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Perfusion deconvolution in DSC-MRI with dispersion-compliant bases
Marco Pizzolato1, Timothé Boutelier2, Rachid Deriche1
1Université Côte d'Azur, Inria, France.
Abstract:
Perfusion imaging of the brain via Dynamic Susceptibility Contrast MRI (DSC-MRI) allows tissue perfusion characterization by recovering the tissue impulse response function and scalar parameters such as the cerebral blood flow (CBF), blood volume (CBV), and mean transit time (MTT). However, the presence of bolus dispersion causes the data to reflect macrovascular properties, in addition to tissue perfusion. In this case, when performing deconvolution of the measured arterial and tissue concentration time-curves it is only possible to recover the effective, i.e. dispersed, response function and parameters. We introduce Dispersion-Compliant Bases (DCB) to represent the response function in the presence and absence of dispersion. We perform in silico and in vivo experiments, and show that DCB deconvolution outperforms oSVD and the state-of-the-art CPI+VTF techniques in the estimation of effective perfusion parameters, regardless of the presence and amount of dispersion. We also show that DCB deconvolution can be used as a pre-processing step to improve the estimation of dispersion-free parameters computed with CPI+VTF, which employs a model of the vascular transport function to characterize dispersion. Indeed, in silico results show a reduction of relative errors up to 50% for dispersion-free CBF and MTT. Moreover, the DCB method recovers effective response functions that comply with healthy and pathological scenarios, and offers the advantage of making no assumptions about the presence, amount, and nature of dispersion.
Insights
Dispersion-Compliant Bases (DCB) deconvolution accurately estimates brain perfusion parameters from Dynamic Susceptibility Contrast MRI (DSC-MRI) data. This method improves accuracy for both effective and dispersion-free parameters, regardless of bolus dispersion.
Area of Science:
- Neuroimaging
- Medical Physics
- Biomedical Engineering
Background:
- Dynamic Susceptibility Contrast MRI (DSC-MRI) is used for brain perfusion imaging, estimating parameters like cerebral blood flow (CBF), blood volume (CBV), and mean transit time (MTT).
- Bolus dispersion in DSC-MRI data complicates the accurate estimation of tissue perfusion by introducing macrovascular effects.
- Current deconvolution methods often recover only effective, dispersed parameters, limiting the precision of perfusion assessment.
Purpose of the Study:
- To introduce Dispersion-Compliant Bases (DCB) as a novel method for representing the response function in DSC-MRI.
- To evaluate the performance of DCB deconvolution against existing techniques (oSVD, CPI+VTF) in estimating perfusion parameters.
- To assess the utility of DCB as a pre-processing step for improving dispersion-free parameter estimation.
Main Methods:
- Development and application of Dispersion-Compliant Bases (DCB) to model the tissue impulse response function.
- In silico and in vivo experiments comparing DCB deconvolution with oSVD and CPI+VTF methods.
- Evaluation of DCB's effectiveness in estimating both effective and dispersion-free perfusion parameters (CBF, MTT).
Main Results:
- DCB deconvolution demonstrated superior performance in estimating effective perfusion parameters compared to oSVD and CPI+VTF.
- DCB deconvolution significantly reduced relative errors (up to 50%) for dispersion-free CBF and MTT when used with CPI+VTF.
- The DCB method accurately recovered effective response functions in both healthy and pathological scenarios without assuming dispersion characteristics.
Conclusions:
- Dispersion-Compliant Bases (DCB) deconvolution offers a robust and accurate approach for DSC-MRI perfusion imaging.
- DCB deconvolution effectively handles bolus dispersion, improving the estimation of both effective and dispersion-free perfusion parameters.
- The DCB method provides a flexible tool for brain perfusion analysis, adaptable to various physiological conditions and dispersion levels.
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