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PubMed · 16093635

Deconvolution analysis of dynamic contrast-enhanced data based on singular value decomposition optimized by generalized cross validation.

Kenya Murase·Youichi Yamazaki·Shohei Miyazaki
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Abstract

PURPOSE: To present an implementation of generalized cross validation (GCV) for automatically determining the regularization parameter--i.e., the threshold value in deconvolution analysis based on truncated singular value decomposition (TSVD) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data--and to investigate the usefulness of this approach in comparison with TSVD with a fixed threshold value (TSVD-F). METHODS: Using computer simulations, we generated a time-dependent concentration of the contrast agent in the volume of interest (VOI) from the arterial input function (AIF) modeled as a gamma-variate function under various cerebral blood flows (CBFs), cerebral blood volumes (CBVs), and signal-to-noise ratios (SNRs) for three different types of residue functions (exponential, triangular, and box-shaped). We also considered the effects of delay and dispersion in AIF. The TSVD with GCV (TSVD-G) and TSVD-F with a fixed threshold value of 0.2 were used to estimate CBF values from the simulated concentration-time curves in the VOI and AIF, and the estimated values were compared with the assumed values. Additionally, the optimal threshold value was determined from the threshold value in TSVD-F giving the mean CBF value closest to the assumed value and was compared with the threshold value determined with TSVD-G. RESULTS: With TSVD-G, the CBF estimation was substantially improved over a wide range of CBFs for all types of residue functions at the cost of more noise than was seen with TSVD-F. The dependency of the threshold value determined with TSVD-G on the CBF, CBV, and SNR was similar to that of the optimal threshold value, with some discrepancy being observed for the box-shaped residue function, although they did not always agree in terms of absolute value. CONCLUSION: Given an improved SNR, TSVD-G is useful for quantification of CBF with deconvolution analysis of DCE-MRI data.

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AlgorithmsBlood Flow VelocityBlood VolumeCerebrovascular CirculationComputer SimulationContrast MediaHumansMagnetic Resonance ImagingModels, TheoreticalMonte Carlo MethodRegional Blood FlowStrokeTime FactorsComparative StudyJournal Article

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Kenya Murase, Youichi Yamazaki, Shohei Miyazaki. 2004. Deconvolution analysis of dynamic contrast-enhanced data based on singular value decomposition optimized by generalized cross validation.. https://doi.org/10.2463/mrms.3.165

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