Benamer HT, Patterson J, Grosset DG, Booij J, De Bruin K, Van Royen E, et al. Accurate differentiation of parkinsonism and essential tremor using visual assessment of [123I]-FP-CIT SPECT imaging: the [123I]-FP-CIT study group. Mov Disord. 2000;15:503–10.
Article CAS PubMed Google Scholar
O’Brien JT, Colloby S, Fenwick J, Williams ED, Firbank M, Burn D, et al. Dopamine transporter loss visualized with FP-CIT SPECT in the differential diagnosis of dementia with Lewy bodies. Arch Neurol. 2004;61:919–25.
Oh M, Kim JS, Kim JY, Shin K-H, Park SH, Kim HO, et al. Subregional patterns of preferential striatal dopamine transporter loss differ in Parkinson disease, progressive supranuclear palsy, and multiple-system atrophy. J Nucl Med. 2012;53:399–406.
Article CAS PubMed Google Scholar
Lee JY, Seo SH, Kim YK, Yoo HB, Kim YE, Song IC, et al. Extrastriatal dopaminergic changes in Parkinson’s disease patients with impulse control disorders. J Neurol Neurosurg Psychiatry. 2014;85:23–30.
Article CAS PubMed Google Scholar
Lee JY, Seo S, Lee JS, Kim HJ, Kim YK, Jeon BS. Putaminal serotonergic innervation: monitoring dyskinesia risk in Parkinson disease. Neurology. 2015;85:853–60.
Article CAS PubMed Google Scholar
Lee I, Kim JS, Park JY, Byun BH, Park SY, Choi JH, et al. Head-to-head comparison of (18) F-FP-CIT and (123) I-FP-CIT for dopamine transporter imaging in patients with Parkinson’s disease: A preliminary study. Synapse. 2018;72:e22032.
Lee JS, Lee DS. Analysis of functional brain images using population-based probabilistic atlas. Curr Med Imaging Rev. 2005;1:81–7.
Lee JS, Lee DS, Kim S-K, Lee S-K, Chung J-K, Lee MC, et al. Localization of epileptogenic zones in F-18 FDG brain PET of patients with temporal lobe epilepsy using artificial neural network. IEEE Trans Med Imaging. 2000;19:347–55.
Article CAS PubMed Google Scholar
Minoshima S, Koeppe RA, Frey KA, Kuhl DE. Anatomic standardization: linear scaling and nonlinear warping of functional brain images. J Nucl Med. 1994;35:1528–37.
Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage. 2011;54:2033–44.
Lee SK, Lee DS, Yeo JS, Lee JS, Kim YK, Jang MJ, et al. FDG-PET images quantified by probabilistic atlas of brain and surgical prognosis of temporal lobe epilepsy. Epilepsia. 2002;43:1032–8.
Kim JS, Cho H, Choi JY, Lee SH, Ryu YH, Lyoo CH, et al. Feasibility of computed tomography-guided methods for spatial normalization of dopamine transporter positron emission tomography image. PLoS One. 2015;10:e0132585.
Article PubMed PubMed Central Google Scholar
Bae S, Choi H, Whi W, Paeng JC, Cheon GJ, Kang KW, et al. Spatial normalization using early-phase [(18)F]FP-CIT PET for quantification of striatal dopamine transporter binding. Nucl Med Mol Imaging. 2020;54:305–14.
Article CAS PubMed PubMed Central Google Scholar
Kang SK, Seo S, Shin SA, Byun MS, Lee DY, Kim YK, et al. Adaptive template generation for amyloid PET using a deep learning approach. Hum Brain Mapp. 2018;39:3769–78.
Article PubMed PubMed Central Google Scholar
Choi H, Lee DS. Generation of structural MR images from amyloid PET: application to MR-less quantification. J Nucl Med. 2018;59:1111–7.
Article CAS PubMed PubMed Central Google Scholar
Kang SK, Kim D, Shin SA, Kim YK, Choi H, Lee JS. Fast and accurate amyloid Brain PET quantification without MRI using deep neural networks. J Nucl Med. 2023;64:659–66.
Article CAS PubMed PubMed Central Google Scholar
Patenaude B, Smith SM, Kennedy DN, Jenkinson M. A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage. 2011;56:907–22.
Perlaki G, Horvath R, Nagy SA, Bogner P, Doczi T, Janszky J, et al. Comparison of accuracy between FSL’s FIRST and Freesurfer for caudate nucleus and putamen segmentation. Sci Rep. 2017;7:2418.
Article PubMed PubMed Central Google Scholar
Rajendran P, Sharma A, Pramanik M. Photoacoustic imaging aided with deep learning: a review. Biomed Eng Lett. 2022;12:155–73.
Rao D, Prakashini K, Singh R, Vijayananda J. Automated segmentation of the larynx on computed tomography images: a review. Biomed Eng Lett. 2022;12:175–83.
Article PubMed PubMed Central Google Scholar
Garcia EV, Piccinelli M. Preparing for the artificial intelligence revolution in nuclear cardiology. Nucl Med Mol Imaging. 2023;57:51–60.
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.
Visvikis D, Lambin P, Beuschau Mauridsen K, Hustinx R, Lassmann M, Rischpler C, et al. Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation. Eur J Nucl Med Mol Imaging. 2022;49:4452–63.
Article PubMed PubMed Central Google Scholar
Berg E, Cherry SR. Using convolutional neural networks to estimate time-of-flight from PET detector waveforms. Phys Med Biol. 2018;63:02LT1.
Lee MS, Hwang D, Kim JH, Lee JS. Deep-dose: a voxel dose estimation method using deep convolutional neural network for personalized internal dosimetry. Sci Rep. 2019;9:1–9.
Kang H, Kang DY. Alzheimer’s disease prediction using attention mechanism with dual-phase (18)F-florbetaben images. Nucl Med Mol Imaging. 2023;57:61–72.
Article CAS PubMed Google Scholar
Kim KM, Lee MS, Suh MS, Cheon GJ, Lee JS. Voxel-based internal dosimetry for 177Lu-labeled radiopharmaceutical therapy using deep residual learning. Nucl Med Mol Imaging. 2023;57:94–102.
Article CAS PubMed Google Scholar
Seo SY, Kim SJ, Oh JS, Chung J, Kim SY, Oh SJ, et al. Unified deep learning-based mouse brain MR segmentation: template-based individual brain positron emission tomography volumes-of-interest generation without spatial normalization in mouse Alzheimer model. Front Aging Neurosci. 2022;14:807903.
Article CAS PubMed PubMed Central Google Scholar
Evans AC, Janke AL, Collins DL, Baillet S. Brain templates and atlases. Neuroimage. 2012;62:911–22.
Ashburner J, Friston KJ. Unified segmentation. Neuroimage. 2005;26:839–51.
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