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Accurate measurement of gradient waveform errors can often improve image quality in sequences with time varying readout and excitation waveforms. Self-encoding or offset-slice sequences are commonly used to measure gradient waveforms. However, the self-encoding method requires a long scan time, while the offset-slice method is often low precision, requiring the thickness of the excited slice to be small compared to the maximal k-space encoded by the test waveform. This work introduces a hybrid these methods, called variable-prephasing. Using a straightforward algebraic model, we demonstrate that variable-prephasing improves the precision of the waveform measurement by allowing acquisition of larger slice thicknesses. Experiments in a phantom were used to validate the theoretical predictions, showing that the precision of variable-prephasing gradient waveform measurements improves with increasing slice thickness.
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http://dx.doi.org/10.1016/j.jmr.2021.106945 | DOI Listing |
bioRxiv
August 2025
Department of Physiology & Membrane Biology, School of Medicine, University of California, Davis, USA.
Pacemaker myocytes of the sinoatrial (SA) node initiate each heartbeat through coupled voltage and Ca oscillators, but whether ATP supply is regulated on a beat-by-beat schedule in these cells has been unclear. Using genetically encoded sensors targeted to the cytosol and mitochondria, we tracked beat-resolved ATP dynamics in intact mouse SA node and isolated myocytes. Cytosolic ATP rose transiently with each Ca transient and segregated into high- and low-gain phenotypes defined by the Ca-ATP coupling slope.
View Article and Find Full Text PDFMed Phys
September 2025
Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan.
Background: Intracranial aneurysms, particularly saccular types, are localized dilations of cerebral vessels prone to rupture, leading to life-threatening complications such as subarachnoid hemorrhage.
Purpose: This study aimed to characterize the localized hemodynamic environment within the aneurysm dome and evaluate how spatial interactions among key flow parameters contribute to rupture risk, using a synergistic analytical framework.
Methods: We applied the targeted evaluation of synergistic links in aneurysms (TESLA) framework to analyze 18 intracranial aneurysms from 15 patients.
Biomed Eng Online
August 2025
College of Chemistry and Life Science, Beijing University of Technology, Beijing, 100124, China.
Background: Coronary artery calcification (CAC) represents a major cardiovascular risk in patients with end-stage renal disease (ESRD) undergoing hemodialysis. Given that radial artery pulse waveforms can reflect vascular status, this study aimed to evaluate their utility in the non-invasive assessment of CAC severity.
Methods: 58 patients with ESRD undergoing hemodialysis were enrolled.
Sci Rep
August 2025
Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Ostrava, 70800, Czech Republic.
The integration of autonomous mobile robots in Smart Home and their secure communication within Internet of Things with 5G networks represents a transformative shift towards more efficient, responsive, and adaptable healthcare and service delivery systems to support independent living for older people at home. This article presents a unique proposal for the possibility of implementing interoperability and secure data transmission within the communication between autonomous mobile robots and building automation technology in a Smart Home using 5G networks and also presents a novel design and application of a time-ahead [Formula: see text]concentration prediction method for sending presence and occupancy information in monitored Smart Home Care spaces without the use of cameras to an autonomous mobile robot for time-ahead detection of deviations from the daily routine. In this study, nonlinear input-output neural network models and nonlinear autoregressive neural network model with exogenous inputs neural network models with the following best results ([Formula: see text] and MAPE = 0.
View Article and Find Full Text PDFInt J Surg
August 2025
Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, South Korea.
Background: Major adverse cardiovascular and cerebrovascular events (MACCEs) after non-cardiac surgery can lead to substantial morbidity, mortality, and healthcare costs. Therefore, accurate and rapid risk prediction is crucial for targeted perioperative management. This study aimed to develop and validate a minimally burdensome multimodal deep learning model integrating demographic data, the International Classification of Diseases (ICD)-10 procedure codes, and raw preoperative 12-lead electrocardiogram (ECG) waveforms to predict 30-day MACCEs and to compare its performance with the established risk indices.
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