x
Volume 42 Issue 1
Apr.  2020
Turn off MathJax
Article Contents
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
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

Implementation of artificial intelligence in functional and molecular imaging

doi: 10.12122/j.issn.1674-4500.2020.01.01
  • Received Date: 2019-12-06
  • Publish Date: 2020-01-01
  • Functional and molecular imaging can visualize and quantitatively measure not only the change of tissue and organ but also cellular and molecular processes in vivo. As new emerging computer technology, artificial intelligence(AI) is widely applied in the area of medical imaging. The implementation of AI on functional and molecular imaging enables the radiologists to make more efficient and full use of the information and explore the biological nature of images, which have a profound impact on the early detection, effective treatment, prognostic prediction, and pathogenesis exploration of disease. This review briefly summarized the implementation and progress of AI on functional and molecular imaging in image processing, image interpretation, and quality control.

     

  • loading
  • [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.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (1730) PDF downloads(99) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return