Deep learning scatter estimation for breast tomosynthesis based on a realistic compressed breast shape model in the CC view

M. C. Pinto, K. Michielsen, A. Rodríguez-Ruiz, R. Biniazan, S. Kappler, I. Sechopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Using anti-scatter grids in digital breast tomosynthesis (DBT) is challenging due to the relative motion between source and detector. Therefore, algorithmic scatter correction methods could be beneficial to compensate for the image quality loss due to scatter radiation. In this work, we present a deep learning model capable of predicting the scatter fraction (SF) image for simulations of realistically shaped homogeneous phantoms. The model was trained, validated, and tested on a dataset of 600 homogeneous phantoms representing the compressed breast with thicknesses ranging from 30 mm to 89 mm with randomly generated breast shapes. Monte Carlo simulations were performed for all cases and at different projection angles to obtain estimates of the DBT primary and scatter projections. The same procedure was performed for patient-based phantoms with realistic internal glandular and adipose texture to evaluate the generalizability of our model results. The median and interquartile range (IQR) of the mean relative difference (MRD) and mean absolute error (MAE) for the homogeneous phantoms between Monte Carlo SF ground truth and model predictions resulted in approximately 0.49% (IQR, 0.26-0.76%) and 2.06% (IQR, 1.83-2.26%), respectively, while the patient-based phantoms resulted in results with a MRD of -1.39% (IQR, -4.13-1.60%) and a MAE of 3.97% (IQR, 3.38-4.71%). This seems to indicate that the model trained on the homogeneous phantoms captures the average representation inside the homogeneous breast, with reasonable accuracy in breasts with texture variations. Therefore, the model has the potential to be used as an algorithmic scatter correction method in the future.

Original languageEnglish
Title of host publicationMedical Imaging 2022
Subtitle of host publicationPhysics of Medical Imaging
EditorsWei Zhao, Lifeng Yu
PublisherSPIE Press
Volume12031
ISBN (Electronic)9781510649378
DOIs
Publication statusPublished - 2022
Externally publishedYes
EventMedical Imaging 2022: Physics of Medical Imaging - Virtual, Online
Duration: 21 Mar 202227 Mar 2022

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume12031
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2022: Physics of Medical Imaging
CityVirtual, Online
Period21/03/2227/03/22

Keywords

  • breast tomosynthesis
  • deep learning
  • scatter correction

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