[논문]Quantification of intravoxel incoherent motion with optimized b‐values using deep neural network

Lee, Wonil; Kim, Byungjai; Park, HyunWook

doi:10.1002/mrm.28708

[해외논문] Quantification of intravoxel incoherent motion with optimized b‐values using deep neural network

Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine, v.86 no.1, 2021년, pp.230 - 244

Lee, Wonil (Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea) , Kim, Byungjai (Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea) , Park, HyunWook (Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea)

Abstract ▼ AI-Helper

PurposeTo develop a framework for quantifying intravoxel incoherent motion (IVIM) parameters, where a neural network for quantification and b‐values for diffusion‐weighted imaging are simultaneously optimized.MethodA deep neural network (DNN) method is proposed for accurate quantification of IVIM parameters from multiple diffusion‐weighted images. In addition, optimal b‐values are selected to acquire the multiple diffusion‐weighted images. The proposed framework consists of an MRI signal generation part and an IVIM parameter quantification part. Monte‐Carlo (MC) simulations were performed to evaluate the accuracy of the IVIM parameter quantification and the efficacy of b‐value optimization. In order to analyze the effect of noise on the optimized b‐values, simulations were performed with five different noise levels. For in vivo data, diffusion images were acquired with the b‐values from four b‐values selection methods for five healthy volunteers at 3T MRI system.ResultsExperiment results showed that both the optimization of b‐values and the training of DNN were simultaneously performed to quantify IVIM parameters. We found that the accuracies of the perfusion coefficient (Dp) and perfusion fraction (f) were more sensitive to b‐values than the diffusion coefficient (D) was. Furthermore, when the noise level changed, the optimized b‐values also changed. Therefore, noise level has to be considered when optimizing b‐values for IVIM quantification.ConclusionThe proposed scheme can simultaneously optimize b‐values and train DNN to minimize quantification errors of IVIM parameters. The trained DNN can quantify IVIM parameters from the diffusion‐weighted images obtained with the optimized b‐values.

Keyword

참고문헌 (36)

Le Bihan D , Breton E , Lallemand D , Aubin M , Vignaud J , Laval‐Jeantet M Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging . Radiology . 1988 ; 168 : 497 ‐ 505 .
Federau C , O'Brien K , Meuli R , Hagmann P , Maeder P Measuring brain perfusion with intravoxel incoherent motion (IVIM): initial clinical experience . J Magn Reson Imaging . 2014 ; 39 : 624 ‐ 632 .
Ichikawa S , Motosugi U , Ichikawa T , Sano K , Morisaka H , Araki T Intravoxel incoherent motion imaging of the kidney: alterations in diffusion and perfusion in patients with renal dysfunction . Magn Reson Imaging . 2013 ; 31 : 414 ‐ 417 .
Le Bihan D What can we see with IVIM MRI? Neuroimage . 2019 ; 187 : 56 ‐ 67 .
Luciani A , Vignaud A , Cavet M , et al. Liver cirrhosis: intravoxel incoherent motion MR imaging—pilot study . Radiology . 2008 ; 249 : 891 ‐ 899 .
Padhani AR , Liu G , Mu‐Koh D , et al. Diffusion‐weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations . Neoplasia . 2009 ; 11 : 102 ‐ 125 .
Yamada I , Aung W , Himeno Y , Nakagawa T , Shibuya H Diffusion coefficients in abdominal organs and hepatic lesions: evaluation with intravoxel incoherent motion echo‐planar MR imaging . Radiology . 1999 ; 210 : 617 ‐ 623 .
Federau C , Cerny M , Roux M , et al. IVIM perfusion fraction is prognostic for survival in brain glioma . Clin Neuroradiol . 2017 ; 27 : 485 ‐ 492 .
Cho GY , Moy L , Kim SG , et al. Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors . Eur Radiol . 2016 ; 26 : 2547 ‐ 2558 .
Selby NM , Blankestijn PJ , Boor P , et al. Magnetic resonance imaging biomarkers for chronic kidney disease: a position paper from the European cooperation in science and technology action PARENCHIMA . Nephrol Dial Transplant . 2018 ; 33 (suppl_2): ii4 ‐ ii14 .
Li YT , Cercueil J‐P , Yuan J , Chen W , Loffroy R , Wáng YXJ Liver intravoxel incoherent motion (IVIM) magnetic resonance imaging: a comprehensive review of published data on normal values and applications for fibrosis and tumor evaluation . Quant Imaging Med Surg . 2017 ; 7 : 59 .
Lemke A , Stieltjes B , Schad LR , Laun FB Toward an optimal distribution of b values for intravoxel incoherent motion imaging . Magn Reson Imaging . 2011 ; 29 : 766 ‐ 776 .
Pekar J , Moonen CT , van Zijl PC On the precision of diffusion/perfusion imaging by gradient sensitization . Magn Reson Med . 1992 ; 23 : 122 ‐ 129 .
Neil JJ , Bretthorst GL On the use of bayesian probability theory for analysis of exponential decay date: an example taken from intravoxel incoherent motion experiments . Magn Reson Med . 1993 ; 29 : 642 ‐ 647 .
Orton MR , Collins DJ , Koh DM , Leach MO Improved intravoxel incoherent motion analysis of diffusion weighted imaging by data driven Bayesian modeling . Magn Reson Med . 2014 ; 71 : 411 ‐ 420 .
Veraart J , Rajan J , Peeters RR , Leemans A , Sunaert S , Sijbers J Comprehensive framework for accurate diffusion MRI parameter estimation . Magn Reson Med . 2013 ; 70 : 972 ‐ 984 .
Freiman M , Perez‐Rossello JM , Callahan MJ , et al. Reliable estimation of incoherent motion parametric maps from diffusion‐weighted MRI using fusion bootstrap moves . Med Image Anal . 2013 ; 17 : 325 ‐ 336 .
Cho GY , Moy L , Zhang JL , et al. Comparison of fitting methods and b‐value sampling strategies for intravoxel incoherent motion in breast cancer . Magn Reson Med . 2015 ; 74 : 1077 ‐ 1085 .
Jalnefjord O , Montelius M , Starck G , Ljungberg M Optimization of b‐value schemes for estimation of the diffusion coefficient and the perfusion fraction with segmented intravoxel incoherent motion model fitting . Magn Reson Med . 2019 ; 82 : 1541 ‐ 1552 .
Zhang JL , Sigmund EE , Rusinek H , et al. Optimization of b‐value sampling for diffusion‐weighted imaging of the kidney . Magn Reson Med . 2012 ; 67 : 89 ‐ 97 .
Dyvorne H , Jajamovich G , Kakite S , Kuehn B , Taouli B Intravoxel incoherent motion diffusion imaging of the liver: optimal b‐value subsampling and impact on parameter precision and reproducibility . Eur J Radiol . 2014 ; 83 : 2109 ‐ 2113 .
Peña‐Nogales Ó , Hernando D , Aja‐Fernández S , de Luis‐Garcia R Determination of optimized set of b‐values for apparent diffusion coefficient mapping in liver diffusion‐weighted MRI . J Magn Reson . 2020 ; 310 : 106634 .
Barbieri S , Donati OF , Froehlich JM , Thoeny HC Impact of the calculation algorithm on biexponential fitting of diffusion‐weighted MRI in upper abdominal organs . Magn Reson Med . 2016 ; 75 : 2175 ‐ 2184 .
Bertleff M , Domsch S , Weingärtner S , et al. Diffusion parameter mapping with the combined intravoxel incoherent motion and kurtosis model using artificial neural networks at 3 T . NMR Biomed . 2017 ; 30 : e3833 .
Barbieri S , Gurney‐Champion OJ , Klaassen R , Thoeny HC Deep learning how to fit an intravoxel incoherent motion model to diffusion‐weighted MRI . Magn Reson Med . 2020 ; 83 : 312 ‐ 321 .
Golkov V , Dosovitskiy A , Sperl JI , et al. Q‐space deep learning: twelve‐fold shorter and model‐free diffusion MRI scans . IEEE Trans Med Imaging . 2016 ; 35 : 1344 ‐ 1351 .
Fang Z , Chen Y , Lin W , Shen D . Quantification of Relaxation Times in MR Fingerprinting Using Deep Learning . In Proceedings of the 27th Annual Meeting of ISMRM. Paris, France ; 2018 : p.3380 .
Jensen JH , Helpern JA , Ramani A , Lu H , Kaczynski K Diffusional kurtosis imaging: the quantification of non‐Gaussian water diffusion by means of magnetic resonance imaging . Magn Reson Med . 2005 ; 53 : 1432 ‐ 1440 .
Wu W‐C , Yang S‐C , Chen Y‐F , Tseng H‐M , My P‐C Simultaneous assessment of cerebral blood volume and diffusion heterogeneity using hybrid IVIM and DK MR imaging: initial experience with brain tumors . Eur Radiol . 2017 ; 27 : 306 ‐ 314 .
Wu W‐C , Chen Y‐F , Tseng H‐M , Yang S‐C , My P‐C Caveat of measuring perfusion indexes using intravoxel incoherent motion magnetic resonance imaging in the human brain . Eur Radiol . 2015 ; 25 : 2485 ‐ 2492 .
Zhu G , Heit JJ , Martin BW , Marcellus DG , Federau C , Wintermark M Optimized combination of b‐values for IVIM perfusion imaging in acute ischemic stroke patients . Clin Neuroradiol . 2020 ; 30 : 535 ‐ 544 .
Chabert S , Verdu J , Huerta G , et al. Impact of b‐value sampling scheme on brain IVIM parameter estimation in healthy subjects . Magn Reson Med Sci . 2020 ; 19 ( 3 ): 216 – 226 .
Federau C , Maeder P , O’Brien K , Browaeys P , Meuli R , Hagmann P Quantitative measurement of brain perfusion with intravoxel incoherent motion MR imaging . Radiology . 2012 ; 265 : 874 ‐ 881 .
Grech‐Sollars M , Hales PW , Miyazaki K , et al. Multi‐centre reproducibility of diffusion MRI parameters for clinical sequences in the brain . NMR Biomed . 2015 ; 28 : 468 ‐ 485 .
Gudbjartsson H , Patz S The Rician distribution of noisy MRI data . Magn Reson Med . 1995 ; 34 : 910 ‐ 914 .
Lee W . Github Repository . 2021 . https://github.com/Wonil521/IVIM/. Accessed February 5, 2021.

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.

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

[해외논문] Quantification of intravoxel incoherent motion with optimized b‐values using deep neural network

Abstract ▼ AI-Helper

Keyword

참고문헌 (36)

이 논문을 인용한 문헌

연구과제 타임라인

활용도 분석정보

활용도 Top5 논문

관련 콘텐츠

원문 URL 링크

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

선택된 텍스트

연합인증

[해외논문] Quantification of intravoxel incoherent motion with optimized b‐values using deep neural network

Abstract ▼ AI-Helper

Keyword

참고문헌 (36)

이 논문을 인용한 문헌

연구과제 타임라인

전체(0) 논문(0) 특허(0) 보고서(0)

전체(0) 논문(0) 특허(0) 보고서(0)

활용도 분석정보

활용도 Top5 논문 더보기

관련 콘텐츠

원문 URL 링크

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

선택된 텍스트

활용도 Top5 논문