Results 11 to 20 of about 412,052 (160)
Deep Learning--Based Dictionary Learning and Tomographic Image Reconstruction
SIAM Journal on Imaging Sciences, 2022 This work presents an approach for image reconstruction in clinical low-dose tomography that combines principles from sparse signal processing with ideas from deep learning. First, we describe sparse signal representation in terms of dictionaries from a statistical perspective and interpret dictionary learning as a process of aligning distribution that
Jevgenija Rudzusika +2 more
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Super-resolution reconstruction based on two-stage residual neural network
Machine Learning with Applications, 2021 With the constant update of deep learning technology, the super-resolution reconstruction technology based on deep learning has also attained a significant breakthrough. This paper primarily discusses the integration of deep learning and super-resolution
Lin Dong, Kohei Inoue
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Personalized brachytherapy dose reconstruction using deep learning [PDF]
Computers in Biology and Medicine, 2021 Accurate calculation of the absorbed dose delivered to the tumor and normal tissues improves treatment gain factor, which is the major advantage of brachytherapy over external radiation therapy. To address the simplifications of TG-43 assumptions that ignore the dosimetric impact of medium heterogeneities, we proposed a deep learning (DL)-based ...
Akhavanallaf, Azadeh +5 more
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Deep Learning Applications in Magnetic Resonance Imaging: Has the Future Become Present?
Diagnostics, 2021 Deep learning technologies and applications demonstrate one of the most important upcoming developments in radiology. The impact and influence of these technologies on image acquisition and reporting might change daily clinical practice.
Sebastian Gassenmaier +8 more
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Stabilizing deep tomographic reconstruction: Part A. Hybrid framework and experimental results
Patterns, 2022 Summary: A recent PNAS paper reveals that several popular deep reconstruction networks are unstable. Specifically, three kinds of instabilities were reported: (1) strong image artefacts from tiny perturbations, (2) small features missed in a deeply ...
Weiwen Wu +9 more
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PYRO-NN: Python Reconstruction Operators in Neural Networks [PDF]
, 2019 Purpose: Recently, several attempts were conducted to transfer deep learning to medical image reconstruction. An increasingly number of publications follow the concept of embedding the CT reconstruction as a known operator into a neural network. However,
Maier, Andreas K. +5 more
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Deep learning for undersampled MRI reconstruction
Physics in Medicine & Biology, 2018 This paper presents a deep learning method for faster magnetic resonance imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for why the proposed approach works well. Uniform subsampling is used in the time-consuming phase-encoding direction to capture high-resolution image information, while permitting ...
Chang Min Hyun +4 more
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Diagnostic and Interventional Radiology, 2022 PURPOSEThe aim of this study was to assess the usefulness of denoising deep-learning-based reconstruction (dDLR) to improve image quality and vessel delineation in noncontrast 3-T wholeheart coronary magnetic resonance angiography (WHCMRA) with sub ...
Toshiya Kariyasu +5 more
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Review of 3D Face Reconstruction Based on Single Image [PDF]
Jisuanji kexue, 2022 In the field of computer vision,3D face reconstruction is a valuable research direction.High quality reconstruction of 3D faces can find applications in face recognition,anti-proofing,animation and medical cosmetology.In the last two decades,although ...
HE Jia-yu, HUANG Hong-bo, ZHANG Hong-yan, SUN Mu-ye, LIU Ya-hui, ZHOU Zhe-hai
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A deep-learning approach for high-speed Fourier ptychographic microscopy [PDF]
, 2018 We demonstrate a new convolutional neural network architecture to perform Fourier ptychographic Microscopy (FPM) reconstruction, which achieves high-resolution phase recovery with considerably less data than standard FPM.https://www.researchgate.net ...
Li, Yunzhe +5 more
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