  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 denoising 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.
