This paper reviewed the deep learning‐based studies for medical imaging synthesis and its
clinical application. Specifically, we summarized the recent developments of deep learning …

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

Background and purpose The promise of the MR-linac is that one can visualize all
anatomical changes during the course of radiotherapy and hence adapt the treatment plan …

Background and purpose Magnetic resonance imaging (MRI)‐based synthetic computed
tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for …

Radiotherapy based on magnetic resonance imaging as the sole modality (MRI-only RT) is
an area of growing scientific interest due to the increasing use of MRI for both target and …

Magnetic resonance imaging (MRI) offers superior soft-tissue contrast as compared with
computed tomography (CT), which is conventionally used for radiation therapy treatment …

Over the past decade, the application of magnetic resonance imaging (MRI) has increased,
and there is growing evidence to suggest that improvements in the accuracy of target …

We study data-parallel training of deep neural networks on high-performance computing
infrastructure. The key problem with scaling data-parallel training is avoiding severe …

Neuroblastoma is an enigmatic disease entity; some tumors disappear spontaneously
without any therapy, while others progress with a fatal outcome despite the implementation …

Purpose To investigate proton density (PD)‐weighted zero TE (ZT) imaging for
morphological depiction and segmentation of cranial bone structures. Methods A rotating …