Post-synchronization of dynamic images of periodically moving organs

The motion of periodically moving organs can be studied by acquisition of d ynamic image series. When time is short, it is necessary either to find a s ync signal to sum synchronous images in real time or to acquire a regular t ime series and to synchronize a posteriori. Dynamic acquisitions were perfo rmed (gastric and lung studies). The activity in each pixel of the moving o rgan can be expressed as h(t)=a(o)+a(1) cos(omega(o)t-phi). The time-activi ty curve u(t) over a region of interest (ROI) of the considered organ was c omputed. 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 D C component, amplitude and phase in each pixel can be then estimated by min imizing the following function: J=Sigma[h(t)-g(t)](2), where g(t) is a nois y 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. Dynami c 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 ima ging was performed with Kr-81(m). The characteristic frequency was also eas ily 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 or gans can be achieved in a simple fashion. This approach overcomes the diffi culty of direct analysis of time-activity curves and provides amplitude and phase images. ((C) 2000 Lippincott Williams & Wilkins).