Deep learning-based correction of PET attenuation correction artifacts at the lung-liver interface

1470 Objectives: A common imaging artifact in PET/CT is the banana-shaped artifact that appears at the lung-liver interface. This artifact occurs when the attenuation correction CT (CTAC) is acquired at a single phase of the respiratory cycle that is spatially mismatched with the emission PET, which is averaged over all respiratory phases. This results in the dome of the liver being under corrected on PET during attenuation correction. This can lead to ambiguity or misdiagnosis when localizing lesions in the dome of the liver, and can significantly impact PET lesion quantitation. This study presents a method for correcting PET-CTAC misalignment at the lung-liver interface through deep learning synthesis of CTAC images from PET data. Methods: Clinical 18F-FDG PET/CT imaging exams for 200 patients acquired on a GE Discovery 710 PET/CT time-of-flight scanner were retrospectively reviewed. The quality of the PET and CTAC alignment at the lung-liver interface were visually scored. Exams that had good PET-CTAC alignment were selected for use as “ground truth” for deep learning. From these exams, the CTACs were intentionally altered using image processing techniques to shift the lung-liver interface in the inferior-superior plane by various distances (0-2 cm), resulting in misaligned CTACs (CTAC-mis). PET images were reconstructed using the CTAC-mis images for attenuation correction, resulting in PET images with banana-shaped artifacts (PET-mis). A 3D patch-based convolutional neural network based on the DeepMedic architecture was trained to predict an aligned but low-resolution CTAC image (CTAC-deep) using the PET-mis as input and the original well-aligned CTAC as the ground truth. The CTAC-deep was then used as a target for deformable registration of CTAC-mis, resulting in a high resolution aligned CTAC (CTAC-reg). Registration was performed using the ANTs package with symmetric normalization to maximize cross correlation. Finally, emission data was reconstructed using CTAC-reg to produce a corrected PET (PET-cor). A blinded experienced nuclear medicine physician scored the severity of the banana artifact on a three-point scale in the ground truth PET, PET-mis and PET-cor images of test subjects not used in training. To compare quantitation/contrast, a 30 mm diameter 2D axial ROI was placed in each subject’s dome of the liver and mean standardized uptake values (SUVs) were compared. Results: Out of the 200 clinical PET/CT exams, 70 were found to have good alignment between PET and CTAC images at the lung-liver interface. Of these, 63 were used in training and 7 for testing. In the test subjects, the proposed method was found to completely eliminate the banana artifact in 6/7 subjects. In 1/7 subjects, the artifact was greatly reduced, but a slight artifact remained. For quantitation, liver dome SUVs in PET-cor were on average within 4 ± 6% of those in the ground truth PET, whereas PET-mis SUVs were 55 ± 10% of the ground truth SUVs. Conclusions: Using deep learning and image registration, a spatially aligned CTAC was generated from PET images for use in attenuation correction. In all cases, the method eliminated or nearly eliminated the banana-shaped attenuation correction artifact at the lung-liver interface. The method may improve diagnosis and PET quantification for hepatic lesions, and is well-suited for clinical workflows as it only requires clinically-acquired PET and CTAC images. Acknowledgement: This word is supported by GE Healthcare.