Citation: | Bowen ZHENG, Weiguo CHEN, Genggeng QIN. Implementation of artificial intelligence in functional and molecular imaging[J]. Journal of Molecular Imaging, 2020, 43(1): 1-6. doi: 10.12122/j.issn.1674-4500.2020.01.01 |
[1] |
Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets[J]. Neural Comput, 2006, 18(7): 1527-54. doi: 10.1162/neco.2006.18.7.1527
|
[2] |
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-6. doi: 10.1016/j.ejca.2011.11.036
|
[3] |
Cui J, Gong K, Guo N, et al. PET image denoising using unsupervised deep learning[J]. Eur J Nucl Med Mol Imaging, 2019, 46(13): 2780-9. doi: 10.1007/s00259-019-04468-4
|
[4] |
Shiri I, Ghafarian P, Geramifar P, et al. Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC)[J]. Eur Radiol, 2019, 29(12): 6867-79. doi: 10.1007/s00330-019-06229-1
|
[5] |
Arabi H, Zeng G, Zheng G, et al. Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI[J]. Eur J Nucl Med Mol Imaging, 2019, 46(13): 2746-59. doi: 10.1007/s00259-019-04380-x
|
[6] |
Tao Q, Yan W, Wang Y, et al. Deep learning-based method for fully automatic quantification of left ventricle function from Cine Mr images: a multivendor, multicenter study[J]. Radiology, 2019, 290(1): 81-8. doi: 10.1148/radiol.2018180513
|
[7] |
Yu M, Lu Z, Shen C, et al. The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning-based FFRCT, or high-risk plaque features?[J]. Eur Radiol, 2019, 29(7): 3647-57. doi: 10.1007/s00330-019-06139-2
|
[8] |
Li Y, Yu M, Dai X, et al. Detection of hemodynamically significant coronary stenosis: CT myocardial perfusion versus machine learning CT fractional flow reserve[J]. Radiology, 2019, 293(2): 305-14. doi: 10.1148/radiol.2019190098
|
[9] |
Tesche C, Otani K, De Cecco CN, et al. Influence of coronary Calcium on diagnostic performance of machine learning CT-FFR: results from MACHINE registry[J]. JACC Cardiovasc Imaging, 2019, 20(8): 30635-7.
|
[10] |
van Hamersvelt RW, Zreik M, Voskuil M, et al. Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis[J]. Eur Radiol, 2019, 29(5): 2350-9. doi: 10.1007/s00330-018-5822-3
|
[11] |
Meier R, Lux P, Med B, et al. Neural network-derived perfusion Maps for the assessment of lesions in patients with acute ischemic stroke[J]. Radiol Artif Intell, 2019, 1(5): e190019. doi: 10.1148/ryai.2019190019
|
[12] |
Park YW, Oh J, You SC, et al. Radiomics and machine learning May accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging[J]. Eur Radiol, 2019, 29(8): 4068-76. doi: 10.1007/s00330-018-5830-3
|
[13] |
Zhang N, Yang G, Gao Z, et al. Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac Cine MRI[J]. Radiology, 2019, 291(3): 606-17. doi: 10.1148/radiol.2019182304
|
[14] |
Bonekamp D, Kohl S, Wiesenfarth M, et al. Radiomic machine learning for characterization of prostate lesions with MRI: comparison to ADC values[J]. Radiology, 2018, 289(1): 128-37. doi: 10.1148/radiol.2018173064
|
[15] |
Aldoj N, Lukas S, Dewey M, et al. Semi-automatic classification of prostate Cancer on multi-parametric Mr imaging using a multi-channel 3D convolutional neural network[J]. Eur Radiol, 2019, 29(8): s00330.
|
[16] |
Antonelli M, Johnston EW, Dikaios NA, et al. Machine learning classifiers can predict Gleason pattern 4 prostate Cancer with greater accuracy than experienced radiologists[J]. Eur Radiol, 2019, 29(9): 4754-64. doi: 10.1007/s00330-019-06244-2
|
[17] |
Zhang S, Song G, Zang Y, et al. Non-invasive radiomics approach potentially predicts non-functioning pituitary adenomas subtypes before surgery[J]. Eur Radiol, 2018, 28(9): 3692-701. doi: 10.1007/s00330-017-5180-6
|
[18] |
Oliveira FM, Faria DB, Costa DC, et al. Extraction, selection and comparison of features for an effective automated computer-aided diagnosis of Parkinson's disease based on[(123)I]FP-CIT SPECT images[J]. Eur J Nucl Med Mol Imaging, 2018, 45(6): 1052-62. doi: 10.1007/s00259-017-3918-7
|
[19] |
Booij J, Dubroff J, Pryma D, et al. Diagnostic performance of the visual reading of(123)I-Ioflupane SPECT images with or without quantification in patients with movement disorders or dementia[J]. J Nucl Med, 2017, 58(11): 1821-6. doi: 10.2967/jnumed.116.189266
|
[20] |
Albert NL, Unterrainer M, Diemling MA, et al. Implementation of the European multicentre database of healthy controls for {[}I-123] FP-CIT SPECT increases diagnostic accuracy in patients with clinically uncertain parkinsonian syndromes[J]. Eur J Nucl Med Mol Imaging, 2016, 43(7): 1315-22. doi: 10.1007/s00259-015-3304-2
|
[21] |
Wenzel M, Milletari F, Krüger J, et al. Automatic classification of dopamine transporter SPECT: deep convolutional neural networks can be trained to be robust with respect to variable image characteristics[J]. Eur J Nucl Med Mol Imaging, 2019, 46(13): 2800-11. doi: 10.1007/s00259-019-04502-5
|
[22] |
Choi H, Ha S, Im HJ, et al. Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging[J]. Neuroimage Clin, 2017, 16(9): 586-94.
|
[23] |
Betancur J, Commandeur F, Motlagh M, et al. Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT: A multicenter study[J]. JACC Cardiovasc Imaging, 2018, 11(11): 1654-63. doi: 10.1016/j.jcmg.2018.01.020
|
[24] |
Xu L, Tetteh G, Lipkova J, et al. Automated Whole-Body bone lesion detection for multiple myeloma on 68 Ga-Pentixafor PET/CT imaging using deep learning methods[J]. Contrast Media Mol Imaging, 2018, 18(1): 1-11.
|
[25] |
Zhang J, Zhao X, Zhao Y, et al. Value of pre-therapy 18F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung Cancer[J]. Eur J Nucl Med Mol Imaging, 2019, 18(11): s00259.
|
[26] |
Vonk-Noordegraaf A, Haddad F, Chin KM, et al. Right heart adaptation to pulmonary arterial hypertension: physiology and pathobiology[J]. J Am Coll Cardiol, 2013, 62(25 Suppl): D22-33.
|
[27] |
Dawes TJ, de Marvao A, Shi WA, et al. Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac Mr imaging study[J]. Radiology, 2017, 283(2): 381-90. doi: 10.1148/radiol.2016161315
|
[28] |
Betancur J, Otaki Y, Motwani M, et al. Prognostic value of combined clinical and myocardial perfusion imaging data using machine learning[J]. JACC Cardiovasc Imaging, 2018, 11(7): 1000-9. doi: 10.1016/j.jcmg.2017.07.024
|
[29] |
Foley KG, Hills RK, Berthon BA, et al. Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal Cancer[J]. Eur Radiol, 2018, 28(1): 428-36. doi: 10.1007/s00330-017-4973-y
|
[30] |
Ruijsink B, Puyol AE, Oksuz I, et al. Fully automated, Quality-Controlled cardiac analysis from CMR: validation and Large-Scale application to characterize cardiac function[J]. JACC Cardiovasc Imaging, 2019, 10(7): 30585-6.
|