Joint estimation of subject motion and tracer kinetic parameters of dynamic PET data in an EM framework

Jieqing Jiao; Cristian A. Salinas; Graham E. Searle; Roger N. Gunn; Julia A. Schnabel

doi:10.1117/12.911497

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14 February 2012 Joint estimation of subject motion and tracer kinetic parameters of dynamic PET data in an EM framework

Jieqing Jiao, Cristian A. Salinas, Graham E. Searle, Roger N. Gunn, Julia A. Schnabel

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Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 83140A (2012) https://doi.org/10.1117/12.911497
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Dynamic Positron Emission Tomography is a powerful tool for quantitative imaging of in vivo biological processes. The long scan durations necessitate motion correction, to maintain the validity of the dynamic measurements, which can be particularly challenging due to the low signal-to-noise ratio (SNR) and spatial resolution, as well as the complex tracer behaviour in the dynamic PET data. In this paper we develop a novel automated expectation-maximisation image registration framework that incorporates temporal tracer kinetic information to correct for inter-frame subject motion during dynamic PET scans. We employ the Zubal human brain phantom to simulate dynamic PET data using SORTEO (a Monte Carlo-based simulator), in order to validate the proposed method for its ability to recover imposed rigid motion. We have conducted a range of simulations using different noise levels, and corrupted the data with a range of rigid motion artefacts. The performance of our motion correction method is compared with pairwise registration using normalised mutual information as a voxel similarity measure (an approach conventionally used to correct for dynamic PET inter-frame motion based solely on intensity information). To quantify registration accuracy, we calculate the target registration error across the images. The results show that our new dynamic image registration method based on tracer kinetics yields better realignment of the simulated datasets, halving the target registration error when compared to the conventional method at small motion levels, as well as yielding smaller residuals in translation and rotation parameters. We also show that our new method is less affected by the low signal in the first few frames, which the conventional method based on normalised mutual information fails to realign.

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Jieqing Jiao, Cristian A. Salinas, Graham E. Searle, Roger N. Gunn, and Julia A. Schnabel "Joint estimation of subject motion and tracer kinetic parameters of dynamic PET data in an EM framework", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83140A (14 February 2012); https://doi.org/10.1117/12.911497

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