SC20 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Deep Learning-Based Low-Dose Tomography Reconstruction with Hybrid-Dose Measurements

Workshop:AI4S: Workshop on Artificial Intelligence and Machine Learning for Scientific Applications

Authors: Ziling Wu (Virginia Tech); Tekin Bicer (Argonne National Laboratory (ANL)); Zhengchun Liu (Argonne National Laboratory, Data Science and Learning Division); Vincent De Andrade (Argonne National Laboratory, X-Ray Science Division); Yunhui Zhu (Virginia Tech); and Ian Foster (Argonne National Laboratory, Data Science and Learning Division)

Abstract: Synchrotron-based X-ray computed tomography is widely used for investigating inner structures of specimens at high spatial resolutions. However, potential beam damage to samples often limits the X-ray exposure during tomography experiments. Proposed strategies for eliminating beam damage also decrease reconstruction quality. Here we present a deep learning-based method to enhance low-dose tomography reconstruction via a hybrid-dose acquisition strategy composed of extremely sparse-view normal-dose projections and full-view low-dose projections. Corresponding image pairs are extracted from low-/normal-dose projections to train a deep convolutional neural network, which is then applied to enhance full-view noisy low-dose projections.

Evaluation on two experimental datasets under different hybrid-dose acquisition conditions show significantly improved structural details and reduced noise levels compared to uniformly distributed acquisitions with the same number of total dosage. The resulting reconstructions also preserve more structural information than reconstructions processed with traditional analytical and regularization-based iterative reconstruction methods from uniform acquisitions. Our performance comparisons show that our implementation, HDrec, can perform de-noising of a real-world experimental data 410x faster than the state-of-the-art Xlearn method while providing better quality. This framework can be applied to other tomographic or scanning based X-ray imaging techniques for enhanced analysis of dose-sensitive samples and has great potential for studying fast dynamic processes.

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