Purpose To develop an advanced deep convolutional neural network (DCNN) architecture
to generate synthetic CT (SCT) images from MR images for intensity-modulated proton …

Radiomics has increasingly been investigated as a potential biomarker in quantitative
imaging to facilitate personalized diagnosis and treatment of head and neck cancer (HNC) …

In this work, we investigate approaches to generating synthetic Computed Tomography (CT)
images from the real Magnetic Resonance Imaging (MRI) data. Generating the radiological …

Purpose Despite the proven utility of multiparametric magnetic resonance imaging (MRI) in
radiation therapy, MRI‐guided radiation treatment planning is limited by the fact that MRI …

Radiotherapy treatment planning requires CT images to accurately calculate the dose
distribution, but sometimes only MR images can be obtained, therefore it is necessary to …

Purpose: To overcome the imaging artifacts and Hounsfield unit inaccuracy limitations of
cone-beam computed tomography, a conditional generative adversarial network is …

Abstract Background and Purpose Magnetic Resonance guided Radiotherapy (MRgRT) still
needs the acquisition of Computed Tomography (CT) images and co-registration between …

Simple Summary Brain MRI scans often require different imaging sequences based on
tissue types, posing a common challenge. In our research, we propose a method that utilizes …

MR-only radiation treatment planning is attractive due to the superior soft tissue definition of
MRI as compared to CT, and the elimination of the uncertainty introduced by CT-MRI …

Purpose Deep learning (DL)‐based super‐resolution (SR) reconstruction for magnetic
resonance imaging (MRI) has recently been receiving attention due to the significant …