Medical Image Quality Assessment

  • Yong Ding


Medical image quality assessment (MIQA) is of great significance to the development of medical imaging technology, which is widely used in computer-aided detection and diagnosis of diseases. However, MIQA evaluates the quality of images according to how well they offer useful and effective presentation to assist with physicians in diagnosing, which is greatly different from the purposes of natural image quality assessment. In this chapter, we present some of the new advances in MIQA by taking some application tasks for instances. The first case concerns evaluating the quality of portable fundus camera photographs, which is used with telemedicine and plays an important role in ophthalmology. The next example is the study on a more advanced type of imaging techniques, which is called susceptibility weighted imaging. The followed case is an adaptive paralleled sinogram noise reduction method based on relative quality assessment provided, which can increase both efficiency and performance of low-dose computed tomography (CT) noise reduction algorithms. The lastly presented study concentrates on the relationship between the image quality and imaging dose in low-dose cone beam CT.


Medical image quality assessment Portable fundus camera photographs Susceptibility weighted imaging Relative quality assessment 


  1. Acharya, T., & Ray, A. K. (2005). Image processing: Principles and applications. Wiley.Google Scholar
  2. Baumueller, S., Winklehner, A., Karlo, C., Goetti, R., Flohr, T., Russi, E. W., et al. (2012). Low-dose CT of the lung: Potential value of iterative reconstructions. European Radiology, 22(12), 2597–2606.CrossRefGoogle Scholar
  3. Beyersdorff, D., Taymoorian, K., Knösel, T., Schnorr, D., Felix, R., Hamm, B., et al. (2005). MRI of prostate cancer at 1.5 and 3.0 T: Comparison of image quality in tumor detection and staging. American Journal of Roentgenology, 185(5), 1214–1220.CrossRefGoogle Scholar
  4. Bian, J., Sharp, G. C., Park, Y., Ouyang, J., Bortfeld, T., & Fakhri, G. E. (2016). Investigation of cone-beam CT image quality trade-off for image-guided radiation therapy. Physics in Medicine & Biology, 61(9), 3317–3346.CrossRefGoogle Scholar
  5. Bohning, D. E., Lomarev, M., Denslow, S., Nahas, Z., Shastri, A., & George, M. (2001). Feasibility of vagus nerve stimulation–synchronized blood oxygenation level–dependent functional MRI. Investigative Radiology, 36(8), 470–479.CrossRefGoogle Scholar
  6. Brenner, D. J., Elliston, C. D., Hall, E. J., & Berdon, W. E. (2001). Estimated risks of radiation-induced fatal cancer from pediatric CT. American Journal of Roentgenology, 176(2), 289–296.CrossRefGoogle Scholar
  7. Brenner, D. J., & Hall, E. J. (2007). Computed tomography—An increasing source of radiation exposure. The New England Journal of Medicine, 357(22), 2277–2284.CrossRefGoogle Scholar
  8. Cavaro-Ménard, C., Zhang, L., & Callet, P. L. (2010). Diagnostic quality assessment of medical images: Challenges and trends. In 2nd European Workshop on Visual Information Processing, Paris, France. Piscataway, USA: IEEE, pp. 277–284.Google Scholar
  9. Chang, C. C., & Lin, C. J. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 1–27.CrossRefGoogle Scholar
  10. Chapman, D., Thomlinson, W., Johnston, R. E., Washburn, D., Pisano, E., Gmür, N., et al. (1997). Diffraction enhanced x-ray imaging. Physics in Medicine & Biology, 42(11), 2015–2025.CrossRefGoogle Scholar
  11. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.MATHGoogle Scholar
  12. Cosman, P. C., Gray, R. M., & Olshen, R. A. (1994). Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic accuracy. Proceedings of the IEEE, 82(6), 919–932.CrossRefGoogle Scholar
  13. Cunningham, P. M., Brennan, D., O’Connell, M., Macmahon, P., O’Neill, P., & Eustace, S. (2007). Patterns of bone and soft-tissue injury at the symphysis pubis in soccer players: Observations at MRI. American Journal of Roentgenology, 188(3), W291–W296.CrossRefGoogle Scholar
  14. Daly, M., Siewerdsen, J., Moseley, D., Jaffray, D., & Irish, J. (2006). Intraoperative cone-beam CT for guidance of head and neck surgery: Assessment of dose and image quality using a C-arm prototype. Medical Physics, 33(10), 3767–3780.CrossRefGoogle Scholar
  15. Deák, Z., Grimm, J. M., Treitl, M., Geyer, L. L., Linsenmaier, U., Körner, M., et al. (2013). Filtered back projection, adaptive statistical iterative reconstruction, and a model-based iterative reconstruction in abdominal CT: An experimental clinical study. Radiology, 266(1), 197–206.CrossRefGoogle Scholar
  16. Deng, C., Ma, L., Lin, W., & Ngan, K. N. (2015). Visual signal quality assessment. Switzerland: Springer International Publishing.CrossRefGoogle Scholar
  17. Denk, C., & Rauscher, A. (2010). Susceptibility weighted imaging with multiple echoes. Journal of Magnetic Resonance Imaging, 31(1), 185–191.CrossRefGoogle Scholar
  18. Dias, J. M. P., Oliveira, C. M., & Cruz, L. A. D. S. (2014). Retinal image quality assessment using generic image quality indicators. Information Fusion, 19(1), 73–90.CrossRefGoogle Scholar
  19. Ding, G. X., & Coffey, C. W. (2009). Radiation dose from kilovoltage cone beam computed tomography in an image-guided radiotherapy procedure. International Journal of Radiation Oncology Biology Physics, 73(2), 610–617.CrossRefGoogle Scholar
  20. Ding, Y., Dai, H., & Wang, S. Z. (2014). Image quality assessment scheme with topographic independent components analysis for sparse feature extraction. Electronics Letters, 50(7), 509–510.CrossRefGoogle Scholar
  21. Dobbin, J. T., III, Samei, E., Ranger, N. T., & Chen, Y. (2006). Intercomparison of methods for image quality characterization. II. Noise power spectrum. Medical Physics, 33(5), 1466–1475.CrossRefGoogle Scholar
  22. Ehman, E. C., Guimarães, L. S., Fidler, J. L., Takahashi, N., Ramirez-Giraldo, J. C., Yu, L., et al. (2012). Noise reduction to decrease radiation dose and improve conspicuity of hepatic lesions at contrast-enhanced 80-kV hepatic CT using projection space denoising. American Journal of Roentgenology, 198(2), 405–411.CrossRefGoogle Scholar
  23. Elbakri, I. A., & Fessler, J. A. (2002). Statistical image reconstruction for polyenergetic X-ray computed tomography. IEEE Transactions on Medical Imaging, 21(2), 89–99.CrossRefGoogle Scholar
  24. Fasih, M., Langlois, J. M. P., Tahar, H. B., & Cheriet, F. (2014). Retinal image quality assessment using generic features. In Proceedings of SPIE (Vol. 9035, pp. 90352Z).Google Scholar
  25. Feldkamp, L., Davis, L., & Kress, J. (1984). Practical cone-beam algorithm. Journal of the Optical Society of America A, 1(6), 612–619.CrossRefGoogle Scholar
  26. Ferzli, R., & Karam, L. J. (2009). A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB). IEEE Transactions on Image Processing, 18(4), 717–728.MathSciNetCrossRefMATHGoogle Scholar
  27. Fessler, J. A., & Booth, S. D. (1999). Conjugate-gradient preconditioning methods for shift-variant PET image reconstruction. IEEE Transactions on Image Processing, 8(5), 688–699.MathSciNetCrossRefMATHGoogle Scholar
  28. Fleming, A. D., Philip, S., Goatman, K. A., Olson, J. A., & Sharp, P. F. (2006). Automated assessment of diabetic retinal image quality based on clarity and field definition. Investigative Ophthalmology & Visual Science, 47(3), 1120–1125.CrossRefGoogle Scholar
  29. Gao, H. (2012). Fast parallel algorithms for the x-ray transform and its adjoint. Medical Physics, 39(11), 7110–7120.CrossRefGoogle Scholar
  30. Ghrare, S. E., Ali, M. A. M., Ismail, M., & Jumari, K. (2008). Diagnostic quality of compressed medical images: Objective and subjective evaluation. In International Conference on Modeling & Simulation, 2008, AICMS 08. Second Asia.Google Scholar
  31. Giancardo, L., Abramoff, M. D., Chaum, E., Karnowski, T. P., Meriaudeau, F., & Tobin, K. W. (2008). Elliptical local vessel density: A fast and robust quality metric for retinal images. In 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008.Google Scholar
  32. Ginesu, G., Massidda, F., & Giusto, D. D. (2006). A multi-factors approach for image quality assessment based on a human visual system model. Signal Processing: Image Communication, 21(4), 316–333.Google Scholar
  33. Gonzalez, A. B. D., & Darby, S. (2004). Risk of cancer from diagnostic X-rays: Estimates for the UK and 14 other countries. Lancet, 363(9406), 345–351.CrossRefGoogle Scholar
  34. Goossens, B., Luong, H., Platiša, L., & Philips, W. (2012). Optimizing image quality using test signals: Trading off blur, noise and contrast. In 4th International Workshop on Quality of Multimedia Experience, Yarra Valley, VIC, Australia (pp. 260–265). Piscataway, USA: IEEE.Google Scholar
  35. Grills, I. S., Hugo, G., Kestin, L. L., Galerani, A. P., Chao, K. K., Wloch, J., et al. (2008). Image-guided radiotherapy via daily online cone-beam CT substantially reduces margin requirements for stereotactic lung radiotherapy. International Journal of Radiation Oncology Biology Physics, 70(4), 1045–1056.CrossRefGoogle Scholar
  36. Haacke, E. M., Mittal, S., Wu, Z., & Neelavalli, J. (2009). Susceptibility-weighted imaging: Technical aspects and clinical applications, part 1. American Journal of Neuroradiology, 30(1), 19–30.CrossRefGoogle Scholar
  37. Han, X., Pearson, E., Bian, J., Cho, S., Sidky, E. Y., Pelizzari, C. A., & Pan, X. (2010). Preliminary investigation of dose allocation in low-dose cone-beam CT. In NSS/MIC: IEEE Nuclear Science Symposium & Medical Imaging Conference, Record (pp. 2051–2054). Knoxville, TN.Google Scholar
  38. Han, X., Pearson, E., Pelizzari, C., Al-Hallaq, H., Sidky, E. Y., Bian, J., et al. (2015). Algorithm-enabled exploration of image-quality potential of cone-beam CT in image-guided radiation therapy. Physics in Medicine & Biology, 60(12), 4601–4633.CrossRefGoogle Scholar
  39. Horie, N., Morikawa, M., Nozaki, A., Hayashi, K., Suyama, K., & Nagata, I. (2011). “Brush sign” on susceptibility-weighted MR imaging indicates the severity of moyamoya disease. American Journal of Neuroradiology, 32(9), 1697–1702.CrossRefGoogle Scholar
  40. Hoxworth, J., Lal, D., Fletcher, G., Patel, A., He, M., Paden, R., et al. (2014). Radiation dose reduction in paranasal sinus CT using model-based iterative reconstruction. AJNR American Journal of Neuroradiology, 35(4), 1–6.CrossRefGoogle Scholar
  41. Hua, Y., Liu, L., & Zhao, Q. (2015). Medical image quality assessment via contrast masking. In 8th International Congress on Image and Signal Processing (CISP), Shenyang, China (pp. 964–968). Piscataway, USA: IEEE.Google Scholar
  42. Iftekharuddin, K. M., Zheng, J., Islam, M. A., & Ogg, R. J. (2009). Fractal-based brain tumor detection in multimodal MRI. Applied Mathematics and Computation, 207(1), 23–41.MathSciNetCrossRefMATHGoogle Scholar
  43. Islam, M. K., Purdie, T. G., Norrlinger, B. D., Alasti, H., Moseley, D. J., Sharpe, M. B., et al. (2006). Patient dose from kilovoltage cone beam computed tomography imaging in radiation therapy. Medical Physics, 33(6), 1573–1582.CrossRefGoogle Scholar
  44. Jaffray, D. A., Siewerdsen, J. H., Wong, J. W., & Martinez, A. A. (2002). Flat-panel cone-beam computed tomography for image-guided radiation therapy. International Journal of Radiation Oncology Biology Physics, 53(5), 1337–1349.CrossRefGoogle Scholar
  45. Jain, A. K., Duin, R. P. W., & Mao, J. (2000). Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 4–37.CrossRefGoogle Scholar
  46. Jensen-Kondering, U., & Böhm, R. (2013). Asymmetrically hypointense veins on T2* w imaging and susceptibility-weighted imaging in ischemic stroke. World Journal of Radiology, 5(4), 156–165.CrossRefGoogle Scholar
  47. Jin, K., Lu, H., Su, Z., Cheng, C., Ye, J., & Qian, D. (2017). Telemedicine screening of retinal diseases with a handheld portable non-mydriatic fundus camera. BMC Ophthalmology, 17(1), 89.CrossRefGoogle Scholar
  48. Karimi, D., Deman, P., Ward, R., & Ford, N. (2016). A sinogram denoising algorithm for low-dose computed tomography. BMC Medical Imaging, 16(1), 11.CrossRefGoogle Scholar
  49. Kawaguchi, A., Sharafeldin, N., Sundaram, A., Campbell, S., Tennant, M., Rudnisky, C., Weis, E., & Damji, K. F. (2017). Tele-ophthalmology for age-related macular degeneration and diabetic retinopathy screening: A systematic review and meta-analysis. Telemedicine and E-Health.Google Scholar
  50. Keller, J. M., Gray, M. R., & Givens, J. A. (1985). A fuzzy k-nearest neighbor algorithm. IEEE Transactions on Systems, Man, and Cybernetics, 15(4), 580–585.CrossRefGoogle Scholar
  51. Khieovongphachanh, V., Hamamoto, K., & Kondo, S. (2008). Study on image quality for medical ultrasonic echo image compression by wavelet transform. In International Symposium on Communications and Information Technologies (ISCIT 2008) (pp. 160–165).Google Scholar
  52. Kim, S., Yoshizumi, T. T., Frush, D. P., Toncheva, G., & Yin, F. F. (2010). Radiation dose from cone beam CT in a pediatric phantom: Risk estimation of cancer incidence. AJR American Journal of Roentgenology, 194(1), 186–190.CrossRefGoogle Scholar
  53. Kircher, M. F., de la Zerda, A., Jokerst, J. V., Zavaleta, C. L., Kempen, P. J., Mittra, E., et al. (2012). A brain tumor molecular imaging strategy using a new triple-modality MRI-photoacoustic-Raman nanoparticle. Nature Medicine, 18(5), 829–834.CrossRefGoogle Scholar
  54. Koopmans, P. J., Manniesing, R., Niessen, W. J., Viergever, M. A., & Barth, M. (2008). MR venography of the human brain using susceptibility weighted imaging at very high field strength. Magnetic Resonance Materials in Physics, Biology and Medicine, 21(1), 149–158.CrossRefGoogle Scholar
  55. Krupinski, E. A., & Jiang, Y. (2008). Anniversary paper: Evaluation of medical imaging systems. Medical Physics, 35(2), 645–659.CrossRefGoogle Scholar
  56. Lee, S. C., & Wang, Y. (1999). Automatic retinal image quality assessment and enhancement. Proceedings of SPIE Image Processing, 3661, 1581–1590.Google Scholar
  57. Leng, S., Yu, L., Zhang, Y., Carter, R., Toledano, A. Y., & McCollough, C. H. (2013). Correlation between model observer and human observer performance in CT imaging when lesion location is uncertain. Medical Physics, 40(8), 081908.CrossRefGoogle Scholar
  58. Li, T., Li, X., Wang, J., Wen, J., Lu, H., Hsieh, J., et al. (2004). Nonlinear sinogram smoothing for low-dose X-ray CT. IEEE Transactions on Nuclear Science, 51(5), 2505–2513.CrossRefGoogle Scholar
  59. Li, Z., Yu, L., Trzasko, J. D., Lake, D. S., Blezek, D. J., Fletcher, J. G., et al. (2014). Adaptive nonlocal means filtering based on local noise level for CT denoising. Medical Physics, 41(1), 011908.CrossRefGoogle Scholar
  60. Lichy, M. P., Aschoff, P., Plathow, C., Stemmer, A., Horger, W., Mueller-Horvat, C., et al. (2007). Tumor detection by diffusion-weighted MRI and ADC-mapping—Initial clinical experiences in comparison to PET-CT. Investigative Radiology, 42(9), 605–613.CrossRefGoogle Scholar
  61. Liu, J., He, J., Chen, H., Ma, L., Zhang, Q., Pan, L. (2012). A comparative study of assessment methods for medical image quality. In 5th International Conference on Biomedical Engineering and Informatics (BMEI), Chongqing, China (131–134). Piscataway, USA: IEEE.Google Scholar
  62. Manduca, A., Yu, L., Trzasko, J. D., Khaylova, N., Kofler, J. M., McCollough, C. M., et al. (2009). Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT. Medical Physics, 36(11), 4911–4919.CrossRefGoogle Scholar
  63. Mansouri, A., Aznaveh, A. M., Torkamani-Azar, F., & Jahanshahi, J. A. (2009). Image quality assessment using the singular value decomposition theorem. Optical Review, 16(2), 49–53.CrossRefGoogle Scholar
  64. Marrugoa, A. G., Millán, M. S., Šorel, M., Kotera, J., & Šroubek, F. (2015). Improving the blind restoration of retinal images by means of point-spread-function estimation assessment. In Tenth International Symposium on Medical Information Processing and Analysis (Vol. 9287, pp 92871D).Google Scholar
  65. Matenine, D., Goussard, Y., & Després, P. (2015). GPU-accelerated regularized iterative reconstruction for few-view cone beam CT. Medical Physics, 42(4), 1505–1517.CrossRefGoogle Scholar
  66. McBain, C. A., Henry, A. M., Sykes, J., Amer, A., Marchant, T., Moore, C. M., et al. (2006). X-ray volumetric imaging in image-guided radiotherapy: the new standard in on-treatment imaging. International Journal of Radiation Oncology Biology Physics, 64(2), 625–634.CrossRefGoogle Scholar
  67. Morita, N., Harada, M., Uno, M., Matsubara, S., Matsuda, T., Nagahiro, S., et al. (2008). Ischemic findings of T2*-weighted 3-tesla MRI in acute stroke patients. Cerebrovascular Diseases, 26(4), 367–375.CrossRefGoogle Scholar
  68. Mucke, J., Möhlenbruch, M., Kickingereder, P., Kieslich, P. J., Bäumer, P., Gumbinger, C., et al. (2015). Asymmetry of deep medullary veins on susceptibility weighted MRI in patients with acute MCA stroke is associated with poor outcome. PLoS ONE, 10(4), e0120801.CrossRefGoogle Scholar
  69. Narvekar, N. D., & Karam, L. J. (2010). An improved no-reference sharpness metric based on the probability of blur detection. In Workshop on Video Processing and Quality Metrics.Google Scholar
  70. Narvekar, N. D., & Karam, L. J. (2011). A no-reference image blur metric based on the cumulative probability of blur detection (CPBD). IEEE Transactions on Image Processing, 20(9), 2678–2683.MathSciNetCrossRefMATHGoogle Scholar
  71. Neitzel, U., Gunther-Kohfahl, S., Borasi, G., & Samei, E. (2004). Determination of the detective quantum efficiency of a digital X-ray detector: Comparison of three evaluations using a common image data set. Medical Physics, 31(8), 2205–2211.CrossRefGoogle Scholar
  72. Ogawa, S., Lee, T. M., Kay, A. R., & Tank, D. W. (1990). Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences, 87(24), 9868–9872.CrossRefGoogle Scholar
  73. Othman, A. E., Brockmann, C., Yang, Z., Kim, C., Afat, S., Pjontek, R., et al. (2016). Impact of image denoising on image quality, quantitative parameters and sensitivity of ultra-low-dose volume perfusion CT imaging. European Radiology, 26(1), 167–174.CrossRefGoogle Scholar
  74. Pambrun, J., & Noumeir, R. (2013). Compressibility variations of JPEG2000 compressed computed tomography. In 35th Annual International Conference of the IEEE EMBS, Osaka, Japan (pp. 3375–3378).Google Scholar
  75. Paulus, J., Meier, J., Bock, R., Hornegger, J., & Michelson, G. (2010). Automated quality assessment of retinal fundus photos. International Journal of Computer Assisted Radiology and Surgery, 5(6), 557–564.CrossRefGoogle Scholar
  76. Ramirez-Giraldo, J. C., Trzasko, J., Leng, S., Yu, L., Manduca, A., & McCollough, C. H. (2011). Nonconvex prior image constrained compressed sensing (NCPICCS): Theory and simulations on perfusion CT. Medical Physics, 38(4), 2157–2167.CrossRefGoogle Scholar
  77. Reichenbach, J. R., Barth, M., Haacke, E. M., Klarhöfer, M., Kaiser, W. A., & Moser, E. (2000). High-resolution MR venography at 3.0 Tesla. Journal of Computer Assisted Tomography, 24(6), 949–957.CrossRefGoogle Scholar
  78. Samei, E., Ranger, N. T., Dobbins, J. T., III, & Chen, Y. (2006). Intercomparison of methods for image characterization. I. Modulation transfer function. Medical Physics, 33(5), 1454–1465.CrossRefGoogle Scholar
  79. Schuhbaeck, A., Achenbach, S., Layritz, C., Eisentopf, J., Hecker, F., Pflederer, T., et al. (2013). Image quality of ultra-low radiation exposure coronary CT angiography with an effective dose <0.1 mSv using high-pitch spiral acquisition and raw data-based iterative reconstruction. European Radiology, 23(3), 597–606.CrossRefGoogle Scholar
  80. Şevik, U., Köse, C., Berber, T., & Erdöl, H. (2014). Identification of suitable fundus images using automated quality assessment methods. Journal of Biomedical Optics, 19(4), 046006.CrossRefGoogle Scholar
  81. Sheikh, H. R., Sabir, M. F., & Bovik, A. C. (2006). A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing, 15(11), 3441–3452.CrossRefGoogle Scholar
  82. Shepp, L. A., & Logan, B. F. (1974). The Fourier reconstruction of a head section. IEEE Transactions on Nuclear Science, 21(3), 21–43.CrossRefGoogle Scholar
  83. Shnayderman, A., Gusev, A., & Eskicioglu, A. M. (2006). An SVD-based grayscale image quality measure for local and global assessment. IEEE Transactions on Image Processing, 15(2), 422–429.CrossRefGoogle Scholar
  84. Siddon, R. L. (1985). Fast calculation of the exact radiological path for a three-dimensional CT array. Medical Physics, 12(2), 252–255.CrossRefGoogle Scholar
  85. Sidky, E. Y., Duchin, Y., & Pan, X. (2011). A constrained, total-variation minimization algorithm for low-intensity X-ray CT. Medical Physics, 38(S1), S117–S125.CrossRefGoogle Scholar
  86. Sutha, V. J., & Latha, P. (2011). Wavelet based quality enhancement for medical images. In International Conference on Recent Advancements in Electrical, Electronics and Control Engineering, Sivakasi, India (pp. 277–280). Piscataway, USA: IEEE.Google Scholar
  87. Szabo, T. L. (2004). Diagnostic ultrasound imaging: Inside out. Academic Press.Google Scholar
  88. Tang, J., Nett, B.E., & Chen, G.H. (2009). Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms. Physics in Medicine & Biology, 54(19): 5781.Google Scholar
  89. Tian, P., Teng, I. C., May, L. D., Kurz, R., Lu, K., Scadeng, M., et al. (2010). Cortical depth-specific microvascular dilation underlies laminar differences in blood oxygenation level-dependent functional MRI signal. Proceedings of the National Academy of Sciences, 107(34), 15246–15251.CrossRefGoogle Scholar
  90. Toet, A., & Lucassen, M. P. (2003). A new universal colour image fidelity metric. Displays, 24(4), 197–207.CrossRefGoogle Scholar
  91. Tsai, D. Y., Lee, Y., & Matsuyama, E. (2008). Information entropy measure for evaluation of image quality. Journal of Digital Imaging, 21(3), 338–347.CrossRefGoogle Scholar
  92. Vaccaro, A. R., Madigan, L., Schweitzer, M. E., Flanders, A. E., Hilibrand, A. S., & Albert, T. J. (2001). Magnetic resonance imaging analysis of soft tissue disruption after flexion-distraction injuries of the subaxial cervical spine. Spine, 26(17), 1866–1872.CrossRefGoogle Scholar
  93. Wagner, R. F., Metz, C. E., & Campbell, G. (2007). Assessment of medical imaging system and computer aids: A tutorial review. Academic Radiology, 14(6), 723–748.CrossRefGoogle Scholar
  94. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612.CrossRefGoogle Scholar
  95. Wang, S., Ding, Y., Dai, H., Qian, D., Yu, X., & Zhang, M. (2014). Generalized relative quality assessment scheme for reconstructed medical images. Bio-Medical Materials and Engineering, 24(6), 2865–2873.Google Scholar
  96. Wang, J., Li, T., Lu, H., & Liang, Z. (2006). Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray computed tomography. IEEE Transactions on Medical Imaging, 25(10), 1272–1283.CrossRefGoogle Scholar
  97. Wang, C., Song, R., Yerfan, J., Yang, L., Wang, S., Zhang, M., et al. (2016). A comparison study of single-echo susceptibility weighted imaging and combined multi-echo susceptibility weighted imaging in visualizing asymmetric medullary veins in stroke patients. PLoS ONE, 11(8), e0159251.CrossRefGoogle Scholar
  98. Xu, Q., Yang, D., Tan, J., Sawatzky, A., & Anastasio, M. A. (2016). Accelerated fast iterative shrinkage thresholding algorithms for sparsity-regularized cone-beam CT image reconstruction. Medical Physics, 43(4), 1849–1872.CrossRefGoogle Scholar
  99. Xu, Q., Yu, H., Mou, X., Zhang, L., Hsieh, J., & Wang, G. (2012). Low-dose X-ray CT reconstruction via dictionary learning. IEEE Transactions on Medical Imaging, 31(9), 1682–1697.CrossRefGoogle Scholar
  100. Xue, W., Zhang, L., Mou, X., & Bovik, A. C. (2014). Gradient magnitude similarity deviation: A highly efficient perceptual image quality index. IEEE Transactions on Image Processing, 23(2), 684–695.MathSciNetCrossRefMATHGoogle Scholar
  101. Yan, H., Cervino, L., Jia, X., & Jiang, S. B. (2012a). A comprehensive study on the relationship between the image quality and imaging dose in low dose CBCT. Physics in Medicine & Biology, 57(7), 2063–2080.CrossRefGoogle Scholar
  102. Yan, S., Sun, J. Z., Yan, Y. Q., Wang, H., & Lou, M. (2012b). Evaluation of brain iron content based on magnetic resonance imaging (MRI): comparison among phase value, R2* and magnitude signal intensity. PLoS ONE, 7(2), e31748.CrossRefGoogle Scholar
  103. Yan, H., Wang, X., Shi, F., Bai, T., Folkerts, M., Cervino, L., et al. (2014). Towards the clinical implementation of iterative low-dose cone-beam CT reconstruction in image-guided radiation therapy: Cone/ring artifact correction and multiple GPU implementation. Medical Physics, 41(11), 119912.Google Scholar
  104. Yang, M. H., Kriegman, D. J., & Ahuja, N. (2002). Detecting faces in images: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1), 34–58.CrossRefGoogle Scholar
  105. Yu, H., & Cai, Y. (2014). Contrast sensitivity function calibration based on image quality prediction. Optical Engineering, 53(11), 113107.CrossRefGoogle Scholar
  106. Zana, F., & Klein, J. C. (2001). Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Transactions on Image Processing, 10(7), 1010–1019.CrossRefMATHGoogle Scholar
  107. Zeileis, A., Smola, A., & Hornik, K. (2004). kernlab-an S4 package for kernel methods in R. Journal of Statistical Software, 11(9), 1–20.Google Scholar
  108. Zhang, L., Cavaro-Ménard, C., Callet, P. L., & Ge, D. (2015). A multi-slice model observer for medical image quality assessment. In International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, Australia (pp. 1667–1671). Piscataway, USA: IEEE.Google Scholar
  109. Zhang, L., Cavaro-Menard, C., Callet, P. L., & Tanguy, J. Y. (2012). A perceptually relevant channelized joint observer (PCJO) for the detection-localization of parametric signals. IEEE Transactions on Medical Imaging, 31(10), 1875–1888.CrossRefGoogle Scholar
  110. Zhang, Y., & Chandler, D. M. (2013). No-reference image quality assessment based on log-derivative statistics of natural scenes. Journal of Electronic Imaging, 22(4), 1–23.Google Scholar
  111. Zhang, Y., Leng, S., Yu, L., Carter, R., & McCollough, C. H. (2014). Correlation between human and model observer performance for discrimination task in CT. Physics in Medicine & Biology, 59(13), 3389–3404.CrossRefGoogle Scholar
  112. Zhu, Y., & Ding, Y. (2017). Auto-optimized paralleled sinogram noise reduction method based on relative quality assessment for low-dose X-ray CT. Journal of Medical Imaging and Health Informatics, 7(1), 278–282.CrossRefGoogle Scholar

Copyright information

© Zhejiang University Press, Hangzhou and Springer-Verlag GmbH Germany 2018

Authors and Affiliations

  1. 1.Zhejiang UniversityHangzhouChina

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