The Relative Impact of Ghosting and Noise on the Perceived Quality of MR Images

Magnetic resonance (MR) imaging is vulnerable to a variety of artifacts, which potentially degrade the perceived quality of MR images and, consequently, may cause inefficient and/or inaccurate diagnosis. In general, these artifacts can be classified as structured or unstructured depending on the correlation of the artifact with the original content. In addition, the artifact can be white or colored depending on the flatness of the frequency spectrum of the artifact. In current MR imaging applications, design choices allow one type of artifact to be traded off with another type of artifact. Hence, to support these design choices, the relative impact of structured versus unstructured or colored versus white artifacts on perceived image quality needs to be known. To this end, we conducted two subjective experiments. Clinical application specialists rated the quality of MR images, distorted with different types of artifacts at various levels of degradation. The results demonstrate that unstructured artifacts deteriorate quality less than structured artifacts, while colored artifacts preserve quality better than white artifacts.

[1]  Julien Milles,et al.  Quantitative evaluation of Compressed Sensing in MRI: Application to 7T time-of-flight angiography , 2010, Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine.

[2]  Oleg S. Pianykh,et al.  Digital Imaging and Communications in Medicine (DICOM) , 2017, Radiopaedia.org.

[3]  J. Clark,et al.  Common artifacts encountered in magnetic resonance imaging. , 1988, Radiologic clinics of North America.

[4]  E Krupinski,et al.  Influence of film and monitor display luminance on observer performance and visual search. , 1999, Academic radiology.

[5]  Ehsan Samei,et al.  Assessment of display performance for medical imaging systems: executive summary of AAPM TG18 report. , 2005, Medical physics.

[6]  Bradley M. Hemminger,et al.  Introduction to perceptual linearization of video display systems for medical image presentation , 1995, Journal of Digital Imaging.

[7]  Charles E. Willis,et al.  Artifacts and misadventures in digital radiography , 2004 .

[8]  Ineke M. C. J. van Overveld Contrast, noise, and blur affect performance and appreciation of digital radiographs , 2009, Journal of Digital Imaging.

[9]  E A Krupinski,et al.  Perception research in medical imaging. , 2005, The British journal of radiology.

[10]  ジョルジ ゴレイ,クサヴィール,et al.  Method and apparatus for magnetic resonance imaging , 1998 .

[11]  A Burgess,et al.  Image quality, the ideal observer, and human performance of radiologic decision tasks. , 1995, Academic radiology.

[12]  Stewart C. Bushong,et al.  Magnetic Resonance Imaging: Physical and Biological Principles , 1988 .

[13]  H L Kundel,et al.  Images, image quality and observer performance: new horizons in radiology lecture. , 1979, Radiology.

[14]  M. S. Chesters,et al.  Human visual perception and ROC methodology in medical imaging. , 1992, Physics in medicine and biology.

[15]  Elizabeth A Krupinski,et al.  The future of image perception in radiology: synergy between humans and computers. , 2003, Academic radiology.

[16]  H L Kundel,et al.  Update on long-term goals for medical image perception research. , 1998, Academic radiology.

[17]  P F Judy,et al.  The Medical Image Perception Society. Key issues for image perception research. , 1998, Radiology.

[18]  H L Kundel,et al.  Visual perception and image display terminals. , 1986, Radiologic clinics of North America.

[19]  Miha Fuderer,et al.  Minimal Artifact Factor SENSE , 2011 .

[20]  H Yan,et al.  Motion artifact suppression: a review of post-processing techniques. , 1992, Magnetic resonance imaging.

[21]  Peter Schelkens,et al.  Visual quality assessment of H.264/AVC compressed laparoscopic video , 2014 .

[22]  Ljiljana Platisa Image quality assessment : utility, beauty, appearance , 2014 .

[23]  Charles E Metz,et al.  ROC analysis in medical imaging: a tutorial review of the literature , 2008, Radiological physics and technology.

[24]  Ingrid Heynderickx,et al.  A No-Reference Metric for Perceived Ringing Artifacts in Images , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[25]  Sugato Chakravarty,et al.  Methodology for the subjective assessment of the quality of television pictures , 1995 .

[26]  Jon-Fredrik Nielsen,et al.  Automatic correction of echo‐planar imaging (EPI) ghosting artifacts in real‐time interactive cardiac MRI using sensitivity encoding , 2008, Journal of magnetic resonance imaging : JMRI.

[27]  Rick Archibald,et al.  A method to reduce the Gibbs ringing artifact in MRI scans while keeping tissue boundary integrity , 2002, IEEE Transactions on Medical Imaging.

[28]  Ketty Savino,et al.  A Study on Quality Assessment for Medical Ultrasound Video Compressed via HEVC , 2014, IEEE Journal of Biomedical and Health Informatics.

[29]  S A Mirowitz MR imaging artifacts. Challenges and solutions. , 1999, Magnetic resonance imaging clinics of North America.

[30]  R. Freeman A handbook of nuclear magnetic resonance , 1987 .

[31]  Alan C. Bovik,et al.  A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms , 2006, IEEE Transactions on Image Processing.

[32]  Kundel Hl,et al.  Visual perception and image display terminals , 1986 .

[33]  Ingrid Heynderickx,et al.  Studying the relative impact of ghosting and noise on the perceived quality of MR images , 2012, Medical Imaging.

[34]  Travis B. Smith,et al.  MRI artifacts and correction strategies , 2010 .

[35]  E. Krupinski,et al.  The importance of perception research in medical imaging. , 2000, Radiation medicine.

[36]  M Fuderer,et al.  The information content of MR images. , 1988, IEEE transactions on medical imaging.

[37]  M Tapiovaara Image quality measurements in radiology. , 2005, Radiation protection dosimetry.

[38]  Zhou Wang,et al.  Modern Image Quality Assessment , 2006, Modern Image Quality Assessment.

[39]  C J Kotre,et al.  Pilot study into optimisation of viewing conditions for electronically displayed images. , 2005, Radiation protection dosimetry.

[40]  W N Hanafee,et al.  Magnetic resonance imaging artifacts: mechanism and clinical significance. , 1986, Radiographics : a review publication of the Radiological Society of North America, Inc.

[41]  J G Pipe,et al.  PROPELLER MRI: Clinical testing of a novel technique for quantification and compensation of head motion , 2001, Journal of magnetic resonance imaging : JMRI.

[42]  I M van Overveld Contrast, noise, and blur affect performance and appreciation of digital radiographs. , 1995, Journal of digital imaging.

[43]  E R McVeigh,et al.  Quantification and reduction of ghosting artifacts in interleaved echo‐planar imaging , 1997, Magnetic resonance in medicine.

[44]  H Jara,et al.  Motion artifact control in body MR imaging. , 1999, Magnetic resonance imaging clinics of North America.