Super-Resolution in Medical Imaging

By Hayit Greenspan. Computer/law journal, 2009

601 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b189282ec4c65f4229105e1b62e0012f7ab551cb

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In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Deep Learning for Image Super-Resolution: A Survey (2019, background): “It enjoys a wide range of real-world applications, such as medical imaging [1], [2], [3], surveillance and security [4], [5]), amongst others.”
Towards a Mathematical Theory of Super‐resolution (2012, background): “This is the reason that resolving subwavelength features is a crucial challenge in fields such as astronomy [33], medical imaging [23], and microscopy [29].”
Image super-resolution: The techniques, applications, and future (2016, background): “13 is the reconstructed image of the single MRI image of the knee in [176].”
Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation (2017, methodology): “In training, however, LR images are synthetically generated from clinical HR data using the MR acquisition model discussed in [20].”
Multiscale brain MRI super-resolution using deep 3D convolutional networks (2019, methodology): “Increas-35 ing image resolution through super-resolution is a key to more accurate understanding of the anatomy (Greenspan 2008).”
7T-guided super-resolution of 3T MRI (2017, background): “In medical imaging, a number of resolution enhancement (RE) methods have been proposed to reconstruct a high-resolution (HR) image from one or more low-resolution (LR) images.(9,10) The traditional RE methods use interpolation functions such as bilinear or bicubic.”
Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation (2018, methodology): “SR is widely used in many fields ranging from medical imaging [7] to satellite imaging [2] and surveillance [38].”
Reconstruction of fetal brain MRI with intensity matching and complete outlier removal (2012, methodology): “More recently, super-resolution methods (Milanfar, 2007; Greenspan, 2009) in combination with slice-tovolume registration have been proposed to reduce the blurring effects during reconstruction of thick-slice data (Gholipour and Warfield, 2009; Gholipour et al.”
Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network (2018, background): “High spatial resolution MRI produces detailed structural information, benefiting clinical diagnosis and decision making as well as accurate quantitative image analysis[1, 2].”
A Deep Journey into Super-resolution (2019, background): “object detection in scenes [6] (particularly small objects [7]), face recognition in surveillance videos [8], medical imaging [9], improving interpretation of images in remote sensing [10], astronomical images [11], and forensics [12].”
Super‐resolution methods in MRI: Can they improve the trade‐off between resolution, signal‐to‐noise ratio, and acquisition time? (2012, background): “While SRR in MRI is a developing field, showing its potential in resolution enhancement (22), a major question from the MRI community is whether SRR has any advantage over direct HR acquisition when SNR and acquisition times are taken into account.”
Densely Residual Laplacian Super-Resolution (2019, methodology): “he output HR image to the input LR image is many-to-one. However, despite being a difﬁcult problem, it is useful in many computer vision applications such as surveillance imaging [1], medical imaging [2], forensics [3], object classiﬁcation [4] etc. Deep convolutional neural network (CNN) super-reso”
CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE) (2018, background): “This computational deblurring job is a major challenge, representing a seriously ill-posed inverse problem [3], [15].”
Super-Resolution from Noisy Data (2012, unknown): “Examples of applications include conventional optical imaging [25], astronomy [32], medical imaging [15], and microscopy [27].”