Phased-array combination of 2D MRS for lipid composition quantification in patients with breast cancer

www.nature.com/scientificreports OPEN Phased‑array combination of 2D MRS for lipid composition quantification in patients with breast cancer Vasiliki Mallikourti1*, Sai Man Cheung1, Tanja Gagliardi1,2, Nicholas Senn1, Yazan Masannat3, Trevor McGoldrick4, Ravi Sharma4, Steven D. Heys1,3,5 & Jiabao He1,5 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. Lipid composition is a central marker for the pathogenesis of breast c ancer1, 2, the most commonly diagnosed cancer among women3. Conventional magnetic resonance spectroscopy (MRS) of stimulated echo acquisition mode (STEAM) with short echo time can detect lipid spectral peaks in the breast non-invasively on standard clinical scanners4, and further enhancement in specificity is valuable for clinical applications. Spectral editing methods of double quantum filtering (DQF), effectively suppress background signals, but only target a single metabolite, such as polyunsaturated fatty acids (PUFA) in 1D MRS5. The two dimensional (2D) MRS method of correlation spectroscopy (COSY)6 resolves lipid composition on a 2D map, but suffers from the dominant water signal and wide peak spread7. DQF-COSY, combining the strength of spectral editing and 2D MRS, allows unobscured identification of individual lipid resonances through sharp peak appearance and suppression of water contamination signals8. However, both the signal retention of only 25% in DQF-COSY7 and depleted lipids within breast tumours59 contribute to low signal to noise ratio (SNR), posing a challenge for accurate quantification. Since DQF-COSY collects a series of 1D spectra demanding a long acquisition time (typical scan time of 15–20 min)10, SNR improvement approaches beyond signal averaging are required for clinically viable applications. Phased-array coils have been widely adopted in routine clinical practice, with signal combination algorithms developed to enhance SNR and reduce acquisition t ime11,12. Adaptively Optimised Combination (AOC)13, amongst current combination algorithms developed for 1D MRS (Table 1)13–16, is the optimal approach for spectra acquired in the brain using conventional M RS13 and PUFA spectra acquired in the breast using spectral editing MRS17. The SNR of a single spectral peak has been adopted as the common assessment criteria in the comparison of combination algorithms. However, lipid composition in 2D MRS is determined utilising multiple spectral peaks across the 2D map, demanding an algorithm with uniform improvement. In contrast to spectral editing MRS, DQF-COSY retains the presence of dominant metabolites, at reduced amplitude, for the estimation 1 Institute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, UK. 2Department of Radiology, Royal Marsden Hospital, London, UK. 3Breast Unit, Aberdeen Royal Infirmary, Aberdeen, UK. 4Department of Oncology, Aberdeen Royal Infirmary, Aberdeen, UK. 5These authors jointly supervised this work: Steven D. Heys and Jiabao He. *email: Scientific Reports | (2020) 10:20041 | https://doi.org/10.1038/s41598-020-74397-y 1 Vol.:(0123456789) www.nature.com/scientificreports/ Algorithms Description Equal weighting Adding after aligning in phase Signal weighting Aligning in phase and weighting with the signal of reference peak S/N weighting Aligning in phase and weighting with the SNR of reference peak S/N2 weighting Aligning in phase and weighting with the signal to the noise squared (S/N2) of reference peak nd-comb Noise decorrelation using PCA, then aligning in phase and weighting the noise decorrelated data using the SNR of reference peak WSVD Noise decorrelation using PCA, then aligning in phase and weighting the noise decorrelated spectra using the first left singular vector obtained from the singular value decomposition of the noise decorrelated spectra AOC Phasing and weighting with the signal of reference peak multiplied by the inverted noise correlation matrix Table 1.  Summary of signal combination algorithms designed for 1D MRS. AOC adaptively optimised combination, CV coefficient of variance, nd-comb noise decorrelated combination, PCA Principal Component Analysis, WSVD whitened singular value decomposition. • Ex vivo study 17 breast tumour specimens, excised from patients 32-phased array coil • • • Raw data In vivo study 15 healthy volunteers 25 patients with breast cancer 16-phased array coil Averaging across repeated acquisitions, apodisation, zero filling Algorithms: 72 DQF-COSY spectra acquired from tumours ex vivo (N=17) and in vivo breast tissue (both breasts, N=30), tumour (N=10), and peritumoural tissue (N=15) Application of signal combination algorithms AOC Signal combination • S/N2 Weighting S/N Weighting Signal Weighting Equal Weighting nd-comb Noise decorrelation - PCA Weighting/phasing all signals at each corresponding coil element Summation of each coil element Comparison using SNR of methylene fat at (1.3,1.3) ppm 2D Fourier transform Evaluation of non-uniformity in SNR improvement across spectral region Combined DQF-COSY spectrum a WSVD b Figure 1.  Diagram of study design and data processing. (a) Study design. Combination algorithms were evaluated on DQF-COSY spectra acquired from ex vivo and in vivo experiments by comparing the SNR. (b) Processing steps. Combination algorithms were applied on DQF-COSY spectra after signal averaging, apodisation and zero filling. AOC = adaptively optimised combination, nd-comb = noise decorrelated combination, WSVD = whitened singular value decomposition. of sensitivities and phases (...truncated)