Small lesion depiction and quantification accuracy of oncological 18F-FDG PET/CT with small voxel and Bayesian penalized likelihood reconstruction

Background To investigate the influence of small voxel Bayesian penalized likelihood (SVB) reconstruction on small lesion detection compared to ordered subset expectation maximization (OSEM) reconstruction using a clinical trials network (CTN) chest phantom and the patients with 18F-FDG-avid small lung tumors, and determine the optimal penalty factor for the lesion depiction and quantification. Methods The CTN phantom was filled with 18F solution with a sphere-to-background ratio of 3.81:1. Twenty-four patients with 18F-FDG-avid lung lesions (diameter < 2 cm) were enrolled. Six groups of PET images were reconstructed: routine voxel OSEM (RVOSEM), small voxel OSEM (SVOSEM), and SVB reconstructions with four penalty factors: 0.6, 0.8, 0.9, and 1.0 (SVB0.6, SVB0.8, SVB0.9, and SVB1.0). The routine and small voxel sizes are 4 × 4 × 4 and 2 × 2 × 2 $ mm^{3} $. The recovery coefficient (RC) was calculated by dividing the measured activity by the injected activity of the hot spheres in the phantom study. The $ SUV_{max} $, target-to-liver ratio (TLR), contrast-to-noise ratio (CNR), the volume of the lesions, and the image noise of the liver were measured and calculated in the patient study. Visual image quality of the patient image was scored by two radiologists using a 5-point scale. Results In the phantom study, SVB0.6, SVB0.8, and SVB0.9 achieved higher RCs than SVOSEM. The RC was higher in SVOSEM than RVOSEM and SVB1.0. In the patient study, the $ SUV_{max} $, TLR, and visual image quality scores of SVB0.6 to SVB0.9 were higher than those of RVOSEM, while the image noise of SVB0.8 to SVB1.0 was equivalent to or lower than that of RVOSEM. All SVB groups had higher CNRs than RVOSEM, but there was no difference between RVOSEM and SVOSEM. The lesion volumes derived from SVB0.6 to SVB0.9 were accurate, but over-estimated by RVOSEM, SVOSEM, and SVB1.0, using the CT measurement as the standard reference. Conclusions The SVB reconstruction improved lesion contrast, TLR, CNR, and volumetric quantification accuracy for small lesions compared to RVOSEM reconstruction without image noise degradation or the need of longer emission time. A penalty factor of 0.8–0.9 was optimal for SVB reconstruction for the small tumor detection with 18F-FDG PET/CT. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xu, Lei [verfasserIn] Li, Ru-Shuai Wu, Run-Ze Yang, Rui You, Qin-Qin Yao, Xiao-Chen Xie, Hui-Fang Lv, Yang Dong, Yun Wang, Feng Meng, Qing-Le

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	FDG PET Lung nodule Small voxel reconstruction Bayesian penalized likelihood reconstruction Small lesion detection

Anmerkung:	© The Author(s) 2022

Übergeordnetes Werk:	Enthalten in: EJNMMI Physics - Berlin : SpringerOpen, 2014, 9(2022), 1 vom: 26. März
Übergeordnetes Werk:	volume:9 ; year:2022 ; number:1 ; day:26 ; month:03

Links:	Volltext

DOI / URN:	10.1186/s40658-022-00451-5

Katalog-ID:	SPR046624740

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245	1	0	\|a Small lesion depiction and quantification accuracy of oncological 18F-FDG PET/CT with small voxel and Bayesian penalized likelihood reconstruction
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520			\|a Background To investigate the influence of small voxel Bayesian penalized likelihood (SVB) reconstruction on small lesion detection compared to ordered subset expectation maximization (OSEM) reconstruction using a clinical trials network (CTN) chest phantom and the patients with 18F-FDG-avid small lung tumors, and determine the optimal penalty factor for the lesion depiction and quantification. Methods The CTN phantom was filled with 18F solution with a sphere-to-background ratio of 3.81:1. Twenty-four patients with 18F-FDG-avid lung lesions (diameter < 2 cm) were enrolled. Six groups of PET images were reconstructed: routine voxel OSEM (RVOSEM), small voxel OSEM (SVOSEM), and SVB reconstructions with four penalty factors: 0.6, 0.8, 0.9, and 1.0 (SVB0.6, SVB0.8, SVB0.9, and SVB1.0). The routine and small voxel sizes are 4 × 4 × 4 and 2 × 2 × 2 $ mm^{3} $. The recovery coefficient (RC) was calculated by dividing the measured activity by the injected activity of the hot spheres in the phantom study. The $ SUV_{max} $, target-to-liver ratio (TLR), contrast-to-noise ratio (CNR), the volume of the lesions, and the image noise of the liver were measured and calculated in the patient study. Visual image quality of the patient image was scored by two radiologists using a 5-point scale. Results In the phantom study, SVB0.6, SVB0.8, and SVB0.9 achieved higher RCs than SVOSEM. The RC was higher in SVOSEM than RVOSEM and SVB1.0. In the patient study, the $ SUV_{max} $, TLR, and visual image quality scores of SVB0.6 to SVB0.9 were higher than those of RVOSEM, while the image noise of SVB0.8 to SVB1.0 was equivalent to or lower than that of RVOSEM. All SVB groups had higher CNRs than RVOSEM, but there was no difference between RVOSEM and SVOSEM. The lesion volumes derived from SVB0.6 to SVB0.9 were accurate, but over-estimated by RVOSEM, SVOSEM, and SVB1.0, using the CT measurement as the standard reference. Conclusions The SVB reconstruction improved lesion contrast, TLR, CNR, and volumetric quantification accuracy for small lesions compared to RVOSEM reconstruction without image noise degradation or the need of longer emission time. A penalty factor of 0.8–0.9 was optimal for SVB reconstruction for the small tumor detection with 18F-FDG PET/CT.
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allfields	10.1186/s40658-022-00451-5 doi (DE-627)SPR046624740 (SPR)s40658-022-00451-5-e DE-627 ger DE-627 rakwb eng Xu, Lei verfasserin aut Small lesion depiction and quantification accuracy of oncological 18F-FDG PET/CT with small voxel and Bayesian penalized likelihood reconstruction 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background To investigate the influence of small voxel Bayesian penalized likelihood (SVB) reconstruction on small lesion detection compared to ordered subset expectation maximization (OSEM) reconstruction using a clinical trials network (CTN) chest phantom and the patients with 18F-FDG-avid small lung tumors, and determine the optimal penalty factor for the lesion depiction and quantification. Methods The CTN phantom was filled with 18F solution with a sphere-to-background ratio of 3.81:1. Twenty-four patients with 18F-FDG-avid lung lesions (diameter < 2 cm) were enrolled. Six groups of PET images were reconstructed: routine voxel OSEM (RVOSEM), small voxel OSEM (SVOSEM), and SVB reconstructions with four penalty factors: 0.6, 0.8, 0.9, and 1.0 (SVB0.6, SVB0.8, SVB0.9, and SVB1.0). The routine and small voxel sizes are 4 × 4 × 4 and 2 × 2 × 2 $ mm^{3} $. The recovery coefficient (RC) was calculated by dividing the measured activity by the injected activity of the hot spheres in the phantom study. The $ SUV_{max} $, target-to-liver ratio (TLR), contrast-to-noise ratio (CNR), the volume of the lesions, and the image noise of the liver were measured and calculated in the patient study. Visual image quality of the patient image was scored by two radiologists using a 5-point scale. Results In the phantom study, SVB0.6, SVB0.8, and SVB0.9 achieved higher RCs than SVOSEM. The RC was higher in SVOSEM than RVOSEM and SVB1.0. In the patient study, the $ SUV_{max} $, TLR, and visual image quality scores of SVB0.6 to SVB0.9 were higher than those of RVOSEM, while the image noise of SVB0.8 to SVB1.0 was equivalent to or lower than that of RVOSEM. All SVB groups had higher CNRs than RVOSEM, but there was no difference between RVOSEM and SVOSEM. The lesion volumes derived from SVB0.6 to SVB0.9 were accurate, but over-estimated by RVOSEM, SVOSEM, and SVB1.0, using the CT measurement as the standard reference. Conclusions The SVB reconstruction improved lesion contrast, TLR, CNR, and volumetric quantification accuracy for small lesions compared to RVOSEM reconstruction without image noise degradation or the need of longer emission time. A penalty factor of 0.8–0.9 was optimal for SVB reconstruction for the small tumor detection with 18F-FDG PET/CT. FDG (dpeaa)DE-He213 PET (dpeaa)DE-He213 Lung nodule (dpeaa)DE-He213 Small voxel reconstruction (dpeaa)DE-He213 Bayesian penalized likelihood reconstruction (dpeaa)DE-He213 Small lesion detection (dpeaa)DE-He213 Li, Ru-Shuai aut Wu, Run-Ze (orcid)0000-0003-0445-9460 aut Yang, Rui aut You, Qin-Qin aut Yao, Xiao-Chen aut Xie, Hui-Fang aut Lv, Yang aut Dong, Yun aut Wang, Feng aut Meng, Qing-Le aut Enthalten in EJNMMI Physics Berlin : SpringerOpen, 2014 9(2022), 1 vom: 26. März (DE-627)785697993 (DE-600)2768912-8 2197-7364 nnns volume:9 year:2022 number:1 day:26 month:03 https://dx.doi.org/10.1186/s40658-022-00451-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022 1 26 03
spelling	10.1186/s40658-022-00451-5 doi (DE-627)SPR046624740 (SPR)s40658-022-00451-5-e DE-627 ger DE-627 rakwb eng Xu, Lei verfasserin aut Small lesion depiction and quantification accuracy of oncological 18F-FDG PET/CT with small voxel and Bayesian penalized likelihood reconstruction 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background To investigate the influence of small voxel Bayesian penalized likelihood (SVB) reconstruction on small lesion detection compared to ordered subset expectation maximization (OSEM) reconstruction using a clinical trials network (CTN) chest phantom and the patients with 18F-FDG-avid small lung tumors, and determine the optimal penalty factor for the lesion depiction and quantification. Methods The CTN phantom was filled with 18F solution with a sphere-to-background ratio of 3.81:1. Twenty-four patients with 18F-FDG-avid lung lesions (diameter < 2 cm) were enrolled. Six groups of PET images were reconstructed: routine voxel OSEM (RVOSEM), small voxel OSEM (SVOSEM), and SVB reconstructions with four penalty factors: 0.6, 0.8, 0.9, and 1.0 (SVB0.6, SVB0.8, SVB0.9, and SVB1.0). The routine and small voxel sizes are 4 × 4 × 4 and 2 × 2 × 2 $ mm^{3} $. The recovery coefficient (RC) was calculated by dividing the measured activity by the injected activity of the hot spheres in the phantom study. The $ SUV_{max} $, target-to-liver ratio (TLR), contrast-to-noise ratio (CNR), the volume of the lesions, and the image noise of the liver were measured and calculated in the patient study. Visual image quality of the patient image was scored by two radiologists using a 5-point scale. Results In the phantom study, SVB0.6, SVB0.8, and SVB0.9 achieved higher RCs than SVOSEM. The RC was higher in SVOSEM than RVOSEM and SVB1.0. In the patient study, the $ SUV_{max} $, TLR, and visual image quality scores of SVB0.6 to SVB0.9 were higher than those of RVOSEM, while the image noise of SVB0.8 to SVB1.0 was equivalent to or lower than that of RVOSEM. All SVB groups had higher CNRs than RVOSEM, but there was no difference between RVOSEM and SVOSEM. The lesion volumes derived from SVB0.6 to SVB0.9 were accurate, but over-estimated by RVOSEM, SVOSEM, and SVB1.0, using the CT measurement as the standard reference. Conclusions The SVB reconstruction improved lesion contrast, TLR, CNR, and volumetric quantification accuracy for small lesions compared to RVOSEM reconstruction without image noise degradation or the need of longer emission time. A penalty factor of 0.8–0.9 was optimal for SVB reconstruction for the small tumor detection with 18F-FDG PET/CT. FDG (dpeaa)DE-He213 PET (dpeaa)DE-He213 Lung nodule (dpeaa)DE-He213 Small voxel reconstruction (dpeaa)DE-He213 Bayesian penalized likelihood reconstruction (dpeaa)DE-He213 Small lesion detection (dpeaa)DE-He213 Li, Ru-Shuai aut Wu, Run-Ze (orcid)0000-0003-0445-9460 aut Yang, Rui aut You, Qin-Qin aut Yao, Xiao-Chen aut Xie, Hui-Fang aut Lv, Yang aut Dong, Yun aut Wang, Feng aut Meng, Qing-Le aut Enthalten in EJNMMI Physics Berlin : SpringerOpen, 2014 9(2022), 1 vom: 26. März (DE-627)785697993 (DE-600)2768912-8 2197-7364 nnns volume:9 year:2022 number:1 day:26 month:03 https://dx.doi.org/10.1186/s40658-022-00451-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022 1 26 03
allfields_unstemmed	10.1186/s40658-022-00451-5 doi (DE-627)SPR046624740 (SPR)s40658-022-00451-5-e DE-627 ger DE-627 rakwb eng Xu, Lei verfasserin aut Small lesion depiction and quantification accuracy of oncological 18F-FDG PET/CT with small voxel and Bayesian penalized likelihood reconstruction 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background To investigate the influence of small voxel Bayesian penalized likelihood (SVB) reconstruction on small lesion detection compared to ordered subset expectation maximization (OSEM) reconstruction using a clinical trials network (CTN) chest phantom and the patients with 18F-FDG-avid small lung tumors, and determine the optimal penalty factor for the lesion depiction and quantification. Methods The CTN phantom was filled with 18F solution with a sphere-to-background ratio of 3.81:1. Twenty-four patients with 18F-FDG-avid lung lesions (diameter < 2 cm) were enrolled. Six groups of PET images were reconstructed: routine voxel OSEM (RVOSEM), small voxel OSEM (SVOSEM), and SVB reconstructions with four penalty factors: 0.6, 0.8, 0.9, and 1.0 (SVB0.6, SVB0.8, SVB0.9, and SVB1.0). The routine and small voxel sizes are 4 × 4 × 4 and 2 × 2 × 2 $ mm^{3} $. The recovery coefficient (RC) was calculated by dividing the measured activity by the injected activity of the hot spheres in the phantom study. The $ SUV_{max} $, target-to-liver ratio (TLR), contrast-to-noise ratio (CNR), the volume of the lesions, and the image noise of the liver were measured and calculated in the patient study. Visual image quality of the patient image was scored by two radiologists using a 5-point scale. Results In the phantom study, SVB0.6, SVB0.8, and SVB0.9 achieved higher RCs than SVOSEM. The RC was higher in SVOSEM than RVOSEM and SVB1.0. In the patient study, the $ SUV_{max} $, TLR, and visual image quality scores of SVB0.6 to SVB0.9 were higher than those of RVOSEM, while the image noise of SVB0.8 to SVB1.0 was equivalent to or lower than that of RVOSEM. All SVB groups had higher CNRs than RVOSEM, but there was no difference between RVOSEM and SVOSEM. The lesion volumes derived from SVB0.6 to SVB0.9 were accurate, but over-estimated by RVOSEM, SVOSEM, and SVB1.0, using the CT measurement as the standard reference. Conclusions The SVB reconstruction improved lesion contrast, TLR, CNR, and volumetric quantification accuracy for small lesions compared to RVOSEM reconstruction without image noise degradation or the need of longer emission time. A penalty factor of 0.8–0.9 was optimal for SVB reconstruction for the small tumor detection with 18F-FDG PET/CT. FDG (dpeaa)DE-He213 PET (dpeaa)DE-He213 Lung nodule (dpeaa)DE-He213 Small voxel reconstruction (dpeaa)DE-He213 Bayesian penalized likelihood reconstruction (dpeaa)DE-He213 Small lesion detection (dpeaa)DE-He213 Li, Ru-Shuai aut Wu, Run-Ze (orcid)0000-0003-0445-9460 aut Yang, Rui aut You, Qin-Qin aut Yao, Xiao-Chen aut Xie, Hui-Fang aut Lv, Yang aut Dong, Yun aut Wang, Feng aut Meng, Qing-Le aut Enthalten in EJNMMI Physics Berlin : SpringerOpen, 2014 9(2022), 1 vom: 26. März (DE-627)785697993 (DE-600)2768912-8 2197-7364 nnns volume:9 year:2022 number:1 day:26 month:03 https://dx.doi.org/10.1186/s40658-022-00451-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022 1 26 03
allfieldsGer	10.1186/s40658-022-00451-5 doi (DE-627)SPR046624740 (SPR)s40658-022-00451-5-e DE-627 ger DE-627 rakwb eng Xu, Lei verfasserin aut Small lesion depiction and quantification accuracy of oncological 18F-FDG PET/CT with small voxel and Bayesian penalized likelihood reconstruction 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background To investigate the influence of small voxel Bayesian penalized likelihood (SVB) reconstruction on small lesion detection compared to ordered subset expectation maximization (OSEM) reconstruction using a clinical trials network (CTN) chest phantom and the patients with 18F-FDG-avid small lung tumors, and determine the optimal penalty factor for the lesion depiction and quantification. Methods The CTN phantom was filled with 18F solution with a sphere-to-background ratio of 3.81:1. Twenty-four patients with 18F-FDG-avid lung lesions (diameter < 2 cm) were enrolled. Six groups of PET images were reconstructed: routine voxel OSEM (RVOSEM), small voxel OSEM (SVOSEM), and SVB reconstructions with four penalty factors: 0.6, 0.8, 0.9, and 1.0 (SVB0.6, SVB0.8, SVB0.9, and SVB1.0). The routine and small voxel sizes are 4 × 4 × 4 and 2 × 2 × 2 $ mm^{3} $. The recovery coefficient (RC) was calculated by dividing the measured activity by the injected activity of the hot spheres in the phantom study. The $ SUV_{max} $, target-to-liver ratio (TLR), contrast-to-noise ratio (CNR), the volume of the lesions, and the image noise of the liver were measured and calculated in the patient study. Visual image quality of the patient image was scored by two radiologists using a 5-point scale. Results In the phantom study, SVB0.6, SVB0.8, and SVB0.9 achieved higher RCs than SVOSEM. The RC was higher in SVOSEM than RVOSEM and SVB1.0. In the patient study, the $ SUV_{max} $, TLR, and visual image quality scores of SVB0.6 to SVB0.9 were higher than those of RVOSEM, while the image noise of SVB0.8 to SVB1.0 was equivalent to or lower than that of RVOSEM. All SVB groups had higher CNRs than RVOSEM, but there was no difference between RVOSEM and SVOSEM. The lesion volumes derived from SVB0.6 to SVB0.9 were accurate, but over-estimated by RVOSEM, SVOSEM, and SVB1.0, using the CT measurement as the standard reference. Conclusions The SVB reconstruction improved lesion contrast, TLR, CNR, and volumetric quantification accuracy for small lesions compared to RVOSEM reconstruction without image noise degradation or the need of longer emission time. A penalty factor of 0.8–0.9 was optimal for SVB reconstruction for the small tumor detection with 18F-FDG PET/CT. FDG (dpeaa)DE-He213 PET (dpeaa)DE-He213 Lung nodule (dpeaa)DE-He213 Small voxel reconstruction (dpeaa)DE-He213 Bayesian penalized likelihood reconstruction (dpeaa)DE-He213 Small lesion detection (dpeaa)DE-He213 Li, Ru-Shuai aut Wu, Run-Ze (orcid)0000-0003-0445-9460 aut Yang, Rui aut You, Qin-Qin aut Yao, Xiao-Chen aut Xie, Hui-Fang aut Lv, Yang aut Dong, Yun aut Wang, Feng aut Meng, Qing-Le aut Enthalten in EJNMMI Physics Berlin : SpringerOpen, 2014 9(2022), 1 vom: 26. März (DE-627)785697993 (DE-600)2768912-8 2197-7364 nnns volume:9 year:2022 number:1 day:26 month:03 https://dx.doi.org/10.1186/s40658-022-00451-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022 1 26 03
allfieldsSound	10.1186/s40658-022-00451-5 doi (DE-627)SPR046624740 (SPR)s40658-022-00451-5-e DE-627 ger DE-627 rakwb eng Xu, Lei verfasserin aut Small lesion depiction and quantification accuracy of oncological 18F-FDG PET/CT with small voxel and Bayesian penalized likelihood reconstruction 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Background To investigate the influence of small voxel Bayesian penalized likelihood (SVB) reconstruction on small lesion detection compared to ordered subset expectation maximization (OSEM) reconstruction using a clinical trials network (CTN) chest phantom and the patients with 18F-FDG-avid small lung tumors, and determine the optimal penalty factor for the lesion depiction and quantification. Methods The CTN phantom was filled with 18F solution with a sphere-to-background ratio of 3.81:1. Twenty-four patients with 18F-FDG-avid lung lesions (diameter < 2 cm) were enrolled. Six groups of PET images were reconstructed: routine voxel OSEM (RVOSEM), small voxel OSEM (SVOSEM), and SVB reconstructions with four penalty factors: 0.6, 0.8, 0.9, and 1.0 (SVB0.6, SVB0.8, SVB0.9, and SVB1.0). The routine and small voxel sizes are 4 × 4 × 4 and 2 × 2 × 2 $ mm^{3} $. The recovery coefficient (RC) was calculated by dividing the measured activity by the injected activity of the hot spheres in the phantom study. The $ SUV_{max} $, target-to-liver ratio (TLR), contrast-to-noise ratio (CNR), the volume of the lesions, and the image noise of the liver were measured and calculated in the patient study. Visual image quality of the patient image was scored by two radiologists using a 5-point scale. Results In the phantom study, SVB0.6, SVB0.8, and SVB0.9 achieved higher RCs than SVOSEM. The RC was higher in SVOSEM than RVOSEM and SVB1.0. In the patient study, the $ SUV_{max} $, TLR, and visual image quality scores of SVB0.6 to SVB0.9 were higher than those of RVOSEM, while the image noise of SVB0.8 to SVB1.0 was equivalent to or lower than that of RVOSEM. All SVB groups had higher CNRs than RVOSEM, but there was no difference between RVOSEM and SVOSEM. The lesion volumes derived from SVB0.6 to SVB0.9 were accurate, but over-estimated by RVOSEM, SVOSEM, and SVB1.0, using the CT measurement as the standard reference. Conclusions The SVB reconstruction improved lesion contrast, TLR, CNR, and volumetric quantification accuracy for small lesions compared to RVOSEM reconstruction without image noise degradation or the need of longer emission time. A penalty factor of 0.8–0.9 was optimal for SVB reconstruction for the small tumor detection with 18F-FDG PET/CT. FDG (dpeaa)DE-He213 PET (dpeaa)DE-He213 Lung nodule (dpeaa)DE-He213 Small voxel reconstruction (dpeaa)DE-He213 Bayesian penalized likelihood reconstruction (dpeaa)DE-He213 Small lesion detection (dpeaa)DE-He213 Li, Ru-Shuai aut Wu, Run-Ze (orcid)0000-0003-0445-9460 aut Yang, Rui aut You, Qin-Qin aut Yao, Xiao-Chen aut Xie, Hui-Fang aut Lv, Yang aut Dong, Yun aut Wang, Feng aut Meng, Qing-Le aut Enthalten in EJNMMI Physics Berlin : SpringerOpen, 2014 9(2022), 1 vom: 26. März (DE-627)785697993 (DE-600)2768912-8 2197-7364 nnns volume:9 year:2022 number:1 day:26 month:03 https://dx.doi.org/10.1186/s40658-022-00451-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2446 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022 1 26 03
language	English
source	Enthalten in EJNMMI Physics 9(2022), 1 vom: 26. März volume:9 year:2022 number:1 day:26 month:03
sourceStr	Enthalten in EJNMMI Physics 9(2022), 1 vom: 26. März volume:9 year:2022 number:1 day:26 month:03
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	FDG PET Lung nodule Small voxel reconstruction Bayesian penalized likelihood reconstruction Small lesion detection
isfreeaccess_bool	true
container_title	EJNMMI Physics
authorswithroles_txt_mv	Xu, Lei @@aut@@ Li, Ru-Shuai @@aut@@ Wu, Run-Ze @@aut@@ Yang, Rui @@aut@@ You, Qin-Qin @@aut@@ Yao, Xiao-Chen @@aut@@ Xie, Hui-Fang @@aut@@ Lv, Yang @@aut@@ Dong, Yun @@aut@@ Wang, Feng @@aut@@ Meng, Qing-Le @@aut@@
publishDateDaySort_date	2022-03-26T00:00:00Z
hierarchy_top_id	785697993
id	SPR046624740
language_de	englisch
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Methods The CTN phantom was filled with 18F solution with a sphere-to-background ratio of 3.81:1. Twenty-four patients with 18F-FDG-avid lung lesions (diameter < 2 cm) were enrolled. Six groups of PET images were reconstructed: routine voxel OSEM (RVOSEM), small voxel OSEM (SVOSEM), and SVB reconstructions with four penalty factors: 0.6, 0.8, 0.9, and 1.0 (SVB0.6, SVB0.8, SVB0.9, and SVB1.0). The routine and small voxel sizes are 4 × 4 × 4 and 2 × 2 × 2 $ mm^{3} $. The recovery coefficient (RC) was calculated by dividing the measured activity by the injected activity of the hot spheres in the phantom study. The $ SUV_{max} $, target-to-liver ratio (TLR), contrast-to-noise ratio (CNR), the volume of the lesions, and the image noise of the liver were measured and calculated in the patient study. Visual image quality of the patient image was scored by two radiologists using a 5-point scale. Results In the phantom study, SVB0.6, SVB0.8, and SVB0.9 achieved higher RCs than SVOSEM. The RC was higher in SVOSEM than RVOSEM and SVB1.0. In the patient study, the $ SUV_{max} $, TLR, and visual image quality scores of SVB0.6 to SVB0.9 were higher than those of RVOSEM, while the image noise of SVB0.8 to SVB1.0 was equivalent to or lower than that of RVOSEM. All SVB groups had higher CNRs than RVOSEM, but there was no difference between RVOSEM and SVOSEM. The lesion volumes derived from SVB0.6 to SVB0.9 were accurate, but over-estimated by RVOSEM, SVOSEM, and SVB1.0, using the CT measurement as the standard reference. Conclusions The SVB reconstruction improved lesion contrast, TLR, CNR, and volumetric quantification accuracy for small lesions compared to RVOSEM reconstruction without image noise degradation or the need of longer emission time. 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author	Xu, Lei
spellingShingle	Xu, Lei misc FDG misc PET misc Lung nodule misc Small voxel reconstruction misc Bayesian penalized likelihood reconstruction misc Small lesion detection Small lesion depiction and quantification accuracy of oncological 18F-FDG PET/CT with small voxel and Bayesian penalized likelihood reconstruction
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topic_title	Small lesion depiction and quantification accuracy of oncological 18F-FDG PET/CT with small voxel and Bayesian penalized likelihood reconstruction FDG (dpeaa)DE-He213 PET (dpeaa)DE-He213 Lung nodule (dpeaa)DE-He213 Small voxel reconstruction (dpeaa)DE-He213 Bayesian penalized likelihood reconstruction (dpeaa)DE-He213 Small lesion detection (dpeaa)DE-He213
topic	misc FDG misc PET misc Lung nodule misc Small voxel reconstruction misc Bayesian penalized likelihood reconstruction misc Small lesion detection
topic_unstemmed	misc FDG misc PET misc Lung nodule misc Small voxel reconstruction misc Bayesian penalized likelihood reconstruction misc Small lesion detection
topic_browse	misc FDG misc PET misc Lung nodule misc Small voxel reconstruction misc Bayesian penalized likelihood reconstruction misc Small lesion detection
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title	Small lesion depiction and quantification accuracy of oncological 18F-FDG PET/CT with small voxel and Bayesian penalized likelihood reconstruction
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title_full	Small lesion depiction and quantification accuracy of oncological 18F-FDG PET/CT with small voxel and Bayesian penalized likelihood reconstruction
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author_browse	Xu, Lei Li, Ru-Shuai Wu, Run-Ze Yang, Rui You, Qin-Qin Yao, Xiao-Chen Xie, Hui-Fang Lv, Yang Dong, Yun Wang, Feng Meng, Qing-Le
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title_sort	small lesion depiction and quantification accuracy of oncological 18f-fdg pet/ct with small voxel and bayesian penalized likelihood reconstruction
title_auth	Small lesion depiction and quantification accuracy of oncological 18F-FDG PET/CT with small voxel and Bayesian penalized likelihood reconstruction
abstract	Background To investigate the influence of small voxel Bayesian penalized likelihood (SVB) reconstruction on small lesion detection compared to ordered subset expectation maximization (OSEM) reconstruction using a clinical trials network (CTN) chest phantom and the patients with 18F-FDG-avid small lung tumors, and determine the optimal penalty factor for the lesion depiction and quantification. Methods The CTN phantom was filled with 18F solution with a sphere-to-background ratio of 3.81:1. Twenty-four patients with 18F-FDG-avid lung lesions (diameter < 2 cm) were enrolled. Six groups of PET images were reconstructed: routine voxel OSEM (RVOSEM), small voxel OSEM (SVOSEM), and SVB reconstructions with four penalty factors: 0.6, 0.8, 0.9, and 1.0 (SVB0.6, SVB0.8, SVB0.9, and SVB1.0). The routine and small voxel sizes are 4 × 4 × 4 and 2 × 2 × 2 $ mm^{3} $. The recovery coefficient (RC) was calculated by dividing the measured activity by the injected activity of the hot spheres in the phantom study. The $ SUV_{max} $, target-to-liver ratio (TLR), contrast-to-noise ratio (CNR), the volume of the lesions, and the image noise of the liver were measured and calculated in the patient study. Visual image quality of the patient image was scored by two radiologists using a 5-point scale. Results In the phantom study, SVB0.6, SVB0.8, and SVB0.9 achieved higher RCs than SVOSEM. The RC was higher in SVOSEM than RVOSEM and SVB1.0. In the patient study, the $ SUV_{max} $, TLR, and visual image quality scores of SVB0.6 to SVB0.9 were higher than those of RVOSEM, while the image noise of SVB0.8 to SVB1.0 was equivalent to or lower than that of RVOSEM. All SVB groups had higher CNRs than RVOSEM, but there was no difference between RVOSEM and SVOSEM. The lesion volumes derived from SVB0.6 to SVB0.9 were accurate, but over-estimated by RVOSEM, SVOSEM, and SVB1.0, using the CT measurement as the standard reference. Conclusions The SVB reconstruction improved lesion contrast, TLR, CNR, and volumetric quantification accuracy for small lesions compared to RVOSEM reconstruction without image noise degradation or the need of longer emission time. A penalty factor of 0.8–0.9 was optimal for SVB reconstruction for the small tumor detection with 18F-FDG PET/CT. © The Author(s) 2022
abstractGer	Background To investigate the influence of small voxel Bayesian penalized likelihood (SVB) reconstruction on small lesion detection compared to ordered subset expectation maximization (OSEM) reconstruction using a clinical trials network (CTN) chest phantom and the patients with 18F-FDG-avid small lung tumors, and determine the optimal penalty factor for the lesion depiction and quantification. Methods The CTN phantom was filled with 18F solution with a sphere-to-background ratio of 3.81:1. Twenty-four patients with 18F-FDG-avid lung lesions (diameter < 2 cm) were enrolled. Six groups of PET images were reconstructed: routine voxel OSEM (RVOSEM), small voxel OSEM (SVOSEM), and SVB reconstructions with four penalty factors: 0.6, 0.8, 0.9, and 1.0 (SVB0.6, SVB0.8, SVB0.9, and SVB1.0). The routine and small voxel sizes are 4 × 4 × 4 and 2 × 2 × 2 $ mm^{3} $. The recovery coefficient (RC) was calculated by dividing the measured activity by the injected activity of the hot spheres in the phantom study. The $ SUV_{max} $, target-to-liver ratio (TLR), contrast-to-noise ratio (CNR), the volume of the lesions, and the image noise of the liver were measured and calculated in the patient study. Visual image quality of the patient image was scored by two radiologists using a 5-point scale. Results In the phantom study, SVB0.6, SVB0.8, and SVB0.9 achieved higher RCs than SVOSEM. The RC was higher in SVOSEM than RVOSEM and SVB1.0. In the patient study, the $ SUV_{max} $, TLR, and visual image quality scores of SVB0.6 to SVB0.9 were higher than those of RVOSEM, while the image noise of SVB0.8 to SVB1.0 was equivalent to or lower than that of RVOSEM. All SVB groups had higher CNRs than RVOSEM, but there was no difference between RVOSEM and SVOSEM. The lesion volumes derived from SVB0.6 to SVB0.9 were accurate, but over-estimated by RVOSEM, SVOSEM, and SVB1.0, using the CT measurement as the standard reference. Conclusions The SVB reconstruction improved lesion contrast, TLR, CNR, and volumetric quantification accuracy for small lesions compared to RVOSEM reconstruction without image noise degradation or the need of longer emission time. A penalty factor of 0.8–0.9 was optimal for SVB reconstruction for the small tumor detection with 18F-FDG PET/CT. © The Author(s) 2022
abstract_unstemmed	Background To investigate the influence of small voxel Bayesian penalized likelihood (SVB) reconstruction on small lesion detection compared to ordered subset expectation maximization (OSEM) reconstruction using a clinical trials network (CTN) chest phantom and the patients with 18F-FDG-avid small lung tumors, and determine the optimal penalty factor for the lesion depiction and quantification. Methods The CTN phantom was filled with 18F solution with a sphere-to-background ratio of 3.81:1. Twenty-four patients with 18F-FDG-avid lung lesions (diameter < 2 cm) were enrolled. Six groups of PET images were reconstructed: routine voxel OSEM (RVOSEM), small voxel OSEM (SVOSEM), and SVB reconstructions with four penalty factors: 0.6, 0.8, 0.9, and 1.0 (SVB0.6, SVB0.8, SVB0.9, and SVB1.0). The routine and small voxel sizes are 4 × 4 × 4 and 2 × 2 × 2 $ mm^{3} $. The recovery coefficient (RC) was calculated by dividing the measured activity by the injected activity of the hot spheres in the phantom study. The $ SUV_{max} $, target-to-liver ratio (TLR), contrast-to-noise ratio (CNR), the volume of the lesions, and the image noise of the liver were measured and calculated in the patient study. Visual image quality of the patient image was scored by two radiologists using a 5-point scale. Results In the phantom study, SVB0.6, SVB0.8, and SVB0.9 achieved higher RCs than SVOSEM. The RC was higher in SVOSEM than RVOSEM and SVB1.0. In the patient study, the $ SUV_{max} $, TLR, and visual image quality scores of SVB0.6 to SVB0.9 were higher than those of RVOSEM, while the image noise of SVB0.8 to SVB1.0 was equivalent to or lower than that of RVOSEM. All SVB groups had higher CNRs than RVOSEM, but there was no difference between RVOSEM and SVOSEM. The lesion volumes derived from SVB0.6 to SVB0.9 were accurate, but over-estimated by RVOSEM, SVOSEM, and SVB1.0, using the CT measurement as the standard reference. Conclusions The SVB reconstruction improved lesion contrast, TLR, CNR, and volumetric quantification accuracy for small lesions compared to RVOSEM reconstruction without image noise degradation or the need of longer emission time. A penalty factor of 0.8–0.9 was optimal for SVB reconstruction for the small tumor detection with 18F-FDG PET/CT. © The Author(s) 2022
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title_short	Small lesion depiction and quantification accuracy of oncological 18F-FDG PET/CT with small voxel and Bayesian penalized likelihood reconstruction
url	https://dx.doi.org/10.1186/s40658-022-00451-5
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author2	Li, Ru-Shuai Wu, Run-Ze Yang, Rui You, Qin-Qin Yao, Xiao-Chen Xie, Hui-Fang Lv, Yang Dong, Yun Wang, Feng Meng, Qing-Le
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up_date	2024-07-03T23:36:13.530Z
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Methods The CTN phantom was filled with 18F solution with a sphere-to-background ratio of 3.81:1. Twenty-four patients with 18F-FDG-avid lung lesions (diameter < 2 cm) were enrolled. Six groups of PET images were reconstructed: routine voxel OSEM (RVOSEM), small voxel OSEM (SVOSEM), and SVB reconstructions with four penalty factors: 0.6, 0.8, 0.9, and 1.0 (SVB0.6, SVB0.8, SVB0.9, and SVB1.0). The routine and small voxel sizes are 4 × 4 × 4 and 2 × 2 × 2 $ mm^{3} $. The recovery coefficient (RC) was calculated by dividing the measured activity by the injected activity of the hot spheres in the phantom study. The $ SUV_{max} $, target-to-liver ratio (TLR), contrast-to-noise ratio (CNR), the volume of the lesions, and the image noise of the liver were measured and calculated in the patient study. Visual image quality of the patient image was scored by two radiologists using a 5-point scale. Results In the phantom study, SVB0.6, SVB0.8, and SVB0.9 achieved higher RCs than SVOSEM. The RC was higher in SVOSEM than RVOSEM and SVB1.0. In the patient study, the $ SUV_{max} $, TLR, and visual image quality scores of SVB0.6 to SVB0.9 were higher than those of RVOSEM, while the image noise of SVB0.8 to SVB1.0 was equivalent to or lower than that of RVOSEM. All SVB groups had higher CNRs than RVOSEM, but there was no difference between RVOSEM and SVOSEM. The lesion volumes derived from SVB0.6 to SVB0.9 were accurate, but over-estimated by RVOSEM, SVOSEM, and SVB1.0, using the CT measurement as the standard reference. Conclusions The SVB reconstruction improved lesion contrast, TLR, CNR, and volumetric quantification accuracy for small lesions compared to RVOSEM reconstruction without image noise degradation or the need of longer emission time. 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Small lesion depiction and quantification accuracy of oncological 18F-FDG PET/CT with small voxel and Bayesian penalized likelihood reconstruction

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