Documentation/4.6/Modules/DSC MRI Analysis

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Introduction and Acknowledgements

DSC logo.png

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></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
  • PkModeling Parameters:
    • CBF/CBV Time Interval Value: Time interval for CBF/CBV calculation.
    • Use Population AIF: A mean AIF is calculated from a functional form instead of from the input using the aifMask or a prescribed AIF.
  • IO
    • Input 4D image: 4D DSC MRI Image.
    • ROI Mask Image: Mask designating the location for DSC analysis.
    • AIF Mask Image: Mask designating the location of the arterial input function (AIF). AIF can either be calculated from the input using the aifMask, prescribed directly in concentration units using the prescribedAIF option, or via a population AIF.
    • Prescribed AIF: Prescribed arterial input function (AIF).
    • Output K2 image: Leakage map.
    • Output RCBV image: Relative Cerebral Blood Volume (rCBV) map.
    • Output RCBF image: Relative Cerebral Blood Flow (rCBF) map.
    • Output MTT image: Mean Transit Time (MTT) map.
  • Advanced options
    • BAT Calculation Mode: PeakGradient(Default) or UseConstantBAT.
    • Constant BAT: Constant bolus arrival time value.
    • Output Bolus Arrival Time Image: The bolus arrival time calculated at each pixel.
    • Output Concentration 4D Image: Delta R2* concentration image.
    • Output Fitted Data 4D Image: Fitted Delta R2* concentration image.

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