Literature DB >> 18230491

The information content of MR images.

M Fuderer1.   

Abstract

The theoretical information content, defined by C.E. Shannon (1948), is proposed as an objective measure of MR (magnetic resonance) image quality. This measure takes into account the contrast-to-noise ratio (CNR), scan resolution, and field of view. It is used to derive an optimum in the tradeoff problem between image resolution and CNR, and as a criterion to assess the usefulness of high-resolution (512(2)) MR images. The result tells that for a given total acquisition time, an optimum value of the resolution can be found. This optimum is very broad. To apply Shannon's theory on information constant to MR images, a model for the spatial spectral power density of these images is required. Such a model has been derived from experimental observations of ordinary MR images, as well as from theoretical considerations.

Year:  1988        PMID: 18230491     DOI: 10.1109/42.14521

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  11 in total

1.  MR CAT scan: a modular approach for hybrid imaging.

Authors:  C Hillenbrand; D Hahn; A Haase; P M Jakob
Journal:  MAGMA       Date:  2000-07       Impact factor: 2.310

2.  Prospective navigator gating with a dual acceptance window technique to reduce respiratory motion artifacts in 3D MR coronary angiography.

Authors:  Yiping P Du
Journal:  Int J Cardiovasc Imaging       Date:  2003-04       Impact factor: 2.357

3.  Optimization of the SNR-resolution tradeoff for registration of magnetic resonance images.

Authors:  Shoan C Kale; Jason P Lerch; R Mark Henkelman; X Josette Chen
Journal:  Hum Brain Mapp       Date:  2008-10       Impact factor: 5.038

4.  Quantification and Segmentation of Brain Tissues from MR Images: A Probabilistic Neural Network Approach.

Authors:  Yue Wang; Tülay Adalý; Sun-Yuan Kung; Zsolt Szabo
Journal:  IEEE Trans Image Process       Date:  1998-08       Impact factor: 10.856

5.  Quantitative image quality evaluation of MR images using perceptual difference models.

Authors:  Jun Miao; Donglai Huo; David L Wilson
Journal:  Med Phys       Date:  2008-06       Impact factor: 4.071

6.  Improved time series reconstruction for dynamic magnetic resonance imaging.

Authors:  Uygar Sümbül; Juan M Santos; John M Pauly
Journal:  IEEE Trans Med Imaging       Date:  2009-01-13       Impact factor: 10.048

7.  An EM approach to MAP solution of segmenting tissue mixtures: a numerical analysis.

Authors:  Zhengrong Liang; Su Wang
Journal:  IEEE Trans Med Imaging       Date:  2009-02       Impact factor: 10.048

8.  A framework for constraining image SNR loss due to MR raw data compression.

Authors:  Matthew C Restivo; Adrienne E Campbell-Washburn; Peter Kellman; Hui Xue; Rajiv Ramasawmy; Michael S Hansen
Journal:  MAGMA       Date:  2018-10-25       Impact factor: 2.310

9.  The EM Method in a Probabilistic Wavelet-Based MRI Denoising.

Authors:  Marcos Martin-Fernandez; Sergio Villullas
Journal:  Comput Math Methods Med       Date:  2015-05-18       Impact factor: 2.238

10.  A consistency evaluation of signal-to-noise ratio in the quality assessment of human brain magnetic resonance images.

Authors:  Shaode Yu; Guangzhe Dai; Zhaoyang Wang; Leida Li; Xinhua Wei; Yaoqin Xie
Journal:  BMC Med Imaging       Date:  2018-05-16       Impact factor: 1.930

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