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Updated: Feb 2, 2026
Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
Published on: January 7, 2021
Automated dynamic motion correction using normalized gradient fields for 82rubidium PET myocardial blood flow
Benjamin C Lee1, Jonathan B Moody1, Alexis Poitrasson-Rivière1
1INVIA Medical Imaging Solutions, 3025 Boardwalk St., Suite 200, Ann Arbor, MI, 48108, USA.
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.
Insights
An automated algorithm corrects patient motion in dynamic PET scans, improving the accuracy of myocardial blood flow and flow reserve measurements. This technique enhances diagnostic precision for cardiovascular conditions.
Area of Science:
- Cardiovascular Imaging
- Nuclear Medicine
- Medical Physics
Background:
- Patient motion during dynamic PET imaging causes misalignment of left ventricular volumes-of-interest (VOIs).
- This misalignment leads to inaccurate quantification of myocardial blood flow (MBF) and myocardial flow reserve (MFR).
- Conventional registration algorithms struggle with motion correction in early blood-phase frames.
Purpose of the Study:
- To develop and validate an image-based, 3D-automated motion-correction algorithm for dynamic PET myocardial perfusion imaging.
- The algorithm aims to correct translational motion across the entire dynamic sequence, especially during the critical first minute post-injection.
- To improve the accuracy of MBF and MFR quantification affected by patient motion.
Main Methods:
- Studied 225 patients undergoing dynamic rubidium-82 chloride (82Rb) PET imaging.
- Developed an automated algorithm using normalized gradient fields and a signed distance function to register dynamic frames.
- Compared automated motion-correction results to manual correction by three physician readers.
Main Results:
- Automated motion-correction shifts closely matched manual shifts (within 5 mm).
- Excellent linear agreement (R=0.99) was observed between automated and manual global MBF values.
- 90% of global MBF and 87% of global MFR values were brought into agreement with manual correction, with significant improvements in the RCA territory.
Conclusions:
- An automated, image-based motion-correction algorithm effectively corrects translational motion in dynamic PET sequences.
- The algorithm demonstrates comparable performance to manual correction, reducing bias and variance in MBF and MFR quantification.
- This technique significantly improves the accuracy of myocardial perfusion quantification, particularly in specific coronary territories like the RCA.
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