Abstract Recently, deep learning (DL)‐based methods for the generation of synthetic
computed tomography (sCT) have received significant research attention as an alternative to …

MRI-linear accelerator (MR-linac) devices have been introduced into clinical practice in
recent years and have enabled MR-guided adaptive radiation therapy (MRgART). However …

Background Lung MRI with ultrashort echo times (UTEs) enables high-resolution and
radiation-free morphologic imaging; however, its image quality is still lower than that of CT …

Multi-modality imaging constitutes a foundation of precision medicine, especially in
oncology where reliable and rapid imaging techniques are needed in order to insure …

The aim of this systematic review is to determine whether Deep Learning (DL) algorithms
can provide a clinically feasible alternative to classic algorithms for synthetic Computer …

In clinical practice, the modality of choice for lung diagnosis is usually computed tomography
(CT), which exposes patients to ionizing radiations and could potentially affect patients' …

The use of deep learning (DL) neural networks (NN) for medical image analysis is
dependent on available datasets and associated ground truths. Often, pathological image …

生成对抗网络(Generative Adversarial Nets, GANs) 模型可以无监督学习到更丰富的数据信息,
其包括生成模型与判别模型, 凭借二者之间的对抗提高性能. 针对传统GANs 存在着梯度消失 …

Purpose Attenuation correction is critical for positron emission tomography (PET) image
reconstruction. The standard protocol for obtaining attenuation information in a clinical PET …

In medical imaging, MR-to-CT synthesis has been extensively studied. The primary
motivation is to benefit from the quality of the CT signal, ie excellent spatial resolution, high …