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

Benjamin C. Lee; Jonathan B. Moody; Alexis Poitrasson-Rivière; Amanda C. Melvin; Richard L. Weinberg; James R. Corbett; Venkatesh L. Murthy; Edward P. Ficaro

doi:10.1007/s12350-018-01471-4

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

Benjamin C. Lee, Jonathan B. Moody, Alexis Poitrasson-Rivière, Amanda C. Melvin, Richard L. Weinberg, James R. Corbett, Venkatesh L. Murthy, Edward P. Ficaro^*

^*Corresponding author for this work

Medicine, Cardiology Division

Research output: Contribution to journal › Article › peer-review

29 Scopus citations

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.

Original language	English (US)
Pages (from-to)	1982-1998
Number of pages	17
Journal	Journal of Nuclear Cardiology
Volume	27
Issue number	6
DOIs	https://doi.org/10.1007/s12350-018-01471-4
State	Published - Dec 2020

Funding

B.C. Lee, J.B. Moody, and A. Poitrasson-Rivière are employees of INVIA Medical Imaging Solutions. A.C. Melvin and R.L. Weinberg have no disclosures. J.R. Corbett and E.P. Ficaro are owners of INVIA Medical Imaging Solutions. V.L. Murthy has received consulting fees from Ionetix, Inc, and owns stock in General Electric and Cardinal Health, and stock options in Ionetix, Inc. V. L. Murthy is supported by 1R01HL136685 from the National, Heart, Lung, Blood Institute, and research Grants from INVIA Medical Imaging Solutions and Siemens Medical Imaging.

Keywords

Myocardial perfusion imaging: PET
coronary blood flow
coronary flow reserve
image artifacts
motion correction

ASJC Scopus subject areas

Cardiology and Cardiovascular Medicine
Radiology Nuclear Medicine and imaging

Access to Document

10.1007/s12350-018-01471-4

Cite this

@article{c4d63edc723e4a86b384a32e96e1cb6b,

title = "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-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.",

keywords = "Myocardial perfusion imaging: PET, coronary blood flow, coronary flow reserve, image artifacts, motion correction",

author = "Lee, {Benjamin C.} and Moody, {Jonathan B.} and Alexis Poitrasson-Rivi{\`e}re and Melvin, {Amanda C.} and Weinberg, {Richard L.} and Corbett, {James R.} and Murthy, {Venkatesh L.} and Ficaro, {Edward P.}",

note = "Publisher Copyright: {\textcopyright} 2018, American Society of Nuclear Cardiology.",

year = "2020",

month = dec,

doi = "10.1007/s12350-018-01471-4",

language = "English (US)",

volume = "27",

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TY - JOUR

T1 - Automated dynamic motion correction using normalized gradient fields for 82rubidium PET myocardial blood flow quantification

AU - Lee, Benjamin C.

AU - Moody, Jonathan B.

AU - Poitrasson-Rivière, Alexis

AU - Melvin, Amanda C.

AU - Weinberg, Richard L.

AU - Corbett, James R.

AU - Murthy, Venkatesh L.

AU - Ficaro, Edward P.

PY - 2020/12

Y1 - 2020/12

N2 - 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.

AB - 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.

KW - Myocardial perfusion imaging: PET

KW - coronary blood flow

KW - coronary flow reserve

KW - image artifacts

KW - motion correction

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