Mayerhoefer ME, Prosch H, Beer L, Tamandl D, Beyer T, Hoeller C, et al. PET/MRI versus PET/CT in oncology: a prospective single-center study of 330 examinations focusing on implications for patient management and cost considerations. Eur J Nucl Med Mol Imaging. 2020;47(1):51–60.
Schwartz M, Gavane SC, Bou-Ayache J, Kolev V, Zakashansky K, Prasad-Hayes M, et al. Feasibility and feasibility and diagnostic performance of hybrid pet/mri compared with pet/ct for gynecological malignancies: a prospective pilot study. Abdom Radiol. 2018;43(12):3462–7. https://doi.org/10.1007/s00261-018-1665-2.
Even AJG, De Ruysscher D, van Elmpt W. The promise of multiparametric imaging in oncology: How do we move forward? Eur J Nucl Med Mol Imaging. 2016;43(7):1195–8. https://doi.org/10.1007/s00259-016-3361-1.
Ahangari S, Littrup Andersen F, Liv Hansen N, Jakobi Nøttrup T, Berthelsen AK, Folsted Kallehauge J, et al. Multi-parametric PET/MRI for enhanced tumor characterization of patients with cervical cancer. Eur J Hybrid Imaging. 2022;6(1):7. https://doi.org/10.1186/s41824-022-00129-2.
Martin O, Schaarschmidt BM, Kirchner J, Suntharalingam S, Grueneisen J, Demircioglu A, et al. PET/MRI versus PET/CT for whole-body staging: results from a single-center observational study on 1,003 sequential examinations. J Nucl Med. 2020;61(8):1131–6.
Mehranian A, Arabi H, Zaidi H. Vision 20/20: magnetic resonance imaging-guided attenuation correction in PET/MRI: challenges, solutions, and opportunities. Med Phys. 2016;43(3):1130–55.
Wagenknecht G, Kaiser HJ, Mottaghy FM, Herzog H. MRI for attenuation correction in PET: methods and challenges. Magn Reson Mater Physics, Biol Med. 2013;26(1):99–113.
Lee JS. A review of deep-learning-based approaches for attenuation correction in positron emission tomography. IEEE Trans Radiat Plasma Med Sci. 2021;5(2):160–84.
Ahangari S, Hansen NL, Olin AB, Nøttrup TJ, Ryssel H, Berthelsen AK, et al. Toward PET/MRI as one-stop shop for radiotherapy planning in cervical cancer patients. Acta Oncol (Madr). 2021. https://doi.org/10.1080/0284186X.2021.1936164.
Martinez-Moller A, Souvatzoglou M, Delso G, Bundschuh RA, Chefdotel C, Ziegler SI, et al. Tissue classification as a potential approach for attenuation correction in whole-body PET/MRI: evaluation with PET/CT data. J Nucl Med. 2009;50(4):520–6.
Hofmann M, Bezrukov I, Mantlik F, Aschoff P, Steinke F, Beyer T, et al. MRI-based attenuation correction for whole-body PET/MRI: quantitative evaluation of segmentation- and atlas-based methods. J Nucl Med. 2011;52(9):1392–9.
Paulus DH, Quick HH, Geppert C, Fenchel M, Zhan Y, Hermosillo G, et al. Whole-body PET/MR imaging: quantitative evaluation of a novel model-based MR attenuation correction method including bone. J Nucl Med. 2015;56(7):1061–6.
Farjam R, Tyagi N, Deasy JO, Hunt MA. Dosimetric evaluation of an atlas-based synthetic CT generation approach for MR-only radiotherapy of pelvis anatomy. J Appl Clin Med Phys. 2019;20(1):101–9.
Keereman V, Fierens Y, Broux T, De Deene Y, Lonneux M, Vandenberghe S. MRI-based attenuation correction for PET/MRI using ultrashort echo time sequences. J Nucl Med. 2010;51(5):812–8.
Ladefoged CN, Benoit D, Law I, Holm S, Kjær A, Hjgaard L, et al. Region specific optimization of continuous linear attenuation coefficients based on UTE (RESOLUTE): application to PET/MR brain imaging. Phys Med Biol. 2015;60(20):8047–65.
Wiesinger F, Bylund M, Yang J, Kaushik S, Shanbhag D, Ahn S, et al. Zero TE-based pseudo-CT image conversion in the head and its application in PET/MR attenuation correction and MR-guided radiation therapy planning. Magn Reson Med. 2018;80(4):1440–51.
Leynes AP, Yang J, Shanbhag DD, Kaushik SS, Seo Y, Hope TA, et al. Hybrid ZTE/Dixon MR-based attenuation correction for quantitative uptake estimation of pelvic lesions in PET/MRI. Med Phys. 2017;44(3):902–13.
Schramm G, Langner J, Hofheinz F, Petr J, Lougovski A, Beuthien-Baumann B, et al. Influence and compensation of truncation artifacts in MR-based attenuation correction in PET/MR. IEEE Trans Med Imaging. 2013;32(11):2056–63.
Delso G, Martinez-Möller A, Bundschuh RA, Nekolla SG, Ziegler SI. The effect of limited MR field of view in MR/PET attenuation correction. Med Phys. 2010;37(6):2804–12.
Nuyts J, Bal G, Kehren F, Fenchel M, Michel C, Watson C. Completion of a truncated attenuation image from the attenuated PET emission data. IEEE Trans Med Imaging. 2013;32(2):237–46.
Blumhagen JO, Ladebeck R, Fenchel M, Scheffler K. MR-based field-of-view extension in MR/PET: B0 homogenization using gradient enhancement (HUGE). Magn Reson Med. 2013;70(4):1047–57.
Lee JS. A review of deep learning-based approaches for attenuation correction in positron emission tomography. IEEE Trans Radiat Plasma Med Sci. 2020;5(2):1.
Ladefoged CN, Hansen AE, Henriksen OM, Bruun FJ, Eikenes L, Øen SK, et al. AI-driven attenuation correction for brain PET/MRI: clinical evaluation of a dementia cohort and importance of the training group size. Neuroimage. 2019;2020(222): 117221. https://doi.org/10.1016/j.neuroimage.2020.117221.
Nie D, Trullo R, Lian J, Wang L, Petitjean C, Ruan S, et al. Medical image synthesis with deep convolutional adversarial networks. IEEE Trans Biomed Eng. 2018;65(12):2720–30.
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(13):2746–59.
Han X. MR-based synthetic CT generation using a deep convolutional neural network method. Med Phys. 2017;44(4):1408–19.
Song X, Qian P, Zheng J, Jiang Y, Xia K, Traughber B, et al. mDixon-based synthetic CT generation via transfer and patch learning. Pattern Recognit Lett. 2020;138:51–9. https://doi.org/10.1016/j.patrec.2020.06.017.
Liu F, Jang H, Kijowski R, BradshawMcmillan TAB. Deep learning Mr imaging-based attenuation correction for PeT, Mr imaging 1 TECHNICAL DEVELOPMENTS: Deep Learning MR Imaging-based Attenuation Correction for PET, MR Imaging Liu et al. Radiol Radiol. 2018. https://doi.org/10.1148/radiol.2017170700.
Olin AB, Hansen AE, Rasmussen JH, Jakoby B, Berthelsen AK, Ladefoged CN, et al. Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients. EJNMMI Phys. 2022. https://doi.org/10.1186/s40658-022-00449-z.
La P, Yang J, Wiesinger F, Kaushik SS, Shanbhag DD, Seo Y, et al. Zero-echo-time and dixon deep pseudo-CT (ZeDD CT): Direct generation of pseudo-CT images for Pelvic PET/MRI Attenuation Correction Using Deep Convolutional Neural Networks with Multiparametric MRI. J Nucl Med. 2018;59(5):852–8.
Torrado-Carvajal A. Dixon-vibe deep learning (divide) pseudo-CT synthesis for pelvis PET/MR attenuation correction (J Nucl Med. (2019) 60:(429–435) https://doi.org/10.2967/jnumed.118.209288). J Nucl Med. 2020;61(1):161.
Ge Y, Xue Z, Cao T, Liao S. Unpaired whole-body MR to CT synthesis with correlation coefficient constrained adversarial learning. 2019;4.
Schaefferkoetter J, Yan J, Moon S, Chan R, Ortega C, Metser U, et al. Deep learning for whole-body medical image generation. Eur J Nucl Med Mol Imaging. 2021;48:3817–26.
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(21): 215016.
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 PET imaging. Phys Med Biol. 2020;65(5): 055011.
Modat M, Ridgway GR, Taylor ZA, Lehmann M, Barnes J, Hawkes DJ, et al. Fast free-form deformation using graphics processing units. Comput Methods Programs Biomed. 2010;98(3):278–84.
Lillington J, Brusaferri L, Kläser K, Shmueli K, Neji R, Hutton BF, et al. PET/MRI attenuation estimation in the lung: a review of past, present, and potential techniques. Med Phys. 2020;47(2):790–811.
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. Tomogr (Ann Arbor, Mich). 2018;4(3):138–47.
Gong K, Yang J, Larson PEZ, Behr SC, Hope TA, Seo Y, et al. MR-based attenuation correction for brain PET using 3-D cycle-consistent adversarial network. IEEE Trans Radiat Plasma Med Sci. 2021;5(2):185–92.
Hwang D, Kim KY, Kang SK, Choi H, Seo S, Paeng JC, et al. Accurate attenuation correction for whole-body Ga-68-DOTATOC PET studies using deep learning. Soc Nuclear Med; 2019.
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 18F-FDG PET/MRI using a deep neural network trained with simultaneously reconstructed activity and attenuation maps. J Nucl Med. 2019;60(8):1183–9.
Arabi H, Zaidi H. MRI-guided attenuation correction in torso PET/MRI: Assessment of segmentation-, atlas-, and deep learning-based approaches in the presence of outliers. Magn Reson Med. 2022;87(2):686–701.
Lindemann ME, Oehmigen M, Blumhagen JO, Gratz M, Quick HH. MR-based truncation and attenuation correction in integrated PET/MR hybrid imaging using HUGE with continuous table motion. Med Phys. 2017;44(9):4559–72.
Grafe H, Lindemann ME, Ruhlmann V, Oehmigen M, Hirmas N, Umutlu L, et al. Evaluation of improved attenuation correction in whole-body PET/MR on patients with bone metastasis using various radiotracers. Eur J Nucl Med Mol Imaging. 2020;47(10):2269–79.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Disclaimer:
This article is autogenerated using RSS feeds and has not been created or edited by OA JF.
Click here for Source link (https://www.springeropen.com/)
