Rectangular Fixed-Gantry CT Prototype: Combining CNT X-Ray Sources and Accelerated Compressed Sensing-Based Reconstruction

Carbon nanotube (CNT)-based multibeam X-ray tubes provide an array of individually controllable X-ray focal spots. The CNT tube allows for flexible placement and distribution of X-ray focal spots in a system. Using a CNT tube, a computed tomography (CT) system with a noncircular geometry and a nonrotating gantry can be created. The noncircular CT geometry can be optimized around a specific imaging problem, utilizing the flexibility of CNT multibeam X-ray tubes to achieve the optimal focal spot distribution for the design constraints of the problem. Iterative reconstruction algorithms provide flexible CT reconstruction to accommodate the noncircular geometry. Compressed sensing-based iterative reconstruction algorithms apply a sparsity constraint to the reconstructed images that can partially account for missing angular coverage due to the noncircular geometry. In this paper, we present a laboratory prototype CT system that uses CNT multibeam X-ray tubes; a rectangular, nonrotating imaging geometry; and an accelerated compressed sensing-based iterative reconstruction algorithm. We apply a total variation minimization as our sparsity constraint. We present the advanced CNT multibeam tubes and show the stability and flexibility of these new tubes. We also present the unique imaging geometry and discuss the design constraints that influenced the specific system design. The reconstruction method is presented along with an overview of the acceleration of the algorithm to near real-time reconstruction. We demonstrate that the prototype reconstructed images have image quality comparable with a conventional CT system. The prototype is optimized for airport checkpoint baggage screening, but the concepts developed may apply to other application-specific CT imaging systems.

[1]  David S. Lalush,et al.  Three-Dimensional Imaging Properties of Rotation-Free Square and Hexagonal Micro-CT Systems , 2010, IEEE Transactions on Medical Imaging.

[2]  Steve B. Jiang,et al.  GPU-based iterative cone-beam CT reconstruction using tight frame regularization , 2010, Physics in medicine and biology.

[3]  Enzhuo Quan,et al.  A Faster Ordered-Subset Convex Algorithm for Iterative Reconstruction , 2006, 2006 IEEE Nuclear Science Symposium Conference Record.

[4]  Otto Zhou,et al.  Dependency of image quality on system configuration parameters in a stationary digital breast tomosynthesis system. , 2013, Medical physics.

[5]  D. Kopans,et al.  Digital tomosynthesis in breast imaging. , 1997, Radiology.

[6]  O. Zhou,et al.  A high-current, large-area, carbon nanotube cathode , 2004, IEEE Transactions on Plasma Science.

[7]  Bartholomew Elias Airport Passenger Screening: Background and Issues for Congress , 2009 .

[8]  A. H. Andersen Algebraic reconstruction in CT from limited views. , 1989, IEEE transactions on medical imaging.

[9]  Guohua Cao,et al.  A carbon nanotube field emission cathode with high current density and long-term stability , 2009, Nanotechnology.

[10]  F Sprenger,et al.  Stationary digital breast tomosynthesis with distributed field emission x-ray tube , 2011, Medical Imaging.

[11]  Otto Zhou,et al.  Stationary scanning x-ray source based on carbon nanotube field emitters , 2005 .

[12]  Hengyong Yu,et al.  A General Total Variation Minimization Theorem for Compressed Sensing Based Interior Tomography , 2009, Int. J. Biomed. Imaging.

[13]  Yuan Cheng,et al.  Rectangular computed tomography using a stationary array of CNT emitters: initial experimental results , 2013, Medical Imaging.

[14]  Yiheng Zhang,et al.  High resolution stationary digital breast tomosynthesis using distributed carbon nanotube x-ray source array. , 2012, Medical physics.

[15]  Kevin Skadron,et al.  Scalable parallel programming , 2008, 2008 IEEE Hot Chips 20 Symposium (HCS).

[16]  Steve B. Jiang,et al.  GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation. , 2010, Medical physics.

[17]  Steve B. Jiang,et al.  GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation , 2010 .

[18]  R. Fowler,et al.  Electron Emission in Intense Electric Fields , 1928 .

[19]  Bin Dong,et al.  ℓ0 Minimization for wavelet frame based image restoration , 2011, Math. Comput..

[20]  P Laganis,et al.  A first generation compact microbeam radiation therapy system based on carbon nanotube X-ray technology. , 2013, Applied physics letters.

[21]  Tuyen Phan,et al.  Design and characterization of a spatially distributed multibeam field emission x-ray source for stationary digital breast tomosynthesis. , 2009, Medical physics.

[22]  Hakan Erdogan,et al.  Ordered subsets algorithms for transmission tomography. , 1999, Physics in medicine and biology.

[23]  Guohua Cao,et al.  Prospective respiratory gated carbon nanotube micro computed tomography. , 2011, Academic radiology.

[24]  Ben Adcock,et al.  The quest for optimal sampling: Computationally efficient, structure-exploiting measurements for compressed sensing , 2014, ArXiv.

[25]  X. Qian,et al.  Distributed source x-ray tube technology for tomosynthesis imaging , 2010, Medical Imaging.

[26]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[27]  Klaus Mueller,et al.  Rapid 3-D cone-beam reconstruction with the simultaneous algebraic reconstruction technique (SART) using 2-D texture mapping hardware , 2000, IEEE Transactions on Medical Imaging.

[28]  Hengyong Yu,et al.  A soft-threshold filtering approach for reconstruction from a limited number of projections , 2010, Physics in medicine and biology.

[29]  M. Vannier,et al.  Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction? , 2009, Inverse problems.

[30]  Freek J. Beekman,et al.  Accelerated iterative transmission CT reconstruction using an ordered subsets convex algorithm , 1998, IEEE Transactions on Medical Imaging.

[31]  D W Holdsworth,et al.  Techniques to alleviate the effects of view aliasing artifacts in computed tomography. , 1999, Medical physics.

[32]  Lei Xing,et al.  GPU computing in medical physics: a review. , 2011, Medical physics.

[33]  E. Sidky,et al.  Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT , 2009, 0904.4495.

[34]  Xiaochuan Pan,et al.  Image reconstruction from few views by non-convex optimization , 2007, 2007 IEEE Nuclear Science Symposium Conference Record.

[35]  R Peng,et al.  A dynamic micro-CT scanner based on a carbon nanotube field emission x-ray source , 2009, Physics in medicine and biology.

[36]  David S. Lalush,et al.  Fast transmission CT reconstruction for SPECT using a block-iterative algorithm , 1999, 1999 IEEE Nuclear Science Symposium. Conference Record. 1999 Nuclear Science Symposium and Medical Imaging Conference (Cat. No.99CH37019).

[37]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.