Whole-body tumor segmentation from FDG-PET/CT: Leveraging a segmentation prior from tissue-wise projections.

: Accurate tumor detection and quantification are important for optimized therapy planning and evaluation. Total tumor burden is also an appealing biomarker for clinical trials. Manual examination and annotation of oncologic PET/CT is labor-intensive and demands a high level of expertise.

Predicting the Antifungal Activity of Small Organic Compounds on Mold using Molecular Dynamics Simulations.

Atomistic models of the plasma membrane of the pathogenic mold are developed. These models are described with an empirical molecular mechanical (MM) force field in combination with molecular dynamics (MD) simulations. The solvated plasma membrane models are brought into contact with 35 small organic compounds to observe their impact on a variety of membrane properties.

A detailed analysis of body composition in relation to cardiopulmonary exercise test indices.

A cardiopulmonary exercise test (CPET) is a test assessing an individual's physiological response during exercise. Results may be affected by body composition, which is best evaluated through imaging techniques like magnetic resonance imaging (MRI). The aim of this study was to assess relationships between body composition and indices obtained from CPET.

Deep learning-based dose prediction for magnetic resonance-guided prostate radiotherapy.

Background: Daily adaptive radiotherapy, as performed with the Elekta Unity MR-Linac, requires choosing between different adaptation methods, namely ATP (Adapt to Position) and ATS (Adapt to Shape), where the latter requires daily re-contouring to obtain a dose plan tailored to the daily anatomy. These steps are inherently resource-intensive, and quickly predicting the dose distribution and the dosimetric evaluation criteria while the patient is on the table could facilitate a fast selection of adaptation method and decrease the treatment times.

Purpose: In this work, we aimed to develop a deep-learning-based dose-prediction pipeline for prostate MR-Linac treatments.

Classification of rotation-invariant biomedical images using equivariant neural networks.

Transmission electron microscopy (TEM) is an imaging technique used to visualize and analyze nano-sized structures and objects such as virus particles. Light microscopy can be used to diagnose diseases or characterize e.g.

Metabolic profiling of galectin-1 and galectin-3: a cross-sectional, multi-omics, association study.

Objectives: Experimental studies indicate a role for galectin-1 and galectin-3 in metabolic disease, but clinical evidence from larger populations is limited.

Methods: We measured circulating levels of galectin-1 and galectin-3 in the Prospective investigation of Obesity, Energy and Metabolism (POEM) study, participants (n = 502, all aged 50 years) and characterized the individual association profiles with metabolic markers, including clinical measures, metabolomics, adipose tissue distribution (Imiomics) and proteomics.

Results: Galectin-1 and galectin-3 were associated with fatty acids, lipoproteins and triglycerides including lipid measurements in the metabolomics analysis adjusted for body mass index (BMI).

Streamlining neuroradiology workflow with AI for improved cerebrovascular structure monitoring.

Radiological imaging to examine intracranial blood vessels is critical for preoperative planning and postoperative follow-up. Automated segmentation of cerebrovascular anatomy from Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) can provide radiologists with a more detailed and precise view of these vessels. This paper introduces a domain generalized artificial intelligence (AI) solution for volumetric monitoring of cerebrovascular structures from multi-center MRAs.

Voxel-wise body composition analysis using image registration of a three-slice CT imaging protocol: methodology and proof-of-concept studies.

Background: Computed tomography (CT) is an imaging modality commonly used for studies of internal body structures and very useful for detailed studies of body composition. The aim of this study was to develop and evaluate a fully automatic image registration framework for inter-subject CT slice registration. The aim was also to use the results, in a set of proof-of-concept studies, for voxel-wise statistical body composition analysis (Imiomics) of correlations between imaging and non-imaging data.

Interactions and effects of a stannous-containing sodium fluoride dentifrice on oral pathogens and the oral microbiome.

Numerous studies have investigated the effects of stannous ions on specific microbes and their efficacy in reducing dental plaque. Nonetheless, our understanding of their impact on the oral microbiome is still a subject of ongoing exploration. Therefore, this study sought to evaluate the effects of a stannous-containing sodium fluoride dentifrice in comparison to a zinc-containing sodium fluoride dentifrice and a control group on intact, healthy oral biofilms.

Improved automated tumor segmentation in whole-body 3D scans using multi-directional 2D projection-based priors.

Early cancer detection, guided by whole-body imaging, is important for the overall survival and well-being of the patients. While various computer-assisted systems have been developed to expedite and enhance cancer diagnostics and longitudinal monitoring, the detection and segmentation of tumors, especially from whole-body scans, remain challenging. To address this, we propose a novel end-to-end automated framework that first generates a tumor probability distribution map (TPDM), incorporating prior information about the tumor characteristics (e.

Automatic segmentation of large-scale CT image datasets for detailed body composition analysis.

Background: Body composition (BC) is an important factor in determining the risk of type 2-diabetes and cardiovascular disease. Computed tomography (CT) is a useful imaging technique for studying BC, however manual segmentation of CT images is time-consuming and subjective. The purpose of this study is to develop and evaluate fully automated segmentation techniques applicable to a 3-slice CT imaging protocol, consisting of single slices at the level of the liver, abdomen, and thigh, allowing detailed analysis of numerous tissues and organs.

A whole-body diffusion MRI normal atlas: development, evaluation and initial use.

Background: Statistical atlases can provide population-based descriptions of healthy volunteers and/or patients and can be used for region- and voxel-based analysis. This work aims to develop whole-body diffusion atlases of healthy volunteers scanned at 1.5T and 3T.

Language trees with sampled ancestors support a hybrid model for the origin of Indo-European languages.

The origins of the Indo-European language family are hotly disputed. Bayesian phylogenetic analyses of core vocabulary have produced conflicting results, with some supporting a farming expansion out of Anatolia ~9000 years before present (yr B.P.

Multi-Level Residual Dual Attention Network for Major Cerebral Arteries Segmentation in MRA Toward Diagnosis of Cerebrovascular Disorders.

Segmentation of major brain vessels is very important for the diagnosis of cerebrovascular disorders and subsequent surgical planning. Vessel segmentation is an important preprocessing step for a wide range of algorithms for the automatic diagnosis or treatment of several vascular pathologies and as such, it is valuable to have a well-performing vascular segmentation pipeline. In this article, we propose an end-to-end multiscale residual dual attention deep neural network for resilient major brain vessel segmentation.

"The challenge is the complexity" - A qualitative study about decision-making in advanced lung cancer treatment.

Introduction: The value of shared decision-making and decision aids (DA) has been well documented yet remain difficult to integrate into clinical practice. We wanted to investigate needs and challenges regarding decision-making about advanced lung cancer treatment after first-line therapy, focusing on DA applicability.

Methods: Qualitative data from separate, semi-structured focus groups with patients/relatives and healthcare professionals were analysed using systematic text condensation.

PhytoOracle: Scalable, modular phenomics data processing pipelines.

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds.

Translating tools and indicators in territorial RRI.

Introduction: By a series of calls within the Horizon 2020 framework programme, the EU funded projects intended to deploy Responsible Research and Innovation (RRI) at a territorial level, in regional research and innovation ecosystems. This paper presents efforts to document and evaluate the achievements in TRANSFORM, one of these projects.

Methods: Evaluative inquiry and theoretical reasoning.

An image registration method for voxel-wise analysis of whole-body oncological PET-CT.

Whole-body positron emission tomography-computed tomography (PET-CT) imaging in oncology provides comprehensive information of each patient's disease status. However, image interpretation of volumetric data is a complex and time-consuming task. In this work, an image registration method targeted towards computer-aided voxel-wise analysis of whole-body PET-CT data was developed.

Erratum for: MIMIR: Deep Regression for Automated Analysis of UK Biobank MRI Scans.

[This corrects the article DOI: 10.1148/ryai.210178.

Improved geometric accuracy of whole body diffusion-weighted imaging at 1.5T and 3T using reverse polarity gradients.

Whole body diffusion-weighted imaging (WB-DWI) is increasingly used in oncological applications, but suffers from misalignments due to susceptibility-induced geometric distortion. As such, DWI and structural images acquired in the same scan session are not geometrically aligned, leading to difficulties in e.g.

Patient specific deep learning based segmentation for magnetic resonance guided prostate radiotherapy.

Background And Purpose: Treatments on combined Magnetic Resonance (MR) scanners and Linear Accelerators (Linacs) for radiotherapy, called MR-Linacs, often require daily contouring. Currently, deformable image registration (DIR) algorithms propagate contours from reference scans, however large shape and size changes can be troublesome. Artificial neural network (ANN) based contouring may alleviate this issue, however generally requires large datasets for training.

MIMIR: Deep Regression for Automated Analysis of UK Biobank MRI Scans.

UK Biobank (UKB) has recruited more than 500 000 volunteers from the United Kingdom, collecting health-related information on genetics, lifestyle, blood biochemistry, and more. Ongoing medical imaging of 100 000 participants with 70 000 follow-up sessions will yield up to 170 000 MRI scans, enabling image analysis of body composition, organs, and muscle. This study presents an experimental inference engine for automated analysis of UKB neck-to-knee body 1.

Serum levels of perfluoroalkyl substances (PFAS) and body composition - A cross-sectional study in a middle-aged population.

Background: It has been suggested that per- and polyfluoroalkyl substances (PFAS) are endocrine disruptors with a potential to influence fat mass.

Objective: The primary hypothesis tested was that we would find positive relationships for PFAS vs measures of adiposity.

Methods: In 321 subjects all aged 50 years in the POEM study, five PFAS (perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorohexane sulfonic acid (PFHxS), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA)) were measured in serum together with a Dual-energy X-ray absorptiometry (DXA) scan for determination of fat and lean mass.

Intrafractional motion models based on principal components in Magnetic Resonance guided prostate radiotherapy.

Background And Purpose: Devices that combine an MR-scanner with a Linac for radiotherapy, referred to as MR-Linac systems, introduce the possibility to acquire high resolution images prior and during treatment. Hence, there is a possibility to acquire individualised learning sets for motion models for each fraction and the construction of intrafractional motion models. We investigated the feasibility for a principal component analysis (PCA) based, intrafractional motion model of the male pelvic region.