[논문]qMTNet: Accelerated quantitative magnetization transfer imaging with artificial neural networks

Luu, Huan Minh; Kim, Dong‐; Hyun; Kim, Jae‐; Woong; Choi, Seung‐; Hong; Park, Sung‐; Hong

doi:10.1002/mrm.28411

[해외논문] qMTNet: Accelerated quantitative magnetization transfer imaging with artificial neural networks

Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine, v.85 no.1, 2021년, pp.298 - 308

Luu, Huan Minh (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea) , Kim, Dong‐Hyun (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea) , Kim, Jae‐Woong (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea) , Choi, Seung‐Hong (Department of Radiology, Seoul National University College of Medicine, Seoul, Korea) , Park, Sung‐Hong (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea)

Abstract ▼ AI-Helper

PurposeTo develop a set of artificial neural networks, collectively termed qMTNet, to accelerate data acquisition and fitting for quantitative magnetization transfer (qMT) imaging.MethodsConventional and interslice qMT data were acquired with two flip angles at six offset frequencies from seven subjects for developing the networks and from four young and four older subjects for testing the generalizability. Two subnetworks, qMTNet‐acq and qMTNet‐fit, were developed and trained to accelerate data acquisition and fitting, respectively. qMTNet‐2 is the sequential application of qMTNet‐acq and qMTNet‐fit to produce qMT parameters (exchange rate, pool fraction) from undersampled qMT data (two offset frequencies rather than six). qMTNet‐1 is one single integrated network having the same functionality as qMTNet‐2. qMTNet‐fit was compared with a Gaussian kernel‐based fitting. qMT parameters generated by the networks were compared with those from ground truth fitted with a dictionary‐driven approach.ResultsThe proposed networks achieved high peak signal‐to‐noise ratio (>30) and structural similarity index (>97) in reference to the ground truth. qMTNet‐fit produced qMT parameters in concordance with the ground truth with better performance than the Gaussian kernel‐based fitting. qMTNet‐2 and qMTNet‐1 could accelerate data acquisition at threefold and accelerate fitting at 5800‐ and 4218‐fold, respectively. qMTNet‐1 showed slightly better performance than qMTNet‐2, whereas qMTNet‐2 was more flexible for applications.ConclusionThe proposed single (qMTNet‐1) and two joint neural networks (qMTNet‐2) can accelerate qMT workflow for both data acquisition and fitting significantly. qMTNet has the potential to accelerate qMT imaging for clinical applications, which warrants further investigation.

Keyword

참고문헌 (47)

Wolff SD , Balaban RS . Magnetization transfer contrast (MTC) and tissue water proton relaxation in vivo . Magn Reson Med . 1989 ; 10 : 135 ‐ 144 .

상세보기
Wolff SD , Chesnick S , Frank JA , Lim KO , Balaban RS . Magnetization transfer contrast: MR imaging of the knee . Radiology . 1991 ; 179 : 623 ‐ 628 .

상세보기
Balaban RS , Ceckler TL . Magnetization transfer contrast in magnetic resonance imaging . Magn Reson Q . 1992 ; 8 : 116 ‐ 137 .
Flamig DP , Pierce WB , Harms SE , Griffey RH . Magnetization transfer contrast in fat‐suppressed steady‐state three‐dimensional MR images . Magn Reson Med . 1992 ; 26 : 122 ‐ 131 .
Henkelman RM , Huang X , Xiang QS , Stanisz GJ , Swanson SD , Bronskill MJ . Quantitative interpretation of magnetization transfer . Magn Reson Med . 1993 ; 29 : 759 ‐ 766 .

상세보기
Harrison NA , Cooper E , Dowell NG , et al. Quantitative magnetization transfer imaging as a biomarker for effects of systemic inflammation on the brain . Biol Psychiatry . 2015 ; 78 : 49 ‐ 57 .

상세보기
Levesque IR , Giacomini PS , Narayanan S , et al. Quantitative magnetization transfer and myelin water imaging of the evolution of acute multiple sclerosis lesions . Magn Reson Med . 2010 ; 63 : 633 ‐ 640 .

상세보기
Mehrabian H , Myrehaug S , Soliman H , Sahgal A , Stanisz GJ . Quantitative magnetization transfer in monitoring glioblastoma (GBM) response to therapy . Sci Rep . 2018 ; 8 : 2475 .

상세보기
Cabana J‐F , Gu Y , Boudreau M , et al. Quantitative magnetization transfer imaging made easy with qMTLab: Software for data simulation, analysis, and visualization . Concepts in Magnetic Resonance Part A . 2015 ; 44A : 263 ‐ 277 .
McLean MA . Accelerated Quantitative Magnetization Transfer (qMT) Imaging (Unpublished master's thesis) . University of Calgary . Calgary, AB ; 2018 : 94 .
Gochberg DF , Gore JC . Quantitative imaging of magnetization transfer using an inversion recovery sequence . Magn Reson Med . 2003 ; 49 : 501 ‐ 505 .

상세보기
Dortch RD , Li K , Gochberg DF , et al. Quantitative magnetization transfer imaging in human brain at 3 T via selective inversion recovery . Magn Reson Med . 2011 ; 66 : 1346 ‐ 1352 .

상세보기
Dortch RD , Moore J , Li K , et al. Quantitative magnetization transfer imaging of human brain at 7T . NeuroImage . 2013 ; 64 : 640 – 649 .
Kim JW , Lee SL , Choi SH , Park SH . Rapid framework for quantitative magnetization transfer imaging with interslice magnetization transfer and dictionary‐driven fitting approaches . Magn Reson Med . 2019 ; 82 : 1671 ‐ 1683 .

상세보기
Dixon WT , Engels H , Castillo M , Sardashti M . Incidental magnetization transfer contrast in standard multislice imaging . Magn Reson Imaging . 1990 ; 8 : 417 ‐ 422 .
Yao L , Gentili A , Thomas A . Incidental magnetization transfer contrast in fast spin‐echo imaging of cartilage . J Magn Reson Imaging . 1996 ; 6 : 180 ‐ 184 .

상세보기
Chang Y , Bae SJ , Lee YJ , et al. Incidental magnetization transfer effects in multislice brain MRI at 3.0T . J Magn Reson Imaging . 2007 ; 25 : 862 ‐ 865 .

상세보기
Shin W , Gu H , Yang Y . Incidental magnetization transfer contrast by fat saturation preparation pulses in multislice Look‐Locker echo planar imaging . Magn Reson Med . 2009 ; 62 : 520 ‐ 526 .

상세보기
Barker JW , Han PK , Choi SH , Bae KT , Park SH . Investigation of inter‐slice magnetization transfer effects as a new method for MTR imaging of the human brain . PLoS ONE . 2015 ; 10 : e0117101 .

상세보기
Park SH , Duong TQ . Alternate ascending/descending directional navigation approach for imaging magnetization transfer asymmetry . Magn Reson Med . 2011 ; 65 : 1702 ‐ 1710 .

상세보기
Park SH , Duong TQ . Brain MR perfusion‐weighted imaging with alternate ascending/descending directional navigation . Magn Reson Med . 2011 ; 65 : 1578 ‐ 1591 .

상세보기
Kim KH , Choi SH , Park SH . Feasibility of quantifying arterial cerebral blood volume using multiphase alternate ascending/descending directional navigation (ALADDIN) . PLoS ONE . 2016 ; 11 : e0156687 .

상세보기
Park H , Lee J , Park SH , Choi SH . Evaluation of tumor blood flow using alternate ascending/descending directional navigation in primary brain tumors: A comparison study with dynamic susceptibility contrast magnetic resonance imaging . Korean J Radiol . 2019 ; 20 : 275 ‐ 282 .

상세보기
Ma D , Gulani V , Seiberlich N , et al. Magnetic resonance fingerprinting . Nature . 2013 ; 495 : 187 ‐ 192 .

상세보기
Kim KH , Park SH . Artificial neural network for suppression of banding artifacts in balanced steady‐state free precession MRI . Magn Reson Imaging . 2017 ; 37 : 139 – 146 .
Hammernik K , Klatzer T , Kobler E , et al. Learning a variational network for reconstruction of accelerated MRI data . Magn Reson Med . 2018 ; 79 : 3055 ‐ 3071 .

상세보기
Kim KH , Choi SH , Park S‐H . Improving arterial spin labeling by using deep learning . Radiology . 2018 ; 287 : 658 ‐ 666 .

상세보기
Kim KH , Do WJ , Park SH . Improving resolution of MR images with an adversarial network incorporating images with different contrast . Med Phys . 2018 ; 45 : 3120 ‐ 3131 .

상세보기
Han YS , Sunwoo L , Ye JC . k‐Space deep learning for accelerated MRI . IEEE Trans Med Imaging . 2020 ; 39 ( 2 ): 377 – 386 .
Seo S , Do W‐J , Luu HM , Kim KH , Choi SH , Park S‐H . Artificial neural network for slice encoding for metal artifact correction (SEMAC) MRI . Magn Reson Med . 2020 ; 84 : 263 – 276 .

상세보기
Cercignani M , Alexander DC . Optimal acquisition schemes for in vivo quantitative magnetization transfer MRI . Magn Reson Med . 2006 ; 56 : 803 ‐ 810 .

상세보기
Abadi AM , Barham P , Chen J , et al. TensorFlow: A System for Large‐Scale Machine Learning . Savannah, GA, USA : USENIX Association ; 2016 : 265 ‐ 283 .
Nair V , Hinton GE . Rectified linear units improve restricted boltzmann machines . In: Proceedings of the 27th International Conference on International Conference on Machine Learning. Haifa, Israel : Omnipress ; 2010 . p 807 ‐ 814 .
Huang G , Liu Z , van der Maaten L , Weinberger KQ . Densely Connected Convolutional Networks . CVPR : IEEE Computer Society ; 2017 : 2261 – 2269 .
Hornik K , Stinchcombe M , White H . Multilayer feedforward networks are universal approximators . Neural Netw . 1989 ; 2 : 359 ‐ 366 .

상세보기
Ioffe S , Szegedy C . Batch normalization: Accelerating deep network training by reducing internal covariate shift . Proceedings of the 32nd International Conference on International Conference on Machine Learning ‐ Volume 37. Lille, France : JMLR.org ; 2015 . p 448 ‐ 456 .
Srivastava N , Hinton GE , Krizhevsky A , Sutskever I , Salakhutdinov R . Dropout: A simple way to prevent neural networks from overfitting . J Mach Learn Res . 2014 ; 15 : 1929 ‐ 1958 .
Kingma D , Ba J . Adam: A method for stochastic optimization . In: 3rd International Conference on Learning Representations. San Diego, CA, USA . 2015 .
Rahimi A , Recht B . Random features for large‐scale kernel machines . In: Proceedings of the 20th International Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada : Curran Associates Inc. ; 2007 . p 1177 ‐ 1184 .
Wilcoxon F . Individual comparisons by ranking methods . Biometrics Bulletin . 1945 ; 1 : 80 ‐ 83 .

상세보기
Horsfield MA , Barker GJ , Barkhof F , Miller DH , Thompson AJ , Filippi M . Guidelines for using quantitative magnetization transfer magnetic resonance imaging for monitoring treatment of multiple sclerosis . J Magn Reson Imaging . 2003 ; 17 : 389 ‐ 397 .

상세보기
Cohen O , Zhu B , Rosen MS . MR fingerprinting Deep RecOnstruction NEtwork (DRONE) . Magn Reson Med . 2018 ; 80 : 885 ‐ 894 .

상세보기
Yoon J , Gong E , Chatnuntawech I , et al. Quantitative susceptibility mapping using deep neural network: QSMnet . NeuroImage . 2018 ; 179 : 199 ‐ 206 .
Lee J , Lee D , Choi JY , Shin D , Shin HG , Lee J . Artificial neural network for myelin water imaging . Magn Reson Med . 2020 ; 83 : 1875 – 1883 .

상세보기
Callaghan MF , Freund P , Draganski B , et al. Widespread age‐related differences in the human brain microstructure revealed by quantitative magnetic resonance imaging . Neurobiol Aging . 2014 ; 35 : 1862 ‐ 1872 .

상세보기
Turati L , Moscatelli M , Mastropietro A , et al. In vivo quantitative magnetization transfer imaging correlates with histology during de‐ and remyelination in cuprizone‐treated mice . NMR Biomed . 2015 ; 28 : 327 ‐ 337 .

상세보기
Caruana R . Multitask learning . Mach Learn . 1997 ; 28 : 41 – 75 .

LOADING...

활용도 분석정보

상세보기

다운로드

내보내기

활용도 Top5 논문

해당 논문의 주제분야에서 활용도가 높은 상위 5개 콘텐츠를 보여줍니다.
더보기 버튼을 클릭하시면 더 많은 관련자료를 살펴볼 수 있습니다.

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증