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> Wang R. >
A feature-based con...
A feature-based convolutional neural network for reconstruction of interventional MRI
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- Zufiria, Blanca (författare)
- KTH,Skolan för kemi, bioteknologi och hälsa (CBH)
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Qiu, S. (författare)
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Yan, K. (författare)
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visa fler...
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Zhao, R. (författare)
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Wang, R. (författare)
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She, H. (författare)
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Zhang, C. (författare)
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Sun, B. (författare)
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- Herman, Pawel, 1979- (författare)
- KTH,Beräkningsvetenskap och beräkningsteknik (CST)
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Du, Y. (författare)
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Feng, Y. (författare)
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(creator_code:org_t)
- 2019-12-19
- 2019
- Engelska.
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Ingår i: NMR in Biomedicine. - : John Wiley and Sons Ltd. - 0952-3480 .- 1099-1492.
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Real-time interventional MRI (I-MRI) could help to visualize the position of the interventional feature, thus improving patient outcomes in MR-guided neurosurgery. In particular, in deep brain stimulation, real-time visualization of the intervention procedure using I-MRI could improve the accuracy of the electrode placement. However, the requirements of a high undersampling rate and fast reconstruction speed for real-time imaging pose a great challenge for reconstruction of the interventional images. Based on recent advances in deep learning (DL), we proposed a feature-based convolutional neural network (FbCNN) for reconstructing interventional images from golden-angle radially sampled data. The method was composed of two stages: (a) reconstruction of the interventional feature and (b) feature refinement and postprocessing. With only five radially sampled spokes, the interventional feature was reconstructed with a cascade CNN. The final interventional image was constructed with a refined feature and a fully sampled reference image. With a comparison of traditional reconstruction techniques and recent DL-based methods, it was shown that only FbCNN could reconstruct the interventional feature and the final interventional image. With a reconstruction time of ~ 500 ms per frame and an acceleration factor of ~ 80, it was demonstrated that FbCNN had the potential for application in real-time I-MRI.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Nyckelord
- deep learning
- image reconstruction
- magnetic resonance imaging
- neuro-intervention
- real-time imaging
- Convolution
- Neural networks
- Neurosurgery
- Acceleration factors
- Convolutional neural network
- Deep brain stimulation
- Fast reconstruction
- Real time visualization
- Realtime imaging
- Reconstruction techniques
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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Till lärosätets databas
- Av författaren/redakt...
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Zufiria, Blanca
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Qiu, S.
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Yan, K.
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Zhao, R.
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Wang, R.
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She, H.
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visa fler...
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Zhang, C.
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Sun, B.
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Herman, Pawel, 1 ...
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Du, Y.
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Feng, Y.
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visa färre...
- Om ämnet
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- TEKNIK OCH TEKNOLOGIER
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TEKNIK OCH TEKNO ...
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och Elektroteknik oc ...
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NMR in Biomedici ...
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Kungliga Tekniska Högskolan