Super-resolution MRI images using Compressive Sensing

Roohi, Samad; Zamani, Jafar; Noorhosseini, Majid; Rahmati, Mohammad

doi:10.1109/iraniancee.2012.6292620

Cited by 14 publications

(7 citation statements)

References 21 publications

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“…Different approaches are proposed in the literature. 12,30,39,42 In Ref. 42, CS-based reference driven image reconstruction method is proposed where the target MR image is represented as a linear combination of a motion compensated reference image and a difference image.…”

Section: Literature Review On the Existing Work And Limitationsmentioning

confidence: 99%

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On curvelet CS reconstructed MR images and GA-based fuzzy conditional entropy maximization for segmentation

Roy¹,

Maity

2019

Int. J. Wavelets Multiresolut Inf. Process.

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In many practical situations, magnetic resonance imaging (MRI) needs reconstruction of images at low measurements, far below the Nyquist rate, as sensing process may be very costly and slow enough so that one can measure the coefficients only a few times. Segmentation of such subsampled reconstructed MR images for medical analysis and diagnosis becomes a challenging task due to the inherent complex characteristics of the MR images. This paper considers reconstruction of MR images at compressive sampling (or compressed sensing (CS)) paradigm followed by its segmentation in an integrated platform. Image reconstruction is done from incomplete measurement space with random noise injection iteratively. A weighted linear prediction is done for the unobserved space followed by spatial domain denoising through adaptive recursive filtering. The reconstructed images, however, suffer from imprecise and/or missing edges, boundaries, lines, curvatures etc. and residual noise. Curvelet transform (CT) is purposely used for removal of noise and for edge enhancement through hard thresholding and suppression of approximate subbands, respectively. Then a fuzzy entropy-based clustering, using genetic algorithms (GAs), is done for segmentation of sharpen MR Image. Extensive simulation results are shown to highlight performance improvement of both image reconstruction and segmentation of the reconstructed images along with relative gain over the existing works.

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Section: Literature Review On the Existing Work And Limitationsmentioning

confidence: 99%

“…In Ref. 30, a method for reconstructing high resolution MR image is presented using CS from the single low resolution image. A Gaussian filter-based CS reconstruction method is proposed.…”

Section: Literature Review On the Existing Work And Limitationsmentioning

confidence: 99%

On curvelet CS reconstructed MR images and GA-based fuzzy conditional entropy maximization for segmentation

Roy¹,

Maity

2019

Int. J. Wavelets Multiresolut Inf. Process.

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“…The practice of image synthesis has been gaining attention recently (Rousseau, 2008; Roy et al, 2010a,b; Rousseau, 2010; Roy et al, 2011; Roohi et al, 2012; Jog et al, 2013a; Rousseau and Studholme, 2013; Konukoglu et al, 2013; Ye et al, 2013; Iglesias et al, 2013; Roy et al, 2013a,b; Jog et al, 2014a; Roy et al, 2014; Burgos et al, 2014; van Tulder and de Bruijne, 2015; Jog et al, 2015a; Cardoso et al, 2015; Van Nguyen et al, 2015; Bahrami, Khosro and Shi, Feng and Zong, Xiaopeng and Shin, Hae Won and An, Hongyu and Shen, Dinggang, 2015; Zikic et al, 2014) and the number of applications where image synthesis methods are being used is also growing. Synthesis of modalities differs from the data imputation literature (Hor and Moradi, 2015) in that the main goal is synthesis of the missing modality as opposed to classification in the absence of it.…”

Section: Introductionmentioning

confidence: 99%

Random forest regression for magnetic resonance image synthesis

Jog

Carass

Roy

et al. 2017

Medical Image Analysis

167

130

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By choosing different pulse sequences and their parameters, magnetic resonance imaging (MRI) can generate a large variety of tissue contrasts. This very flexibility, however, can yield inconsistencies with MRI acquisitions across datasets or scanning sessions that can in turn cause inconsistent automated image analysis. Although image synthesis of MR images has been shown to be helpful in addressing this problem, an inability to synthesize both T2-weighted brain images that include the skull and FLuid Attenuated Inversion Recovery (FLAIR) images has been reported. The method described herein, called REPLICA, addresses these limitations. REPLICA is a supervised random forest image synthesis approach that learns a nonlinear regression to predict intensities of alternate tissue contrasts given specific input tissue contrasts. Experimental results include direct image comparisons between synthetic and real images, results from image analysis tasks on both synthetic and real images, and comparison against other state-of-the-art image synthesis methods. REPLICA is computationally fast, and is shown to be comparable to other methods on tasks they are able to perform. Additionally REPLICA has the capability to synthesize both T2-weighted images of the full head and FLAIR images, and perform intensity standardization between different imaging datasets.

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“…Image synthesis in general has gained significant attention in the medical imaging community in the last seven years (Rousseau, 2008; Roy et al, 2010a,b; Rousseau, 2010; Roy et al, 2011; Roohi et al, 2012; Jog et al, 2013a; Rousseau and Studholme, 2013; Konukoglu et al, 2013; Burgos et al, 2013; Ye et al, 2013; Iglesias et al, 2013; Roy et al, 2013a,b, 2014). A classical registration-based solution to this problem was presented in (Miller et al, 1993): Given a subject image b 1 with contrast 1 and a pair of co-registered atlas images a 1 and a 2 of contrasts 1 and 2 , respectively, a 1 is registered to b 1 using a deformable registration algorithm, and the transformation is then applied to a 2 to produce the synthetic image b̂ 2 with contrast 2 .…”

Section: Introductionmentioning

confidence: 99%

MR image synthesis by contrast learning on neighborhood ensembles

Jog

Carass

Roy

et al. 2015

Medical Image Analysis

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Automatic processing of magnetic resonance images is a vital part of neuroscience research. Yet even the best and most widely used medical image processing methods will not produce consistent results when their input images are acquired with different pulse sequences. Although intensity standardization and image synthesis methods have been introduced to address this problem, their performance remains dependent on knowledge and consistency of the pulse sequences used to acquire the images. In this paper, an image synthesis approach that first estimates the pulse sequence parameters of the subject image is presented. The estimated parameters are then used with a collection of atlas or training images to generate a new atlas image having the same contrast as the subject image. This additional image provides an ideal source from which to synthesize any other target pulse sequence image contained in the atlas. In particular, a nonlinear regression intensity mapping is trained from the new atlas image to the target atlas image and then applied to the subject image to yield the particular target pulse sequence within the atlas. Both intensity standardization and synthesis of missing tissue contrasts can be achieved using this framework. The approach was evaluated on both simulated and real data, and shown to be superior in both intensity standardization and synthesis to other established methods.

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Super-resolution MRI images using Compressive Sensing

Cited by 14 publications

References 21 publications

On curvelet CS reconstructed MR images and GA-based fuzzy conditional entropy maximization for segmentation

On curvelet CS reconstructed MR images and GA-based fuzzy conditional entropy maximization for segmentation

Random forest regression for magnetic resonance image synthesis

MR image synthesis by contrast learning on neighborhood ensembles

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