P-LORAKS: Low-rank modeling of local k-space neighborhoods with parallel imaging data.

Ac-loraks: Autocalibrated Low-rank Modeling of Local K-space Neighborhoods

Introduction: Low-rank modeling of local k-space neighborhoods (LORAKS) is a recent constrained MRI framework that can enable accurate image reconstruction from sparselyand unconventionally-sampled k-space data [1,2]. Specifically, Ref. [1] showed that the k-space data for MR images that have limited spatial support or slowly-varying image phase can be mapped into structured low-rank matrices, ...

Low-Rank Modeling of Local $k$-Space Neighborhoods (LORAKS) for Constrained MRI

Recent theoretical results on low-rank matrix reconstruction have inspired significant interest in low-rank modeling of MRI images. Existing approaches have focused on higher-dimensional scenarios with data available from multiple channels, timepoints, or image contrasts. The present work demonstrates that single-channel, single-contrast, single-timepoint k-space data can also be mapped to low-...

Low-Rank Modeling of Local k-Space Neighborhoods (LORAKS): Implementation and Examples for Reproducible Research

Simultaneous Multi-slice MRI Reconstruction using LORAKS

Introduction The combination of simultaneous multi-slice (SMS) acquisition with parallel imaging enables highly-accelerated MRI experiments [1,2]. However, most existing methods use highly structured k-space sampling, and require either prior knowledge of the coil sensitivity maps [1,2] or autocalibration data [3]. In this work, we propose a constrained reconstruction framework that works with ...

Improving parallel imaging by jointly reconstructing multi-contrast data.

PURPOSE To develop parallel imaging techniques that simultaneously exploit coil sensitivity encoding, image phase prior information, similarities across multiple images, and complementary k-space sampling for highly accelerated data acquisition. METHODS We introduce joint virtual coil (JVC)-generalized autocalibrating partially parallel acquisitions (GRAPPA) to jointly reconstruct data acquir...