Synergistic motion compensation strategies for positron emission tomography when acquired simultaneously with magnetic resonance imaging

Abstract

Subject motion in positron emission tomography (PET) is a key factor that degrades image resolution and quality, limiting its potential capabilities. Correcting for it is complicated due to the lack of sufficient measured PET data from each position. This poses a significant barrier in calculating the amount of motion occurring during a scan. Motion correction can be implemented at different stages of data processing either during or after image reconstruction, and once applied accurately can substantially improve image quality and information accuracy. With the development of integrated PET-MRI (magnetic resonance imaging) scanners, internal organ motion can be measured concurrently with both PET and MRI. In this review paper, we explore the synergistic use of PET and MRI data to correct for any motion that affects the PET images. Different types of motion that can occur during PET-MRI acquisitions are presented and the associated motion detection, estimation and correction methods are reviewed. Finally, some highlights from recent literature in selected human and animal imaging applications are presented and the importance of motion correction for accurate kinetic modelling in dynamic PET-MRI is emphasized. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

Keywords: motion correction; positron emission tomography and magnetic resonance imaging; resolution.

Figure 1.

Diagram of motion management strategies in PET-MRI. Motion can be tracked by creating surrogate signals from PET and/or MRI. Likewise, motion vectors can be created from both modalities. PET data can be subdivided in short frames/gates which include only a small fraction of motion. Motion correction of the PET data can be achieved by integrating motion vectors within reconstruction (MCIR) or by applying them to previously reconstructed images (RTA). (Online version in colour.)

Figure 2.

Motion correction results. A single slice of the motion-corrupted, corrected and reference PET and MRI images are shown. The difference images are presented in the fourth and fifth columns. The colour bars indicate the range of pixel intensities in the difference images (original images were scaled from 0 to 1). (Reproduced from Johnson et al. 2019. Rigid-body motion correction in hybrid PET/MRI using spherical navigator echoes. Physics in Medicine and Biology, 64, doi:10.1088/1361-6560/ab10b2 © Institute of Physics & Engineering in Medicine. Reproduced by permission of IOP Publishing. All rights reserved). [40]. (Online version in colour.)

Figure 3.

[¹⁸F]FDG PET-MRI study. (a) Maximum-intensity projection (MIP) non–attenuation-corrected, non-motion-corrected (NAC U); attenuation-corrected, non-motion-corrected (U); attenuation-corrected & motion-corrected (MC) PET images. (b) Axial PET slices with three lesions (arrows) that wrongly appear in the lungs in the uncorrected image (U) and correctly appear in the liver in the motion-corrected image (MC), along with fused T2-weighted half-Fourier–acquired single-shot turbo spin-echo MRI-PET images and MRI image alone. (This research was originally published in JNM. Manber, Thielemans, et al. Clinical impact of respiratory motion correction in simultaneous PET-MRI using a joint PET/MRI predictive motion model. The Journal of Nuclear Medicine 2018; 59, 1467–1473. © SNMMI) [42]. (Online version in colour)

Figure 4.

Visual comparison of PET images as obtained by: uncorrected data (a), motion correction (b), and gated reconstruction (c) containing a (motion-affected) lesion next to the hilum and a (static) osseous lesion in the lower spine. Magnified regions around these lesions are shown in the adjacent images. Note the enhanced sharpness and signal-to-noise ratio of the hilar lesion in the motion corrected data in comparison to the other two methods, while the bone lesion in the region with less motion varies less between the uncorrected and motion compensated reconstructions (Reproduced from Gratz M, Ruhlmann V, Umutlu L, Fenchel M, Hong I, Quick HH (2020) Impact of respiratory motion correction on lesion visibility and quantification in thoracic PET/MRI imaging. PLoS ONE 15, e0233209) [78]. (Online version in colour.)

Figure 5.

Identifying regions affected by motion: blurring and signal loss within the myocardium when non-motion corrected (non-MC) non-gated compared to motion corrected (MC) PET images as in the antero-lateral wall (solid line). Background regions were drawn in the blood pool on the right or left ventricle (dashed line). (Reproduced from Robson PM, Trivieri MG, Karakatsanis NA, Padilla M, Abgral R, Dweck MR, Kovacic JC, Fayad ZA. (2018) Correction of respiratory and cardiac motion in cardiac PET/MRI using MR-based motion modelling. Physics in Medicine and Biology 63, doi:10.1088/1361-6560/aaea97 © Institute of Physics & Engineering in Medicine. Reproduced by permission of IOP Publishing. All rights reserved). [62]. (Online version in colour.)

Figure 6.

Basal (top) and midventricular (bottom) short-axis slices as shown without motion correction and with motion compensated image reconstruction. (This research was originally published in JNM. Kolbitsch et al. Cardiac and respiratory motion correction for simultaneous cardiac PET/MR. The Journal of Nuclear Medicine 2017; 58, 846-852. © SNMMI [94]). (Online version in colour.)