Clinical image benefits after model-based reconstruction for low dose dedicated breast tomosynthesis

Model-based iterative reconstruction (MBIR) is implemented to process full clinical data sets of dedicated breast tomosynthesis (DBT) in a low dose condition and achieves less spreading of anatomical structure between slices. MBIR is a statistical based reconstruction which can control the trade-off between data fitting and image regularization. In this study, regularization is formulated with anisotropic prior weighting that independently controls the image regularization between in-plane and out-of-plane voxel neighbors. Studies at complete and partial convergence show that the appropriate formulation of data-fit and regularization terms along with anisotropic prior weighting leads to a solution with improved localization of objects within a more narrow range of slices. This result is compared with the solutions using simultaneous iterative reconstruction technique (SIRT), which is one of the state of art reconstruction in DBT. MBIR yields higher contrast-to-noise for medium and large size microcalcifications and diagnostic structures in volumetric breast images and supports opportunity for dose reduction for 3D breast imaging.

[1]  Ehsan Samei,et al.  Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology. , 2014, Medical physics.

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

[3]  Masaki Katsura,et al.  Model-based iterative reconstruction technique for radiation dose reduction in chest CT: comparison with the adaptive statistical iterative reconstruction technique , 2012, European Radiology.

[4]  I. Sechopoulos.  A review of breast tomosynthesis. Part I. The image acquisition process. , 2013, Medical physics.

[5]  Bruno De Man,et al.  Toward a dose reduction strategy using model-based reconstruction with limited-angle tomosynthesis , 2014, Medical Imaging.

[6]  Jean-Baptiste Thibault,et al.  A three-dimensional statistical approach to improved image quality for multislice helical CT. , 2007, Medical physics.

[7]  B. De Man,et al.  Distance-driven projection and backprojection in three dimensions. , 2004, Physics in medicine and biology.

[8]  Lubomir M. Hadjiiski,et al.  Selective-diffusion regularization for enhancement of microcalcifications in digital breast tomosynthesis reconstruction. , 2010, Medical physics.

[9]  Martin J. Yaffe,et al.  Characterization of a constrained paired-view technique in iterative reconstruction for breast tomosynthesis. , 2013, Medical physics.

[10]  Xiaochuan Pan,et al.  Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms. , 2009, Medical physics.

[11]  Daniel B Kopans,et al.  Digital breast tomosynthesis from concept to clinical care. , 2014, AJR. American journal of roentgenology.

[12]  Ming Jiang,et al.  Convergence of the simultaneous algebraic reconstruction technique (SART) , 2003, Conference Record of Thirty-Fifth Asilomar Conference on Signals, Systems and Computers (Cat.No.01CH37256).

[13]  I. Sechopoulos.  A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications. , 2013, Medical physics.

[14]  Ken D. Sauer,et al.  A local update strategy for iterative reconstruction from projections , 1993, IEEE Trans. Signal Process..