The importance of resolution is crucial when working with medical images. The possibility to visualize details lead to a more accurate diagnosis and makes segmentation easier. However, obtention of high-resolution medical images requires of long acquisition times. In clinical environments, lack of time leads to the acquisition of low-resolution images.
Super Resolution (SR) consist in post-processing images in order to enhance its resolution. During the last years, a branch of SR is getting promising results. This branch focuses in the application of Convolutional Neural Networks (CNNs) to the images.
This project is intended to create a network able to enhance resolution of knee MR stored in DICOM format. Different networks are proposed, and evaluation is made by computing Peak Signal-to-Noise Ratio (PSNR) and normalized Cross-Correlation. One of the networks proposed, SR-DCNN, presented better results than the conventional method, bicubic interpolation. Finally, visual comparison of the SR-DCNN and bicubic interpolation also showed that the network proposed outperforms the conventional methods.Ingenierรญa Biomรฉdic
