Automated dynamic motion correction using normalized gradient fields for 82rubidium PET myocardial blood flow quantification

Abstract

Background.-Patient motion can lead to misalignment of left ventricular (LV) volumes-ofinterest (VOIs) and subsequently inaccurate quantification of myocardial blood flow (MBF) and flow reserve (MFR) from dynamic PET myocardial perfusion images. We aimed to develop an image-based 3D-automated motion-correction algorithm that corrects the full dynamic sequence for translational motion, especially in the early blood phase frames (~ first minute) where the injected tracer activity is transitioning from the blood pool to the myocardium and where conventional image registration algorithms have had limited success. Methods.-We studied 225 consecutive patients who underwent dynamic rest/stress rubidium-82 chloride (82 Rb) PET imaging. Dynamic image series consisting of 30 frames were reconstructed with frame durations ranging from 5 to 80 seconds. An automated algorithm localized the RV and LV blood pools in space and time and then registered each frame to a tissue reference image volume using normalized gradient fields with a modification of a signed distance function. The computed shifts and their global and regional flow estimates were compared to those of reference shifts that were assessed by three physician readers.

Patient motion correction for dynamic cardiac PET: Current status and challenges

“…Motion magnitude was found larger in the stress studies than that in the rest studies. 1 In another study, the incidence and magnitude of motion reported in Ref. 7 were much larger than those reported in the current study.…”

“…In a recent study, an 82 rubidium ( 82 Rb)-dynamic-tailored motion-correction framework has been proposed to address the voluntary body motion for all the dynamic frames, including both early and late phases. 1 This approach brings us one step closer to the practical and full motion correction for dynamic cardiac PET studies. In this editorial, we discussed the current status and limitations of motion-correction methods for dynamic cardiac PET, including the recent publication at JNC, and also pointed out the remaining challenges for future developments.…”

“…However, there has been no such application for the entire dynamic cardiac PET frames. The current study by Lee et al, 1 instead, proposed to use a normalized gradient-field-based similarity metric, defined as the first order derivatives of a smoothed PET image, in the motion estimation process. With the assumption that ''two image volumes were considered similar if the normalized gradient directions were aligned at a given position'', 1 each dynamic image frame was rigidly aligned to match a fixed later phase summed frame, i.e., 120 to 400 seconds post-injection, to achieve motion correction.…”

Motion correction to enhance absolute myocardial blood flow quantitation by PET

“…25 Previously, Poitrasson-Rivière et al introduced an automated algorithm localizing the left and right ventricular blood pools in 4-D, with registration of each frame to a tissue reference image volume using normalized gradient fields. 22 This image-based, automated motion correction algorithm using normalized gradient fields applied to dynamic PET sequences matched the results of manual motion correction, reducing bias and variance in MBF and MFR. 22 It is also important to realize that motion correction must be practical in the clinical setting.…”

“…Evidence supporting this is the far greater fraction of stress studies containing motion than rest studies. 22,23 Proper reconstruction of images when the heart has shifted with respect to the attenuating organs is non-trivial.…”