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.

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.

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.

Variability and magnitude of brain glutamate levels in schizophrenia: a meta and mega-analysis.

Glutamatergic dysfunction is implicated in schizophrenia pathoaetiology, but this may vary in extent between patients. It is unclear whether inter-individual variability in glutamate is greater in schizophrenia than the general population. We conducted meta-analyses to assess (1) variability of glutamate measures in patients relative to controls (log coefficient of variation ratio: CVR); (2) standardised mean differences (SMD) using Hedges g; (3) modal distribution of individual-level glutamate data (Hartigan's unimodality dip test).

Reduction of Gadolinium-Based Contrast Agents in MRI Using Convolutional Neural Networks and Different Input Protocols: Limited Interchangeability of Synthesized Sequences With Original Full-Dose Images Despite Excellent Quantitative Performance.

Objectives: The purpose of this study was to implement a state-of-the-art convolutional neural network used to synthesize artificial T1-weighted (T1w) full-dose images from corresponding noncontrast and low-dose images (using various settings of input sequences) and test its performance on a patient population acquired prospectively.

Materials And Methods: In this monocentric, institutional review board-approved study, a total of 138 participants were included who received an adapted imaging protocol with acquisition of a T1w low dose after administration of 10% of the standard dose and acquisition of a T1w full dose after administration of the remaining 90% of the standard dose of a gadolinium-containing contrast agent. A total of 83 participants formed the training sample (51.

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.

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 supports the differentiation of alcoholic and other-than-alcoholic cirrhosis based on MRI.

Although CT and MRI are standard procedures in cirrhosis diagnosis, differentiation of etiology based on imaging is not established. This proof-of-concept study explores the potential of deep learning (DL) to support imaging-based differentiation of the etiology of liver cirrhosis. This retrospective, monocentric study included 465 patients with confirmed diagnosis of (a) alcoholic (n = 221) and (b) other-than-alcoholic (n = 244) cirrhosis.

Comparison of different ROI analysis methods for liver lesion characterization with simplified intravoxel incoherent motion (IVIM).

This study investigated the impact of different ROI placement and analysis methods on the diagnostic performance of simplified IVIM-DWI for differentiating liver lesions. 1.5/3.

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.

Simplified intravoxel incoherent motion diffusion-weighted MRI of liver lesions: feasibility of combined two-colour index maps.

Background: To evaluate the feasibility of two-colour index maps containing combined diffusion and perfusion information from simplified intravoxel incoherent motion (IVIM) for liver lesion malignancy assessment.

Methods: Diffusion-weighted data from a respiratory-gated 1.5-T magnetic resonance sequence were analysed in 109 patients with liver lesions.

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.

Feasibility of CT-derived myocardial strain measurement in patients with advanced cardiac valve disease.

To explore the feasibility of CT-derived myocardial strain measurement in patients with advanced cardiac valve disease and to compare it to strain measurements derived from transthoracic echocardiography (TTE). 43 consecutive patients with advanced cardiac valve disease and clinically indicated retrospectively gated cardiac CTs were retrospectively analyzed. The longitudinal, circumferential as well as radial systolic strain were determined in all patients utilizing a commercially available CT strain software.

Association of Age, Antipsychotic Medication, and Symptom Severity in Schizophrenia With Proton Magnetic Resonance Spectroscopy Brain Glutamate Level: A Mega-analysis of Individual Participant-Level Data.

Importance: Proton magnetic resonance spectroscopy (1H-MRS) studies indicate that altered brain glutamatergic function may be associated with the pathophysiology of schizophrenia and the response to antipsychotic treatment. However, the association of altered glutamatergic function with clinical and demographic factors is unclear.

Objective: To assess the associations of age, symptom severity, level of functioning, and antipsychotic treatment with brain glutamatergic metabolites.

A pilot study of magnetic resonance fingerprinting in Parkinson's disease.

Parkinson's disease (PD) affects more than six million people, but reliable MRI biomarkers with which to diagnose patients have not been established. Magnetic resonance fingerprinting (MRF) is a recent quantitative technique that can provide relaxometric maps from a single sequence. The purpose of this study is to assess the potential of MRF to identify PD in patients and their disease severity, as well as to evaluate comfort during MRF.

Body composition analysis using CT and MRI: intra-individual intermodal comparison of muscle mass and myosteatosis.

Computed tomography (CT) and magnetic resonance imaging (MRI) can quantify muscle mass and quality. However, it is still unclear if CT and MRI derived measurements can be used interchangeable. In this prospective study, fifty consecutive participants of a cancer screening program underwent same day low-dose chest CT and MRI.

Fully Automated Segmentation of Connective Tissue Compartments for CT-Based Body Composition Analysis: A Deep Learning Approach.

Objective: Body composition comprises prognostic information in patients with various malignancies and can be opportunistically determined from routine computed tomography (CT) scans. However, accurate assessment of patients with alterations, for example, due to ascites or anasarca, and accurate identification of intermuscular fat remain challenging. In this study, we aimed to develop a fully automated and highly accurate segmentation tool for connective tissue compartments from abdominal CT scans using the open-source Convolutional Neural Network (CNN) DeepMedic.

Yttrium-90 radioembolization for hepatocellular carcinoma: Outcome prediction with MRI derived fat-free muscle area.

Purpose: Sarcopenia is associated with adverse outcomes in several gastrointestinal malignancies and liver cirrhosis. We aimed to study the utility of magnetic resonance imaging (MRI) derived fat-free muscle area (FFMA) to predict clinical outcome in patients receiving yttrium-90 radioembolization (RE) for treatment of hepatocellular carcinoma (HCC).

Methods: Fifty-eight patients with unresectable HCC and pre-interventional liver MRI undergoing salvage RE were retrospectively evaluated.

MR fingerprinting as a diagnostic tool in patients with frontotemporal lobe degeneration: A pilot study.

Several very rare forms of dementia are associated with characteristic focal atrophy predominantly of the frontal and/or temporal lobes and currently lack imaging solutions to monitor disease. Magnetic resonance fingerprinting (MRF) is a recently developed technique providing quantitative relaxivity maps and images with various tissue contrasts out of a single sequence acquisition. This pilot study explores the utility of MRF-based T1 and T2 mapping to discover focal differences in relaxation times between patients with frontotemporal lobe degenerative dementia and healthy controls.

Quantification of liver fibrosis: extracellular volume fraction using an MRI bolus-only technique in a rat animal model.

Background: To determine the utility of single-contrast-bolus hepatic extracellular volume (ECV) fraction measurement at different time points to detect and quantify hepatic fibrosis.

Methods: Different grades of liver fibrosis were induced in 23 male Sprague-Dawley rats by carbon-tetrachloride (CCl) intoxication. In ten control rats, no fibrosis was induced.

Evaluation of Exponential ADC (eADC) and Computed DWI (cDWI) for the Detection of Prostate Cancer.

Purpose: To directly compare different methods proposed for enhanced conspicuity and discriminability of prostate cancer on diffusion-weighted imaging (DWI) and to compare the results to original DWI images and conventional apparent diffusion coefficient (ADC) maps.

Materials And Methods: Clinical routine prostate DWI datasets (b = 0, 50, 800 s/mm², acquired at a field strength of 3 T) of 104 consecutive patients with subsequent MR-guided prostate biopsy were included in this retrospective study. For each dataset exponential ADC maps (eADC), computed DWI images (cDWI), and additionally eADC maps for computed b-values of 2000 and 3000 s/mm² were generated (c_eADC).

Quantification of Liver Fibrosis at T1 and T2 Mapping with Extracellular Volume Fraction MRI: Preclinical Results.

Purpose To evaluate MRI T1 and T2 mapping with calculation of extracellular volume (ECV) for diagnosis and grading of liver fibrosis. Materials and Methods Different grades of fibrosis were induced in 60 male Sprague-Dawley rats by bile duct ligation (BDL) and carbon-tetrachloride (CCl) intoxication. Portal pressure was measured invasively, whereas hepatic fibrosis was quantified by hydroxyproline content, Sirius red staining, and α smooth muscle actin staining.