Determining individual glomerular proteinuria and periglomerular infiltration in a cleared murine kidney by a 3-dimensional fast marching algorithm.

Three-dimensional (3D) imaging has advanced basic research and clinical medicine. However, limited resolution and imperfections of real-world 3D image material often preclude algorithmic image analysis. Here, we present a methodologic framework for such imaging and analysis for functional and spatial relations in experimental nephritis. First, optical tissue-clearing protocols were optimized to preserve fluorescence signals for light sheet fluorescence microscopy and compensated attenuation effects using adjustable 3D correction fields. Next, we adapted the fast marching algorithm to conduct backtracking in 3D environments and developed a tool to determine local concentrations of extractable objects. As a proof-of-concept application, we used this framework to determine in a glomerulonephritis model the individual proteinuria and periglomerular immune cell infiltration for all glomeruli of half a mouse kidney. A correlation between these parameters surprisingly did not support the intuitional assumption that the most inflamed glomeruli are the most proteinuric. Instead, the spatial density of adjacent glomeruli positively correlated with the proteinuria of a given glomerulus. Because proteinuric glomeruli appear clustered, this suggests that the exact location of a kidney biopsy may affect the observed severity of glomerular damage. Thus, our algorithmic pipeline described here allows analysis of various parameters of various organs composed of functional subunits, such as the kidney, and can theoretically be adapted to processing other image modalities.

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.

Deeplasia: deep learning for bone age assessment validated on skeletal dysplasias.

BACKGROUND: Skeletal dysplasias collectively affect a large number of patients worldwide. Most of these disorders cause growth anomalies. Hence, evaluating skeletal maturity via the determination of bone age (BA) is a useful tool. Moreover, consecutive BA measurements are crucial for monitoring the growth of patients with such disorders, especially for timing hormonal treatment or orthopedic interventions. However, manual BA assessment is time-consuming and suffers from high intra- and inter-rater variability. This is further exacerbated by genetic disorders causing severe skeletal malformations. While numerous approaches to automate BA assessment have been proposed, few are validated for BA assessment on children with skeletal dysplasias. OBJECTIVE: We present Deeplasia, an open-source prior-free deep-learning approach designed for BA assessment specifically validated on patients with skeletal dysplasias. MATERIALS AND METHODS: We trained multiple convolutional neural network models under various conditions and selected three to build a precise model ensemble. We utilized the public BA dataset from the Radiological Society of North America (RSNA) consisting of training, validation, and test subsets containing 12,611, 1,425, and 200 hand and wrist radiographs, respectively. For testing the performance of our model ensemble on dysplastic hands, we retrospectively collected 568 radiographs from 189 patients with molecularly confirmed diagnoses of seven different genetic bone disorders including achondroplasia and hypochondroplasia. A subset of the dysplastic cohort (149 images) was used to estimate the test-retest precision of our model ensemble on longitudinal data. RESULTS: The mean absolute difference of Deeplasia for the RSNA test set (based on the average of six different reference ratings) and dysplastic set (based on the average of two different reference ratings) were 3.87 and 5.84 months, respectively. The test-retest precision of Deeplasia on longitudinal data (2.74 months) is estimated to be similar to a human expert. CONCLUSION: We demonstrated that Deeplasia is competent in assessing the age and monitoring the development of both normal and dysplastic bones.

Linear multi-scale modeling of diffusion MRI data: A framework for characterization of oriented structures across length scales.

Diffusion-weighted magnetic resonance imaging (DW-MRI) has evolved to provide increasingly sophisticated investigations of the human brain's structural connectome in vivo. Restriction spectrum imaging (RSI) is a method that reconstructs the orientation distribution of diffusion within tissues over a range of length scales. In its original formulation, RSI represented the signal as consisting of a spectrum of Gaussian diffusion response functions. Recent technological advances have enabled the use of ultra-high b-values on human MRI scanners, providing higher sensitivity to intracellular water diffusion in the living human brain. To capture the complex diffusion time dependence of the signal within restricted water compartments, we expand upon the RSI approach to represent restricted water compartments with non-Gaussian response functions, in an extended analysis framework called linear multi-scale modeling (LMM). The LMM approach is designed to resolve length scale and orientation-specific information with greater specificity to tissue microstructure in the restricted and hindered compartments, while retaining the advantages of the RSI approach in its implementation as a linear inverse problem. Using multi-shell, multi-diffusion time DW-MRI data acquired with a state-of-the-art 3 T MRI scanner equipped with 300 mT/m gradients, we demonstrate the ability of the LMM approach to distinguish different anatomical structures in the human brain and the potential to advance mapping of the human connectome through joint estimation of the fiber orientation distributions and compartment size characteristics.

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.

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.

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.

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.

Percutaneous transhepatic biliary drainage: a retrospective single-center study of 372 patients.

BACKGROUND: Complication rates in percutaneous transhepatic biliary drainage (PTBD) are non-uniform and vary considerably. In addition, the impact of peri-procedural risk factors is under-investigated. PURPOSE: To compare success and complication rates of PTBD in patients with and without accompanying technical risk factors. MATERIAL AND METHODS: A single-center retrospective study was conducted from January 2004 to December 2016. Patients receiving PTBD due to biliary obstruction or biliary leakage were included. Technical risk factors (non-distended bile ducts, ascites, obesity, anasarca, non-compliance) were assessed. Complications were classified according to the Society of Interventional Radiology. RESULTS: In total, 372 patients were included (57.3% men, 42.7% women; mean age = 66 years). Overall, 466 PTBDs were performed. Of the patients, 70.1% presented with malignancy and biliary obstruction; 26.8% had benign biliary obstruction; 3.1% had biliary leakage. Technical risk factors were reported in 57 (15.3%) patients. Overall technical success of initial PTBD was 98.7%, primary technical success was 97.9%. In patients with non-dilatated bile ducts, primary technical success was 68.2%. Overall complication rate was 15.0% (8.1% major complications, 6.9% minor complications). Neither major nor minor complications were more frequent in patients with technical risk factors (P > 0.05). In left-sided PTBD, hemorrhage was more frequent (P = 0.015). Patients with malignancy were significantly more affected by drainage-related complications (P = 0.004; odds ratio = 2.03). The mortality rate was 0.5% (n = 2). CONCLUSION: PTBD is a safe and effective method for the treatment of biliary obstruction and biliary leaks. Complication rates are low, even in procedures with risk factors.

End-to-end automated body composition analyses with integrated quality control for opportunistic assessment of sarcopenia in CT.

OBJECTIVES: To develop a pipeline for automated body composition analysis and skeletal muscle assessment with integrated quality control for large-scale application in opportunistic imaging. METHODS: First, a convolutional neural network for extraction of a single slice at the L3/L4 lumbar level was developed on CT scans of 240 patients applying the nnU-Net framework. Second, a 2D competitive dense fully convolutional U-Net for segmentation of visceral and subcutaneous adipose tissue (VAT, SAT), skeletal muscle (SM), and subsequent determination of fatty muscle fraction (FMF) was developed on single CT slices of 1143 patients. For both steps, automated quality control was integrated by a logistic regression model classifying the presence of L3/L4 and a linear regression model predicting the segmentation quality in terms of Dice score. To evaluate the performance of the entire pipeline end-to-end, body composition metrics, and FMF were compared to manual analyses including 364 patients from two centers. RESULTS: Excellent results were observed for slice extraction (z-deviation = 2.46 ± 6.20 mm) and segmentation (Dice score for SM = 0.95 ± 0.04, VAT = 0.98 ± 0.02, SAT = 0.97 ± 0.04) on the dual-center test set excluding cases with artifacts due to metallic implants. No data were excluded for end-to-end performance analyses. With a restrictive setting of the integrated segmentation quality control, 39 of 364 patients were excluded containing 8 cases with metallic implants. This setting ensured a high agreement between manual and fully automated analyses with mean relative area deviations of ΔSM = 3.3 ± 4.1%, ΔVAT = 3.0 ± 4.7%, ΔSAT = 2.7 ± 4.3%, and ΔFMF = 4.3 ± 4.4%. CONCLUSIONS: This study presents an end-to-end automated deep learning pipeline for large-scale opportunistic assessment of body composition metrics and sarcopenia biomarkers in clinical routine. KEY POINTS: • Body composition metrics and skeletal muscle quality can be opportunistically determined from routine abdominal CT scans. • A pipeline consisting of two convolutional neural networks allows an end-to-end automated analysis. • Machine-learning-based quality control ensures high agreement between manual and automatic analysis.

Cardiac MRI in Patients with Prolonged Cardiorespiratory Symptoms after Mild to Moderate COVID-19.

Background Myocardial injury and inflammation at cardiac MRI in patients with COVID-19 have been described in recent publications. Concurrently, a chronic COVID-19 syndrome (CCS) after SARS-CoV-2 infection has been observed and manifests with symptoms such as fatigue and exertional dyspnea. Purpose To explore the relationship between CCS and myocardial injury and inflammation as an underlying cause of the persistent complaints in previously healthy individuals. Materials and Methods In this prospective study from January 2021 to April 2021, study participants without known cardiac or pulmonary diseases prior to SARS-CoV-2 infection who had persistent CCS symptoms such as fatigue or exertional dyspnea after convalescence and healthy control participants underwent cardiac MRI. The cardiac MRI protocol included evaluating the T1 and T2 relaxation times, extracellular volume, T2 signal intensity ratio, and late gadolinium enhancement (LGE). Student t tests, Mann-Whitney U tests, and χ2 tests were used for statistical analysis. Results Forty-one participants with CCS (mean age, 39 years ± 13 [standard deviation]; 18 men) and 42 control participants (mean age, 39 years ± 16; 26 men) were evaluated. The median time between the initial incidence of mild to moderate COVID-19 not requiring hospitalization and undergoing cardiac MRI was 103 days (interquartile range, 88-158 days). Troponin T levels were normal. Parameters indicating myocardial inflammation and edema were comparable between participants with CCS and control participants (T1 relaxation times: 978 msec ± 23 vs 971 msec ± 25 [P = .17]; T2 relaxation times: 53 msec ± 2 vs 52 msec ± 2 [P = .47]; T2 signal intensity ratios: 1.6 ± 0.2 vs 1.6 ± 0.3 [P = .10]). Visible myocardial edema was present in none of the participants. Three of 41 (7%) participants with CCS demonstrated nonischemic LGE, whereas no participants in the control group demonstrated nonischemic LGE (0 of 42 [0%]; P = .07). None of the participants fulfilled the 2018 Lake Louise criteria for the diagnosis of myocarditis. Conclusion Individuals with chronic COVID-19 syndrome who did not undergo hospitalization for COVID-19 did not demonstrate signs of active myocardial injury or inflammation at cardiac MRI. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Lima and Bluemke in this issue.

Diagnostic Benefit of MRI for Exclusion of Ligamentous Injury in Patients with Lateral Atlantodental Interval Asymmetry at Initial Trauma CT.

Background Cervical spine CT is regularly performed to exclude cervical spine injury during the initial evaluation of trauma patients. Patients with asymmetry of the lateral atlantodental interval (LADI) often undergo subsequent MRI to rule out ligamentous injuries. The clinical relevance of an asymmetric LADI and the benefit of additional MRI remain unclear. Purpose To evaluate the diagnostic benefit of additional MRI in patients with blunt trauma who have asymmetry of the LADI and no other cervical injuries. Materials and Methods Patients who underwent cervical spine CT during initial trauma evaluation between March 2017 and August 2019 were retrospectively evaluated. Those who underwent subsequent MRI because of LADI asymmetry of 1 mm or greater with no other signs of cervical injury were identified and reevaluated by two readers blinded to clinical data and initial study reports regarding possible ligamentous injuries. Results Among 1553 patients, 146 (9%) had LADI asymmetry of 1 mm or greater. Of these, 46 patients (mean age ± standard deviation, 39 years ± 22; 28 men; median LADI asymmetry, 2.4 mm [interquartile range, 1.8-3.1 mm]) underwent supplementary MRI with no other signs of cervical injury at initial CT. Ten of the 46 patients (22%) showed cervical tenderness at clinical examination, and 36 patients (78%) were asymptomatic. In two of the 46 patients (4%), MRI revealed alar ligament injury; both of these patients showed LADI asymmetry greater than 3 mm, along with cervical tenderness at clinical examination, and underwent treatment for ligamentous injury. In 13 of the 46 patients (28%), signal intensity alterations of alar ligaments without signs of rupture were observed. Four of these 13 patients (31%) were subsequently treated for ligamentous injury despite being asymptomatic. Conclusion Subsequent MRI following CT of the cervical spine in trauma patients with lateral atlantodental interval asymmetry may have diagnostic benefit only in symptomatic patients. In asymptomatic patients without proven cervical injuries, subsequent MRI showed no diagnostic benefit and may even lead to overtreatment. © RSNA, 2021 Online supplemental material is available for this article.

Detection of liver cirrhosis in standard T2-weighted MRI using deep transfer learning.

OBJECTIVES: To investigate the diagnostic performance of deep transfer learning (DTL) to detect liver cirrhosis from clinical MRI. METHODS: The dataset for this retrospective analysis consisted of 713 (343 female) patients who underwent liver MRI between 2017 and 2019. In total, 553 of these subjects had a confirmed diagnosis of liver cirrhosis, while the remainder had no history of liver disease. T2-weighted MRI slices at the level of the caudate lobe were manually exported for DTL analysis. Data were randomly split into training, validation, and test sets (70%/15%/15%). A ResNet50 convolutional neural network (CNN) pre-trained on the ImageNet archive was used for cirrhosis detection with and without upstream liver segmentation. Classification performance for detection of liver cirrhosis was compared to two radiologists with different levels of experience (4th-year resident, board-certified radiologist). Segmentation was performed using a U-Net architecture built on a pre-trained ResNet34 encoder. Differences in classification accuracy were assessed by the χ2-test. RESULTS: Dice coefficients for automatic segmentation were above 0.98 for both validation and test data. The classification accuracy of liver cirrhosis on validation (vACC) and test (tACC) data for the DTL pipeline with upstream liver segmentation (vACC = 0.99, tACC = 0.96) was significantly higher compared to the resident (vACC = 0.88, p < 0.01; tACC = 0.91, p = 0.01) and to the board-certified radiologist (vACC = 0.96, p < 0.01; tACC = 0.90, p < 0.01). CONCLUSION: This proof-of-principle study demonstrates the potential of DTL for detecting cirrhosis based on standard T2-weighted MRI. The presented method for image-based diagnosis of liver cirrhosis demonstrated expert-level classification accuracy. KEY POINTS: • A pipeline consisting of two convolutional neural networks (CNNs) pre-trained on an extensive natural image database (ImageNet archive) enables detection of liver cirrhosis on standard T2-weighted MRI. • High classification accuracy can be achieved even without altering the pre-trained parameters of the convolutional neural networks. • Other abdominal structures apart from the liver were relevant for detection when the network was trained on unsegmented images.

Free-breathing high resolution modified Dixon steady-state angiography with compressed sensing for the assessment of the thoracic vasculature in pediatric patients with congenital heart disease.

BACKGROUND: Cardiovascular magnetic resonance angiography (CMRA) is a non-invasive imaging modality of choice in pediatric patients with congenital heart disease (CHD). This study was aimed to evaluate the diagnostic utility of a respiratory- and electrocardiogram-gated steady-state CMRA with modified Dixon (mDixon) fat suppression technique and compressed sensing in comparison to standard first-pass CMRA in pediatric patients with CHD at 3 T. METHODS: In this retrospective single center study, pediatric CHD patients who underwent CMR with first-pass CMRA followed by mDixon steady-state CMRA at 3 T were analyzed. Image quality using a Likert scale from 5 (excellent) to 1 (non-diagnostic) and quality of fat suppression were assessed in consensus by two readers. Blood-to-tissue contrast and quantitative measurements of the thoracic vasculature were assessed separately by two readers. CMRA images were reevaluated by two readers for additional findings, which could be identified only on either one of the CMRA types. Paired Student t test, Wilcoxon test, and intraclass correlation coefficients (ICCs) were used for statistical analysis. RESULTS: 32 patients with CHD (3.3 ± 1.7 years, 13 female) were included. Overall image quality of steady-state mDixon CMRA was higher compared to first-pass CMRA (4.5 ± 0.5 vs. 3.3 ± 0.5; P < 0.001). Blood-to-tissue contrast ratio of steady-state mDixon CMRA was comparable to first-pass CMRA (7.85 ± 4.75 vs. 6.35 ± 2.23; P = 0.133). Fat suppression of steady-state mDixon CMRA was perfect in 30/32 (94%) cases. Vessel diameters were greater in first-pass CMRA compared to steady-state mDixon CMRA with the greatest differences at the level of pulmonary arteries and veins (e.g., right pulmonary artery for reader 1: 10.4 ± 2.4 vs. 9.9 ± 2.3 mm, P < 0.001). Interobserver agreement was higher for steady-state mDixon CMRA for all measurements compared to first-pass CMRA (ICCs > 0.92). In 9/32 (28%) patients, 10 additional findings were identified on mDixon steady-state CMRA (e.g., partial anomalous venous return, abnormalities of coronary arteries, subclavian artery stenosis), which were not depicted using first-pass CMRA. CONCLUSIONS: Steady-state mDixon CMRA offers a robust fat suppression, a high image quality, and diagnostic utility for the assessment of the thoracic vasculature in pediatric CHD patients.

Diagnostic value of magnetic resonance parametric mapping for non-invasive assessment of liver fibrosis in patients with primary sclerosing cholangitis.

BACKGROUND: Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease, characterized by bile duct inflammation and destruction, leading to biliary fibrosis and cirrhosis. The purpose of this study was to investigate the utility of T1 and T2 mapping parameters, including extracellular volume fraction (ECV) for non-invasive assessment of fibrosis severity in patients with PSC. METHODS: In this prospective study, patients with PSC diagnosis were consecutively enrolled from January 2019 to July 2020 and underwent liver MRI. Besides morphological sequences, MR elastography (MRE), and T1 and T2 mapping were performed. ECV was calculated from T1 relaxation times. The presence of significant fibrosis (≥ F2) was defined as MRE-derived liver stiffness ≥ 3.66 kPa and used as the reference standard, against which the diagnostic performance of MRI mapping parameters was tested. Student t test, ROC analysis and Pearson correlation were used for statistical analysis. RESULTS: 32 patients with PSC (age range 19-77 years) were analyzed. Both, hepatic native T1 (r = 0.66; P < 0.001) and ECV (r = 0.69; P < 0.001) correlated with MRE-derived liver stiffness. To diagnose significant fibrosis (≥ F2), ECV revealed a sensitivity of 84.2% (95% confidence interval (CI) 62.4-94.5%) and a specificity of 84.6% (CI 57.8-95.7%); hepatic native T1 revealed a sensitivity of 52.6% (CI 31.7-72.7%) and a specificity of 100.0% (CI 77.2-100.0%). Hepatic ECV (area under the curve (AUC) 0.858) and native T1 (AUC 0.711) had an equal or higher diagnostic performance for the assessment of significant fibrosis compared to serologic fibrosis scores (APRI (AUC 0.787), FIB-4 (AUC 0.588), AAR (0.570)). CONCLUSIONS: Hepatic T1 and ECV can diagnose significant fibrosis in patients with PSC. Quantitative mapping has the potential to be a new non-invasive biomarker for liver fibrosis assessment and quantification in PSC patients.

Morphological and functional assessment of the uterus: "one-stop shop imaging" using a compressed-sensing accelerated, free-breathing T1-VIBE sequence.

BACKGROUND: The combination of motion-insensitive, high-temporal, and spatial resolution imaging with evaluation of quantitative perfusion has the potential to increase the diagnostic capabilities of magnetic resonance imaging (MRI) in the female pelvis. PURPOSE: To compare a free-breathing compressed-sensing VIBE (fbVIBE) with flexible temporal resolution (range = 4.6-13.8 s) with breath-hold VIBE (bhVIBE) and to evaluate the potential value of quantifying uterine perfusion. MATERIAL AND METHODS: A total of 70 datasets from 60 patients (bhVIBE: n = 30; fbVIBE: n = 40) were evaluated by two radiologists. Only temporally resolved reconstruction (fbVIBE) was performed on 30 of the fbVIBE datasets. For a subset (n = 10) of the fbVIBE acquisitions, a time- and motion-resolved reconstruction (mrVIBE) was evaluated. Image quality (IQ), artifacts, diagnostic confidence (DC), and delineation of uterine structures (DoS) were graded on Likert scales (IQ/DC/DoS: 1 (non-diagnostic) to 5 (perfect); artifacts: 1 (no artifacts) to 5 (severe artifacts)). A Tofts model was applied for perfusion analysis. Ktrans was obtained in the myometrium (Mm), junctional zone (Jz), and cervix (Cx). RESULTS: The median IQ/DoS/DC scores of fbVIBE (4/5/5 κ >0.7-0.9) and bhVIBE (4/4/4; κ = 0.5-0.7; P > 0.05) were high, but Artifacts were graded low (fbVIBE/bhVIBE: 2/2; κ = 0.6/0.5; P > 0.05). Artifacts were only slightly improved by the additional motion-resolved reconstruction (fbVIBE/mrVIBE: 2/1.5; P = 0.08); fbVIBE was preferred in most cases (7/10). Significant differences of Ktrans values were found between Cx, Jz, and Mm (0.12/0.21/0.19; P < 0.05). CONCLUSION: The fbVIBE sequence allows functional and morphological assessment of the uterus at comparable IQ to bhVIBE.

Women in radiology: gender diversity is not a metric-it is a tool for excellence.

Women in Focus: Be Inspired was a unique programme held at the 2019 European Congress of Radiology that was structured to address a range of topics related to gender and healthcare, including leadership, mentoring and the generational progression of women in medicine. In most countries, women constitute substantially fewer than half of radiologists in academia or private practice despite frequently accounting for at least half of medical school enrolees. Furthermore, the proportion of women decreases at higher academic ranks and levels of leadership, a phenomenon which has been referred to as a "leaky pipeline". Gender diversity in the radiologic workplace, including in academic and leadership positions, is important for the present and future success of the field. It is a tool for excellence that helps to optimize patient care and research; moreover, it is essential to overcome the current shortage of radiologists. This article reviews the current state of gender diversity in academic and leadership positions in radiology internationally and explores a wide range of potential reasons for gender disparities, including the lack of role models and mentorship, unconscious bias and generational changes in attitudes about the desirability of leadership positions. Strategies for both individuals and institutions to proactively increase the representation of women in academic and leadership positions are suggested. KEY POINTS: • Gender-diverse teams perform better. Thus, gender diversity throughout the radiologic workplace, including in leadership positions, is important for the current and future success of the field. • Though women now make up roughly half of medical students, they remain underrepresented among radiology trainees, faculty and leaders. • Factors leading to the gender gap in academia and leadership positions in Radiology include a lack of role models and mentors, unconscious biases, other societal barriers and generational changes.

Developing a Roadmap for Interventional Oncology.

Interventional oncology uses image-guided procedures to enhance cancer care. Today, this specialty plays an increasingly critical role in cancer diagnosis (e.g., biopsy), cancer therapy (e.g., ablation or embolization), and cancer symptom palliation (e.g., nephrostomies or biliary drainages). Although the number of procedures and technical capabilities has improved over the last few years, challenges remain. In this article we discuss the need to advance existing procedures, develop new ones, and focus on several operational aspects that will dictate future interventional techniques to enhance cancer care, particularly by accelerating drug development and improving patient outcomes. IMPLICATIONS FOR PRACTICE: Interventional oncology is vital for cancer diagnosis, therapy, and symptom palliation. This report focuses on current interventional procedures and techniques with a look toward future improvements that will improve cancer care and patient outcomes.

The impact of injector-based contrast agent administration in time-resolved MRA.

OBJECTIVES: Time-resolved contrast-enhanced MR angiography (4D-MRA), which allows the simultaneous visualization of the vasculature and blood-flow dynamics, is widely used in clinical routine. In this study, the impact of two different contrast agent injection methods on 4D-MRA was examined in a controlled, standardized setting in an animal model. METHODS: Six anesthetized Goettingen minipigs underwent two identical 4D-MRA examinations at 1.5 T in a single session. The contrast agent (0.1 mmol/kg body weight gadobutrol, followed by 20 ml saline) was injected using either manual injection or an automated injection system. A quantitative comparison of vascular signal enhancement and quantitative renal perfusion analyses were performed. RESULTS: Analysis of signal enhancement revealed higher peak enhancements and shorter time to peak intervals for the automated injection. Significantly different bolus shapes were found: automated injection resulted in a compact first-pass bolus shape clearly separated from the recirculation while manual injection resulted in a disrupted first-pass bolus with two peaks. In the quantitative perfusion analyses, statistically significant differences in plasma flow values were found between the injection methods. CONCLUSIONS: The results of both qualitative and quantitative 4D-MRA depend on the contrast agent injection method, with automated injection providing more defined bolus shapes and more standardized examination protocols. KEY POINTS: • Automated and manual contrast agent injection result in different bolus shapes in 4D-MRA. • Manual injection results in an undefined and interrupted bolus with two peaks. • Automated injection provides more defined bolus shapes. • Automated injection can lead to more standardized examination protocols.

Diffusion kurtosis imaging of the liver at 3 Tesla: in vivo comparison to standard diffusion-weighted imaging.

Background Functional techniques like diffusion-weighted imaging (DWI) are gaining more and more importance in liver magnetic resonance imaging (MRI). Diffusion kurtosis imaging (DKI) is an advanced technique that might help to overcome current limitations of DWI. Purpose To evaluate DKI for the differentiation of hepatic lesions in comparison to conventional DWI at 3 Tesla. Material and Methods Fifty-six consecutive patients were examined using a routine abdominal MR protocol at 3 Tesla which included DWI with b-values of 50, 400, 800, and 1000 s/mm2. Apparent diffusion coefficient maps were calculated applying a standard mono-exponential fit, while a non-Gaussian kurtosis fit was used to obtain DKI maps. ADC as well as Kurtosis-corrected diffusion ( D) values were quantified by region of interest analysis and compared between lesions. Results Sixty-eight hepatic lesions (hepatocellular carcinoma [HCC] [n = 25]; hepatic adenoma [n = 4], cysts [n = 18]; hepatic hemangioma [HH] [n = 18]; and focal nodular hyperplasia [n = 3]) were identified. Differentiation of malignant and benign lesions was possible based on both DWI ADC as well as DKI D-values ( P values were in the range of 0.04 to < 0.0001). Conclusion In vivo abdominal DKI calculated using standard b-values is feasible and enables quantitative differentiation between malignant and benign liver lesions. Assessment of conventional ADC values leads to similar results when using b-values below 1000 s/mm2 for DKI calculation.