We have implemented a whole-heart 3D cones coronary magnetic resonance angiography (CMRA) method that acquires a 3D image-based navigator (iNAV) every heartbeat to monitor respiratory motion in every region of the heart (Fig. 1)¹. In this work, we develop a nonrigid-motion-correction method that derives both global and localized motion trajectories from these 3D iNAVs.

The proposed motion-correction method uses an autofocusing method that assembles the motion-corrected image on a pixel-by-pixel basis by selecting the best-focused pixel from a bank of candidate motion-corrected 3D images^2,3. Localized gradient entropy serves as the focusing metric⁴. 3D iNAVs are processed to extract both global and localized motion trajectories that generate the following three categories of candidate images.

1) Rigid-body-corrected image. A global rigid-body motion trajectory (translation and rotation) is estimated from the 3D iNAVs and used to correct the main scan data.

2) Localized-motion-corrected images based on 3D iNAVs. The 3D iNAVs consist of low spatial resolution (4.4 mm) frames acquired with high temporal resolution (per heartbeat). To estimate residual motion trajectories in localized regions of the heart, the 3D iNAVs are first rigid-body corrected and then processed by i) estimating nonrigid motion deformation fields between frames; ii) clustering pixels into regions with similar deformation-field behavior via k-means clustering (Fig. 2); and iii) averaging the deformation fields within each cluster to derive a 3D translational motion trajectory for that cluster. The number of clusters was empirically set to 32, leading to 32 localized motion trajectories. Correction based on each of these 32 trajectories is applied to the main scan data to augment the rigid-body correction, resulting in 32 candidate images.

3) Localized-motion-corrected images based on binned main data. To derive localized motion trajectories from images with higher spatial resolution than the 3D iNAVs, the main scan data (rigid-body-corrected) is divided into three bins, each representing a different respiratory phase. A similarity matrix analysis of the 3D iNAVs guides the binning of the main scan data. The resultant binned images have low temporal resolution (three frames) but high spatial resolution (1.2 mm), with relatively mild artifacts from k-space undersampling (Fig. 3). These frames are subjected to the same deformation-field clustering procedure described above to obtain localized translational motion trajectories that are each applied to the main scan data to augment the rigid-body correction. In this case, the number of clusters was set to 16, leading to 16 additional candidate images with localized motion correction.

To assess the method's performance, six normal subjects were scanned on a 1.5T GE Signa system using an 8-channel cardiac receiver array. The 3D cones CMRA scan was acquired with ATR-SSFP contrast with 1.2 mm isotropic resolution over a 28x28x14 cm³ FOV. Scan time was 509 heartbeats with 100% acquisition efficiency. A 3D iNAV with 4.4 mm isotropic resolution over the same 28x28x14 cm³ FOV was acquired every heartbeat in 176 ms using a 32-interleave variable-density cones scan reconstructed with ESPIRiT¹.

Quantitative vessel sharpness measurements using image edge profile acutance (IEPA)⁵ was compared between CMRA images reconstructed with no motion correction, basic 3D translational motion correction, and the proposed method. Five segments from each of three vessels--right coronary artery (RCA), left anterior descending (LAD) artery, left circumflex (LCx)--were analyzed, resulting in IEPA scores for a total of 90 vessel segments.

Figure 4 shows an example of images reconstructed with no correction, 3D translational motion correction, and the proposed method. The proposed method exhibits improved vessel sharpness in this subject. A summary of the vessel-sharpness measurements with IEPA is given in Fig. 5. Higher IEPA scores indicate increased sharpness. Compared to no correction, 3D translational motion correction and the proposed method increased the vessel sharpness by 13% and 19%, respectively. Out of the 90 vessel segments that were examined, 75 segments showed improved IEPA scores with the proposed method as compared to 3D translational motion correction.The proposed method extracts global and localized motion information from 3D iNAVs to perform nonrigid motion correction for CMRA. While global rigid-body correction by itself substantially improves vessel sharpnesss, additional correction based on localized motion trajectories leads to further improvement. 3D iNAVs acquired every heartbeat offer the flexibility to do binning and direct beat-to-beat localized motion estimation. The high temporal resolution of 3D iNAVs captures beat-to-beat nonrigid motion, while the binned data complements the iNAV information with high spatial resolution.