Investigating the impact of data consistency in task-informed learned image reconstruction method

Various supervised learning-based medical image reconstruction methods have been developed with the goal of improving image quality (IQ). These methods typically use loss functions that minimize pixel-level differences between the reconstructed and high-quality target images. While they may seemingly perform well based on traditional image quality metrics such as mean squared error, they do not consistently improve objective IQ measures based on diagnostic task performance. This work introduces a task-informed learned image reconstruction method. To establish the method, a measure of signal detection performance is incorporated in a hybrid loss function that is used for training. The proposed method is inspired by null space learning, and a task-informed data-consistent (DC) U-Net is utilized to estimate a null space component of the object that enhances task performance, while ensuring that the measurable component is stably reconstructed using a regularized pseudoinverse operator. The impact of changing the specified task or observer at inference time to be different from that employed for model training, a phenomenon we refer to as”task-shift” or”observer-shift”, respectively, was also investigated.