Abstract
Medical image reconstruction algorithms such as Penalized Weighted Least Squares (PWLS) typically rely on a good choice of tuning parameters such as the number of iterations, the strength of regularizar, etc. However, obtaining a good estimate of such parameters is often done using trial and error methods. This process is very time consuming and laborious especially for high resolution images. To solve this problem we propose an interactive framework. We focus on the regularization parameter and train a CNN to imitate its impact on image for varying values. The trained CNN can be used by a human practitioner to tune the regularization strength on-the-fly as per the requirements. Taking the example of Digital Breast Tomosynthesis reconstruction, we demonstrate the feasibility of our approach and also discuss the future applications of this interactive reconstruction approach. We also test the proposed methodology on public Walnut and Lodopab CT reconstruction datasets to show it can be generalized to CT reconstruction as well.
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This material is based upon work supported by the National Science Foundation under Grant No. IIS-1715985 and IIS-1812606.
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Sahu, P., Huang, H., Zhao, W., Qin, H. (2021). Interactive Smoothing Parameter Optimization in DBT Reconstruction Using Deep Learning. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_6
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DOI: https://doi.org/10.1007/978-3-030-87234-2_6
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