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

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 spectroscop...

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Published inPloS one Vol. 11; no. 11; p. e0165730
Main Authors Bellomo, Giovanni, Marcocci, Francesco, Bianchini, David, Mezzenga, Emilio, D’Errico, Vincenzo, Menghi, Enrico, Zannoli, Romano, Sarnelli, Anna
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 10.11.2016
Public Library of Science (PLoS)
Subjects
Algorithms
Biology and Life Sciences
Brain
Cancer
Choline
Choline - analysis
Citric acid
Citric Acid - analysis
Computer and Information Sciences
Computer programs
Computer simulation
Coupling
Creatine
Creatine - analysis
Developed countries
Engineering and Technology
Health aspects
Humans
In vivo methods and tests
Ingenia
Liver
Liver cancer
Magnetic resonance
Magnetic resonance imaging
Magnetic resonance spectroscopy
Magnetic Resonance Spectroscopy - instrumentation
Magnetic Resonance Spectroscopy - methods
Male
Medicine and Health Sciences
Metabolites
Neuroimaging
NMR
Nuclear magnetic resonance
Nuclear magnetic resonance spectroscopy
Optimization
Phantoms, Imaging
Physical Sciences
Polyamines
Prostate - chemistry
Prostate - pathology
Prostate cancer
Prostatic Neoplasms - chemistry
Prostatic Neoplasms - pathology
Proton magnetic resonance
Proton Magnetic Resonance Spectroscopy
Research and Analysis Methods
Resonance
Signal to noise ratio
Software
Spectral analysis
Spectroscopy
Spectrum analysis
Online AccessGet full text
ISSN1932-6203
1932-6203
DOI10.1371/journal.pone.0165730

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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.
AbstractList 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 ( 1 H-MRS) and proton magnetic resonance spectroscopy imaging ( 1 H-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 .
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.
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.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.
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 (.sup.1 H-MRS) and proton magnetic resonance spectroscopy imaging (.sup.1 H-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.
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 ( 1 H-MRS) and proton magnetic resonance spectroscopy imaging ( 1 H-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 .
Audience Academic
Author Marcocci, Francesco
Bianchini, David
Mezzenga, Emilio
Menghi, Enrico
Zannoli, Romano
Bellomo, Giovanni
Sarnelli, Anna
D’Errico, Vincenzo
AuthorAffiliation Mayo Clinic Rochester, UNITED STATES
2 Experimental, Diagnostic and Specialty Medicine Department DIMES, University of Bologna, Bologna, Italy
1 Medical Physics Unit, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, FC, Italy
AuthorAffiliation_xml – name: 1 Medical Physics Unit, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, FC, Italy
– name: 2 Experimental, Diagnostic and Specialty Medicine Department DIMES, University of Bologna, Bologna, Italy
– name: Mayo Clinic Rochester, UNITED STATES
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  surname: Bellomo
  fullname: Bellomo, Giovanni
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  givenname: David
  surname: Bianchini
  fullname: Bianchini, David
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  givenname: Emilio
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  fullname: Mezzenga, Emilio
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  surname: D’Errico
  fullname: D’Errico, Vincenzo
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  surname: Zannoli
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CitedBy_id crossref_primary_10_1007_s00261_019_02114_z
crossref_primary_10_1148_rg_2019180123
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crossref_primary_10_1016_j_pnmrs_2018_01_001
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Copyright COPYRIGHT 2016 Public Library of Science
2016 Bellomo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
2016 Bellomo et al 2016 Bellomo et al
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Competing Interests: The authors have declared that no competing interests exist.
Conceptualization: FM DB. Data curation: GB FM. Formal analysis: GB FM AS EM. Investigation: GB FM. Methodology: GB FM. Project administration: FM. Resources: EM VD RZ. Software: GB. Supervision: AS EM. Validation: AS FM EM. Visualization: GB FM EM AS. Writing – original draft: GB FM EM AS. Writing – review & editing: GB FM EM AS.
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Snippet 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...
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StartPage e0165730
SubjectTerms Algorithms
Biology and Life Sciences
Brain
Cancer
Choline
Choline - analysis
Citric acid
Citric Acid - analysis
Computer and Information Sciences
Computer programs
Computer simulation
Coupling
Creatine
Creatine - analysis
Developed countries
Engineering and Technology
Health aspects
Humans
In vivo methods and tests
Ingenia
Liver
Liver cancer
Magnetic resonance
Magnetic resonance imaging
Magnetic resonance spectroscopy
Magnetic Resonance Spectroscopy - instrumentation
Magnetic Resonance Spectroscopy - methods
Male
Medicine and Health Sciences
Metabolites
Neuroimaging
NMR
Nuclear magnetic resonance
Nuclear magnetic resonance spectroscopy
Optimization
Phantoms, Imaging
Physical Sciences
Polyamines
Prostate - chemistry
Prostate - pathology
Prostate cancer
Prostatic Neoplasms - chemistry
Prostatic Neoplasms - pathology
Proton magnetic resonance
Proton Magnetic Resonance Spectroscopy
Research and Analysis Methods
Resonance
Signal to noise ratio
Software
Spectral analysis
Spectroscopy
Spectrum analysis
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Title MR Spectroscopy in Prostate Cancer: New Algorithms to Optimize Metabolite Quantification
URI https://www.ncbi.nlm.nih.gov/pubmed/27832096
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http://dx.doi.org/10.1371/journal.pone.0165730
Volume 11
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