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


CONCLUSIONS
Given an improved SNR, TSVD-G is useful for quantification of CBF with deconvolution analysis of DCE-MRI data.
Murase
Kenya
K
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
Yamazaki
Youichi
Y
Miyazaki
Shohei
S
D000465:Algorithms; D001783:Blood Flow Velocity / Q000502:physiology; D001810:Blood Volume; D002560:Cerebrovascular Circulation / Q000502:physiology*; D003198:Computer Simulation; D003287:Contrast Media; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008962:Models, Theoretical; D009010:Monte Carlo Method; D012039:Regional Blood Flow / Q000502:physiology*; D020521:Stroke / Q000175:diagnosis; D013997:Time Factors
D003287:Contrast Media