[논문]Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss

Quan, Tran Minh; Nguyen-Duc, Thanh; Jeong, Won-Ki

doi:10.1109/tmi.2018.2820120

[해외논문] Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss 원문보기

IEEE transactions on medical imaging, v.37 no.6, 2018년, pp.1488 - 1497

Quan, Tran Minh (Ulsan National Institute of Science and Technology, Ulsan, South Korea) , Nguyen-Duc, Thanh (Ulsan National Institute of Science and Technology, Ulsan, South Korea) , Jeong, Won-Ki (Ulsan National Institute of Science and Technology, Ulsan, South Korea)

Abstract ▼ AI-Helper

Compressed sensing magnetic resonance imaging (CS-MRI) has provided theoretical foundations upon which the time-consuming MRI acquisition process can be accelerated. However, it primarily relies on iterative numerical solvers, which still hinders their adaptation in time-critical applications. In addition, recent advances in deep neural networks have shown their potential in computer vision and image processing, but their adaptation to MRI reconstruction is still in an early stage. In this paper, we propose a novel deep learning-based generative adversarial model, RefineGAN, for fast and accurate CS-MRI reconstruction. The proposed model is a variant of fully-residual convolutional autoencoder and generative adversarial networks (GANs), specifically designed for CS-MRI formulation; it employs deeper generator and discriminator networks with cyclic data consistency loss for faithful interpolation in the given under-sampled $k$-space data. In addition, our solution leverages a chained network to further enhance the reconstruction quality. RefineGAN is fast and accurate—the reconstruction process is extremely rapid, as low as tens of milliseconds for reconstruction of a $256\times 256$ image, because it is one-way deployment on a feed-forward network, and the image quality is superior even for extremely low sampling rate (as low as 10%) due to the data-driven nature of the method. We demonstrate that RefineGAN outperforms the state-of-the-art CS-MRI methods by a large margin in terms of both running time and image quality via evaluation using several open-source MRI databases.

참고문헌 (37)

Deep de-aliasing for fast compressive sensing MRI yu 2017
Deep generative adversarial networks for compressed sensing automates MRI mardani 2017
Learning to discover cross-domain relations with generative adversarial networks kim 2017
Proc NIPS Generative adversarial nets goodfellow 2014 2672
Proc MICCAI Conf Dictionary learning and time sparsity in dynamic MRI caballero 2012 256
Adam A method for stochastic optimization kingma 2014
10.1109/CVPR.2016.90
Wasserstein GAN arjovsky 2017
Jung, Hong, Ye, Jong Chul, Kim, Eung Yeop. Improved k–t BLAST and k–t SENSE using FOCUSS. Physics in medicine & biology, vol.52, no.11, 3201-3226.

상세보기
Jung, Hong, Sung, Kyunghyun, Nayak, Krishna S., Kim, Eung Yeop, Ye, Jong Chul. k-t FOCUSS: A general compressed sensing framework for high resolution dynamic MRI. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine, vol.61, no.1, 103-116.

상세보기
Proc MICCAI Conf Multi-GPU reconstruction of dynamic compressed sensing MRI quan 2015 484
Proc MICCAI Conf Accelerated dynamic MRI reconstruction with total variation and nuclear norm regularization yao 2015 635
Otazo, Ricardo, Candès, Emmanuel, Sodickson, Daniel K.. Low‐rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine, vol.73, no.3, 1125-1136.

상세보기
Tremoulheac, Benjamin, Dikaios, Nikolaos, Atkinson, David, Arridge, Simon R.. Dynamic MR Image Reconstruction–Separation From Undersampled ( ${\bf k},t$)-Space via Low-Rank Plus Sparse Prior. IEEE transactions on medical imaging, vol.33, no.8, 1689-1701.

상세보기
Aharon, M., Elad, M., Bruckstein, A.. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society, vol.54, no.11, 4311-4322.

상세보기
10.1109/ISBI.2012.6235548
Caballero, Jose, Price, Anthony N., Rueckert, Daniel, Hajnal, Joseph V.. Dictionary Learning and Time Sparsity for Dynamic MR Data Reconstruction. IEEE transactions on medical imaging, vol.33, no.4, 979-994.

상세보기
Ravishankar, Saiprasad, Bresler, Yoram. MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning. IEEE transactions on medical imaging, vol.30, no.5, 1028-1041.

상세보기
Deep artifact learning for compressed sensing and parallel MRI lee 2017
Liang, Dong, Liu, Bo, Wang, JiunJie, Ying, Leslie. Accelerating SENSE using compressed sensing. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine, vol.62, no.6, 1574-1584.

상세보기
Schlemper, Jo, Caballero, Jose, Hajnal, Joseph V., Price, Anthony N., Rueckert, Daniel. A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction. IEEE transactions on medical imaging, vol.37, no.2, 491-503.

상세보기
Lustig, M., Donoho, D.L., Santos, J.M., Pauly, J.M.. Compressed Sensing MRI [A look at how CS can improve on current imaging techniques]. IEEE signal processing magazine, vol.25, no.2, 72-82.

상세보기
Goldstein, Tom, Osher, Stanley. The Split Bregman Method for L1-Regularized Problems. SIAM journal on imaging sciences, vol.2, no.2, 323-343.

상세보기
Jin, Kyong Hwan, McCann, Michael T., Froustey, Emmanuel, Unser, Michael. Deep Convolutional Neural Network for Inverse Problems in Imaging. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol.26, no.9, 4509-4522.

상세보기
10.1137/1.9781611970104
Nature Deep learning lecun 2015 10.1038/nature14539 521 436

상세보기
Boyd, Stephen. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers. Foundations and trends in machine learning, vol.3, no.1, 1-122.

상세보기
Donoho, D.L.. Compressed sensing. IEEE transactions on information theory, vol.52, no.4, 1289-1306.

상세보기
10.1109/ICCV.2017.244
Heidemann, Robin M., �zsarlak, �zkan, Parizel, Paul M., Michiels, Johan, Kiefer, Berthold, Jellus, Vladimir, M�ller, Mathias, Breuer, Felix, Blaimer, Martin, Griswold, Mark A., Jakob, Peter M.. A brief review of parallel magnetic resonance imaging. European radiology, vol.13, no.10, 2323-2337.

상세보기
10.1109/ISBI.2016.7493321
10.1109/CVPR.2013.57
Proc MICCAI Conf Compressed sensing dynamic MRI reconstruction using GPU-accelerated 3D convolutional sparse coding quan 2016 484
10.1109/ISBI.2016.7493320
10.1109/ICASSP.2014.6854992
Proc NIPS Deep ADMM-net for compressive sensing MRI sun 2016 10
10.1109/ISBI.2017.7950457

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] Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss 원문보기

Abstract ▼ AI-Helper

참고문헌 (37)

이 논문을 인용한 문헌

연구과제 타임라인

활용도 분석정보

활용도 Top5 논문

관련 콘텐츠

원문 보기

원문 URL 링크

오픈액세스(OA) 유형

이 논문과 함께 이용한 콘텐츠

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

선택된 텍스트

연합인증

[해외논문] Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss 원문보기

Abstract ▼ AI-Helper

참고문헌 (37)

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 보기

원문 URL 링크

오픈액세스(OA) 유형

이 논문과 함께 이용한 콘텐츠

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

선택된 텍스트

활용도 Top5 논문