Rapid 3D dynamic arterial spin labeling with a sparse model-based image reconstruction

Abstract

Dynamic arterial spin labeling (ASL) MRI measures the perfusion bolus at multiple observation times and yields accurate estimates of cerebral blood flow in the presence of variations in arterial transit time. ASL has intrinsically low signal-to-noise ratio (SNR) and is sensitive to motion, so that extensive signal averaging is typically required, leading to long scan times for dynamic ASL. The goal of this study was to develop an accelerated dynamic ASL method with improved SNR and robustness to motion using a model-based image reconstruction that exploits the inherent sparsity of dynamic ASL data. The first component of this method is a single-shot 3D turbo spin echo spiral pulse sequence accelerated using a combination of parallel imaging and compressed sensing. This pulse sequence was then incorporated into a dynamic pseudo continuous ASL acquisition acquired at multiple observation times, and the resulting images were jointly reconstructed enforcing a model of potential perfusion time courses. Performance of the technique was verified using a numerical phantom and it was validated on normal volunteers on a 3-Tesla scanner. In simulation, a spatial sparsity constraint improved SNR and reduced estimation errors. Combined with a model-based sparsity constraint, the proposed method further improved SNR, reduced estimation error and suppressed motion artifacts. Experimentally, the proposed method resulted in significant improvements, with scan times as short as 20s per time point. These results suggest that the model-based image reconstruction enables rapid dynamic ASL with improved accuracy and robustness.

Keywords: Arterial spin labeling; Brain perfusion MRI; Compressed sensing; Dynamic ASL; Model-based sparsity; Single-shot spiral ASL.

Fig. A

Performance of model-based image reconstruction on dynamic ASL images with background suppression for two volunteers. Each image was reconstructed using the standard gridding method and the proposed method. For each subject, the top two rows show ASL images at 9 OTs, and the bottom two rows show the CBF and ATT maps calculated from these images using different methods. The arrows illustrate regions with improved SNR with the proposed method. With the model-based constraint, the proposed method suppressed the background noise and reduced the residual in perfusion model regression. Units: Dynamic model fitting residual (a.u.), CBF maps (ml/100g/min) and ATT maps (seconds).

Fig. B

Second case of accelerated dynamic ASL with model-based reconstruction, single-shot 3D spiral k-space trajectories and background suppression. Results from the first case are shown in Fig. 10. The residual in the 60s scan and conventional reconstruction with gridding method (mean residual = 4.2e-6) was reduced by the model-based constraint (mean residual = 2.3e-6). The quality of images was maintained with 40 s / OT (mean residual = 2.7e-6) and 20 s / OT (mean residual = 4.3e-6). Units: Dynamic model fitting residual (a.u.). CBF maps (ml/100g/min). ATT maps (seconds).

Fig. 1

Evolution of dynamic ASL signals in a numerical phantom illustrating the underlying assumptions of this work. The signal from an individual pixel is temporally slowly varying. The dynamic signals in different pixels (blue and red) follow the same nonlinear perfusion model with different parameter values.

Fig. 2

Overview of model-based reconstruction. The acquired data corresponding to each spiral interleaf are processed separately. Each label image dataset is subtracted from the corresponding control image dataset to generate k-space data with ASL contrast. The ASL images are recovered in a model-based iterative reconstruction by pursuing model-based sparsity, spatial sparsity and data consistency across multiple-channel measurements.

Fig. 3

Dual-density 3D spiral k-space trajectory. In this 3D spiral TSE pulse sequence, each echo train samples a particular spiral interleaf while the z direction is encoded with a centric view order (a). Each dual-density spiral interleaf fully samples the center of k-space (dashed line in c), providing auto-calibration data for parallel image reconstruction. With different initial spiral angles (b), multiple interleaves can be combined into an oversampled k-space (solid line in c) and reconstructed by a simple gridding method for comparison with accelerated image reconstruction methods.

Fig. 4

The trained dictionary represents the ASL signal. For noiseless data (a), only a few prototypes are needed to represent the signal with high accuracy. For noisy data (b), image denoising can be performed by projecting the signal onto a few prototypes. One selected ASL pixel illustrates that a dictionary representation can approximate the noiseless signal more accurately than the noisy signal itself (c).

Fig. 5

Model-based sparsity reduces artifacts in multi-OT ASL images. In the noiseless case, one of the dynamic ASL images contained motion artifacts (a) from subtraction between control and label images. By projecting the multi-OT signal onto the model-based dictionary, the motion artifact was reduced dramatically (b), because its dynamic pattern was distinct from the ASL model. As a reference, the noiseless and artifact-free image is shown in (c). Similar results were seen with noisy data (d, e). All images are windowed the same, demonstrating the magnitude of the motion artifacts.

Fig. 6

Regularization parameter searching in compressed sensing. Left: RMSE changed with spatial TV weights only. Based on the RMSE, the best TV weight was fixed at 0.0033. By adding the K-SVD constraint, the error is further reduced (right).

Fig. 7

Simulated ASL images reconstructed by compressed sensing. The complex Gaussian noise in initial images (a) was suppressed by the compressed sensing reconstruction with spatial TV constraint (b) and model-based sparsity (c). By combining spatial and model-based sparsity, the image quality is improved further (d). The noiseless image (e) is shown for reference.

Fig. 8

Dynamic pCASL perfusion images from selected OTs (2600, 3100, 3600, 4100 ms) in a normal volunteer. The gridding reconstruction (a) shows high motion artifacts and background noise. The SPIRiT reconstruction (b) eliminated many of the artifacts in the gridding reconstruction. The model-based reconstruction suppressed background noise and reduced the estimation error in the CBF (c). As the arrow highlights, the motion artifacts obtained using the gridding method (a) and SPIRiT reconstruction (b) were suppressed when the model-based sparsity constraint was used (c). When using only 1/3 of the acquired data and reducing the scan time to 40s at each OT, the SNR of perfusion images dropped substantially in the Grid (d) and SPIRiT (e) reconstruction and there was more fitting error, as shown by the CBF calculation residual. Again, the proposed method improved the images, reduced the fitting error, and provided a similar CBF map to the high-SNR results (f). Units: Dynamic model fitting residual (a.u.). CBF maps (ml/100g/min). ATT maps (seconds).

Fig. 9

ASL image SNR and CBF estimation residual in volunteers (N = 6, mean ± standard deviation). ROIs of grey matter (GM) and white matter (WM) were chosen based on T1 value. Compared with gridding and parallel image reconstruction, the proposed method improved SNR (a) and reduced estimation residuals (b) significantly. With 1/3 of the data, the proposed method also provided better structural similarity to the high SNR results (c). (In the same ROI, * P < 0.05 versus the parallel reconstruction method; ^$ P < 0.05 versus the gridding method).

Fig. 10

Accelerated dynamic ASL with model-based reconstruction, single-shot 3D spiral k-space trajectories and background suppression. With a scan time of 60 s / OT measurement, the model-based reconstruction reduced the background noise and model residual (mean residual = 4.6e-6), compared with conventional gridding reconstruction (mean residual = 7.1e-6). The proposed method maintained the image quality with a scan time of 40 s / OT (mean residual = 5.7e-6) and provided moderate image quality with a 20 s / OT measurement (mean residual = 9.5e-6). Units: Dynamic model fitting residual (a.u.). CBF maps (ml/100g/min). ATT maps (seconds).

Fig. 11

3D CBF and ATT maps for the second case of accelerated dynamic ASL. CBF maps (ml/100g/min). ATT maps (seconds).