The spatial statistics of structural magnetic resonance images: application to post-acquisition quality assessment of brain MRI images

References

• Pedersen M, Zheng Y, Hardeberg J. Evaluation of image quality metrics for color prints. In: Heyden A, Kahl F, editors. Image analysis. Berlin: Springer; 2011. p. 317–326. (Lecture Notes in Computer Science; Vol. 6688).
   Google Scholar
• Pedersen M, Bonnier N, Hardeberg JY, et al., Attributes of image quality for color prints. J Electron Imag. 2010;19(1):011016.
   Web of Science ®Google Scholar
• Wang Z, Bovik A. Modern image quality assessment. Morgan & Claypool; 2006. DOI:10.2200/S00010ED1V01Y200508IVM003
   Google Scholar
• Janssen R. Computational image quality. Society of Photo Optical. Bellingham, WA: SPIE Press; 2001. (Press Monographs).
   Google Scholar
• Mansoor AB, Anwar A, Khan S. Subjective evaluation of image quality measures for white noise and gaussian blur-distorted images. Imag Sci J. 2013;61(1):13–21.
   Web of Science ®Google Scholar
• Duvernoy HM. The human brain: surface, three-dimensional sectional anatomy with MRI, and blood supply. Vienna: Springer Science & Business Media; 2012.
   Google Scholar
• Mori S, Wakana S, Van Zijl PC, et al., MRI atlas of human white matter. Vol. 16. Amsterdam: American Society of Neuroradiology; 2005.
   Google Scholar
• Frisoni GB, Fox NC, Jack CR, et al., The clinical use of structural MRI in alzheimer disease. Nat Rev Neurol. 2010;6(2):67–77.
   PubMed Web of Science ®Google Scholar
• Scheltens P, Leys D, Barkhof F, et al., Atrophy of medial temporal lobes on MRI in ‘probable’ Alzheimer's disease and normal ageing: diagnostic value and neuropsychological correlates. J Neurol, Neurosurg Psychiatry. 1992;55(10):967–972.
   PubMed Web of Science ®Google Scholar
• Arnold DL, Calabresi PA, Kieseier BC, et al., Effect of peginterferon beta-1a on MRI measures and achieving no evidence of disease activity: results from a randomized controlled trial in relapsing-remitting multiple sclerosis. BMC Neurol. 2014;14(1):777–0.
   Web of Science ®Google Scholar
• Selmaj K, Kappos L, Arnold D, et al., Safety and tolerability results from the decide study: A phase 3 active-comparator study of daclizumab hyp in relapsing-remitting multiple sclerosis (p7.230). Neurology. 2015;84(14 Supplement):P7.230.
   Google Scholar
• Comi G, Cohen J, Arnold D, et al., Efficacy results of the phase 2 portion of the radiance trial: a randomized, double-blind, placebo-controlled trial of oral rpc1063 in adults with relapsing multiple sclerosis (p7.198). Neurology. 2015;84(14 Supplement):P7.198.
   Google Scholar
• Yalman Y, Akar F, Bayilmis C. A web-based digital analysis interface for image quality assessment. Imag Sci J. 2014;62(3):149–160.
   Web of Science ®Google Scholar
• Wang Z, Bovik AC, Sheikh HR, et al., Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004;13(4):600–612.
   PubMed Web of Science ®Google Scholar
• Narwaria M, Lin W. Objective image quality assessment based on support vector regression. IEEE Trans Neural Netw. 2010 Mar;21(3):515–519.
   PubMed Web of Science ®Google Scholar
• Prieto F, Guarini M, Tejos C, et al., Metrics for quantifying the quality of MR images. In: Proceedings of the 17th Annual Meeting of ISMRM; Honolulu, Hawaii; 2009. p. 4696.
   Google Scholar
• Miao J, Huang F, Narayan S, et al., A new perceptual difference model for diagnostically relevant quantitative image quality evaluation: A preliminary study. Magn Reson Imag. 2013;31(4):596–603.
   PubMed Web of Science ®Google Scholar
• Miao J, Huo D, Wilson DL. Quantitative image quality evaluation of MR images using perceptual difference models. Med Phys. 2008;35(6):2541–2553.
   PubMed Web of Science ®Google Scholar
• ul Haque MI, Qadri MT, Siddiqui N. Reduced reference blockiness and blurriness meter for image quality assessment. Imag Sci J. 2015;63(5):296–302.
   Web of Science ®Google Scholar
• Woodard JP, Carley-Spencer MP. No-reference image quality metrics for structural MRI. Neuroinformatics. 2006;4(3):243–262.
   PubMed Web of Science ®Google Scholar
• Mittal A, Moorthy AK, Bovik AC. No-reference image quality assessment in the spatial domain. IEEE Trans Image Process. 2012 Dec;21(12):4695–4708.
   PubMed Web of Science ®Google Scholar
• Fang Y, Ma K, Wang Z, et al., No-reference quality assessment of contrast-distorted images based on natural scene statistics. IEEE Signal Process Lett. 2015 Jul;22(7):838–842.
   Web of Science ®Google Scholar
• Mortamet B, Bernstein MA, Jack CR, et al., Automatic quality assessment in structural brain magnetic resonance imaging. Magn Reson Med. 2009;62(2):365–372.
   PubMed Web of Science ®Google Scholar
• Saad MA, Bovik AC, Charrier C. Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Trans Image Process. 2012;21(8):3339–3352.
   PubMed Web of Science ®Google Scholar
• Kim WG, Kwon OJ, Pang HS. Wavelet-based distortion measure for binary images. Imag Sci J. 2013;61(5):408–418.
   Web of Science ®Google Scholar
• Sinha N, Ramakrishnan AG. Quality assessment in magnetic resonance images. Crit Rev^TM Biomed Eng. 2010;38(2):127–141.
   PubMedGoogle Scholar
• Pedersen M, Hardeberg JY. Full-reference image quality metrics: classification and evaluation. Foundations Trends® Comput Graph Vis. 2012;7(1):1–80.
   Google Scholar
• Pedersen M. Evaluation of 60 full-reference image quality metrics on the CID: IQ. In: 2015 IEEE International Conference on Image Processing, ICIP 2015, Quebec City, QC, Canada, September 27–30, 2015; 2015. p. 1588–1592.
   Google Scholar
• Cavaro-Menard C, Zhang L, Le Callet P. Diagnostic quality assessment of medical images: challenges and trends. In: 2010 2nd European Workshop on Visual Information Processing (EUVIP); July; 2010. p. 277–284.
   Google Scholar
• Thung KH, Raveendran P. A survey of image quality measures. In: 2009 International Conference for Technical Postgraduates (TECHPOS); Kuala Lumpur, Malaysia; Dec; 2009. p. 1–4.
   Google Scholar
• Moraru L, Moldovanu SS, Obreja CD. A survey over image quality analysis techniques for brain MR images. Int J Radiol. 2015;2(1):24–28.
   Google Scholar
• Chandler DM. Seven challenges in image quality assessment: past, present, and future research. ISRN Signal Processing. 2013.
   Google Scholar
• Wang Z. Objective image quality assessment: facing the real-world challenges. In: Society for Imaging Science and Technology, International Symposium on Electronic Imaging, Image Quality and System Performance XII; 2016. p. 1–6.
   Google Scholar
• Lee JC, Su Y, Tu TM, et al., A novel approach to image quality assessment in iris recognition systems. Imag Sci J. 2010;58(3):136–145.
   Web of Science ®Google Scholar
• Cressie N. Statistics for spatial data. New York: John Wiley & Sons; 2015.
   Google Scholar
• Colliot O, Camara O, Dewynter R, et al., Description of brain internal structures by means of spatial relations for MR image segmentation. In: Fitzpatrick M, Sonka, M, editors. Medical Imaging 2004. San Diego, CA: International Society for Optics and Photonics; 2004. p. 444–455.
   Google Scholar
• Bowden DM, Martin RF. Neuronames brain hierarchy. Neuroimage. 1995;2(1):63–83.
   PubMed Web of Science ®Google Scholar
• Osadebey M, Pedersen M, Arnold D, et al., Bayesian framework inspired no-reference region-of-interest quality measure for brain MRI images. J Med Imag. 2017;4(2):025504–025504.
   Web of Science ®Google Scholar
• Wendel K, Osadebey M, Malmivuo J. Incorporating craniofacial anthropometry into realistically-shaped head models. In: Dössel O, Schlegel W, editors. World congress on medical physics and biomedical engineering; September 7–12, 2009; Munich, Germany. Berlin: Springer; 2010. p. 1706–1709. (IFMBE Proceedings; Vol. 25/4).
   Google Scholar
• Osadebey M. Simulation of realistic head geometry using radial vector representation of magnetic resonance image data [master's thesis]. Tampereen teknillinen yliopisto. Julkaisu-Tampere University of Technology; 2009.
   Google Scholar
• Wendel KE. The influence of tissue conductivity and head geometry on eeg measurement sensitivity distributions [dissertation]. Tampereen teknillinen yliopisto. Julkaisu-Tampere University of Technology; 2010.
   Google Scholar
• Shiee N, Bazin PL, Cuzzocreo JL, et al., Reconstruction of the human cerebral cortex robust to white matter lesions: method and validation. Human Brain Mapp. 2014;35(7):3385–3401.
   PubMed Web of Science ®Google Scholar
• Mugler JP, Brookeman JR. Three-dimensional magnetization-prepared rapid gradient-echo imaging (3d mp rage). Magn Reson Med. 1990;15(1):152–157.
   PubMed Web of Science ®Google Scholar
• Reimer P, Meaney JF, Parizel PM, et al., Clinical MR imaging. Berlin: Springer; 2010.
   Google Scholar
• Jack CR, Bernstein MA, Fox NC, et al., The alzheimer's disease neuroimaging initiative (adni): MRI methods. J Magn Reson Imag. 2008;27(4):685–691.
   PubMed Web of Science ®Google Scholar
• Ruderman DL. The statistics of natural images. Network: Comput Neural Syst. 1994;5(4):517–548.
   Web of Science ®Google Scholar
• Leon-Garcia A. Probability, statistics and random processes for electrical engineering. 3rd ed. Upper Saddle River (NJ): Prentice-Hall, Pearson Education Inc.; 2008. Chapter 3.
   Google Scholar
• Norman G, Streiner D. Biostatistics: the bare essentials. 3rd ed. Hamilton: People's Medical Publishing House; 2008. Chapter 4.
   Google Scholar
• Lüders E, Steinmetz H, Jäncke L. Brain size and grey matter volume in the healthy human brain. Neuroreport. 2002;13(17):2371–2374.
   PubMed Web of Science ®Google Scholar
• Grafarend EW. Linear and nonlinear models: fixed effects, random effects, and mixed models. Berlin: Walter de Gruyter; 2006.
   Google Scholar
• Coupé P, Manjón JV, Gedamu E, et al., Robust Rician noise estimation for MR images. Med Image Anal. 2010;14(4):483–493.
   PubMed Web of Science ®Google Scholar
• Deshpande AM, Patnaik S. A novel modified cepstral based technique for blind estimation of motion blur. Optik-Int J Light Electron Opt. 2014;125(2):606–615.
   Google Scholar
• Chow LS, Paramesran R. Review of medical image quality assessment. Biomed Signal Process Control. 2016;27:145–154.
   Web of Science ®Google Scholar
• Kupinski MA, Barrett HH. Small-animal spect imaging. Vol. 233. New York: Springer; 2005.
   Google Scholar
• Vovk U, Pernus F, Likar B. A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans Med Imag. 2007;26(3):405–421.
   PubMed Web of Science ®Google Scholar