Stability of the interior problem with polynomial attenuation in the region of interest

In many practical applications, it is desirable to solve the interior problem of tomography without requiring knowledge of the attenuation function fa on an open set within the region of interest (ROI). It was proved recently that the interior problem has a unique solution if fa is assumed to be piecewise polynomial on the ROI. In this paper, we tackle the related question of stability. It is well known that lambda tomography allows one to stably recover the locations and values of the jumps of fa inside the ROI from only the local data. Hence, we consider here only the case of a polynomial, rather than piecewise polynomial, fa on the ROI. Assuming that the degree of the polynomial is known, along with some other fairly mild assumptions on fa, we prove a stability estimate for the interior problem. Additionally, we prove the following general uniqueness result. If there is an open set U on which fa is the restriction of a real-analytic function, then fa is uniquely determined by only the line integrals through U. It turns out that two known uniqueness theorems are corollaries of this result.

[1]  A. Ramm,et al.  The RADON TRANSFORM and LOCAL TOMOGRAPHY , 1996 .

[2]  Xiaochuan Pan,et al.  Exact image reconstruction on PI-lines from minimum data in helical cone-beam CT. , 2004, Physics in medicine and biology.

[3]  Hengyong Yu,et al.  Exact Interior Reconstruction from Truncated Limited-Angle Projection Data , 2008, Int. J. Biomed. Imaging.

[4]  Irene A. Stegun,et al.  Handbook of Mathematical Functions. , 1966 .

[5]  M. Defrise,et al.  Tiny a priori knowledge solves the interior problem , 2007 .

[6]  Hiroyuki Kudo,et al.  Truncated Hilbert transform and image reconstruction from limited tomographic data , 2006 .

[7]  L. Hörmander The analysis of linear partial differential operators , 1990 .

[8]  Rolf Clackdoyle,et al.  Cone-beam reconstruction using the backprojection of locally filtered projections , 2005, IEEE Transactions on Medical Imaging.

[9]  Hengyong Yu,et al.  Gel'fand-Graev's reconstruction formula in the 3D real space. , 2010, Medical physics.

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

[11]  Hengyong Yu,et al.  Lambda tomography with discontinuous scanning trajectories. , 2007, Physics in medicine and biology.

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

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

[14]  M. Jiang,et al.  Supplemental analysis on compressed sensing based interior tomography. , 2009, Physics in medicine and biology.

[15]  Harold R. Parks,et al.  A Primer of Real Analytic Functions , 1992 .

[16]  E. T. Quinto,et al.  Local Tomography in Electron Microscopy , 2008, SIAM J. Appl. Math..

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

[18]  M. Kachelriess,et al.  Improved total variation-based CT image reconstruction applied to clinical data , 2011, Physics in medicine and biology.

[19]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[20]  Erik L. Ritman,et al.  Local Tomography II , 1997, SIAM J. Appl. Math..

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

[22]  Erik L. Ritman,et al.  Local tomography , 1992 .

[23]  I. Gel'fand,et al.  Crofton's function and inversion formulas in real integral geometry , 1991 .

[24]  Hengyong Yu,et al.  Exact Interior Reconstruction with Cone-Beam CT , 2008, Int. J. Biomed. Imaging.

[25]  E. T. Quinto Local algorithms in exterior tomography , 2007 .

[26]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

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

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