Magnetic resonance spectroscopy with high-resolution and exact quantification in the presence of noise for improving ovarian cancer detection

摘要

Ovarian cancer is the most common cause of death from gynecological malignancies in many developed countries. When confined to the ovary, the 5-year survival rates are over 90%, but detection methods are insufficiently accurate for widespread application. Consequently, ovarian cancer is typically diagnosed late, with poor prognosis. Magnetic resonance spectroscopy (MRS) could potentially improve ovarian cancer diagnosis, but is currently hampered by poor resolution for this small, moving organ. Advanced signal processing methods through the fast Padé transform (FPT) can improve resolution and provide quantitative metabolic information. We applied the FPT to noise-corrupted time signals generated according to in vitro MRS data as encoded from malignant ovarian cyst fluid. In the presence of background noise, the FPT converged using merely 1/8 of the full signal length N = 1024, amounting to some 128 data points. This number of time signal points permits reconstruction of altogether 128 spectral parameters for 64 ensuing resonances. Each resonance is quantified by 2 spectral parameters, the complex-valued frequencies and amplitudes. The FPT accurately reconstructed the spectral parameters for all twelve genuine resonances from which the input time signal is intrinsically built. Thereby, in the presence of noise, the FPT provided fully reliable estimates of metabolite concentrations characteristic of malignant ovarian cyst fluid. Through the practical concept of signal-noise separation by means of so-called pole-zero cancellations (Froissart doublets), the remaining 52 resonances from the total of 64 resonances were unequivocally identified as spurious and, as such, could confidently be discarded. Given that magnetic resonance-based modalities entail no exposure to ionizing radiation, if their diagnostic accuracy were improved, magnetic resonance imaging and spectroscopy could have broader applications in screening surveillance for early ovarian cancer detection, especially among women at high risk. The present results suggest that Padé-optimized MRS could help achieve that goal.