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

Lookup NU author(s): Dr Jiabao He



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2019, The Author(s).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.

Publication metadata

Author(s): Mallikourti V, Cheung SM, Gagliardi T, Masannat Y, Heys SD, He J

Publication type: Article

Publication status: Published

Journal: Scientific Reports

Year: 2019

Volume: 9

Issue: 1

Print publication date: 01/12/2019

Online publication date: 25/06/2019

Acceptance date: 05/06/2019

Date deposited: 01/11/2022

ISSN (electronic): 2045-2322

Publisher: Nature Publishing Group


DOI: 10.1038/s41598-019-45710-1

PubMed id: 31239527


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