FlowNet-PET: Unsupervised Learning to Perform Respiratory Motion Correction in PET Imaging. (arXiv:2205.14147v1 [eess.IV])

To correct for breathing motion in PET imaging, an interpretable and
unsupervised deep learning technique, FlowNet-PET, was constructed. The network
was trained to predict the optical flow between two PET frames from different
breathing amplitude ranges. As a result, the trained model groups different
retrospectively-gated PET images together into a motion-corrected single bin,
providing a final image with similar counting statistics as a non-gated image,
but without the blurring effects that were initially observed. As a
proof-of-concept, FlowNet-PET was applied to anthropomorphic digital phantom
data, which provided the possibility to design robust metrics to quantify the
corrections. When comparing the predicted optical flows to the ground truths,
the median absolute error was found to be smaller than the pixel and slice
widths, even for the phantom with a diaphragm movement of 21 mm. The
improvements were illustrated by comparing against images without motion and
computing the intersection over union (IoU) of the tumors as well as the
enclosed activity and coefficient of variation (CoV) within the no-motion tumor
volume before and after the corrections were applied. The average relative
improvements provided by the network were 54%, 90%, and 76% for the IoU, total
activity, and CoV, respectively. The results were then compared against the
conventional retrospective phase binning approach. FlowNet-PET achieved similar
results as retrospective binning, but only required one sixth of the scan
duration. The code and data used for training and analysis has been made
publicly available (https://github.com/teaghan/FlowNet_PET).

Source: https://arxiv.org/abs/2205.14147

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