRI parameters and CKD stages or Pox levels was found. However, MR imaging of oxalate osteopathy revealed changes attributed to clinical status which differed clearly to that in secondary hyperparathyroidism. CONCLUSIONS: MRI is able to visually detect (early) oxalate osteopathy in PHI. It can be used for its monitoring and is distinguished from renal osteodystrophy. In the future, machine learning algorithms may aid in the objective assessment of oxalate deposition in bone. Graphical Abstract A higher resolution version of the Graphical abstract is available as Supplementary information.

Association between single-slice and whole heart measurements of epicardial and pericardial fat in cardiac MRI.

BACKGROUND: Epicardial (ECF) and pericardial fat (PCF) are important prognostic markers for various cardiac diseases. However, volumetry of the fat compartments is time-consuming. PURPOSE: To investigate whether total volume of ECF and PCF can be estimated by axial single-slice measurements and in a four-chamber view. MATERIAL AND METHODS: A total of 113 individuals (79 patients and 34 healthy) were included in this retrospective magnetic resonance imaging (MRI) study. The total volume of ECF and PCF was determined using a 3D-Dixon sequence. Additionally, the area of ECF and PCF was obtained in single axial layers at five anatomical landmarks (left coronary artery, right coronary artery, right pulmonary artery, mitral valve, coronary sinus) of the Dixon sequence and in a four-chamber view of a standard cine sequence. Pearson's correlation coefficient was calculated between the total volume and each single-slice measurement. RESULTS: Axial single-slice measurements of ECF and PCF correlated strongly with the total fat volumes at all landmarks (ECF: r = 0.85-0.94, P < 0.001; PCF: r = 0.89-0.94, P < 0.001). The best correlation was found at the level of the left coronary artery for ECF and PCF (r = 0.94, P < 0.001). Correlation between single-slice measurement in the four-chamber view and the total ECF and PCF volume was lower (r = 0.75 and r = 0.8, respectively, P < 0.001). CONCLUSION: Single-slice measurements allow an estimation of ECF and PCF volume. This time-efficient analysis allows studies of larger patient cohorts and the opportunistic determination of ECF/PCF from routine examinations.

Deep learning for standardized, MRI-based quantification of subcutaneous and subfascial tissue volume for patients with lipedema and lymphedema.

OBJECTIVES: To contribute to a more in-depth assessment of shape, volume, and asymmetry of the lower extremities in patients with lipedema or lymphedema utilizing volume information from MR imaging. METHODS: A deep learning (DL) pipeline was developed including (i) localization of anatomical landmarks (femoral heads, symphysis, knees, ankles) and (ii) quality-assured tissue segmentation to enable standardized quantification of subcutaneous (SCT) and subfascial tissue (SFT) volumes. The retrospectively derived dataset for method development consisted of 45 patients (42 female, 44.2 ± 14.8 years) who underwent clinical 3D DIXON MR-lymphangiography examinations of the lower extremities. Five-fold cross-validated training was performed on 16,573 axial slices from 40 patients and testing on 2187 axial slices from 5 patients. For landmark detection, two EfficientNet-B1 convolutional neural networks (CNNs) were applied in an ensemble. One determines the relative foot-head position of each axial slice with respect to the landmarks by regression, the other identifies all landmarks in coronal reconstructed slices using keypoint detection. After landmark detection, segmentation of SCT and SFT was performed on axial slices employing a U-Net architecture with EfficientNet-B1 as encoder. Finally, the determined landmarks were used for standardized analysis and visualization of tissue volume, distribution, and symmetry, independent of leg length, slice thickness, and patient position. RESULTS: Excellent test results were observed for landmark detection (z-deviation = 4.5 ± 3.1 mm) and segmentation (Dice score: SCT = 0.989 ± 0.004, SFT = 0.994 ± 0.002). CONCLUSIONS: The proposed DL pipeline allows for standardized analysis of tissue volume and distribution and may assist in diagnosis of lipedema and lymphedema or monitoring of conservative and surgical treatments. KEY POINTS: • Efficient use of volume information that MRI inherently provides can be extracted automatically by deep learning and enables in-depth assessment of tissue volumes in lipedema and lymphedema. • The deep learning pipeline consisting of body part regression, keypoint detection, and quality-assured tissue segmentation provides detailed information about the volume, distribution, and asymmetry of lower extremity tissues, independent of leg length, slice thickness, and patient position.

Simplified intravoxel incoherent motion DWI for differentiating malignant from benign breast lesions.

BACKGROUND: To evaluate simplified intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for differentiating malignant versus benign breast lesions as (i) stand-alone tool and (ii) add-on to dynamic contrast-enhanced magnetic resonance imaging. METHODS: 1.5-T DWI data (b = 0, 50, 250, 800 s/mm2) were retrospectively analysed for 126 patients with malignant or benign breast lesions. Apparent diffusion coefficient (ADC) ADC (0, 800) and IVIM-based parameters D1' = ADC (50, 800), D2' = ADC (250, 800), f1' = f (0, 50, 800), f2' = f (0, 250, 800) and D*' = D* (0, 50, 250, 800) were voxel-wise calculated without fitting procedures. Regions of interest were analysed in vital tumour and perfusion hot spots. Beside the single parameters, the combined use of D1' with f1' and D2' with f2' was evaluated. Lesion differentiation was investigated for lesions (i) with hyperintensity on DWI with b = 800 s/mm2 (n = 191) and (ii) with suspicious contrast-enhancement (n = 135). RESULTS: All lesions with suspicious contrast-enhancement appeared also hyperintense on DWI with b = 800 s/mm2. For task (i), best discrimination was reached for the combination of D1' and f1' using perfusion hot spot regions-of-interest (accuracy 93.7%), which was higher than that of ADC (86.9%, p = 0.003) and single IVIM parameters D1' (88.0%) and f1' (87.4%). For task (ii), best discrimination was reached for single parameter D1' using perfusion hot spot regions-of-interest (92.6%), which were slightly but not significantly better than that of ADC (91.1%) and D2' (88.1%). Adding f1' to D1' did not improve discrimination. CONCLUSIONS: IVIM analysis yielded a higher accuracy than ADC. If stand-alone DWI is used, perfusion analysis is of special relevance.

Assessment of liver cirrhosis severity with extracellular volume fraction MRI.

We aimed to investigate the diagnostic utility of MRI extracellular volume fraction (ECV) for the assessment of liver cirrhosis severity as defined by Child-Pugh class. In this retrospective study, 90 patients (68 cirrhotic patients and 22 controls), who underwent multiparametric liver MRI, were identified. Hepatic T1 relaxation times and ECV were assessed. Clinical scores of liver disease severity were calculated. One-way analysis of variance (ANOVA) followed by Tukey's multiple comparison test, Spearman's correlation coefficient, and receiver operating characteristic (ROC) analysis were used for statistical analysis. In cirrhotic patients, hepatic native T1 increased depending on Child-Pugh class (620.5 ± 78.9 ms (Child A) vs. 666.6 ± 73.4 ms (Child B) vs. 828.4 ± 91.2 ms (Child C), P < 0.001). ECV was higher in cirrhotic patients compared to the controls (40.1 ± 11.9% vs. 25.9 ± 4.5%, P < 0.001) and increased depending of Child-Pugh class (33.3 ± 6.0% (Child A) vs. 39.6 ± 4.9% (Child B) vs. 52.8 ± 1.2% (Child C), P < 0.001). ECV correlated with Child-Pugh score (r = 0.64, P < 0.001). ECV allowed differentiating between Child-Pugh classes A and B, and B and C with an AUC of 0.785 and 0.944 (P < 0.001, respectively). The diagnostic performance of ECV for differentiating between Child-Pugh classes A and B, and B and C was higher compared to hepatic native T1 (AUC: 0.651 and 0.910) and MELD score (AUC: 0.740 and 0.795) (P < 0.05, respectively). MRI-derived ECV correlated with Child-Pugh score and had a high diagnostic performance for the discrimination of different Child-Pugh classes. ECV might become a valuable non-invasive biomarker for the assessment of liver cirrhosis severity.

Peripartum Cardiomyopathy: Diagnostic and Prognostic Value of Cardiac Magnetic Resonance in the Acute Stage.

This study aimed to evaluate the diagnostic and prognostic value of cardiac magnetic resonance in acute peripartum cardiomyopathy (PPCM). A total of 17 patients with PPCM in the acute stage and 15 healthy controls were retrospectively analyzed regarding myocardial function, edema, late gadolinium enhancement (LGE), and T1 and T2 mappings (T1, T2). Echocardiographic follow-ups were performed. Functional recovery was defined as a left ventricular ejection fraction (LVEF) of ≥50%. Patients with PPCM displayed biventricular dysfunction with reduced myocardial strain parameters and left ventricular and atrial dilatation, as well as diffuse myocardial edema (T2 signal intensity ratio: 2.10 ± 0.34 vs. 1.58 ± 0.21, p < 0.001; T1: 1070 ± 51 ms vs. 980 ± 28 ms, p = 0.001; T2: 63 ± 5 ms vs. 53 ± 2 ms, p < 0.001). Visual myocardial edema was present in 10 patients (59%). LGE was positive in 2 patients (12%). A total of 13 patients (76%) showed full LVEF recovery. The absence of visual myocardial edema and impairment of strain parameters were associated with delayed LVEF recovery. Multivariable Cox regression analysis revealed global longitudinal strain as an independent prognostic factor for LVEF recovery. In conclusion, biventricular systolic dysfunction with diffuse myocardial edema seems to be present in acute PPCM. Myocardial edema and strain may have prognostic value for LVEF recovery.