The computed tomography (CT) technique is extensively employed as an imaging modality
in clinical settings. The radiation dose of CT, however, is significantly high, thereby raising …

Medical images are helpful for the diagnosis, treatment, and evaluation of diseases. Precise
medical image segmentation improves diagnosis and decision-making, aiding intelligent …

In this paper, we propose Hformer, a novel supervised learning model for low-dose
computer tomography (LDCT) denoising. Hformer combines the strengths of convolutional …

The low-dose computed tomography (CT) scan is clinically significant to reduce the radiation
risk for radiologists and patients, especially in repeative examination. However, it inherently …

A mobile ad-hoc network (MANET) necessitates appropriate routing techniques to enable
optimal data transfer. The selection of appropriate routing protocols while utilizing the default …

Background To improve myocardial delayed enhancement (MDE) CT, a deep learning (DL)–
based post hoc denoising method supervised with averaged MDE CT data was developed …

Excessive ionizing radiation in cone-beam computed tomography (CBCT) causes damage
to patients, whereas a low radiation dose degrades the imaging quality. To improve the …

Proton FLASH therapy with an ultra-high dose rate is in urgent need of more accurate
treatment plan system (TPS) to promote the development of proton computed tomography …

Spectral computed tomography (CT) based on photon counting detectors can resolve the
energy of every single photon interacting with the sensor layer and be used to analyze …

Many deep learning-based approaches have been authenticated well performed for low-
dose computed tomography (LDCT) image postprocessing. Unfortunately, most of them …