Dataset Information

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.

SUBMITTER: Bellomo G

PROVIDER: S-EPMC5104319 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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

Bellomo Giovanni G Marcocci Francesco F Bianchini David D Mezzenga Emilio E D'Errico Vincenzo V Menghi Enrico E Zannoli Romano R Sarnelli Anna A

PloS one 20161110 11

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 poly ...[more]

PMID: 27832096

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Project description:Purpose To assess the feasibility of luminal water imaging, a quantitative T2-based magnetic resonance (MR) imaging technique, for the detection and grading of prostate cancer (PCa). Materials and Methods Eighteen patients with biopsy-proven PCa provided informed consent to be included in this institutional human ethics board-approved prospective study between January 2015 and January 2016. Patients underwent 3.0-T MR imaging shortly before radical prostatectomy. T2 distributions were generated with a regularized non-negative least squares algorithm from multiecho spin-echo MR imaging data. From T2 distributions, maps of seven MR parameters, Ncomp, T2short, T2long, geometric mean T2 (gmT2), luminal water fraction (LWF), Ashort, and Along, were generated and compared with digitized images of hematoxylin-eosin-stained whole-mount histologic slices. A paired t test was used to determine significant differences between MR parameters in malignant and nonmalignant tissue. Correlation with Gleason score (GS) was evaluated with the Spearman rank correlation test. Diagnostic accuracy was evaluated by using logistic generalized linear mixed-effect models and receiver operating characteristic (ROC) analysis. Results The average values of four MR parameters (gmT2, Ashort, Along, and LWF) were significantly different between malignant and nonmalignant tissue. All MR parameters except for T2long showed significant correlation (P < .05) with GS in the peripheral zone. The highest correlation with GS was obtained for LWF (-0.78 ± 0.11, P < .001). ROC analysis demonstrated high accuracy for tumor detection, with the highest area under the ROC curve obtained for LWF (0.97 in the peripheral zone and 0.98 in the transition zone). Conclusion Results of this pilot study demonstrated the feasibility of luminal water imaging in the detection and grading of PCa. A study with a larger cohort of patients and a broader range of GS is required to further evaluate this new technique in clinical settings. © RSNA, 2017.

Project description:BackgroundAbsolute quantification of metabolites in MR spectroscopic imaging (MRSI) requires a stable reference signal of known concentration. The Electronic REference To access In vivo Concentrations (ERETIC) has shown great promise but has not been applied in patients and 3D MRSI. ERETIC hardware has not been integrated with receive arrays due to technical challenges, such as coil combination and unwanted coupling between multiple ERETIC and receive channels, for which we developed mitigation strategies.PurposeTo develop absolute quantification for whole-brain MRSI in glioma patients.Study typeProspective.PopulationFive healthy volunteers and three patients with isocitrate dehydrogenase mutant glioma (27% female). Calibration and coil loading phantoms.Field strength/sequenceA 3 T; Adiabatic spin-echo spiral 3D MRSI with real-time motion correction, Fluid Attenuated Inversion Recovery (FLAIR), Gradient Recalled Echo (GRE), Multi-echo Magnetization Prepared Rapid Acquisition of Gradient Echo (MEMPRAGE).AssessmentAbsolute quantification was performed for five brain metabolites (total N-acetyl-aspartate [NAA]/creatine/choline, glutamine + glutamate, myo-inositol) and the oncometabolite 2-hydroxyglutarate using a custom-built 4x-ERETIC/8x-receive array coil. Metabolite quantification was performed with both EREIC and internal water reference methods. ERETIC signal was transmitted via optical link and used to correct coil loading. Inductive and radiative coupling between ERETIC and receive channels were measured.Statistical testsERETIC and internal water methods for metabolite quantification were compared using Bland-Altman (BA) analysis and the nonparametric Mann-Whitney test. P < 0.05 was considered statistically significant.ResultsERETIC could be integrated in receive arrays and inductive coupling dominated (5-886 times) radiative coupling. Phantoms show proportional scaling of the ERETIC signal with coil loading. The BA analysis demonstrated very good agreement (3.3% ± 1.6%) in healthy volunteers, while there was a large difference (36.1% ± 3.8%) in glioma tumors between metabolite concentrations by ERETIC and internal water quantification.ConclusionOur results indicate that ERETIC integrated with receive arrays and whole-brain MRSI is feasible for brain metabolites quantification. Further validation is required to probe that ERETIC provides more accurate metabolite concentration in glioma patients.Evidence level2 TECHNICAL EFFICACY: Stage 1.

Dataset Information

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

Publications

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

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