A CT-free deep-learning-based attenuation and scatter correction for copper-64 PET in different time-point scans

Kadhim AA, Sheikhzadeh P, Abbasi M, Afshar S, Vahidfar N, Asidkar S, Karimipourfard M, Valibeiglou Z, Ay MR. Investigating patient-specific absorbed dose assessment for copper-64 PET/CT. Front Biomed Technol. 2024.

Monsef A, Sheikhzadeh P, Steiner JR, Sadeghi F, Yazdani M, Ghafarian P. Optimizing scan time and bayesian penalized likelihood reconstruction algorithm in copper-64 PET/CT imaging: a phantom study. Biomed Phys Eng Express. 2024;10(4): 045019.

Article  Google Scholar 

Vahidfar N, Bakhshi Kashi M, Afshar S, Sheikhzadeh P, Farzanefar S, Salehi Y, Eppard E. Recent advances of copper-64 based radiopharmaceuticals in nuclear medicine. In: Advances in dosimetry and new trends in radiopharmaceuticals. London: IntechOpen; 2024.

Google Scholar 

Bushberg JT, Seibert JA, Leidholdt EM, Boone JM, Mahesh M. The essential physics of medical imaging. Med Phys. 2013;40(7): 077301.

Article  Google Scholar 

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(7):922–7.

Article  CAS  PubMed  Google Scholar 

Kinahan PE, Hasegawa BH, Beyer T. X-ray-based attenuation correction for positron emission tomography/computed tomography scanners. In: Seminars in nuclear medicine, vol. 33. Philadelphia: WB Saunders; 2003. p. 166–79.

Google Scholar 

Yang J, Sohn JH, Behr SC, Gullberg GT, Seo Y. CT-less direct correction of attenuation and scatter in the image space using deep learning for whole-body FDG PET: potential benefits and pitfalls. Radiology. 2020;3(2): e200137.

PubMed  PubMed Central  Google Scholar 

Barrett JF, Keat N. Artifacts in CT: recognition and avoidance. Radiographics. 2004;24(6):1679–91.

Article  PubMed  Google Scholar 

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):160–84.

Article  Google Scholar 

Yoo HJ, Lee JS, Lee JM. Integrated whole body MR/PET: where are we? Korean J Radiol. 2015;16(1):32–49.

Article  PubMed  PubMed Central  Google Scholar 

González AJ, Sánchez F, Benlloch JM. Organ-dedicated molecular imaging systems. IEEE Trans Radiat Plasma Med Sci. 2018;2(5):388–403.

Article  Google Scholar 

Jang H, Liu F, Zhao G, Bradshaw T, McMillan AB. Deep learning based MRAC using rapid ultrashort echo time imaging. Med Phys. 2018;45(8):3697–704.

Article  Google Scholar 

Dong X, Wang T, Lei Y, Higgins K, Liu T, Curran WJ, Yang X. Synthetic CT generation from non-attenuation corrected PET images for whole-body PET imaging. Phys Med Biol. 2019;64(21): 215016.

Article  PubMed  PubMed Central  Google Scholar 

Armanious K, Hepp T, Küstner T, Dittmann H, Nikolaou K, La Fougère C, Gatidis S. Independent attenuation correction of whole body [18 F] FDG-PET using a deep learning approach with generative adversarial networks. EJNMMI Res. 2020;10:1–9.

Article  Google Scholar 

Mostafapour S, Gholamiankhah F, Dadgar H, Arabi H, Zaidi H. Feasibility of deep learning-guided attenuation and scatter correction of whole-body 68Ga-PSMA PET studies in the image domain. Clin Nucl Med. 2021;46(8):609–15.

Article  PubMed  Google Scholar 

Shiri I, Vafaei Sadr A, Akhavan A, Salimi Y, Sanaat A, Amini M, Zaidi H. Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning. Eur J Nucl Med Mol Imaging. 2023;50(4):1034–50.

Article  PubMed  Google Scholar 

Dong X, Lei Y, Wang T, Higgins K, Liu T, Curran WJ, Yang X. Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging. Phys Med Biol. 2020;65(5): 055011.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Shiri I, Arabi H, Geramifar P, Hajianfar G, Ghafarian P, Rahmim A, Zaidi H. Deep-JASC: joint attenuation and scatter correction in whole-body 18 F-FDG PET using a deep residual network. Eur J Nucl Med Mol Imaging. 2020;47:2533–48.

Article  PubMed  Google Scholar 

Cardoso MJ, Li W, Brown R, Ma N, Kerfoot E, Wang Y et al. Monai: an open-source framework for deep learning in healthcare.arXiv preprintarXiv:2211.02701. 2022.

Hatamizadeh A, Nath V, Tang Y, Yang D, Roth HR, Xu D. Swin unetr: Swin transformers for semantic segmentation of brain tumors in MRI images. In: International MICCAI brainlesion workshop. Cham: Springer; 2021. p. 272–84.

Google Scholar 

Salimi Y, Mansouri Z, Shiri I, Mainta I, Zaidi H. Deep learning-powered CT-less multitracer organ segmentation from PET images: a solution for unreliable CT segmentation in PET/CT imaging. Clin Nucl Med. 2022;50:10–1097.

Google Scholar 

Lord MS, Islamian JP, Seyyedi N, Samimi R, Farzanehfar S, Shahrbabki M, Sheikhzadeh P. Deep-learning-based attenuation correction for 68Ga-DOTATATE whole-body PET imaging: a dual-center clinical study. Mol Imaging Radionuclide Ther. 2024. https://doi.org/10.4274/mirt.galenos.2024.86422.

Article  Google Scholar 

Comments (0)

No login
gif