ABSTRACT Fast, robust and reliable quantitative knee joint MR imaging would be a significant step forward in studying joint degeneration, injury and osteoarthritis (OA). Automation of compositional and morphological feature extraction of the tissues in the knee it is an essential step for translation to clinical practice of promising quantitative techniques. It would enable the analysis of large patient cohorts and assist the radiologist/clinician in augmenting the value of MRI. Automation of several human tasks has been achieved in the last few years by the usage of Deep Learning techniques. With the availability of large amounts of annotated data and processing power, using the concepts of transforming data to knowledge by the observation of examples, supervised learning can today accomplish challenges never demonstrated before. In addition to image analysis and interpretation, Deep Learning is revolutionizing the acquisition and reconstruction aspects of the pipeline. Models can learn a direct mapping between under sampled k-space and image domain. While Deep Learning application to musculoskeletal imaging showed promising results when applied in a controlled setting, it is well understood that generalization beyond the statistical distribution of the training set is still an unmet challenge. In MRI this translates into poor performances when trained models are tested on different imaging protocols or images acquired on different MRI systems. With this proposal, we aim to leverage on this recent advancement and filling the existing gaps. We aim to study novel integrated models able to simultaneously accelerate MRI acquisition and automate the image processing that can overcome the limitation of single domain application. Fast image acquisition and accurate image post processing are typically considered to be separate problems. However, the neural networks optimization design gives us an opportunity to integrate the two to maximize both acceleration and machine-based image processing and interpretation. We will use both publicly available benchmark dataset (FastMRI) and internally collected dataset to build deep learning models able to accurately reconstruct under sampled MRI acquisitions. We will use a dataset prospectively acquired during the course of this study to validate the clinical applicability of the developed methods. Specifically, we will test the hypothesis that the proposed integrated pipeline can be applied in clinical setting for a fast and intelligent knee scan obtaining image quality comparable to standard acquisition and automated processing accuracy comparable with human reproducibility. Additionally, we propose to make our annotated image datasets and trained models a shared resource, a centralized, open evaluation platform for MRI reconstruction and image post processing techniques.