Technical Note: DTI measurements of fractional anisotropy and mean diffusivity at 1.5 T: Comparison of two radiofrequency head coils with different functional designs and sensitivities
Purpose: Diffusion tensor imaging (DTI) is highly sensitive to noise and improvement of radiofrequency coil technology represents a straightforward way for augmenting signal-to-noise ratio (SNR) performance in magnetic resonance imaging (MRI) scanners. The aim of this study was to characterize the d...
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Published in | Medical physics (Lancaster) Vol. 38; no. 6; pp. 3205 - 3211 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
American Association of Physicists in Medicine
01.06.2011
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Subjects | |
Online Access | Get full text |
ISSN | 0094-2405 2473-4209 |
DOI | 10.1118/1.3592013 |
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Abstract | Purpose:
Diffusion tensor imaging (DTI) is highly sensitive to noise and improvement of radiofrequency coil technology represents a straightforward way for augmenting signal-to-noise ratio (SNR) performance in magnetic resonance imaging (MRI) scanners. The aim of this study was to characterize the dependence of DTI measurements of fractional anisotropy (FA) and mean diffusivity (MD) on the choice of head coil, comparing two head coils with different functional designs and sensitivities.
Methods:
Fourteen healthy subjects underwent DTI acquisitions at 1.5 T. Every subject was scanned twice, using a standard quadrature birdcage head coil (coil-A) and an eight-channel array head coil (coil-B). FA and MD maps, estimated using both the linear least squares (LLS) and nonlinear least squares (NLLS) methods, were nonlinearly normalized into a standard space. Then, volumetric regions of interest encompassing typical white and gray matter structures [splenium of the corpus callosum (SCC), internal capsule (IC), cerebral peduncles (CP), middle cerebellar peduncles (MCP), globus pallidus (GP), thalamus (TH), caudate (CA), and putamen (PU)] were analyzed. Significant differences and trends of variation in DTI measurements were assessed by the Wilcoxon test for paired samples with and without Bonferroni correction for multiple comparisons, respectively.
Results:
The overall SNR of coil-B was ∼30% higher than that of coil-A. When comparing DTI measurements (coil-B versus coil-A), mean FA values (SCC, IC, and TH), mean MD values (IC, CP, GP, and TH), FA standard deviation (CP, MCP, GP, and CA), and MD standard deviation (IC, CP, TH, and PU) resulted decreased (significant difference,p
cor < 0.05, or trend of variation, p
uncor < 0.05) in several gray and white matter regions of the human brain. With the exception of CP, the results in terms of revealed significant difference or trend of variation were independent of the method (LLS and NLLS) used for estimating the diffusion tensor.
Conclusions:
In various gray and white matter structures, the eight-channel array head coil yielded more precise and accurate measurements of DTI derived indices compared to the standard quadrature birdcage head coil. |
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AbstractList | Purpose:
Diffusion tensor imaging (DTI) is highly sensitive to noise and improvement of radiofrequency coil technology represents a straightforward way for augmenting signal‐to‐noise ratio (SNR) performance in magnetic resonance imaging (MRI) scanners. The aim of this study was to characterize the dependence of DTI measurements of fractional anisotropy (FA) and mean diffusivity (MD) on the choice of head coil, comparing two head coils with different functional designs and sensitivities.
Methods:
Fourteen healthy subjects underwent DTI acquisitions at 1.5 T. Every subject was scanned twice, using a standard quadrature birdcage head coil (coil‐A) and an eight‐channel array head coil (coil‐B). FA and MD maps, estimated using both the linear least squares (LLS) and nonlinear least squares (NLLS) methods, were nonlinearly normalized into a standard space. Then, volumetric regions of interest encompassing typical white and gray matter structures [splenium of the corpus callosum (SCC), internal capsule (IC), cerebral peduncles (CP), middle cerebellar peduncles (MCP), globus pallidus (GP), thalamus (TH), caudate (CA), and putamen (PU)] were analyzed. Significant differences and trends of variation in DTI measurements were assessed by the Wilcoxon test for paired samples with and without Bonferroni correction for multiple comparisons, respectively.
Results:
The overall SNR of coil‐B was ∼30% higher than that of coil‐A. When comparing DTI measurements (coil‐B versus coil‐A), mean FA values (SCC, IC, and TH), mean MD values (IC, CP, GP, and TH), FA standard deviation (CP, MCP, GP, and CA), and MD standard deviation (IC, CP, TH, and PU) resulted decreased (significant difference,pcor < 0.05, or trend of variation, puncor < 0.05) in several gray and white matter regions of the human brain. With the exception of CP, the results in terms of revealed significant difference or trend of variation were independent of the method (LLS and NLLS) used for estimating the diffusion tensor.
Conclusions:
In various gray and white matter structures, the eight‐channel array head coil yielded more precise and accurate measurements of DTI derived indices compared to the standard quadrature birdcage head coil. Purpose: Diffusion tensor imaging (DTI) is highly sensitive to noise and improvement of radiofrequency coil technology represents a straightforward way for augmenting signal-to-noise ratio (SNR) performance in magnetic resonance imaging (MRI) scanners. The aim of this study was to characterize the dependence of DTI measurements of fractional anisotropy (FA) and mean diffusivity (MD) on the choice of head coil, comparing two head coils with different functional designs and sensitivities. Methods: Fourteen healthy subjects underwent DTI acquisitions at 1.5 T. Every subject was scanned twice, using a standard quadrature birdcage head coil (coil-A) and an eight-channel array head coil (coil-B). FA and MD maps, estimated using both the linear least squares (LLS) and nonlinear least squares (NLLS) methods, were nonlinearly normalized into a standard space. Then, volumetric regions of interest encompassing typical white and gray matter structures [splenium of the corpus callosum (SCC), internal capsule (IC), cerebral peduncles (CP), middle cerebellar peduncles (MCP), globus pallidus (GP), thalamus (TH), caudate (CA), and putamen (PU)] were analyzed. Significant differences and trends of variation in DTI measurements were assessed by the Wilcoxon test for paired samples with and without Bonferroni correction for multiple comparisons, respectively. Results: The overall SNR of coil-B was ∼30% higher than that of coil-A. When comparing DTI measurements (coil-B versus coil-A), mean FA values (SCC, IC, and TH), mean MD values (IC, CP, GP, and TH), FA standard deviation (CP, MCP, GP, and CA), and MD standard deviation (IC, CP, TH, and PU) resulted decreased (significant difference, p cor <0.05, or trend of variation, p uncor <0.05) in several gray and white matter regions of the human brain. With the exception of CP, the results in terms of revealed significant difference or trend of variation were independent of the method (LLS and NLLS) used for estimating the diffusion tensor. Conclusions: In various gray and white matter structures, the eight-channel array head coil yielded more precise and accurate measurements of DTI derived indices compared to the standard quadrature birdcage head coil. Diffusion tensor imaging (DTI) is highly sensitive to noise and improvement of radiofrequency coil technology represents a straightforward way for augmenting signal-to-noise ratio (SNR) performance in magnetic resonance imaging (MRI) scanners. The aim of this study was to characterize the dependence of DTI measurements of fractional anisotropy (FA) and mean diffusivity (MD) on the choice of head coil, comparing two head coils with different functional designs and sensitivities. Fourteen healthy subjects underwent DTI acquisitions at 1.5 T. Every subject was scanned twice, using a standard quadrature birdcage head coil (coil-A) and an eight-channel array head coil (coil-B). FA and MD maps, estimated using both the linear least squares (LLS) and nonlinear least squares (NLLS) methods, were nonlinearly normalized into a standard space. Then, volumetric regions of interest encompassing typical white and gray matter structures [splenium of the corpus callosum (SCC), internal capsule (IC), cerebral peduncles (CP), middle cerebellar peduncles (MCP), globus pallidus (GP), thalamus (TH), caudate (CA), and putamen (PU)] were analyzed. Significant differences and trends of variation in DTI measurements were assessed by the Wilcoxon test for paired samples with and without Bonferroni correction for multiple comparisons, respectively. The overall SNR of coil-B was 30% higher than that of coil-A. When comparing DTI measurements (coil-B versus coil-A), mean FA values (SCC, IC, and TH), mean MD values (IC, CP, GP, and TH), FA standard deviation (CP, MCP, GP, and CA), and MD standard deviation (IC, CP, TH, and PU) resulted decreased (significant difference, p(cor) < 0.05, or trend of variation, P(uncor) < 0.05) in several gray and white matter regions of the human brain. With the exception of CP, the results in terms of revealed significant difference or trend of variation were independent of the method (LLS and NLLS) used for estimating the diffusion tensor. In various gray and white matter structures, the eight-channel array head coil yielded more precise and accurate measurements of DTI derived indices compared to the standard quadrature birdcage head coil. Purpose: Diffusion tensor imaging (DTI) is highly sensitive to noise and improvement of radiofrequency coil technology represents a straightforward way for augmenting signal-to-noise ratio (SNR) performance in magnetic resonance imaging (MRI) scanners. The aim of this study was to characterize the dependence of DTI measurements of fractional anisotropy (FA) and mean diffusivity (MD) on the choice of head coil, comparing two head coils with different functional designs and sensitivities. Methods: Fourteen healthy subjects underwent DTI acquisitions at 1.5 T. Every subject was scanned twice, using a standard quadrature birdcage head coil (coil-A) and an eight-channel array head coil (coil-B). FA and MD maps, estimated using both the linear least squares (LLS) and nonlinear least squares (NLLS) methods, were nonlinearly normalized into a standard space. Then, volumetric regions of interest encompassing typical white and gray matter structures [splenium of the corpus callosum (SCC), internal capsule (IC), cerebral peduncles (CP), middle cerebellar peduncles (MCP), globus pallidus (GP), thalamus (TH), caudate (CA), and putamen (PU)] were analyzed. Significant differences and trends of variation in DTI measurements were assessed by the Wilcoxon test for paired samples with and without Bonferroni correction for multiple comparisons, respectively. Results: The overall SNR of coil-B was ∼30% higher than that of coil-A. When comparing DTI measurements (coil-B versus coil-A), mean FA values (SCC, IC, and TH), mean MD values (IC, CP, GP, and TH), FA standard deviation (CP, MCP, GP, and CA), and MD standard deviation (IC, CP, TH, and PU) resulted decreased (significant difference,p cor < 0.05, or trend of variation, p uncor < 0.05) in several gray and white matter regions of the human brain. With the exception of CP, the results in terms of revealed significant difference or trend of variation were independent of the method (LLS and NLLS) used for estimating the diffusion tensor. Conclusions: In various gray and white matter structures, the eight-channel array head coil yielded more precise and accurate measurements of DTI derived indices compared to the standard quadrature birdcage head coil. Diffusion tensor imaging (DTI) is highly sensitive to noise and improvement of radiofrequency coil technology represents a straightforward way for augmenting signal-to-noise ratio (SNR) performance in magnetic resonance imaging (MRI) scanners. The aim of this study was to characterize the dependence of DTI measurements of fractional anisotropy (FA) and mean diffusivity (MD) on the choice of head coil, comparing two head coils with different functional designs and sensitivities.PURPOSEDiffusion tensor imaging (DTI) is highly sensitive to noise and improvement of radiofrequency coil technology represents a straightforward way for augmenting signal-to-noise ratio (SNR) performance in magnetic resonance imaging (MRI) scanners. The aim of this study was to characterize the dependence of DTI measurements of fractional anisotropy (FA) and mean diffusivity (MD) on the choice of head coil, comparing two head coils with different functional designs and sensitivities.Fourteen healthy subjects underwent DTI acquisitions at 1.5 T. Every subject was scanned twice, using a standard quadrature birdcage head coil (coil-A) and an eight-channel array head coil (coil-B). FA and MD maps, estimated using both the linear least squares (LLS) and nonlinear least squares (NLLS) methods, were nonlinearly normalized into a standard space. Then, volumetric regions of interest encompassing typical white and gray matter structures [splenium of the corpus callosum (SCC), internal capsule (IC), cerebral peduncles (CP), middle cerebellar peduncles (MCP), globus pallidus (GP), thalamus (TH), caudate (CA), and putamen (PU)] were analyzed. Significant differences and trends of variation in DTI measurements were assessed by the Wilcoxon test for paired samples with and without Bonferroni correction for multiple comparisons, respectively.METHODSFourteen healthy subjects underwent DTI acquisitions at 1.5 T. Every subject was scanned twice, using a standard quadrature birdcage head coil (coil-A) and an eight-channel array head coil (coil-B). FA and MD maps, estimated using both the linear least squares (LLS) and nonlinear least squares (NLLS) methods, were nonlinearly normalized into a standard space. Then, volumetric regions of interest encompassing typical white and gray matter structures [splenium of the corpus callosum (SCC), internal capsule (IC), cerebral peduncles (CP), middle cerebellar peduncles (MCP), globus pallidus (GP), thalamus (TH), caudate (CA), and putamen (PU)] were analyzed. Significant differences and trends of variation in DTI measurements were assessed by the Wilcoxon test for paired samples with and without Bonferroni correction for multiple comparisons, respectively.The overall SNR of coil-B was 30% higher than that of coil-A. When comparing DTI measurements (coil-B versus coil-A), mean FA values (SCC, IC, and TH), mean MD values (IC, CP, GP, and TH), FA standard deviation (CP, MCP, GP, and CA), and MD standard deviation (IC, CP, TH, and PU) resulted decreased (significant difference, p(cor) < 0.05, or trend of variation, P(uncor) < 0.05) in several gray and white matter regions of the human brain. With the exception of CP, the results in terms of revealed significant difference or trend of variation were independent of the method (LLS and NLLS) used for estimating the diffusion tensor.RESULTSThe overall SNR of coil-B was 30% higher than that of coil-A. When comparing DTI measurements (coil-B versus coil-A), mean FA values (SCC, IC, and TH), mean MD values (IC, CP, GP, and TH), FA standard deviation (CP, MCP, GP, and CA), and MD standard deviation (IC, CP, TH, and PU) resulted decreased (significant difference, p(cor) < 0.05, or trend of variation, P(uncor) < 0.05) in several gray and white matter regions of the human brain. With the exception of CP, the results in terms of revealed significant difference or trend of variation were independent of the method (LLS and NLLS) used for estimating the diffusion tensor.In various gray and white matter structures, the eight-channel array head coil yielded more precise and accurate measurements of DTI derived indices compared to the standard quadrature birdcage head coil.CONCLUSIONSIn various gray and white matter structures, the eight-channel array head coil yielded more precise and accurate measurements of DTI derived indices compared to the standard quadrature birdcage head coil. |
Author | Pesaresi, Ilaria Giannelli, Marco Belmonte, Gina Ghedin, Piero Bartolozzi, Carlo Claudio Traino, Antonio Toschi, Nicola Cosottini, Mirco |
Author_xml | – sequence: 1 givenname: Marco surname: Giannelli fullname: Giannelli, Marco email: m.giannelli@ao-pisa.toscana.it organization: Unit of Medical Physics, Azienda Ospedaliero-Universitaria Pisana, 56126 Pisa, Italy – sequence: 2 givenname: Gina surname: Belmonte fullname: Belmonte, Gina organization: Unit of Medical Physics, Azienda Ospedaliera Universitaria Senese, 53100 Siena, Italy – sequence: 3 givenname: Nicola surname: Toschi fullname: Toschi, Nicola organization: Medical Physics Section, Faculty of Medicine, University of Rome “Tor Vergata,” 00133 Roma, Italy – sequence: 4 givenname: Ilaria surname: Pesaresi fullname: Pesaresi, Ilaria organization: Unit of Neuroradiology, Azienda Ospedaliero-Universitaria Pisana, 56126 Pisa, Italy – sequence: 5 givenname: Piero surname: Ghedin fullname: Ghedin, Piero organization: GE Medical Systems Italia, GE Healthcare, 20126 Milano, Italy – sequence: 6 givenname: Antonio surname: Claudio Traino fullname: Claudio Traino, Antonio organization: Unit of Medical Physics, Azienda Ospedaliero-Universitaria Pisana, 56126 Pisa, Italy – sequence: 7 givenname: Carlo surname: Bartolozzi fullname: Bartolozzi, Carlo organization: Department of Oncology, Transplants and Advanced Technologies in Medicine, University of Pisa, 56126 Pisa, Italy – sequence: 8 givenname: Mirco surname: Cosottini fullname: Cosottini, Mirco organization: Unit of Neuroradiology, Azienda Ospedaliero-Universitaria Pisana, 56126 Pisa, Italy; and Department of Neuroscience, University of Pisa, 56126 Pisa, Italy |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/21815395$$D View this record in MEDLINE/PubMed |
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Copyright | American Association of Physicists in Medicine 2011 American Association of Physicists in Medicine |
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Keywords | DTI diffusion tensor imaging fractional anisotropy magnetic resonance imaging mean diffusivity |
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Notes | Telephone: 0039 050 993359; Fax: 0039 050 992513. m.giannelli@ao‐pisa.toscana.it Author to whom correspondence should be addressed. Electronic mail ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
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Diffusion tensor imaging (DTI) is highly sensitive to noise and improvement of radiofrequency coil technology represents a straightforward way for... Diffusion tensor imaging (DTI) is highly sensitive to noise and improvement of radiofrequency coil technology represents a straightforward way for augmenting... |
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SubjectTerms | Adult Anisotropy Applied neuroscience biodiffusion Biomedical imaging biomedical measurement biomedical MRI Brain Computer software data acquisition Diffusion diffusion tensor imaging Diffusion Tensor Imaging - instrumentation DTI Equipment Design Female fractional anisotropy Humans Image Processing, Computer-Assisted Instrumentation least mean squares methods Magnetic anisotropy Magnetic fields magnetic resonance imaging Male mean diffusivity Medical image noise medical image processing Medical imaging Radio Waves Tensor methods Young Adult |
Title | Technical Note: DTI measurements of fractional anisotropy and mean diffusivity at 1.5 T: Comparison of two radiofrequency head coils with different functional designs and sensitivities |
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