multicontrast

Multicontrast refers to imaging and visualization approaches that produce more than one form of image contrast in order to better differentiate structures, materials, or tissues. The term covers both acquiring data from multiple imaging modalities (or multiple channels within a modality) and processing data to generate distinct contrast representations from a single dataset. By combining contrasts, multicontrast techniques can reveal features that are not apparent with a single contrast mechanism, improve tissue classification, and support more robust analysis.

Examples of acquisition strategies include multiparametric magnetic resonance imaging, which collects T1- and T2-weighted images as well as diffusion and perfusion metrics; dual-energy computed tomography, which highlights different materials by using different X-ray energies; and microscopy using different stains or illumination modes (bright-field, phase contrast, differential interference contrast, fluorescence) to create complementary views. In some contexts, computational methods extract multiple contrast channels from one modality, such as color-mapped overlays or fused images that encode several contrasts simultaneously.

Applications span medicine, pathology, neuroscience, materials science, and remote sensing. The common aim is to increase contrast between features of interest, enable quantitative metrics, and reduce ambiguity in interpretation. Challenges include ensuring proper alignment across modalities, calibrating contrast scales, avoiding artifacts introduced by fusion, and maintaining clear labeling so that the meaning of each contrast channel is understood.

See also: multiparametric imaging, dual-energy imaging, multispectral imaging, and image fusion.