Single-image super-resolution of brain MR images using overcomplete dictionaries

Rueda, Andrea; Malpica, Norberto; Romero, Eduardo

doi:10.1016/j.media.2012.09.003

Cited by 161 publications

(109 citation statements)

References 38 publications

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“…For instance, [2,3] use example patches from HR images to super-resolve scalar MR and DW images respectively, with an explicitly defined generative model relating a HR patch to a LR patch and carefully crafted regularisation. Another generative approach is the sparse-representation methods [4,5], which construct a coupled library of HR and LR images from training data and solve the SR problem through projection onto it. Image quality transfer (IQT) [6] is a general quality-enhancement framework based on patch regression, which shows great promise in SR of DT images and requires no special acquisition, so is applicable to large varieties of existing data.…”

Section: Introductionmentioning

confidence: 99%

“…We incorporate Bayesian inference into the framework and name the new method Bayesian IQT (BIQT). Although many SR methods [2][3][4][5] can be cast as maximum a posteriori (MAP) optimisation problems, the dimensionality or complexity of the posterior distribution make the computation of uncertainty very expensive. In contrast, the random forest implementation of the original IQT is amenable to uncertainty estimation thanks to the simple linear model at each leaf node, but the current approach computes maximum likelihood (ML) solution.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Image Quality Transfer

Tanno

Ghosh

Grussu

et al. 2016

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

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Abstract. Image quality transfer (IQT) aims to enhance clinical images of relatively low quality by learning and propagating high-quality structural information from expensive or rare data sets. However, the original framework gives no indication of confidence in its output, which is a significant barrier to adoption in clinical practice and downstream processing. In this article, we present a general Bayesian extension of IQT which enables efficient and accurate quantification of uncertainty, providing users with an essential prediction of the accuracy of enhanced images. We demonstrate the efficacy of the uncertainty quantification through super-resolution of diffusion tensor images of healthy and pathological brains. In addition, the new method displays improved performance over the original IQT and standard interpolation techniques in both reconstruction accuracy and robustness to anomalies in input images. AQ1 AQ2

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian Image Quality Transfer

Tanno

Ghosh

Grussu

et al. 2016

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

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show abstract

“…By using training data that covers the whole cardiac cycle, we can account for the variation of motion likely in the test image. A Gaussian kernel, with full width half max (FWHM) equal to the slice thickness, is used to blur in the slice-select direction only, in order to simulate the acquisition process [9]. The resulting LR images are then up sampled using bi-cubic interpolation back to the original size, giving.…”

Section: Trainingmentioning

confidence: 99%

Patch Based Coupled Dictionary Approach for Cardiac Mri Images Using Sr Reconstruction Algorithm

2017

IJMTER

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Abstract-Although tremendous progress has been made in the past to get High resolution 3D cardiac MRI but still it is considered as area of concern in the medical image processing filed. The existing anisotropic 2D stack volumes are typical, and improving the resolution of these is strongly motivated by both visualization and analysis. The lack ofsuitable reconstruction techniques that handle non-rigid motion means that cardiac image enhancement is still often attained by simple interpolation. A novel approach is proposed in this paper which accurately precise the High resolution 3D cardiac MRI in easy way. We explore the use of example-based super-resolution, to enable high fidelity patchbasedreconstruction, using training data that does not need to be accurately aligned with the target data.

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“…The PANO operator incorporated prior information learned from under-sampled data or another contrast image, which led to optimized sparse representation of images to be reconstructed. Other medical approaches, for example [19], generate overcomplete dictionaries to enhance the quality of Magnetic Resonance (MR) images. This couples high and low frequency information, so an HR version of an LR brain MR image is generated.…”

Section: Introductionmentioning

confidence: 99%