MR-based cardiac and respiratory motion correction of PET: application to static and dynamic cardiac 18F-FDG imaging

Abstract

Motion of the myocardium deteriorates the quality and quantitative accuracy of cardiac PET images. We present a method for MR-based cardiac and respiratory motion correction of cardiac PET data and evaluate its impact on estimation of activity and kinetic parameters in human subjects. Three healthy subjects underwent simultaneous dynamic ¹⁸F-FDG PET and MRI on a hybrid PET/MR scanner. A cardiorespiratory motion field was determined for each subject using navigator, tagging and golden-angle radial MR acquisitions. Acquired coincidence events were binned into cardiac and respiratory phases using electrocardiogram and list mode-driven signals, respectively. Dynamic PET images were reconstructed with MR-based motion correction (MC) and without motion correction (NMC). Parametric images of ¹⁸F-FDG consumption rates (K_i) were estimated using Patlak's method for both MC and NMC images. MC alleviated motion artifacts in PET images, resulting in improved spatial resolution, improved recovery of activity in the myocardium wall and reduced spillover from the myocardium to the left ventricle cavity. Significantly higher myocardium contrast-to-noise ratio and lower apparent wall thickness were obtained in MC versus NMC images. Likewise, parametric images of K_i calculated with MC data had improved spatial resolution as compared to those obtained with NMC. Consistent with an increase in reconstructed activity concentration in the frames used during kinetic analyses, MC led to the estimation of higher K_i values almost everywhere in the myocardium, with up to 18% increase (mean across subjects) in the septum as compared to NMC. This study shows that MR-based motion correction of cardiac PET results in improved image quality that can benefit both static and dynamic studies.

Figure 1:

Schematic timeline of data acquisition. MR-AC: MR-based attenuation correction sequence, ‘tMR_x’: tagged MRI sequence with tag lines orientation parallel to x-axis.

Figure 2:

Illustration of positioning of (A) ROIs for one short-axis plane and (B) line profiles for calculation of myocardium CNR and apparent wall thickness, respectively.

Figure 3:

Examples of images and motion fields estimated using tagged (A) and golden-angle radial (B) MR acquisitions in subject 1. Tagged and golden-angle images are shown in transverse and coronal orientations, respectively. The motion fields estimated with tagged (resp. golden-angle radial) MRI capture the movement of voxels between the reference end-diastolic (resp. end-exhalation) phases and all other cardiac (resp. respiratory) phases. The blue arrow in (B) indicates the placement of the navigator. LV: left ventricle, RV: right ventricle.

Figure 4:

Respiratory traces generated for all three subjects using list mode and MR navigator-based techniques. The left panels show plots of the two curves for a 150-sec window, while the right panels show the corresponding scatter plots.

Figure 5:

Short-axis and horizontal long-axis images of a late dynamic frame reconstructed with MC and NMC for subject 1. White arrows indicate locations where reconstructed wall activity is clearly higher in MC compared to NMC. Red arrows point to papillary muscles whose structure is more visible in MC images, indicating improved spatial resolution. Orange arrows indicate areas where spillover from the myocardium to the left-ventricle cavity is visibly reduced in MC images.

Figure 6:

Short-axis and horizontal long-axis images of a late dynamic frame reconstructed with MC and NMC for subject 2. White arrows indicate locations where reconstructed wall activity is clearly higher in MC compared to NMC. Red arrows point to papillary muscles whose structure is more visible in MC images, indicating improved spatial resolution. Orange arrows indicate areas where spillover from the myocardium to the left-ventricle cavity is visibly reduced in MC images.

Figure 7:

Percent changes in myocardium CNR and wall thickness between MC vs. NMC images for all three subjects. Overall, MC images have higher CNR and lower wall thickness than NMC in all four myocardium sectors.

Figure 8:

Same transverse slice across dynamic frames for NMC and MC methods (subject 2). Note that, to facilitate comparison of NMC and NMC images, the display range was adapted specifically for each dynamic frame. The first 4 frames after injection are not included in the figure as they were not corrected for motion. TOI = time of injection.

Figure 9:

Short-axis and horizontal long-axis K_i slices obtained using MC and NMC dynamic images for subject 1. MC yielded higher K_i values than NMC, especially in regions evidenced by white arrows. Structures such as papillary muscles are also easier to delineate in MC K_i maps (see red arrows).

Figure 10:

Short-axis and horizontal long-axis K_i slices obtained using MC and NMC dynamic images for subject 2. MC yielded higher K_i values than NMC, especially in regions evidenced by the white arrows. Structures such as papillary muscles are also easier to delineate in MC K_i maps (see red arrows).

Figure 11:

Examples of Patlak plots for MC and NMC in one ROI taken in the base of the septum. (normalized time: integrated plasma activity over [0-t] / plasma activity (t)).

Figure 12:

Percent differences in myocardium K_i for MC vs. NMC for all three subjects. Motion correction yields higher K_i values almost everywhere but especially in the septum.