Improved total sensitivity estimation for multiple receive coils in MRI using ratios of first-order statistics
Object Spatial variation in the sensitivity profiles of receive coils in MRI leads to spatially dependent scaling of the signal amplitude across an image. In practice, total sensitivity of the coil array is either calibrated or corrected directly by comparison to a uniform sensitivity image, fitting...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Miloushev, Vesselin Z. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Magnetic resonance materials in physics, biology and medicine - Heidelberg : Springer, 1993, 35(2022), 6 vom: 25. Juli, Seite 895-901 |
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| Übergeordnetes Werk: |
volume:35 ; year:2022 ; number:6 ; day:25 ; month:07 ; pages:895-901 |
| Links: |
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| DOI / URN: |
10.1007/s10334-022-01028-0 |
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SPR048446491 |
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| 100 | 1 | |a Miloushev, Vesselin Z. |e verfasserin |0 (orcid)0000-0002-1899-8791 |4 aut | |
| 245 | 1 | 0 | |a Improved total sensitivity estimation for multiple receive coils in MRI using ratios of first-order statistics |
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| 520 | |a Object Spatial variation in the sensitivity profiles of receive coils in MRI leads to spatially dependent scaling of the signal amplitude across an image. In practice, total sensitivity of the coil array is either calibrated or corrected directly by comparison to a uniform sensitivity image, fitting of coil profiles, or indirectly by constraining the reconstructed image or coil profiles. In the absence of these corrections, popular coil summation strategies are often designed to maximize the signal-to-noise ratio or optimize under-sampled encoding but not necessarily estimate the value of the signal unscaled by the coil spatial sensitivity. Materials and Methods We use ratios of first-order statistics to approach the unscaled value of the signal at any position. Motivated by the assumption that the coil array is a sample from much larger number of possible coils, we present two approaches to scale the mean signal in all coils: (1) an argument for use of the mode of the normalized signals, and (2) using a one-dimensional analog derive an approximate expression for scaling with the ratio of the square-of-the-mean to the mean-of-the-squares. We test these approaches with simulation where idealized coil elements are arrayed around an object, and on directly acquired data with an 8-channel coil array on a uniform 13C phantom, and on Hyperpolarized 13C pyruvate brain MRI. Results We show improved image uniformity using the ratios of first order statistics compared to a simple homomorphic filter, noting that these approaches are more sensitive to noise. Discussion We present simple methods for correcting the spatial variation in sensitivity profiles in the context of a coil array. These methods can be used as an initial or adjunct step in data post-processing. | ||
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| 700 | 1 | |a Boltyanskiy, Rostislav |4 aut | |
| 700 | 1 | |a Granlund, Kristin L. |4 aut | |
| 700 | 1 | |a Keshari, Kayvan R. |4 aut | |
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10.1007/s10334-022-01028-0 doi (DE-627)SPR048446491 (SPR)s10334-022-01028-0-e DE-627 ger DE-627 rakwb eng Miloushev, Vesselin Z. verfasserin (orcid)0000-0002-1899-8791 aut Improved total sensitivity estimation for multiple receive coils in MRI using ratios of first-order statistics 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) 2022 Object Spatial variation in the sensitivity profiles of receive coils in MRI leads to spatially dependent scaling of the signal amplitude across an image. In practice, total sensitivity of the coil array is either calibrated or corrected directly by comparison to a uniform sensitivity image, fitting of coil profiles, or indirectly by constraining the reconstructed image or coil profiles. In the absence of these corrections, popular coil summation strategies are often designed to maximize the signal-to-noise ratio or optimize under-sampled encoding but not necessarily estimate the value of the signal unscaled by the coil spatial sensitivity. Materials and Methods We use ratios of first-order statistics to approach the unscaled value of the signal at any position. Motivated by the assumption that the coil array is a sample from much larger number of possible coils, we present two approaches to scale the mean signal in all coils: (1) an argument for use of the mode of the normalized signals, and (2) using a one-dimensional analog derive an approximate expression for scaling with the ratio of the square-of-the-mean to the mean-of-the-squares. We test these approaches with simulation where idealized coil elements are arrayed around an object, and on directly acquired data with an 8-channel coil array on a uniform 13C phantom, and on Hyperpolarized 13C pyruvate brain MRI. Results We show improved image uniformity using the ratios of first order statistics compared to a simple homomorphic filter, noting that these approaches are more sensitive to noise. Discussion We present simple methods for correcting the spatial variation in sensitivity profiles in the context of a coil array. These methods can be used as an initial or adjunct step in data post-processing. Multi-channel MRI (dpeaa)DE-He213 Artifacts (dpeaa)DE-He213 Post-processing (dpeaa)DE-He213 Boltyanskiy, Rostislav aut Granlund, Kristin L. aut Keshari, Kayvan R. aut Enthalten in Magnetic resonance materials in physics, biology and medicine Heidelberg : Springer, 1993 35(2022), 6 vom: 25. Juli, Seite 895-901 (DE-627)308449711 (DE-600)1502491-X 1352-8661 nnns volume:35 year:2022 number:6 day:25 month:07 pages:895-901 https://dx.doi.org/10.1007/s10334-022-01028-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2022 6 25 07 895-901 |
| spelling |
10.1007/s10334-022-01028-0 doi (DE-627)SPR048446491 (SPR)s10334-022-01028-0-e DE-627 ger DE-627 rakwb eng Miloushev, Vesselin Z. verfasserin (orcid)0000-0002-1899-8791 aut Improved total sensitivity estimation for multiple receive coils in MRI using ratios of first-order statistics 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) 2022 Object Spatial variation in the sensitivity profiles of receive coils in MRI leads to spatially dependent scaling of the signal amplitude across an image. In practice, total sensitivity of the coil array is either calibrated or corrected directly by comparison to a uniform sensitivity image, fitting of coil profiles, or indirectly by constraining the reconstructed image or coil profiles. In the absence of these corrections, popular coil summation strategies are often designed to maximize the signal-to-noise ratio or optimize under-sampled encoding but not necessarily estimate the value of the signal unscaled by the coil spatial sensitivity. Materials and Methods We use ratios of first-order statistics to approach the unscaled value of the signal at any position. Motivated by the assumption that the coil array is a sample from much larger number of possible coils, we present two approaches to scale the mean signal in all coils: (1) an argument for use of the mode of the normalized signals, and (2) using a one-dimensional analog derive an approximate expression for scaling with the ratio of the square-of-the-mean to the mean-of-the-squares. We test these approaches with simulation where idealized coil elements are arrayed around an object, and on directly acquired data with an 8-channel coil array on a uniform 13C phantom, and on Hyperpolarized 13C pyruvate brain MRI. Results We show improved image uniformity using the ratios of first order statistics compared to a simple homomorphic filter, noting that these approaches are more sensitive to noise. Discussion We present simple methods for correcting the spatial variation in sensitivity profiles in the context of a coil array. These methods can be used as an initial or adjunct step in data post-processing. Multi-channel MRI (dpeaa)DE-He213 Artifacts (dpeaa)DE-He213 Post-processing (dpeaa)DE-He213 Boltyanskiy, Rostislav aut Granlund, Kristin L. aut Keshari, Kayvan R. aut Enthalten in Magnetic resonance materials in physics, biology and medicine Heidelberg : Springer, 1993 35(2022), 6 vom: 25. Juli, Seite 895-901 (DE-627)308449711 (DE-600)1502491-X 1352-8661 nnns volume:35 year:2022 number:6 day:25 month:07 pages:895-901 https://dx.doi.org/10.1007/s10334-022-01028-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2022 6 25 07 895-901 |
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10.1007/s10334-022-01028-0 doi (DE-627)SPR048446491 (SPR)s10334-022-01028-0-e DE-627 ger DE-627 rakwb eng Miloushev, Vesselin Z. verfasserin (orcid)0000-0002-1899-8791 aut Improved total sensitivity estimation for multiple receive coils in MRI using ratios of first-order statistics 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) 2022 Object Spatial variation in the sensitivity profiles of receive coils in MRI leads to spatially dependent scaling of the signal amplitude across an image. In practice, total sensitivity of the coil array is either calibrated or corrected directly by comparison to a uniform sensitivity image, fitting of coil profiles, or indirectly by constraining the reconstructed image or coil profiles. In the absence of these corrections, popular coil summation strategies are often designed to maximize the signal-to-noise ratio or optimize under-sampled encoding but not necessarily estimate the value of the signal unscaled by the coil spatial sensitivity. Materials and Methods We use ratios of first-order statistics to approach the unscaled value of the signal at any position. Motivated by the assumption that the coil array is a sample from much larger number of possible coils, we present two approaches to scale the mean signal in all coils: (1) an argument for use of the mode of the normalized signals, and (2) using a one-dimensional analog derive an approximate expression for scaling with the ratio of the square-of-the-mean to the mean-of-the-squares. We test these approaches with simulation where idealized coil elements are arrayed around an object, and on directly acquired data with an 8-channel coil array on a uniform 13C phantom, and on Hyperpolarized 13C pyruvate brain MRI. Results We show improved image uniformity using the ratios of first order statistics compared to a simple homomorphic filter, noting that these approaches are more sensitive to noise. Discussion We present simple methods for correcting the spatial variation in sensitivity profiles in the context of a coil array. These methods can be used as an initial or adjunct step in data post-processing. Multi-channel MRI (dpeaa)DE-He213 Artifacts (dpeaa)DE-He213 Post-processing (dpeaa)DE-He213 Boltyanskiy, Rostislav aut Granlund, Kristin L. aut Keshari, Kayvan R. aut Enthalten in Magnetic resonance materials in physics, biology and medicine Heidelberg : Springer, 1993 35(2022), 6 vom: 25. Juli, Seite 895-901 (DE-627)308449711 (DE-600)1502491-X 1352-8661 nnns volume:35 year:2022 number:6 day:25 month:07 pages:895-901 https://dx.doi.org/10.1007/s10334-022-01028-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2022 6 25 07 895-901 |
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10.1007/s10334-022-01028-0 doi (DE-627)SPR048446491 (SPR)s10334-022-01028-0-e DE-627 ger DE-627 rakwb eng Miloushev, Vesselin Z. verfasserin (orcid)0000-0002-1899-8791 aut Improved total sensitivity estimation for multiple receive coils in MRI using ratios of first-order statistics 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) 2022 Object Spatial variation in the sensitivity profiles of receive coils in MRI leads to spatially dependent scaling of the signal amplitude across an image. In practice, total sensitivity of the coil array is either calibrated or corrected directly by comparison to a uniform sensitivity image, fitting of coil profiles, or indirectly by constraining the reconstructed image or coil profiles. In the absence of these corrections, popular coil summation strategies are often designed to maximize the signal-to-noise ratio or optimize under-sampled encoding but not necessarily estimate the value of the signal unscaled by the coil spatial sensitivity. Materials and Methods We use ratios of first-order statistics to approach the unscaled value of the signal at any position. Motivated by the assumption that the coil array is a sample from much larger number of possible coils, we present two approaches to scale the mean signal in all coils: (1) an argument for use of the mode of the normalized signals, and (2) using a one-dimensional analog derive an approximate expression for scaling with the ratio of the square-of-the-mean to the mean-of-the-squares. We test these approaches with simulation where idealized coil elements are arrayed around an object, and on directly acquired data with an 8-channel coil array on a uniform 13C phantom, and on Hyperpolarized 13C pyruvate brain MRI. Results We show improved image uniformity using the ratios of first order statistics compared to a simple homomorphic filter, noting that these approaches are more sensitive to noise. Discussion We present simple methods for correcting the spatial variation in sensitivity profiles in the context of a coil array. These methods can be used as an initial or adjunct step in data post-processing. Multi-channel MRI (dpeaa)DE-He213 Artifacts (dpeaa)DE-He213 Post-processing (dpeaa)DE-He213 Boltyanskiy, Rostislav aut Granlund, Kristin L. aut Keshari, Kayvan R. aut Enthalten in Magnetic resonance materials in physics, biology and medicine Heidelberg : Springer, 1993 35(2022), 6 vom: 25. Juli, Seite 895-901 (DE-627)308449711 (DE-600)1502491-X 1352-8661 nnns volume:35 year:2022 number:6 day:25 month:07 pages:895-901 https://dx.doi.org/10.1007/s10334-022-01028-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2022 6 25 07 895-901 |
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10.1007/s10334-022-01028-0 doi (DE-627)SPR048446491 (SPR)s10334-022-01028-0-e DE-627 ger DE-627 rakwb eng Miloushev, Vesselin Z. verfasserin (orcid)0000-0002-1899-8791 aut Improved total sensitivity estimation for multiple receive coils in MRI using ratios of first-order statistics 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) 2022 Object Spatial variation in the sensitivity profiles of receive coils in MRI leads to spatially dependent scaling of the signal amplitude across an image. In practice, total sensitivity of the coil array is either calibrated or corrected directly by comparison to a uniform sensitivity image, fitting of coil profiles, or indirectly by constraining the reconstructed image or coil profiles. In the absence of these corrections, popular coil summation strategies are often designed to maximize the signal-to-noise ratio or optimize under-sampled encoding but not necessarily estimate the value of the signal unscaled by the coil spatial sensitivity. Materials and Methods We use ratios of first-order statistics to approach the unscaled value of the signal at any position. Motivated by the assumption that the coil array is a sample from much larger number of possible coils, we present two approaches to scale the mean signal in all coils: (1) an argument for use of the mode of the normalized signals, and (2) using a one-dimensional analog derive an approximate expression for scaling with the ratio of the square-of-the-mean to the mean-of-the-squares. We test these approaches with simulation where idealized coil elements are arrayed around an object, and on directly acquired data with an 8-channel coil array on a uniform 13C phantom, and on Hyperpolarized 13C pyruvate brain MRI. Results We show improved image uniformity using the ratios of first order statistics compared to a simple homomorphic filter, noting that these approaches are more sensitive to noise. Discussion We present simple methods for correcting the spatial variation in sensitivity profiles in the context of a coil array. These methods can be used as an initial or adjunct step in data post-processing. Multi-channel MRI (dpeaa)DE-He213 Artifacts (dpeaa)DE-He213 Post-processing (dpeaa)DE-He213 Boltyanskiy, Rostislav aut Granlund, Kristin L. aut Keshari, Kayvan R. aut Enthalten in Magnetic resonance materials in physics, biology and medicine Heidelberg : Springer, 1993 35(2022), 6 vom: 25. Juli, Seite 895-901 (DE-627)308449711 (DE-600)1502491-X 1352-8661 nnns volume:35 year:2022 number:6 day:25 month:07 pages:895-901 https://dx.doi.org/10.1007/s10334-022-01028-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2022 6 25 07 895-901 |
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Enthalten in Magnetic resonance materials in physics, biology and medicine 35(2022), 6 vom: 25. Juli, Seite 895-901 volume:35 year:2022 number:6 day:25 month:07 pages:895-901 |
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Enthalten in Magnetic resonance materials in physics, biology and medicine 35(2022), 6 vom: 25. Juli, Seite 895-901 volume:35 year:2022 number:6 day:25 month:07 pages:895-901 |
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Miloushev, Vesselin Z. @@aut@@ Boltyanskiy, Rostislav @@aut@@ Granlund, Kristin L. @@aut@@ Keshari, Kayvan R. @@aut@@ |
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In practice, total sensitivity of the coil array is either calibrated or corrected directly by comparison to a uniform sensitivity image, fitting of coil profiles, or indirectly by constraining the reconstructed image or coil profiles. In the absence of these corrections, popular coil summation strategies are often designed to maximize the signal-to-noise ratio or optimize under-sampled encoding but not necessarily estimate the value of the signal unscaled by the coil spatial sensitivity. Materials and Methods We use ratios of first-order statistics to approach the unscaled value of the signal at any position. Motivated by the assumption that the coil array is a sample from much larger number of possible coils, we present two approaches to scale the mean signal in all coils: (1) an argument for use of the mode of the normalized signals, and (2) using a one-dimensional analog derive an approximate expression for scaling with the ratio of the square-of-the-mean to the mean-of-the-squares. We test these approaches with simulation where idealized coil elements are arrayed around an object, and on directly acquired data with an 8-channel coil array on a uniform 13C phantom, and on Hyperpolarized 13C pyruvate brain MRI. Results We show improved image uniformity using the ratios of first order statistics compared to a simple homomorphic filter, noting that these approaches are more sensitive to noise. Discussion We present simple methods for correcting the spatial variation in sensitivity profiles in the context of a coil array. 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Miloushev, Vesselin Z. |
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Improved total sensitivity estimation for multiple receive coils in MRI using ratios of first-order statistics Multi-channel MRI (dpeaa)DE-He213 Artifacts (dpeaa)DE-He213 Post-processing (dpeaa)DE-He213 |
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improved total sensitivity estimation for multiple receive coils in mri using ratios of first-order statistics |
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Improved total sensitivity estimation for multiple receive coils in MRI using ratios of first-order statistics |
| abstract |
Object Spatial variation in the sensitivity profiles of receive coils in MRI leads to spatially dependent scaling of the signal amplitude across an image. In practice, total sensitivity of the coil array is either calibrated or corrected directly by comparison to a uniform sensitivity image, fitting of coil profiles, or indirectly by constraining the reconstructed image or coil profiles. In the absence of these corrections, popular coil summation strategies are often designed to maximize the signal-to-noise ratio or optimize under-sampled encoding but not necessarily estimate the value of the signal unscaled by the coil spatial sensitivity. Materials and Methods We use ratios of first-order statistics to approach the unscaled value of the signal at any position. Motivated by the assumption that the coil array is a sample from much larger number of possible coils, we present two approaches to scale the mean signal in all coils: (1) an argument for use of the mode of the normalized signals, and (2) using a one-dimensional analog derive an approximate expression for scaling with the ratio of the square-of-the-mean to the mean-of-the-squares. We test these approaches with simulation where idealized coil elements are arrayed around an object, and on directly acquired data with an 8-channel coil array on a uniform 13C phantom, and on Hyperpolarized 13C pyruvate brain MRI. Results We show improved image uniformity using the ratios of first order statistics compared to a simple homomorphic filter, noting that these approaches are more sensitive to noise. Discussion We present simple methods for correcting the spatial variation in sensitivity profiles in the context of a coil array. These methods can be used as an initial or adjunct step in data post-processing. © The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) 2022 |
| abstractGer |
Object Spatial variation in the sensitivity profiles of receive coils in MRI leads to spatially dependent scaling of the signal amplitude across an image. In practice, total sensitivity of the coil array is either calibrated or corrected directly by comparison to a uniform sensitivity image, fitting of coil profiles, or indirectly by constraining the reconstructed image or coil profiles. In the absence of these corrections, popular coil summation strategies are often designed to maximize the signal-to-noise ratio or optimize under-sampled encoding but not necessarily estimate the value of the signal unscaled by the coil spatial sensitivity. Materials and Methods We use ratios of first-order statistics to approach the unscaled value of the signal at any position. Motivated by the assumption that the coil array is a sample from much larger number of possible coils, we present two approaches to scale the mean signal in all coils: (1) an argument for use of the mode of the normalized signals, and (2) using a one-dimensional analog derive an approximate expression for scaling with the ratio of the square-of-the-mean to the mean-of-the-squares. We test these approaches with simulation where idealized coil elements are arrayed around an object, and on directly acquired data with an 8-channel coil array on a uniform 13C phantom, and on Hyperpolarized 13C pyruvate brain MRI. Results We show improved image uniformity using the ratios of first order statistics compared to a simple homomorphic filter, noting that these approaches are more sensitive to noise. Discussion We present simple methods for correcting the spatial variation in sensitivity profiles in the context of a coil array. These methods can be used as an initial or adjunct step in data post-processing. © The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) 2022 |
| abstract_unstemmed |
Object Spatial variation in the sensitivity profiles of receive coils in MRI leads to spatially dependent scaling of the signal amplitude across an image. In practice, total sensitivity of the coil array is either calibrated or corrected directly by comparison to a uniform sensitivity image, fitting of coil profiles, or indirectly by constraining the reconstructed image or coil profiles. In the absence of these corrections, popular coil summation strategies are often designed to maximize the signal-to-noise ratio or optimize under-sampled encoding but not necessarily estimate the value of the signal unscaled by the coil spatial sensitivity. Materials and Methods We use ratios of first-order statistics to approach the unscaled value of the signal at any position. Motivated by the assumption that the coil array is a sample from much larger number of possible coils, we present two approaches to scale the mean signal in all coils: (1) an argument for use of the mode of the normalized signals, and (2) using a one-dimensional analog derive an approximate expression for scaling with the ratio of the square-of-the-mean to the mean-of-the-squares. We test these approaches with simulation where idealized coil elements are arrayed around an object, and on directly acquired data with an 8-channel coil array on a uniform 13C phantom, and on Hyperpolarized 13C pyruvate brain MRI. Results We show improved image uniformity using the ratios of first order statistics compared to a simple homomorphic filter, noting that these approaches are more sensitive to noise. Discussion We present simple methods for correcting the spatial variation in sensitivity profiles in the context of a coil array. These methods can be used as an initial or adjunct step in data post-processing. © The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) 2022 |
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| title_short |
Improved total sensitivity estimation for multiple receive coils in MRI using ratios of first-order statistics |
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https://dx.doi.org/10.1007/s10334-022-01028-0 |
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Boltyanskiy, Rostislav Granlund, Kristin L. Keshari, Kayvan R. |
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| doi_str |
10.1007/s10334-022-01028-0 |
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2024-07-03T19:15:54.708Z |
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|
| score |
7.40102 |