Delay and dispersion effects in dynamic susceptibility contrast MRI: simulations using singular value decomposition.

Area of Science:

• Medical Imaging
• Neuroimaging
• Radiology

Background:

• Dynamic susceptibility contrast (DSC) MRI is a vital tool for assessing tissue perfusion.
• Accurate quantification of DSC MRI data relies on precise measurement of the arterial input function (AIF) and deconvolution of tissue concentration curves.
• Singular value decomposition (SVD) is a widely adopted method for DSC data deconvolution.

Purpose of the Study:

• To investigate the impact of delay and dispersion in the estimated AIF on DSC MRI perfusion quantification.
• To evaluate the accuracy of cerebral blood flow (CBF) and mean transit time (MTT) measurements under these conditions.

Main Methods:

• Simulations were conducted to model the effects of AIF delay and dispersion on DSC quantification.
• The study focused on the widely used SVD deconvolution technique.
• Analysis included assessing the resulting errors in CBF and MTT estimations.

Main Results:

• Both delay and dispersion in the AIF led to significant underestimation of CBF.
• Overestimation of MTT was observed due to AIF delay and dispersion.
• While AIF arrival time can correct for delay errors, dispersion correction remains challenging.

Conclusions:

• Inaccurate AIF estimation, specifically delay and dispersion, introduces substantial errors in DSC MRI perfusion parameters.
• Developing robust methods to model and correct for vascular dispersion is crucial for reliable DSC MRI quantification.
• Further research into vascular modeling is needed to improve the accuracy of perfusion measurements.