Use of the Hotelling observer to optimize image reconstruction in digital breast tomosynthesis

Abstract. We propose an implementation of the Hotelling observer that can be applied to the optimization of linear image reconstruction algorithms in digital breast tomosynthesis. The method is based on considering information within a specific region of interest, and it is applied to the optimization of algorithms for detectability of microcalcifications. Several linear algorithms are considered: simple back-projection, filtered back-projection, back-projection filtration, and Λ-tomography. The optimized algorithms are then evaluated through the reconstruction of phantom data. The method appears robust across algorithms and parameters and leads to the generation of algorithm implementations which subjectively appear optimized for the task of interest.

[1]  J. Baker,et al.  A mathematical model platform for optimizing a multiprojection breast imaging system. , 2008, Medical physics.

[2]  Rongping Zeng,et al.  Evaluating the sensitivity of the optimization of acquisition geometry to the choice of reconstruction algorithm in digital breast tomosynthesis through a simulation study. , 2015, Physics in medicine and biology.

[3]  Eric Todd Quinto,et al.  Characterization and reduction of artifacts in limited angle tomography , 2013 .

[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]  Robert M. Nishikawa,et al.  Effect of Scan Angle and Reconstruction Algorithm on Model Observer Performance in Tomosynthesis , 2008, Digital Mammography / IWDM.

[6]  Aldo Badano,et al.  A statistical, task-based evaluation method for three-dimensional x-ray breast imaging systems using variable-background phantoms. , 2010, Medical physics.

[7]  M. Glas,et al.  Principles of Computerized Tomographic Imaging , 2000 .

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

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

[10]  Xiaochuan Pan,et al.  Task-based optimization of dedicated breast CT via Hotelling observer metrics. , 2014, Medical physics.

[11]  H H Barrett,et al.  Addition of a channel mechanism to the ideal-observer model. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[12]  P. Kuchment,et al.  On local tomography , 1995 .

[13]  Harrison H Barrett,et al.  Validating the use of channels to estimate the ideal linear observer. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

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

[15]  Bernd Weissmuller,et al.  Radiological Imaging The Theory Of Image Formation Detection And Processing , 2016 .

[16]  Adrian A. Sanchez,et al.  Task-Based Optimization of Computed Tomography Imaging Systems , 2015, ArXiv.

[17]  Kyle J Myers,et al.  A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data. , 2013, Medical physics.

[18]  Xiaochuan Pan,et al.  Region of interest based Hotelling observer for computed tomography with comparison to alternative methods , 2014, Journal of medical imaging.

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

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

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