Autocalibrated wave-CAIPI reconstruction; Joint optimization of k-space trajectory and parallel imaging reconstruction

作者：Cauley Stephen F^*; Setsompop Kawin; Bilgic Berkin; Bhat Himanshu; Gagoski Borjan; Wald Lawrence L

来源：Magnetic Resonance in Medicine, 2017, 78(3): 1093-1099.

DOI：10.1002/mrm.26499

• joint optimization
• gradient trajectory
• wave-CAIPI
• autocalibrated

PurposeFast MRI acquisitions often rely on efficient traversal of k-space and hardware limitations, or other physical effects can cause the k-space trajectory to deviate from a theoretical path in a manner dependent on the image prescription and protocol parameters. Additional measurements or generalized calibrations are typically needed to characterize the discrepancies. We propose an autocalibrated technique to determine these discrepancies.
MethodsA joint optimization is used to estimate the trajectory simultaneously with the parallel imaging reconstruction, without the need for additional measurements. Model reduction is introduced to make this optimization computationally efficient, and to ensure final image quality.
ResultsWe demonstrate our approach for the wave-CAIPI fast acquisition method that uses a corkscrew k-space path to efficiently encode k-space and spread the voxel aliasing. Model reduction allows for the 3D trajectory to be automatically calculated in fewer than 30 s on standard vendor hardware. The method achieves equivalent accuracy to full-gradient calibration scans.
ConclusionsThe proposed method allows for high-quality wave-CAIPI reconstruction across wide ranges of protocol parameters, such as field of view (FOV) location/orientation, bandwidth, echo time (TE), resolution, and sinusoidal amplitude/frequency. Our framework should allow for the autocalibration of gradient trajectories from many other fast MRI techniques in clinically relevant time. Magn Reson Med 78:1093-1099, 2017.

• 出版日期2017-9
• 单位河北医科大学; MIT