DICOM PS3.17 2019a - Explanatory Information

FFFF Advanced Blending Presentation State Storage Encoding Example (Informative) (Current)
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# FFFF Advanced Blending Presentation State Storage Encoding Example (Informative)

This section illustrates the usage of the Advanced Blending Presentation State for a functional MRI study.

## FFFF.1 Introduction

Quantitative imaging provides measurements of physical properties, in vivo and non-invasively, for research and clinical practice. DICOM support for parametric maps provides a structure for organizing these results as an extension of the already widely-used imaging standard. The addition of color LUT support for parametric maps bridges the gap between data handling and visualization.

An example of quantitative imaging in clinical practice today is the use of MRI, PET and other modalities in brain mapping for diagnostic assessment in pre-treatment planning for tumor, epilepsy, arterio-venous malformations (AVMs) and other conditions. MR Diffusion tensor imaging (DTI) results in fractional anisotropy (FA) and other parametric maps highlighting white matter structures. Task-based functional MRI (fMRI) highlights specific areas of eloquent cortex (gray matter) as expressed in statistical activation maps. Other parameters and modalities including perfusion, MR spectroscopy, and PET are often employed to locate and characterize lesions by means of their hyper- and hypo-metabolism and -perfusion in parametric maps.

The visualization of multiple parametric maps and sources of anatomical information in the same space requires the tools to highlight areas of interest (and hide irrelevant areas) in parametric maps. Two important tools provided in this supplement are thresholding of parametric maps by their real-world values, and blending of multiple images in a single view.

In this example the series 2 to 5 have a lower resolution and are expected to be resampled to have the same resolution as series 1 as this is identified as series to be used for target Geometry.

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EEEE.5 Informative References | Home | FFFF.2 Example

DICOM PS3.17 2019a - Explanatory Information