Singular-value decomposition of a tomosynthesis system.

Tomosynthesis is an emerging technique with potential to replace mammography, since it gives 3D information at a relatively small increase in dose and cost. We present an analytical singular-value decomposition of a tomosynthesis system, which provides the measurement component of any given object. The method is demonstrated on an example object. The measurement component can be used as a reconstruction of the object, and can also be utilized in future observer studies of tomosynthesis image quality.

[1]  L. Goddard.  Linear Differential Operators , 1962, Nature.

[2]  Tao Wu,et al.  A comparison of reconstruction algorithms for breast tomosynthesis. , 2004, Medical physics.

[3]  Lubomir M. Hadjiiski,et al.  A comparative study of limited-angle cone-beam reconstruction methods for breast tomosynthesis. , 2006, Medical physics.

[4]  James T Dobbins,et al.  Digital x-ray tomosynthesis: current state of the art and clinical potential. , 2003, Physics in medicine and biology.

[5]  David Gur,et al.  Digital breast tomosynthesis: a pilot observer study. , 2008, AJR. American journal of roentgenology.

[6]  J. Dobbins.  Tomosynthesis imaging: at a translational crossroads. , 2009, Medical physics.

[7]  R M Nishikawa,et al.  Task-based assessment of breast tomosynthesis: effect of acquisition parameters and quantum noise. , 2010, Medical physics.

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

[9]  Ehsan Samei,et al.  Optimized image acquisition for breast tomosynthesis in projection and reconstruction space. , 2009, Medical physics.

[10]  I. B. Russak,et al.  Manifolds , 2019, Spacetime and Geometry.

[11]  Kyle J Myers,et al.  Singular-value decomposition for through-focus imaging systems. , 2006, Journal of the Optical Society of America. A, Optics, image science, and vision.

[12]  Anders Tingberg,et al.  Breast tomosynthesis and digital mammography: a comparison of breast cancer visibility and BIRADS classification in a population of cancers with subtle mammographic findings , 2008, European Radiology.

[13]  Tor D Tosteson,et al.  Digital breast tomosynthesis: initial experience in 98 women with abnormal digital screening mammography. , 2007, AJR. American journal of roentgenology.

[14]  A Liseno,et al.  In-depth resolution for a strip source in the Fresnel zone. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[15]  Craig K. Abbey,et al.  An Ideal Observer for a Model of X-Ray Imaging in Breast Parenchymal Tissue , 2008, Digital Mammography / IWDM.

[16]  C Abbey,et al.  Statistical texture synthesis of mammographic images with super-blob lumpy backgrounds. , 1999, Optics express.

[17]  John M Boone,et al.  Characterizing anatomical variability in breast CT images. , 2008, Medical physics.

[18]  Gene Gindi,et al.  Three-dimensional radiographic imaging with a restricted view angle , 1979 .

[19]  A E Burgess.  Visual signal detection with two-component noise: low-pass spectrum effects. , 1999, Journal of the Optical Society of America. A, Optics, image science, and vision.

[20]  H.H. Barrett,et al.  Model observers for assessment of image quality , 1993, 2002 IEEE Nuclear Science Symposium Conference Record.

[21]  Jie Yao,et al.  Predicting human performance by a channelized Hotelling observer model , 1992, Optics & Photonics.

[22]  Andrew D. A. Maidment,et al.  Development of an anthropomorphic breast software phantom based on region growing algorithm , 2008, SPIE Medical Imaging.

[23]  Gisella Gennaro,et al.  Digital breast tomosynthesis versus digital mammography: a clinical performance study , 2010, European Radiology.

[24]  H H Barrett,et al.  Null functions and eigenfunctions: tools for the analysis of imaging systems. , 1991, Progress in clinical and biological research.

[25]  Kyle J. Myers,et al.  Singular Vectors of a Linear Imaging System as Efficient Channels for the Bayesian Ideal Observer , 2009, IEEE Transactions on Medical Imaging.