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Deep Equilibrium Unfolding Learning for Noise Estimation and Removal in Optical Molecular Imaging.
Comput Med Imaging Graph
January 2025
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; National Key Laboratory of Kidney Diseases, Beijing 100853, China. Electronic address:
In clinical optical molecular imaging, the need for real-time high frame rates and low excitation doses to ensure patient safety inherently increases susceptibility to detection noise. Faced with the challenge of image degradation caused by severe noise, image denoising is essential for mitigating the trade-off between acquisition cost and image quality. However, prevailing deep learning methods exhibit uncontrollable and suboptimal performance with limited interpretability, primarily due to neglecting underlying physical model and frequency information.
View Article and Find Full Text PDFImpact of intravenous CT contrast agents on internal calibration techniques to determine trabecular BMD of the lumbar spine.
Eur J Radiol
January 2025
Department of Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany; Institute of Medical Physics, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany. Electronic address:
Objectives: Contrast agents are frequently administered in computed tomography (CT) scans used for opportunistic screening of osteoporosis. The objective of this study is to compare the impact of contrast-related bone mineral density (BMD) increase between phantom-based and internal CT calibration techniques.
Materials And Methods: Phantom-based and internal CT calibration techniques were used to determine trabecular BMD in 93 existing clinical CT scans of the lumbar spine of 34 subjects, scanned before and after administration of contrast agents.
Efficient Second Harmonic Generation via Plasmonic-Photonic Mode Matching in Hybrid Waveguide.
Nano Lett
January 2025
School of Physics and Technology, and Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, Wuhan University, Wuhan 430072, China.
Hybrid nonlinear plasmonic waveguides, characterized by a small mode area and large nonlinear susceptibility, present an intriguing and practical platform for the minimization of nonlinear photonic devices. Nevertheless, the intrinsic Ohmic loss associated with surface plasmon polaritons (SPPs) and modal dispersion imposes constraints on the effective interaction length and, consequently, the ultimate efficiency of nonlinear processes. In this study, we demonstrate an efficient second harmonic generation (SHG) within a hybrid plasmonic waveguide by leveraging SPP-like modes at the fundamental wave and photonic-like modes at the SHG under phase matching conditions.
View Article and Find Full Text PDFProtocol to infer off-target effects of drugs on cellular signaling using interactome-based deep learning.
STAR Protoc
January 2025
Department of Cell and Molecular Biology, SciLifeLab, Karolinska Institutet, 171 77 Stockholm, Sweden. Electronic address:
Drugs that target specific proteins often have off-target effects. We present a protocol using artificial neural networks to model cellular transcriptional responses to drugs, aiming to understand their mechanisms of action. We detail steps for predicting transcriptional activities, inferring drug-target interactions, and explaining the off-target mechanism of action.
View Article and Find Full Text PDFValidating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcare.
Learn Health Syst
January 2025
Bioethics Research Center, Division of General Medical Sciences, Department of Medicine Washington University School of Medicine St. Louis Missouri USA.
Objectives: Patient engagement is critical for the effective development and use of artificial intelligence (AI)-enabled tools in learning health systems (LHSs). We adapted a previously validated measure from pediatrics to assess adults' openness and concerns about the use of AI in their healthcare.
Study Design: Cross-sectional survey.
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