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Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
Critical Reviews™ in Biomedical Engineering
SJR: 0.207 SNIP: 0.376 CiteScore™: 0.79

ISSN Печать: 0278-940X
ISSN Онлайн: 1943-619X

Выпуски:
Том 47, 2019 Том 46, 2018 Том 45, 2017 Том 44, 2016 Том 43, 2015 Том 42, 2014 Том 41, 2013 Том 40, 2012 Том 39, 2011 Том 38, 2010 Том 37, 2009 Том 36, 2008 Том 35, 2007 Том 34, 2006 Том 33, 2005 Том 32, 2004 Том 31, 2003 Том 30, 2002 Том 29, 2001 Том 28, 2000 Том 27, 1999 Том 26, 1998 Том 25, 1997 Том 24, 1996 Том 23, 1995

Critical Reviews™ in Biomedical Engineering

DOI: 10.1615/CritRevBiomedEng.v38.i2.20
pages 127-141

Quality Assessment in Magnetic Resonance Images

Neelam Sinha
Department of Electrical Engineering, Indian Institute of Science, Bangalore, India
A.G. Ramakrishnan
Department of Electrical Engineering, Indian Institute of Science, Bangalore, India

Краткое описание

Assessing quality of medical images is critical because the subsequent course of actions depend on it. Extensive use of clinical magnetic resonance (MR) imaging warrants a study in image indices used for MR images. The quality of MR images assumes particular significance in the determination of their reliability for diagnostics, response to therapies, synchronization across different imaging cycles, optimization of interventional imaging, and image restoration. In this paper, we review various techniques developed for the assessment of MR image quality. The reported quality indices can be broadly classified as subjective/objective, automatic/semi-automatic, region-of-interest/non-region-of-interest−based, full-reference/no-reference and HVS incorporated/non-HVS incorporated. The trade-of across the various indices lies in the computational complexity, assumptions, repeatability, and resemblance to human perception. Because images are eventually viewed by the human eye, it is found that it is important to incorporate aspects of human visual response, sensitivity, and characteristics in computing quality indices. Additionally, no-reference metrics are the most relevant due to the lack of availability of a golden standard against which images could be compared. Techniques that are objective and automatic are preferred for their repeatability and to eliminate avoidable errors due to factors like stress, which arise in human intervention.


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