A deep learning-based approach for direct whole-body PET attenuation correction.

569 Introduction: Attenuation correction (AC) and scatter correction (SC) is a required step to enable diagnostic and quantitative PET imaging. While this is most commonly performed utilizing a sequential CT acquisition for PET/CT imaging, there may still exist limitations, such as patient movement between PET and CT or concerns about ionizing radiation dose. Recent work has explored PET-only AC approaches [1] and more recently, artificial intelligence (AI)-based approaches have been proposed for the brain [2,3]. The goal of this study is to develop and evaluate a whole-body PET-only attenuation correction approach using such an AI approach. Specifically, we seek to convert non-attenuation corrected (NAC) PET images directly into AC and SC PET images using a convolutional neural network. Methods: A deep convolutional encoder-decoder (CED) network using the UNet structure with 50% dropout, batch normalization, and max pooling was implemented in Keras. Model parameters included: starting input channels=16, depth=3, optimizer=Adam(lr=1e-4), loss function=mean squared error, batch size=20, # of training epochs=500. 50 clinical FDG PET/CT datasets were retrospectively obtained under IRB approval of subjects scanned on a single scanner (Discovery 710, GE Healthcare). 40 subjects were used to train the model with a validation split of 20%. 10 training-naive subjects were used to evaluate the model. Input images were non-time-of-flight (non-TOF) NAC PET images with an output target of TOF AC PET images. All images were resampled to 3x3x3mm prior to training. Structural similarity index (SSIM), mean average error (MAE), percent error (%-error), absolute percent error (%-abserror) were calculated for pixels inside the body (air not included) as performance metrics. Results: In the 10 evaluation subjects, the performance measures for the AI-AC image compared to AC were: SSIM=0.9429±0.0339; MAE=121.4803±73.3756 Bq/cc; %-error=-0.9163±10.4934 %; %-abserror=28.2073±5.3542 %. Performance measures for the NAC image compared to AC were: SSIM=.8331±.0716; MAE=472.9379± 165.5215 Bq/cc; %-error=-87.1119±3.4715 %; %-abserror=87.1221±3.4679 %. Compared to a NAC image alone, it is feasible that the AI-AC approach could potentially provide feasible AC images when a CT is not available. Discussion/Conclusion: We have presented a preliminary demonstration of direct NAC to AC conversion using a convolutional neural network. While absolute percent error of the proposed approach is on the order of 30%, this represents an interesting new capability for using direct conversion of input data into corrected data. Note that the NAC images used also lacked time-of-flight and scatter correction, which must also be inherently learned by the network. Additional interesting features of this output include reasonable performance for average-sized patients in areas with high amounts of focal activity, such as the bladder. However, in patients of very large girth, the method can be challenged by significant central soft tissue-related attenuation and lack of signal in the originating NAC image. Thus future work is necessary to further improve this approach, including optimization of the deep learning network, input and output image reconstruction parameters (e.g., use of TOF NAC), and generalizability between agents and scanner hardware. Ultimately, it may be feasible to perform direct reconstruction including attenuation and scatter correction directly from PET sinogram data using AI approaches [4]. References:1) Berker, Li. (2016). Med Phys. 43(2). 2) Liu, Jang, Kijowski, et al. (2018). EJNMMI Physics. 5:24. 3) Hwang, Kim, Kang, et al. (2018). J Nucl Med. 59(10). 4) Zhu, Liu, Cauley et al. (2018). Nature 555(7697).