Translation of quantitative MRI analysis tools for clinical neuroradiology application

Quantification of imaging features can assist radiologists by reducing subjectivity, aiding detection of subtle pathology, and increasing reporting consistency. Translation of quantitative image analysis techniques to clinical use is currently uncommon and challenging. This thesis explores translation of quantitative imaging support tools for clinical neuroradiology use. I have proposed a translational framework for development of quantitative imaging tools, using dementia as an exemplar application. This framework emphasises the importance of clinical validation, which is not currently prioritised. Aspects of the framework were then applied to four disease areas: hippocampal sclerosis (HS) as a cause of epilepsy; dementia; multiple sclerosis (MS) and gliomas. A clinical validation study for an HS quantitative report showed that when image interpreters used the report, they were more accurate and confident in their assessments, particularly for challenging bilateral cases. A similar clinical validation study for a dementia reporting tool found improved sensitivity for all image interpreters and increased assessment accuracy for consultant radiologists. These studies indicated benefits from quantitative reports that contextualise a patient’s results with appropriate normative reference data. For MS, I addressed a technical translational challenge by applying lesion and brain quantification tools to standard clinical image acquisitions which do not include a conventional T1-weighted sequence. Results were consistent with those from conventional sequence inputs and therefore I pursued this concept to establish a clinically applicable normative reference dataset for development of a quantitative reporting tool for clinical use. I focused on current radiology reporting of gliomas to establish which features are commonly missed and may be important for clinical management decisions. This informs both the potential utility of a quantitative report for gliomas and its design and content. I have identified numerous translational challenges for quantitative reporting and explored aspects of how to address these for several applications across clinical neuroradiology

Artificial Intelligence for the Detection of Focal Cortical Dysplasia: Challenges in Translating Algorithms into Clinical Practice

Focal cortical dysplasias (FCDs) are malformations of cortical development and one of the most common pathologies causing pharmacoresistant focal epilepsy. Resective neurosurgery yields high success rates, especially if the full extent of the lesion is correctly identified and completely removed. The visual assessment of magnetic resonance imaging does not pinpoint the FCD in 30%–50% of cases, and half of all patients with FCD are not amenable to epilepsy surgery, partly because the FCD could not be sufficiently localized. Computational approaches to FCD detection are an active area of research, benefitting from advancements in computer vision. Automatic FCD detection is a significant challenge and one of the first clinical grounds where the application of artificial intelligence may translate into an advance for patients' health. The emergence of new methods from the combination of health and computer sciences creates novel challenges. Imaging data need to be organized into structured, well-annotated datasets and combined with other clinical information, such as histopathological subtypes or neuroimaging characteristics. Algorithmic output, that is, model prediction, requires a technically correct evaluation with adequate metrics that are understandable and usable for clinicians. Publication of code and data is necessary to make research accessible and reproducible. This critical review introduces the field of automatic FCD detection, explaining underlying medical and technical concepts, highlighting its challenges and current limitations, and providing a perspective for a novel research environment

A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images

Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of unlabelled data. This review summarizes recent research into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging, concentrating on studies that compare self-supervised pretraining to fully supervised learning for diagnostic tasks such as classification and segmentation. The most pertinent finding is that self-supervised pretraining generally improves downstream task performance compared to full supervision, most prominently when unlabelled examples greatly outnumber labelled examples. Based on the aggregate evidence, recommendations are provided for practitioners considering using self-supervised learning. Motivated by limitations identified in current research, directions and practices for future study are suggested, such as integrating clinical knowledge with theoretically justified self-supervised learning methods, evaluating on public datasets, growing the modest body of evidence for ultrasound, and characterizing the impact of self-supervised pretraining on generalization.Comment: 32 pages, 6 figures, a literature survey submitted to BMC Medical Imagin

Deep learning approaches for segmentation of multiple sclerosis lesions on brain MRI

Multiple Sclerosis (MS) is a demyelinating disease of the central nervous system which causes lesions in brain tissues, especially visible in white matter with magnetic resonance imaging (MRI). The diagnosis of MS lesions, which is often performed visually with MRI, is an important task as it can help characterizing the progression of the disease and monitoring the efficacy of a candidate treatment. automatic detection and segmentation of MS lesions from MRI images offer the potential for a faster and more cost-effective performance which could also be immune to expert bias segmentation. In this thesis, we study automated approaches to segment MS lesions from MRI images. The thesis begins with a review of the existing literature on MS lesion segmentation and discusses their general limitations. We then propose three novel approaches that rely on Convolutional Neural Networks (CNNs) to segment MS lesions. The first approach demonstrates that the parameters of a CNN learned from natural images, transfer well to the tasks of MS lesion segmentation. In the second approach, we describe a novel multi-branch CNN architecture with end-to-end training that can take advantage of each MRI modalities individually. In that work, we also investigated the combination of MRI modalities leading to the best segmentation performance. In the third approach, we show an effective and novel generalization method for MS lesion segmentation when data are collected from multiple MRI scanning sites and as suffer from (site-)domain shifts. Finally, this thesis concludes with open questions that may benefit from future work. This thesis demonstrates the potential role of CNNs as a common methodological building block to address clinical problems in MS segmentation

Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence

Multiple sclerosis (MS) patients have heterogeneous clinical presentations, symptoms and progression over time, making MS difficult to assess and comprehend in vivo. The combination of large-scale data-sharing and artificial intelligence creates new opportunities for monitoring and understanding MS using magnetic resonance imaging (MRI).First, development of validated MS-specific image analysis methods can be boosted by verified reference, test and benchmark imaging data. Using detailed expert annotations, artificial intelligence algorithms can be trained on such MS-specific data. Second, understanding disease processes could be greatly advanced through shared data of large MS cohorts with clinical, demographic and treatment information. Relevant patterns in such data that may be imperceptible to a human observer could be detected through artificial intelligence techniques. This applies from image analysis (lesions, atrophy or functional network changes) to large multi-domain datasets (imaging, cognition, clinical disability, genetics, etc.).After reviewing data-sharing and artificial intelligence, this paper highlights three areas that offer strong opportunities for making advances in the next few years: crowdsourcing, personal data protection, and organized analysis challenges. Difficulties as well as specific recommendations to overcome them are discussed, in order to best leverage data sharing and artificial intelligence to improve image analysis, imaging and the understanding of MS