Title: Medical image synthesis – MRI to CT

Collaborator Institute: University of Cape Town, South Africa; SUHRC Anatomy; SUHRC Radiology

Magnetic resonance imaging (MRI) and computed tomography(CT) are often used for image- based diagnosis of musculoskeletal (MSK) disease and disorders. To leverage complementary information in MRI and CT, and overcome limitations related to either, clinical protocols routinely require both for diagnosis or treatment of MSK disease and disorders. However, image capture from more than one modality results in increased cost to the patient and to the health care system. Furthermore, radiation exposure during CT acquisition limits its use, particularly in immuno-compromised patients or in young children. The synthesis of CT from MRI has become an active research area and particular focus has been on application in radiotherapy..

The aim of this project is to explore two approaches (Atlas -, and VAE - based) for generating sCT from routine MRI images (T1, T2), and evaluate the sCT for use in orthopaedic applications. VAE has not been used for sCT synthesis (a problem defined as mapping two domains) due to the fact it learns mapping within one domain while GAN has shown an ability for multi-domain mapping.

Title: Osteoarthritis initiative project

Collaborator Institute: SUHRC Radiology

Osteoarthritis (OA) affects 27 million US adults and often leads to severe disability. The prevalence of OA is 33.6% in adults older than 65 years. Although OA is a widespread and debilitating disease, treatment options are currently limited, and disease modifying therapies have not been established yet. In an effort to develop quantitative biomarkers for OA and to fill the void that exists for diagnosing, monitoring and assessing the extent of early whole joint degeneration in OA, the past decade has shown an increase in using noninvasive imaging for OA. Magnetic Resonance Imaging (MRI) is a central component of large-scale epidemiologic observational studies such as the Osteoarthritis Initiative (OAI), where it can provide a rich array of structural and functional features of musculoskeletal tissues, which in turn shed light on disease etiology, potential treatment pathways, and prognostic tools for long-term disease outcomes. Magnetic resonance (MR)-derived compositional imaging techniques, such as T2 relaxation times, assess the structural and biochemical properties of cartilage since they are sensitive to changes in collagen orientation and water content. Previous studies reported that spatially assessing relaxation times of the knee cartilage using laminar and sub-compartmental analyses could lead to better and possibly earlier identification of cartilage matrix abnormalities..

In this project, we aim to build a fully automated method for the analysis of T2 relaxation time maps with the aim of extracting relevant relaxometry patterns to classify radiographic knee OA in the entire OAI baseline dataset (publicly available). We aim to establish the role of data driven feature extraction to exploit the potential of T2 relaxation times in comparison to classic feature handcrafting. We hypothesize that the coupling of quantitative compositional MRI and deep learning can uncover latent feature representations, non-linear aggregation among elementary features, and thus better characterize OA as compared to compartmental averages or linear patterns decompositions..

Title: quantitative MRI acquisition and analysis

Collaborator Institute: IMT Atlantique, Brest, France; SUHRC Radiology

Magnetic resonance imaging (MRI) has emerged as a useful tool for clinicians and scientists to assess the health of cartilage and other soft tissues. Conventional MRI provides sufficient tissue contrast to detect morphological changes in cartilage. However, such changes are detectable only when the cartilage morphology is affected and changes in cartilage physiology prior to morphological changes cannot be visualized or measured with conventional MRI [6]. Detecting these changes through the quantification of physiological, physical, and chemical characterization of the cartilage becomes a priority.

In this project, we aim to address the technical and clinical research gaps by having three objectives as follows: 1) Develop a cartilage specific phantom to be able to acquire multi- scanner data and validate the reliability of the qMRI protocols, 2) develop and validate a non- negative matrix factorization based deep learning framework for the reconstruction of diagnostic quality cartilage relaxation maps from conventional morphologic images, and 3) illustrate the use of the developed phantom and the framework in a cohort with knee OA. The Radiological Society of North America (RSNA) has established the Quantitative Imaging Biomarkers Alliance (QIBA) to address the research gaps by developing qMR imaging guidelines and technical standards documents, referred to as profiles. In our proposal, the phantom development is proposed as per the guidelines of QIBA. No customized phantom exists to date for calibration and multi-site use of cartilage biomarkers. Our proposed phantom development will bridge this gap and will create the necessary ability for conducting multi-center and multi- vendor studies. This will not only establish the reliability of the qMRI protocols, it will also allow us to pool the data together for broader machine learning applications. The second objective of developing deep learning framework will make use of an available repository of Osteoarthritis Initiative imaging dataset. Use of such database will not only allow us to start the algorithm development right from the beginning of the project but will also provide us with the ability to test and explore our abilities for multiple advanced deep learning techniques. We propose to use a non-negative matrix factorization approach to be embedded within the deep learning layer so that we can interpret what our deep learning algorithm is learning. The third objective will allow us to recruit and monitor the OA progression in a borderline OA cohort. This will allow us to prove the efficacy of qMRI protocols and developed phantom. Each of these objectives can be regarded as stand-alone projects that can start simultaneously. Apart from these, the successful completion of the project marks the beginning of new era of qMRI based clinical practice in the management of joint disorders.

Title: Femoroacetabular impingement project

Collaborator Institute: University of Cambridge, UK

Femoroacetabular impingement (FAI) is a common cause of hip pain, decreased function, and progression to early osteoarthritis (OA). FAI syndrome is prevalent in young and active adult population such as professional athletes or ballet dancers with impingement exacerbated during the athletic/dance activity. FAI pathomechanics relates to functional impingement due to altered bony anatomy of the acetabulum and proximal femur. FAI can be further categorized as cam-type and pincer-type based on the specific bony deformity present. Impingement due to asphericity of the femoral head and neck is described as cam-type FAI, whereas global or focal over-coverage of the acetabulum results in pincer-type FAI. The most common form of FAI,

However, it is mixed-type impingement where there are features of both cam and pincer morphology. Repetitive abnormal contact between these structures can damage the labrum and articular cartilage if not properly treated and is recognized as a major risk factor in the non- dysplastic hip.

This research project proposes to develop a framework to build personalized treatment strategies and reduce healthcare costs during the management of patients with FAI. The proposal has two aims. The first aim is to develop a medical imaging and modeling framework to provide an in vivo dynamic understanding of FAI using advanced MR imaging techniques and integrate this knowledge in pre-operative planning stage of open/arthroscopic surgery. The second aim is to develop and illustrate the efficacy and validity of medical image synthesis algorithm to create MRI to synthetic CT image volumes and eliminate the need for CT scanning during the open/arthroscopic surgery planning of the individuals with FAI.

Title: Federated learning project

Collaborator Institute: University of Missouri-Kansas City, Kansas City, USA

Federated learning avoids the long process of transferring or sharing of datasets from one location (institute) to another. Such data sharing almost always need to have data transfer agreement in place between collaborating institutes. Further, the medical imaging data needs anonymization which is a tricky and time-consuming task. Federated learning is a deep learning technique which allows researchers to train the deep learning algorithms without transferring or sharing data. Data always remain within their respective security walls. This allows data sharing and model training a much easier exercise.

Federated Learning (FL) is simply the decentralized form of Machine Learning. In Machine Learning, we usually train our data that is aggregated from several edge devices like mobile phones, laptops, etc. and is brought together to a centralized server. Machine Learning algorithms, then grab this data and trains itself and finally predicts results for new data generated. Federated Learning is born at the intersection of on-device AI, blockchain, and edge computing/IoT. Here we train a centralized Machine Learning model on decentralized data! The FL architecture in it’s basic form consists of a curator or server that sits at its centre and coordinates the training activities. Clients are mainly edge devices which could run into millions in number. These devices communicate at least twice with the server per training iteration. To start with, they each receive the current global model’s weights from the server, train it on each of their local data to generate updated parameters which are then uploaded back to the server for aggregation. This cycle of communication persists until a pre-set epoch number or an accuracy condition is reached. In the Federated Averaging Algorithm, aggregation simply means an averaging operation. That is all there is to the training of a FL model.

We plan to apply the FL approach in multiple projects where our collaborators are internationally located and are having a huge database. Project specific training and travel may be needed for this project.

Title: Obesity and neuroimaging

Collaborator Institute: SUHRC Nutrition

Obesity has become a global pandemic with multiple adverse clinical consequences. Accumulating evidence demonstrates that cognition is affected by an excess of body adiposity in both adults and children. Epidemiological studies also indicate that midlife obesity increases the risk of progression to mild cognitive impairment (MCI) and Alzheimer's disease (AD). On the contrary, higher body mass index (BMI) in late-life might be protective.8 This obesity paradox might be associated with the confounding effect of weight loss in preclinical AD. The exact mechanisms leading to cognitive impairment and neurodegeneration in persons with an excess of body adiposity remain to be fully elucidated. Animal models suggest a significant contribution of obesity and obesity-related metabolic disturbances to AD pathophysiology. In contrast, human studies assessing the impact of obesity on amyloid and tau pathology report conflicting findings both in vivo and in post-mortem studies. Thus, higher BMI has been related to higher, but also to lower, AD burden. On the other hand, obesity might contribute to neurodegeneration by mechanisms unrelated to AD. Obesity is a state of peripheral low-grade chronic inflammation, and it is frequently associated with an abnormal peripheral sensitivity to insulin effects. In experimental models, obesity-related peripheral inflammation has been linked to blood-brain barrier dysfunction, neuroinflammation, and neurodegeneration, whereas central insulin resistance has been associated with impaired synaptic plasticity and memory. Furthermore, obesity is a strong risk factor for hypertension, type 2 diabetes, and dyslipidemia, and it is a well-established cerebrovascular risk factor.

The proposed project is a new project and we aim to collect quantitative neuroimaging data on obese and healthy population to study the inflammatory and cognitive markers of obesity.