Literature DB >> 20566298

Non-local MRI upsampling.

José V Manjón1, Pierrick Coupé, Antonio Buades, Vladimir Fonov, D Louis Collins, Montserrat Robles.   

Abstract

In Magnetic Resonance Imaging, image resolution is limited by several factors such as hardware or time constraints. In many cases, the acquired images have to be upsampled to match a specific resolution. In such cases, image interpolation techniques have been traditionally applied. However, traditional interpolation techniques are not able to recover high frequency information of the underlying high resolution data. In this paper, a new upsampling method is proposed to recover some of this high frequency information by using a data-adaptive patch-based reconstruction in combination with a subsampling coherence constraint. The proposed method has been evaluated on synthetic and real clinical cases and compared with traditional interpolation methods. The proposed method is shown to outperform classical interpolation methods compared in terms of quantitative measures and visual observation. Copyright 2010 Elsevier B.V. All rights reserved.

Mesh:

Year:  2010        PMID: 20566298     DOI: 10.1016/j.media.2010.05.010

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  54 in total

1.  MRI upsampling using feature-based nonlocal means approach.

Authors:  Kourosh Jafari-Khouzani
Journal:  IEEE Trans Med Imaging       Date:  2014-06-12       Impact factor: 10.048

2.  Applications of a deep learning method for anti-aliasing and super-resolution in MRI.

Authors:  Can Zhao; Muhan Shao; Aaron Carass; Hao Li; Blake E Dewey; Lotta M Ellingsen; Jonghye Woo; Michael A Guttman; Ari M Blitz; Maureen Stone; Peter A Calabresi; Henry Halperin; Jerry L Prince
Journal:  Magn Reson Imaging       Date:  2019-06-24       Impact factor: 2.546

3.  Super-resolution reconstruction of neonatal brain magnetic resonance images via residual structured sparse representation.

Authors:  Yongqin Zhang; Pew-Thian Yap; Geng Chen; Weili Lin; Li Wang; Dinggang Shen
Journal:  Med Image Anal       Date:  2019-04-18       Impact factor: 8.545

4.  Non-local means resolution enhancement of lung 4D-CT data.

Authors:  Yu Zhang; Guorong Wu; Pew-Thian Yap; Qianjin Feng; Jun Lian; Wufan Chen; Dinggang Shen
Journal:  Med Image Comput Comput Assist Interv       Date:  2012

5.  Low-rank total variation for image super-resolution.

Authors:  Feng Shi; Jian Cheng; Li Wang; Pew-Thian Yap; Dinggang Shen
Journal:  Med Image Comput Comput Assist Interv       Date:  2013

6.  Medical Image Imputation from Image Collections.

Authors:  Adrian V Dalca; Katherine L Bouman; William T Freeman; Natalia S Rost; Mert R Sabuncu; Polina Golland
Journal:  IEEE Trans Med Imaging       Date:  2018-08-22       Impact factor: 10.048

7.  Accounting for the Confound of Meninges in Segmenting Entorhinal and Perirhinal Cortices in T1-Weighted MRI.

Authors:  Long Xie; Laura E M Wisse; Sandhitsu R Das; Hongzhi Wang; David A Wolk; Jose V Manjón; Paul A Yushkevich
Journal:  Med Image Comput Comput Assist Interv       Date:  2016-10-02

8.  IMPROVING MAGNETIC RESONANCE RESOLUTION WITH SUPERVISED LEARNING.

Authors:  Amod Jog; Aaron Carass; Jerry L Prince
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2014

9.  BTK: an open-source toolkit for fetal brain MR image processing.

Authors:  François Rousseau; Estanislao Oubel; Julien Pontabry; Marc Schweitzer; Colin Studholme; Mériam Koob; Jean-Louis Dietemann
Journal:  Comput Methods Programs Biomed       Date:  2012-10-01       Impact factor: 5.428

10.  Population Based Image Imputation.

Authors:  Adrian V Dalca; Katherine L Bouman; William T Freeman; Natalia S Rost; Mert R Sabuncu; Polina Golland
Journal:  Inf Process Med Imaging       Date:  2017-05-23
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