DeLaBE: A Deep Learning architecture for Bio-images enhancing

In order to improve bioimages quality and improve diagnosis reliability, physicians need to use injectable contrast medium. Recently, a lot of studies reported the toxicity of these agents such as the correlation between repeated administration of Gadolinium MRI examinations and nephrogenic systemic fibrosis. The direction is to define innovative techniques able to improve bioimages quality, minimizing the quantity of contrast medium, or define methods able to enhance images. Latter can be performed by using deep learning based machine learning techniques. In this work, we report preliminary work about the design of a deep learning architecture for bio-images enhancing, called DeLaBE, able to enrich information from images obtained with low or zero quantities of contrast liquid. DeLaBe is based on using a convolutional neural network reversing the standard workflow by using non-contrast images as ground truth. The cost function compares the prediction and the reference ground-truth image, which enables the optimization of the network parameters. During training of the DL model, the network parameters are updated with respect to the cost function comparing predicted and true non-contrast images. The goal is to improve the performance of the enhancing task by increasing the number of inputs as non-contrast images require a specific examination without agents administrations.