Cardiac Positron Emission Tomography (PET) can provide diagnostic information about myocardial perfusion and metabolism with excellent sensitivity. The main challenge is physiological motion of the heart due to breathing and heartbeat. Here we present a joint PET-MR image registration approach which provides non-rigid cardiac and respiratory motion information utilising image data from a simultaneous PET-MR scan of less than 4min. In addition, attenuation correction information is also derived from the MR scan. The motion information is utilised in a motion-corrected PET image reconstruction which improves PET image quality, enhances visualisation of small features and increases measured uptake values by 22±7%.
Patient population: Three patients (49±11years, 80±8kg, all male) were imaged 175±19min after 18F-FDG injection (334±17MBq).
Free-breathing data acquisition: Simultaneous PET-MR data was obtained during a 3:18min free-breathing scan. MR data acquisition was carried out with a triple-echo prototype Dixon-based GRE Golden angle Radial Phase Encoding (GRPE) sequence (TE = 1.2/2.7/4.2ms, FA = 10°) allowing for the reconstruction of attenuation correction (AC) maps and dynamic motion-resolved 3D image data7. Spatial resolution of MR was 1.9x3.2x3.2mm3 and 2.1x2.1x2mm3 for PET with a FOV covering the entire thorax including the arms.
Reconstruction of respiratory and cardiac motion resolved MR and PET images: The MR and PET data were first binned into 8 respiratory motion states based on a 1D MR self-navigator signal7. In a second step MR and PET data were separated into 8 cardiac motion states based on an external ECG. 4D motion-resolved MR images were reconstructed offline using a non-Cartesian iterative SENSE approach with temporal and spatial regularization8. 4D motion-resolved PET images were reconstructed using STIR (3D OSEM, 23 subsets, 3 iterations, 4 mm isotropic Gaussian post-filtering)9.
Respiratory and cardiac motion estimation: 3D non-rigid respiratory and cardiac motion was estimated using a spline-based registration algorithm (MIRTK10) by minimizing the following function:
$$\min_T \big( ( 1-\lambda ) S ( I_i^{MR} \odot T_i, I_{ref}^{MR}) + \lambda S ( I_i^{PET} \odot T_i, I_{ref}^{PET} ) + \sigma B(T_i) \big)$$
where S is the image similarity metric (normalised mutual information), Ii are the 3D images at different motion states i, Ti is a 3D non-rigid transformation for a certain motion state and B is a regularisation term of the spline interpolation. The weighting between MR and PET image information is determined by λ. Combining both respiratory and cardiac motion information provided 8 respiratory x 8 cardiac = 64 different motion fields. The reference motion states ref were end-expiration for respiratory and mid-diastole for cardiac motion.
AC images: From the three-point Dixon MR data fat and water images were reconstructed and separated into six different tissue types11. Literature-based attenuation values were assigned to the different tissue types to obtain AC images.
Motion-corrected PET image reconstruction (PET-MCIR): Respiratory and cardiac motion fields were utilised in PET-MCIR to compensate for both types of physiological motion. The AC images were transformed to the different motion states to ensure accurate quantification. The reconstruction was carried out with STIR (3D OSEM, 23 subsets, 3 iterations, 4 mm isotropic Gaussian post-filtering).
Evaluation: We reconstructed PET images from motion fields with λ = 0.5 (joint PET-MR) and compared them to λ = 0 (PET-only) and λ = 1 (MR-only).
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