In vivo magnetic resonance spectroscopy for ovarian cancer diagnostics: quantification by the fast Padé transform

Abstract

Early-stage ovarian cancer has an excellent prognosis, but due mainly to late detection, ovarian cancer remains a major cause of cancer deaths among women. In vivo magnetic resonance spectroscopy (MRS) would be an excellent candidate for early ovarian cancer detection, being non-invasive, surpassing anatomic imaging to identify metabolic features of cancer, and free of ionizing radiation. However, the present meta-analysis of 13 studies indicates that with conventional Fourier-based processing, in vivo MRS insufficiently distinguished 134 cancerous from 114 benign ovarian lesions. The fast Padé transform (FPT), an advanced signal processor with high-resolution and parametric (quantification-equipped) capabilities is best qualified for MRS time signals from the ovary, as demonstrated in our earlier proof-of-concept studies. We now apply the FPT to MRS time signals encoded in vivo on a 3 T scanner, echo time of 30 ms, from a borderline serous cystic ovarian tumor. The FPT-produced total shape spectrum was better resolved than with Fourier processing. Spectra averaging through the FPT generated a denoised total shape spectrum. Subsequent parametric analysis reconstructed dense component spectra in the "usual" mode: absorption and dispersion components mixed and "ersatz" mode: reconstructed phases set to zero, thus eliminating interference effects. Numerous metabolites, including potential cancer biomarkers, were identified and quantified by the FPT, including isoleucine, valine, lipids, lactate, alanine, lysine, N-acetyl aspartate, N-acetylneuraminic acid, gluta- mine, choline, phosphocholine, myoinositol. Many of these are difficult or impossible to detect with Fourier plus fitting techniques for in vivo MRS of the ovary. These Padé-generated results are promising, overcoming major barriers hindering MRS from becoming a key method for non-invasively assessing ovarian lesions.

Iterative averaging of spectra as a powerful way of suppressing spurious resonances in signal processing

“…9a. In our benchmarking studies based on synthesized data associated with in vitro encoded time signals [50] from benign and cancerous ovary with added noise [21,51], the powerful noise separation capabilities of the FPT (+) were demonstrated. These capabilities of the FPT (+) are seen to be of vital importance in the clinical setting, as well, in which the FIDs are encoded at quite short total signal lengths using MR scanners of relatively weak magnetic field strength, B 0 = 1.5 T.…”

Matrix methods for Padé approximation: Numerical calculation of poles, zeros and residues

“…In these latter cases the signal is usually analyzed by looking at the signal frequencies in sliding "time windows" and plotting the frequencies found in each window vs. the window starting time; "super-resolution" (point c.(i) above) allows the use of shorter "windows" than FFT and to thus follow the changes in time of the system frequencies avoiding the often complicated sidebands that appear when using the longer "windows" required to obtain a sufficient resolution with FFT. For applications involving damped signals, we can mention magnetic resonance spectroscopy [8]; for damped chirped signals in high noise, gravitational wave bursts [24]; and for signals with frequencies oscillating in a complicated way, syncronized neuronal hyppocampal rythms.…”

Visualizing hidden components of envelopes non-parametrically in magnetic resonance spectroscopy: Phosphocholine, a breast cancer biomarker

“…Internal cross-validation is provided by the FPT (+) and FPT (−) against each other within the same Padé methodology, but using different algorithms [2,4,6]. Once convergence has been achieved by both of these two variants of the FPT, the final joint output list is produced from the spectral parameters that are generated by the FPT (+) and the FPT (−) .…”

“…The two approximations of the Green function G N (z −1 ) are provided by the expressions for G MRS time signals [1,4,6,[16][17][18]. Conversely, the FPT (−) attains the same resolution as that of the FFT by using fewer signal points, e.g.…”

“…Conversely, the FPT (−) attains the same resolution as that of the FFT by using fewer signal points, e.g. N /2 or so [6,16].…”