Automated dynamic motion correction using normalized gradient fields for 82rubidium PET myocardial blood flow quantification.
Academic Article
Overview
abstract
BACKGROUND: Patient motion can lead to misalignment of left ventricular (LV) volumes-of-interest (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 (82Rb) 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. RESULTS: The automated motion-correction shifts were within 5 mm of the manual motion-correction shifts across the entire sequence. The automated and manual motion-correction global MBF values had excellent linear agreement (R = 0.99, y = 0.97x + 0.06). Uncorrected flows outside of the limits of agreement with the manual motion-corrected flows were brought into agreement in 90% of the cases for global MBF and in 87% of the cases for global MFR. The limits of agreement for stress MBF were also reduced twofold globally and by fourfold in the RCA territory. CONCLUSIONS: An image-based, automated motion-correction algorithm for dynamic PET across the entire dynamic sequence using normalized gradient fields matched the results of manual motion correction in reducing bias and variance in MBF and MFR, particularly in the RCA territory.
