This paper presents a review of deep learning (DL)-based medical image registration
methods. We summarized the latest developments and applications of DL-based registration …

Supervised learning aims to build a function or model that seeks as many mappings as
possible between the training data and outputs, where each training data will predict as a …

Charged particle beam radiotherapy requires dedicated measures to compensate for the
dosimetric influence of inter-and intra-fractional target motion. Independent of the delivery …

A four-dimensional deformable image registration (4D DIR) algorithm, referred to as 4D local
trajectory modeling (4DLTM), is presented and applied to thoracic 4D computed tomography …

Over the last few decades, deformable image registration (DIR) has gained popularity in
image‐guided radiation therapy for a number of applications, such as contour propagation …

Purpose: Four‐dimensional computed tomography (4D CT) can provide patient‐specific
motion information for radiotherapy planning and delivery. Motion estimation in 4D CT is …

Abstract Three-dimensional (3D) deformable image registration is a fundamental technique
in medical image analysis tasks. Although it has been extensively investigated, current deep …

Quantitative validation of deformable image registration (DIR) algorithms is extremely
difficult because of the complexity involved in constructing a deformable phantom that can …

In this article, our goal is twofold. First, we propose and compare two methods which process
deformable registration to estimate patient specific lung and tumor displacements and …

Deformable registration is needed for a variety of tasks in establishing the voxel
correspondence between respiratory phases. Most registration algorithms assume or imply …