A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images

Igor Stępień; Mariusz Oszust

doi:10.3390/jimaging8060160

journal article Open Access Jun 04, 2022

A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images

Igor Stępień

Mariusz Oszust

Journal of Imaging Vol. 8 No. 6 pp. 160 · MDPI AG

View at Publisher Save 10.3390/jimaging8060160

Abstract

No-reference image quality assessment (NR-IQA) methods automatically and objectively predict the perceptual quality of images without access to a reference image. Therefore, due to the lack of pristine images in most medical image acquisition systems, they play a major role in supporting the examination of resulting images and may affect subsequent treatment. Their usage is particularly important in magnetic resonance imaging (MRI) characterized by long acquisition times and a variety of factors that influence the quality of images. In this work, a survey covering recently introduced NR-IQA methods for the assessment of MR images is presented. First, typical distortions are reviewed and then popular NR methods are characterized, taking into account the way in which they describe MR images and create quality models for prediction. The survey also includes protocols used to evaluate the methods and popular benchmark databases. Finally, emerging challenges are outlined along with an indication of the trends towards creating accurate image prediction models.

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References

87

[1]

Krupinski "Anniversary paper: Evaluation of medical imaging systems" Med. Phys. (2008) 10.1118/1.2830376

[2]

Kolind "Quantitative evaluation of metal artifact reduction techniques" J. Magn. Reson. Imaging: Off. J. Int. Soc. Magn. Reson. Med. (2004) 10.1002/jmri.20144

[3]

Roy "Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging" EBioMedicine (2020) 10.1016/j.ebiom.2020.102963

[4]

Westbrook, C., and Talbot, J. (2018). MRI in Practice, John Wiley and Sons.

[5]

Soher "A Review of MR Physics: 3 T versus 1.5 T" Magn. Reson. Imaging Clin. N. Am. (2007) 10.1016/j.mric.2007.06.002

[6]

Largent "Image quality assessment of fetal brain MRI using multi-instance deep learning methods" J. Magn. Reson. Imaging (2021) 10.1002/jmri.27649

[7]

Xu, J., Lala, S., Gagoski, B., Abaci Turk, E., Grant, P.E., Golland, P., and Adalsteinsson, E. (2020, January 4–8). Semi-supervised learning for fetal brain MRI quality assessment with ROI consistency. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Virtual Conference. 10.1007/978-3-030-59725-2_37

[8]

Current Trends and Advances in Image Quality Assessment

Krzysztof Okarma

Elektronika ir Elektrotechnika 2019 10.5755/j01.eie.25.3.23681

[9]

Wang "Modern Image Quality Assessment" Synth. Lect. Image Video, Multimed. Process. (2006) 10.1007/978-3-031-02238-8

[10]

Wang "Reduced- and No-Reference Image Quality Assessment" IEEE Signal Process. Mag. (2011) 10.1109/msp.2011.942471

[11]

Zhai "Perceptual image quality assessment: A survey" Sci. China Inf. Sci. (2020) 10.1007/s11432-019-2757-1

[12]

Chow "Review of medical image quality assessment" Biomed. Signal Process. Control (2016) 10.1016/j.bspc.2016.02.006

[13]

Zhang, W., Li, D., Ma, C., Zhai, G., Yang, X., and Ma, K. (2021). Continual Learning for Blind Image Quality Assessment. arXiv. 10.1109/tpami.2022.3178874

[14]

Varga, D. (2022). No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion. Appl. Sci., 12. 10.3390/app12010101

[15]

Garcia Freitas, P., Da Eira, L.P., Santos, S.S., and Farias, M.C.Q.d. (2018). On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment. J. Imaging, 4. 10.3390/jimaging4100114

[16]

Varga, D. (2021). Analysis of Benford’s Law for No-Reference Quality Assessment of Natural, Screen-Content, and Synthetic Images. Electronics, 10. 10.3390/electronics10192378

[17]

Leonardi, M., Napoletano, P., Schettini, R., and Rozza, A. (2021). No Reference, Opinion Unaware Image Quality Assessment by Anomaly Detection. Sensors, 21. 10.3390/s21030994

[18]

Tsougos, I. (2016). Image Principles, Neck, and the Brain, CRC Press.

[19]

Patton "Techniques, pitfalls and artifacts in magnetic resonance imaging" Radiographics (1987) 10.1148/radiographics.7.3.3448645

[20]

Mezrich "A perspective on K-space" Radiology (1995) 10.1148/radiology.195.2.7724743

[21]

Graves "Body MRI artifacts in clinical practice: A physicist’s and radiologist’s perspective" J. Magn. Reson. Imaging (2013) 10.1002/jmri.24288

[22]

Zhuo "MR Artifacts, Safety, and Quality Control" RadioGraphics (2006) 10.1148/rg.261055134

[23]

Hashemi, R.H., Bradley, W.G., and Lisanti, C.J. (2012). MRI: The Basics: The Basics, Lippincott Williams & Wilkins.

[24]

Allisy-Roberts, P.J., and Williams, J. (2007). Farr’s Physics for Medical Imaging, Elsevier Health Sciences.

[25]

Parallel MR imaging

Anagha Deshmane, Vikas Gulani, Mark A. Griswold et al.

Journal of Magnetic Resonance Imaging 2012 10.1002/jmri.23639

[26]

Hamilton "Recent advances in parallel imaging for MRI" Prog. Nucl. Magn. Reson. Spectrosc. (2017) 10.1016/j.pnmrs.2017.04.002

[27]

Ehman "Flow artifact reduction in MRI: A review of the roles of gradient moment nulling and spatial presaturation" Magn. Reson. Med. (1990) 10.1002/mrm.1910140214

[28]

Wood "The magnetic field dependence of the breathing artifact" Magn. Reson. Imaging (1986) 10.1016/0730-725x(86)90044-5

[29]

Zaitsev "Motion artifacts in MRI: A complex problem with many partial solutions" J. Magn. Reson. Imaging (2015) 10.1002/jmri.24850

[30]

Osadebey "Blind blur assessment of MRI images using parallel multiscale difference of Gaussian filters" BioMedical Eng. OnLine (2018) 10.1186/s12938-018-0514-4

[31]

Hong, R., Cheng, W.H., Yamasaki, T., Wang, M., and Ngo, C.W. (2018, January 21–22). Subjective Quality Assessment of Stereoscopic Omnidirectional Image. Proceedings of the Advances in Multimedia Information Processing—PCM 2018, Hefei, China.

[32]

Morelli "An image-based approach to understanding the physics of MR artifacts" Radiographics (2011) 10.1148/rg.313105115

[33]

Pusey "Magnetic resonance imaging artifacts: Mechanism and clinical significance" Radiographics (1986) 10.1148/radiographics.6.5.3685515

[34]

Block "Suppression of MRI truncation artifacts using total variation constrained data extrapolation" Int. J. Biomed. Imaging (2008) 10.1155/2008/184123

[35]

Kucharczyk "Effect of multislice interference on image contrast in T2-and T1-weighted MR images" Am. J. Neuroradiol. (1988)

[36]

Doran "A complete distortion correction for MR images: I. Gradient warp correction" Phys. Med. Biol. (2005) 10.1088/0031-9155/50/7/001

[37]

Caramanos "Gradient distortions in MRI: Characterizing and correcting for their effects on SIENA-generated measures of brain volume change" Neuroimage (2010) 10.1016/j.neuroimage.2009.08.008

[38]

Bammer "Parallel imaging artifacts in body magnetic resonance imaging" Can. Assoc. Radiol. J. (2009) 10.1016/j.carj.2009.02.036

[39]

Kaur "Protocol error artifacts in MRI: Sources and remedies revisited" Radiography (2007) 10.1016/j.radi.2006.03.011

[40]

Gao "Image quality assessment and human visual system" SPIE Proc. (2010)

[41]

Suthaharan "No-reference visually significant blocking artifact metric for natural scene images" Signal Process. (2009) 10.1016/j.sigpro.2009.02.007

[42]

Bhateja "Two-stage multi-modal MR images fusion method based on parametric logarithmic image processing (PLIP) model" Pattern Recognit. Lett. (2020) 10.1016/j.patrec.2020.05.027

[43]

Liu "Hierarchical nonlocal residual networks for image quality assessment of pediatric diffusion MRI With Limited and Noisy Annotations" IEEE Trans. Med. Imaging (2020) 10.1109/tmi.2020.3002708

[44]

Iqbal "Generative Adversarial Network for Medical Images (MI-GAN)" J. Med. Syst. (2018) 10.1007/s10916-018-1072-9

[45]

Qi, K., Li, H., Rong, C., Gong, Y., Li, C., Zheng, H., and Wang, S. (2021). Blind Image Quality Assessment for MRI with A Deep Three-dimensional content-adaptive Hyper-Network. arXiv.

[46]

Modified-BRISQUE as no reference image quality assessment for structural MR images

Li Sze Chow, Heshalini Rajagopal

Magnetic Resonance Imaging 2017 10.1016/j.mri.2017.07.016

[47]

Mittal, A., Moorthy, A.K., and Bovik, A.C. (2011, January 6–9). Blind/Referenceless Image Spatial Quality Evaluator. Proceedings of the 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), Pacific Grove, CA, USA. 10.1109/acssc.2011.6190099

[48]

Jang "Quality evaluation of no-reference MR images using multidirectional filters and image statistics" Magn. Reson. Med. (2018) 10.1002/mrm.27084

[49]

Nabavi, S., Simchi, H., Moghaddam, M.E., Frangi, A.F., and Abin, A.A. (2021). Automatic Multi-Class Cardiovascular Magnetic Resonance Image Quality Assessment using Unsupervised Domain Adaptation in Spatial and Frequency Domains. arXiv.

[50]

Stępień, I., Obuchowicz, R., Piórkowski, A., and Oszust, M. (2021). Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment. Sensors, 21. 10.3390/s21041043

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Details

Published: Jun 04, 2022
Vol/Issue: 8(6)
Pages: 160
License: View

Authors

I

Igor Stępień

Doctoral School of Engineering and Technical Sciences, Rzeszow University of Technology, al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland

M

Mariusz Oszust

Department of Computer and Control Engineering, Rzeszow University of Technology, Wincentego Pola 2, 35-959 Rzeszow, Poland

Cite This Article

Igor Stępień, Mariusz Oszust (2022). A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images. Journal of Imaging, 8(6), 160. https://doi.org/10.3390/jimaging8060160

A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images

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