Image quality in CT: From physical measurements to model observers.

Evaluation of image quality (IQ) in Computed Tomography (CT) is important to ensure that diagnostic questions are correctly answered, whilst keeping radiation dose to the patient as low as is reasonably possible. The assessment of individual aspects of IQ is already a key component of routine quality control of medical x-ray devices. These values together with standard dose indicators can be used to give rise to 'figures of merit' (FOM) to characterise the dose efficiency of the CT scanners operating in certain modes. The demand for clinically relevant IQ characterisation has naturally increased with the development of CT technology (detectors efficiency, image reconstruction and processing), resulting in the adaptation and evolution of assessment methods. The purpose of this review is to present the spectrum of various methods that have been used to characterise image quality in CT: from objective measurements of physical parameters to clinically task-based approaches (i.e. model observer (MO) approach) including pure human observer approach. When combined together with a dose indicator, a generalised dose efficiency index can be explored in a framework of system and patient dose optimisation. We will focus on the IQ methodologies that are required for dealing with standard reconstruction, but also for iterative reconstruction algorithms. With this concept the previously used FOM will be presented with a proposal to update them in order to make them relevant and up to date with technological progress. The MO that objectively assesses IQ for clinically relevant tasks represents the most promising method in terms of radiologist sensitivity performance and therefore of most relevance in the clinical environment.

[1]  Ehsan Samei,et al.  Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology. , 2014, Medical physics.

[2]  William Hendee,et al.  The Handbook of Medical Image Perception and Techniques. , 2010, Medical physics.

[3]  Adam Wunderlich,et al.  Utility as a rationale for choosing observer performance assessment paradigms for detection tasks in medical imaging. , 2013, Medical physics.

[4]  François O Bochud,et al.  Effects of computing parameters and measurement locations on the estimation of 3D NPS in non-stationary MDCT images. , 2013, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[5]  M F McNitt-Gray,et al.  Application of the noise power spectrum in modern diagnostic MDCT: part I. Measurement of noise power spectra and noise equivalent quanta , 2007, Physics in medicine and biology.

[6]  Michael F. McNitt-Gray,et al.  Effect of Edge-Preserving Adaptive Image Filter on Low-Contrast Detectability in CT Systems: Application of ROC Analysis , 2008, Int. J. Biomed. Imaging.

[7]  Noboru Niki,et al.  A method for determining the modulation transfer function from thick microwire profiles measured with x-ray microcomputed tomography. , 2012, Medical physics.

[8]  A. Burgess Statistically defined backgrounds: performance of a modified nonprewhitening observer model. , 1994, Journal of the Optical Society of America. A, Optics, image science, and vision.

[9]  Jean-Baptiste Thibault,et al.  A three-dimensional statistical approach to improved image quality for multislice helical CT. , 2007, Medical physics.

[10]  J. C. Dainty,et al.  Image Science: Principles, Analysis and Evaluation of Photographic-Type Imaging Processes , 1974 .

[11]  A Burgess,et al.  Visual signal detection. III. On Bayesian use of prior knowledge and cross correlation. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[12]  Kristina Hellén-Halme,et al.  A phantom for simplified image quality control of dental cone beam computed tomography units. , 2014, Oral surgery, oral medicine, oral pathology and oral radiology.

[13]  R. F. Wagner,et al.  SNR and DQE analysis of broad spectrum X-ray imaging , 1985 .

[14]  F R Verdun,et al.  Computed tomography commissioning programmes: how to obtain a reliable MTF with an automatic approach? , 2010, Radiation protection dosimetry.

[15]  Jessica C. Ramella-Roman,et al.  Three-dimensional phantoms for curvature correction in spatial frequency domain imaging , 2012, Biomedical optics express.

[16]  Ehsan Samei,et al.  Erratum: "Contrast-detail analysis of three flat panel detectors for digital radiography" [Med. Phys., - (2006)]. , 2006, Medical physics.

[17]  Ce. Metz,et al.  Receiver operating characteristic (ROC) analysis in medical imaging , 1997 .

[18]  F R Verdun,et al.  Image quality assessment in digital mammography: part II. NPWE as a validated alternative for contrast detail analysis , 2011, Physics in medicine and biology.

[19]  Åke Björck,et al.  Numerical Methods , 1995, Handbook of Marine Craft Hydrodynamics and Motion Control.

[20]  John M Boone,et al.  Reply to "Comment on the 'Report of AAPM TG 204: Size-specific dose estimates (SSDE) in pediatric and adult body CT examinations'" [AAPM Report 204, 2011]. , 2012, Medical physics.

[21]  M J Tapiovaara Efficiency of low-contrast detail detectability in fluoroscopic imaging. , 1997, Medical physics.

[22]  K. Doi,et al.  Radiologists' performance for differentiating benign from malignant lung nodules on high-resolution CT using computer-estimated likelihood of malignancy. , 2004, AJR. American journal of roentgenology.

[23]  H H Barrett,et al.  Addition of a channel mechanism to the ideal-observer model. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[24]  R. F. Wagner,et al.  Aperture optimization for emission imaging: effect of a spatially varying background. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[25]  A. Burgess Visual perception studies and observer models in medical imaging. , 2011, Seminars in nuclear medicine.

[26]  Neil A. Macmillan,et al.  Detection Theory: A User's Guide , 1991 .

[27]  Matthew A Kupinski,et al.  Assessing image quality and dose reduction of a new x-ray computed tomography iterative reconstruction algorithm using model observers. , 2014, Medical physics.

[28]  K Doi,et al.  A comparison of physical image quality indices and observer performance in the radiographic detection of nylon beads. , 1984, Physics in medicine and biology.

[29]  Jim Thurston,et al.  NCRP Report No. 160: Ionizing Radiation Exposure of the Population of the United States , 2010 .

[30]  C J Kotre,et al.  Receptor dose in digital fluorography: a comparison between theory and practice. , 2001, Physics in medicine and biology.

[31]  M P Eckstein,et al.  Visual signal detection in structured backgrounds. III. Calculation of figures of merit for model observers in statistically nonstationary backgrounds. , 2000, Journal of the Optical Society of America. A, Optics, image science, and vision.

[32]  Ehsan Samei,et al.  Evaluating iterative reconstruction performance in computed tomography. , 2014, Medical physics.

[33]  W J H Veldkamp,et al.  Comparison between human and model observer performance in low-contrast detection tasks in CT images: application to images reconstructed with filtered back projection and iterative algorithms. , 2014, The British journal of radiology.

[34]  Willi A. Kalender,et al.  On the Correlation of Pixel Noise, Spatial Resolution and Dose in Computed Tomography : Theoretical Prediction and Verification by Simulation and Measurement , 2003 .

[35]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[36]  L. G. Månsson Methods for the Evaluation of Image Quality: A Review , 2000 .

[37]  Melanie Grunwald,et al.  Foundations Of Image Science , 2016 .

[38]  Arthur E. Burgess Evaluation of detection model performance in power-law noise , 2001, SPIE Medical Imaging.

[39]  William Vennart,et al.  ICRU Report 54: Medical imaging—the assessment of image quality: ISBN 0-913394-53-X. April 1996, Maryland, U.S.A. , 1997 .

[40]  B J Craven A table ofd′ forM-alternative odd-man-out forced-choice procedures , 1992, Perception & psychophysics.

[41]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .

[42]  Zhou Yu,et al.  Recent Advances in CT Image Reconstruction , 2013, Current Radiology Reports.

[43]  H. O. Wyckoff,et al.  The International Commission on Radiation Units and Measurements , 2001, Journal of the ICRU.

[44]  R. F. Wagner,et al.  SNR and noise measurements for medical imaging: I. A practical approach based on statistical decision theory. , 1993, Physics in medicine and biology.

[45]  Vladimir Mironov,et al.  Organ printing: computer-aided jet-based 3D tissue engineering. , 2003, Trends in biotechnology.

[46]  Christin Wirth The Essential Physics of Medical Imaging , 2003, European Journal of Nuclear Medicine and Molecular Imaging.

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

[48]  Abbas Aroua,et al.  EXPOSURE OF THE SWISS POPULATION BY MEDICAL X-RAYS: 2008 REVIEW , 2012, Health physics.

[49]  Dev P Chakraborty,et al.  A brief history of free-response receiver operating characteristic paradigm data analysis. , 2013, Academic radiology.

[50]  Ehsan Samei,et al.  Quantum noise properties of CT images with anatomical textured backgrounds across reconstruction algorithms: FBP and SAFIRE. , 2014, Medical physics.

[51]  Harold L. Kundel,et al.  Handbook of Medical Imaging, Volume 1. Physics and Psychophysics , 2000 .

[52]  A E Burgess,et al.  Visual signal detection. II. Signal-location identification. , 1984, Journal of the Optical Society of America. A, Optics and image science.

[53]  Fabio Becce,et al.  Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms , 2014, Physics in medicine and biology.

[54]  Euclid Seeram,et al.  Computed Tomography: Physical Principles, Clinical Applications, and Quality Control , 1994 .

[55]  Miguel P Eckstein,et al.  Evaluation of internal noise methods for Hotelling observer models. , 2007, Medical physics.

[56]  R. F. Wagner,et al.  Application of information theory to the assessment of computed tomography. , 1979, Medical physics.

[57]  Andrew Hyatt,et al.  Computed tomography: physical principles, clinical applications, and quality control , 2009 .

[58]  Ehsan Samei,et al.  Contrast-detail analysis of three flat panel detectors for digital radiography. , 2006, Medical physics.

[59]  H H Barrett,et al.  Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[60]  M P Eckstein,et al.  Visual signal detection in structured backgrounds. IV. Figures of merit for model performance in multiple-alternative forced-choice detection tasks with correlated responses. , 2000, Journal of the Optical Society of America. A, Optics, image science, and vision.

[61]  Jovan G Brankov,et al.  Evaluation of the channelized Hotelling observer with an internal-noise model in a train-test paradigm for cardiac SPECT defect detection , 2013, Physics in medicine and biology.

[62]  D G Brown,et al.  Detection performance of the ideal decision function and its McLaurin expansion: signal position unknown. , 1995, The Journal of the Acoustical Society of America.

[63]  Nico Lanconelli,et al.  A new clinical unit for digital radiography based on a thick amorphous selenium plate: physical and psychophysical characterization. , 2011, Medical physics.

[64]  A. Booth Numerical Methods , 1957, Nature.

[65]  G. Norman Likert scales, levels of measurement and the “laws” of statistics , 2010, Advances in health sciences education : theory and practice.

[66]  R. Brooks,et al.  Statistical limitations in x-ray reconstructive tomography. , 1976, Medical physics.

[67]  R. Likert “Technique for the Measurement of Attitudes, A” , 2022, The SAGE Encyclopedia of Research Design.

[68]  W J H Veldkamp,et al.  Automated assessment of low contrast sensitivity for CT systems using a model observer. , 2011, Medical physics.

[69]  John M. Boone,et al.  Radiation dose and image-quality assessment in computed tomography , 2012 .

[70]  Kyle J. Myers,et al.  Maximum a-posteriori detection and figures of merit for detection under uncertainty , 1990, Medical Imaging.

[71]  H H Barrett,et al.  Effect of random background inhomogeneity on observer detection performance. , 1992, Journal of the Optical Society of America. A, Optics and image science.

[72]  S. Jamieson Likert scales: how to (ab)use them , 2004, Medical education.

[73]  J. Swets Signal detection and recognition by human observers : contemporary readings , 1964 .

[74]  Christoph Hoeschen,et al.  Signal detection and location-dependent noise in cone-beam computed tomography using the spatial definition of the Hotelling SNR. , 2012, Medical physics.

[75]  Shuai Leng,et al.  Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized Hotelling observer: impact of radiation dose and reconstruction algorithms. , 2013, Medical physics.

[76]  L. Popescu Nonparametric signal detectability evaluation using an exponential transformation of the FROC curve. , 2011, Medical physics.

[77]  P. Allisy-Roberts,et al.  Farr's Physics for Medical Imaging , 2007 .

[78]  Shuai Leng,et al.  Correlation between model observer and human observer performance in CT imaging when lesion location is uncertain. , 2013, Medical physics.

[79]  Jeffrey H Siewerdsen,et al.  A simple approach to measure computed tomography (CT) modulation transfer function (MTF) and noise-power spectrum (NPS) using the American College of Radiology (ACR) accreditation phantom. , 2013, Medical physics.

[80]  Yi Zhang,et al.  Correlation between human and model observer performance for discrimination task in CT , 2014, Physics in medicine and biology.

[81]  Roger Ratcliff,et al.  A revised table of d’ for M-alternative forced choice , 1979 .

[82]  S. Riederer,et al.  The noise power spectrum in computed X-ray tomography. , 1978, Physics in medicine and biology.

[83]  J M Boone,et al.  Determination of the presampled MTF in computed tomography. , 2001, Medical physics.

[84]  ICRU Report No. 87: Radiation dose and image-quality assessment in computed tomography. , 2012, Journal of the ICRU.

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

[86]  E L Nickoloff,et al.  A simplified approach for modulation transfer function determinations in computed tomography. , 1985, Medical physics.

[87]  P. Judy,et al.  The line spread function and modulation transfer function of a computed tomographic scanner. , 1976, Medical physics.

[88]  Willi A. Kalender,et al.  Computed tomography : fundamentals, system technology, image quality, applications , 2000 .

[89]  E. Kazerooni,et al.  Computer-aided diagnosis of lung nodules on CT scans: ROC study of its effect on radiologists' performance. , 2010, Academic radiology.

[90]  Michael J. Flynn,et al.  Measurement of the spatial resolution of a clinical volumetric computed tomography scanner using a sphere phantom , 2006, SPIE Medical Imaging.

[91]  A. Burgess Comparison of receiver operating characteristic and forced choice observer performance measurement methods. , 1995, Medical physics.

[92]  M. Tapiovaara SNR and noise measurements for medical imaging. II. Application to fluoroscopic X-ray equipment , 1993 .

[93]  Marc Kachelriess,et al.  Assessment of spatial resolution in CT , 2008, 2008 IEEE Nuclear Science Symposium Conference Record.

[94]  A. Rose,et al.  Vision: human and electronic , 1973 .

[95]  H.H. Barrett,et al.  Model observers for assessment of image quality , 1993, 2002 IEEE Nuclear Science Symposium Conference Record.

[96]  Jovan G. Brankov,et al.  Optimization of the internal noise models for channelized Hotelling observer , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[97]  Xiang Li,et al.  Predictive models for observer performance in CT: applications in protocol optimization , 2011, Medical Imaging.

[98]  C. J. Kotre,et al.  The effect of background structure on the detection of low contrast objects in mammography. , 1998, The British journal of radiology.

[99]  J Y Vaishnav,et al.  Objective assessment of image quality and dose reduction in CT iterative reconstruction. , 2014, Medical physics.

[100]  F R Verdun,et al.  Estimation of the noisy component of anatomical backgrounds. , 1999, Medical physics.

[101]  D. Altman,et al.  Agreement Between Methods of Measurement with Multiple Observations Per Individual , 2007, Journal of biopharmaceutical statistics.

[102]  Ehsan Samei,et al.  Towards task-based assessment of CT performance: System and object MTF across different reconstruction algorithms. , 2012, Medical physics.