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One of the most important methods in medical ultrasound imaging is the synthetic transmit aperture (STA). Despite the image quality improvement in the STA, this method suffers from several limitations, including a limited data acquisition rate and an increase in the overall time to form a single frame. Tensor completion (TC) is a powerful technique that uses rank minimization to recover missing information from a low-rank tensor. This paper provides a novel random synthetic transmit aperture (RSTA) method based on using only a randomly selected part (a fraction) of the linear array elements in the transmit mode to increase the data acquisition rate and then applying the tensor completion (TC) to improve the image quality. By the proposed method, as it is not necessary to transmit all elements sequentially, the data acquisition rate is improved and the overall time for creating an image is also significantly reduced. We investigated the proposed idea by using several simulated and experimental phantoms. Results showed that the proposed method could increase the data acquisition rate up to three times with the image quality difference of less than 6% compared to the original STA method.
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http://dx.doi.org/10.1016/j.ultras.2021.106553 | DOI Listing |
J Dent Educ
September 2025
Department of Oral and Maxillofacial Surgery, Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, P. R. China.
Background: Virtual reality (VR) and artificial intelligence (AI) technologies have advanced significantly over the past few decades, expanding into various fields, including dental education.
Purpose: To comprehensively review the application of VR and AI technologies in dentistry training, focusing on their impact on cognitive load management and skill enhancement. This study systematically summarizes the existing literature by means of a scoping review to explore the effects of the application of these technologies and to explore future directions.
Int J Radiat Oncol Biol Phys
September 2025
Radiation Oncology, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143. Electronic address:
Purpose: Accelerating MR acquisition is essential for image guided therapeutic applications. Compressed sensing (CS) has been developed to minimize image artifacts in accelerated scans, but the required iterative reconstruction is computationally complex and difficult to generalize. Convolutional neural networks (CNNs)/Transformers-based deep learning (DL) methods emerged as a faster alternative but face challenges in modeling continuous k-space, a problem amplified with non-Cartesian sampling commonly used in accelerated acquisition.
View Article and Find Full Text PDFComput Biol Med
September 2025
Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain. Electronic address:
Modelling the diffusion-relaxation magnetic resonance (MR) signal obtained from multi-parametric sequences has recently gained immense interest in the community due to new techniques significantly reducing data acquisition time. A preferred approach for examining the diffusion-relaxation MR data is to follow the continuum modelling principle that employs kernels to represent the tissue features, such as the relaxations or diffusion properties. However, constructing reasonable dictionaries with predefined signal components depends on the sampling density of model parameter space, thus leading to a geometrical increase in the number of atoms per extra tissue parameter considered in the model.
View Article and Find Full Text PDFBrief Bioinform
September 2025
Department of Computer Science, Hanyang University, 222 Wangsimni-ro, Seoul 04763, Republic of Korea.
Motivation: Mobile genetic elements (MGEs) play an important role in facilitating the acquisition of antibiotic resistance genes (ARGs) within microbial communities, significantly impacting the evolution of antibiotic resistance. Understanding the mechanism and trajectory of ARG acquisition requires a comprehensive analysis of the ARG-carrying mobilome-a collective set of MGEs carrying ARGs. However, identifying the mobilome within complex microbiomes poses considerable challenges.
View Article and Find Full Text PDFBrain Res
September 2025
Institute of Cognitive Neuroscience and Psychology, HUN-REN Research Centre for Natural Sciences, Hungary.
Identifying early predictors of language development is essential for understanding how infants acquire vocabulary during the first years of life. While previous studies have established the importance of infant-directed speech (IDS) and neural speech processing, this longitudinal study introduces a novel approach by combining EEG-based functional connectivity analysis and machine learning to assess the joint contribution of maternal and infant neural factors to language outcomes. Data were collected at birth and nine months, including maternal personality and speech characteristics, alongside infant EEG responses during speech processing.
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