Lesion quantification in oncological positron emission tomography: A maximum likelihood partial volume correction strategy
A maximum likelihood (ML) partial volume effect correction (PVEC) strategy for the quantification of uptake and volume of oncological lesions in F 18 -FDG positron emission tomography is proposed. The algorithm is based on the application of ML reconstruction on volumetric regional basis functions i...
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| Published in | Medical physics (Lancaster) Vol. 36; no. 7; pp. 3040 - 3049 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
American Association of Physicists in Medicine
01.07.2009
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0094-2405 2473-4209 |
| DOI | 10.1118/1.3130019 |
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| Abstract | A maximum likelihood (ML) partial volume effect correction (PVEC) strategy for the quantification of uptake and volume of oncological lesions in
F
18
-FDG positron emission tomography is proposed. The algorithm is based on the application of ML reconstruction on volumetric regional basis functions initially defined on a smooth standard clinical image and iteratively updated in terms of their activity and volume. The volume of interest (VOI) containing a previously detected region is segmented by a
k
-means algorithm in three regions: A central region surrounded by a partial volume region and a spill-out region. All volume outside the VOI (background with all other structures) is handled as a unique basis function and therefore “frozen” in the reconstruction process except for a gain coefficient. The coefficients of the regional basis functions are iteratively estimated with an attenuation-weighted ordered subset expectation maximization (AWOSEM) algorithm in which a 3D, anisotropic, space variant model of point spread function (PSF) is included for resolution recovery. The reconstruction-segmentation process is iterated until convergence; at each iteration, segmentation is performed on the reconstructed image blurred by the system PSF in order to update the partial volume and spill-out regions. The developed PVEC strategy was tested on sphere phantom studies with activity contrasts of 7.5 and 4 and compared to a conventional recovery coefficient method. Improved volume and activity estimates were obtained with low computational costs, thanks to blur recovery and to a better local approximation to ML convergence. |
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| AbstractList | A maximum likelihood (ML) partial volume effect correction (PVEC) strategy for the quantification of uptake and volume of oncological lesions in F18‐FDG positron emission tomography is proposed. The algorithm is based on the application of ML reconstruction on volumetric regional basis functions initially defined on a smooth standard clinical image and iteratively updated in terms of their activity and volume. The volume of interest (VOI) containing a previously detected region is segmented by a k‐means algorithm in three regions: A central region surrounded by a partial volume region and a spill‐out region. All volume outside the VOI (background with all other structures) is handled as a unique basis function and therefore “frozen” in the reconstruction process except for a gain coefficient. The coefficients of the regional basis functions are iteratively estimated with an attenuation‐weighted ordered subset expectation maximization (AWOSEM) algorithm in which a 3D, anisotropic, space variant model of point spread function (PSF) is included for resolution recovery. The reconstruction‐segmentation process is iterated until convergence; at each iteration, segmentation is performed on the reconstructed image blurred by the system PSF in order to update the partial volume and spill‐out regions. The developed PVEC strategy was tested on sphere phantom studies with activity contrasts of 7.5 and 4 and compared to a conventional recovery coefficient method. Improved volume and activity estimates were obtained with low computational costs, thanks to blur recovery and to a better local approximation to ML convergence. A maximum likelihood (ML) partial volume effect correction (PVEC) strategy for the quantification of uptake and volume of oncological lesions in 18F-FDG positron emission tomography is proposed. The algorithm is based on the application of ML reconstruction on volumetric regional basis functions initially defined on a smooth standard clinical image and iteratively updated in terms of their activity and volume. The volume of interest (VOI) containing a previously detected region is segmented by a k-means algorithm in three regions: A central region surrounded by a partial volume region and a spill-out region. All volume outside the VOI (background with all other structures) is handled as a unique basis function and therefore "frozen" in the reconstruction process except for a gain coefficient. The coefficients of the regional basis functions are iteratively estimated with an attenuation-weighted ordered subset expectation maximization (AWOSEM) algorithm in which a 3D, anisotropic, space variant model of point spread function (PSF) is included for resolution recovery. The reconstruction-segmentation process is iterated until convergence; at each iteration, segmentation is performed on the reconstructed image blurred by the system PSF in order to update the partial volume and spill-out regions. The developed PVEC strategy was tested on sphere phantom studies with activity contrasts of 7.5 and 4 and compared to a conventional recovery coefficient method. Improved volume and activity estimates were obtained with low computational costs, thanks to blur recovery and to a better local approximation to ML convergence.A maximum likelihood (ML) partial volume effect correction (PVEC) strategy for the quantification of uptake and volume of oncological lesions in 18F-FDG positron emission tomography is proposed. The algorithm is based on the application of ML reconstruction on volumetric regional basis functions initially defined on a smooth standard clinical image and iteratively updated in terms of their activity and volume. The volume of interest (VOI) containing a previously detected region is segmented by a k-means algorithm in three regions: A central region surrounded by a partial volume region and a spill-out region. All volume outside the VOI (background with all other structures) is handled as a unique basis function and therefore "frozen" in the reconstruction process except for a gain coefficient. The coefficients of the regional basis functions are iteratively estimated with an attenuation-weighted ordered subset expectation maximization (AWOSEM) algorithm in which a 3D, anisotropic, space variant model of point spread function (PSF) is included for resolution recovery. The reconstruction-segmentation process is iterated until convergence; at each iteration, segmentation is performed on the reconstructed image blurred by the system PSF in order to update the partial volume and spill-out regions. The developed PVEC strategy was tested on sphere phantom studies with activity contrasts of 7.5 and 4 and compared to a conventional recovery coefficient method. Improved volume and activity estimates were obtained with low computational costs, thanks to blur recovery and to a better local approximation to ML convergence. A maximum likelihood (ML) partial volume effect correction (PVEC) strategy for the quantification of uptake and volume of oncological lesions in ‐FDG positron emission tomography is proposed. The algorithm is based on the application of ML reconstruction on volumetric regional basis functions initially defined on a smooth standard clinical image and iteratively updated in terms of their activity and volume. The volume of interest (VOI) containing a previously detected region is segmented by a ‐means algorithm in three regions: A central region surrounded by a partial volume region and a spill‐out region. All volume outside the VOI (background with all other structures) is handled as a unique basis function and therefore “frozen” in the reconstruction process except for a gain coefficient. The coefficients of the regional basis functions are iteratively estimated with an attenuation‐weighted ordered subset expectation maximization (AWOSEM) algorithm in which a 3D, anisotropic, space variant model of point spread function (PSF) is included for resolution recovery. The reconstruction‐segmentation process is iterated until convergence; at each iteration, segmentation is performed on the reconstructed image blurred by the system PSF in order to update the partial volume and spill‐out regions. The developed PVEC strategy was tested on sphere phantom studies with activity contrasts of 7.5 and 4 and compared to a conventional recovery coefficient method. Improved volume and activity estimates were obtained with low computational costs, thanks to blur recovery and to a better local approximation to ML convergence. A maximum likelihood (ML) partial volume effect correction (PVEC) strategy for the quantification of uptake and volume of oncological lesions in 18F-FDG positron emission tomography is proposed. The algorithm is based on the application of ML reconstruction on volumetric regional basis functions initially defined on a smooth standard clinical image and iteratively updated in terms of their activity and volume. The volume of interest (VOI) containing a previously detected region is segmented by a k-means algorithm in three regions: A central region surrounded by a partial volume region and a spill-out region. All volume outside the VOI (background with all other structures) is handled as a unique basis function and therefore "frozen" in the reconstruction process except for a gain coefficient. The coefficients of the regional basis functions are iteratively estimated with an attenuation-weighted ordered subset expectation maximization (AWOSEM) algorithm in which a 3D, anisotropic, space variant model of point spread function (PSF) is included for resolution recovery. The reconstruction-segmentation process is iterated until convergence; at each iteration, segmentation is performed on the reconstructed image blurred by the system PSF in order to update the partial volume and spill-out regions. The developed PVEC strategy was tested on sphere phantom studies with activity contrasts of 7.5 and 4 and compared to a conventional recovery coefficient method. Improved volume and activity estimates were obtained with low computational costs, thanks to blur recovery and to a better local approximation to ML convergence. A maximum likelihood (ML) partial volume effect correction (PVEC) strategy for the quantification of uptake and volume of oncological lesions in F 18 -FDG positron emission tomography is proposed. The algorithm is based on the application of ML reconstruction on volumetric regional basis functions initially defined on a smooth standard clinical image and iteratively updated in terms of their activity and volume. The volume of interest (VOI) containing a previously detected region is segmented by a k -means algorithm in three regions: A central region surrounded by a partial volume region and a spill-out region. All volume outside the VOI (background with all other structures) is handled as a unique basis function and therefore “frozen” in the reconstruction process except for a gain coefficient. The coefficients of the regional basis functions are iteratively estimated with an attenuation-weighted ordered subset expectation maximization (AWOSEM) algorithm in which a 3D, anisotropic, space variant model of point spread function (PSF) is included for resolution recovery. The reconstruction-segmentation process is iterated until convergence; at each iteration, segmentation is performed on the reconstructed image blurred by the system PSF in order to update the partial volume and spill-out regions. The developed PVEC strategy was tested on sphere phantom studies with activity contrasts of 7.5 and 4 and compared to a conventional recovery coefficient method. Improved volume and activity estimates were obtained with low computational costs, thanks to blur recovery and to a better local approximation to ML convergence. |
| Author | Faggiano, Elena Gerundini, Paolo De Bernardi, Elisabetta Baselli, Giuseppe Zito, Felicia |
| Author_xml | – sequence: 1 givenname: Elisabetta surname: De Bernardi fullname: De Bernardi, Elisabetta email: elisabetta.debernardi@polimi.it organization: Department of Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy – sequence: 2 givenname: Elena surname: Faggiano fullname: Faggiano, Elena organization: Department of Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy – sequence: 3 givenname: Felicia surname: Zito fullname: Zito, Felicia organization: Department of Nuclear Medicine, Fondazione Ospedale Maggiore Policlinico, Mangiagalli e Regina Elena, 20122 Milano, Italy – sequence: 4 givenname: Paolo surname: Gerundini fullname: Gerundini, Paolo organization: Department of Nuclear Medicine, Fondazione Ospedale Maggiore Policlinico, Mangiagalli e Regina Elena, 20122 Milano, Italy – sequence: 5 givenname: Giuseppe surname: Baselli fullname: Baselli, Giuseppe organization: Department of Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19673203$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1088/0031-9155/53/10/009 10.1007/s00259-006-0363-4 10.2967/jnumed.106.035774 10.1109/42.938248 10.1109/TNS.2007.905167 10.1007/s00259-004-1532-y 10.1109/42.563660 10.1109/JPROC.2003.817882 10.1007/s002590050022 10.1016/j.ctrv.2006.02.002 10.1088/0031-9155/52/12/010 10.1109/TMI.2006.873222 10.1118/1.2712043 10.1118/1.2432404 10.1097/00004728-198607000-00021 10.1109/42.222672 10.1088/0031-9155/38/1/005 10.1109/TMI.2006.876171 10.1007/s00259‐006‐0363‐4 10.1088/0031‐9155/38/1/005 10.1007/s00259‐004‐1532‐y 10.1088/0031‐9155/53/10/009 10.1088/0031‐9155/52/12/010 10.1097/00004728‐198607000‐00021 |
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| Keywords | partial volume effect correction volume estimate maximum likelihood reconstruction uptake quantification PET regional basis functions |
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| References | Panin, Kehren, Michel, Casey (c5) 2006; 25 Soret, Bacharach, Buvat (c10) 2007; 48 Carson (c16) 1986; 10 Montgomery, Amira, Zaidi (c13) 2007; 34 Formiconi (c17) 1993; 12 Defrise, Kinahan, Townsend, Michel, Sibomana, Newport (c21) 1997; 16 Alessio, Kinahan, Lewellen (c6) 2006; 25 Drever, Roa, McEwan, Robinson (c11) 2007; 34 Lange, Carson (c1) 1984; 8 Lewitt, Matej (c20) 2003; 91 Teo, Seo, Bacharach, Carrasquillo, Ributti, Shukla, Hasegawa, Hawkins, Franc (c9) 2007; 48 van Baardwijk, Baumert, Bosmans, van Kroonenburgh, Stroobants, Gregoire, Lambin, De Ruysscher (c24) 2006; 32 Liow, Strother (c3) 1993; 38 Hatt, Lamare, Boussion, Turzo, Collet, Salzenstein, Roux, Jarritt, Carson, Cheze-Le Rest, Visvikis (c12) 2007; 52 De Bernardi, Mazzoli, Zito, Baselli (c4) 2007; 54 Liu, Comtat, Michel, Kinahan, Defrise, Townsend (c2) 2001; 20 Kirov, Piao, Schmidtlein (c8) 2008; 53 Krak, Hoekstra, Lammertsma (c23) 2004; 31 Geets, Lee, Bol, Lonneux, Gregoire (c14) 2007; 34 Geworski, Knoop, de Cabrejas, Knapp, Munz (c22) 2000; 27 Panin, V.; Kehren, F.; Michel, C.; Casey, M. 2006; 25 Teo, B.; Seo, Y.; Bacharach, S.; Carrasquillo, J.; Ributti, S.; Shukla, H.; Hasegawa, B.; Hawkins, R.; Franc, B. 2007; 48 Defrise, M.; Kinahan, P.; Townsend, D.; Michel, C.; Sibomana, M.; Newport, D. 1997; 16 Formiconi, A. 1993; 12 Carson, R. 1986; 10 Lange, K.; Carson, R. 1984; 8 Geets, X.; Lee, J.; Bol, A.; Lonneux, M.; Gregoire, V. 2007; 34 Lewitt, R.; Matej, S. 2003; 91 Liu, X.; Comtat, C.; Michel, C.; Kinahan, P.; Defrise, M.; Townsend, D. 2001; 20 Kirov, A.; Piao, J.; Schmidtlein, C. 2008; 53 De Bernardi, E.; Mazzoli, M.; Zito, F.; Baselli, G. 2007; 54 van Baardwijk, A.; Baumert, B.; Bosmans, G.; van Kroonenburgh, M.; Stroobants, S.; Gregoire, V.; Lambin, P.; De Ruysscher, D. 2006; 32 Soret, M.; Bacharach, S.; Buvat, I. 2007; 48 Hatt, M.; Lamare, F.; Boussion, N.; Turzo, A.; Collet, C.; Salzenstein, F.; Roux, C.; Jarritt, P.; Carson, K.; Cheze-Le Rest, C.; Visvikis, D. 2007; 52 Alessio, A.; Kinahan, P.; Lewellen, T. 2006; 25 Drever, L.; Roa, W.; McEwan, A.; Robinson, D. 2007; 34 Montgomery, D.; Amira, A.; Zaidi, H. 2007; 34 Krak, N.; Hoekstra, O.; Lammertsma, A. 2004; 31 Liow, J.; Strother, S. 1993; 38 Geworski, L.; Knoop, B.; de Cabrejas, M.; Knapp, W.; Munz, D. 2000; 27 1993; 12 2004; 31 2003; 91 2000; 27 1993; 38 1986; 10 2006; 32 1984; 8 2006; 25 2007 2006 1997; 16 2008; 53 2007; 52 2007; 54 2007; 34 2001; 20 2007; 48 e_1_2_7_6_1 La Rivière P. (e_1_2_7_20_1) 2006 e_1_2_7_5_1 e_1_2_7_4_1 e_1_2_7_3_1 e_1_2_7_9_1 e_1_2_7_7_1 e_1_2_7_18_1 e_1_2_7_17_1 Boussion N. (e_1_2_7_8_1) 2007 El Bitar Z. (e_1_2_7_19_1) 2006 e_1_2_7_15_1 e_1_2_7_14_1 e_1_2_7_25_1 e_1_2_7_13_1 e_1_2_7_24_1 e_1_2_7_12_1 e_1_2_7_23_1 e_1_2_7_11_1 e_1_2_7_22_1 e_1_2_7_21_1 Lange K. (e_1_2_7_2_1) 1984; 8 Hatt M. (e_1_2_7_16_1) 2007 Teo B. (e_1_2_7_10_1) 2007; 48 |
| References_xml | – volume: 16 start-page: 145 issn: 0278-0062 year: 1997 ident: c21 article-title: Exact and approximate rebinning algorithms for 3D PET data publication-title: IEEE Trans. Med. Imaging – volume: 48 start-page: 932 issn: 0161-5505 year: 2007 ident: c10 article-title: Partial-volume effect in PET tumor imaging publication-title: J. Nucl. Med. – volume: 10 start-page: 654 issn: 0363-8715 year: 1986 ident: c16 article-title: A maximum likelihood method for region-of-interest evaluation in emission tomography publication-title: J. Comput. Assist. Tomogr. – volume: 31 start-page: S103 issn: 1619-7070 year: 2004 ident: c23 article-title: Measuring response to chemotherapy in locally advanced breast cancer: Methodological considerations publication-title: Eur. J. Nucl. Med. Mol. Imaging – volume: 27 start-page: 161 issn: 0340-6997 year: 2000 ident: c22 article-title: Recovery correction for quantization in emission tomography: A feasibility study publication-title: Eur. J. Nucl. Med. – volume: 91 start-page: 1588 issn: 0018-9219 year: 2003 ident: c20 article-title: Overview of methods for image reconstruction form projections in emission computed tomography publication-title: Proc. IEEE – volume: 34 start-page: 1253 issn: 0094-2405 year: 2007 ident: c11 article-title: Iterative threshold segmentation for PET target volume delineation publication-title: Med. Phys. – volume: 25 start-page: 907 issn: 0278-0062 year: 2006 ident: c5 article-title: Fully 3-D PET reconstruction with system matrix derived from point source measurements publication-title: IEEE Trans. Med. Imaging – volume: 34 start-page: 722 issn: 0094-2405 year: 2007 ident: c13 article-title: Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model publication-title: Med. Phys. – volume: 53 start-page: 2577 issn: 0031-9155 year: 2008 ident: c8 article-title: Partial volume effect correction in PET using regularized iterative deconvolution with variance control based on local topology publication-title: Phys. Med. Biol. – volume: 48 start-page: 802 issn: 0161-5505 year: 2007 ident: c9 article-title: Partial-volume correction in PET: validation of an iterative postreconstruction method with phantom and patient data publication-title: J. Nucl. Med. – volume: 52 start-page: 3467 issn: 0031-9155 year: 2007 ident: c12 article-title: Fuzzy hidden Markov chains segmentation for volume determination and quantization in PET publication-title: Phys. Med. Biol. – volume: 34 start-page: 1427 issn: 1619-7070 year: 2007 ident: c14 article-title: A gradient-based method for segmenting FDG-PET images: Methodology and validation publication-title: Eur. J. Nucl. Med. Mol. Imaging – volume: 38 start-page: 55 issn: 0031-9155 year: 1993 ident: c3 article-title: The convergence of object dependent resolution in maximum likelihood based tomographic image reconstruction publication-title: Phys. Med. Biol. – volume: 32 start-page: 245 issn: 0305-7372 year: 2006 ident: c24 article-title: The current status of FDG-PET in tumor volume definition in radiotherapy treatment planning publication-title: Cancer Treat. Rev. – volume: 25 start-page: 828 issn: 0278-0062 year: 2006 ident: c6 article-title: Modeling and incorporation of system response functions in 3-D whole body PET publication-title: IEEE Trans. Med. Imaging – volume: 8 start-page: 306 issn: 0363-8715 year: 1984 ident: c1 article-title: EM reconstruction algorithms for emission and transmission tomography publication-title: J. Comput. Assist. Tomogr. – volume: 20 start-page: 804 issn: 0278-0062 year: 2001 ident: c2 article-title: Comparison of 3-D reconstruction with 3D-OSEM and with publication-title: IEEE Trans. Med. Imaging – volume: 54 start-page: 1626 issn: 0018-9499 year: 2007 ident: c4 article-title: Resolution recovery in PET during AWOSEM reconstruction: A performance evaluation study publication-title: IEEE Trans. Nucl. Sci. – volume: 12 start-page: 90 issn: 0278-0062 year: 1993 ident: c17 article-title: Least squares algorithm for region-of-interest evaluation in emission tomography publication-title: IEEE Trans. Med. Imaging – volume: 48 start-page: 802-810 year: 2007 publication-title: J. Nucl. Med. – volume: 53 start-page: 2577-2591 year: 2008 publication-title: Phys. Med. Biol. doi: 10.1088/0031-9155/53/10/009 – volume: 34 start-page: 1427-1438 year: 2007 publication-title: Eur. J. Nucl. Med. Mol. Imaging doi: 10.1007/s00259-006-0363-4 – volume: 8 start-page: 306-316 year: 1984 publication-title: J. Comput. Assist. Tomogr. – volume: 48 start-page: 932-945 year: 2007 publication-title: J. Nucl. Med. doi: 10.2967/jnumed.106.035774 – volume: 20 start-page: 804-814 year: 2001 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/42.938248 – volume: 54 start-page: 1626-1638 year: 2007 publication-title: IEEE Trans. Nucl. Sci. doi: 10.1109/TNS.2007.905167 – volume: 31 start-page: S103-S111 year: 2004 publication-title: Eur. J. Nucl. Med. Mol. Imaging doi: 10.1007/s00259-004-1532-y – volume: 16 start-page: 145-158 year: 1997 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/42.563660 – volume: 91 start-page: 1588-1611 year: 2003 publication-title: Proc. IEEE doi: 10.1109/JPROC.2003.817882 – volume: 27 start-page: 161-169 year: 2000 publication-title: Eur. J. Nucl. Med. doi: 10.1007/s002590050022 – volume: 32 start-page: 245-260 year: 2006 publication-title: Cancer Treat. Rev. doi: 10.1016/j.ctrv.2006.02.002 – volume: 52 start-page: 3467-3491 year: 2007 publication-title: Phys. Med. Biol. doi: 10.1088/0031-9155/52/12/010 – volume: 25 start-page: 828-837 year: 2006 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2006.873222 – volume: 34 start-page: 1253-1265 year: 2007 publication-title: Med. Phys. doi: 10.1118/1.2712043 – volume: 34 start-page: 722-736 year: 2007 publication-title: Med. Phys. doi: 10.1118/1.2432404 – volume: 10 start-page: 654-663 year: 1986 publication-title: J. Comput. Assist. Tomogr. doi: 10.1097/00004728-198607000-00021 – volume: 12 start-page: 90-100 year: 1993 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/42.222672 – volume: 38 start-page: 55-70 year: 1993 publication-title: Phys. Med. Biol. doi: 10.1088/0031-9155/38/1/005 – volume: 25 start-page: 907-921 year: 2006 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2006.876171 – volume: 20 start-page: 804 year: 2001 end-page: 814 article-title: Comparison of 3‐D reconstruction with 3D‐OSEM and with publication-title: IEEE Trans. Med. Imaging – start-page: 2812 year: 2007 end-page: 2816 article-title: Fully automated partial volume correction in PET based on a wavelet approach without the use of anatomical information – volume: 34 start-page: 722 year: 2007 end-page: 736 article-title: Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model publication-title: Med. Phys. – volume: 34 start-page: 1253 year: 2007 end-page: 1265 article-title: Iterative threshold segmentation for PET target volume delineation publication-title: Med. Phys. – volume: 27 start-page: 161 year: 2000 end-page: 169 article-title: Recovery correction for quantization in emission tomography: A feasibility study publication-title: Eur. J. Nucl. Med. – volume: 16 start-page: 145 year: 1997 end-page: 158 article-title: Exact and approximate rebinning algorithms for 3D PET data publication-title: IEEE Trans. Med. Imaging – volume: 8 start-page: 306 year: 1984 end-page: 316 article-title: EM reconstruction algorithms for emission and transmission tomography publication-title: J. Comput. Assist. Tomogr. – volume: 53 start-page: 2577 year: 2008 end-page: 2591 article-title: Partial volume effect correction in PET using regularized iterative deconvolution with variance control based on local topology publication-title: Phys. Med. Biol. – volume: 38 start-page: 55 year: 1993 end-page: 70 article-title: The convergence of object dependent resolution in maximum likelihood based tomographic image reconstruction publication-title: Phys. Med. Biol. – volume: 12 start-page: 90 year: 1993 end-page: 100 article-title: Least squares algorithm for region‐of‐interest evaluation in emission tomography publication-title: IEEE Trans. Med. Imaging – volume: 32 start-page: 245 year: 2006 end-page: 260 article-title: The current status of FDG‐PET in tumor volume definition in radiotherapy treatment planning publication-title: Cancer Treat. Rev. – volume: 54 start-page: 1626 year: 2007 end-page: 1638 article-title: Resolution recovery in PET during AWOSEM reconstruction: A performance evaluation study publication-title: IEEE Trans. Nucl. Sci. – volume: 91 start-page: 1588 year: 2003 end-page: 1611 article-title: Overview of methods for image reconstruction form projections in emission computed tomography publication-title: Proc. IEEE – volume: 31 start-page: S103 year: 2004 end-page: S111 article-title: Measuring response to chemotherapy in locally advanced breast cancer: Methodological considerations publication-title: Eur. J. Nucl. Med. Mol. Imaging – start-page: 2924 year: 2006 end-page: 2928 article-title: Monotonic iterative reconstruction algorithms for targeted reconstruction in emission and transmission computed tomography – volume: 52 start-page: 3467 year: 2007 end-page: 3491 article-title: Fuzzy hidden Markov chains segmentation for volume determination and quantization in PET publication-title: Phys. Med. Biol. – volume: 25 start-page: 907 year: 2006 end-page: 921 article-title: Fully 3‐D PET reconstruction with system matrix derived from point source measurements publication-title: IEEE Trans. Med. Imaging – volume: 34 start-page: 1427 year: 2007 end-page: 1438 article-title: A gradient‐based method for segmenting FDG‐PET images: Methodology and validation publication-title: Eur. J. Nucl. Med. Mol. Imaging – volume: 48 start-page: 932 year: 2007 end-page: 945 article-title: Partial‐volume effect in PET tumor imaging publication-title: J. Nucl. Med. – start-page: 3410 year: 2006 end-page: 3413 article-title: Targeted fully 3D Monte Carlo reconstruction in SPECT – volume: 48 start-page: 802 year: 2007 end-page: 810 article-title: Partial‐volume correction in PET: validation of an iterative postreconstruction method with phantom and patient data publication-title: J. Nucl. Med. – start-page: 3939 year: 2007 end-page: 3945 article-title: A segmentation algorithm for heterogeneous tumor automatic delineation in PET – volume: 25 start-page: 828 year: 2006 end-page: 837 article-title: Modeling and incorporation of system response functions in 3‐D whole body PET publication-title: IEEE Trans. Med. Imaging – volume: 10 start-page: 654 year: 1986 end-page: 663 article-title: A maximum likelihood method for region‐of‐interest evaluation in emission tomography publication-title: J. Comput. Assist. Tomogr. – ident: e_1_2_7_12_1 doi: 10.1118/1.2712043 – volume: 8 start-page: 306 year: 1984 ident: e_1_2_7_2_1 article-title: EM reconstruction algorithms for emission and transmission tomography publication-title: J. Comput. Assist. Tomogr. – ident: e_1_2_7_11_1 doi: 10.2967/jnumed.106.035774 – ident: e_1_2_7_14_1 doi: 10.1118/1.2432404 – ident: e_1_2_7_25_1 doi: 10.1016/j.ctrv.2006.02.002 – ident: e_1_2_7_15_1 doi: 10.1007/s00259‐006‐0363‐4 – ident: e_1_2_7_18_1 doi: 10.1109/42.222672 – ident: e_1_2_7_4_1 doi: 10.1088/0031‐9155/38/1/005 – ident: e_1_2_7_7_1 doi: 10.1109/TMI.2006.873222 – start-page: 2924 year: 2006 ident: e_1_2_7_20_1 – ident: e_1_2_7_24_1 doi: 10.1007/s00259‐004‐1532‐y – start-page: 3939 year: 2007 ident: e_1_2_7_16_1 – ident: e_1_2_7_23_1 doi: 10.1007/s002590050022 – start-page: 3410 year: 2006 ident: e_1_2_7_19_1 – ident: e_1_2_7_9_1 doi: 10.1088/0031‐9155/53/10/009 – ident: e_1_2_7_5_1 doi: 10.1109/TNS.2007.905167 – ident: e_1_2_7_3_1 doi: 10.1109/42.938248 – ident: e_1_2_7_21_1 doi: 10.1109/JPROC.2003.817882 – ident: e_1_2_7_22_1 doi: 10.1109/42.563660 – ident: e_1_2_7_6_1 doi: 10.1109/TMI.2006.876171 – ident: e_1_2_7_13_1 doi: 10.1088/0031‐9155/52/12/010 – ident: e_1_2_7_17_1 doi: 10.1097/00004728‐198607000‐00021 – start-page: 2812 year: 2007 ident: e_1_2_7_8_1 – volume: 48 start-page: 802 year: 2007 ident: e_1_2_7_10_1 article-title: Partial‐volume correction in PET: validation of an iterative postreconstruction method with phantom and patient data publication-title: J. Nucl. Med. |
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| SubjectTerms | Algorithms Cancer Fluorodeoxyglucose F18 Image Interpretation, Computer-Assisted - methods image reconstruction image resolution Image scanners image segmentation Iteration theory iterative methods Likelihood Functions maximum likelihood estimation maximum likelihood reconstruction Medical image artifacts Medical image noise medical image processing Medical image reconstruction Medical image smoothing Medical imaging Neoplasms - diagnostic imaging Numerical approximation and analysis partial volume effect correction PET phantoms Phantoms, Imaging positron emission tomography Positron emission tomography (PET) Positron-Emission Tomography - methods Positron-Emission Tomography - statistics & numerical data Probability theory, stochastic processes, and statistics Radiography Reconstruction regional basis functions Segmentation tumours uptake quantification volume estimate wounds |
| Title | Lesion quantification in oncological positron emission tomography: A maximum likelihood partial volume correction strategy |
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