New reconstruction algorithm for digital breast tomosynthesis: better image quality for humans and computers

  title={New reconstruction algorithm for digital breast tomosynthesis: better image quality for humans and computers},
  author={Alejandro Rodr{\'i}guez-Ruiz and Jonas Teuwen and Suzan Vreemann and Ramona W. Bouwman and Ruben E. van Engen and Nico Karssemeijer and Ritse M. Mann and Albert Gubern-M{\'e}rida and Ioannis Sechopoulos},
  journal={Acta Radiologica (Stockholm, Sweden : 1987)},
  pages={1051 - 1059}
Background The image quality of digital breast tomosynthesis (DBT) volumes depends greatly on the reconstruction algorithm. [] Key Method In parallel, a three-dimensional deep-learning convolutional neural network (3D-CNN) was trained (n = 259 patients, 51 positives with BI-RADS 3, 4, or 5 calcifications) and tested (n = 46 patients, nine positives), separately with FBP and EMPIRE volumes, to discriminate between samples with and without calcifications.

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