Deep-learning-based methods of attenuation correction for SPECT and PET.
Attenuation correction
PET
SPECT
deep learning
Journal
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
ISSN: 1532-6551
Titre abrégé: J Nucl Cardiol
Pays: United States
ID NLM: 9423534
Informations de publication
Date de publication:
10 2023
10 2023
Historique:
received:
18
03
2022
accepted:
02
05
2022
medline:
23
10
2023
pubmed:
11
6
2022
entrez:
10
6
2022
Statut:
ppublish
Résumé
Attenuation correction (AC) is essential for quantitative analysis and clinical diagnosis of single-photon emission computed tomography (SPECT) and positron emission tomography (PET). In clinical practice, computed tomography (CT) is utilized to generate attenuation maps (μ-maps) for AC of hybrid SPECT/CT and PET/CT scanners. However, CT-based AC methods frequently produce artifacts due to CT artifacts and misregistration of SPECT-CT and PET-CT scans. Segmentation-based AC methods using magnetic resonance imaging (MRI) for PET/MRI scanners are inaccurate and complicated since MRI does not contain direct information of photon attenuation. Computational AC methods for SPECT and PET estimate attenuation coefficients directly from raw emission data, but suffer from low accuracy, cross-talk artifacts, high computational complexity, and high noise level. The recently evolving deep-learning-based methods have shown promising results in AC of SPECT and PET, which can be generally divided into two categories: indirect and direct strategies. Indirect AC strategies apply neural networks to transform emission, transmission, or MR images into synthetic μ-maps or CT images which are then incorporated into AC reconstruction. Direct AC strategies skip the intermediate steps of generating μ-maps or CT images and predict AC SPECT or PET images from non-attenuation-correction (NAC) SPECT or PET images directly. These deep-learning-based AC methods show comparable and even superior performance to non-deep-learning methods. In this article, we first discussed the principles and limitations of non-deep-learning AC methods, and then reviewed the status and prospects of deep-learning-based methods for AC of SPECT and PET.
Identifiants
pubmed: 35680755
doi: 10.1007/s12350-022-03007-3
pii: 10.1007/s12350-022-03007-3
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1859-1878Subventions
Organisme : NHLBI NIH HHS
ID : R01 HL154345
Pays : United States
Informations de copyright
© 2022. The Author(s) under exclusive licence to American Society of Nuclear Cardiology.
Références
Danad I, Raijmakers PG, Driessen RS, Leipsic J, Raju R, Naoum C. Comparison of coronary CT angiography, SPECT, PET, and hybrid imaging for diagnosis of ischemic heart disease determined by fractional flow reserve. JAMA Cardiol 2017;2:1100‐7.
pubmed: 28813561
pmcid: 5710451
doi: 10.1001/jamacardio.2017.2471
Acampa W, Gaemperli O, Gimelli A, Knaapen P, Schindler TH, Verberne HJ, et al. Role of risk stratification by SPECT, PET, and hybrid imaging in guiding management of stable patients with ischaemic heart disease: Expert Panel of the EANM Cardiovascular Committee and EACVI. Eur Heart J Cardiovasc Imaging 2015;16:1289‐98.
pubmed: 25902767
doi: 10.1093/ehjci/jev093
Uematsu T, Yuen S, Yukisawa S, Aramaki T, Morimoto N, Endo M, et al. Comparison of FDG PET and SPECT for detection of bone metastases in breast cancer. Am J Roentgenol 2005;184:1266‐73.
doi: 10.2214/ajr.184.4.01841266
Even-Sapir E, Metser U, Mishani E, Lievshitz G, Lerman H, Leibovitch I. The detection of bone metastases in patients with high-risk prostate cancer:
pubmed: 16455635
Zhu L, Ploessl K, Kung HF. PET/SPECT imaging agents for neurodegenerative diseases. Chem Soc Rev 2014;43:6683‐91.
pubmed: 24676152
pmcid: 4159420
doi: 10.1039/C3CS60430F
Lu F-M, Yuan Z. PET/SPECT molecular imaging in clinical neuroscience: Recent advances in the investigation of CNS diseases. Quant Imaging Med Surg 2015;5:433.
pubmed: 26029646
pmcid: 4426104
Burger C, Goerres G, Schoenes S, Buck A, Lonn A, Von Schulthess G. PET attenuation coefficients from CT images: Experimental evaluation of the transformation of CT into PET 511-keV attenuation coefficients. Eur J Nucl Med Mol Imaging 2002;29:922‐7.
pubmed: 12111133
doi: 10.1007/s00259-002-0796-3
Garcia EV. SPECT attenuation correction: An essential tool to realize nuclear cardiology’s manifest destiny. J Nucl Cardiol 2007;14:16‐24.
pubmed: 17276302
doi: 10.1016/j.nuclcard.2006.12.144
Lee TC, Alessio AM, Miyaoka RM, Kinahan PE. Morphology supporting function: Attenuation correction for SPECT/CT, PET/CT, and PET/MR imaging. Q J Nucl Med Mol Imaging Off Publ Ital Assoc Nucl Med Int Assoc Radiopharmacol Sect Soc 2016;60:25.
Lee JS. A review of deep-learning-based approaches for attenuation correction in positron emission tomography. IEEE Trans Radiat Plasma Med Sci 2020;5:160‐84.
doi: 10.1109/TRPMS.2020.3009269
Patton JA, Turkington TG. SPECT/CT physical principles and attenuation correction. J Nucl Med Technol 2008;36:1‐10.
pubmed: 18287196
doi: 10.2967/jnmt.107.046839
Blankespoor S, Xu X, Kaiki K, Brown J, Tang H, Cann C, et al. Attenuation correction of SPECT using X-ray CT on an emission–transmission CT system: Myocardial perfusion assessment. IEEE Trans Nucl Sci 1996;43:2263‐74.
doi: 10.1109/23.531891
Kinahan PE, Hasegawa BH, Beyer T. X-ray-based attenuation correction for positron emission tomography/computed tomography scanners. Semin Nucl Med 2003;33:166‐79.
pubmed: 12931319
doi: 10.1053/snuc.2003.127307
Zaidi H, Hasegawa B. Determination of the attenuation map in emission tomography. J Nucl Med 2003;44:291‐315.
pubmed: 12571222
Rahman MA, Zhu Y, Clarkson E, Kupinski MA, Frey EC, Jha AK. Fisher information analysis of list-mode SPECT emission data for joint estimation of activity and attenuation distribution. Inverse Probl 2020;36:084002.
pubmed: 33071423
pmcid: 7561050
doi: 10.1088/1361-6420/ab958b
Chen X, Zhou B, Xie H, Shi L, Liu H, Holler W, et al. Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT. Eur J Nucl Med Mol Imaging 2022. https://doi.org/10.1007/s00259-022-05718-8 .
doi: 10.1007/s00259-022-05718-8
pubmed: 36512067
pmcid: 10027784
Barrett JF, Keat N. Artifacts in CT: Recognition and avoidance. Radiographics 2004;24:1679‐91.
pubmed: 15537976
doi: 10.1148/rg.246045065
Boas FE, Fleischmann D. CT artifacts: Causes and reduction techniques. Imaging Med 2012;4:229‐40.
doi: 10.2217/iim.12.13
Goetze S, Brown TL, Lavely WC, Zhang Z, Bengel FM. Attenuation correction in myocardial perfusion SPECT/CT: Effects of misregistration and value of reregistration. J Nucl Med 2007;48:1090‐5.
pubmed: 17574985
doi: 10.2967/jnumed.107.040535
Gould KL, Pan T, Loghin C, Johnson NP, Guha A, Sdringola S. Frequent diagnostic errors in cardiac PET/CT due to misregistration of CT attenuation and emission PET images: A definitive analysis of causes, consequences, and corrections. J Nucl Med 2007;48:1112‐21.
pubmed: 17574974
doi: 10.2967/jnumed.107.039792
Martinez-Möller A, Souvatzoglou M, Navab N, Schwaiger M, Nekolla SG. Artifacts from misaligned CT in cardiac perfusion PET/CT studies: Frequency, effects, and potential solutions. J Nucl Med 2007;48:188‐93.
pubmed: 17268013
Bockisch A, Freudenberg LS, Schmidt D, Kuwert T. Hybrid imaging by SPECT/CT and PET/CT: Proven outcomes in cancer imaging. Semin Nucl Med 2009;39:276‐89.
pubmed: 19497404
doi: 10.1053/j.semnuclmed.2009.03.003
Brix G, Lechel U, Glatting G, Ziegler SI, Münzing W, Müller SP, et al. Radiation exposure of patients undergoing whole-body dual-modality
pubmed: 15809483
Larkin AM, Serulle Y, Wagner S, Noz ME, Friedman K. Quantifying the increase in radiation exposure associated with SPECT/CT compared to SPECT alone for routine nuclear medicine examinations. Int J Mol Imaging 2011;2011:897202.
pubmed: 21755054
pmcid: 3132661
doi: 10.1155/2011/897202
Vandenberghe S, Marsden PK. PET–MRI: A review of challenges and solutions in the development of integrated multimodality imaging. Phys Med Biol 2015;60:R115.
pubmed: 25650582
doi: 10.1088/0031-9155/60/4/R115
Arabi H, Zaidi H. Magnetic resonance imaging-guided attenuation correction in whole-body PET/MRI using a sorted atlas approach. Med Image Anal 2016;31:1‐15.
pubmed: 26948109
doi: 10.1016/j.media.2016.02.002
Chen Y, An H. Attenuation correction of PET/MR imaging. Magn Reson Imaging Clin 2017;25:245‐55.
doi: 10.1016/j.mric.2016.12.001
Berker Y, Franke J, Salomon A, Palmowski M, Donker HC, Temur Y, et al. MRI-based attenuation correction for hybrid PET/MRI systems: A 4-class tissue segmentation technique using a combined ultrashort-echo-time/Dixon MRI sequence. J Nucl Med 2012;53:796‐804.
pubmed: 22505568
doi: 10.2967/jnumed.111.092577
Cabello J, Lukas M, Förster S, Pyka T, Nekolla SG, Ziegler SI. MR-based attenuation correction using ultrashort-echo-time pulse sequences in dementia patients. J Nucl Med 2015;56:423‐9.
pubmed: 25678486
doi: 10.2967/jnumed.114.146308
Wang G, Ye JC, Mueller K, Fessler JA. Image reconstruction is a new frontier of machine learning. IEEE Trans Med Imaging 2018;37:1289‐96.
pubmed: 29870359
doi: 10.1109/TMI.2018.2833635
Xie H, Shan H, Cong W, Liu C, Zhang X, Liu S, et al. Deep efficient end-to-end reconstruction (DEER) network for few-view breast CT image reconstruction. IEEE Access 2020;8:196633‐46.
pubmed: 33251081
pmcid: 7695229
doi: 10.1109/ACCESS.2020.3033795
Zhou B, Liu C, Duncan JS. Anatomy-constrained contrastive learning for synthetic segmentation without ground-truth. In: International conference on medical image computing and computer-assisted intervention, 2021. p. 47-56.
Zhou B, Chen X, Zhou SK, Duncan JS, Liu C. DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography. Med Image Anal 2022;75:102289.
pubmed: 34758443
doi: 10.1016/j.media.2021.102289
Guo Z, Li X, Huang H, Guo N, Li Q. Deep learning-based image segmentation on multimodal medical imaging. IEEE Trans Radiat Plasma Med Sci 2019;3:162‐9.
pubmed: 34722958
pmcid: 8553020
doi: 10.1109/TRPMS.2018.2890359
Zhou B, Tsai Y-J, Chen X, Duncan JS, Liu C. MDPET: A unified motion correction and denoising adversarial network for low-dose gated PET. IEEE Trans Med Imaging 2021;40:3154‐64.
pubmed: 33909561
pmcid: 8588635
doi: 10.1109/TMI.2021.3076191
Hashimoto F, Ohba H, Ote K, Teramoto A, Tsukada H. Dynamic PET image denoising using deep convolutional neural networks without prior training datasets. IEEE Access 2019;7:96594‐603.
doi: 10.1109/ACCESS.2019.2929230
Chen M, Shi X, Zhang Y, Wu D, Guizani M. Deep feature learning for medical image analysis with convolutional autoencoder neural network. IEEE Trans Big Data 2017;7:750‐8.
doi: 10.1109/TBDATA.2017.2717439
Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, 2015. p. 234-41.
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. In: Advances in neural information processing systems, 2014. p. 27.
Zhu J-Y, Park T, Isola P, Efros AA. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, 2017. p. 2223-32.
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017. p. 4700-8.
Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018. p. 7132-41.
Targ S, Almeida D, Lyman K. Resnet in ResNet: Generalizing residual architectures, 2016. arXiv preprint arXiv: 160308029.
McMillan AB, Bradshaw TJ. Artificial intelligence-based data corrections for attenuation and scatter in position emission tomography and single-photon emission computed tomography. PET Clin 2021;16:543‐52.
pubmed: 34364816
doi: 10.1016/j.cpet.2021.06.010
Bradshaw TJ, Zhao G, Jang H, Liu F, McMillan AB. Feasibility of deep learning-based PET/MR attenuation correction in the pelvis using only diagnostic MR images. Tomography 2018;4:138‐47.
pubmed: 30320213
pmcid: 6173790
doi: 10.18383/j.tom.2018.00016
Dong X, Wang T, Lei Y, Higgins K, Liu T, Curran WJ, et al. Synthetic CT generation from non-attenuation corrected PET images for whole-body PET imaging. Phys Med Biol 2019;64:215016.
pubmed: 31622962
pmcid: 7759014
doi: 10.1088/1361-6560/ab4eb7
Shi L, Onofrey JA, Liu H, Liu Y-H, Liu C. Deep learning-based attenuation map generation for myocardial perfusion SPECT. Eur J Nucl Med Mol Imaging 2020;47:2383‐95.
pubmed: 32219492
doi: 10.1007/s00259-020-04746-6
Choi B-H, Hwang D, Kang S-K, Kim K-Y, Choi H, Seo S, et al. Accurate transmission-less attenuation correction method for amyloid-β brain PET using deep neural network. Electronics 2021;10:1836.
doi: 10.3390/electronics10151836
Hwang D, Kang SK, Kim KY, Choi H, Lee JS. Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography. Eur J Nucl Med Mol Imaging 2021;49:1‐10.
Chen X, Zhou B, Shi L, Liu H, Pang Y, Wang R, et al. CT-free attenuation correction for dedicated cardiac SPECT using a 3D dual squeeze-and-excitation residual dense network. J Nucl Cardiol 2021. https://doi.org/10.1007/s12350-021-02672-0 .
doi: 10.1007/s12350-021-02672-0
pubmed: 34535872
Torkaman M, Yang J, Shi L, Wang R, Miller EJ, Sinusas AJ, et al. Direct image-based attenuation correction using conditional generative adversarial network for SPECT myocardial perfusion imaging. In: Medical imaging 2021: biomedical applications in molecular, structural, and functional imaging, 2021. p. 116000U.
Yang J, Shi L, Wang R, Miller EJ, Sinusas AJ, Liu C, et al. Direct attenuation correction using deep learning for cardiac SPECT: A feasibility study. J Nucl Med 2021;62:1645‐52.
pubmed: 33637586
pmcid: 8612332
doi: 10.2967/jnumed.120.256396
Shiri I, Arabi H, Geramifar P, Hajianfar G, Ghafarian P, Rahmim A, et al. Deep-JASC: Joint attenuation and scatter correction in whole-body
pubmed: 32415552
doi: 10.1007/s00259-020-04852-5
Mostafapour S, Gholamiankhah F, Dadgar H, Arabi H, Zaidi H. Feasibility of deep learning-guided attenuation and scatter correction of whole-body
pubmed: 33661195
doi: 10.1097/RLU.0000000000003585
Pan T-S, King MA, De Vries DJ, Ljungberg M. Segmentation of the body and lungs from Compton scatter and photopeak window data in SPECT: A Monte-Carlo investigation. IEEE Trans Med Imaging 1996;15:13‐24.
pubmed: 18215885
doi: 10.1109/42.481437
Pan T-S, King MA, Luo D-S, Dahlberg ST, Villegas BJ. Estimation of attenuation maps from scatter and photopeak window single photon-emission computed tomographic images of technetium 99m-labeled sestamibi. J Nucl Cardiol 1997;4:42‐51.
pubmed: 9138839
doi: 10.1016/S1071-3581(97)90048-9
Hosoba M, Wani H, Toyama H, Murata H, Tanaka E. Automated body contour detection in SPECT: Effects on quantitative studies. J Nucl Med 1986;27:1184‐91.
pubmed: 3487628
Ben Younes R, Mas J, Bidet R. A fully automated contour detection algorithm the preliminary step for scatter and attenuation compensation in SPECT. Eur J Nucl Med 1988;14:586‐9.
pubmed: 3266599
doi: 10.1007/BF00251780
Hebert TJ, Gopal S, Murphy P. A fully automated optimization algorithm for determining the 3-D patient contour from photo-peak projection data in SPECT. IEEE Trans Med Imaging 1995;14:122‐31.
pubmed: 18215816
doi: 10.1109/42.370408
Censor Y, Gustafson DE, Lent A, Tuy H. A new approach to the emission computerized tomography problem: Simultaneous calculation of attenuation and activity coefficients. IEEE Trans Nucl Sci 1979;26:2775‐9.
doi: 10.1109/TNS.1979.4330535
Nuyts J, Dupont P, Stroobants S, Benninck R, Mortelmans L, Suetens P. Simultaneous maximum a posteriori reconstruction of attenuation and activity distributions from emission sinograms. IEEE Trans Med Imaging 1999;18:393‐403.
pubmed: 10416801
doi: 10.1109/42.774167
Krol A, Bowsher JE, Manglos SH, Feiglin DH, Tornai MP, Thomas FD. An EM algorithm for estimating SPECT emission and transmission parameters from emission data only. IEEE Trans Med Imaging 2001;20:218‐32.
pubmed: 11341711
doi: 10.1109/42.918472
Bronnikov AV. Reconstruction of attenuation map using discrete consistency conditions. IEEE Trans Med Imaging 2000;19:451‐62.
pubmed: 11021688
doi: 10.1109/42.870255
Gourion D, Noll D, Gantet P, Celler A, Esquerré J-P. Attenuation correction using SPECT emission data only. IEEE Trans Nucl Sci 2002;49:2172‐9.
doi: 10.1109/TNS.2002.803862
Yan Y, Zeng GL. Attenuation map estimation with SPECT emission data only. Int J Imaging Syst Technol 2009;19:271‐6.
pubmed: 20148196
pmcid: 2818122
doi: 10.1002/ima.20200
Chen Y, Goorden MC, Beekman FJ. Automatic attenuation map estimation from SPECT data only for brain perfusion scans using convolutional neural networks. Phys Med Biol 2021;66:065006.
pubmed: 33571975
doi: 10.1088/1361-6560/abe557
Chen Y, Goorden MC, Beekman FJ. Convolutional neural network based attenuation correction for
doi: 10.1088/1361-6560/ac2470
Liu H, Wu J, Shi L, Liu Y, Miller E, Sinusas A, et al. Post-reconstruction attenuation correction for SPECT myocardium perfusion imaging facilitated by deep learning-based attenuation map generation. J Nucl Cardiol 2021. https://doi.org/10.1007/s12350-021-02817-1 .
doi: 10.1007/s12350-021-02817-1
pubmed: 34877638
pmcid: 9426651
Yu Z, Rahman MA, Schindler T, Laforest R, Jha AK. A physics and learning-based transmission-less attenuation compensation method for SPECT. In: Medical imaging 2021: physics of medical imaging, 2021. p. 1159512.
Torrey L, Shavlik J. Transfer learning. In: Handbook of research on machine learning applications and trends: Algorithms, methods, and techniques. Hershey: IGI Global; 2010. p. 242-64.
Shin H-C, Roth HR, Gao M, Lu L, Xu Z, Nogues I, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 2016;35:1285‐98.
pubmed: 26886976
doi: 10.1109/TMI.2016.2528162
Chen X, Hendrik Pretorius P, Zhou B, Liu H, Johnson K, Liu Y-H, et al. Cross-vender, cross-tracer, and cross-protocol deep transfer learning for attenuation map generation of cardiac SPECT. J Nucl Cardiol 2022. https://doi.org/10.1007/s12350-022-02978-7 .
doi: 10.1007/s12350-022-02978-7
pubmed: 36123568
Garcia EV, Faber TL, Esteves FP. Cardiac dedicated ultrafast SPECT cameras: New designs and clinical implications. J Nucl Med 2011;52:210‐7.
pubmed: 21233190
doi: 10.2967/jnumed.110.081323
Xu EZ, Mullani NA, Gould KL, Anderson WL. A segmented attenuation correction for PET. J Nucl Med 1991;32:161‐5.
pubmed: 1988625
Sekine T, Buck A, Delso G, Ter Voert EE, Huellner M, Veit-Haibach P, et al. Evaluation of atlas-based attenuation correction for integrated PET/MR in human brain: Application of a head atlas and comparison to true CT-based attenuation correction. J Nucl Med 2016;57:215‐20.
pubmed: 26493207
doi: 10.2967/jnumed.115.159228
Rezaei A, Defrise M, Bal G, Michel C, Conti M, Watson C, et al. Simultaneous reconstruction of activity and attenuation in time-of-flight PET. IEEE Trans Med Imaging 2012;31:2224‐33.
pubmed: 22899574
doi: 10.1109/TMI.2012.2212719
Rezaei A, Defrise M, Nuyts J. ML-reconstruction for TOF-PET with simultaneous estimation of the attenuation factors. IEEE Trans Med Imaging 2014;33:1563‐72.
pubmed: 24760903
doi: 10.1109/TMI.2014.2318175
Rezaei A, Michel C, Casey ME, Nuyts J. Simultaneous reconstruction of the activity image and registration of the CT image in TOF-PET. Phys Med Biol 2016;61:1852.
pubmed: 26854817
doi: 10.1088/0031-9155/61/4/1852
Gong K, Yang J, Kim K, El Fakhri G, Seo Y, Li Q. Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images. Phys Med Biol 2018;63:125011.
pubmed: 29790857
pmcid: 6031313
doi: 10.1088/1361-6560/aac763
Jang H, Liu F, Zhao G, Bradshaw T, McMillan AB. Deep learning based MRAC using rapid ultrashort echo time imaging. Med Phys 2018;45:3697‐704.
doi: 10.1002/mp.12964
Liu F, Jang H, Kijowski R, Bradshaw T, McMillan AB. Deep learning MR imaging-based attenuation correction for PET/MR imaging. Radiology 2018;286:676‐84.
pubmed: 28925823
doi: 10.1148/radiol.2017170700
Arabi H, Zeng G, Zheng G, Zaidi H. Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI. Eur J Nucl Med Mol Imaging 2019;46:2746‐59.
pubmed: 31264170
doi: 10.1007/s00259-019-04380-x
Blanc-Durand P, Khalifé M, Sgard B, Kaushik S, Soret M, Tiss A, et al. Attenuation correction using 3D deep convolutional neural network for brain
pubmed: 31589623
pmcid: 6779234
doi: 10.1371/journal.pone.0223141
Liu F, Jang H, Kijowski R, Zhao G, Bradshaw T, McMillan AB. A deep learning approach for
doi: 10.1186/s40658-018-0225-8
Reimold M, Nikolaou K, Christian La Fougère M, Gatidis S. 18 Independent brain F-FDG PET attenuation correction using a deep learning approach with Generative Adversarial Networks. Hell J Nucl Med 2019;22:179‐86.
pubmed: 31587027
Armanious K, Hepp T, Küstner T, Dittmann H, Nikolaou K, La Fougère C, et al. Independent attenuation correction of whole body [
doi: 10.1186/s13550-020-00644-y
Hwang D, Kim KY, Kang SK, Seo S, Paeng JC, Lee DS, et al. Improving the accuracy of simultaneously reconstructed activity and attenuation maps using deep learning. J Nucl Med 2018;59:1624‐9.
pubmed: 29449446
doi: 10.2967/jnumed.117.202317
Hwang D, Kang SK, Kim KY, Seo S, Paeng JC, Lee DS, et al. Generation of PET attenuation map for whole-body time-of-flight
pubmed: 30683763
pmcid: 6681691
doi: 10.2967/jnumed.118.219493
Shi L, Onofrey JA, Revilla EM, Toyonaga T, Menard D, Ankrah J, et al. A novel loss function incorporating imaging acquisition physics for PET attenuation map generation using deep learning. In: International conference on medical image computing and computer-assisted intervention, 2019. p. 723-31.
Rezaei A, Schramm G, Willekens SM, Delso G, Van Laere K, Nuyts J. A quantitative evaluation of joint activity and attenuation reconstruction in TOF PET/MR brain imaging. J Nucl Med 2019;60:1649‐55.
pubmed: 30979823
pmcid: 6836858
doi: 10.2967/jnumed.118.220871
Shiri I, Ghafarian P, Geramifar P, Leung KH-Y, Ghelichoghli M, Oveisi M, et al. Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder–decoder (Deep-DAC). Eur Radiol 2019;29:6867‐79.
pubmed: 31227879
doi: 10.1007/s00330-019-06229-1
Dong X, Lei Y, Wang T, Higgins K, Liu T, Curran WJ, et al. Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging. Phys Med Biol 2020;65:055011.
pubmed: 31869826
pmcid: 7099429
doi: 10.1088/1361-6560/ab652c
Yang J, Park D, Gullberg GT, Seo Y. Joint correction of attenuation and scatter in image space using deep convolutional neural networks for dedicated brain
pubmed: 30743246
pmcid: 6449185
doi: 10.1088/1361-6560/ab0606
Gerlot-Chiron P, Bizais Y. Registration of multimodality medical images using a region overlap criterion. CVGIP Graph Models Image Process 1992;54:396‐406.
doi: 10.1016/1049-9652(92)90024-R
Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P. Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 1997;16:187‐98.
pubmed: 9101328
doi: 10.1109/42.563664
Hwang D, Kang SK, Kim KY, Choi H, Seo S, Lee JS. Data-driven respiratory phase-matched PET attenuation correction without CT. Phys Med Biol 2021;66:115009.
doi: 10.1088/1361-6560/abfc8f
Murata T, Yokota H, Yamato R, Horikoshi T, Tsuneda M, Kurosawa R, et al. Development of attenuation correction methods using deep learning in brain-perfusion single-photon emission computed tomography. Med Phys 2021;48:4177‐90.
pubmed: 34061380
doi: 10.1002/mp.15016
Sakaguchi K, Kaida H, Yoshida S, Ishii K. Attenuation correction using deep learning for brain perfusion SPECT images. Ann Nucl Med 2021;35:589‐99.
pubmed: 33751364
doi: 10.1007/s12149-021-01600-z