Rigid-body motion correction in hybrid PET/MRI using spherical navigator echoes

Abstract

Integrated positron emission tomography and magnetic resonance imaging (PET/MRI) is an imaging technology that provides complementary anatomical and functional information for medical diagnostics. Both PET and MRI are highly susceptible to motion artifacts due, in part, to long acquisition times. The simultaneous acquisition of the two modalities presents the opportunity to use MRI navigator techniques for motion correction of both PET and MRI data. For this task, we propose spherical navigator echoes (SNAVs)—3D k-space navigators that can accurately and rapidly measure rigid body motion in all six degrees of freedom. SNAVs were incorporated into turbo FLASH (tfl)—a product fast gradient echo sequence—to create the tfl-SNAV pulse sequence. Acquiring in vivo brain images from a healthy volunteer with both sequences first compared the tfl-SNAV and product tfl sequences. It was observed that incorporation of the SNAVs into the image sequence did not have any detrimental impact on the image quality. The SNAV motion correction technique was evaluated using an anthropomorphic brain phantom. Following a stationary reference image where the tfl-SNAV sequence was acquired along with simultaneous list-mode PET, three identical PET/MRI scans were performed where the phantom was moved several times throughout each acquisition. This motion—up to 11° and 14 mm—resulted in motion artifacts in both PET and MR images. Following SNAV motion correction of the MRI and PET list-mode data, artifact reduction was achieved for both the PET and MR images in all three motion trials. The corrected images have improved image quality and are quantitatively more similar to the ground truth reference images.

3D rigid‐body motion information from spherical Lissajous navigators at small k‐space radii: A proof of concept

“…The technique offers a simple way to qualitatively detect and assess in vivo motion (see Figure 4); this feature is useful for hybrid MR-PET applications where motion detection guides rebinning of PET data before its reconstruction and subsequent realignment before a final averaging. 28 "Artificial" and real in vivo motion was quantitatively measured with sub-degree and sub-millimeter accuracies (Figures 3 and 5), similar to the results in previous works. 4,10 In the quantitative in vivo experiments, it was assumed that the reference methods, i.e., the FOV movements and image registrations, worked flawlessly, although they too certainly contain errors.…”

“…Phantom experiments revealed precisions of 0.014° and 0.013 mm (see Figure ), and motion parameters were stable over clinically relevant times. The technique offers a simple way to qualitatively detect and assess in vivo motion (see Figure ); this feature is useful for hybrid MR‐PET applications where motion detection guides rebinning of PET data before its reconstruction and subsequent realignment before a final averaging . “Artificial” and real in vivo motion was quantitatively measured with sub‐degree and sub‐millimeter accuracies (Figures and ), similar to the results in previous works .…”

CALIPER: A software for blood-free parametric Patlak mapping using PET/MRI input function

“…Retrospective motion correction approaches can effectively correct patient motion, if motion vectors can be optimally estimated throughout the lengthy PET acquisition. We previously demonstrated the feasibility of using ultra-fast MRI pulses embedded as navigator echoes in anatomical MRI sequences to retrospectively correct motion in brain PET and anatomical MRI, including relatively large head movements of up to 11 o rotations and 14 mm translations (Johnson et al, 2019). MRI navigators can be incorporated into serial anatomical and functional MRI acquisitions during standard PET/MR imaging to capture motion throughout the dynamic PET scan without increasing MRI scan time.…”

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

“…Scientists are trying to develop sequences that can operate in an interleaving fashion with other sequences or motion models which can help predict motion at any given time during the acquisition. An example of parallel sequences is given by Johnson et al ., who incorporated six degrees of freedom motion tracking spherical three-dimensional navigators into a turbo-FLASH sequence with no detrimental impact to image quality [ 40 ]. These models use minimal MRI information to correlate each motion of different parts of the body with a surrogate signal, which can be obtained from an external device or another MRI sequence—and this is why we refer to them as hybrid tracking methods.…”