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      KCI등재후보

      Artificial Intelligence in Neuroimaging: Clinical Applications

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      https://www.riss.kr/link?id=A108072198

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      다국어 초록 (Multilingual 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 neuro...

      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.

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      참고문헌 (Reference)

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      1 Han Y, "k-space deep learning for accelerated MRI" 39 : 377-386, 2020

      2 Knoll F, "fastMRI : a publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning" 2 : e190007-, 2020

      3 Andrews DW, "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" 363 : 1665-1672, 2004

      4 Balakrishnan G, "VoxelMorph : a learning framework for deformable medical image registration" 38 : 1788-1800, 2019

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      6 Wen PY, "Updated response assessment criteria for high-grade gliomas : response assessment in neuro-oncology working group" 28 : 1963-1972, 2010

      7 Chung H, "Two-stage deep learning for accelerated 3D time-of-flight MRA without matched training data" 71 : 102047-, 2021

      8 Langa KM, "The diagnosis and management of mild cognitive impairment : a clinical review" 312 : 2551-2561, 2014

      9 Louis DN, "The 2021 WHO classification of tumors of the central nervous system : a summary" 23 : 1231-1251, 2021

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      23 Park JS, "Modelbased high-definition dynamic contrast enhanced MRI for concurrent estimation of perfusion and microvascular permeability" 59 : 101566-, 2020

      24 Park SH, "Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction" 286 : 800-809, 2018

      25 Kim T, "Machine learning for detecting moyamoya disease in plain skull radiography using a convolutional neural network" 40 : 636-642, 2019

      26 Lei Y, "MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks" 46 : 3565-3581, 2019

      27 Bashyam VM, "MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide" 143 : 2312-2324, 2020

      28 Kim JY, "Incorporating diffusionand perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients" 21 : 404-414, 2019

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      30 Choi KS, "Improving the reliability of pharmacokinetic parameters at dynamic contrast-enhanced MRI in astrocytomas : a deep learning approach" 297 : 178-188, 2020

      31 Mateen BA, "Improving the quality of machine learning in health applications and clinical research" 2 : 554-556, 2020

      32 Brady SL, "Improving image quality and reducing radiation dose for pediatric CT by using deep learning reconstruction" 298 : 180-188, 2021

      33 Kim KH, "Improving arterial spin labeling by using deep learning" 287 : 658-666, 2018

      34 Cole EB, "Impact of computer-aided detection systems on radiologist accuracy with digital mammography" 203 : 909-916, 2014

      35 Wang G, "Image reconstruction is a new frontier of machine learning" 37 : 1289-1296, 2018

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      40 Dashtbani Moghari M, "Efficient radiation dose reduction in whole-brain CT perfusion imaging using a 3D GAN : performance and clinical feasibility" 66 : 2021

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      42 Sounderajah V, "Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions : the STARD-AI Steering Group" 26 : 807-808, 2020

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      45 Cho J, "Deep learning-based computer-aided detection system for automated treatment response assessment of brain metastases on 3D MRI" 11 : 739639-, 2021

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      47 Jo T, "Deep learning in Alzheimer's disease : diagnostic classification and prognostic prediction using neuroimaging data" 11 : 220-, 2019

      48 Jeon Y, "Deep learning for diagnosis of paranasal sinusitis using multi-view radiographs" 11 : 2021

      49 Lin L, "Deep learning for automated contouring of primary tumor volumes by MRI for nasopharyngeal carcinoma" 291 : 677-686, 2019

      50 Grovik E, "Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI" 51 : 175-182, 2020

      51 Chilamkurthy S, "Deep learning algorithms for detection of critical findings in head CT scans : a retrospective study" 392 : 2388-2396, 2018

      52 Liu F, "Deep learning MR imaging-based attenuation correction for PET/MR imaging" 286 : 676-684, 2018

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      54 Jin CB, "Deep CT to MR synthesis using paired and unpaired data" 19 : 2361-, 2019

      55 Aerts HJ, "Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach" 5 : 4006-, 2014

      56 Kim B, "CycleMorph : cycle consistent unsupervised deformable image registration" 71 : 102036-, 2021

      57 Kang E, "Cycle-consistent adversarial denoising network for multiphase coronary CT angiography" 46 : 550-562, 2019

      58 Zhou Z, "Computer-aided detection of brain metastases in T1-weighted MRI for stereotactic radiosurgery using deep learning single-shot detectors" 295 : 407-415, 2020

      59 Lee D, "CollaGAN: collaborative GAN for missing image data imputation" 2487-2496, 2019

      60 Kim HY, "Classification of true progression after radiotherapy of brain metastasis on MRI using artificial intelligence : a systematic review and metaanalysis" 3 : vdab080-, 2021

      61 Kim HE, "Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study" 2 : e138-e148, 2020

      62 Cho SJ, "Brain metastasis detection using machine learning : a systematic review and meta-analysis" 23 : 214-225, 2021

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      64 Titano JJ, "Automated deepneural-network surveillance of cranial images for acute neurologic events" 24 : 1337-1341, 2018

      65 Rauschecker AM, "Artificial intelligence system approaching neuroradiologist-level differential diagnosis accuracy at brain MRI" 295 : 626-637, 2020

      66 Lee H, "An explainable deeplearning algorithm for the detection of acute intracranial haemorrhage from small datasets" 3 : 173-182, 2019

      67 Grigorescu S, "A survey of deep learning techniques for autonomous driving" 37 : 362-386, 2020

      68 Avants BB, "A reproducible evaluation of ANTs similarity metric performance in brain image registration" 54 : 2033-2044, 2011

      69 Chang PD, "A multiparametric model for mapping cellularity in glioblastoma using radiographically localized biopsies" 38 : 890-898, 2017

      70 Ho KC, "A machine learning approach for classifying ischemic stroke onset time from imaging" 38 : 1666-1676, 2019

      71 Lao J, "A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme" 7 : 10353-, 2017

      72 Liu F, "A deep learning approach for 18F-FDG PET attenuation correction" 5 : 24-, 2018

      73 Arpit D, "A closer look at memorization in deep networks" 233-242, 2017

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      공동연구자 (7)

      유사연구자 (20) 활용도상위20명

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가예정 계속평가 신청대상 (계속평가)
      2021-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
      2020-12-01 평가 등재후보 탈락 (계속평가)
      2019-12-01 평가 등재후보로 하락 (계속평가) KCI등재후보
      2018-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2015-03-31 학술지명변경 한글명 : 대한자기공명의과학회지 -> Investigative Magnetic Resonance Imaging
      외국어명 : Journal of the Korean Society of Magnetic Resonance in Medicine -> Investigative Magnetic Resonance Imaging
      KCI등재
      2015-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2011-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2010-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2009-01-01 평가 등재후보학술지 유지 (등재후보1차) KCI등재후보
      2008-01-01 평가 등재후보학술지 유지 (등재후보1차) KCI등재후보
      2007-01-01 평가 등재후보학술지 유지 (등재후보2차) KCI등재후보
      2006-06-23 학술지명변경 외국어명 : Journal of Korean Society of Magnetic Resonancein Medicine -> Journal of the Korean Society of Magnetic Resonance in Medicine KCI등재후보
      2006-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2004-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.03 0.03 0.02
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.03 0.03 0.178 0.03
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