Modelling and Simulation in Digital Tomosynthesis

Digital Tomosynthesis (DTS) is a method of reconstruction of tomographic images produced at variable heights, on the basis of a set of limited angle angular projections taken around human anatomy. With the development of flat panel detectors, this technique obtained a large popularity, as a potential diagnostic x-ray imaging technique, to detect breast microcalcifications, pulmonary nodules in the chest or early stages of dental caries, to mention but a few. Before clinical exploitation of DTS, a large number of feasibility studies should demonstrate its advantage and ability as a screening or diagnostic tool. Emphasis is given to the benefits of applying DTS to areas of x-ray imaging that have demonstrated evidence of low rate of early cancer detection. To facilitate these studies and accelerate the clinical application of DTS, modelling and simulation tools are exploited. With the impressive progress in computing power, the use of GRID infrastructures and the appearance of advance interactive software, Modelling and Simulation has been established as a new powerful tool in successful problem solving prior to implementation. The aim of this paper is to highlight the role of modelling and simulation in investigating the eventual application of DTS in diagnostic x-ray imaging. The tomosynthesis method and various modelling and simulation approaches are described. Examples of application of modelling and simulation for studying different aspects of DTS are highlighted and demonstrate their importance in testing novel ideas and optimising imaging parameters.

[1]  N Pallikarakis,et al.  A wavelet-based method for removal of out-of-plane structures in digital tomosynthesis. , 1998, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[2]  Anders Tingberg,et al.  X-ray tomosynthesis: a review of its use for breast and chest imaging. , 2010, Radiation protection dosimetry.

[3]  H Zaidi,et al.  Relevance of accurate Monte Carlo modeling in nuclear medical imaging. , 1999, Medical physics.

[4]  Kristina Bliznakova,et al.  A novel simulation algorithm for soft tissue compression , 2007, Medical & Biological Engineering & Computing.

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

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

[7]  G Panayiotakis,et al.  A multiple projection method for digital tomosynthesis. , 1992, Medical physics.

[8]  S Suryanarayanan,et al.  Evaluation of an improved algorithm for producing realistic 3D breast software phantoms: application for mammography. , 2010, Medical physics.

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

[10]  K Bliznakova,et al.  Evaluation of digital breast tomosynthesis reconstruction algorithms using synchrotron radiation in standard geometry. , 2010, Medical physics.

[11]  D. G. Grant Tomosynthesis: a three-dimensional radiographic imaging technique. , 1972, IEEE transactions on bio-medical engineering.

[12]  Z. Kamarianakis,et al.  Robust identification and localization of intramedullary nail holes for distal locking using CBCT: a simulation study. , 2011, Medical engineering & physics.