Abstract
With the rapidly-expanding sophistication in our understanding of cancer cell biology, molecular imaging offers a critical bridge to oncology. Molecular imaging through magnetic resonance spectroscopy (MRS) can provide information about many metabolites at the same time. Since MRS entails no ionizing radiation, repeated monitoring, including screening can be performed. However, MRS via the fast Fourier transform (FFT) has poor resolution and signal-to-noise ratio (SNR). Moreover, subjective and non-unique (ambiguous) fittings of FFT spectra cannot provide reliable quantification of clinical usefulness. In sharp contrast, objective and unique (unambiguous) signal processing by the fast Padé transform (FPT) can increase resolution and retrieve the true quantitative metabolic information. To illustrate, we apply the FPT to in vitro MRS data as encoded from malignant ovarian cyst fluid and perform detailed analysis. This problem area is particularly in need of timely diagnostics by more advanced modalities, such as high-resolution MRS, since conventional methods usually detect ovarian cancers at late stages with poor prognosis, whereas at an early stage the prognosis is excellent. The reliability and robustness of the FPT is assessed for time signals contaminated with varying noise levels. In the presence of higher background noise, all physical metabolites were unequivocally identified and their concentrations precisely extracted, using small fractions of the total signal length. Via the “signal-noise separation” concept alongside the “stability test”, all non-physical information was binned, such that fully denoised spectra were generated. These results imply that a reformulation of data acquisition is needed, as guided by the FPT in MRS, since a small number of short transient time signals can provide high resolution and good SNR. This would enhance the diagnostic accuracy of MRS and shorten examination times, thereby improving efficiency and cost-effectiveness of this high throughput cancer diagnostic modality. Such advantages could be particularly important for more effective ovarian cancer detection, as well as more broadly for improved diagnostics and treatment within oncology.