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A key component in developing atrial digital twins (ADT) - virtual representations of patients' atria - is the accurate prescription of myocardial fibers which are essential for the tissue characterization. Due to the difficulty of reconstructing atrial fibers from medical imaging, a widely used strategy for fiber generation in ADT relies on mathematical models. Existing methodologies utilize semi-automatic approaches, are tailored to specific morphologies, and lack rigorous validation against imaging fiber data. In this study, we introduce a novel atrial Laplace-Dirichlet-Rule-Based Method (LDRBM) for prescribing highly detailed myofiber orientations and providing robust regional annotation in bi-atrial morphologies of any complexity. The robustness of our approach is verified in eight extremely detailed bi-atrial geometries, derived from a sub-millimeter Diffusion-Tensor-Magnetic-Resonance Imaging (DTMRI) human atrial fiber dataset. We validate the LDRBM by quantitatively recreating each of the DTMRI fiber architectures: a comprehensive comparison with DTMRI ground truth data is conducted, investigating differences between electrophysiology (EP) simulations provided by either LDRBM and DTMRI fibers. Finally, we demonstrate that the novel LDRBM outperforms current state-of-the-art (LDRBMs and Universal Atrial Coordinates) fiber models, confirming the exceptional accuracy of our methodology and the critical importance of incorporating detailed fiber orientations in EP simulations. Ultimately, this work represents a fundamental step towards the development of physics-based digital twins of the human atria, establishing a new standard for prescribing fibers in ADT.
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http://dx.doi.org/10.1016/j.compbiomed.2025.109774 | DOI Listing |
J Invest Dermatol
September 2025
Department of Dermatology, CHU Nice, University Côte d'Azur, Nice, France; C3M, INSERM U1065, University Côte d'Azur, Nice, France.
Nat Med
September 2025
Freelance writer, Toronto, Ontario, Canada.
Clin Transl Med
September 2025
Department of Computer Science and Biomedical Engineering, Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Graz, Austria.
Computational modeling and simulation are playing an increasingly important role in oncology, bridging biological research, data science and clinical practice to better understand cancer complexity and inform therapeutic development. This special issue presents recent advances in multiscale modeling, artificial intelligence-driven systems, digital twins, and in silico trials, illustrating the evolving potential of computational tools to support innovation from bench to bedside. Together, these contributions outline a future in which precision medicine, adaptive therapies and personalized diagnostics are guided by integrative and predictive modeling approaches.
View Article and Find Full Text PDFChaos
September 2025
Department of Information Physics and Computing, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.
The increasing energy consumption required for information processing has become a significant challenge, leading to growing interest in optical and optoelectronic reservoir computing as a more efficient alternative. Trained reservoir computers are especially suited for low-energy applications near the edge. However, the computational cost of training the reservoir output weights, particularly due to matrix operations, adds potentially unwanted complexity to the architecture.
View Article and Find Full Text PDFMed Phys
August 2025
GE HealthCare MICT, Stockholm, Sweden.
Background: Photon-counting computed tomography (CT) bears promise to substantially improve spectral and spatial resolution. One reason for the relatively slow evolution of photon-counting detectors in CT-the technology has been used in nuclear medicine and planar radiology for decades-is pulse pileup, that is, the random staggering of pulses, resulting in count loss and spectral distortion, which in turn cause image bias and reduced contrast-to-noise ratio (CNR). The deterministic effects of pileup can be mitigated with a pileup-correction algorithm, but the loss of CNR cannot be recovered, and must be minimized by hardware design.
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