Sparsity-Promoting Calibration for GRAPPA Accelerated Parallel MRI Reconstruction
Year: 2013
Type: article
Abstract: The amount of calibration data needed to produce images of adequate quality can prevent auto-calibrating parallel imaging reconstruction methods like generalized autocalibrating partially parallel acquisitions (GRAPPA) from achieving a high total acceleration factor. To improve the quality of calibration when the number of auto-calibration signal (ACS) lines is restricted, we propose a sparsity-promoting regularized calibration method that finds a GRAPPA kernel consistent with the ACS fit equations that yields jointly sparse reconstructed coil channel images. Several experiments evaluate the performance of the proposed method relative to unregularized and existing regularized calibration methods for both low-quality and underdetermined fits from the ACS lines. These experiments demonstrate that the proposed method, like other regularization methods, is capable of mitigating noise amplification, and in addition, the proposed method is particularly effective at minimizing coherent aliasing artifacts caused by poor kernel calibration in real data. Using the proposed method, we can increase the total achievable acceleration while reducing degradation of the reconstructed image better than existing regularized calibration methods.
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: G2776647444, Parallel Excitation Methods for High Field MRI, Biological Spatial Resolution Limits in fMRI, G434551613, G5520316217, Center for Functional Imaging Technologies, Adaptive Techniques for Robust High-Resolution Functional Magnetic Resonance Imag, G6671297155, Spiral Spectroscopic Human Neuroimaging, CAREER: Acquisition, Approximation, and Compression - An Integrated Study
OpenAlex ID: W2086903403