Computer-Assisted Scan Protocol and Reconstruction (CASPAR)—Reduction of Image Noise and Patient Dose

X-ray computed tomography is a powerful medical imaging device. It allows high-resolution 3-D visualization of the human body. However, one drawback is the health risk associated with ionizing radiation. Simply downscaling the radiation intensities over the entire scan results in increased quantum noise. This paper proposes the concept of computer-assisted scan protocol and reconstruction. More specifically, we propose a method to compute patient and task-specific intensity profiles that achieve an optimal tradeoff between radiation dose and image quality. Therefore, reasonable image variance and dose metrics are derived. Conventional third-generation systems as well as inverted geometry concepts are considered. Two dose/noise minimization problems are formulated and solved by an efficient algorithm providing optimized milliampere (mA)-profiles. Thorax phantom simulations demonstrate the promising advantage of this technique: in this particular example, the dose is reduced by 53% for third-generation systems and by 86% for an inverted geometry in comparison to a sinusoidal mA-profile at a constant upper noise limit.

[1]  Leon S. Lasdon,et al.  Optimization in engineering design , 1967 .

[2]  C. G. Broyden The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations , 1970 .

[3]  C. G. Broyden The Convergence of a Class of Double-rank Minimization Algorithms 2. The New Algorithm , 1970 .

[4]  R. Brooks,et al.  Statistical limitations in x-ray reconstructive tomography. , 1976, Medical physics.

[5]  Paul T. Boggs,et al.  Sequential Quadratic Programming , 1995, Acta Numerica.

[6]  W A Kalender,et al.  Dose reduction in CT by anatomically adapted tube current modulation. I. Simulation studies. , 1999, Medical physics.

[7]  Avinash C. Kak,et al.  Principles of computerized tomographic imaging , 2001, Classics in applied mathematics.

[8]  B. De Man,et al.  Distance-driven projection and backprojection , 2002, 2002 IEEE Nuclear Science Symposium Conference Record.

[9]  J. Valentin Basic anatomical and physiological data for use in radiological protection: reference values , 2002, Annals of the ICRP.

[10]  M. Kalra,et al.  Strategies for CT radiation dose optimization. , 2004, Radiology.

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

[12]  Patrick J. La Rivière,et al.  Reduction of noise-induced streak artifacts in X-ray computed tomography through spline-based penalized-likelihood sinogram smoothing , 2005, IEEE Transactions on Medical Imaging.

[13]  J. O’Sullivan,et al.  Properties of preprocessed sinogram data in x-ray computed tomography. , 2006, Medical physics.

[14]  Bruno De Man,et al.  Multi-source inverse geometry CT: a new system concept for x-ray computed tomography , 2007, SPIE Medical Imaging.

[15]  Zhye Yin,et al.  Inverse geometry CT: The next-generation CT architecture? , 2007, 2007 IEEE Nuclear Science Symposium Conference Record.

[16]  Jeffrey H. Siewerdsen,et al.  Intensity-modulated fluence patterns for task-specific imaging in cone-beam CT , 2007, SPIE Medical Imaging.

[17]  Bruno De Man,et al.  CatSim: a new computer assisted tomography simulation environment , 2007, SPIE Medical Imaging.

[18]  Adam Wunderlich,et al.  Image covariance and lesion detectability in direct fan-beam x-ray computed tomography , 2008, Physics in medicine and biology.