Privacy-ensuring Open-weights Large Language Models Are Competitive with Closed-weights GPT-4o in Extracting Chest Radiography Findings from Free-Text Reports.

Background Large-scale secondary use of clinical databases requires automated tools for retrospective extraction of structured content from free-text radiology reports. Purpose To share data and insights on the application of privacy-preserving open-weights large language models (LLMs) for reporting content extraction with comparison to standard rule-based systems and the closed-weights LLMs from OpenAI. Materials and Methods In this retrospective exploratory study conducted between May 2024 and September 2024, zero-shot prompting of 17 open-weights LLMs was preformed.

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.

Neural network for autonomous segmentation and volumetric assessment of clot and edema in acute and subacute intracerebral hemorrhages.

Introduction: Minimally-invasive surgical techniques for intracerebral hemorrhage (ICH) evacuation use imaging to guide the suction, lysing and/or drainage from the hemorrhage site via various designs. A previous international surgical study has shown that reduction of hematoma volume below 15 ml is indicative of improved long term patient outcomes. The study noted a need for tools to periodically visualize remaining clot during intervention to increase the likelihood of evacuating sufficient clot volumes without endangering rebleeds.

Transformer-based structuring of free-text radiology report databases.

Objectives: To provide insights for on-site development of transformer-based structuring of free-text report databases by investigating different labeling and pre-training strategies.

Methods: A total of 93,368 German chest X-ray reports from 20,912 intensive care unit (ICU) patients were included. Two labeling strategies were investigated to tag six findings of the attending radiologist.

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.

Multiscale Label-Free Imaging of Fibrillar Collagen in the Tumor Microenvironment.

With recent advances in cancer therapeutics, there is a great need for improved imaging methods for characterizing cancer onset and progression in a quantitative and actionable way. Collagen, the most abundant extracellular matrix protein in the tumor microenvironment (and the body in general), plays a multifaceted role, both hindering and promoting cancer invasion and progression. Collagen deposition can defend the tumor with immunosuppressive effects, while aligned collagen fiber structures can enable tumor cell migration, aiding invasion and metastasis.

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.

Real-time trajectory guide tracking for intraoperative MRI-guided neurosurgery.

Purpose: In current intraoperative MRI (IMRI) methods, an iterative approach is used to aim trajectory guides at intracerebral targets: image MR-visible features, determine current aim by fitting model to image, manipulate device, repeat. Infrequent updates are produced by such methods, compared to rapid optically tracked stereotaxy used in the operating room. Our goal was to develop a real-time interactive IMRI method for aiming.

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.

An Anthropomorphic Digital Reference Object (DRO) for Simulation and Analysis of Breast DCE MRI Techniques.

Advances in accelerated magnetic resonance imaging (MRI) continue to push the bounds on achievable spatial and temporal resolution while maintaining a clinically acceptable image quality. Validation tools, including numerical simulations, are needed to characterize the repeatability and reproducibility of such methods for use in quantitative imaging applications. We describe the development of a simulation framework for analyzing and optimizing accelerated MRI acquisition and reconstruction techniques used in dynamic contrast enhanced (DCE) breast imaging.

Convalescent plasma with a high level of virus-specific antibody effectively neutralizes SARS-CoV-2 variants of concern.

The ongoing evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants severely limits available effective monoclonal antibody therapies. Effective drugs are also supply limited. COVID-19 convalescent plasma (CCP) qualified for high antibody levels effectively reduces immunocompetent outpatient hospitalization.

High Viral Specific Antibody Convalescent Plasma Effectively Neutralizes SARS-CoV-2 Variants of Concern.

Unlabelled: The ongoing evolution of SARS-Co-V2 variants to omicron severely limits available effective monoclonal antibody therapies. Effective drugs are also supply limited. Covid-19 convalescent plasma (CCP) qualified for high antibody levels effectively reduces immunocompetent outpatient hospitalization.

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.

Autologous transplant therapy alleviates motor and depressive behaviors in parkinsonian monkeys.

Degeneration of dopamine (DA) neurons in the midbrain underlies the pathogenesis of Parkinson's disease (PD). Supplement of DA via L-DOPA alleviates motor symptoms but does not prevent the progressive loss of DA neurons. A large body of experimental studies, including those in nonhuman primates, demonstrates that transplantation of fetal mesencephalic tissues improves motor symptoms in animals, which culminated in open-label and double-blinded clinical trials of fetal tissue transplantation for PD.