Optimizing breast-tomosynthesis acquisition parameters with scanning model observers

In breast tomosynthesis (BT), multiple x-ray projections obtained over a limited angular span are reconstructed to produce a three-dimensional (3D) volume. This 3D imagery can lead to reduced structural masking effects compared to conventional mammography. Accordingly, there has been considerable interest in optimizing acquisition and reconstruction parameters associated with BT. In this work, we evaluate the use of a scanning model observer and localization ROC (LROC) methodology for performing a task-based optimization of the angular span and number of projection angles for a simulated BT system. The observer was applied to extracted slices of 3D volumes reconstructed with filtered backprojection. Both "background-known-exactly" (BKE) and "quasi-BKE" (QBKE) tasks were conducted. The latter task attempts to account for limited observer training by preserving structural noise in the detection task. Reduced noise in the form of fewer projections was important with the BKE task, although wider angular spans were also advantageous. Higher sampling densities may improve performance for the more-realistic QBKE tasks.

[1]  Bo Zhao,et al.  Optimization of detector operation and imaging geometry for breast tomosynthesis , 2007, SPIE Medical Imaging.

[2]  Michael A. King,et al.  Comparison of human- and model-observer LROC studies , 2003, SPIE Medical Imaging.

[3]  R. Swensson.  Unified measurement of observer performance in detecting and localizing target objects on images. , 1996, Medical physics.

[4]  H H Barrett,et al.  Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[5]  L. Feldkamp,et al.  Practical cone-beam algorithm , 1984 .

[6]  Aruna A. Vedula,et al.  A computer simulation study comparing lesion detection accuracy with digital mammography, breast tomosynthesis, and cone-beam CT breast imaging. , 2006, Medical physics.

[7]  Bo Zhao,et al.  A computer simulation platform for the optimization of a breast tomosynthesis system. , 2007, Medical physics.

[8]  Joseph Y. Lo,et al.  Visual image quality metrics for optimization of breast tomosynthesis acquisition technique , 2007, SPIE Medical Imaging.

[9]  H.C. Gifford,et al.  Evaluation of multiclass model observers in PET LROC studies , 2004, IEEE Symposium Conference Record Nuclear Science 2004..

[10]  Ying Chen,et al.  Methodology of NEQ (f) analysis for optimization and comparison of digital breast tomosynthesis acquisition techniques and reconstruction algorithms , 2007, SPIE Medical Imaging.

[11]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[12]  Andrew D. A. Maidment,et al.  Mammogram synthesis using a 3D simulation. I. Breast tissue model and image acquisition simulation. , 2002, Medical physics.

[13]  Renuka Uppaluri,et al.  Effect of acquisition parameters on image quality in digital tomosynthesis , 2007, SPIE Medical Imaging.

[14]  Stephen J. Glick,et al.  Optimal spectra for indirect detector breast tomosynthesis , 2006, SPIE Medical Imaging.

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

[16]  P.H. Pretorius,et al.  A comparison of human and model observers in multislice LROC studies , 2005, IEEE Transactions on Medical Imaging.

[17]  A. Mushlin,et al.  Estimating the accuracy of screening mammography: a meta-analysis. , 1998, American journal of preventive medicine.