Browse > Article
http://dx.doi.org/10.13104/imri.2022.26.1.1

Artificial Intelligence in Neuroimaging: Clinical Applications  

Choi, Kyu Sung (Department of Radiology, Seoul National University Hospital)
Sunwoo, Leonard (Department of Radiology, Seoul National University Bundang Hospital)
Publication Information
Investigative Magnetic Resonance Imaging / v.26, no.1, 2022 , pp. 1-9 More about this Journal
Abstract
Artificial intelligence (AI) powered by deep learning (DL) has shown remarkable progress in image recognition tasks. Over the past decade, AI has proven its feasibility for applications in medical imaging. Various aspects of clinical practice in neuroimaging can be improved with the help of AI. For example, AI can aid in detecting brain metastases, predicting treatment response of brain tumors, generating a parametric map of dynamic contrast-enhanced MRI, and enhancing radiomics research by extracting salient features from input images. In addition, image quality can be improved via AI-based image reconstruction or motion artifact reduction. In this review, we summarize recent clinical applications of DL in various aspects of neuroimaging.
Keywords
Artificial intelligence; Deep learning; Radiomics; Neuroimaging; Clinical application;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Andrews DW, Scott CB, Sperduto PW, et al. Whole brain radiation therapy with or without stereotactic radiosurgery boost for patients with one to three brain metastases: phase III results of the RTOG 9508 randomised trial. Lancet 2004;363:1665-1672   DOI
2 Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector. In European conference on computer vision: Springer, 2016:21-37
3 Grovik E, Yi D, Iv M, Tong E, Rubin D, Zaharchuk G. Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI. J Magn Reson Imaging 2020;51:175-182   DOI
4 Cho SJ, Sunwoo L, Baik SH, Bae YJ, Choi BS, Kim JH. Brain metastasis detection using machine learning: a systematic review and meta-analysis. Neuro Oncol 2021;23:214-225   DOI
5 Zhou Z, Sanders JW, Johnson JM, et al. Computer-aided detection of brain metastases in T1-weighted MRI for stereotactic radiosurgery using deep learning single-shot detectors. Radiology 2020;295:407-415   DOI
6 Bae S, Choi YS, Ahn SS, et al. Radiomic MRI phenotyping of glioblastoma: improving survival prediction. Radiology 2018;289:797-806   DOI
7 Tamada D, Kromrey ML, Ichikawa S, Onishi H, Motosugi U. Motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MR imaging of the liver. Magn Reson Med Sci 2020;19:64-76   DOI
8 Kim Y, Lee KJ, Sunwoo L, et al. Deep learning in diagnosis of maxillary sinusitis using conventional radiography. Invest Radiol 2019;54:7-15   DOI
9 Brady SL, Trout AT, Somasundaram E, Anton CG, Li Y, Dillman JR. Improving image quality and reducing radiation dose for pediatric CT by using deep learning reconstruction. Radiology 2021;298:180-188   DOI
10 Bashyam VM, Erus G, Doshi J, et al. MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide. Brain 2020;143:2312-2324   DOI
11 Jeon Y, Lee K, Sunwoo L, et al. Deep learning for diagnosis of paranasal sinusitis using multi-view radiographs. Diagnostics (Basel) 2021;11
12 Kim T, Heo J, Jang DK, et al. Machine learning for detecting moyamoya disease in plain skull radiography using a convolutional neural network. EBioMedicine 2019;40:636-642   DOI
13 Yu Y, Xie Y, Thamm T, et al. Use of deep learning to predict final ischemic stroke lesions from initial magnetic resonance imaging. JAMA Netw Open 2020;3:e200772   DOI
14 Langa KM, Levine DA. The diagnosis and management of mild cognitive impairment: a clinical review. JAMA 2014;312:2551-2561   DOI
15 Shim KY, Chung SW, Jeong JH, et al. Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI. Sci Rep 2021;11:9974   DOI
16 Grigorescu S, Trasnea B, Cocias T, Macesanu G. A survey of deep learning techniques for autonomous driving. J Field Robot 2020;37:362-386   DOI
17 Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115-118   DOI
18 Kim HE, Kim HH, Han BK, et al. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. Lancet Digit Health 2020;2:e138-e148   DOI
19 Mazurowski MA. Radiogenomics: what it is and why it is important. J Am Coll Radiol 2015;12:862-866   DOI
20 Choi YS, Bae S, Chang JH, et al. Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics. Neuro Oncol 2021;23:304-313   DOI
21 Park JE, Park SY, Kim HJ, Kim HS. Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives. Korean J Radiol 2019;20:1124-1137   DOI
22 Chang PD, Malone HR, Bowden SG, et al. A multiparametric model for mapping cellularity in glioblastoma using radiographically localized biopsies. AJNR Am J Neuroradiol 2017;38:890-898   DOI
23 Choi KS, Choi SH, Jeong B. Prediction of IDH genotype in gliomas with dynamic susceptibility contrast perfusion MR imaging using an explainable recurrent neural network. Neuro Oncol 2019;21:1197-1209   DOI
24 Patel RR, Mehta MP. Targeted therapy for brain metastases: improving the therapeutic ratio. Clin Cancer Res 2007;13:1675-1683   DOI
25 Bae S, An C, Ahn SS, et al. Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features: model development and validation. Sci Rep 2020;10:12110   DOI
26 Knoll F, Zbontar J, Sriram A, et al. fastMRI: a publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning. Radiol Artif Intell 2020;2:e190007   DOI
27 Rauschecker AM, Rudie JD, Xie L, et al. Artificial intelligence system approaching neuroradiologist-level differential diagnosis accuracy at brain MRI. Radiology 2020;295:626-637   DOI
28 Lin L, Dou Q, Jin YM, et al. Deep learning for automated contouring of primary tumor volumes by MRI for nasopharyngeal carcinoma. Radiology 2019;291:677-686   DOI
29 Kim HY, Cho SJ, Sunwoo L, et al. Classification of true progression after radiotherapy of brain metastasis on MRI using artificial intelligence: a systematic review and meta-analysis. Neurooncol Adv 2021;3:vdab080
30 Szegedy C, Ioffe S, Vanhoucke V, Alemi AA. Inception-v4, inception-ResNet and the impact of residual connections on learning. In:Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. San Francisco, California, USA: AAAI Press, 2017: 4278-4284
31 Lee KJ, Ryoo I, Choi D, Sunwoo L, You SH, Jung HN. Performance of deep learning to detect mastoiditis using multiple conventional radiographs of mastoid. PLoS One 2020;15:e0241796   DOI
32 Ho KC, Speier W, Zhang H, Scalzo F, El-Saden S, Arnold CW. A machine learning approach for classifying ischemic stroke onset time from imaging. IEEE Trans Med Imaging 2019;38:1666-1676   DOI
33 Dashtbani Moghari M, Zhou L, Yu B, et al. Efficient radiation dose reduction in whole-brain CT perfusion imaging using a 3D GAN: performance and clinical feasibility. Phys Med Biol 2021;66
34 Lao J, Chen Y, Li ZC, et al. A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Sci Rep 2017;7:10353   DOI
35 Han Y, Sunwoo L, Ye JC. k-space deep learning for accelerated MRI. IEEE Trans Med Imaging 2020;39:377-386   DOI
36 Chung H, Cha E, Sunwoo L, Ye JC. Two-stage deep learning for accelerated 3D time-of-flight MRA without matched training data. Med Image Anal 2021;71:102047   DOI
37 Lee D, Kim J, Moon W-J, Ye JC. CollaGAN: collaborative GAN for missing image data imputation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019:2487-2496
38 Choi KS, You SH, Han Y, Ye JC, Jeong B, Choi SH. Improving the reliability of pharmacokinetic parameters at dynamic contrast-enhanced MRI in astrocytomas: a deep learning approach. Radiology 2020;297:178-188   DOI
39 Balakrishnan G, Zhao A, Sabuncu MR, Guttag J, Dalca AV. VoxelMorph: a learning framework for deformable medical image registration. IEEE Transactions on Medical Imaging 2019;38:1788-1800   DOI
40 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;54:2033-2044   DOI
41 Lee D, Lee J, Ko J, Yoon J, Ryu K, Nam Y. Deep learning in MR image processing. Investig Magn Reson Imaging 2019;23:81-99   DOI
42 Louis DN, Perry A, Wesseling P, et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol 2021;23:1231-1251   DOI
43 Singh G, Manjila S, Sakla N, et al. Radiomics and radiogenomics in gliomas: a contemporary update. Br J Cancer 2021;125:641-657   DOI
44 Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014;5:4006   DOI
45 Kim JY, Park JE, Jo Y, et al. Incorporating diffusion-and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients. Neuro Oncol 2019;21:404-414   DOI
46 Wang G, Ye JC, Mueller K, Fessler JA. Image reconstruction is a new frontier of machine learning. IEEE Trans Med Imaging 2018;37:1289-1296   DOI
47 Kim B, Kim DH, Park SH, Kim J, Lee JG, Ye JC. CycleMorph: cycle consistent unsupervised deformable image registration. Med Image Anal 2021;71:102036   DOI
48 Park JS, Lim E, Choi SH, Sohn CH, Lee J, Park J. Model-based high-definition dynamic contrast enhanced MRI for concurrent estimation of perfusion and microvascular permeability. Med Image Anal 2020;59:101566   DOI
49 Lei Y, Harms J, Wang T, et al. MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks. Med Phys 2019;46:3565-3581   DOI
50 Ramalho J, Semelka RC, Ramalho M, Nunes RH, AlObaidy M, Castillo M. Gadolinium-based contrast agent accumulation and toxicity: an update. AJNR Am J Neuroradiol 2016;37:1192-1198   DOI
51 Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Netw 1989;2:359-366   DOI
52 Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016;316:2402-2410   DOI
53 Cole EB, Zhang Z, Marques HS, Edward Hendrick R, Yaffe MJ, Pisano ED. Impact of computer-aided detection systems on radiologist accuracy with digital mammography. AJR Am J Roentgenol 2014;203:909-916   DOI
54 Kim KH, Choi SH, Park SH. Improving arterial spin labeling by using deep learning. Radiology 2018;287:658-666   DOI
55 Kang E, Koo HJ, Yang DH, Seo JB, Ye JC. Cycle-consistent adversarial denoising network for multiphase coronary CT angiography. Med Phys 2019;46:550-562   DOI
56 Jin CB, Kim H, Liu M, et al. Deep CT to MR synthesis using paired and unpaired data. Sensors 2019;19:2361   DOI
57 Chilamkurthy S, Ghosh R, Tanamala S, et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet 2018;392:2388-2396   DOI
58 Jo T, Nho K, Saykin AJ. Deep learning in Alzheimer's disease: diagnostic classification and prognostic prediction using neuroimaging data. Front Aging Neurosci 2019;11:220   DOI
59 Yun J, Park JE, Lee H, Ham S, Kim N, Kim HS. Radiomic features and multilayer perceptron network classifier: a robust MRI classification strategy for distinguishing glioblastoma from primary central nervous system lymphoma. Sci Rep 2019;9:5746   DOI
60 Liu F, Jang H, Kijowski R, Zhao G, Bradshaw T, McMillan AB. A deep learning approach for 18F-FDG PET attenuation correction. EJNMMI Phys 2018;5:24   DOI
61 Wen PY, Macdonald DR, Reardon DA, et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 2010;28:1963-1972   DOI
62 Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology 2018;286:800-809   DOI
63 Mateen BA, Liley J, Denniston AK, Holmes CC, Vollmer SJ. Improving the quality of machine learning in health applications and clinical research. Nat Mach Intell 2020;2:554-556   DOI
64 Liu F, Jang H, Kijowski R, Bradshaw T, McMillan AB. Deep learning MR imaging-based attenuation correction for PET/MR imaging. Radiology 2018;286:676-684   DOI
65 Titano JJ, Badgeley M, Schefflein J, et al. Automated dee-pneural-network surveillance of cranial images for acute neurologic events. Nat Med 2018;24:1337-1341   DOI
66 Kuo W, Hne C, Mukherjee P, Malik J, Yuh EL. Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning. Proc Natl Acad Sci U S A 2019;116:22737-22745   DOI
67 Lee H, Yune S, Mansouri M, et al. An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nat Biomed Eng 2019;3:173-182   DOI
68 Kickingereder P, Isensee F, Tursunova I, et al. Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol 2019;20:728-740   DOI
69 Cho J, Kim YJ, Sunwoo L, et al. Deep learning-based computer-aided detection system for automated treatment response assessment of brain metastases on 3D MRI. Front Oncol 2021;11:739639   DOI
70 Kim DW, Jang HY, Kim KW, Shin Y, Park SH. Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: results from recently published papers. Korean J Radiol 2019;20:405-410   DOI
71 Harvey H, Oakden-Rayner L. Guidance for interventional trials involving artificial intelligence. Radiol Artif Intell 2020;2:e200228   DOI
72 Arpit D, Jastrzebski S, Ballas N, et al. A closer look at memorization in deep networks. In International Conference on Machine Learning: PMLR, 2017:233-242
73 Sounderajah V, Ashrafian H, Aggarwal R, et al. Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: the STARD-AI Steering Group. Nat Med 2020;26:807-808   DOI