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Lookup NU author(s): Dr Jiabao He
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2020, The Author(s).Lipid composition in breast cancer, a central marker of disease progression, can be non-invasively quantified using 2D MRS method of double quantum filtered correlation spectroscopy (DQF-COSY). The low signal to noise ratio (SNR), arising from signal retention of only 25% and depleted lipids within tumour, demands improvement approaches beyond signal averaging for clinically viable applications. We therefore adapted and examined combination algorithms, designed for 1D MRS, for 2D MRS with both internal and external references. Lipid composition spectra were acquired from 17 breast tumour specimens, 15 healthy female volunteers and 25 patients with breast cancer on a clinical 3 T MRI scanner. Whitened singular value decomposition (WSVD) with internal reference yielded maximal SNR with an improvement of 53.3% (40.3–106.9%) in specimens, 84.4 ± 40.6% in volunteers, 96.9 ± 54.2% in peritumoural adipose tissue and 52.4% (25.1–108.0%) in tumours in vivo. Non-uniformity, as variance of improvement across peaks, was low at 21.1% (13.7–28.1%) in specimens, 5.5% (4.2–7.2%) in volunteers, 6.1% (5.0–9.0%) in peritumoural tissue, and 20.7% (17.4–31.7%) in tumours in vivo. The bias (slope) in improvement ranged from − 1.08 to 0.21%/ppm along the diagonal directions. WSVD is therefore the optimal algorithm for lipid composition spectra with highest SNR uniformly across peaks, reducing acquisition time by up to 70% in patients, enabling clinical applications.
Author(s): Mallikourti V, Cheung SM, Gagliardi T, Senn N, Masannat Y, McGoldrick T, Sharma R, Heys SD, He J
Publication type: Article
Publication status: Published
Journal: Scientific Reports
Year: 2020
Volume: 10
Issue: 1
Print publication date: 01/12/2020
Online publication date: 18/11/2020
Acceptance date: 24/08/2020
Date deposited: 01/11/2022
ISSN (electronic): 2045-2322
Publisher: Nature Publishing Group
URL: https://doi.org/10.1038/s41598-020-74397-y
DOI: 10.1038/s41598-020-74397-y
PubMed id: 33208767
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