An investigation of quantitative accuracy for deep learning based denoising in oncological PET.

An investigation of quantitative accuracy for deep learning based denoising in oncological PET. Phys Med Biol. 2019 Jul 15;: Authors: Lu W, Onofrey JA, Lu Y, Shi L, Ma T, Liu Y, Liu C Abstract Reducing radiation dose is important for PET imaging. However, reducing injection doses causes increased image noise and low signal-to-noise ratio (SNR), subsequently affecting diagnostic and quantitative accuracies. Deep learning methods have shown a great potential to reduce the noise and improve the SNR in low dose PET data. 
 In this work, we comprehensively investigated the quantitative accuracy of small lung nodules, in addition to visual image quality, using deep learning based denoising methods for oncological PET imaging. We applied and optimized an advanced deep learning method based on the U-net architecture to predict the standard dose PET image from 10% low-dose PET data. We also investigated the effect of different network architectures, image dimensions, labels and inputs for deep learning methods with respect to both noise reduction performance and quantitative accuracy. Normalized mean square error, SNR, and standard uptake value (SUV) bias of different nodule regions of interest (ROIs) were used for evaluation.
 Our results showed that U-net and GAN are superior to CAE with smaller SUV<sub>mean</sub> and SUV<sub>max</sub&am...
Source: Physics in Medicine and Biology - Category: Physics Authors: Tags: Phys Med Biol Source Type: research