Validation of a data-driven motion-compensated PET brain image reconstruction algorithm in clinical patients using four radiotracers

TY - JOUR

T1 - Validation of a data-driven motion-compensated PET brain image reconstruction algorithm in clinical patients using four radiotracers

AU - Munk, Ole L.

AU - Rodell, Anders B.

AU - Danielsen, Patricia B.

AU - Madsen, Josefine R.

AU - Sørensen, Mie T.

AU - Okkels, Niels

AU - Horsager, Jacob

AU - Andersen, Katrine B.

AU - Borghammer, Per

AU - Aanerud, Joel

AU - Jones, Judson

AU - Hong, Inki

AU - Zuehlsdorff, Sven

N1 - Publisher Copyright: © The Author(s) 2025.

PY - 2025/12

Y1 - 2025/12

N2 - Purpose: Patients with dementia symptoms often struggle to limit movements during PET examinations, necessitating motion compensation in brain PET imaging to ensure the high image quality needed for diagnostic accuracy. This study validates a data-driven motion-compensated (MoCo) PET brain image reconstruction algorithm that corrects head motion by integrating the detected motion frames and their associated rigid body transformations into the iterative image reconstruction. Validation was conducted using phantom scans, healthy volunteers, and clinical patients using four radiotracers with distinct tracer activity distributions. Methods: We conducted technical validation experiments of the algorithm using Hoffman brain phantom scans during a series of controlled movements, followed by two blinded reader studies assessing image quality between standard images and MoCo images in 38 clinical patients receiving dementia scans with [18F]Fluorodeoxyglucose, [18F]N-(3-iodopro-2E-enyl)-2beta-carbomethoxy-3beta-(4’-methylphenyl)-nortropane, [18F]flutemetamol, and a research group comprising 25 elderly subjects scanned with [18F]fluoroethoxybenzovesamicol. Results: The Hoffman brain phantom study demonstrated the algorithm’s capability to detect and correct for even minimal movements, 1-mm translations and 1⁰ rotations, applied to the phantom. Within the clinical cohort, where standard images were deemed suboptimal or non-diagnostic, all MoCo images were classified as having acceptable diagnostic quality. In the research cohort, MoCo images consistently matched or surpassed the standard image quality even in cases with minimal head movement, and the MoCo algorithm never led to degraded image quality. Conclusion: The PET brain MoCo reconstruction algorithm was robust and worked well for four different tracers with markedly different uptake patterns. Moco images markedly improved the image quality for patients who were unable to lie still during a PET examination and obviated the need for any repeat scans. Thus, the method was clinically feasible and has the potential for improving diagnostic accuracy.

AB - Purpose: Patients with dementia symptoms often struggle to limit movements during PET examinations, necessitating motion compensation in brain PET imaging to ensure the high image quality needed for diagnostic accuracy. This study validates a data-driven motion-compensated (MoCo) PET brain image reconstruction algorithm that corrects head motion by integrating the detected motion frames and their associated rigid body transformations into the iterative image reconstruction. Validation was conducted using phantom scans, healthy volunteers, and clinical patients using four radiotracers with distinct tracer activity distributions. Methods: We conducted technical validation experiments of the algorithm using Hoffman brain phantom scans during a series of controlled movements, followed by two blinded reader studies assessing image quality between standard images and MoCo images in 38 clinical patients receiving dementia scans with [18F]Fluorodeoxyglucose, [18F]N-(3-iodopro-2E-enyl)-2beta-carbomethoxy-3beta-(4’-methylphenyl)-nortropane, [18F]flutemetamol, and a research group comprising 25 elderly subjects scanned with [18F]fluoroethoxybenzovesamicol. Results: The Hoffman brain phantom study demonstrated the algorithm’s capability to detect and correct for even minimal movements, 1-mm translations and 1⁰ rotations, applied to the phantom. Within the clinical cohort, where standard images were deemed suboptimal or non-diagnostic, all MoCo images were classified as having acceptable diagnostic quality. In the research cohort, MoCo images consistently matched or surpassed the standard image quality even in cases with minimal head movement, and the MoCo algorithm never led to degraded image quality. Conclusion: The PET brain MoCo reconstruction algorithm was robust and worked well for four different tracers with markedly different uptake patterns. Moco images markedly improved the image quality for patients who were unable to lie still during a PET examination and obviated the need for any repeat scans. Thus, the method was clinically feasible and has the potential for improving diagnostic accuracy.

KW - Brain

KW - Data driven

KW - Dementia

KW - Motion correction

KW - PET

KW - Reconstruction

UR - http://www.scopus.com/inward/record.url?scp=85218129500&partnerID=8YFLogxK

U2 - 10.1186/s40658-025-00723-w

DO - 10.1186/s40658-025-00723-w

M3 - Journal article

C2 - 39899171

AN - SCOPUS:85218129500

SN - 2197-7364

VL - 12

JO - EJNMMI Physics

JF - EJNMMI Physics

IS - 1

M1 - 11

ER -