A Perspective on Deep Imaging

Wang, Ge

doi:10.1109/access.2016.2624938

Cited by 408 publications

(246 citation statements)

References 35 publications

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“…A strong theoretical understanding of deep learning is yet to be established 101 despite the reported successes across many fields — explaining why deep learning layers that lie between inputs and outputs are labelled as ‘hidden layers’ (Box 1; fig. 2b).…”

Section: Ai Challenges In Medical Imagingmentioning

confidence: 99%

Artificial intelligence in radiology

et al. 2018

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Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this O pinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

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Section: Ai Challenges In Medical Imagingmentioning

confidence: 99%

Artificial intelligence in radiology

et al. 2018

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“…Shen et al [37] surveyed the most recent research in deep learning for medical image analysis and Wang [38] provided an insightful perspective on deep imaging that proposed to incorporate deep learning into tomographic image reconstruction. Essentially, CS-MRI reconstruction solves a generalised inverse problem that is analogous to image superresolution (SR) [39], de-noising and inpainting [40], [41] that have been successfully solved using deep neural network architectures, e.g., using convolutional neural networks (CNN).…”

Section: B Deep Learning-based Cs-mrimentioning

confidence: 99%

DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

Yang

Dong

et al. 2018

IEEE Trans. Med. Imaging

886

628

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Abstract-Compressed Sensing Magnetic Resonance Imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce motion artefacts and the effect of contrast washout, thus yielding better image quality. Different from parallel imaging based fast MRI, which utilises multiple coils to simultaneously receive MR signals, CS-MRI breaks the Nyquist-Shannon sampling barrier to reconstruct MRI images with much less required raw data. This paper provides a deep learning based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working only on data from a single image, and prior knowledge from large training datasets. In particular, a novel conditional Generative Adversarial Networks-based model (DAGAN) is proposed to reconstruct CS-MRI. In our DAGAN architecture, we have designed a refinement learning method to stabilise our U-Net based generator, which provides an endto-end network to reduce aliasing artefacts. To better preserve texture and edges in the reconstruction, we have coupled the adversarial loss with an innovative content loss. In addition, we incorporate frequency domain information to enforce similarity in both the image and frequency domains. We have performed comprehensive comparison studies with both conventional CS-MRI reconstruction methods and newly investigated deep learning approaches. Compared to these methods, our DAGAN method provides superior reconstruction with preserved perceptual image details. Furthermore, each image is reconstructed in about 5 ms, which is suitable for real-time processing.

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“…The potential contributions of DNNs to medical image classification have been and continue to be investigated [11]. In contrast, the impact of DNNs on medical image reconstruction are just starting to be examined [12], [13]. The goal of this paper was to significantly expand and report on our previous work to integrate DNNs into an ultrasound beamformer and to train them to improve the quality of the resulting ultrasound images [14].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Ultrasound Beamforming

Luchies

Byram

2018

IEEE Trans. Med. Imaging

145

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We investigate the use of deep neural networks (DNNs) for suppressing off-axis scattering in ultrasound channel data. Our implementation operates in the frequency domain via the short-time Fourier transform. The inputs to the DNN consisted of the separated real and imaginary components (i.e. in-phase and quadrature components) observed across the aperture of the array, at a single frequency and for a single depth. Different networks were trained for different frequencies. The output had the same structure as the input and the real and imaginary components were combined as complex data before an inverse short-time Fourier transform was used to reconstruct channel data. Using simulation, physical phantom experiment, and in vivo scans from a human liver, we compared this DNN approach to standard delay-and-sum (DAS) beamforming and an adaptive imaging technique that uses the coherence factor (CF). For a simulated point target, the side lobes when using the DNN approach were about 60 dB below those of standard DAS. For a simulated anechoic cyst, the DNN approach improved contrast ratio (CR) and contrast-to-noise (CNR) ratio by 8.8 dB and 0.3 dB, respectively, compared to DAS. For an anechoic cyst in a physical phantom, the DNN approach improved CR and CNR by 17.1 dB and 0.7 dB, respectively. For two in vivo scans, the DNN approach improved CR and CNR by 13.8 dB and 9.7 dB, respectively. We also explored methods for examining how the networks in this work function.

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A Perspective on Deep Imaging

Cited by 408 publications

References 35 publications

Artificial intelligence in radiology

Artificial intelligence in radiology

DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

Deep Neural Networks for Ultrasound Beamforming

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