Free-breathing cine imaging with motion-corrected reconstruction at 3T using SPiral Acquisition with Respiratory correction and Cardiac Self-gating (SPARCS)

Abstract

Purpose: To develop a continuous-acquisition cardiac self-gated spiral pulse sequence and a respiratory motion-compensated reconstruction strategy for free-breathing cine imaging.

Methods: Cine data were acquired continuously on a 3T scanner for 8 seconds per slice without ECG gating or breath-holding, using a golden-angle gradient echo spiral pulse sequence. Cardiac motion information was extracted by applying principal component analysis on the gridded 8 × 8 k-space center data. Respiratory motion was corrected by rigid registration on each heartbeat. Images were reconstructed using a low-rank and sparse (L+S) technique. This strategy was evaluated in 37 healthy subjects and 8 subjects undergoing clinical cardiac MR studies. Image quality was scored (1-5 scale) in a blinded fashion by 2 experienced cardiologists. In 13 subjects with whole-heart coverage, left ventricular ejection fraction (LVEF) from SPiral Acquisition with Respiratory correction and Cardiac Self-gating (SPARCS) was compared to that from a standard ECG-gated breath-hold balanced steady-state free precession (bSSFP) cine sequence.

Results: The self-gated signal was successfully extracted in all cases and demonstrated close agreement with the acquired ECG signal (mean bias, -0.22 ms). The mean image score across all subjects was 4.0 for reconstruction using the L+S model. There was good agreement between the LVEF derived from SPARCS and the gold-standard bSSFP technique.

Conclusion: SPARCS successfully images cardiac function without the need for ECG gating or breath-holding. With an 8-second data acquisition per slice, whole-heart cine images with clinically acceptable spatial and temporal resolution and image quality can be acquired in <90 seconds of free-breathing acquisition.

Keywords: cardiac MRI; golden angle; low-rank and sparse; motion correction; self-gating; spiral trajectory.

Figure 1:. Pipeline of cardiac self-gating.

(a) In the first step, the 8×8 center region of k-space is gridded across all coils through time. (b) Principal component analysis is then performed across this data to derive temporal-basis functions. (c) Frequency spectrum analysis is performed to separate the cardiac and respiratory components. (d) Extracted filtered cardiac motion component and peak detection is performed to detect the cardiac triggers. (e) A respiratory motion component can also be derived and used for self-gating as alternative to rigid registration.

Figure 2:. Pipeline of respiratory motion correction.

In the first step (a), retrospective cardiac binning of the data across multiple R-R intervals, was performed so that the heart could be located based on the variation in signal intensity due to cardiac motion throughout the cardiac cycle. Once the heart ROI was selected, further processing used only this defined ROI and data were averaged over part of individual heartbeats. In the second step (b), by using all of the spiral interleafs for each heartbeat, a single static image was created for each heartbeat and these images were registered to determine the respiratory motion. For the automatic coil selection (c), a reference image (blue) was reconstructed from a single heartbeat using a large temporal window and an aliased image (red) was reconstructed using a small temporal window. The images reconstructed from these two temporal windows provided an assessment of the artifact power, and coils with high SNR and low artifact power (circled by green dashed line) were selected appropriately.

Figure 3:. Cardiac gating consistency.

(a) Bland-Altman plot indicates a non-significant bias of −0.22 ms for the R-R interval length difference between self-gated signals and ECG signals across all the subjects. (b) There is a strong positive correlation relationship (R² = 0.96) between self-gated and ECG R-R interval lengths.

Figure 4:. Motion correction performance in a representative subject.

(a) A static image was reconstructed from each heartbeat to generate the images used for respiratory motion compensation. (b) The rigid registration displacement in x (anterior-posterior: A-P) and y (head-foot: H-F) directions was determined and used to correct the respiratory position. X-t and Y-t profiles before and after registration in the H-F and A-P directions (c) are shown in (d). The heart borders are more closely aligned following respiratory motion correction.

Figure 5:. Automatic coil selection.

(a) and (b) show the reconstructed images before and after the proposed coil selection method, respectively. (c) is the difference image between (a) and (b), with 10-fold scaling to better visualize aliasing artifacts. The red arrow indicates aliasing caused by remote coils.

Figure 6:. Short axis SPARCS images from a healthy volunteer.

The first and second rows show SPARCS reconstructed images using NUFFT and L+S, respectively. The third row shows the clinically used breath-hold ECG-gated bSSFP images for comparison. End diastolic and end systolic images are shown in the first and second columns, respectively, and x-t profiles are shown in the last column. X positions are indicated as dashed yellow lines in end-diastolic images. Red arrows indicate aliasing artifacts that are obvious in NUFFT images but are reduced by the L+S technique. The image-quality scores for this subject from 2 cardiologists were: 4 and 3 for SPARCS NUFFT images; 5 and 4.5 for SPARCS L+S images; and 5 and 5 for breath-hold ECG-gated bSSFP images.

Figure 7:. Short axis SPARCS images from a clinical patient subject.

The first and second rows show SPARCS reconstructed images using NUFFT and L+S, respectively. The third row shows the clinically used breath-hold ECG-gated bSSFP images for comparison. End-diastolic and end-systolic images are shown in the first and second columns, respectively, and x-t profiles are shown in the last column. X positions are indicated as dashed yellow lines in end-diastolic images. Red arrows indicate susceptibility artifacts that often occur in clinical bSSFP images. The image-quality scores for this subject from 2 cardiologists were: 4 and 3.5 for SPARCS NUFFT images; 5 and 4.5 for SPARCS L+S images; and 3 and 3.5 for breath-hold ECG-gated bSSFP images.

Figure 8:. Blinded image quality scores for all subjects.

The bar plot shows the scores for SPARCS images using NUFFT and L+S, as well as breath-hold ECG-gated bSSFP images, graded in a blinded fashion by 2 cardiologists. * indicates a significant difference (p < 0.001).

Figure 9:. Whole-heart reconstruction results.

(a) The top 2 rows of images are L+S diastolic frames across all slices. (b) The bottom 2 rows of images are L+S systolic frames across all slices.

Figure 10:. Bland-Altman plots of EF for the subjects with whole-heart coverage.

(a) Bland-Altman plot of EF calculated from Cartesian bSSFP image results (Cartesian) and SPARCS NUFFT image results (NUFFT). (b) Bland-Altman plot of EF calculated from Cartesian bSSFP image results and SPARCS L+S image results (L+S).