Compressed sensing-sensitivity encoding may reduce scan time without sacrificing image quality.

Background and purpose: Compressed sensing-sensitivity encoding is a promising MR imaging acceleration technique.

Compressed sensing (CS) may be useful for accelerating data acquisitions in high-resolution fMRI.

The proposed method outperformed the compressed sensing methods.

This study compares the image quality of compressed sensing-sensitivity encoding accelerated imaging with conventional MR imaging sequences.

Here we developed a sparse-signal recovery technique using compressed sensing to analyze images with highly overlapping fluorescent spots.

The performance of the proposed method was also compared to that of several compressed sensing (CS) algorithms.

Materials and methods: A 16-fold accelerated real-time cine CMR pulse sequence was developed using gradient echo readout, Cartesian k-space sampling, and compressed sensing.

The multiple respiratory-resolved 3D k-space data were subsequently reconstructed using a joint parallel imaging and compressed sensing method with spatial and temporal regularization.

Using methods and ideas from compressed sensing and group testing, we have developed a cost-effective genotyping protocol to detect carriers for severe genetic disorders.

By combining parallel imaging and compressed sensing in the reconstruction, streaking artifacts were mostly removed with improved delineation of fine structures using the proposed strategy.

There was 25% and 35% scan-time reduction with compressed sensing-sensitivity encoding for FLAIR and echo-spoiled gradient echo sequences, respectively.

For T2-FLAIR, image quality of the compressed sensing-sensitivity encoding images was within 10% of the conventional images on average for 3 of 5 metrics.