An auto-calibrated method for parallel imaging with spiral trajectory using sector-specific interpolation kernels was experimentally demonstrated to be robust and efficient.
The proposed auto-calibrated parallel spiral imaging method allows for efficient implementation and robust, accurate image reconstruction.
This work describes an auto-calibrated method for parallel imaging with spiral trajectory. The method is a k-space approach where an interpolation kernel, accounting for coil sensitivity factors, is derived from experimental data and used to interpolate the reduced data set in parallel imaging to estimate the missing k-space data. For the case of spiral imaging, this interpolation kernel is defined along radial directions so that missing spiral interleaves can be estimated directly from neighboring interleaves. This kernel is invariant along the radial direction but varies azimuthally. Therefore, the k-space is divided into angular sectors and sector-specific kernels are used. It is demonstrated experimentally that relatively few sectors are sufficient for accurate reconstruction, allowing for efficient implementation. The interpolation kernels can be derived either from a separate calibration scan or self-calibration data available with a dual-density spiral acquisition. The reconstruction method is implemented with two sampling strategies and experimentally demonstrated to be robust.
and et al. (Wed,) conducted a other in Magnetic Resonance Imaging. Auto-calibrated parallel spiral imaging was evaluated. An auto-calibrated method for parallel imaging with spiral trajectory using sector-specific interpolation kernels was experimentally demonstrated to be robust and efficient.