Optimal Phased-Array Signal Combination For Polyunsaturated Fatty Acids Measurement In Breast Cancer Using Multiple Quantum Coherence MR Spectroscopy At 3T

Vasiliki Mallikourti,Steven D Heys,Tanja Gagliardi,Yazan Masannat,Jiabao He,Sai Man Cheung

doi:10.1038/s41598-019-45710-1

Vasiliki Mallikourti, Steven D Heys + Show 4 more

Open Access

https://doi.org/10.1038/s41598-019-45710-1

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Abstract

Polyunsaturated fatty acid (PUFA), a key marker in breast cancer, is non-invasively quantifiable using multiple quantum coherence (MQC) magnetic resonance spectroscopy (MRS) at the expense of losing half of the signal. Signal combination for phased array coils provides potential pathways to enhance the signal to noise ratio (SNR), with current algorithms developed for conventional brain MRS. Since PUFA spectra and the biochemical environment in the breast deviate significantly from those in the brain, we set out to identify the optimal algorithm for PUFA in breast cancer. Combination algorithms were compared using PUFA spectra from 17 human breast tumour specimens, 15 healthy female volunteers, and 5 patients with breast cancer on a clinical 3 T MRI scanner. Adaptively Optimised Combination (AOC) yielded the maximum SNR improvement in specimens (median, 39.5%; interquartile range: 35.5–53.2%, p < 0.05), volunteers (82.4 ± 37.4%, p < 0.001), and patients (median, 61%; range: 34–105%, p < 0.05), while independent from voxel volume (rho = 0.125, p = 0.632), PUFA content (rho = 0.256, p = 0.320) or water/fat ratio (rho = 0.353, p = 0.165). Using AOC, acquisition in patients is 1.5 times faster compared to non-noise decorrelated algorithms. Therefore, AOC is the most suitable current algorithm to improve SNR or accelerate the acquisition of PUFA MRS from breast in a clinical setting.

Highlights

Polyunsaturated fatty acid (PUFA) spectra were acquired using multiple quantum coherence (MQC) magnetic resonance spectroscopy (MRS) from whole breast tumour specimens freshly excised from patients, healthy female volunteers and patients with breast cancer
There was a clear difference in signal to noise ratio (SNR) improvement between the noise decorrelated algorithms and the linear algorithms
Whitened Singular Value Decomposition (WSVD) gave a large variance of SNR improvement ranging from −100 to 77% dependent on the baseline SNR, since WSVD uses the PUFA spectra to estimate the weighting

Summary

Introduction

Weighting based on Signal of reference peak (typically water) multiplied by the inverted noise correlation matrix. Noise decorrelation combination (nd-comb) initially employs the Principal Component Analysis (PCA) to remove correlated noise before applying the S/N Weighting algorithm[18,19]. Whitened Singular Value Decomposition (WSVD)[21,22] applies the singular value decomposition[23,24] on the noise-decorrelated data to derive the weighting. WSVD exploits the whole noise-decorrelated spectra to estimate the weighting compared to the single reference peak used in nd-comb. Instead of eliminating the correlated noise as a preparation step, Adaptively Optimised Combination (AOC) algorithm derives the weighting from the amplitude of a reference peak (typically water) incorporating the inverted noise correlation matrix[10]. We systematically examined current signal combination algorithms on PUFA spectra acquired using MQC MRS in breast tumours

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