Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data
Purpose The combination of positron emission tomography (PET) and magnetic resonance (MR) tomography in a single device is anticipated to be the next step following PET/CT for future molecular imaging application. Compared to CT, the main advantages of MR are versatile soft tissue contrast and its c...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Schulz, V. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2010 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag 2010 |
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| Übergeordnetes Werk: |
Enthalten in: European journal of nuclear medicine and molecular imaging - Heidelberg [u.a.] : Springer-Verl., 2002, 38(2010), 1 vom: 05. Okt., Seite 138-152 |
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| Übergeordnetes Werk: |
volume:38 ; year:2010 ; number:1 ; day:05 ; month:10 ; pages:138-152 |
| Links: |
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| DOI / URN: |
10.1007/s00259-010-1603-1 |
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| 100 | 1 | |a Schulz, V. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data |
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| 520 | |a Purpose The combination of positron emission tomography (PET) and magnetic resonance (MR) tomography in a single device is anticipated to be the next step following PET/CT for future molecular imaging application. Compared to CT, the main advantages of MR are versatile soft tissue contrast and its capability to acquire functional information without ionizing radiation. However, MR is not capable of measuring a physical quantity that would allow a direct derivation of the attenuation values for high-energy photons. Methods To overcome this problem, we propose a fully automated approach that uses a dedicated T1-weighted MR sequence in combination with a customized image processing technique to derive attenuation maps for whole-body PET. The algorithm automatically identifies the outer contour of the body and the lungs using region-growing techniques in combination with an intensity analysis for automatic threshold estimation. No user interaction is required to generate the attenuation map. Results The accuracy of the proposed MR-based attenuation correction (AC) approach was evaluated in a clinical study using whole-body PET/CT and MR images of the same patients (n = 15). The segmentation of the body and lung contour (L-R directions) was evaluated via a four-point scale in comparison to the original MR image (mean values >3.8). PET images were reconstructed using elastically registered MR-based and CT-based (segmented and non-segmented) attenuation maps. The MR-based AC showed similar behaviour as CT-based AC and similar accuracy as offered by segmented CT-based AC. Standardized uptake value (SUV) comparisons with reference to CT-based AC using predefined attenuation coefficients showed the largest difference for bone lesions (mean value ± standard variation of $ SUV_{max} $: −3.0% ± 3.9% for MR; −6.5% ± 4.1% for segmented CT). A blind comparison of PET images corrected with segmented MR-based, CT-based and segmented CT-based AC afforded identical lesion detectability, but slight differences in image quality were found. Conclusion Our MR‐based attenuation correction method offers similar correction accuracy as offered by segmented CT. According to the specialists involved in the blind study, these differences do not affect the diagnostic value of the PET images. | ||
| 650 | 4 | |a Magnetic resonance imaging |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a MRI |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a MR |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Positron emission tomography |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a PET |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Attenuation correction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Whole body |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a PET/MR |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a MR-AC |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a MR-based |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Torres-Espallardo, I. |4 aut | |
| 700 | 1 | |a Renisch, S. |4 aut | |
| 700 | 1 | |a Hu, Z. |4 aut | |
| 700 | 1 | |a Ojha, N. |4 aut | |
| 700 | 1 | |a Börnert, P. |4 aut | |
| 700 | 1 | |a Perkuhn, M. |4 aut | |
| 700 | 1 | |a Niendorf, T. |4 aut | |
| 700 | 1 | |a Schäfer, W. M. |4 aut | |
| 700 | 1 | |a Brockmann, H. |4 aut | |
| 700 | 1 | |a Krohn, T. |4 aut | |
| 700 | 1 | |a Buhl, A. |4 aut | |
| 700 | 1 | |a Günther, R. W. |4 aut | |
| 700 | 1 | |a Mottaghy, F. M. |4 aut | |
| 700 | 1 | |a Krombach, G. A. |4 aut | |
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10.1007/s00259-010-1603-1 doi (DE-627)SPR00313900X (SPR)s00259-010-1603-1-e DE-627 ger DE-627 rakwb eng Schulz, V. verfasserin aut Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2010 Purpose The combination of positron emission tomography (PET) and magnetic resonance (MR) tomography in a single device is anticipated to be the next step following PET/CT for future molecular imaging application. Compared to CT, the main advantages of MR are versatile soft tissue contrast and its capability to acquire functional information without ionizing radiation. However, MR is not capable of measuring a physical quantity that would allow a direct derivation of the attenuation values for high-energy photons. Methods To overcome this problem, we propose a fully automated approach that uses a dedicated T1-weighted MR sequence in combination with a customized image processing technique to derive attenuation maps for whole-body PET. The algorithm automatically identifies the outer contour of the body and the lungs using region-growing techniques in combination with an intensity analysis for automatic threshold estimation. No user interaction is required to generate the attenuation map. Results The accuracy of the proposed MR-based attenuation correction (AC) approach was evaluated in a clinical study using whole-body PET/CT and MR images of the same patients (n = 15). The segmentation of the body and lung contour (L-R directions) was evaluated via a four-point scale in comparison to the original MR image (mean values >3.8). PET images were reconstructed using elastically registered MR-based and CT-based (segmented and non-segmented) attenuation maps. The MR-based AC showed similar behaviour as CT-based AC and similar accuracy as offered by segmented CT-based AC. Standardized uptake value (SUV) comparisons with reference to CT-based AC using predefined attenuation coefficients showed the largest difference for bone lesions (mean value ± standard variation of $ SUV_{max} $: −3.0% ± 3.9% for MR; −6.5% ± 4.1% for segmented CT). A blind comparison of PET images corrected with segmented MR-based, CT-based and segmented CT-based AC afforded identical lesion detectability, but slight differences in image quality were found. Conclusion Our MR‐based attenuation correction method offers similar correction accuracy as offered by segmented CT. According to the specialists involved in the blind study, these differences do not affect the diagnostic value of the PET images. Magnetic resonance imaging (dpeaa)DE-He213 MRI (dpeaa)DE-He213 MR (dpeaa)DE-He213 Positron emission tomography (dpeaa)DE-He213 PET (dpeaa)DE-He213 Attenuation correction (dpeaa)DE-He213 Whole body (dpeaa)DE-He213 PET/MR (dpeaa)DE-He213 MR-AC (dpeaa)DE-He213 MR-based (dpeaa)DE-He213 Torres-Espallardo, I. aut Renisch, S. aut Hu, Z. aut Ojha, N. aut Börnert, P. aut Perkuhn, M. aut Niendorf, T. aut Schäfer, W. M. aut Brockmann, H. aut Krohn, T. aut Buhl, A. aut Günther, R. W. aut Mottaghy, F. M. aut Krombach, G. A. aut Enthalten in European journal of nuclear medicine and molecular imaging Heidelberg [u.a.] : Springer-Verl., 2002 38(2010), 1 vom: 05. Okt., Seite 138-152 (DE-627)359787258 (DE-600)2098375-X 1619-7089 nnns volume:38 year:2010 number:1 day:05 month:10 pages:138-152 https://dx.doi.org/10.1007/s00259-010-1603-1 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_65 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 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 38 2010 1 05 10 138-152 |
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10.1007/s00259-010-1603-1 doi (DE-627)SPR00313900X (SPR)s00259-010-1603-1-e DE-627 ger DE-627 rakwb eng Schulz, V. verfasserin aut Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2010 Purpose The combination of positron emission tomography (PET) and magnetic resonance (MR) tomography in a single device is anticipated to be the next step following PET/CT for future molecular imaging application. Compared to CT, the main advantages of MR are versatile soft tissue contrast and its capability to acquire functional information without ionizing radiation. However, MR is not capable of measuring a physical quantity that would allow a direct derivation of the attenuation values for high-energy photons. Methods To overcome this problem, we propose a fully automated approach that uses a dedicated T1-weighted MR sequence in combination with a customized image processing technique to derive attenuation maps for whole-body PET. The algorithm automatically identifies the outer contour of the body and the lungs using region-growing techniques in combination with an intensity analysis for automatic threshold estimation. No user interaction is required to generate the attenuation map. Results The accuracy of the proposed MR-based attenuation correction (AC) approach was evaluated in a clinical study using whole-body PET/CT and MR images of the same patients (n = 15). The segmentation of the body and lung contour (L-R directions) was evaluated via a four-point scale in comparison to the original MR image (mean values >3.8). PET images were reconstructed using elastically registered MR-based and CT-based (segmented and non-segmented) attenuation maps. The MR-based AC showed similar behaviour as CT-based AC and similar accuracy as offered by segmented CT-based AC. Standardized uptake value (SUV) comparisons with reference to CT-based AC using predefined attenuation coefficients showed the largest difference for bone lesions (mean value ± standard variation of $ SUV_{max} $: −3.0% ± 3.9% for MR; −6.5% ± 4.1% for segmented CT). A blind comparison of PET images corrected with segmented MR-based, CT-based and segmented CT-based AC afforded identical lesion detectability, but slight differences in image quality were found. Conclusion Our MR‐based attenuation correction method offers similar correction accuracy as offered by segmented CT. According to the specialists involved in the blind study, these differences do not affect the diagnostic value of the PET images. Magnetic resonance imaging (dpeaa)DE-He213 MRI (dpeaa)DE-He213 MR (dpeaa)DE-He213 Positron emission tomography (dpeaa)DE-He213 PET (dpeaa)DE-He213 Attenuation correction (dpeaa)DE-He213 Whole body (dpeaa)DE-He213 PET/MR (dpeaa)DE-He213 MR-AC (dpeaa)DE-He213 MR-based (dpeaa)DE-He213 Torres-Espallardo, I. aut Renisch, S. aut Hu, Z. aut Ojha, N. aut Börnert, P. aut Perkuhn, M. aut Niendorf, T. aut Schäfer, W. M. aut Brockmann, H. aut Krohn, T. aut Buhl, A. aut Günther, R. W. aut Mottaghy, F. M. aut Krombach, G. A. aut Enthalten in European journal of nuclear medicine and molecular imaging Heidelberg [u.a.] : Springer-Verl., 2002 38(2010), 1 vom: 05. Okt., Seite 138-152 (DE-627)359787258 (DE-600)2098375-X 1619-7089 nnns volume:38 year:2010 number:1 day:05 month:10 pages:138-152 https://dx.doi.org/10.1007/s00259-010-1603-1 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_65 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 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 38 2010 1 05 10 138-152 |
| allfields_unstemmed |
10.1007/s00259-010-1603-1 doi (DE-627)SPR00313900X (SPR)s00259-010-1603-1-e DE-627 ger DE-627 rakwb eng Schulz, V. verfasserin aut Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2010 Purpose The combination of positron emission tomography (PET) and magnetic resonance (MR) tomography in a single device is anticipated to be the next step following PET/CT for future molecular imaging application. Compared to CT, the main advantages of MR are versatile soft tissue contrast and its capability to acquire functional information without ionizing radiation. However, MR is not capable of measuring a physical quantity that would allow a direct derivation of the attenuation values for high-energy photons. Methods To overcome this problem, we propose a fully automated approach that uses a dedicated T1-weighted MR sequence in combination with a customized image processing technique to derive attenuation maps for whole-body PET. The algorithm automatically identifies the outer contour of the body and the lungs using region-growing techniques in combination with an intensity analysis for automatic threshold estimation. No user interaction is required to generate the attenuation map. Results The accuracy of the proposed MR-based attenuation correction (AC) approach was evaluated in a clinical study using whole-body PET/CT and MR images of the same patients (n = 15). The segmentation of the body and lung contour (L-R directions) was evaluated via a four-point scale in comparison to the original MR image (mean values >3.8). PET images were reconstructed using elastically registered MR-based and CT-based (segmented and non-segmented) attenuation maps. The MR-based AC showed similar behaviour as CT-based AC and similar accuracy as offered by segmented CT-based AC. Standardized uptake value (SUV) comparisons with reference to CT-based AC using predefined attenuation coefficients showed the largest difference for bone lesions (mean value ± standard variation of $ SUV_{max} $: −3.0% ± 3.9% for MR; −6.5% ± 4.1% for segmented CT). A blind comparison of PET images corrected with segmented MR-based, CT-based and segmented CT-based AC afforded identical lesion detectability, but slight differences in image quality were found. Conclusion Our MR‐based attenuation correction method offers similar correction accuracy as offered by segmented CT. According to the specialists involved in the blind study, these differences do not affect the diagnostic value of the PET images. Magnetic resonance imaging (dpeaa)DE-He213 MRI (dpeaa)DE-He213 MR (dpeaa)DE-He213 Positron emission tomography (dpeaa)DE-He213 PET (dpeaa)DE-He213 Attenuation correction (dpeaa)DE-He213 Whole body (dpeaa)DE-He213 PET/MR (dpeaa)DE-He213 MR-AC (dpeaa)DE-He213 MR-based (dpeaa)DE-He213 Torres-Espallardo, I. aut Renisch, S. aut Hu, Z. aut Ojha, N. aut Börnert, P. aut Perkuhn, M. aut Niendorf, T. aut Schäfer, W. M. aut Brockmann, H. aut Krohn, T. aut Buhl, A. aut Günther, R. W. aut Mottaghy, F. M. aut Krombach, G. A. aut Enthalten in European journal of nuclear medicine and molecular imaging Heidelberg [u.a.] : Springer-Verl., 2002 38(2010), 1 vom: 05. Okt., Seite 138-152 (DE-627)359787258 (DE-600)2098375-X 1619-7089 nnns volume:38 year:2010 number:1 day:05 month:10 pages:138-152 https://dx.doi.org/10.1007/s00259-010-1603-1 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_65 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 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 38 2010 1 05 10 138-152 |
| allfieldsGer |
10.1007/s00259-010-1603-1 doi (DE-627)SPR00313900X (SPR)s00259-010-1603-1-e DE-627 ger DE-627 rakwb eng Schulz, V. verfasserin aut Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2010 Purpose The combination of positron emission tomography (PET) and magnetic resonance (MR) tomography in a single device is anticipated to be the next step following PET/CT for future molecular imaging application. Compared to CT, the main advantages of MR are versatile soft tissue contrast and its capability to acquire functional information without ionizing radiation. However, MR is not capable of measuring a physical quantity that would allow a direct derivation of the attenuation values for high-energy photons. Methods To overcome this problem, we propose a fully automated approach that uses a dedicated T1-weighted MR sequence in combination with a customized image processing technique to derive attenuation maps for whole-body PET. The algorithm automatically identifies the outer contour of the body and the lungs using region-growing techniques in combination with an intensity analysis for automatic threshold estimation. No user interaction is required to generate the attenuation map. Results The accuracy of the proposed MR-based attenuation correction (AC) approach was evaluated in a clinical study using whole-body PET/CT and MR images of the same patients (n = 15). The segmentation of the body and lung contour (L-R directions) was evaluated via a four-point scale in comparison to the original MR image (mean values >3.8). PET images were reconstructed using elastically registered MR-based and CT-based (segmented and non-segmented) attenuation maps. The MR-based AC showed similar behaviour as CT-based AC and similar accuracy as offered by segmented CT-based AC. Standardized uptake value (SUV) comparisons with reference to CT-based AC using predefined attenuation coefficients showed the largest difference for bone lesions (mean value ± standard variation of $ SUV_{max} $: −3.0% ± 3.9% for MR; −6.5% ± 4.1% for segmented CT). A blind comparison of PET images corrected with segmented MR-based, CT-based and segmented CT-based AC afforded identical lesion detectability, but slight differences in image quality were found. Conclusion Our MR‐based attenuation correction method offers similar correction accuracy as offered by segmented CT. According to the specialists involved in the blind study, these differences do not affect the diagnostic value of the PET images. Magnetic resonance imaging (dpeaa)DE-He213 MRI (dpeaa)DE-He213 MR (dpeaa)DE-He213 Positron emission tomography (dpeaa)DE-He213 PET (dpeaa)DE-He213 Attenuation correction (dpeaa)DE-He213 Whole body (dpeaa)DE-He213 PET/MR (dpeaa)DE-He213 MR-AC (dpeaa)DE-He213 MR-based (dpeaa)DE-He213 Torres-Espallardo, I. aut Renisch, S. aut Hu, Z. aut Ojha, N. aut Börnert, P. aut Perkuhn, M. aut Niendorf, T. aut Schäfer, W. M. aut Brockmann, H. aut Krohn, T. aut Buhl, A. aut Günther, R. W. aut Mottaghy, F. M. aut Krombach, G. A. aut Enthalten in European journal of nuclear medicine and molecular imaging Heidelberg [u.a.] : Springer-Verl., 2002 38(2010), 1 vom: 05. Okt., Seite 138-152 (DE-627)359787258 (DE-600)2098375-X 1619-7089 nnns volume:38 year:2010 number:1 day:05 month:10 pages:138-152 https://dx.doi.org/10.1007/s00259-010-1603-1 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_65 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 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 38 2010 1 05 10 138-152 |
| allfieldsSound |
10.1007/s00259-010-1603-1 doi (DE-627)SPR00313900X (SPR)s00259-010-1603-1-e DE-627 ger DE-627 rakwb eng Schulz, V. verfasserin aut Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2010 Purpose The combination of positron emission tomography (PET) and magnetic resonance (MR) tomography in a single device is anticipated to be the next step following PET/CT for future molecular imaging application. Compared to CT, the main advantages of MR are versatile soft tissue contrast and its capability to acquire functional information without ionizing radiation. However, MR is not capable of measuring a physical quantity that would allow a direct derivation of the attenuation values for high-energy photons. Methods To overcome this problem, we propose a fully automated approach that uses a dedicated T1-weighted MR sequence in combination with a customized image processing technique to derive attenuation maps for whole-body PET. The algorithm automatically identifies the outer contour of the body and the lungs using region-growing techniques in combination with an intensity analysis for automatic threshold estimation. No user interaction is required to generate the attenuation map. Results The accuracy of the proposed MR-based attenuation correction (AC) approach was evaluated in a clinical study using whole-body PET/CT and MR images of the same patients (n = 15). The segmentation of the body and lung contour (L-R directions) was evaluated via a four-point scale in comparison to the original MR image (mean values >3.8). PET images were reconstructed using elastically registered MR-based and CT-based (segmented and non-segmented) attenuation maps. The MR-based AC showed similar behaviour as CT-based AC and similar accuracy as offered by segmented CT-based AC. Standardized uptake value (SUV) comparisons with reference to CT-based AC using predefined attenuation coefficients showed the largest difference for bone lesions (mean value ± standard variation of $ SUV_{max} $: −3.0% ± 3.9% for MR; −6.5% ± 4.1% for segmented CT). A blind comparison of PET images corrected with segmented MR-based, CT-based and segmented CT-based AC afforded identical lesion detectability, but slight differences in image quality were found. Conclusion Our MR‐based attenuation correction method offers similar correction accuracy as offered by segmented CT. According to the specialists involved in the blind study, these differences do not affect the diagnostic value of the PET images. Magnetic resonance imaging (dpeaa)DE-He213 MRI (dpeaa)DE-He213 MR (dpeaa)DE-He213 Positron emission tomography (dpeaa)DE-He213 PET (dpeaa)DE-He213 Attenuation correction (dpeaa)DE-He213 Whole body (dpeaa)DE-He213 PET/MR (dpeaa)DE-He213 MR-AC (dpeaa)DE-He213 MR-based (dpeaa)DE-He213 Torres-Espallardo, I. aut Renisch, S. aut Hu, Z. aut Ojha, N. aut Börnert, P. aut Perkuhn, M. aut Niendorf, T. aut Schäfer, W. M. aut Brockmann, H. aut Krohn, T. aut Buhl, A. aut Günther, R. W. aut Mottaghy, F. M. aut Krombach, G. A. aut Enthalten in European journal of nuclear medicine and molecular imaging Heidelberg [u.a.] : Springer-Verl., 2002 38(2010), 1 vom: 05. Okt., Seite 138-152 (DE-627)359787258 (DE-600)2098375-X 1619-7089 nnns volume:38 year:2010 number:1 day:05 month:10 pages:138-152 https://dx.doi.org/10.1007/s00259-010-1603-1 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_65 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 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 38 2010 1 05 10 138-152 |
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Enthalten in European journal of nuclear medicine and molecular imaging 38(2010), 1 vom: 05. Okt., Seite 138-152 volume:38 year:2010 number:1 day:05 month:10 pages:138-152 |
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Magnetic resonance imaging MRI MR Positron emission tomography PET Attenuation correction Whole body PET/MR MR-AC MR-based |
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Schulz, V. @@aut@@ Torres-Espallardo, I. @@aut@@ Renisch, S. @@aut@@ Hu, Z. @@aut@@ Ojha, N. @@aut@@ Börnert, P. @@aut@@ Perkuhn, M. @@aut@@ Niendorf, T. @@aut@@ Schäfer, W. M. @@aut@@ Brockmann, H. @@aut@@ Krohn, T. @@aut@@ Buhl, A. @@aut@@ Günther, R. W. @@aut@@ Mottaghy, F. M. @@aut@@ Krombach, G. A. @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR00313900X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519235638.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201001s2010 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00259-010-1603-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR00313900X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00259-010-1603-1-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Schulz, V.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2010</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag 2010</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Purpose The combination of positron emission tomography (PET) and magnetic resonance (MR) tomography in a single device is anticipated to be the next step following PET/CT for future molecular imaging application. Compared to CT, the main advantages of MR are versatile soft tissue contrast and its capability to acquire functional information without ionizing radiation. However, MR is not capable of measuring a physical quantity that would allow a direct derivation of the attenuation values for high-energy photons. Methods To overcome this problem, we propose a fully automated approach that uses a dedicated T1-weighted MR sequence in combination with a customized image processing technique to derive attenuation maps for whole-body PET. The algorithm automatically identifies the outer contour of the body and the lungs using region-growing techniques in combination with an intensity analysis for automatic threshold estimation. No user interaction is required to generate the attenuation map. Results The accuracy of the proposed MR-based attenuation correction (AC) approach was evaluated in a clinical study using whole-body PET/CT and MR images of the same patients (n = 15). The segmentation of the body and lung contour (L-R directions) was evaluated via a four-point scale in comparison to the original MR image (mean values >3.8). PET images were reconstructed using elastically registered MR-based and CT-based (segmented and non-segmented) attenuation maps. The MR-based AC showed similar behaviour as CT-based AC and similar accuracy as offered by segmented CT-based AC. Standardized uptake value (SUV) comparisons with reference to CT-based AC using predefined attenuation coefficients showed the largest difference for bone lesions (mean value ± standard variation of $ SUV_{max} $: −3.0% ± 3.9% for MR; −6.5% ± 4.1% for segmented CT). A blind comparison of PET images corrected with segmented MR-based, CT-based and segmented CT-based AC afforded identical lesion detectability, but slight differences in image quality were found. Conclusion Our MR‐based attenuation correction method offers similar correction accuracy as offered by segmented CT. According to the specialists involved in the blind study, these differences do not affect the diagnostic value of the PET images.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Magnetic resonance imaging</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">MRI</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">MR</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Positron emission tomography</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">PET</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Attenuation correction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Whole body</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">PET/MR</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">MR-AC</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">MR-based</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Torres-Espallardo, I.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Renisch, S.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hu, Z.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ojha, N.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Börnert, P.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Perkuhn, M.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Niendorf, T.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Schäfer, W. 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|
| author |
Schulz, V. |
| spellingShingle |
Schulz, V. misc Magnetic resonance imaging misc MRI misc MR misc Positron emission tomography misc PET misc Attenuation correction misc Whole body misc PET/MR misc MR-AC misc MR-based Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data |
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Schulz, V. |
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Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data Magnetic resonance imaging (dpeaa)DE-He213 MRI (dpeaa)DE-He213 MR (dpeaa)DE-He213 Positron emission tomography (dpeaa)DE-He213 PET (dpeaa)DE-He213 Attenuation correction (dpeaa)DE-He213 Whole body (dpeaa)DE-He213 PET/MR (dpeaa)DE-He213 MR-AC (dpeaa)DE-He213 MR-based (dpeaa)DE-He213 |
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Schulz, V. |
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Schulz, V. Torres-Espallardo, I. Renisch, S. Hu, Z. Ojha, N. Börnert, P. Perkuhn, M. Niendorf, T. Schäfer, W. M. Brockmann, H. Krohn, T. Buhl, A. Günther, R. W. Mottaghy, F. M. Krombach, G. A. |
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10.1007/s00259-010-1603-1 |
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automatic, three-segment, mr-based attenuation correction for whole-body pet/mr data |
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Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data |
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Purpose The combination of positron emission tomography (PET) and magnetic resonance (MR) tomography in a single device is anticipated to be the next step following PET/CT for future molecular imaging application. Compared to CT, the main advantages of MR are versatile soft tissue contrast and its capability to acquire functional information without ionizing radiation. However, MR is not capable of measuring a physical quantity that would allow a direct derivation of the attenuation values for high-energy photons. Methods To overcome this problem, we propose a fully automated approach that uses a dedicated T1-weighted MR sequence in combination with a customized image processing technique to derive attenuation maps for whole-body PET. The algorithm automatically identifies the outer contour of the body and the lungs using region-growing techniques in combination with an intensity analysis for automatic threshold estimation. No user interaction is required to generate the attenuation map. Results The accuracy of the proposed MR-based attenuation correction (AC) approach was evaluated in a clinical study using whole-body PET/CT and MR images of the same patients (n = 15). The segmentation of the body and lung contour (L-R directions) was evaluated via a four-point scale in comparison to the original MR image (mean values >3.8). PET images were reconstructed using elastically registered MR-based and CT-based (segmented and non-segmented) attenuation maps. The MR-based AC showed similar behaviour as CT-based AC and similar accuracy as offered by segmented CT-based AC. Standardized uptake value (SUV) comparisons with reference to CT-based AC using predefined attenuation coefficients showed the largest difference for bone lesions (mean value ± standard variation of $ SUV_{max} $: −3.0% ± 3.9% for MR; −6.5% ± 4.1% for segmented CT). A blind comparison of PET images corrected with segmented MR-based, CT-based and segmented CT-based AC afforded identical lesion detectability, but slight differences in image quality were found. Conclusion Our MR‐based attenuation correction method offers similar correction accuracy as offered by segmented CT. According to the specialists involved in the blind study, these differences do not affect the diagnostic value of the PET images. © Springer-Verlag 2010 |
| abstractGer |
Purpose The combination of positron emission tomography (PET) and magnetic resonance (MR) tomography in a single device is anticipated to be the next step following PET/CT for future molecular imaging application. Compared to CT, the main advantages of MR are versatile soft tissue contrast and its capability to acquire functional information without ionizing radiation. However, MR is not capable of measuring a physical quantity that would allow a direct derivation of the attenuation values for high-energy photons. Methods To overcome this problem, we propose a fully automated approach that uses a dedicated T1-weighted MR sequence in combination with a customized image processing technique to derive attenuation maps for whole-body PET. The algorithm automatically identifies the outer contour of the body and the lungs using region-growing techniques in combination with an intensity analysis for automatic threshold estimation. No user interaction is required to generate the attenuation map. Results The accuracy of the proposed MR-based attenuation correction (AC) approach was evaluated in a clinical study using whole-body PET/CT and MR images of the same patients (n = 15). The segmentation of the body and lung contour (L-R directions) was evaluated via a four-point scale in comparison to the original MR image (mean values >3.8). PET images were reconstructed using elastically registered MR-based and CT-based (segmented and non-segmented) attenuation maps. The MR-based AC showed similar behaviour as CT-based AC and similar accuracy as offered by segmented CT-based AC. Standardized uptake value (SUV) comparisons with reference to CT-based AC using predefined attenuation coefficients showed the largest difference for bone lesions (mean value ± standard variation of $ SUV_{max} $: −3.0% ± 3.9% for MR; −6.5% ± 4.1% for segmented CT). A blind comparison of PET images corrected with segmented MR-based, CT-based and segmented CT-based AC afforded identical lesion detectability, but slight differences in image quality were found. Conclusion Our MR‐based attenuation correction method offers similar correction accuracy as offered by segmented CT. According to the specialists involved in the blind study, these differences do not affect the diagnostic value of the PET images. © Springer-Verlag 2010 |
| abstract_unstemmed |
Purpose The combination of positron emission tomography (PET) and magnetic resonance (MR) tomography in a single device is anticipated to be the next step following PET/CT for future molecular imaging application. Compared to CT, the main advantages of MR are versatile soft tissue contrast and its capability to acquire functional information without ionizing radiation. However, MR is not capable of measuring a physical quantity that would allow a direct derivation of the attenuation values for high-energy photons. Methods To overcome this problem, we propose a fully automated approach that uses a dedicated T1-weighted MR sequence in combination with a customized image processing technique to derive attenuation maps for whole-body PET. The algorithm automatically identifies the outer contour of the body and the lungs using region-growing techniques in combination with an intensity analysis for automatic threshold estimation. No user interaction is required to generate the attenuation map. Results The accuracy of the proposed MR-based attenuation correction (AC) approach was evaluated in a clinical study using whole-body PET/CT and MR images of the same patients (n = 15). The segmentation of the body and lung contour (L-R directions) was evaluated via a four-point scale in comparison to the original MR image (mean values >3.8). PET images were reconstructed using elastically registered MR-based and CT-based (segmented and non-segmented) attenuation maps. The MR-based AC showed similar behaviour as CT-based AC and similar accuracy as offered by segmented CT-based AC. Standardized uptake value (SUV) comparisons with reference to CT-based AC using predefined attenuation coefficients showed the largest difference for bone lesions (mean value ± standard variation of $ SUV_{max} $: −3.0% ± 3.9% for MR; −6.5% ± 4.1% for segmented CT). A blind comparison of PET images corrected with segmented MR-based, CT-based and segmented CT-based AC afforded identical lesion detectability, but slight differences in image quality were found. Conclusion Our MR‐based attenuation correction method offers similar correction accuracy as offered by segmented CT. According to the specialists involved in the blind study, these differences do not affect the diagnostic value of the PET images. © Springer-Verlag 2010 |
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| score |
7.4000015 |