Correcting for Patient Breathing Motion in PET Imaging




O'Briain, Teaghan

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Positron emission tomography (PET) requires imaging times that last several minutes long. Therefore, when imaging areas that are prone to respiratory motion, blurring effects are often observed. This blurring can impair our ability to use these images for diagnostics purposes as well for treatment planning. While there are methods that are used to account for this effect, they often rely on adjustments to the imaging protocols in the form of longer scan times or subjecting the patient to higher doses of radiation. This dissertation explores an alternative approach that leverages state-of-the-art deep learning techniques to align the PET signal acquired at different points of the breathing motion. This method does not require adjustments to standard clinical protocols; and therefore, is more efficient and/or safer than the most widely adopted approach. To help validate this method, Monte Carlo (MC) simulations were conducted to emulate the PET imaging process, which represent the focus of our first experiment. The next experiment was the development and testing of our motion correction method. A clinical four-ring PET imaging system was modelled using GATE (v. 9.0). To validate the simulations, PET images were acquired of a cylindrical phantom, point source, and image quality phantom with the modeled system and the experimental procedures were also simulated. The simulations were compared against the measurements in terms of their count rates and sensitivity as well as their image uniformity, resolution, recovery coefficients, coefficients of variation, contrast, and background variability. When compared to the measured data, the number of true detections in the MC simulations was within 5%. The scatter fraction was found to be (31.1 ± 1.1)% and (29.8 ± 0.8)% in the measured and simulated scans, respectively. Analyzing the measured and simulated sinograms, the sensitivities were found to be 10.0 cps/kBq and 9.5 cps/kBq, respectively. The fraction of random coincidences were 19% in the measured data and 25% in the simulation. When calculating the image uniformity within the axial slices, the measured image exhibited a uniformity of (0.015 ± 0.005), while the simulated image had a uniformity of (0.029 ± 0.011). In the axial direction, the uniformity was measured to be (0.024 ± 0.006) and (0.040 ± 0.015) for the measured and simulated data, respectively. Comparing the image resolution, an average percentage difference of 2.9% was found between the measurements and simulations. The recovery coefficients calculated in both the measured and simulated images were found to be within the EARL ranges, except for that of the simulation of the smallest sphere. The coefficients of variation for the measured and simulated images were found to be 12% and 13%, respectively. Lastly, the background variability was consistent between the measurements and simulations, while the average percentage difference in the sphere contrasts was found to be 8.8%. The code used to run the GATE simulations and evaluate the described metrics has been made available ( Next, 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 ( The encouraging results provided by our motion correction method present the opportunity for many possible future applications. For instance, this method can be transferred to clinical patient PET images or applied to alternative imaging modalities that would benefit from similar motion corrections. When applied to clinical PET images, FlowNet-PET would provide the capability of acquiring high quality images without the requirement for either longer scan times or subjecting the patients to higher doses of radiation. Accordingly, the imaging process would likely become more efficient and/or safer, which would be appreciated by both the health care institutions and their patients.



Convolutional Neural Networks, FlowNet, Motion correction, PET imaging, Unsupervised learning, Optical flow