Deep learning-based pipeline to improve sharpness in knee imaging at both 1.5T and 3T: a clinical evaluation

In MRI, edge sharpness is one of the main criteria to allow structure’s delineation and relevant clinical diagnosis. One way to improve the sharpness is to artificially increase the reconstructed matrix size with methods such as zero-padding interpolation (ZIP). In a previous study, we created a deep learning reconstruction (DLR) pipeline, combining ZIP with two CNN’s: the first one trained to reduce image noise, and the second one to reduce ringing artifacts. The goal of this work was to evaluate the clinical impact of this DLR pipeline on pathological knee images performed at 1.5T and 3T, compared to standard reconstructions.