Medical Image Analysis
Volume 15, Issue 1 , Pages 53-70 , February 2011

Task-based performance analysis of FBP, SART and ML for digital breast tomosynthesis using signal CNR and Channelised Hotelling Observers

  • Dominique Van de Sompel

      Affiliations

    • Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
    • Corresponding Author InformationCorresponding author.
    • ,
  • Sir Michael Brady

      Affiliations

    • Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
    • ,
  • John Boone

      Affiliations

    • Department of Radiology, UC Davis, 4860 Y Street, Suite 3100, Sacramento, CA 95817, USA

Received 16 December 2009 ,Revised 14 July 2010 ,Accepted 19 July 2010.

References 

  1. Andersen A, Kak A. Simultaneous algebraic reconstruction technique (SART): a superior implementation of the art algorithm. Ultrasonic Imaging. 1984;6(1):81–94
  2. Bakic P, Albert M, Brzakovic D, Maidment A. Mammogram synthesis using a 3D simulation. I. Breast tissue model and image acquisition simulation. Medical Physics. 2002;29:2131
  3. Barrett, H., Abbey, C., Gallas, B., Eckstein, M., 1998. Stabilized estimates of hotelling-observer detection performance in patient-structured noise. In: Proceedings of the SPIE, vol. 3340, pp. 27–43.
  4. Beutel J, Kundel H, Van Metter R. Handbook of Medical Imaging. Vol. 1: Physics and Psychophysics. SPIE Press; 2000;
  5. Bissonnette, M., Hansroul, M., Masson, E., Savard, S., Cadieux, S., Warmoes, P., Gravel, D., Agopyan, J., Polischuk, B., Haerer, W., et al., 2005. Digital breast tomosynthesis using an amorphous selenium flat panel detector. In: Proceedings of the SPIE, vol. 5745, p. 529.
  6. Bliznakova K, Bliznakov Z, Bravou V, Kolitsi Z, Pallikarakis N. A three-dimensional breast software phantom for mammography simulation. Physics in Medicine and Biology. 2003;48(22):3699–3719
  7. Boone, J., 1998. Spectral modeling and compilation of quantum fluence in radiography and mammography. In: Proceedings of the SPIE, vol. 3336, p. 592.
  8. Burgess A. The Rose model, revisited. Journal of the Optical Society of America A. 1999;16(3):633–646
  9. Cai, S., Li, K., Selesnick, I., 2003. Matlab implementation of wavelet transforms: image denoising. <http://taco.poly.edu/WaveletSoftware/denoise.html> (last accessed 12.05.09).
  10. Chan H, Wei J, Zhang Y, Sahiner B, Hadjiiski L, Helvie M. Detection of masses in digital breast tomosynthesis mammography: effects of the number of projection views and dose. Lecture Notes in Computer Science. 2008;5116:279–285
  11. Chawla A, Samei E, Saunders R, Lo J, Baker J. A mathematical model platform for optimizing a multiprojection breast imaging system. Medical Physics. 2008;35:1337
  12. Claus, B., Eberhard, J., Schmitz, A., Carson, P., Goodsitt, M., Chan, H., 2006. Generalized filtered back-projection reconstruction in breast tomosynthesis. IWDM 2006 4046, 167–174.
  13. Claus B, Eberhard J, Thomas J, Galbo C, Pakenas W, Muller S. Preference study of reconstructed image quality in mammographic tomosynthesis. In:  Peitgen H-O editors. IWDM 2002. Bremen, Germany: Springer; 2002;
  14. De Pierro A. On the relation between the ISRA and the EM algorithm for positron emission tomography. IEEE Transactions on Medical Imaging. 1993;12(2):328–333
  15. De Pierro A. A modified expectation maximization algorithm for penalized likelihood estimation in emission tomography. IEEE Transactions on Medical Imaging. 1995;14(1):132–137
  16. Diekmann F, Meyer H, Diekmann S, Puong S, Muller S, Bick U, et al. Thick slices from tomosynthesis data sets: phantom study for the evaluation of different algorithms. Journal of Digital Imaging. 2007;1–8
  17. Dobbins J, Godfrey D. Digital X-ray tomosynthesis: current state of the art and clinical potential. Physics in Medicine and Biology. 2003;48(19):65–106
  18. Eckstein M, Bartroff J, Abbey C, Whiting J, Bochud F. Automated computer evaluation and optimization of image compression of X-ray coronary angiograms for signal known exactly detection tasks. Optics Express. 2003;11(5):460–475
  19. Erdogan, H., Fessler, J., 1998. Accelerated monotonic algorithms for transmission tomography. In: ICIP, vol. 2, pp. 680–684.
  20. Erdogan H, Fessler J. Monotonic algorithms for transmission tomography. IEEE Transactions on Medical Imaging. 1999;18(9):801–814
  21. Feldkamp L, Davis L, Kress J. Practical cone-beam algorithm. Journal of the Optical Society of America A. 1984;1(6):612–619
  22. Gallas B, Barrett H. Validating the use of channels to estimate the ideal linear observer. Journal of the Optical Society of America A. 2003;20(9):1725–1738
  23. Gilbert P. Iterative methods for the three-dimensional reconstruction of an object from projections. Journal of Theoretical Biology. 1972;36:105–117
  24. Gong X, Glick S, Liu B, Vedula A, Thacker S. A computer simulation study comparing lesion detection accuracy with digital mammography, breast tomosynthesis, and cone-beam CT breast imaging. Medical Physics. 2006;33:1041
  25. Gordon R, Bender R, Herman G. Algebraic reconstruction techniques (art) for three-dimensional electron microscopy and X-ray photography. Journal of Theoretical Biology. 1970;29(3):471–481
  26. Hipwell J, Tanner T, Crum W, Hawkes D. A new validation method for X-ray mammogram registration algorithms using a projection model of breast X-ray compression. IEEE Transactions on Medical Imaging. 2007;26(9):1190–1200
  27. Hoeschen C, Fill U, Zankl M, Panzer W, Regulla D, Dohring W. A high-resolution voxel phantom of the breast for dose calculations in mammography. Radiation Protection Dosimetry. 2005;114(1–3):406–409
  28. Hotelling H. The generalization of Student’s ratio. The Annals of Mathematical Statistics. 1931;360–378
  29. Kaczmarz S. Angenaherte auflosung von systemen linearer gleichungen. Bulletin International de l’Academie Polonaise des Sciences et des Lettres A. 1937;35:355–357(Approximate solution to systems of linear equations)
  30. Kak, A.C., Slaney, M., 2001. Principles of Computerized Tomographic Imaging. Society of Industrial and Applied Mathematics.
  31. Kempston M, Mainprize J, Yaffe M. Evaluating the effect of dose on reconstructed image quality in digital tomosynthesis. Lecture Notes in Computer Science. 2006;4046:490–497
  32. Kinahan P, Defrise M. Analytic image reconstruction methods. Emission Tomography: The Fundamentals of PET and SPECT. 2004;421–442
  33. Lange K, Carson R. EM reconstruction algorithms for emission and transmission tomography. Journal of Computer Assisted Tomography. 1984;8:306–316
  34. Lauritsch G, Haerer W. A theoretical framework for filtered backprojection in tomosynthesis. SPIE Conference on Image Processing. 1998;3338:1127–1137
  35. Ludwig J, Mertelmeier T, Kunze H, Heirer W. A novel approach for filtered backprojection in tomosynthesis based on filter kernels determined by iterative reconstruction techniques. Lecture Notes in Computer Science. 2008;5116:612–620
  36. Ma A, Gunn S, Darambara D. Introducing DeBRa: a detailed breast model for radiological studies. Physics in Medicine and Biology. 2009;54(14):4533–4545
  37. Maidment, A., Albert, M., Thunberg, S., Adelow, L., Blom, O., Egerstrom, J., Eklund, M., Francke, T., Jordung, U., Kristoffersson, T., et al., 2005. Evaluation of a photon-counting breast tomosynthesis imaging system. In: Proceedings of the SPIE, vol. 5745, pp. 572–582.
  38. Maidment A, Ullberg C, Francke T, Lindqvist L, Sokolov S, Lindman K, et al. Clinical evaluation of a photon-counting tomosynthesis mammography system. Lecture Notes in Computer Science. 2006;4046:144–151
  39. Mertelmeier, T., Orman, J., Haerer, W., Dudam, M., 2006. Optimizing filtered backprojection reconstruction for a breast tomosynthesis prototype device. SPIE, vol. 6142, p. 61420F.
  40. Näppi J, Dean P, Nevalainen O, Toikkanen S. Algorithmic 3D simulation of breast calcifications for digital mammography. Computer Methods and Programs in Biomedicine. 2001;66(1):115–124
  41. Orman J, Mertelmeier T, Haerer W. Adaptation of image quality using various filter setups in the filtered backprojection approach for digital breast tomosynthesis. IWDM 2006. 2006;4046:175–182
  42. Park S, Barrett H, Clarkson E, Kupinski M, Myers K. Channelized-ideal observer using Laguerre–Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal. Journal of the Optical Society of America A. 2007;24(12):136–150
  43. Pathmanathan, P., 2006. Predicting tumour location by simulating the deformation of the breast using nonlinear elasticity and the finite element method. Ph.D. thesis, University of Oxford.
  44. Pathmanathan P, Gavaghan D, Whiteley J, Brady M, Nash M, Nielsen P, et al. Predicting tumour location by simulating large deformations of the breast using a 3d finite element model and nonlinear elasticity. Medical Image Computing and Computer-Assisted Intervention (MICCAI). 2004;3217:217–224
  45. Pineda A, Yoon S, Paik D, Fahrig R. Optimization of a tomosynthesis system for the detection of lung nodules. Medical Physics. 2006;33:1372
  46. Qiu Y, Sun X, Manohar V, Goldgof D. Towards registration of temporal mammograms by finite element simulation of MR breast volumes. Proceedings of the SPIE Medical Imaging: Visualization, Image-guided Procedures, and Modeling. 2008;6918:2008
  47. Rajagopal, V., Nash, M., Highnam, R., Nielsen, P., 2008. The breast biomechanics reference state for multi-modal image analysis. International Workshop of Digital Mammography, pp. 385–392.
  48. Reiser I, Sidky E, Nishikawa R, Pan X. Development of an analytic breast phantom for quantitative comparison of reconstruction algorithms for digital breast tomosynthesis. Lecture Notes in Computer Science. 2006;4046:190–196
  49. Ren, B., Ruth, C., Stein, J., Smith, A., Shaw, I., Jing, Z., 2005. Design and performance of the prototype full field breast tomosynthesis system with selenium based flat panel detector. In: Proceedings of the SPIE, vol. 5745, p. 550.
  50. Samei E, Flynn M, Eyler W. Relative influence of quantum and anatomical noise on the detection of subtle lung nodules in chest radiographs. Radiology. 1999;213:727–734
  51. Sarkar, S., Zhang, Y., Qiu, Y., Goldgof, D., Li, L., 2007. 3D finite element modeling of nonrigid breast deformation for feature registration in-ray and MR images. In: IEEE Workshop on Applications of Computer Vision, WACV’07, pp. 38–38.
  52. Siddon RL. Fast calculation of the exact radiological path for a three-dimensional CT array. Medical Physics. 1985;12:252–255
  53. Smith A, Niklason L, Ren B, Wu T, Ruth C, Jing Z. Lesion visibility in low dose tomosynthesis. Lecture Notes in Computer Science. 2006;4046:160–166
  54. Stevens G, Fahrig R, Pelc N. Filtered backprojection for modifying the impulse response of circular tomosynthesis. Medical Physics. 2001;28(3):372–380
  55. Suryanarayanan S, Karellas A, Srinivasan V, Bakerph S, Glick S, D’Orsi C, et al. Evaluation of linear and nonlinear tomosynthetic reconstruction methods in digital mammography. Academic Radiology. 2001;8(3):219–224
  56. Suryanarayanan S, Karellas A, Vedantham S, Glick S, D’Orsi C, Baker S, et al. Comparison of tomosynthesis methods used with digital mammography. Academic radiology. 2000;7(12):1085
  57. Taylor P, Owens R, Ingram D. Simulated mammography using synthetic 3D breasts. Computational Imaging and Vision. 1998;13:283–290
  58. Tromans, C., 2006. Measuring breast density from X-ray mammograms. Ph.D. thesis, University of Oxford, Wolfson Medical Vision Laboratory, Department of Engineering Science, Oxford, OX1 3PJ, UK.
  59. Tromans C, Brady M, Van de Sompel D, Lorenzon M, Bazzocchi M, Zuiani C. Progress toward a quantitative scale for describing radiodensity in mammographic images. Digital Mammography. 2008;553–560
  60. Van de Sompel, D., Brady, M., 2010. Task-based performance analysis of SART for digital breast tomosynthesis using signal CNR and channelized hotelling observers. In: Accepted for oral presentation at SPIE Medical Imaging 2010, San Diego, CA, USA.
  61. Wu G, Mainprize J, Yaffe M. Characterization of projection ordering in iterative reconstruction methods for breast tomosynthesis. Lecture Notes in Computer Science. 2008;5116:601–605
  62. Wu T, Moore R, Rafferty E, Kopans D. A comparison of reconstruction algorithms for breast tomosynthesis. Medical Physics. 2004;31(9):2636–2647
  63. Wu T, Stewart A, Stanton M, McCauley T, Phillips W, Kopans D, et al Tomographic mammography using a limited number of low-dose cone-beam projection images. Medical Physics. 2003;30(3):365–380
  64. Yang W, Carkaci S, Chen L, Lai C, Sahin A, Whitman G, et al. Dedicated cone-beam breast CT: feasibility study with surgical mastectomy specimens. American Journal of Roentgenology. 2007;189(6):1312
  65. Yoon, S., Gang, J., Tward, D., Siewerdsen, J., Fahrig, R., 2009. Analysis of lung nodule detectability and anatomical clutter in tomosynthesis imaging of the chest. In: Proceedings of the SPIE, vol. 7258, p. 72581M.
  66. Young, S., Park, S., Anderson, S., Badano, A., Myers, K., Bakic, P., 2009. Estimating breast tomosynthesis performance in detection tasks with variable-background phantoms. In: Proceedings of SPIE, vol. 7258, pp. 72580O.1–72580O.9.
  67. Zhang, Y., Chan, H., Goodsitt, M., Schmitz, A., Eberhard, J., Claus, B., 2008. Investigation of different PV distributions in digital breast tomosynthesis (DBT) mammography. In: IWDM 2008. Springer, pp. 593–600.
  68. Zhang Y, Chan H, Sahiner B, Wei J, Goodsitt M, Hadjiiski L, et al. A comparative study of limited-angle cone-beam reconstruction methods for breast tomosynthesis. Medical Physics. 2006;33(10):3781–3795
  69. Zhang, Y., Chan, H., Sahiner, B., Wei, J., Goodsitt, M., Hadjiiski, L., Ge, J., Zhou, C., 2006b. Tomosynthesis reconstruction using the simultaneous algebraic reconstruction technique (SART) on breast phantom data. In: Proceedings of the SPIE, vol. 6142, p. 614249.
  70. Zhang Y, Chan H, Sahiner B, Wu Y, Zhou C, Ge J, et al. Application of boundary detection information in breast tomosynthesis reconstruction. Medical Physics. 2007;34:3603
  71. Zhou J, Zhao B, Zhao W. A computer simulation platform for the optimization of a breast tomosynthesis system. Medical Physics. 2007;34(3):1098–1109
  72. Zhou, L., Oldan, J., Fisher, P., Gindi, G., 2006. Low-contrast lesion detection in tomosynthetic breast imaging using a realistic breast phantom. In: Proceedings of the SPIE Medical Imaging 2006: Physics of Medical Imaging, vol. 6142, pp. 1685–1696.

PII: S1361-8415(10)00096-4

doi: 10.1016/j.media.2010.07.004

Medical Image Analysis
Volume 15, Issue 1 , Pages 53-70 , February 2011

Article Tools

* Image Usage & Resolution