Simulated scatter performance of an inverse-geometry dedicated breast CT system.

The purpose of this work was to quantify the effects of scatter for inverse-geometry dedicated breast CT compared to cone-beam breast CT through simulations. The inverse geometry was previously proposed as an alternative to cone-beam acquisition for volumetric CT. The inverse geometry consists of a large-area scanned-source opposite a detector array that is smaller in the transverse direction. While the gantry rotates, the x-ray beam is rapidly sequenced through an array of positions, acquiring a truncated projection image at each position. Inverse-geometry CT (IGCT) is expected to detect less scatter than cone-beam methods because only a fraction of the object is irradiated at any time and the fast detector isolates the measurements from sequential x-ray beams. An additional scatter benefit is the increased air gap due to the inverted geometry. In this study, we modeled inverse-geometry and cone-beam dedicated breast CT systems of equivalent resolution, field of view, and photon fluence. Monte Carlo simulations generated scatter and primary projections of three cylindrical phantoms of diameters 10, 14, and 18 cm composed of 50% adipose/50% glandular tissue. The scatter-to-primary ratio (SPR) was calculated for each breast diameter. Monte Carlo simulations were combined with analytical simulations to generate inverse-geometry and cone-beam images of breast phantoms embedded with tumors. Noise reprehenting the photon fluence of a realistic breast CT scan was added to the simulated projections. Cone-beam data were reconstructed with and without an ideal scatter correction. The CNR between breast tumor and background was compared for the inverse and cone-beam geometries for the three phantom diameters. Results demonstrated an order of magnitude reduction in SPR for the IGCT system compared to the cone-beam system. For example, the peak IGCT SPRs were 0.05 and 0.09 for the 14 and 18 cm phantoms, respectively, compared to 0.42 and 1 for the cone-beam system. For both geometries, the effects of scatter on contrast-to-noise ratio (CNR) were small for the 10 cm diameter phantom. The inverse-geometry improved the CNR by factors of 1.16 for the 14 cm phantom and 1.48 for the 18 cm phantom compared to a cone-beam acquisition without scatter correction. When an ideal scatter correction was applied to the cone-beam acquisition, the IGCT CNR improvements were 1.03 and 1.25 for the 14 and 18 cm phantoms. Overall, the results suggest that the inverse geometry may be advantageous for dedicated breast CT, an application that requires high-contrast resolution, spatial resolution, and dose efficiency.

[1]  Ehsan Samei,et al.  Simulation study of a quasi-monochromatic beam for x-ray computed mammotomography. , 2004, Medical physics.

[2]  John M. Boone,et al.  Computed Tomography for Imaging the Breast , 2006, Journal of Mammary Gland Biology and Neoplasia.

[3]  Lingyun Chen,et al.  Visibility of microcalcification in cone beam breast CT: effects of X-ray tube voltage and radiation dose. , 2007, Medical physics.

[4]  Lei Zhu,et al.  Scatter Correction Method for X-Ray CT Using Primary Modulation: Theory and Preliminary Results , 2006, IEEE Transactions on Medical Imaging.

[5]  J. Boone,et al.  Dedicated breast CT: initial clinical experience. , 2008, Radiology.

[6]  D. Jaffray,et al.  Cone-beam computed tomography with a flat-panel imager: magnitude and effects of x-ray scatter. , 2001, Medical physics.

[7]  D. Jaffray,et al.  The influence of antiscatter grids on soft-tissue detectability in cone-beam computed tomography with flat-panel detectors. , 2004, Medical physics.

[8]  Taly Gilat Schmidt,et al.  A prototype table‐top inverse‐geometry volumetric CT system , 2006 .

[9]  Aruna A. Vedula,et al.  Microcalcification detection using cone-beam CT mammography with a flat-panel imager. , 2004, Physics in medicine and biology.

[10]  X Liu,et al.  A post-reconstruction method to correct cupping artifacts in cone beam breast computed tomography. , 2007, Medical physics.

[11]  Biao Chen,et al.  Cone-beam volume CT breast imaging: feasibility study. , 2002, Medical physics.

[12]  W. Kalender,et al.  Combining deterministic and Monte Carlo calculations for fast estimation of scatter intensities in CT , 2006, Physics in medicine and biology.

[13]  J A Seibert,et al.  X-ray scatter removal by deconvolution. , 1988, Medical physics.

[14]  N. Pelc,et al.  An inverse-geometry volumetric CT system with a large-area scanned source: a feasibility study. , 2004, Medical physics.

[15]  John M Boone,et al.  Technique factors and their relationship to radiation dose in pendant geometry breast CT. , 2005, Medical physics.

[16]  J. Boone,et al.  Dedicated breast CT: radiation dose and image quality evaluation. , 2001, Radiology.

[17]  S. Mori,et al.  Magnitude and effects of x-ray scatter in a 256-slice CT scanner. , 2006, Medical physics.

[18]  R. Ning,et al.  A cone beam filtered backprojection (CB-FBP) reconstruction algorithm for a circle-plus-two-arc orbit. , 2001, Medical physics.

[19]  Selin Carkaci,et al.  Dedicated cone-beam breast CT: feasibility study with surgical mastectomy specimens. , 2007, AJR. American journal of roentgenology.

[20]  J. S. Laughlin,et al.  Absorbed radiation dose in mammography. , 1979, Radiology.

[21]  Edward G. Solomon,et al.  Scanning-beam digital x-ray (SBDX) system for cardiac angiography , 1999, Medical Imaging.

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

[23]  Ruola Ning,et al.  Flat panel detector-based cone beam computed tomography with a circle-plus-two-arcs data acquisition orbit: preliminary phantom study. , 2003, Medical physics.

[24]  J. Boone,et al.  Evaluation of x-ray scatter properties in a dedicated cone-beam breast CT scanner. , 2005, Medical physics.

[25]  Freek J. Beekman,et al.  Accelerated simulation of cone beam X-ray scatter projections , 2004, IEEE Transactions on Medical Imaging.

[26]  C. D'Orsi,et al.  Monte Carlo and phantom study of the radiation dose to the body from dedicated CT of the breast. , 2008, Radiology.

[27]  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.

[28]  P. C. Johns,et al.  X-ray characterisation of normal and neoplastic breast tissues. , 1987, Physics in medicine and biology.

[29]  M. Defrise,et al.  A fast rebinning algorithm for 3D positron emission tomography using John's equation , 1999 .

[30]  Hengyong Yu,et al.  Cone-beam mammo-computed tomography from data along two tilting arcs. , 2006, Medical physics.

[31]  J. Boone,et al.  Evaluation of x-ray scatter properties in a dedicated cone-beam breast CT scanner. , 2005, Medical physics.

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

[33]  J A Sorenson,et al.  Scatter rejection by air gaps: an empirical model. , 1985, Medical physics.

[34]  Norbert J. Pelc,et al.  A fast 3D reconstruction algorithm for inverse-geometry CT based on an exact PET rebinning algorithm , 2007, SPIE Medical Imaging.

[35]  Stephen J Glick,et al.  Normalized glandular dose (DgN) coefficients for flat-panel CT breast imaging. , 2004, Physics in medicine and biology.

[36]  Ruola Ning,et al.  X-ray scatter correction algorithm for cone beam CT imaging. , 2004, Medical physics.

[37]  T R Nelson,et al.  A comprehensive analysis of DgN(CT) coefficients for pendant-geometry cone-beam breast computed tomography. , 2004, Medical physics.

[38]  M Honda,et al.  Method for estimating the intensity of scattered radiation using a scatter generation model. , 1991, Medical physics.

[39]  Freek J. Beekman,et al.  Efficient Monte Carlo based scatter artifact reduction in cone-beam micro-CT , 2006, IEEE Transactions on Medical Imaging.

[40]  P M Joseph,et al.  The effects of scatter in x-ray computed tomography. , 1982, Medical physics.

[41]  Robert E. Melen,et al.  CdZnTe detector array for a scanning-beam digital x-ray system , 1999, Medical Imaging.

[42]  Samta Thacker,et al.  Evaluating the impact of X-ray spectral shape on image quality in flat-panel CT breast imaging. , 2007, Medical physics.

[43]  S. Molloi,et al.  Photon counting computed tomography: concept and initial results. , 2005, Medical physics.

[44]  Kai Yang,et al.  Computer modeling of the spatial resolution properties of a dedicated breast CT system. , 2007, Medical physics.

[45]  J. Boone,et al.  An accurate method for computer-generating tungsten anode x-ray spectra from 30 to 140 kV. , 1997, Medical physics.

[46]  Gary H. Glover,et al.  Compton scatter effects in CT reconstructions , 1982 .