An independent component analysis approach to perfusion weighted imaging

In dynamic susceptibility contrast perfusion weighted imaging, the recirculation effect is normally removed by gamma-variate fitting from concentration curves before estimating hemodynamic parameters. At lower SNR, however, many fitting failures may result. Moreover, when cerebral hemodynamics is compromised e.g., cerebral ischemia, a substantially broadened concentration curve is anticipated, resulting in the first passage overlapping with recirculation, which again causes a gamma-fit to fail to consistently discern recirculation contributions from the first passage. We propose to exploit independent component analysis to obviate the recirculation effect. We demonstrate that such a technique can remove recirculation in normal and ischemic brain tissues while preserving the first passage. This in turn allows for accurate recirculation elimination and hence improved estimation of cerebral blood volume particularly when overlapping between first passage and recirculation is suspected as in the case of an ischemic lesion.