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.

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 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.

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.

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.

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 Depicts Immune Checkpoint Inhibitor-induced Myocarditis: A Prospective Study.

Background Immune checkpoint inhibitors (ICIs) for cancer treatment are associated with a spectrum of immune-related adverse events, including ICI-induced myocarditis; however, the extent of subclinical acute cardiac effects related to ICI treatment is unclear. Purpose To explore the extent of cardiac injury and inflammation related to ICI therapy that can be detected with use of cardiac MRI. Materials and Methods In this prospective study from November 2019 to April 2021, oncologic participants, without known underlying structural heart disease or cardiac symptoms, underwent multiparametric cardiac MRI before planned ICI therapy (baseline) and 3 months after starting ICI therapy (follow-up). The cardiac MRI protocol incorporated assessment of cardiac function, including systolic myocardial strain, myocardial edema, late gadolinium enhancement (LGE), T1 and T2 relaxation times, and extracellular volume fraction. The paired t test, Wilcoxon signed-rank test, and McNemar test were used for intraindividual comparisons. Results Twenty-two participants (mean age ± standard deviation, 65 years ± 14; 13 men) were evaluated, receiving a median of four infusions of ICI therapy (interquartile range, four to six infusions). Compared with baseline MRI, participants displayed increased markers of diffuse myocardial edema at follow-up (T1 relaxation time, 972 msec ± 26 vs 1006 msec ± 36 [P < .001]; T2 relaxation time, 54 msec ± 3 vs 58 msec ± 4 [P < .001]; T2 signal intensity ratio, 1.5 ± 0.3 vs 1.7 ± 0.3 [P = .03]). Left ventricular average systolic longitudinal strain had decreased at follow-up MRI (-23.4% ± 4.8 vs -19.6% ± 5.1, respectively; P = .005). New nonischemic LGE lesions were prevalent in two of 22 participants (9%). Compared with baseline, small pericardial effusions were more evident at follow-up (one of 22 participants [5%] vs 10 of 22 [45%]; P = .004). Conclusion In participants who received immune checkpoint inhibitor therapy for cancer treatment, follow-up cardiac MRI scans showed signs of systolic dysfunction and increased parameters of myocardial edema and inflammation. © 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.

Magnetic resonance parametric mapping of the spleen for non-invasive assessment of portal hypertension.

OBJECTIVES: In patients with advanced liver disease, portal hypertension is an important risk factor, leading to complications such as esophageal variceal bleeding, ascites, and hepatic encephalopathy. This study aimed to determine the diagnostic value of T1 and T2 mapping and extracellular volume fraction (ECV) for the non-invasive assessment of portal hypertension. METHODS: In this prospective study, 50 participants (33 patients with indication for trans-jugular intrahepatic portosystemic shunt (TIPS) and 17 healthy volunteers) underwent MRI. The derivation and validation cohorts included 40 and 10 participants, respectively. T1 and T2 relaxation times and ECV of the liver and the spleen were assessed using quantitative mapping techniques. Direct hepatic venous pressure gradient (HVPG) and portal pressure measurements were performed during TIPS procedure. ROC analysis was performed to compare diagnostic performance. RESULTS: Splenic ECV correlated with portal pressure (r = 0.72; p < 0.001) and direct HVPG (r = 0.50; p = 0.003). No significant correlations were found between native splenic T1 and T2 relaxation times with portal pressure measurements (p > 0.05, respectively). In the derivation cohort, splenic ECV revealed a perfect diagnostic performance with an AUC of 1.000 for the identification of clinically significant portal hypertension (direct HVPG ≥ 10 mmHg) and outperformed other parameters: hepatic T2 (AUC, 0.731), splenic T2 (AUC, 0.736), and splenic native T1 (AUC, 0.806) (p < 0.05, respectively). The diagnostic performance of mapping parameters was comparable in the validation cohort. CONCLUSION: Splenic ECV was associated with portal pressure measurements in patients with advanced liver disease. Future studies should explore the diagnostic value of parametric mapping accross a broader range of pressure values. KEY POINTS: • Non-invasive assessment and monitoring of portal hypertension is an area of unmet interest. • Splenic extracellular volume fraction is strongly associated with portal pressure in patients with end-stage liver disease. • Quantitative splenic and hepatic MRI-derived parameters have a potential to become a new non-invasive diagnostic parameter to assess and monitor portal pressure.

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.

Multiparametric cardiac magnetic resonance imaging in pediatric and adolescent patients with acute myocarditis.

BACKGROUND: The diagnostic value of cardiac magnetic resonance imaging (MRI) employing the 2018 Lake Louise criteria in pediatric and adolescent patients with acute myocarditis is undefined. OBJECTIVE: To evaluate the diagnostic value of the Lake Louise criteria in pediatric and adolescent patients with suspected acute myocarditis and to show the utility of cardiac MRI for follow-up in this patient cohort. MATERIALS AND METHODS: Forty-three patients (age range: 8-21 years) with suspected acute myocarditis and 13 control patients who underwent cardiac MRI were retrospectively analyzed. T2-weighted and late gadolinium enhancement imaging were performed in all patients. T1 and T2 mapping were available in 26/43 patients (60%). The Lake Louise criteria were assessed. In 27/43 patients (63%), cardiac MRI follow-up was available. Receiver operating characteristic analysis, Pearson's correlation coefficient and paired Student's t-test were used for statistical analysis. RESULTS: In the total cohort, the Lake Louise criteria achieved a sensitivity of 86% (95% confidence interval [CI]: 72-95%) and a specificity of 100% (95% CI: 79-100%) for the diagnosis of acute myocarditis. In the subgroup of patients with available mapping parameters, the diagnostic performance of the Lake Louise criteria was higher when mapping parameters were implemented into the score (area under the receiver operating characteristic curve: 0.944 vs. 0.870; P=0.033). T2 relaxation times were higher in patients with admission to the intermediate care unit and were associated with the length of intermediate care unit stay (r=0.879, P=0.049). Cardiac MRI markers of active inflammation decreased on follow-up examinations (e.g., T1 relaxation times: 1,032±39 ms vs. 975±33 ms, P<0.001; T2 relaxation times: 58±5 ms vs. 54±5 ms, P=0.003). CONCLUSION: The Lake Louise criteria have a high diagnostic performance for the diagnosis of acute myocarditis and are a valuable tool for follow-up in pediatric and adolescent patients. The mapping techniques enhance the diagnostic performance of the 2018 Lake Louise criteria.

MRI Assessment of Chylous and Nonchylous Effusions: Use of Multipoint Dixon Fat Quantification.

Background Diagnosis of chylous effusions normally requires invasive paracentesis. Purpose To assess whether MRI with multipoint Dixon fat quantification allows for noninvasive differentiation of chylous and nonchylous ascites and pleural effusions. Materials and Methods Phantom, ex vivo, and in vivo MRI examinations were performed by using a commercially available multipoint Dixon pulse sequence with a 1.5-T MRI system. Fat fraction values were measured with a region of interest-based approach on reconstructed maps. For phantom evaluation, eight titrated fatty fluid solutions (nonhuman samples) with varying triglyceride content (145-19 000 mg/dL [1.64-214.7 mmol/L]) were examined. For ex vivo evaluation, 15 chylous and five nonchylous study participant fluid samples were examined. In a prospective study performed from June 2016 to February 2018, 29 study participants with known chylous (n = 17) and nonchylous (n = 12) effusions were evaluated with MRI. All clinical samples underwent laboratory testing for triglyceride level, total protein level, white blood cells, and red blood cells. Laboratory values were correlated with fat fraction values; the optimal fat fraction threshold was determined to differentiate chylous and nonchylous fluids. Results Phantom analysis showed that fat fraction values correlated with triglyceride content (r = 0.99, P < .001). In ex vivo studies, multipoint Dixon-derived fat fraction was higher in chylous versus nonchylous fluids (mean, 2.5% ± 1.2 [standard deviation] vs 0.8% ± 0.2; P = .001). Fat fraction was correlated with triglyceride content (r = 0.96, P < .001). For in vivo studies, fat fraction was greater for chylous versus nonchylous fluids (mean, 6.2% ± 4.3 vs 0.6% ± 0.6; P < .001). In vivo fat fraction was correlated with triglyceride content (r = 0.96, P < .001). Use of a fat fraction cutoff value greater than 1.8% yielded a sensitivity of 14 of 17 (82% [95% confidence interval (CI): 57%, 97%]) and a specificity of 12 of 12 (100% [95% CI: 74%, 100%]) for differentiation of chylous and nonchylous effusions. Conclusion MRI can help identify chylous versus nonchylous ascites and pleural effusions through use of multipoint Dixon fat quantification. © RSNA, 2020 Online supplemental material is available for this article.

Myocardial Fibrosis and Inflammation in Liver Cirrhosis: MRI Study of the Liver-Heart Axis.

Background Cardiac involvement in liver cirrhosis in the absence of underlying cardiac disease is termed cirrhotic cardiomyopathy. The pathophysiology of this condition is still poorly understood. Purpose To investigate the extent of subclinical imaging changes in terms of fibrosis and inflammation and to explore the relationship between the severity of liver disease and the degree of myocardial involvement. Materials and Methods In this prospective study from November 2018 to December 2019, participants with liver cirrhosis and healthy control participants underwent hepatic and cardiac MRI. The multiparametric scan protocol assessed hepatic (T1 and T2 relaxation times, extracellular volume [ECV], and MR elastography-based liver stiffness) and cardiac (T1 and T2 relaxation times, ECV, myocardial edema, late gadolinium enhancement [LGE], and myocardial strain) parameters. Student t tests, one-way analysis of variance, Pearson correlation, and multivariable binary regression analysis were used for statistical analyses. Results A total of 42 participants with liver cirrhosis (mean age ± standard deviation, 57 years ± 11; 23 men) and 18 control participants (mean age, 54 years ± 19; 11 men) were evaluated. Compared with control participants, the participants with liver cirrhosis displayed reduced longitudinal strain and elevated markers of myocardial disease (T1 and T2 relaxation times, ECV, and qualitative and quantitative LGE). Myocardial T1 (978 msec ± 23 vs 1006 msec ± 29 vs 1044 msec ± 14; P < .001) and T2 relaxation times (56 msec ± 4 vs 59 msec ± 3 vs 62 msec ± 8; P = .04) and ECV (30% ± 5 vs 33% ± 5 vs 38% ± 7; P = .009) were higher depending on Child-Pugh class (A vs B vs C). Positive LGE lesions (three of 11 [27%] vs 10 of 19 [53%] vs nine of 11 [82%]; P = .04) were more prevalent in advanced Child-Pugh classes. MR elastography-based liver stiffness was an independent predictor for LGE (odds ratio, 1.6; 95% confidence interval: 1.2%, 2.1%; P = .004) and correlated with quantitative LGE (r = 0.67; P < .001), myocardial T1 relaxation times (r = 0.55; P < .001), and ECV (r = 0.39; P = .01). Conclusion In participants with liver cirrhosis, systolic dysfunction and elevated parameters of myocardial edema and fibrosis were observed at MRI, which were more abnormal with greater severity of liver disease. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by de Roos and Lamb in this issue.

Influence of hydration status on cardiovascular magnetic resonance myocardial T1 and T2 relaxation time assessment: an intraindividual study in healthy subjects.

BACKGROUND: Myocardial native T1 and T2 relaxation time mapping are sensitive to pathological increase of myocardial water content (e.g. myocardial edema). However, the influence of physiological hydration changes as a possible confounder of relaxation time assessment has not been studied. The purpose of this study was to evaluate, whether changes in myocardial water content due to dehydration and hydration might alter myocardial relaxation times in healthy subjects. METHODS: A total of 36 cardiovascular magnetic resonance (CMR) scans were performed in 12 healthy subjects (5 men, 25.8 ± 3.2 years). Subjects underwent three successive CMR scans: (1) baseline scan, (2) dehydration scan after 12 h of fasting (no food or water), (3) hydration scan after hydration. CMR scans were performed for the assessment of myocardial native T1 and T2 relaxation times and cardiac function. For multiple comparisons, repeated measures ANOVA or the Friedman test was used. RESULTS: There was no change in systolic blood pressure or left ventricular ejection fraction between CMR scans (P > 0.05, respectively). T1 relaxation times were significantly reduced with dehydration (987 ± 27 ms [baseline] vs. 968 ± 29 ms [dehydration] vs. 986 ± 28 ms [hydration]; P = 0.006). Similar results were observed for T2 relaxation times (52.9 ± 1.8 ms [baseline] vs. 51.5 ± 2.0 ms [dehydration] vs. 52.2 ± 1.9 ms [hydration]; P = 0.020). CONCLUSIONS: Dehydration may lead to significant alterations in relaxation times and thereby may influence precise, repeatable and comparable assessment of native T1 and T2 relaxation times. Hydration status should be recognized as new potential confounder of native T1 and T2 relaxation time assessment in clinical routine.

Feature-tracking-based strain analysis - a comparison of tracking algorithms.

PURPOSE: Optical flow feature-tracking (FT) strain assessment is increasingly being employed scientifically and clinically. Several software packages, employing different algorithms, enable computation of FT-derived strains. The aim of this study is to investigate the impact of the underlying algorithm on the validity and robustness of FT-derived strain results. MATERIAL AND METHODS: CSPAMM and SSFP cine sequences were acquired in 30 subjects (15 patients with aortic stenosis and associated secondary hypertrophic cardiomyopathy, and 15 controls) in identical midventricular short-axis locations. Global peak systolic circumferential strain (PSCS) was calculated using tagging and feature-tracking software with different algorithms (non-rigid, elastic image registration, and blood myocardial border tracing). Intermodality agreement and intra- as well inter-observer variability were assessed. RESULTS: Intermodality/inter-algorithm comparison for global PSCS using Friedman's test revealed statistically significant differences (tagging vs. blood myocardial border tracing algorithm). Intermodality assessment revealed the highest correlation between tagging and non-rigid, elastic image registration (r = 0.84), while correlation between tagging and blood myocardial border tracing (r = 0.36) and between the two feature-tracking software packages (r = 0.5) were considerably lower. CONCLUSIONS: The type of algorithm employed during feature-tracking strain assessment has a significant impact on the results. The non-rigid, elastic image registration algorithm produces more precise and reproducible results than the blood myocardium tracing algorithm.

Effects of a 24-hr-shift-related short-term sleep deprivation on cardiac function: A cardiac magnetic resonance-based study.

Fatigue and sleep deprivation are common phenomena, especially among medical professionals and shift workers. Studies have proven that short episodes of sleep deprivation can lead to sympathetic hyperactivity with an elevation in blood pressure, heart rate, and an increased secretion of stress hormones (e.g. cortisol, noradrenaline, thyroid hormones). In this study investigating cardiac strain in 20 healthy subjects undergoing short-term sleep deprivation, it could be shown for the first time that 24-hr-shift-related short-term sleep deprivation leads to a significant increase in cardiac contractility, blood pressure, heart rate and stress hormone secretion. These findings may help better understand how workload and shift duration affect public health, and lay the foundation for further investigations.

Fat-free muscle area measured by magnetic resonance imaging predicts overall survival of patients undergoing radioembolization of colorectal cancer liver metastases.

OBJECTIVES: To investigate the clinical potential of fat-free muscle area (FFMA) to predict outcome in patients with liver-predominant metastatic colorectal cancer (mCRC) undergoing radioembolization (RE) with 90Yttrium microspheres. METHODS: Patients with mCRC who underwent RE in our center were included in this retrospective study. All patients received liver magnetic resonance imaging including standard T2-weighted images. The total erector spinae muscle area and the intramuscular adipose tissue area were measured at the level of the origin of the superior mesenteric artery and subtracted to calculate FFMA. Cutoff values for definition of low FFMA were 3644 mm2 in men and 2825 mm2 in women. The main outcome was overall survival (OS). For survival analysis, the Kaplan-Meier method and Cox regressions comparing various clinic-oncological parameters which potentially may affect OS were performed. RESULTS: Seventy-seven patients (28 female, mean age 60 ± 11 years) were analyzed. Mean time between MRI and the following RE was 17 ± 31 days. Median OS after RE was 178 days. Patients with low FFMA had significantly shortened OS compared to patients with high FFMA (median OS: 128 vs. 273 days, p = 0.017). On multivariate Cox regression analysis, OS was best predicted by FFMA (hazard ratio (HR) 2.652; p < 0.001). Baseline bilirubin (HR 1.875; p = 0.030), pattern of tumor manifestation (HR 1.679; p = 0.001), and model of endstage liver disease (MELD) score (HR 1.164; p < 0.001) were also significantly associated with OS. CONCLUSIONS: FFMA was associated with OS in patients receiving RE for treatment of mCRC and might be a new prognostic biomarker for survival prognosis. KEY POINTS: • Fat-free muscle area (FFMA) as a measure of lean muscle area predicts survival in metastatic colorectal liver cancer following radioembolization. • FFMA can easily be assessed from routine pre-interventional liver magnetic resonance imaging. • FFMA might be a new promising biomarker for assessment of sarcopenia.