Klunk WE, Engler H, Nordberg A, Wang Y, Blomqvist G, Holt DP, et al. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh compound-B. Ann Neurol. 2004;55:306–19.
CAS PubMed Article Google Scholar
Mathis CA, Wang Y, Holt DP, Huang G-F, Debnath ML, Klunk WE. Synthesis and evaluation of 11C-labeled 6-substituted 2-arylbenzothiazoles as amyloid imaging agents. J Med Chem. 2003;46:2740–54.
CAS PubMed Article Google Scholar
Nelissen N, Laere KV, Thurfjell L, Owenius R, Vandenbulcke M, Koole M, et al. Phase 1 study of the pittsburgh compound b derivative 18F-flutemetamol in healthy volunteers and patients with probable Alzheimer disease. J Nucl Med. 2009;50:1251–9.
CAS PubMed Article Google Scholar
Vandenberghe R, Van Laere K, Ivanoiu A, Salmon E, Bastin C, Triau E, et al. 18F-flutemetamol amyloid imaging in Alzheimer disease and mild cognitive impairment: a phase 2 trial. Ann Neurol. 2010;68:319–29.
Chien DT, Bahri S, Szardenings AK, Walsh JC, Mu F, Su M-Y, et al. Early clinical pet imaging results with the novel phf-tau radioligand [F-18]-T807. J Alzheimers Dis. 2013;34:457–68.
CAS PubMed Article Google Scholar
Harada R, Okamura N, Furumoto S, Furukawa K, Ishiki A, Tomita N, et al. 18F-THK5351: a novel pet radiotracer for imaging neurofibrillary pathology in Alzheimer disease. J Nucl Med. 2016;57:208–14.
CAS PubMed Article Google Scholar
Maruyama M, Shimada H, Suhara T, Shinotoh H, Ji B, Maeda J, et al. Imaging of tau pathology in a tauopathy mouse model and in Alzheimer patients compared to normal controls. Neuron. 2013;79:1094–108.
CAS PubMed Article Google Scholar
Okamura N, Furumoto S, Harada R, Tago T, Yoshikawa T, Fodero-Tavoletti M, et al. Novel 18F-labeled arylquinoline derivatives for noninvasive imaging of tau pathology in Alzheimer disease. J Nucl Med. 2013;54:1420–7.
CAS PubMed Article Google Scholar
Hoffman EJ, Huang S-C, Phelps ME. Quantitation in positron emission computed tomography: 1. Effect of object size. J Comput Assist Tomogr. 1979;3:299–308.
CAS PubMed Article Google Scholar
Alessio AM, Kinahan PE. Improved quantitation for PET/CT image reconstruction with system modeling and anatomical priors. Med Phys. 2006;33:4095–103.
Baete K, Nuyts J, Laere KV, Van Paesschen W, Ceyssens S, De Ceuninck L, et al. Evaluation of anatomy based reconstruction for partial volume correction in brain FDG-PET. Neuroimage. 2004;23:305–17.
Erlandsson K, Dickson J, Arridge S, Atkinson D, Ourselin S, Hutton BF. MR imaging-guided partial volume correction of PET data in PET/MR imaging. PET Clin. 2016;11:161–77.
Meltzer CC, Leal JP, Mayberg HS, Wagner HN, Frost JJ. Correction of PET data for partial volume effects in human cerebral cortex by MR imaging. J Comput Assist Tomogr. 1990;14:561–70.
CAS PubMed Article Google Scholar
Müller-Gärtner HW, Links JM, Prince JL, Bryan RN, McVeigh E, Leal JP, et al. Measurement of radiotracer concentration in brain gray matter using positron emission tomography: MRI-based correction for partial volume effects. J Cereb Blood Flow Metab. 1992;12:571–83.
Rousset OG, Ma Y, Evans AC. Correction for partial volume effects in PET: principle and validation. J Nucl Med. 1998;39:904–11.
Shidahara M, Tsoumpas C, Hammers A, Boussion N, Visvikis D, Suhara T, et al. Functional and structural synergy for resolution recovery and partial volume correction in brain PET. Neuroimage. 2009;44:340–8.
Thomas BA, Erlandsson K, Modat M, Thurfjell L, Vandenberghe R, Ourselin S, et al. The importance of appropriate partial volume correction for PET quantification in Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2011;38:1104–19.
Arakawa R, Stenkrona P, Takano A, Nag S, Maior RS, Halldin C. Test-retest reproducibility of [11C]-l-deprenyl-D2 binding to MAO-B in the human brain. EJNMMI Res. 2017;7:54.
PubMed PubMed Central Article CAS Google Scholar
Brendel M, Högenauer M, Delker A, Sauerbeck J, Bartenstein P, Seibyl J, et al. Improved longitudinal [18F]-AV45 amyloid PET by white matter reference and VOI-based partial volume effect correction. Neuroimage. 2015;108:450–9.
Habert M-O, Bertin H, Labit M, Diallo M, Marie S, Martineau K, et al. Evaluation of amyloid status in a cohort of elderly individuals with memory complaints: validation of the method of quantification and determination of positivity thresholds. Ann Nucl Med. 2018;32:75–86.
CAS PubMed Article Google Scholar
LaPoint MR, Chhatwal JP, Sepulcre J, Johnson KA, Sperling RA, Schultz AP. The association between tau PET and retrospective cortical thinning in clinically normal elderly. Neuroimage. 2017;157:612–22.
Schaeverbeke J, Evenepoel C, Declercq L, Gabel S, Meersmans K, Bruffaerts R, et al. Distinct [18F]THK5351 binding patterns in primary progressive aphasia variants. Eur J Nucl Med Mol Imaging. 2018;45:1–16.
Bengio Y, Lamblin P, Popovici D, Larochelle H. Greedy Layer-wise Training of Deep Networks. In: Proc 19th Int Conf Neural Inf Process Syst [Internet]. Cambridge, MA, USA: MIT Press; 2006 [cited 2018 Jan 10]. p. 153–60. Available from: http://dl.acm.org/citation.cfm?id=2976456.2976476
Hinton GE, Osindero S, Teh Y-W. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18:1527–54.
Bangalore Yogananda CG, Shah BR, Vejdani-Jahromi M, Nalawade SS, Murugesan GK, Yu FF, et al. A fully automated deep learning network for brain tumor segmentation. Tomography. 2020;6:186–93.
PubMed PubMed Central Article Google Scholar
Ben Naceur M, Akil M, Saouli R, Kachouri R. Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy. Med Image Anal. 2020;63:101692.
Naser MA, Deen MJ. Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images. Comput Biol Med. 2020;121: 103758.
Windisch P, Weber P, Fürweger C, Ehret F, Kufeld M, Zwahlen D, et al. Implementation of model explainability for a basic brain tumor detection using convolutional neural networks on MRI slices. Neuroradiology. 2020. https://doi.org/10.1007/s00234-020-02465-1.
Feng W, Halm-Lutterodt NV, Tang H, Mecum A, Mesregah MK, Ma Y, et al. Automated MRI-based deep learning model for detection of Alzheimer’s disease process. Int J Neural Syst. 2020;30:2050032.
Pan D, Zeng A, Jia L, Huang Y, Frizzell T, Song X. Early detection of Alzheimer’s disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning. Front Neurosci. 2020. https://doi.org/10.3389/fnins.2020.00259/full.
Article PubMed PubMed Central Google Scholar
Wen J, Thibeau-Sutre E, Diaz-Melo M, Samper-González J, Routier A, Bottani S, et al. Convolutional neural networks for classification of Alzheimer’s disease: Overview and reproducible evaluation. Med Image Anal. 2020;63: 101694.
Clèrigues A, Valverde S, Bernal J, Freixenet J, Oliver A, Lladó X. Acute and sub-acute stroke lesion segmentation from multimodal MRI. Comput Methods Programs Biomed. 2020;194: 105521.
Kumar A, Upadhyay N, Ghosal P, Chowdhury T, Das D, Mukherjee A, et al. CSNet: a new DeepNet framework for ischemic stroke lesion segmentation. Comput Methods Programs Biomed. 2020;193: 105524.
Tomita N, Jiang S, Maeder ME, Hassanpour S. Automatic post-stroke lesion segmentation on MR images using 3D residual convolutional neural network. NeuroImage Clin. 2020;27: 102276.
PubMed PubMed Central Article Google Scholar
Henschel L, Conjeti S, Estrada S, Diers K, Fischl B, Reuter M. FastSurfer - a fast and accurate deep learning based neuroimaging pipeline. Neuroimage. 2020;219: 117012.
Thyreau B, Taki Y. Learning a cortical parcellation of the brain robust to the MRI segmentation with convolutional neural networks. Med Image Anal. 2020;61: 101639.
Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. 2015 [cited 2018 Feb 5]; Available from: https://arxiv.org/abs/1505.04597
Fischl B, van der Kouwe A, Destrieux C, Halgren E, Ségonne F, Salat DH, et al. Automatically parcellating the human cerebral cortex. Cereb Cortex. 2004;14:11–22.
Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33:341–55.
CAS PubMed Article Google Scholar
Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31:968–80.
Jagust WJ, Bandy D, Chen K, Foster NL, Landau SM, Mathis CA, et al. The ADNI PET core. Alzheimers Dement J Alzheimers Assoc. 2010;6:221–9.
Matsubara K, Ibaraki M, Shidahara M, Kinoshita T, for the Alzheimer’s Disease Neuroimaging Initiative. Iterative framework for image registration and partial volume correction in brain positron emission tomography. Radiol Phys Technol. 2020;13:349–57.
Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. ArXiv150203167 Cs [Internet]. 2015 [cited 2017 Jul 20]; Available from: http://arxiv.org/abs/1502.03167
Nair V, Hinton GE. Rectified li
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