Researchers and clinicians interested in high resolution diffusion imaging.For high resolution DWI, multishot EPI with fewer distortions is a preferred option against single-shot EPI. However, there are motion-induced shot-to-shot phase variations due to diffusion sensitization gradients. SYMPHONY ¹ and realigned GRAPPA ² are k-space reconstruction methods for navigated multishot DWI, which can obtain high-quality diffusion images by integrating information of all shots and coils. Nevertheless, SYMPHONY suffers a relatively long computation time, especially when large number of shots or coil arrays are applied. In this work, we proposed a shot-coil compression method for SYMPHONY to reduce its reconstruction time without sacrificing image quality.

For large coil arrays, the computation time of parallel imaging methods becomes longer due to the large datasets. To overcome the computation burden, coil compression techniques have been developed which can remove the redundancy in highly correlated multi-coil data. For multishot diffusion imaging, conventional coil compression methods that compress data only in coil dimension still result in relatively long reconstruction time due to a large number of shots. Since there are correlations between the data of different shots, we performed data compression along both shot and coil dimensions as an extension of geometric-decomposition coil compression (GCC) ³ method for multishot diffusion imaging. As shown in Fig. 1, the proposed method is divided into three steps,

1. Shift the k-space data of different shots to the same sampling pattern. The same operation is then performed to the corresponding 2D navigator of each shot.

2. Reshape the data to combine the coil and shot dimensions, then we get a new encoding dimension of $$$N_{c}\times N_{s}$$$, where $$$N_{c}$$$ is the number of coils, $$$N_{s}$$$ is the number of shots. 2D navigator data are used to solve the following minimization problem and obtain the compression matrix $$$A$$$ ³.

$$minimize(A_{x}) \; \sum_{x,k_{y}}\parallel (A_{x}^HA_{x}-I)d_{x}(k_{y}) \parallel$$

$$subject \, to \; A_{x}A_{x}^H=I$$

Here, $$$A_{x}$$$ is the compression matrix of the encoding dimension ($$$N_{c}\times N_{s}$$$) at spatial location $$$x$$$ and $$$d_{x}(k_{y})$$$ is the k-space data from all dimensions at spatial location $$$x$$$ and k-space coordinate $$$k_{y}$$$ .

3. Compress the aligned shot-coil encoding dimension using the compression matrix $$$A$$$.

After compression, the data size is largely reduced and SYMPHONY ¹ is used to reconstruct the diffusion images.

A simulation was designed to compare the shot-coil compression method with the conventional GCC method. A 32-channel non-diffusion weighted 8-shot dual spin-echo EPI image was used as a reference. Spatially random phases (third-order) were added to 8-shot data respectively, to simulate the motion-induced phase variations in diffusion weighted images. The matrix size of the data was 240×232. 240×16 ACS data in the center of k-space were used to calculate the compression matrix. Compression rate is defined as the ratio of original and compressed encoding dimensions. The proposed method was compared with the conventional GCC method at various compression rates. Single kernel GRAPPA SYMPHONY was used to reconstruct the simulated data.

In-vivo brain DTI data was also acquired to validate the feasibility of the proposed method. The multishot diffusion tensor images were acquired from a healthy volunteer on a Philips 3T scanner (Philips Healthcare, Best, The Netherlands) with the following parameters: number of shot=8, FOV=240×240 mm², slice thickness=4 mm, TR/TE=2500/77 ms, in-plane image resolution=1×1 mm², the number of diffusion directions=12 with b value=800 s/mm², navigator size=240×25, Number of Signals Averaged (NSA)=2.

Fig. 2 shows the nRMSEs of the proposed method and the conventional GCC at different compression rates in the simulation. When the compression rate is higher than 10, the shot-coil compression is much better than the conventional coil compression. The reconstruction time of SYMPHONY without compression is 8.02s for a single image, and it is significantly reduced to 0.29s when the compression rate of the proposed method is 16. Fig. 3 shows the reconstructed images by SYMPHONY with and without the two compression methods, and the corresponding error maps (×10) with a compression rate of 16. The nRMSE of shot-coil compression method is lower than that of conventional GCC (3.32% versus 5.16%). In-vivo brain results are shown in Fig. 4. The high resolution FA maps reconstructed by the proposed method are close to those by SYMPHONY without compression.The simulation and the in-vivo experiment validated the ability of the shot-coil compression method to remarkably improve the computation efficiency of SYMPHONY without obvious image degradation. The proposed method can achieve about 20-fold acceleration in our experiments. Therefore, shot-coil compression is an effective computation acceleration method for high resolution multishot diffusion imaging.