Abstract
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.
Highlights
Lipid composition in breast cancer, a central marker of disease progression, can be non-invasively quantified using 2D magnetic resonance spectroscopy (MRS) method of double quantum filtered correlation spectroscopy (DQF-COSY)
Optimised Combination (AOC)[13], amongst current combination algorithms developed for 1D MRS (Table 1)[13,14,15,16], is the optimal approach for spectra acquired in the brain using conventional M RS13 and polyunsaturated fatty acids (PUFA) spectra acquired in the breast using spectral editing MRS17
Current combination algorithms, designed for 1D MRS, were adapted and evaluated for lipid composition spectra from breast acquired using 2D MRS, with a particular focus on noise decorrelation algorithms. WSVDi was identified as the most effective signal combination approach in 2D MRS, instead of Adaptively Optimised Combination (AOC), the optimal algorithm for 1D M RS13,17. WSVDi provided maximal signal to noise ratio (SNR) improvement in patients (97% in peritumoural adipose tissue, 52% in tumour) and low non-uniformity of 6% and 21% respectively. WSVDi, eliminating the need for acquiring an additional reference spectrum, reduces scan time by 50–70% from 17 to 8 min in tumour and 5 min in peritumoural adipose tissue
Summary
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. 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 signals[8]. Both the signal retention of only 25% in DQF-COSY7 and depleted lipids within breast tumours[59] contribute to low signal to noise ratio (SNR), posing a challenge for accurate quantification. Noise decorrelation using PCA, aligning in phase and weighting the noise decorrelated data using the SNR of reference peak
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