A comparative study of three fast algorithms to estimate cerebral blood flow and distribution volume using...

To estimate regional cerebral blood flow (rCBF) and distribution volume (Vd) using N-isopropyl-p-[123]iodoamphetamine (IMP) and two SPECT data (early and delayed data) measured at two discrete time points, three fast algorithms are implemented, which are the table look-up method (TLU), the linear least-squares fitting method (LLSF), and the linear search method (LS). A two-compartment model was used as the IMP kinetic model. These algorithms were applied to the early and delayed data measured by the two scan protocols, t1 = 12 min, t2 = 54 min (scan A) and t1 = 12 min, t2 = 85 min (scan B). The estimated rCBF and Vd values using the present three algorithms were in good agreement with those using the nonlinear least-squares fitting technique and dynamic SPECT data. The quantitative value and signal to noise (S/N) ratio of rCBF images by TLU was not changed whether scan A or scan B was used. However, the S/N of the Vd image by TLU using scan B improved over that using scan A. The same tendency was observed in the results of LLSF and LS. The CPU times for the parametric imaging of CBF and Vd images for one slice (64 x 64 matrix) were 0.04, 0.09, and 0.14 s using TLU, LLSF, and LS, respectively. In conclusion, the present three algorithms gave reasonably accurate estimates of rCBF and Vd using two SPECT data, and are well suited for parametric imaging because of their good statistical properties and high computational efficiency.