Kenya Murase1, Youichi Yamazaki, Shohei Miyazaki. 1. Department of Medical Physics and Engineering, Division of Medical Technology and Science, Course of Health Science, Graduate School of Medicine, Osaka University, Suita, Japan. murase@sahs.med.osaka-u.ac.jp
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.
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.
Authors: Chong Duan; Jesper F Kallehauge; Carlos J Pérez-Torres; G Larry Bretthorst; Scott C Beeman; Kari Tanderup; Joseph J H Ackerman; Joel R Garbow Journal: Mol Imaging Biol Date: 2018-02 Impact factor: 3.488
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Authors: Raymond C Wadlow; Ben S Wittner; S Aidan Finley; Henry Bergquist; Rabi Upadhyay; Stephen Finn; Massimo Loda; Umar Mahmood; Sridhar Ramaswamy Journal: PLoS One Date: 2009-09-03 Impact factor: 3.240