Post-synchronization of dynamic images of periodically moving organs

Abstract

The motion of periodically moving organs can be studied by acquisition of dynamic image series. When time is short, it is necessary either to find a sync signal to sum synchronous images in real time or to acquire a regular time series and to synchronize a posteriori. Dynamic acquisitions were performed (gastric and lung studies). The activity in each pixel of the moving organ can be expressed as h(t)=a0 + a1 cos(omegao0t - phi). The time-activity curve u(t) over a region of interest (ROI) of the considered organ was computed. When the ROI is well chosen, the power spectrum of u(t) exhibits a sharp peak near the characteristic frequency of the periodic motion. The DC component, amplitude and phase in each pixel can be then estimated by minimizing the following function: J=sigma[h(t) - g(t)]2, where g(t) is a noisy measurement of h(t). It is then easy to reconstruct an a posteriori gated time series by computing h(t) for various times over a single period. This approach was successful in characterizing lung and gastric motions. Dynamic series were acquired as for gastric emptying studies. The characteristic frequency of antral motility was easily and unambiguously estimated and DC, amplitude and phase images were computed. Dynamic pulmonary functional imaging was performed with 81Krm. The characteristic frequency was also easily estimated from the time-activity curve power spectrum using a ROI drawn over the lower part of the lungs. The DC, amplitude and phase images were then computed from the dynamic series and the characteristic frequency. In conclusion, a posteriori gating of dynamic series of periodically moving organs can be achieved in a simple fashion. This approach overcomes the difficulty of direct analysis of time-activity curves and provides amplitude and phase images.