Laboratoire Traitement du Signal et de l'Image

Coronary CT angiography (CCTA) is an essential technique for clinical coronary assessment. However, the risks associated with ionizing radiation cannot be ignored, especially its stochastic effects, which increase the risk of cancer. Although it can effectively alleviate radiation problems, low-dose CCTA can reduce imaging quality and interfere with the diagnosis of the radiologist. Existing deep learning methods based on image restoration suffer from subtle structure degradation after noise suppression, leading to unclear coronary boundaries. Furthermore, in the absence of prior guidance on coronary location, subtle coronary branches will be lost after aggressive noise suppression and are difficult to restore successfully. To address the above issues, this paper proposes a novel Guided Refinement Network (GRN) model based on joint learning for restoring high-quality images from low-dose CCTA. GRN integrates coronary segmentation, which provides coronary location, into the denoising, and the two leverage mutual guidance for effective interaction and collaborative optimization. On the one hand, denoising provides images with lower noise levels for segmentation to assist in generating coronary masks. Furthermore, segmentation provides a prior coronary location for denoising, aiming to preserve and restore subtle coronary branches. GRN achieves noise suppression and subtle structure enhancement for low-dose CCTA imaging through joint denoising and segmentation, while also generating segmentation results with reference value. Quantitative and qualitative results show that GRN outperforms existing methods in noise suppression, subtle structure restoration, and visual perception improvement, and generates coronary masks that can serve as a reference for radiologists to assist in diagnosing coronary disease.

This work aims to identify the Key Research Areas for building and deploying the semantic interoperability framework for the Intensive Medicine Data Space in Europe. A set of European experts defined four research areas and associated challenges: i) to characterize the value of Common Data Models, vocabularies and standardized data elements for creating the foundation for the standardization of Intensive Care Unit (ICU) data sets, ii) to derive the Common Data Model ensuring seamless data interoperability supporting the querying and access to high-quality, multi-modal data located on federated nodes according to the FAIR principles, iii) to define and support the data standardization process enabling efficient secondary use of ICU data set during the execution of key decision support use cases through the Intensive Medicine Data Space in Europe and beyond, and iv) to define requirements for anonymization and pseudonymization for the good balance between data protection and innovation.

Dual-energy CT exploits the different attenuation characteristics of substances under different energy X-rays and collects high- and low-energy data from the same area to differentiate and quantify specific substances, which is now widely used in clinical diagnosis, disease monitoring, and other fields. If the object scanned during dual-energy CT imaging contains metallic material, the reconstructed image will suffer from metal artifacts. Metal artifacts in dual-energy CT result in surrounding tissue structures presenting erroneous CT values, blurred image details, and unclear border demarcation lines, which seriously affects the quality of the images as well as the accuracy of clinical diagnosis. To reduce metal artifacts in dual-energy CT, we combine material decomposition techniques with metal artifact reduction to explore metal artifact reduction methods applied to dual-energy CT.

Although women with severe symptomatic aortic stenosis have more complications than men when undergoing surgical valve replacement, they are under-represented in clinical trials. The Randomized researcH in womEn all comers wIth Aortic stenosis (RHEIA) trial investigates the balance of benefits and risks of transcatheter aortic valve implantation (TAVI) vs. surgery in women. Women were randomized 1:1 to transfemoral TAVI with a balloon-expandable valve or surgery. The primary composite endpoint was death, stroke, or (valve, procedure or heart failure related) rehospitalization at 1 year. Non-inferiority testing with a pre-specified 6% margin and superiority testing were performed in the as-treated population. At 48 European centres, 443 women underwent randomization, and 420 were treated as randomized. Mean age was 73 years, and the mean estimated surgical risk of death was 2.1% (Society of Thoracic Surgeons risk score). Kaplan-Meier estimates of the primary endpoint event rates at 1 year were 8.9% in the TAVI and 15.6% in the surgery group. This difference of -6.8% with an upper 95% confidence limit of -1.5% demonstrated the non-inferiority of TAVI (P < .001). The two-sided 95% confidence interval of -13.0% to -.5% further resulted in superiority (P = .034). The 1-year incidence of the primary endpoint components was: .9% with TAVI vs. 2.0% with surgery for death from any cause, 3.3% vs. 3.0% for stroke, and 5.8% vs. 11.4% for rehospitalization. Among women with severe aortic stenosis, the incidence of the composite of death, stroke, or rehospitalization at 1 year was lower with TAVI than with surgery. NCT04160130.

Crises in the operating room, often resulting from human factors, endangers patient safety. Simulation-based training to develop non-technical skills shows promise in managing these crises. This review examines the simulation techniques, targeted healthcare professionals, non-technical skills, crisis scenarios, and evaluation metrics used in operating room crisis management training. Systematic review. MEDLINE, APA PsycInfo and Web of Science databases were searched for peer-reviewed articles published between January 2004 and March 2024. This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The review protocol has been registered on the Open Science Framework (OSF) (https://osf.io/7bsc8). The inclusion criteria were as follows: (1) The study population comprised healthcare and medical professionals or students; (2) the intervention involved a simulated learning or training experience; (3) the outcomes focused on non-technical skills or crew resource management; (4) the training setting was the operating room (simulated or real); and (5) the learning scenarios depicted a crisis or an adverse event. This systematic review identified 29 eligible articles. The findings highlight the predominance of high-fidelity simulations, primarily targeting medical staff rather than nurses or other healthcare professionals. Training focused on communication, teamwork, situation awareness, problem solving, and decision making, with scenarios mostly addressing patient deterioration. Assessments reached up to Kirkpatrick's Level 3, demonstrating a positive training impact through learners' reactions and learning metrics rather than behavior and organizational results. Despite their effectiveness, current training practices exhibit limitations. Incorporating nurses and other paramedical staff in interprofessional training, as well as emphasizing team-related scenarios and evaluating behavioral changes in practice, could enhance training effectiveness. This has implications for interprofessional healthcare education and skills transfer to real-world settings, ultimately improving patient safety.