Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept

Abstract Background To generate high-quality bone scan SPECT images from only 1/7 scan time SPECT images using deep learning-based enhancement method. Materials and methods Normal-dose (925–1110 MBq) clinical technetium 99 m-methyl diphosphonate (99mTc-MDP) SPECT/CT images and corresponding SPECT/CT images with 1/7 scan time from 20 adult patients with bone disease and a phantom were collected to develop a lesion-attention weighted U2-Net (Qin et al. in Pattern Recognit 106:107404, 2020), which produces high-quality SPECT images from fast SPECT/CT images. The quality of synthesized SPECT images from different deep learning models was compared using PSNR and SSIM. Clinic evaluation on 5-point Likert scale (5 = excellent) was performed by two experienced nuclear physicians. Average score and Wilcoxon test were constructed to assess the image quality of 1/7 SPECT, DL-enhanced SPECT and the standard SPECT. SUVmax, SUVmean, SSIM and PSNR from each detectable sphere filled with imaging agent were measured and compared for different images. Results U2-Net-based model reached the best PSNR (40.8) and SSIM (0.788) performance compared with other advanced deep learning methods. The clinic evaluation showed the quality of the synthesized SPECT images is much higher than that of fast SPECT images (P < 0.05). Compared to the standard SPECT images, enhanced images exhibited the same general image quality (P < 0.999), similar detail of 99mTc-MDP (P = 0.125) and the same diagnostic confidence (P = 0.1875). 4, 5 and 6 spheres could be distinguished on 1/7 SPECT, DL-enhanced SPECT and the standard SPECT, respectively. The DL-enhanced phantom image outperformed 1/7 SPECT in SUVmax, SUVmean, SSIM and PSNR in quantitative assessment. Conclusions Our proposed method can yield significant image quality improvement in the noise level, details of anatomical structure and SUV accuracy, which enabled applications of ultra fast SPECT bone imaging in real clinic settings. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Boyang Pan [verfasserIn] Na Qi [verfasserIn] Qingyuan Meng [verfasserIn] Jiachen Wang [verfasserIn] Siyue Peng [verfasserIn] Chengxiao Qi [verfasserIn] Nan-Jie Gong [verfasserIn] Jun Zhao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	SPECT Bone Image quality enhancement Deep learning

Übergeordnetes Werk:	In: EJNMMI Physics - SpringerOpen, 2015, 9(2022), 1, Seite 15
Übergeordnetes Werk:	volume:9 ; year:2022 ; number:1 ; pages:15

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s40658-022-00472-0

Katalog-ID:	DOAJ043349714

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520			\|a Abstract Background To generate high-quality bone scan SPECT images from only 1/7 scan time SPECT images using deep learning-based enhancement method. Materials and methods Normal-dose (925–1110 MBq) clinical technetium 99 m-methyl diphosphonate (99mTc-MDP) SPECT/CT images and corresponding SPECT/CT images with 1/7 scan time from 20 adult patients with bone disease and a phantom were collected to develop a lesion-attention weighted U2-Net (Qin et al. in Pattern Recognit 106:107404, 2020), which produces high-quality SPECT images from fast SPECT/CT images. The quality of synthesized SPECT images from different deep learning models was compared using PSNR and SSIM. Clinic evaluation on 5-point Likert scale (5 = excellent) was performed by two experienced nuclear physicians. Average score and Wilcoxon test were constructed to assess the image quality of 1/7 SPECT, DL-enhanced SPECT and the standard SPECT. SUVmax, SUVmean, SSIM and PSNR from each detectable sphere filled with imaging agent were measured and compared for different images. Results U2-Net-based model reached the best PSNR (40.8) and SSIM (0.788) performance compared with other advanced deep learning methods. The clinic evaluation showed the quality of the synthesized SPECT images is much higher than that of fast SPECT images (P < 0.05). Compared to the standard SPECT images, enhanced images exhibited the same general image quality (P < 0.999), similar detail of 99mTc-MDP (P = 0.125) and the same diagnostic confidence (P = 0.1875). 4, 5 and 6 spheres could be distinguished on 1/7 SPECT, DL-enhanced SPECT and the standard SPECT, respectively. The DL-enhanced phantom image outperformed 1/7 SPECT in SUVmax, SUVmean, SSIM and PSNR in quantitative assessment. Conclusions Our proposed method can yield significant image quality improvement in the noise level, details of anatomical structure and SUV accuracy, which enabled applications of ultra fast SPECT bone imaging in real clinic settings.
650		4	\|a SPECT
650		4	\|a Bone
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653		0	\|a Medical physics. Medical radiology. Nuclear medicine
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allfields	10.1186/s40658-022-00472-0 doi (DE-627)DOAJ043349714 (DE-599)DOAJ934d1084dd4447a4ad6337acf8fc3de1 DE-627 ger DE-627 rakwb eng R895-920 Boyang Pan verfasserin aut Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background To generate high-quality bone scan SPECT images from only 1/7 scan time SPECT images using deep learning-based enhancement method. Materials and methods Normal-dose (925–1110 MBq) clinical technetium 99 m-methyl diphosphonate (99mTc-MDP) SPECT/CT images and corresponding SPECT/CT images with 1/7 scan time from 20 adult patients with bone disease and a phantom were collected to develop a lesion-attention weighted U2-Net (Qin et al. in Pattern Recognit 106:107404, 2020), which produces high-quality SPECT images from fast SPECT/CT images. The quality of synthesized SPECT images from different deep learning models was compared using PSNR and SSIM. Clinic evaluation on 5-point Likert scale (5 = excellent) was performed by two experienced nuclear physicians. Average score and Wilcoxon test were constructed to assess the image quality of 1/7 SPECT, DL-enhanced SPECT and the standard SPECT. SUVmax, SUVmean, SSIM and PSNR from each detectable sphere filled with imaging agent were measured and compared for different images. Results U2-Net-based model reached the best PSNR (40.8) and SSIM (0.788) performance compared with other advanced deep learning methods. The clinic evaluation showed the quality of the synthesized SPECT images is much higher than that of fast SPECT images (P < 0.05). Compared to the standard SPECT images, enhanced images exhibited the same general image quality (P < 0.999), similar detail of 99mTc-MDP (P = 0.125) and the same diagnostic confidence (P = 0.1875). 4, 5 and 6 spheres could be distinguished on 1/7 SPECT, DL-enhanced SPECT and the standard SPECT, respectively. The DL-enhanced phantom image outperformed 1/7 SPECT in SUVmax, SUVmean, SSIM and PSNR in quantitative assessment. Conclusions Our proposed method can yield significant image quality improvement in the noise level, details of anatomical structure and SUV accuracy, which enabled applications of ultra fast SPECT bone imaging in real clinic settings. SPECT Bone Image quality enhancement Deep learning Medical physics. Medical radiology. Nuclear medicine Na Qi verfasserin aut Qingyuan Meng verfasserin aut Jiachen Wang verfasserin aut Siyue Peng verfasserin aut Chengxiao Qi verfasserin aut Nan-Jie Gong verfasserin aut Jun Zhao verfasserin aut In EJNMMI Physics SpringerOpen, 2015 9(2022), 1, Seite 15 (DE-627)785697993 (DE-600)2768912-8 21977364 nnns volume:9 year:2022 number:1 pages:15 https://doi.org/10.1186/s40658-022-00472-0 kostenfrei https://doaj.org/article/934d1084dd4447a4ad6337acf8fc3de1 kostenfrei https://doi.org/10.1186/s40658-022-00472-0 kostenfrei https://doaj.org/toc/2197-7364 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 15
spelling	10.1186/s40658-022-00472-0 doi (DE-627)DOAJ043349714 (DE-599)DOAJ934d1084dd4447a4ad6337acf8fc3de1 DE-627 ger DE-627 rakwb eng R895-920 Boyang Pan verfasserin aut Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background To generate high-quality bone scan SPECT images from only 1/7 scan time SPECT images using deep learning-based enhancement method. Materials and methods Normal-dose (925–1110 MBq) clinical technetium 99 m-methyl diphosphonate (99mTc-MDP) SPECT/CT images and corresponding SPECT/CT images with 1/7 scan time from 20 adult patients with bone disease and a phantom were collected to develop a lesion-attention weighted U2-Net (Qin et al. in Pattern Recognit 106:107404, 2020), which produces high-quality SPECT images from fast SPECT/CT images. The quality of synthesized SPECT images from different deep learning models was compared using PSNR and SSIM. Clinic evaluation on 5-point Likert scale (5 = excellent) was performed by two experienced nuclear physicians. Average score and Wilcoxon test were constructed to assess the image quality of 1/7 SPECT, DL-enhanced SPECT and the standard SPECT. SUVmax, SUVmean, SSIM and PSNR from each detectable sphere filled with imaging agent were measured and compared for different images. Results U2-Net-based model reached the best PSNR (40.8) and SSIM (0.788) performance compared with other advanced deep learning methods. The clinic evaluation showed the quality of the synthesized SPECT images is much higher than that of fast SPECT images (P < 0.05). Compared to the standard SPECT images, enhanced images exhibited the same general image quality (P < 0.999), similar detail of 99mTc-MDP (P = 0.125) and the same diagnostic confidence (P = 0.1875). 4, 5 and 6 spheres could be distinguished on 1/7 SPECT, DL-enhanced SPECT and the standard SPECT, respectively. The DL-enhanced phantom image outperformed 1/7 SPECT in SUVmax, SUVmean, SSIM and PSNR in quantitative assessment. Conclusions Our proposed method can yield significant image quality improvement in the noise level, details of anatomical structure and SUV accuracy, which enabled applications of ultra fast SPECT bone imaging in real clinic settings. SPECT Bone Image quality enhancement Deep learning Medical physics. Medical radiology. Nuclear medicine Na Qi verfasserin aut Qingyuan Meng verfasserin aut Jiachen Wang verfasserin aut Siyue Peng verfasserin aut Chengxiao Qi verfasserin aut Nan-Jie Gong verfasserin aut Jun Zhao verfasserin aut In EJNMMI Physics SpringerOpen, 2015 9(2022), 1, Seite 15 (DE-627)785697993 (DE-600)2768912-8 21977364 nnns volume:9 year:2022 number:1 pages:15 https://doi.org/10.1186/s40658-022-00472-0 kostenfrei https://doaj.org/article/934d1084dd4447a4ad6337acf8fc3de1 kostenfrei https://doi.org/10.1186/s40658-022-00472-0 kostenfrei https://doaj.org/toc/2197-7364 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 15
allfields_unstemmed	10.1186/s40658-022-00472-0 doi (DE-627)DOAJ043349714 (DE-599)DOAJ934d1084dd4447a4ad6337acf8fc3de1 DE-627 ger DE-627 rakwb eng R895-920 Boyang Pan verfasserin aut Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background To generate high-quality bone scan SPECT images from only 1/7 scan time SPECT images using deep learning-based enhancement method. Materials and methods Normal-dose (925–1110 MBq) clinical technetium 99 m-methyl diphosphonate (99mTc-MDP) SPECT/CT images and corresponding SPECT/CT images with 1/7 scan time from 20 adult patients with bone disease and a phantom were collected to develop a lesion-attention weighted U2-Net (Qin et al. in Pattern Recognit 106:107404, 2020), which produces high-quality SPECT images from fast SPECT/CT images. The quality of synthesized SPECT images from different deep learning models was compared using PSNR and SSIM. Clinic evaluation on 5-point Likert scale (5 = excellent) was performed by two experienced nuclear physicians. Average score and Wilcoxon test were constructed to assess the image quality of 1/7 SPECT, DL-enhanced SPECT and the standard SPECT. SUVmax, SUVmean, SSIM and PSNR from each detectable sphere filled with imaging agent were measured and compared for different images. Results U2-Net-based model reached the best PSNR (40.8) and SSIM (0.788) performance compared with other advanced deep learning methods. The clinic evaluation showed the quality of the synthesized SPECT images is much higher than that of fast SPECT images (P < 0.05). Compared to the standard SPECT images, enhanced images exhibited the same general image quality (P < 0.999), similar detail of 99mTc-MDP (P = 0.125) and the same diagnostic confidence (P = 0.1875). 4, 5 and 6 spheres could be distinguished on 1/7 SPECT, DL-enhanced SPECT and the standard SPECT, respectively. The DL-enhanced phantom image outperformed 1/7 SPECT in SUVmax, SUVmean, SSIM and PSNR in quantitative assessment. Conclusions Our proposed method can yield significant image quality improvement in the noise level, details of anatomical structure and SUV accuracy, which enabled applications of ultra fast SPECT bone imaging in real clinic settings. SPECT Bone Image quality enhancement Deep learning Medical physics. Medical radiology. Nuclear medicine Na Qi verfasserin aut Qingyuan Meng verfasserin aut Jiachen Wang verfasserin aut Siyue Peng verfasserin aut Chengxiao Qi verfasserin aut Nan-Jie Gong verfasserin aut Jun Zhao verfasserin aut In EJNMMI Physics SpringerOpen, 2015 9(2022), 1, Seite 15 (DE-627)785697993 (DE-600)2768912-8 21977364 nnns volume:9 year:2022 number:1 pages:15 https://doi.org/10.1186/s40658-022-00472-0 kostenfrei https://doaj.org/article/934d1084dd4447a4ad6337acf8fc3de1 kostenfrei https://doi.org/10.1186/s40658-022-00472-0 kostenfrei https://doaj.org/toc/2197-7364 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 15
allfieldsGer	10.1186/s40658-022-00472-0 doi (DE-627)DOAJ043349714 (DE-599)DOAJ934d1084dd4447a4ad6337acf8fc3de1 DE-627 ger DE-627 rakwb eng R895-920 Boyang Pan verfasserin aut Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background To generate high-quality bone scan SPECT images from only 1/7 scan time SPECT images using deep learning-based enhancement method. Materials and methods Normal-dose (925–1110 MBq) clinical technetium 99 m-methyl diphosphonate (99mTc-MDP) SPECT/CT images and corresponding SPECT/CT images with 1/7 scan time from 20 adult patients with bone disease and a phantom were collected to develop a lesion-attention weighted U2-Net (Qin et al. in Pattern Recognit 106:107404, 2020), which produces high-quality SPECT images from fast SPECT/CT images. The quality of synthesized SPECT images from different deep learning models was compared using PSNR and SSIM. Clinic evaluation on 5-point Likert scale (5 = excellent) was performed by two experienced nuclear physicians. Average score and Wilcoxon test were constructed to assess the image quality of 1/7 SPECT, DL-enhanced SPECT and the standard SPECT. SUVmax, SUVmean, SSIM and PSNR from each detectable sphere filled with imaging agent were measured and compared for different images. Results U2-Net-based model reached the best PSNR (40.8) and SSIM (0.788) performance compared with other advanced deep learning methods. The clinic evaluation showed the quality of the synthesized SPECT images is much higher than that of fast SPECT images (P < 0.05). Compared to the standard SPECT images, enhanced images exhibited the same general image quality (P < 0.999), similar detail of 99mTc-MDP (P = 0.125) and the same diagnostic confidence (P = 0.1875). 4, 5 and 6 spheres could be distinguished on 1/7 SPECT, DL-enhanced SPECT and the standard SPECT, respectively. The DL-enhanced phantom image outperformed 1/7 SPECT in SUVmax, SUVmean, SSIM and PSNR in quantitative assessment. Conclusions Our proposed method can yield significant image quality improvement in the noise level, details of anatomical structure and SUV accuracy, which enabled applications of ultra fast SPECT bone imaging in real clinic settings. SPECT Bone Image quality enhancement Deep learning Medical physics. Medical radiology. Nuclear medicine Na Qi verfasserin aut Qingyuan Meng verfasserin aut Jiachen Wang verfasserin aut Siyue Peng verfasserin aut Chengxiao Qi verfasserin aut Nan-Jie Gong verfasserin aut Jun Zhao verfasserin aut In EJNMMI Physics SpringerOpen, 2015 9(2022), 1, Seite 15 (DE-627)785697993 (DE-600)2768912-8 21977364 nnns volume:9 year:2022 number:1 pages:15 https://doi.org/10.1186/s40658-022-00472-0 kostenfrei https://doaj.org/article/934d1084dd4447a4ad6337acf8fc3de1 kostenfrei https://doi.org/10.1186/s40658-022-00472-0 kostenfrei https://doaj.org/toc/2197-7364 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 15
allfieldsSound	10.1186/s40658-022-00472-0 doi (DE-627)DOAJ043349714 (DE-599)DOAJ934d1084dd4447a4ad6337acf8fc3de1 DE-627 ger DE-627 rakwb eng R895-920 Boyang Pan verfasserin aut Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background To generate high-quality bone scan SPECT images from only 1/7 scan time SPECT images using deep learning-based enhancement method. Materials and methods Normal-dose (925–1110 MBq) clinical technetium 99 m-methyl diphosphonate (99mTc-MDP) SPECT/CT images and corresponding SPECT/CT images with 1/7 scan time from 20 adult patients with bone disease and a phantom were collected to develop a lesion-attention weighted U2-Net (Qin et al. in Pattern Recognit 106:107404, 2020), which produces high-quality SPECT images from fast SPECT/CT images. The quality of synthesized SPECT images from different deep learning models was compared using PSNR and SSIM. Clinic evaluation on 5-point Likert scale (5 = excellent) was performed by two experienced nuclear physicians. Average score and Wilcoxon test were constructed to assess the image quality of 1/7 SPECT, DL-enhanced SPECT and the standard SPECT. SUVmax, SUVmean, SSIM and PSNR from each detectable sphere filled with imaging agent were measured and compared for different images. Results U2-Net-based model reached the best PSNR (40.8) and SSIM (0.788) performance compared with other advanced deep learning methods. The clinic evaluation showed the quality of the synthesized SPECT images is much higher than that of fast SPECT images (P < 0.05). Compared to the standard SPECT images, enhanced images exhibited the same general image quality (P < 0.999), similar detail of 99mTc-MDP (P = 0.125) and the same diagnostic confidence (P = 0.1875). 4, 5 and 6 spheres could be distinguished on 1/7 SPECT, DL-enhanced SPECT and the standard SPECT, respectively. The DL-enhanced phantom image outperformed 1/7 SPECT in SUVmax, SUVmean, SSIM and PSNR in quantitative assessment. Conclusions Our proposed method can yield significant image quality improvement in the noise level, details of anatomical structure and SUV accuracy, which enabled applications of ultra fast SPECT bone imaging in real clinic settings. SPECT Bone Image quality enhancement Deep learning Medical physics. Medical radiology. Nuclear medicine Na Qi verfasserin aut Qingyuan Meng verfasserin aut Jiachen Wang verfasserin aut Siyue Peng verfasserin aut Chengxiao Qi verfasserin aut Nan-Jie Gong verfasserin aut Jun Zhao verfasserin aut In EJNMMI Physics SpringerOpen, 2015 9(2022), 1, Seite 15 (DE-627)785697993 (DE-600)2768912-8 21977364 nnns volume:9 year:2022 number:1 pages:15 https://doi.org/10.1186/s40658-022-00472-0 kostenfrei https://doaj.org/article/934d1084dd4447a4ad6337acf8fc3de1 kostenfrei https://doi.org/10.1186/s40658-022-00472-0 kostenfrei https://doaj.org/toc/2197-7364 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 15
language	English
source	In EJNMMI Physics 9(2022), 1, Seite 15 volume:9 year:2022 number:1 pages:15
sourceStr	In EJNMMI Physics 9(2022), 1, Seite 15 volume:9 year:2022 number:1 pages:15
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	SPECT Bone Image quality enhancement Deep learning Medical physics. Medical radiology. Nuclear medicine
isfreeaccess_bool	true
container_title	EJNMMI Physics
authorswithroles_txt_mv	Boyang Pan @@aut@@ Na Qi @@aut@@ Qingyuan Meng @@aut@@ Jiachen Wang @@aut@@ Siyue Peng @@aut@@ Chengxiao Qi @@aut@@ Nan-Jie Gong @@aut@@ Jun Zhao @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	785697993
id	DOAJ043349714
language_de	englisch
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Materials and methods Normal-dose (925–1110 MBq) clinical technetium 99 m-methyl diphosphonate (99mTc-MDP) SPECT/CT images and corresponding SPECT/CT images with 1/7 scan time from 20 adult patients with bone disease and a phantom were collected to develop a lesion-attention weighted U2-Net (Qin et al. in Pattern Recognit 106:107404, 2020), which produces high-quality SPECT images from fast SPECT/CT images. The quality of synthesized SPECT images from different deep learning models was compared using PSNR and SSIM. Clinic evaluation on 5-point Likert scale (5 = excellent) was performed by two experienced nuclear physicians. Average score and Wilcoxon test were constructed to assess the image quality of 1/7 SPECT, DL-enhanced SPECT and the standard SPECT. SUVmax, SUVmean, SSIM and PSNR from each detectable sphere filled with imaging agent were measured and compared for different images. Results U2-Net-based model reached the best PSNR (40.8) and SSIM (0.788) performance compared with other advanced deep learning methods. The clinic evaluation showed the quality of the synthesized SPECT images is much higher than that of fast SPECT images (P < 0.05). Compared to the standard SPECT images, enhanced images exhibited the same general image quality (P < 0.999), similar detail of 99mTc-MDP (P = 0.125) and the same diagnostic confidence (P = 0.1875). 4, 5 and 6 spheres could be distinguished on 1/7 SPECT, DL-enhanced SPECT and the standard SPECT, respectively. The DL-enhanced phantom image outperformed 1/7 SPECT in SUVmax, SUVmean, SSIM and PSNR in quantitative assessment. 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callnumber-first	R - Medicine
author	Boyang Pan
spellingShingle	Boyang Pan misc R895-920 misc SPECT misc Bone misc Image quality enhancement misc Deep learning misc Medical physics. Medical radiology. Nuclear medicine Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept
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topic_title	R895-920 Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept SPECT Bone Image quality enhancement Deep learning
topic	misc R895-920 misc SPECT misc Bone misc Image quality enhancement misc Deep learning misc Medical physics. Medical radiology. Nuclear medicine
topic_unstemmed	misc R895-920 misc SPECT misc Bone misc Image quality enhancement misc Deep learning misc Medical physics. Medical radiology. Nuclear medicine
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title	Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept
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title_full	Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept
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author_browse	Boyang Pan Na Qi Qingyuan Meng Jiachen Wang Siyue Peng Chengxiao Qi Nan-Jie Gong Jun Zhao
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title_sort	ultra high speed spect bone imaging enabled by a deep learning enhancement method: a proof of concept
callnumber	R895-920
title_auth	Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept
abstract	Abstract Background To generate high-quality bone scan SPECT images from only 1/7 scan time SPECT images using deep learning-based enhancement method. Materials and methods Normal-dose (925–1110 MBq) clinical technetium 99 m-methyl diphosphonate (99mTc-MDP) SPECT/CT images and corresponding SPECT/CT images with 1/7 scan time from 20 adult patients with bone disease and a phantom were collected to develop a lesion-attention weighted U2-Net (Qin et al. in Pattern Recognit 106:107404, 2020), which produces high-quality SPECT images from fast SPECT/CT images. The quality of synthesized SPECT images from different deep learning models was compared using PSNR and SSIM. Clinic evaluation on 5-point Likert scale (5 = excellent) was performed by two experienced nuclear physicians. Average score and Wilcoxon test were constructed to assess the image quality of 1/7 SPECT, DL-enhanced SPECT and the standard SPECT. SUVmax, SUVmean, SSIM and PSNR from each detectable sphere filled with imaging agent were measured and compared for different images. Results U2-Net-based model reached the best PSNR (40.8) and SSIM (0.788) performance compared with other advanced deep learning methods. The clinic evaluation showed the quality of the synthesized SPECT images is much higher than that of fast SPECT images (P < 0.05). Compared to the standard SPECT images, enhanced images exhibited the same general image quality (P < 0.999), similar detail of 99mTc-MDP (P = 0.125) and the same diagnostic confidence (P = 0.1875). 4, 5 and 6 spheres could be distinguished on 1/7 SPECT, DL-enhanced SPECT and the standard SPECT, respectively. The DL-enhanced phantom image outperformed 1/7 SPECT in SUVmax, SUVmean, SSIM and PSNR in quantitative assessment. Conclusions Our proposed method can yield significant image quality improvement in the noise level, details of anatomical structure and SUV accuracy, which enabled applications of ultra fast SPECT bone imaging in real clinic settings.
abstractGer	Abstract Background To generate high-quality bone scan SPECT images from only 1/7 scan time SPECT images using deep learning-based enhancement method. Materials and methods Normal-dose (925–1110 MBq) clinical technetium 99 m-methyl diphosphonate (99mTc-MDP) SPECT/CT images and corresponding SPECT/CT images with 1/7 scan time from 20 adult patients with bone disease and a phantom were collected to develop a lesion-attention weighted U2-Net (Qin et al. in Pattern Recognit 106:107404, 2020), which produces high-quality SPECT images from fast SPECT/CT images. The quality of synthesized SPECT images from different deep learning models was compared using PSNR and SSIM. Clinic evaluation on 5-point Likert scale (5 = excellent) was performed by two experienced nuclear physicians. Average score and Wilcoxon test were constructed to assess the image quality of 1/7 SPECT, DL-enhanced SPECT and the standard SPECT. SUVmax, SUVmean, SSIM and PSNR from each detectable sphere filled with imaging agent were measured and compared for different images. Results U2-Net-based model reached the best PSNR (40.8) and SSIM (0.788) performance compared with other advanced deep learning methods. The clinic evaluation showed the quality of the synthesized SPECT images is much higher than that of fast SPECT images (P < 0.05). Compared to the standard SPECT images, enhanced images exhibited the same general image quality (P < 0.999), similar detail of 99mTc-MDP (P = 0.125) and the same diagnostic confidence (P = 0.1875). 4, 5 and 6 spheres could be distinguished on 1/7 SPECT, DL-enhanced SPECT and the standard SPECT, respectively. The DL-enhanced phantom image outperformed 1/7 SPECT in SUVmax, SUVmean, SSIM and PSNR in quantitative assessment. Conclusions Our proposed method can yield significant image quality improvement in the noise level, details of anatomical structure and SUV accuracy, which enabled applications of ultra fast SPECT bone imaging in real clinic settings.
abstract_unstemmed	Abstract Background To generate high-quality bone scan SPECT images from only 1/7 scan time SPECT images using deep learning-based enhancement method. Materials and methods Normal-dose (925–1110 MBq) clinical technetium 99 m-methyl diphosphonate (99mTc-MDP) SPECT/CT images and corresponding SPECT/CT images with 1/7 scan time from 20 adult patients with bone disease and a phantom were collected to develop a lesion-attention weighted U2-Net (Qin et al. in Pattern Recognit 106:107404, 2020), which produces high-quality SPECT images from fast SPECT/CT images. The quality of synthesized SPECT images from different deep learning models was compared using PSNR and SSIM. Clinic evaluation on 5-point Likert scale (5 = excellent) was performed by two experienced nuclear physicians. Average score and Wilcoxon test were constructed to assess the image quality of 1/7 SPECT, DL-enhanced SPECT and the standard SPECT. SUVmax, SUVmean, SSIM and PSNR from each detectable sphere filled with imaging agent were measured and compared for different images. Results U2-Net-based model reached the best PSNR (40.8) and SSIM (0.788) performance compared with other advanced deep learning methods. The clinic evaluation showed the quality of the synthesized SPECT images is much higher than that of fast SPECT images (P < 0.05). Compared to the standard SPECT images, enhanced images exhibited the same general image quality (P < 0.999), similar detail of 99mTc-MDP (P = 0.125) and the same diagnostic confidence (P = 0.1875). 4, 5 and 6 spheres could be distinguished on 1/7 SPECT, DL-enhanced SPECT and the standard SPECT, respectively. The DL-enhanced phantom image outperformed 1/7 SPECT in SUVmax, SUVmean, SSIM and PSNR in quantitative assessment. Conclusions Our proposed method can yield significant image quality improvement in the noise level, details of anatomical structure and SUV accuracy, which enabled applications of ultra fast SPECT bone imaging in real clinic settings.
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title_short	Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept
url	https://doi.org/10.1186/s40658-022-00472-0 https://doaj.org/article/934d1084dd4447a4ad6337acf8fc3de1 https://doaj.org/toc/2197-7364
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author2	Na Qi Qingyuan Meng Jiachen Wang Siyue Peng Chengxiao Qi Nan-Jie Gong Jun Zhao
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up_date	2024-07-03T17:04:38.516Z
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