Multimodal fusion in neuroimaging combines data from multiple imaging modalities to
overcome the fundamental limitations of individual modalities. Neuroimaging fusion can …

In this Review article, we systematically summarize the design and applications of various
kinds of long-lived emissive probes for bioimaging and biosensing via time-resolved …

The establishment of image correspondence through robust image registration is critical to
many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring …

Traditional feature matching methods, such as scale-invariant feature transform (SIFT),
usually use image intensity or gradient information to detect and describe feature points; …

One of the fundamental challenges in supervised learning for multimodal image registration
is the lack of ground-truth for voxel-level spatial correspondence. This work describes a …

Image registration and fusion algorithms exist in almost every software system that creates
or uses images in radiotherapy. Most treatment planning systems support some form of …

This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver
registration for image-pairs works by patch-wise prediction of a deformation model based …

Deep learning technologies have dramatically reshaped the field of medical image
registration over the past decade. The initial developments, such as regression-based and U …

Feature matching, which refers to establishing reliable correspondence between two sets of
features (particularly point features), is a critical prerequisite in feature-based registration. In …

This paper presents a review of automated image registration methodologies that have been
used in the medical field. The aim of this paper is to be an introduction to the field, provide …