Documentation/Nightly/Modules/DSC MRI Analysis

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Introduction and Acknowledgements

Extension: DSC_MRI_Analysis Acknowledgments: This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, and by National Cancer Institute as part of the Quantitative Imaging Network initiative (U01CA154601) and QIICR (U24CA180918). Implementation of the DSC MRI Analysis was contributed by Xiao Da from MGH. Author: Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Steve Pieper (Isomics), Jayashree Kalpathy-Cramer (MGH) Contact: Xiao Da, <email>XDA@mgh.harvard.edu</email>

National Alliance for Medical Image Computing (NA-MIC)   Quantitative Image Informatics for Cancer Research   Martinos Center for Biomedical Imaging

Module Description

Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning. In the context of glial neoplasms, perfusion characteristics have been shown to correlate with tumor type and grade and hence influence treatment decisions.
DSC MRI imaging is based on the principle that flow of a paramagnetic contrast agent through a capillary bed will transiently change the magnetic susceptibility of the given tissue. Decreased signal intensity on spin-echo or gradient-echo images after the first pass of the contrast agent, frequently described as susceptibility-induced T2* shortening, is the result of this temporal change in magnetic susceptibility. This signal time curve is then converted into a concentration time curve, and use of tracer kinetic analysis various hemodynamic variables, such as cerebral blood volume, cerebral blood flow, and mean transit time, as well as metrics that address vessel leakage may be estimated. Combined, these metrics enable microvascular imaging, providing a visual correlate of blood flow, volume, and vessel permeability.

Use Cases

• Estimation of quantitative parameters(rCBV,rCBF and MTT) from DSC MRI.
• Guide diagnosis, prognosis and therapy planning.
• Brain Tumor, Stroke, Adrenoleukodystrophy and other diseases.

Framework to Estimate DSC Parametric Maps

Panels and their use

DSC MRI Analysis GUI

Input Input image:

Similar Modules

• MultiVolumeExplorer
• MultiVolumeImporter
• PkModeling

References

• Introduction of DSC MRI from Siemens
• Equations for conversion of signal to concentration for DSC
• Other method to investigate DSC MRI

Information for Developers

• Source code for analysis of DSC MRI