MR Spectroscopy in Prostate Cancer: New Algorithms to Optimize Metabolite Quantification

Abstract

Prostate cancer (PCa) is the most common non-cutaneous cancer in male subjects and the second leading cause of cancer-related death in developed countries. The necessity of a non-invasive technique for the diagnosis of PCa in early stage has grown through years. Proton magnetic resonance spectroscopy (1H-MRS) and proton magnetic resonance spectroscopy imaging (1H-MRSI) are advanced magnetic resonance techniques that can mark the presence of metabolites such as citrate, choline, creatine and polyamines in a selected voxel, or in an array of voxels (in MRSI) inside prostatic tissue. Abundance or lack of these metabolites can discriminate between pathological and healthy tissue. Although the use of magnetic resonance spectroscopy (MRS) is well established in brain and liver with dedicated software for spectral analysis, quantification of metabolites in prostate can be very difficult to achieve, due to poor signal to noise ratio and strong J-coupling of the citrate. The aim of this work is to develop a software prototype for automatic quantification of citrate, choline and creatine in prostate. Its core is an original fitting routine that makes use of a fixed step gradient descent minimization algorithm (FSGD) and MRS simulations developed with the GAMMA libraries in C++. The accurate simulation of the citrate spin systems allows to predict the correct J-modulation under different NMR sequences and under different coupling parameters. The accuracy of the quantifications was tested on measurements performed on a Philips Ingenia 3T scanner using homemade phantoms. Some acquisitions in healthy volunteers have been also carried out to test the software performance in vivo.

Fig 1. Block diagram of the homemade software for MRS signal analysis.

Fig 2

Comparison between acquired smoothed (green lines) and simulated (red lines) spectra, together with their absolute difference (blue lines), for the phantoms considered in the study and reported in Table 1: phantom a) n°1, b) n°2, c) n°3, d) n°4 and e) n°5. f) An example of the output file of the homemade software with the final simulation parameters obtained for phantom n°4.

Fig 3

Results of the homemade software relative to the metabolite spectra amplitudes versus their concentrations for a) Cit, b) Cho and c) Cr in phantoms. d) (Cho+Cr)/Cit estimated ratios versus real concentration ratios in phantoms.

Fig 4

Results of the AMARES (jMRUI software) relative to the metabolite spectra amplitudes versus their concentrations for a) Cit, b) Cho and c) Cr in phantoms. d) (Cho+Cr)/Cit estimated ratios versus real ratio concentrations in phantoms.

Fig 5. In vivo spectra comparison between acquired smoothed (green lines) and homemade software simulated (red lines) spectra, together with their absolute difference (blue lines).

a) Single voxel spectrum of the peripheral zone of the prostate (Voxel size: 1.5⨯1.5⨯1.5 cm³, 1024 pts in resolution), b-c) two spectra taken from 3-D PRESS of subject 1 with 1024 pts in resolution, d-e) two spectra taken from 3-D PRESS of subject 2 with 2048 pts in resolution, f) output of the quantification of the spectrum d).

Fig 6. Methylene protons of Cit are a strong-coupled spin system.

Fig 7. Linear regression analysis used to estimate the T₂ parameters.