Deep learning for opportunistic, end-to-end automated assessment of epicardial adipose tissue in pre-interventional, ECG-gated spiral computed tomography.

Objectives: Recently, epicardial adipose tissue (EAT) assessed by CT was identified as an independent mortality predictor in patients with various cardiac diseases. Our goal was to develop a deep learning pipeline for robust automatic EAT assessment in CT.

Methods: Contrast-enhanced ECG-gated cardiac and thoraco-abdominal spiral CT imaging from 1502 patients undergoing transcatheter aortic valve replacement (TAVR) was included.

Ultra-high gradient performance 3-Tesla MRI for super-fast and high-quality prostate imaging: initial experience.

Objectives: To implement and evaluate a super-fast and high-quality biparametric MRI (bpMRI) protocol for prostate imaging acquired at a new ultra-high gradient 3.0-T MRI system.

Methods: Participants with clinically suspected prostate cancer prospectively underwent a multiparametric MRI (mpMRI) on a new 3.

Deep Learning Virtual Contrast-Enhanced T1 Mapping for Contrast-Free Myocardial Extracellular Volume Assessment.

Background: The acquisition of contrast-enhanced T1 maps to calculate extracellular volume (ECV) requires contrast agent administration and is time consuming. This study investigates generative adversarial networks for contrast-free, virtual extracellular volume (vECV) by generating virtual contrast-enhanced T1 maps.

Methods And Results: This retrospective study includes 2518 registered native and contrast-enhanced T1 maps from 1000 patients who underwent cardiovascular magnetic resonance at 1.

The impact of lenvatinib on sarcopenia in patients with advanced unresectable hepatocellular carcinoma.

Lenvatinib is a multiple receptor tyrosine kinase inhibitor (TKI) approved for first-line treatment of patients with unresectable hepatocellular carcinoma (HCC). TKI are suspected of exacerbating muscle loss in patients with cancer. In this study, we analyze the role of muscle loss in patients with advanced HCC treated with lenvatinib.

Computer tomography-based assessment of perivascular adipose tissue in patients with abdominal aortic aneurysms.

This retrospective study investigates perivascular adipose tissue (PVAT) alterations in CT as a marker of inflammation in patients with abdominal aortic aneurysms (AAA). 100 abdominal CT scans of patients with abdominal aortic aneurysms and 100 age and sex matched controls without underlying aortic disease were included. Artificial Intelligence (AI) assisted segmentation of the aorta and the surrounding adipose tissue was performed.

[Artificial intelligence in diagnostic radiology for dose management : Advances and perspectives using the example of computed tomography].

Clinical-methodological Problem: Imaging procedures employing ionizing radiation require compliance with European directives and national regulations in order to protect patients. Each exposure must be indicated, individually adapted, and documented. Unacceptable dose exceedances must be detected and reported.

[Large language models from OpenAI, Google, Meta, X and Co. : The role of "closed" and "open" models in radiology].

Background: In 2023, the release of ChatGPT triggered an artificial intelligence (AI) boom. The underlying large language models (LLM) of the nonprofit organization "OpenAI" are not freely available under open-source licenses, which does not allow on-site implementation inside secure clinic networks. However, efforts are being made by open-source communities, start-ups and large tech companies to democratize the use of LLMs.

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

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.

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".

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.

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.

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.

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").

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.

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.

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.

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.

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.

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.

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.

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.

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/mm) were retrospectively analysed for 126 patients with malignant or benign breast lesions.

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.

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.