A Task-informed Model Training Method for Deep Neural Network-based Image Denoising

A variety of deep neural network (DNN)-based image denoising methods have been proposed for use with medical images. These methods are typically trained by minimizing loss functions that quantify a distance between the denoised image and the defined target image (e.g., a noise-free or low noise image). They have demonstrated high performance in terms of traditional image quality metrics such as root mean square error (RMSE), structural similarity index metric (SSIM), or peak signal-To-noise ratio (PSNR). However, it has been reported that these denoising methods may not improve the objective measures of image quality (IQ). In this work, a task-informed model training method that preserves task-specific information is established and systematically evaluated with clinical realistic simulated low-dose X-ray computed tomography (CT) images. Specifically, binary signal de-tection tasks under signal-known-statistically (SKS) with background-known-statistically (BKS) conditions are considered. The low-dose CT denoising networks are first pretrained by use of a mean-square-error (MSE) loss function. A fully connected layer with a sigmoid activation function is subsequently appended to the denoising network, which can be interpreted as a single layer neural network-based numerical observer (SLNN-NO). A hy-brid loss function consisting of a binary cross-entropy loss function and mean square loss function is employed to jointly fine-Tune the denoising network and train the SLNN-NO. The performance of the SLNN-NO on denoised data is quantified to evaluate the impact of the task-informed training procedure on the denoising network. The presented results indicate that the task-informed training method can improve observer performance while providing control over the trade off between traditional and task-based measures of image quality. 2022 SPIE.