Interior tomography with radial Hilbert filtering and a priori information in a small circular area

Interior tomography problem can be solved using the so-called differentiated backprojection-projection onto convex sets (DBP-POCS) method, which requires a priori information within a small area interior to the region of interest (ROI) to be imaged. In theory, the small area wherein the a priori information is required can be in any shape, but most of the existing implementations carry out the Hilbert filtering either horizontally or vertically, leading to a vertical or horizontal strip that may be across a large area in the object. In this work, we specifically re-derive the reconstruction formula in the DBP-POCS fashion with radial Hilbert filtering (namely radial DBP-POCS method henceforth). We implement the radial DBP-POCS method, and thus the small area with the a priori information can be roughly circular (e.g., a sinus or ventricles among other anatomic cavities in human or animal body). We also conduct an experimental evaluation to verify the performance of this practical implementation. The performance of the radial DBP-POCS method with the a priori information in a small circular area is evaluated with projection data of the standard Shepp-Logan phantom simulated by computer. The preliminary performance study shows that, if the a priori information in a small circular area is available, the radial DBP-POCS method can solve the interior tomography problem in a much more practical way at high accuracy. In comparison to the implementations of DBP-POCS method demanding the a priori information in horizontal or vertical strip, the radial DBP-POCS method requires the a priori information within a small circular area only. Such a relaxed requirement on the availability of a priori information can be readily met in practice, because a variety of small circular areas (e.g., air-filled sinuses or fluid-filled ventricles among other anatomic cavities) exist in human or animal body. Therefore, the radial DBP-POCS method with a priori information in a small circular area is feasible in clinical and preclinical practice.

[1]  B. Logan,et al.  Reconstructing Interior Head Tissue from X-Ray Transmissions , 1974 .

[2]  C. McCollough,et al.  Radiation dose reduction in computed tomography: techniques and future perspective. , 2009, Imaging in medicine.

[3]  Hengyong Yu,et al.  A General Local Reconstruction Approach Based on a Truncated Hilbert Transform , 2007, Int. J. Biomed. Imaging.

[4]  Hengyong Yu,et al.  Compressed sensing based interior tomography , 2009, Physics in medicine and biology.

[5]  John M Boone,et al.  Dose reduction in pediatric CT: a rational approach. , 2003, Radiology.

[6]  Donald P Frush,et al.  Computed tomography and radiation risks: what pediatric health care providers should know. , 2003, Pediatrics.

[7]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Hengyong Yu,et al.  A scheme for multisource interior tomography. , 2009, Medical physics.

[9]  Thomas L Toth,et al.  Comparison of Z-axis automatic tube current modulation technique with fixed tube current CT scanning of abdomen and pelvis. , 2004, Radiology.

[10]  M. Jiang,et al.  High-order total variation minimization for interior tomography , 2010, Inverse problems.

[11]  Zhengrong Liang,et al.  An experimental study on the noise properties of x-ray CT sinogram data in Radon space , 2008, Physics in medicine and biology.

[12]  M. Defrise,et al.  Solving the interior problem of computed tomography using a priori knowledge , 2008, Inverse problems.

[13]  Hengyong Yu,et al.  Interior Reconstruction Using the Truncated Hilbert Transform via Singular Value Decomposition. , 2008, Journal of X-ray science and technology.

[14]  Xuanqin Mou,et al.  Noise reduction by projection direction dependent diffusion for low dose fan-beam x-ray computed tomography , 2011, Medical Imaging.

[15]  M. Kalra,et al.  Techniques and applications of automatic tube current modulation for CT. , 2004, Radiology.

[16]  E. Sidky,et al.  Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization , 2008, Physics in medicine and biology.

[17]  F. Natterer The Mathematics of Computerized Tomography , 1986 .

[18]  Jiahua Fan,et al.  Minimization of over-ranging in helical volumetric CT via hybrid cone beam image reconstruction-Benefits in dose efficiency. , 2008, Medical physics.

[19]  L. Tanoue Computed Tomography — An Increasing Source of Radiation Exposure , 2009 .

[20]  F. Noo,et al.  A two-step Hilbert transform method for 2D image reconstruction. , 2004, Physics in medicine and biology.

[21]  Guillermo Sapiro,et al.  Robust anisotropic diffusion , 1998, IEEE Trans. Image Process..