Analysis of Fourier-domain task-based detectability index in tomosynthesis and cone-beam CT in relation to human observer performance.

PURPOSE Design and optimization of medical imaging systems benefit from accurate theoretical modeling that identifies the physical factors governing image quality, particularly in the early stages of system development. This work extends Fourier metrics of imaging performance and detectability index (d') to tomosynthesis and cone-beam CT (CBCT) and investigates the extent to which d' is a valid descriptor of task-based imaging performance as assessed by human observers, METHODS The detectability index for tasks presented in 2D slices (d'(slice)) was derived from 3D cascaded systems analysis of tomosynthesis and CBCT. Anatomical background noise measured in a physical phantom presenting power-law spectral density was incorporated in the "generalized" noise-equivalent quanta. Theoretical calculations of d'(slice) were performed as a function of total angular extent (theta(tot)) of source-detector orbit ranging 10 degrees - 360 degrees under two acquisition schemes: (i) Constant angular separation between projections (constant-delta theta), giving variable number of projections (N(proj)) and dose vs theta(tot) and (ii) constant number of projections (constant-N(proj)), giving constant dose (but variable angular sampling) with theta(tot). Five simple observer models were investigated: Prewhitening (PW), prewhitening with eye filter and internal noise (PWEi), nonprewhitening (NPW), nonprewhitening with eye filter (NPWE), and nonprewhitening with eye filter and internal noise (NPWEi). Human observer performance was measured in 9AFC tests for five simple imaging tasks presented within uniform and power-law clutter backgrounds. Measurements (from 9AFC tests) and theoretical calculations (from cascaded systems analysis of d'(slice)) were compared in terms of area under the ROC curve (A(z)) RESULTS Reasonable correspondence between theoretical calculations and human observer performance was achieved for all imaging tasks over the broad range of experimental conditions and acquisition schemes. The PW and PWEi observer models tended to overestimate detectability, while the various NPW models predicted observer performance fairly well, with NPWEi giving the best overall agreement. Detectability was shown to increase with theta(tot) due to the reduction of out-of-plane clutter, reaching a plateau after a particular theta(tot) that depended on the imaging task. Depending on the acquisition scheme, however (i.e., constant-N(proj) or delta theta), detectability was seen in some cases to decline at higher theta(tot) due to tradeoffs among quantum noise, background clutter, and view sampling. CONCLUSIONS Generalized detectability index derived from a 3D cascaded systems model shows reasonable correspondence with human observer performance over a fairly broad range of imaging tasks and conditions, although discrepancies were observed in cases relating to orbits intermediate to 180 degrees and 360 degrees. The basic correspondence of theoretical and measured performance supports the application of such a theoretical framework for system design and optimization of tomosynthesis and CBCT.

[1]  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.

[2]  Jeffrey H Siewerdsen,et al.  Cascaded systems analysis of the 3D noise transfer characteristics of flat-panel cone-beam CT. , 2008, Medical physics.

[3]  Jeffrey H Siewerdsen,et al.  Comparison of model and human observer performance for detection and discrimination tasks using dual-energy x-ray images. , 2008, Medical physics.

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

[5]  H. Barrett,et al.  Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood-generating functions. , 1998, Journal of the Optical Society of America. A, Optics, image science, and vision.

[6]  J A Rowlands,et al.  Effects of characteristic x rays on the noise power spectra and detective quantum efficiency of photoconductive x-ray detectors. , 2001, Medical physics.

[7]  David Gur,et al.  Tomosynthesis: potential clinical role in breast imaging. , 2007, AJR. American journal of roentgenology.

[8]  D. Jaffray,et al.  A framework for noise-power spectrum analysis of multidimensional images. , 2002, Medical physics.

[9]  H H Barrett,et al.  Objective assessment of image quality: effects of quantum noise and object variability. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[10]  H. Barrett,et al.  Ideal-observer computation in medical imaging with use of Markov-chain Monte Carlo techniques. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[11]  Wei Zhao,et al.  Imaging performance of amorphous selenium based flat-panel detectors for digital mammography: characterization of a small area prototype detector. , 2003, Medical physics.

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

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

[14]  J H Siewerdsen,et al.  Anatomical background and generalized detectability in tomosynthesis and cone-beam CT. , 2010, Medical physics.

[15]  R P Velthuizen,et al.  On the statistical nature of mammograms. , 1999, Medical physics.

[16]  Aruna A. Vedula,et al.  A computer simulation study comparing lesion detection accuracy with digital mammography, breast tomosynthesis, and cone-beam CT breast imaging. , 2006, Medical physics.

[17]  C E Metz,et al.  Digital image processing: effect on detectability of simulated low-contrast radiographic patterns. , 1984, Radiology.

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

[19]  C. Metz Basic principles of ROC analysis. , 1978, Seminars in nuclear medicine.

[20]  Jeffrey H. Siewerdsen,et al.  Cone-beam CT with a flat-panel imager: noise considerations for fully 3D computed tomography , 2000, Medical Imaging.

[21]  Christobel Saunders,et al.  New diagnostic techniques for breast cancer detection. , 2008, Future oncology.

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

[23]  Laurie L Fajardo,et al.  Breast tomosynthesis: present considerations and future applications. , 2007, Radiographics : a review publication of the Radiological Society of North America, Inc.

[24]  Aldo Badano,et al.  A statistical, task-based evaluation method for three-dimensional x-ray breast imaging systems using variable-background phantoms. , 2010, Medical physics.

[25]  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.

[26]  Arthur E. Burgess,et al.  Mammographic structure: data preparation and spatial statistics analysis , 1999, Medical Imaging.

[27]  Thomas Mertelmeier,et al.  Experimental validation of a three-dimensional linear system model for breast tomosynthesis. , 2008, Medical physics.

[28]  Ehsan Samei,et al.  Optimization of dual energy contrast enhanced breast tomosynthesis for improved mammographic lesion detection and diagnosis , 2008, SPIE Medical Imaging.

[29]  Ehsan Samei,et al.  Quantification of radiographic image quality based on patient anatomical contrast-to-noise ratio: a preliminary study with chest images , 2010, Medical Imaging.

[30]  Ehsan Samei,et al.  Physical measures of image quality in photostimulable phosphor radiographic systems , 1997, Medical Imaging.

[31]  Rebecca Fahrig,et al.  Cascaded systems analysis of the 3D NEQ for cone-beam CT and tomosynthesis , 2008, SPIE Medical Imaging.

[32]  Ruola Ning,et al.  Cone-beam CT for breast imaging: Radiation dose, breast coverage, and image quality. , 2010, AJR. American journal of roentgenology.

[33]  R. F. Wagner,et al.  Objective assessment of image quality. II. Fisher information, Fourier crosstalk, and figures of merit for task performance. , 1995, Journal of the Optical Society of America. A, Optics, image science, and vision.

[34]  Peter G. J. Barten,et al.  Contrast sensitivity of the human eye and its e ects on image quality , 1999 .

[35]  J Yorkston,et al.  Signal, noise power spectrum, and detective quantum efficiency of indirect-detection flat-panel imagers for diagnostic radiology. , 1998, 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.

[37]  J H Siewerdsen,et al.  Intraoperative cone-beam CT for guidance of head and neck surgery: Assessment of dose and image quality using a C-arm prototype. , 2006, Medical physics.

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

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

[40]  Wei Zhao,et al.  Three-dimensional linear system analysis for breast tomosynthesis. , 2008, Medical physics.

[41]  D. C. Barber,et al.  Medical Imaging-The Assessment of Image Quality , 1996 .

[42]  R M Nishikawa,et al.  Task-based assessment of breast tomosynthesis: effect of acquisition parameters and quantum noise. , 2010, Medical physics.

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

[44]  Ehsan Samei,et al.  Subtle lung nodules: influence of local anatomic variations on detection. , 2003, Radiology.

[45]  Kyle J Myers,et al.  Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.

[46]  Kyle J. Myers,et al.  Detection And Discrimination Of Known Signals In Inhomogeneous, Random Backgrounds , 1989, Medical Imaging.

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

[48]  Jie Yao,et al.  Predicting human performance by a channelized Hotelling observer model , 1992, Optics & Photonics.

[49]  B. Dosher,et al.  Characterizing human perceptual inefficiencies with equivalent internal noise. , 1999, Journal of the Optical Society of America. A, Optics, image science, and vision.

[50]  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.

[51]  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.

[52]  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.

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

[54]  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.

[55]  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.

[56]  S. Mukherji,et al.  Conebeam CT of the Head and Neck, Part 2: Clinical Applications , 2009, American Journal of Neuroradiology.

[57]  M D Silver A method for including redundant data in computed tomography. , 2000, Medical physics.

[58]  James T. Dobbins,et al.  Chest tomosynthesis: technical principles and clinical update. , 2009, European journal of radiology.

[59]  Jie Liu,et al.  Observer efficiency in discrimination tasks Simulating Malignant and benign breast lesions imaged with ultrasound , 2006, IEEE Transactions on Medical Imaging.

[60]  James T. Dobbins,et al.  Digital tomosynthesis of the chest. , 2008, Journal of thoracic imaging.

[61]  Joseph Y. Lo,et al.  Visual image quality metrics for optimization of breast tomosynthesis acquisition technique , 2007, SPIE Medical Imaging.

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

[63]  A. Burgess,et al.  Human observer detection experiments with mammograms and power-law noise. , 2001, Medical physics.

[64]  L B Lusted,et al.  Radiographic applications of signal detection theory. , 1972, Radiology.