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Purpose: To develop a deep learning approach for channelization of the Hotelling model observer (DL-CHO) and apply to the task based image quality evaluation of digital breast tomosynthesis (DBT) using a structured phantom. Methods: An acrylic semi-cylindrical container was filled with different sizes of acrylic spheres and water. Five 3D printed non-spiculated mass models were also inserted in the phantom, each with different size (diameter from 1.5mm to 5.7mm). The phantom was scanned on 8 different DBT systems, at 3 dose levels on each system, giving a total of 594 DBT scans. Nearly half of the image dataset was read by human readers using a 4-alternative forced choice (4-AFC) paradigm. From the human results, an anthropomorphic DL-CHO was developed and trained, utilizing a single convolutional layer with five kernels functioning like channels. After 50 training epochs, the convolutional kernels were fixed and then validated with the second half of the image dataset. Statistical analysis of the goodness of the fit between the newly developed DL-CHO and human observers was performed to estimate the appropriateness of the new CHO for multivendor tomosynthesis studies. Results: The DL-CHO shows good agreement with human observers for all 8 DBT systems, with Pearson’s correlation between 0.90 and 0.99; linear regression slope between 0.60 and 1.17; and mean error between -5.6PC and 12PC. The DL-CHO shows better reproducibility compared to human observers for most of the lesion sizes. Conclusions: The DL-CHO offers a robust and efficient means of evaluating DBT test object images, for the purpose of DBT system image quality evaluation.
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