Separation of parallel encoded complex-valued slices (SPECS) from a single complex-valued aliased coil image

Abstract

Purpose: Achieving a reduction in scan time with minimal inter-slice signal leakage is one of the significant obstacles in parallel MR imaging. In fMRI, multiband-imaging techniques accelerate data acquisition by simultaneously magnetizing the spatial frequency spectrum of multiple slices. The SPECS model eliminates the consequential inter-slice signal leakage from the slice unaliasing, while maintaining an optimal reduction in scan time and activation statistics in fMRI studies.

Materials and methods: When the combined k-space array is inverse Fourier reconstructed, the resulting aliased image is separated into the un-aliased slices through a least squares estimator. Without the additional spatial information from a phased array of receiver coils, slice separation in SPECS is accomplished with acquired aliased images in shifted FOV aliasing pattern, and a bootstrapping approach of incorporating reference calibration images in an orthogonal Hadamard pattern.

Result: The aliased slices are effectively separated with minimal expense to the spatial and temporal resolution. Functional activation is observed in the motor cortex, as the number of aliased slices is increased, in a bilateral finger tapping fMRI experiment.

Conclusion: The SPECS model incorporates calibration reference images together with coefficients of orthogonal polynomials into an un-aliasing estimator to achieve separated images, with virtually no residual artifacts and functional activation detection in separated images.

Keywords: MRI; Multiband; Parallel slice; SPECS; Simultaneous multi-slice (SMS); fMRI.

Fig. 1

Aliasing N_S=4 slices (a) without applying any FOV shift, and (b) applying a FOV shift of (j-1)FOV/N_S to slice j=[1,..,N_S=4] prior to aliasing.

Fig. 2

A diagram outlining the data acquisition and processing pipeline to implement the SPECS model in four main steps.

Fig. 3

Aliased phantom acquisitions for A=2, the N_z=8 slices are acquired in N_p=2 packets, (a) the magnitude and phase for packet 1, and (b) the magnitude and phase for packet 2, each containing N_s=4 slices in N_acq=2 acquisitions.

Fig. 4

For the first TR, the SPECS model separates the magnitude and phase images acquired by a single coil, as shown in Fig. 3, (a) with the mean of randomly selecting calibration slices into eight magnitude and phase images with no visual residual aliasing artifacts for (b) A=2, and (c) A=4. The white numbering scheme in (b) and (c) corresponds to the packet the slice was initially aliased into.

Fig. 5

With the phantom simulation, fMRI activation statistics estimated with a complex-valued model in each voxel of each slice after separating 8 aliased slices with the SPECS model. Activation statistics are presented for data reconstructed from (a) eight acquisitions and eight packets, A=1, (b) two acquisitions and two packets, A=2, and (c) two acquisitions and one packet, A=4. Pink squares indicate regions of true functional activity in slices. The white numbering scheme corresponds to the packet the slice was initially aliased into.

Fig. 6

Aliased experimental data acquisitions for A=2, the N_z=8 slices are acquired in N_p=2 packets, (a) the magnitude and phase for packet 1, and (b) the magnitude and phase for packet 2, each containing N_s=4 slices in N_acq=2 acquisitions.

Fig. 7

For the first TR, the SPECS model separates the magnitude and phase images acquired by a single coil, as shown in Fig. 6, (a) with the mean of randomly selecting calibration slices into eight magnitude and phase images with no visual residual aliasing artifacts for (b) A=2, and (c) A=4. The white numbering scheme in (b) and (c) corresponds to the packet the slice was initially aliased into.

Fig. 8

With the experimental data simulation, fMRI activation statistics estimated with a complex-valued model in each voxel of each slice after separating 8 aliased slices with the SPECS model. Activation statistics are presented for data reconstructed from (a) eight acquisitions and eight packets, A=1, (b) two acquisitions and two packets, A=2, and (c) two acquisitions and one packet, A=4. Pink squares indicate regions of true functional activity in slices. The white numbering scheme corresponds to the packet the slice was initially aliased into.