Highly accelerated real-time cardiac cine MRI using k-t SPARSE-SENSE

Abstract

For patients with impaired breath-hold capacity and/or arrhythmias, real-time cine MRI may be more clinically useful than breath-hold cine MRI. However, commercially available real-time cine MRI methods using parallel imaging typically yield relatively poor spatio-temporal resolution due to their low image acquisition speed. We sought to achieve relatively high spatial resolution (∼2.5 × 2.5 mm(2)) and temporal resolution (∼40 ms), to produce high-quality real-time cine MR images that could be applied clinically for wall motion assessment and measurement of left ventricular function. In this work, we present an eightfold accelerated real-time cardiac cine MRI pulse sequence using a combination of compressed sensing and parallel imaging (k-t SPARSE-SENSE). Compared with reference, breath-hold cine MRI, our eightfold accelerated real-time cine MRI produced significantly worse qualitative grades (1-5 scale), but its image quality and temporal fidelity scores were above 3.0 (adequate) and artifacts and noise scores were below 3.0 (moderate), suggesting that acceptable diagnostic image quality can be achieved. Additionally, both eightfold accelerated real-time cine and breath-hold cine MRI yielded comparable left ventricular function measurements, with coefficient of variation <10% for left ventricular volumes. Our proposed eightfold accelerated real-time cine MRI with k-t SPARSE-SENSE is a promising modality for rapid imaging of myocardial function.

Figure 1

a) 8-fold accelerated k_y-t sampling pattern varied along time. b) A schematic illustrating how the k_x-k_y-t sampling pattern is averaged over time to produce the resulting k_x-k_y sampling pattern. This k_x-k_y pattern represents the sampling used to perform self-calibration of coil sensitivities (see Figure 6 for cross-reference). White lines represent acquired samples.

Figure 2

Simulation results comparing the fully sampled reference cardiac cine data to retrospectively 8-fold accelerated k-t SPARSE-SENSE results with different sparsifying transforms with regularization weight 0.01: temporal FFT, temporal PCA, and temporal TV. a) In the zoomed view of the heart, temporal TV yielded the lowest RMSE. b) In the chest wall, temporal FFT yielded the lowest RMSE. c,d) Corresponding plots of RMSE for the heart and chest wall regions, respectively, as a function of regularization weight ranging from 0.005 to 0.05. These results show that temporal TV is superior to the other two sparsifying transforms for the dynamic region, whereas temporal FFT is superior to the other two transforms for the static region. Based on these results, we elected to use temporal TV as the primary sparsifying transform with regularization weight 0.01 and temporal FFT as the secondary transform with regularization weight 0.001.

Figure 3

Numerical simulation results (top row: end-diastolic images, middle row: end-systolic images, bottom row: spatial-temporal plots through the blood-myocardium boundary) comparing different R values using temporal TV with weight 0.01 and temporal FFT with weight 0.001: (first column) R = 1, (second column) R = 2, (third column) R =4, (fourth column) R = 6, (fifth column) R=8, and (sixth column) R = 10. These results show good results can be obtained up to R = 8.

Figure 4

Numerical simulation results comparing the a) fully-sampled data (R=1) to the retrospectively 8-fold undersampled reconstruction results using four different sparsifying transforms: b) temporal FFT, c) temporal PCA, d) temporal TV, and e) temporal TV + FFT. (First row) end-systolic SAX image, (second row) spatial-temporal profile from the SAX image, (third row) end-systolic LAX image, and (fourth row) spatial-temporal profile from the LAX image. Both temporal FFT and temporal PCA yielded more temporal blurring artifacts within the wall (arrows) than temporal TV and temporal TV+FFT.

Figure 5

a) Coil sensitivities calculated using an (left column) external reference acquisition (pre-scan) and (right column) self-calibration method. b) The resulting k-t SPARSE-SENSE images using externally and self-calibrated coil sensitivities. Note that two sets of data are very similar, suggesting that our self-calibration of coil sensitivities was robust.

Figure 6

Schematic flowchart of the image reconstruction method. a) Coil sensitivity maps were self-calibrated by averaging undersampled k-space data over time and computed using the adaptive array combination method. b) Multicoil, zero-filled k-space data, along with the corresponding coil sensitivity maps, were reconstructed using both temporal TV and temporal FFT as the sparsifying transforms, where regularization weight of temporal TV (λ₁) is ten times larger than that for temporal FFT (λ₂). F: 2D spatial FFT; S: coil sensitivity data; T₁: temporal TV; T₂: temporal FFT; x: image to be reconstructed; y: acquired undersampled k-space; λ₁ and λ₂: regularization parameters.

Figure 7

(Rows 1–2) End-diastolic and (rows 3–4) end-systolic images at multiple cardiac phases comparing (row 1, 3) breath-hold cine MRI and (row 2, 4) real-time cine MRI. Both image sets were acquired from a 29-year old (male) healthy subject. The real-time cine MRI produced averaged scores of 3.25, 4.3, 2.6, and 2.4 for image quality, temporal fidelity, artifact and noise level, respectively, whereas the corresponding scores for breath-hold cine MRI were 4.9, 5, 1.1, and 1.1, respectively. RT: real-time; BH: breath-hold. Note that the breath-hold cine MR images had higher spatial resolution than the real-time cine MR images (1.6 mm vs. 2.3 mm, respectively).

Figure 8

Bland-Altman plots illustrating good agreement between breath-hold cine MRI and real-time cine MRI for the following LV function measurements: (top, left) EDV (mean difference −15.2 ml [solid line]; lower and upper 95% limits of agreement −27.6 and −2.8 ml [dashed lines], respectively), (top, right) ESV (mean difference 2.1 ml [solid line]; lower and upper 95% limits of agreement −4.7 and 8.9 ml [dashed lines], respectively), (bottom, left) SV (mean difference −17.3 ml [solid line]; lower and upper 95% limits of agreement −31.3 and −3.3 ml [dashed lines], respectively), and (bottom, right) EF (mean difference −5.7 % [solid line]; lower and upper 95% limits of agreement −11.3 % and −0.1 % [dashed lines], respectively).

Figure 9

Proposed real-time cine MRI protocol with prospective ECG triggering to capture true end diastole, where images are continuously acquired through the second R-wave to visually identify true end diastole. This proposed approach yielded global function measurements in excellent agreement with breath-hold cine MRI with retrospective ECG gating.

Figure 10

Representative end-diastolic and end-systolic real-time cine images: (Top row) SAX view of a 26-year old (female) patient and (Bottom row) LAX view of a 36-year old (male) patient. The patient in SAX view exhibited averaged scores of 3.5, 4.5, 2, and 1.75 for image quality, temporal fidelity, artifact and noise level, respectively. The patient in LAX view exhibited averaged scores of 3.5, 3.75, 1.75, and 1.75 for image quality, temporal fidelity, artifact and noise level, respectively.