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

Area of Science:

• Magnetic Resonance Imaging
• Functional Magnetic Resonance Imaging (fMRI)
• Image Reconstruction

Background:

• Parallel imaging techniques in fMRI aim to reduce scan time but often suffer from inter-slice signal leakage.
• Multiband imaging accelerates fMRI acquisition by exciting multiple slices simultaneously, leading to aliasing artifacts.

Purpose of the Study:

• To present the Slice-wise EPI Calibration and Separation (SPECS) model for accelerated fMRI acquisition.
• To eliminate inter-slice signal leakage in multiband fMRI while minimizing scan time.
• To maintain optimal activation statistics in fMRI studies.

Main Methods:

• The SPECS model utilizes a least squares estimator to separate aliased slices from the inverse Fourier reconstruction of the combined k-space data.
• Slice separation is achieved without additional receiver coil information by employing aliased images with shifted FOV patterns and a bootstrapping approach with orthogonal Hadamard-patterned calibration images.

Main Results:

• The SPECS model successfully separates aliased slices with minimal impact on spatial and temporal resolution.
• Functional activation in the motor cortex was detected in a bilateral finger tapping fMRI experiment, even with an increased number of aliased slices.

Conclusions:

• The SPECS model effectively separates aliased images using calibration reference images and orthogonal polynomial coefficients.
• The method achieves virtually artifact-free separated images and reliable functional activation detection in fMRI studies.