Sensitivity Enhancement of (Hyper-)CEST Image Series by Exploiting Redundancies in the Spectral Domain

TL;DR

This paper introduces two spectral redundancy exploitation techniques, PCA-based post-processing and low-rank reconstruction, to significantly enhance SNR in Hyper-CEST imaging, facilitating in vivo detection of xenon biosensors.

Contribution

It presents novel spectral domain methods to improve SNR in Hyper-CEST, enabling more effective in vivo biosensor detection.

Findings

Both methods significantly increase SNR in Hyper-CEST data.

PCA-based processing is applicable to proton CEST experiments.

Low-rank reconstruction benefits hyperpolarized nuclei sampling.

Abstract

CEST has proven to be a valuable technique for the detection of hyperpolarized xenon-based functionalized contrast agents. Additional information can be encoded in the spectral dimension, allowing the simultaneous detection of multiple different biosensors. However, due to the low concentration of dissolved xenon in biological tissue, the signal to noise ratio (SNR) of Hyper-CEST data is still a critical issue. In this work, we present two techniques aiming to increase SNR by exploiting the typically high redundancy in spectral CEST image series: PCA-based post-processing and sub-sampled acquisition with low-rank reconstruction. Each of them yields a significant SNR enhancement, demonstrating the feasibility of the two approaches. While the first method is directly applicable to proton CEST experiments as well, the second one is particularly beneficial when dealing with hyperpolarized nuclei, since it distributes the non-renewable initial polarization more efficiently over the sampling points. The results obtained are a further step towards the detection of xenon biosensors with spectral Hyper-CEST imaging in vivo.