Evaluating different methods of MR-based motion correction in simultaneous PET/MR using a head phantom moved by a robotic system

Abstract

Background: Due to comparatively long measurement times in simultaneous positron emission tomography and magnetic resonance (PET/MR) imaging, patient movement during the measurement can be challenging. This leads to artifacts which have a negative impact on the visual assessment and quantitative validity of the image data and, in the worst case, can lead to misinterpretations. Simultaneous PET/MR systems allow the MR-based registration of movements and enable correction of the PET data. To assess the effectiveness of motion correction methods, it is necessary to carry out measurements on phantoms that are moved in a reproducible way. This study explores the possibility of using such a phantom-based setup to evaluate motion correction strategies in PET/MR of the human head.

Method: An MR-compatible robotic system was used to generate rigid movements of a head-like phantom. Different tools, either from the manufacturer or open-source software, were used to estimate and correct for motion based on the PET data itself (SIRF with SPM and NiftyReg) and MR data acquired simultaneously (e.g. MCLFIRT, BrainCompass). Different motion estimates were compared using data acquired during robot-induced motion. The effectiveness of motion correction of PET data was evaluated by determining the segmented volume of an activity-filled flask inside the phantom. In addition, the segmented volume was used to determine the centre-of-mass and the change in maximum activity concentration.

Results: The results showed a volume increase between 2.7 and 36.3% could be induced by the experimental setup depending on the motion pattern. Both, BrainCompass and MCFLIRT, produced corrected PET images, by reducing the volume increase to 0.7-4.7% (BrainCompass) and to -2.8-0.4% (MCFLIRT). The same was observed for example for the centre-of-mass, where the results show that MCFLIRT (0.2-0.6 mm after motion correction) had a smaller deviation from the reference position than BrainCompass (0.5-1.8 mm) for all displacements.

Conclusions: The experimental setup is suitable for the reproducible generation of movement patterns. Using open-source software for motion correction is a viable alternative to the vendor-provided motion-correction software.

Keywords: BrainCompass; MCFLIRT; Motion correction; NiftyReg; PET-MRI; Phantom; SIRF; SPM.

Fig. 1

Schematic representation of the InnoMotion robotic system. The possible directions of movement are indicated by the coloured arrows. Translational movements were performed along the z-axis and rotations around the x-axis (${\theta }_1$). Innomedic GmbH [28]

Fig. 2

Commercial skull model and head phantom (left, [30]), corresponding $T_1$ MPRAGE of the head phantom (middle, for the sequence parameters see Table 1) and PET image of the filled flask within the phantom (right). The PET image (right) shows one of the three markers

Fig. 3

InnoMotion robot system and experimental setup for generating a translation motion (left). Extension of the setup to create rotations around the transverse axis (right). The pulling motion of the robot arm can be converted into a rotational motion (see red arrows)

Fig. 4

Framework for reconstruction of equidistant frames using MCFLIRT. (1) The PET data was split into frames and reconstructed by the manufacturer software (Siemens). (2) The MR data (EPI) were spatially and temporally adapted to the PET data. EPI data were interpolated by miconv (ODIN [35]) which uses an Akima spline [40] for this purpose. (3) MCFLIRT was executed based on the adapted EPI data. (4) The resulting transformation matrices are then applied to the PET frames. (5) Finally, all motion-corrected frames are summed

Fig. 5

Framework for motion-adapted reconstruction using MCFLIRT. (1) MCFLIRT was applied to the EPI data. (2) An algorithm then determined the frames to be reconstructed. (3) Based on the defined movement intervals, the PET data were reconstructed on the Siemens console. (4) The framework from Fig. 4 was used to obtain a motion-corrected image

Fig. 6

Schematic representation of the evaluation method. After reconstruction, three or four datasets were available: motion-uncorrected, BrainCompass and MCFLIRT (30 $\times$ 20s and if relevant motion-adapted). To obtain MCFLIRT corrected images, the frameworks of Figs. 4 and 5 had to be applied

Fig. 7

Results of MoCo procedures for different translation amplitudes by pulling the head phantom. Shown are the motion-uncorrected volume (black), BrainCompass (green) and MCFLIRT with equidistant frames (blue). The values were averaged over four identical motion amplitudes with the error bar in y-direction as the SD. The error bar in x-direction shows the fluctuations of the measured amplitude (SD)

Fig. 8

Representation of the absolute deviation of the COM without any MoCo and after MoCo using BrainCompass and MCFLIRT. The red line symbolizes 0 mm distance to the COM before the motion (reference). The values represent the Euclidean distance between two points in space

Fig. 9

Representation of the relative deviation of $A_{\mathit{max}}$ without any MoCo and after application of BrainCompass and MCFLIRT. The red line symbolizes $A_{\mathit{max}}$ for the reference (before motion)

Fig. 10

Schematic representation of the motion-adapted frames for Alternative 1 (left) and for Alternative 2 (right). The blue line represents the absolute translation amplitude and the red lines symbolize the frame boundaries

Fig. 11

Comparison of different registration methods on a single translation movement. The robotic movement (black line) symbolizes the ground truth. Upper image: EPI-based; lower image: NAC-based. Shown is the absolute translation displacement depending on time

Fig. 12

Comparison of different registration methods of multiple translation movements. The robotic movement (black line) symbolizes the ground truth. Upper image: EPI-based; lower image: NAC-based. Shown is the absolute translation displacement depending on time