Deep Learning Technology for Rapid Morphological and Quantitative Imaging of Knee Pathology

PROJECT SUMMARY The high prevalence of knee pain in the general population has presented an immense challenge to public health, with significant health care and economic burden to our society. Magnetic resonance imaging (MRI) is the imaging modality of choice to evaluate patients with knee pain. Indeed, peripheral joints rank third as the

most frequent body parts imaged using MRI, with the knee being by far the most common joint evaluated. Given the rise of the number of knee MRI examinations over the next decade with the increasing incidence of knee injuries and the increasing prevalence of knee osteoarthritis, there is an urgent clinical need to reduce the

economic burden of knee MRI, with the most direct approach being to decrease the overall time required to perform the MRI examination. Over the past decade, multiple techniques have been attempted to accelerate knee MRI including parallel imaging, compressed sensing, multi-slice acquisition, and three-dimensional

isotropic resolution imaging. However, all current methods have limitations, including decreased signal-to-noise ratio, image blurring, incompatibility to present necessary tissue contrasts, and inability to evaluate all joint structures. Lack of appropriate acceleration methods also prevents quantitative MRI such as T2 relaxation time

mapping from being used clinically, despite its evident value for detecting early signs of joint degeneration. This application aims to develop novel rapid acquisition and reconstruction techniques to maximize MR scanner efficiency, improve imaging management, and automate scanning workflow, with the final goal of reducing the

economic burden of knee MRI and facilitating clinical imaging operation. Our proposed new methods will be based on developing advanced deep learning reconstruction, combined with novel rapid image acquisition and automatic processing, all of which are pioneered by our research group. We propose developing, optimizing,

and evaluating a rapid 5-minute knee MRI protocol consisting of all clinical sequences and additional T2 mapping sequences, enabling rapid imaging of the whole knee for both morphological and quantitative assessment with seamless incorporation into clinical workflow. The overall hypothesis is that a rapid 5-minute knee MRI protocol

can be equivalent to the standard 35-minute clinical knee MR protocol. Our proposal includes three specific aims: (i) development of a robust deep learning method for a 4-minute rapid multi-planar morphological knee imaging, (ii) development of a deep learning method for a 1-minute whole-knee-covered high-resolution T2 mapping, and

(iii) investigation of a comprehensive evaluation for rapid knee MR protocol in patients with knee osteoarthritis. Successful completion of this project will deliver a rapid 5-minute knee MRI protocol, including routine clinical imaging and additional T2 mapping that can fit into a standard 15-minute clinical time slot. This protocol will be

well-evaluated and implemented in clinical settings to facilitate dissemination for further validation. Our methods would offer a unique opportunity to improve joint health care, reduce healthcare costs, and benefit a large population that suffers knee pain and joint discomfort.