Stable spline deconvolution for dynamic susceptibility contrast MRI.

Area of Science:

• Medical Imaging
• Neuroimaging
• Biophysics

Background:

• Dynamic susceptibility contrast MRI (DSC-MRI) is crucial for assessing cerebral blood flow (CBF).
• Accurate deconvolution methods are essential for reliable CBF quantification from DSC-MRI data.
• Existing methods like singular value decomposition (SVD) have limitations in reconstructing physiological parameters.

Purpose of the Study:

• To introduce and evaluate the stable spline (SS) deconvolution method for CBF quantification in DSC-MRI.
• To compare the performance of the SS method against established techniques, specifically block-circulant singular value decomposition (oSVD) and nonlinear stochastic regularization (NSR).

Main Methods:

• The study employed simulated DSC-MRI data and two clinical datasets for comparative analysis.
• The stable spline (SS) deconvolution method was implemented and assessed.
• Performance was evaluated against oSVD and NSR methods, focusing on residue function reconstruction and CBF quantification.

Main Results:

• The SS method accurately reconstructed the dispersed residue function and its peak, even with dispersion and delay.
• In the absence of dispersion, SS performance was comparable to oSVD, with both failing to fully reconstruct the residue function.
• Both SS and NSR demonstrated superior differentiation of healthy and pathological CBF values compared to oSVD across all simulated conditions.
• Clinical data analysis revealed that SS and NSR provided more physiologically plausible CBF maps than oSVD.

Conclusions:

• The stable spline (SS) method addresses limitations of oSVD, such as unphysiological residue function estimates.
• SS overcomes the computational expense of NSR, making it suitable for large datasets.
• The SS method represents a valuable and practical alternative for quantitative CBF analysis in DSC-MRI.