Cardiovascular drug therapy in adults is steadily progressing. However, the extent to which children with heart disease in Switzerland benefit from this progress remains unknown. This survey aimed to investigate the current outpatient prescribing practices among paediatric cardiologists in Switzerland. We conducted a cross-sectional survey among Swiss paediatric cardiologists. The physicians were asked to state how often they administer drugs on a pre-defined list for the following indications: heart failure, arrhythmia and thromboembolism. Forty-three (56
Magnetic resonance imaging (MRI) has played a crucial role in fetal neurodevelopmental research. Structural annotations of MR images are an important step for quantitative analysis of the developing human brain, with Deep Learning providing an automated alternative for this otherwise tedious manual process. However, segmentation performances of Convolutional Neural Networks often suffer from domain shift, where the network fails when applied to subjects that deviate from the distribution with which it is trained on. In this work, we aim to train networks capable of automatically segmenting fetal brain MRIs with a wide range of domain shifts pertaining to differences in subject physiology and acquisition environments, in particular shape-based differences commonly observed in pathological cases. We introduce a novel data-driven train-time sampling strategy that seeks to fully exploit the diversity of a given training dataset to enhance the domain generalizability of the trained networks. We adapted our sampler, together with other existing data augmentation techniques, to the SynthSeg framework, a generator that utilizes domain randomization to generate diverse training data. We ran thorough experimentations and ablation studies on a wide range of training/testing data to test the validity of the approaches. Our networks achieved notable improvements in the segmentation quality on testing subjects with intense anatomical abnormalities (p < 1e-4), though at the cost of a slighter decrease in performance in cases with fewer abnormalities. Our work also lays the foundation for future works on creating and adapting data-driven sampling strategies for other training pipelines.
Intravoxel incoherent motion (IVIM) MRI allows for simultaneous assessment of tissue microcirculation (perfusion) and diffusion of water. In single-center studies, IVIM has shown great potential for diagnosis, treatment outcome prediction, and treatment monitoring for many different diseases and organs. However, heterogeneity in data acquisition protocols, pre-processing pipelines, and post-processing routines yields differences in reported IVIM parameters, which has constrained large-scale deployment of IVIM. Moreover, deploying IVIM protocols and analysis typically requires technical expertise, further challenging wider use, especially for clinicians. In this consensus paper, to accelerate the deployment of IVIM, we provide recommendations and harmonize protocols for brain, breast, kidney, liver, muscle, and pancreas IVIM studies. For this goal we organized multiple questionnaires and held a dedicated workshop. To ensure a level of standardized, reproducible results, without restricting innovation, we suggest a small subset of b-values to always be measured and analyzed separately, and to which more extensive b-value sampling can be added for advanced investigations. We further introduce detailed recommendations on acquisition protocols and analysis pipelines. To increase consistency, repeatability, and reproducibility, we highly recommend that these protocols and pipelines be deployed by scientists and clinicians for IVIM studies. For advanced users who desire different protocols or analysis approaches, we suggest adding results from our suggested protocols and analysis pipeline in the supplemental part of their paper to enable retrospective studies.
Specific learning disabilities are neurodevelopmental conditions characterized by persistent academic challenges. This narrative review synthesizes behavioral and neuroimaging evidence from 48 peer-reviewed studies on working memory impairments in reading, written expression or mathematics difficulties. Key findings reveal domain-specific deficits: phonological loop impairments dominate in reading difficulties, visuospatial sketchpad in math difficulties, and central executive dysfunction, especially in comorbid reading and mathematics disorder. This review addresses inconsistencies in prior literature due to methodological heterogeneity in task selection. By analyzing specific working memory tasks, we reveal that divergent findings stem from inconsistent task frameworks. Neuroimaging evidence links task-specific working memory profiles to atypical activation in language-related (e.g., angular gyrus) and number-processing (e.g., intraparietal sulcus) networks. This review is the first to attribute working memory inconsistencies in specific learning disabilities to task heterogeneity, offering a unified framework for research and clinical practice. It challenges IQ-based diagnostics, advocating working memory profiling for targeted interventions.
