Cascaded systems analysis of the 3D noise transfer characteristics of flat-panel cone-beam CT.

The physical factors that govern 2D and 3D imaging performance may be understood from quantitative analysis of the spatial-frequency-dependent signal and noise transfer characteristics [e.g., modulation transfer function (MTF), noise-power spectrum (NPS), detective quantum efficiency (DQE), and noise-equivalent quanta (NEQ)] along with a task-based assessment of performance (e.g., detectability index). This paper advances a theoretical framework based on cascaded systems analysis for calculation of such metrics in cone-beam CT (CBCT). The model considers the 2D projection NPS propagated through a series of reconstruction stages to yield the 3D NPS and allows quantitative investigation of tradeoffs in image quality associated with acquisition and reconstruction techniques. While the mathematical process of 3D image reconstruction is deterministic, it is shown that the process is irreversible, the associated reconstruction parameters significantly affect the 3D DQE and NEQ, and system optimization should consider the full 3D imaging chain. Factors considered in the cascade include: system geometry; number of projection views; logarithmic scaling; ramp, apodization, and interpolation filters; 3D back-projection; and 3D sampling (noise aliasing). The model is validated in comparison to experiment across a broad range of dose, reconstruction filters, and voxel sizes, and the effects of 3D noise correlation on detectability are explored. The work presents a model for the 3D NPS, DQE, and NEQ of CBCT that reduces to conventional descriptions of axial CT as a special case and provides a fairly general framework that can be applied to the design and optimization of CBCT systems for various applications.

[1]  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.

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

[3]  Norbert J. Pelc,et al.  The noise power spectrum in computed X-ray tomography. , 1978 .

[4]  B. Fallone,et al.  Detective quantum efficiency of a direct-detection active matrix flat panel imager at megavoltage energies. , 2001, Medical physics.

[5]  I A Cunningham,et al.  Parallel cascades: new ways to describe noise transfer in medical imaging systems. , 2001, Medical physics.

[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]  K. Hanson,et al.  Detectability in computed tomographic images. , 1979, Medical physics.

[8]  Jeffrey H. Siewerdsen,et al.  Unified iso-SNR approach to task-directed imaging in flat-panel cone-beam CT , 2002, SPIE Medical Imaging.

[9]  J H Siewerdsen,et al.  Geometric calibration of a mobile C-arm for intraoperative cone-beam CT. , 2008, Medical physics.

[10]  M F Kijewski,et al.  The noise power spectrum of CT images. , 1987, Physics in medicine and biology.

[11]  J H Siewerdsen,et al.  Spektr: a computational tool for x-ray spectral analysis and imaging system optimization. , 2004, Medical physics.

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

[13]  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.

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

[15]  A Fenster,et al.  A spatial-frequency dependent quantum accounting diagram and detective quantum efficiency model of signal and noise propagation in cascaded imaging systems. , 1994, Medical physics.

[16]  Andrew D. A. Maidment,et al.  Linear response theory for detectors consisting of discrete arrays. , 2000, Medical physics.

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

[18]  H H Barrett,et al.  Statistical limitations in transaxial tomography. , 1976, Computers in biology and medicine.

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

[20]  J H Siewerdsen,et al.  Soft-tissue detectability in cone-beam CT: evaluation by 2AFC tests in relation to physical performance metrics. , 2007, Medical physics.

[21]  Jeffrey H. Siewerdsen,et al.  Incorporation of task in 3D imaging performance evaluation: the impact of asymmetric NPS on detectability , 2004, SPIE Medical Imaging.

[22]  L. Feldkamp,et al.  Practical cone-beam algorithm , 1984 .

[23]  Jeffrey H. Siewerdsen,et al.  Analysis of image noise in 3D cone-beam CT: spatial and Fourier domain approaches under conditions of varying stationarity , 2008, SPIE Medical Imaging.

[24]  M. Rabbani,et al.  Detective quantum efficiency of imaging systems with amplifying and scattering mechanisms. , 1987, Journal of the Optical Society of America. A, Optics and image science.

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

[26]  I A Cunningham,et al.  Optimal phosphor thickness for portal imaging. , 1997, Medical physics.

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

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

[29]  S Suryanarayanan,et al.  Full breast digital mammography with an amorphous silicon-based flat panel detector: physical characteristics of a clinical prototype. , 2000, Medical physics.

[30]  Srinivasan Vedantham,et al.  Investigation of optimal kVp settings for CT mammography using a flat-panel imager , 2002, SPIE Medical Imaging.

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

[32]  H. Blume,et al.  DQE(f) of four generations of computed radiography acquisition devices. , 1995, Medical physics.

[33]  J. Wong,et al.  Characterization of a fluoroscopic imaging system for kV and MV radiography. , 2000, Medical physics.

[34]  Ian A. Cunningham,et al.  Can a Fourier-based cascaded-systems analysis describe noise in complex shift-variant spatially sampled detectors? , 2004, SPIE Medical Imaging.

[35]  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.

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

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

[38]  Aldo Badano,et al.  Anisotropic imaging performance in indirect x-ray imaging detectors. , 2006, Medical physics.

[39]  Jeffrey H. Siewerdsen,et al.  Generalized DQE analysis of dual-energy imaging using flat-panel detectors , 2005, SPIE Medical Imaging.

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

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

[42]  K Faulkner,et al.  Analysis of x-ray computed tomography images using the noise power spectrum and autocorrelation function. , 1984, Physics in medicine and biology.