Dynamic relaxation in algebraic reconstruction technique (ART) for breast tomosynthesis imaging

BACKGROUND AND OBJECTIVES A major challenge in Digital Breast Tomosynthesis (DBT) is handling image noise since the 3D reconstructed images are obtained from low dose projections and limited angular range. The use of the iterative reconstruction algorithm Algebraic Reconstruction Technique (ART) in clinical context depends on two key factors: the number of iterations needed (time consuming) and the image noise after iterations. Both factors depend highly on a relaxation coefficient (λ), which may give rise to slow or noisy reconstructions, when a single λ value is considered for the entire iterative process. The aim of this work is to present a new implementation for the ART that takes into account a dynamic mode to calculate λ in DBT image reconstruction. METHODS A set of initial reconstructions of real phantom data was done using constant λ values. The results were used to choose, for each iteration, the suitable λ value, taking into account the image noise level and the convergence speed. A methodology to optimize λ automatically during the image reconstruction was proposed. RESULTS Results showed we can dynamically choose λ values in such a way that the time needed to reconstruct the images can be significantly reduced (up to 70%) while achieving similar image quality. These results were confirmed with one clinical dataset. CONCLUSIONS With simple methodology we were able to dynamically choose λ in DBT image reconstruction with ART, allowing a shorter image reconstruction time without increasing image noise.

[1]  P. Toft.  The Radon Transform - Theory and Implementation , 1996 .

[2]  D. Ros,et al.  The influence of a relaxation parameter on SPECT iterative reconstruction algorithms. , 1996, Physics in medicine and biology.

[3]  Chris C. Shaw.  Cone Beam Computed Tomography , 2014 .

[4]  John M. Boone,et al.  Task-based performance analysis of FBP, SART and ML for digital breast tomosynthesis using signal CNR and Channelised Hotelling Observers , 2011, Medical Image Anal..

[5]  T M Buzug,et al.  Weighted simultaneous algebraic reconstruction technique for tomosynthesis imaging of objects with high-attenuation features. , 2013, Medical physics.

[6]  Gabor T. Herman,et al.  Relaxation methods for image reconstruction , 1978, CACM.

[7]  Nuno Matela,et al.  Choosing the ART relaxation parameter for Clear-PEM 2D image reconstruction , 2010, Comput. Methods Programs Biomed..

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

[9]  Niko Papanikolaou,et al.  The effect of a limited number of projections and reconstruction algorithms on the image quality of megavoltage digital tomosynthesis , 2009, Journal of applied clinical medical physics.

[10]  Howard C. Gifford,et al.  Penalized Maximum Likelihood Reconstruction for Improved Microcalcification Detection in Breast Tomosynthesis , 2011, IEEE Transactions on Medical Imaging.

[11]  Kazemi Ehsan Mohammad,et al.  An Empirical Study of Algebraic Reconstruction Techniques , 2011 .

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

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

[14]  M. Wernick,et al.  Emission Tomography: The Fundamentals of PET and SPECT , 2004 .

[15]  Gabor T. Herman,et al.  Fundamentals of Computerized Tomography: Image Reconstruction from Projections , 2009, Advances in Pattern Recognition.

[16]  Gabor T. Herman,et al.  Image reconstruction from projections : the fundamentals of computerized tomography , 1980 .

[17]  Gabor T. Herman,et al.  Algebraic reconstruction techniques can be made computationally efficient [positron emission tomography application] , 1993, IEEE Trans. Medical Imaging.