Joint Altered Calibration with Linear Interpolation for Cross-Sampling

EasyChair Preprint 15905

Abstract

Among the methods of sampling trajectories, cross-sampling can provide more autocalibration signal (ACS) data and enables better incoherent sampling with lower gradient control system requirements. However, there were still data registration regarding magnetic field inhomogeneities. So in this study, a self-calibrated off-resonance correction is proposed to coregistrate the inconsistency between the orthogonal readout data. This method employs a linear prediction operator to estimate the cross-sampled data and the corrected data undergo the same calibration, and reconstruction process as that used in conventional generalized autocalibrating partially parallel acquisitions (GRAPPA), in order to recover the missing k-space data and generate the final image. We will compare this calibration method with the traditional first-order k-space trajectory correction for cross-sampling autocalibrating partially parallel acquisitions (CS-GRAPPA) and the experimental results on phantoms and human subjects demonstrate that the images reconstructed by this method exhibit fewer artifacts and higher signal-to-noise ratio (SNR).

Keyphrases: Autocalibration, Magnetic Resonance Imaging (MRI), cross-sampling, linear interpolation

@booklet{EasyChair:15905,
  author    = {Lin Kaiwen and Zhou Jiantai and Yuan Pei and Wang Changliang Wang and Luo Penghui and Chen Lin},
  title     = {Joint Altered Calibration with Linear Interpolation for Cross-Sampling},
  howpublished = {EasyChair Preprint 15905},
  year      = {EasyChair, 2025}}