Comparison of model and human observer performance for detection and discrimination tasks using dual-energy x-ray images.

Model observer performance, computed theoretically using cascaded systems analysis (CSA), was compared to the performance of human observers in detection and discrimination tasks. Dual-energy (DE) imaging provided a wide range of acquisition and decomposition parameters for which observer performance could be predicted and measured. This work combined previously derived observer models (e.g., Fisher-Hotelling and non-prewhitening) with CSA modeling of the DE image noise-equivalent quanta (NEQ) and imaging task (e.g., sphere detection, shape discrimination, and texture discrimination) to yield theoretical predictions of detectability index (d') and area under the receiver operating characteristic (Az). Theoretical predictions were compared to human observer performance assessed using 9-alternative forced-choice tests to yield measurement of Az as a function of DE image acquisition parameters (viz., allocation of dose between the low- and high-energy images) and decomposition technique [viz., three DE image decomposition algorithms: standard log subtraction (SLS), simple-smoothing of the high-energy image (SSH), and anti-correlated noise reduction (ACNR)]. Results showed good agreement between theory and measurements over a broad range of imaging conditions. The incorporation of an eye filter and internal noise in the observer models demonstrated improved correspondence with human observer performance. Optimal acquisition and decomposition parameters were shown to depend on the imaging task; for example, ACNR and SSH yielded the greatest performance in the detection of soft-tissue and bony lesions, respectively. This study provides encouraging evidence that Fourier-based modeling of NEQ computed via CSA and imaging task provides a good approximation to human observer performance for simple imaging tasks, helping to bridge the gap between Fourier metrics of detector performance (e.g., NEQ) and human observer performance.

[1]  Chris C Shaw,et al.  Dual-energy digital mammography for calcification imaging: scatter and nonuniformity corrections. , 2005, Medical physics.

[2]  Srinivasan Vedantham,et al.  Solid-state fluoroscopic imager for high-resolution angiography: parallel-cascaded linear systems analysis. , 2004, Medical physics.

[3]  C A Mistretta,et al.  Single-exposure dual-energy computed radiography: improved detection and processing. , 1990, Radiology.

[4]  Miguel P. Eckstein,et al.  Metrics of medical image quality: task-based model observers vs. image discrimination/perceptual difference models , 2004, SPIE Medical Imaging.

[5]  M. Webster,et al.  Contrast adaptation and the spatial structure of natural images. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

[6]  C E Metz,et al.  Transfer function analysis of radiographic imaging systems. , 1979, Physics in medicine and biology.

[7]  R. Alvarez,et al.  Comparison of dual energy detector system performance. , 2004, Medical physics.

[8]  M J Yaffe,et al.  Theoretical optimization of dual-energy x-ray imaging with application to mammography. , 1985, Medical physics.

[9]  Ehsan Samei,et al.  Does image quality matter? Impact of resolution and noise on mammographic task performance. , 2007, Medical physics.

[10]  R. F. Wagner,et al.  Efficiency of human visual signal discrimination. , 1981, Science.

[11]  J. Baker,et al.  A mathematical model platform for optimizing a multiprojection breast imaging system. , 2008, Medical physics.

[12]  J Yorkston,et al.  Empirical and theoretical investigation of the noise performance of indirect detection, active matrix flat-panel imagers (AMFPIs) for diagnostic radiology. , 1997, Medical physics.

[13]  J Yorkston,et al.  Optimization of image acquisition techniques for dual-energy imaging of the chest. , 2007, Medical physics.

[14]  J H Siewerdsen,et al.  Optimization of dual-energy imaging systems using generalized NEQ and imaging task. , 2006, Medical physics.

[15]  Jeffrey H. Siewerdsen,et al.  Three-dimensional NEQ transfer characteristics of volume CT using direct- and indirect-detection flat-panel imagers , 2003, SPIE Medical Imaging.

[16]  Frank Fischbach,et al.  Dual-energy chest radiography with a flat-panel digital detector: revealing calcified chest abnormalities. , 2003, AJR. American journal of roentgenology.

[17]  J H Siewerdsen,et al.  Generalized DQE analysis of radiographic and dual-energy imaging using flat-panel detectors. , 2005, Medical physics.

[18]  Jeffrey H. Siewerdsen,et al.  NEQ and task in dual-energy imaging: from cascaded systems analysis to human observer performance , 2008, SPIE Medical Imaging.

[19]  W. Kalender,et al.  An algorithm for noise suppression in dual energy CT material density images. , 1988, IEEE transactions on medical imaging.

[20]  D. Jaffray,et al.  Optimization of x-ray imaging geometry (with specific application to flat-panel cone-beam computed tomography). , 2000, Medical physics.

[21]  Craig K. Abbey,et al.  Model observer based optimization of JPEG image compression , 2000, Medical Imaging.

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

[23]  H. Barrett,et al.  Effect of noise correlation on detectability of disk signals in medical imaging. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[24]  Elizabeth A. Krupinski,et al.  Use of a human visual system model to predict the effects of display veiling glare on observer performance , 2004, SPIE Medical Imaging.

[25]  Jeffrey H Siewerdsen,et al.  Cascaded systems analysis of noise reduction algorithms in dual-energy imaging. , 2008, Medical physics.

[26]  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.

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

[28]  J A Rowlands,et al.  Digital radiology using active matrix readout of amorphous selenium: theoretical analysis of detective quantum efficiency. , 1997, Medical physics.

[29]  C A Mistretta,et al.  A correlated noise reduction algorithm for dual-energy digital subtraction angiography. , 1989, Medical physics.

[30]  P. Munro,et al.  A quantum accounting and detective quantum efficiency analysis for video-based portal imaging. , 1997, Medical physics.

[31]  Sabee Molloi,et al.  Optimization of a flat-panel based real time dual-energy system for cardiac imaging. , 2006, Medical physics.

[32]  E. Samei,et al.  Dose dependence of mass and microcalcification detection in digital mammography: free response human observer studies. , 2007, Medical physics.

[33]  James T Dobbins,et al.  Quantitative , 2020, Psychology through Critical Auto-Ethnography.

[34]  E Samei,et al.  Detection of subtle lung nodules: relative influence of quantum and anatomic noise on chest radiographs. , 1999, Radiology.

[35]  Stephen Rudin,et al.  Micro-angiography for neuro-vascular imaging. II. Cascade model analysis. , 2003, Medical physics.

[36]  A E Burgess,et al.  Visual signal detectability with two noise components: anomalous masking effects. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.