Development of a Clinical Quantitative Neuroradiology MR Report for Parkinson’s disease

Parkinson’s disease (PD) is the second most common neurodegenerative disorder, with progressive and disabling symptoms. Diagnosis of PD is currently made on clinical grounds as there are no objective tests to diagnose or stage PD. Depending on the experience of the clinician, the rate of misdiagnosis ranges between 5-26%. Although neuroimaging has revolutionised diagnosis and staging of many common neurological diseases, it currently plays a very limited role in the diagnosis of PD. In practice magnetic resonance imaging (MRI) is used qualitatively, mainly to exclude other secondary causes and to help guide the differential diagnosis of other movement disorders diagnosis. Sensitive biomarkers of disease will transform the clinical reporting of MR examinations by providing objective and quantifiable measures which will decrease inter-rater variability and increase the reliability of diagnosis in daily clinical routine, thereby improving patient care. They will be also essential in clinical trials. Three main MRI biomarkers have been suggested to date: a) iron assessed by susceptibility-weighted imaging or quantitative susceptibility mapping (QSM), b) neuromelanin (NM) assessed by neuromelanin-sensitive sequences and c) brain-volume assessed by 3D-T1 weighted sequences.Translating quantitative techniques into the clinical setting, however, presents significant challenges, which must be addressed to meet the demand for accurate, timely and impactful clinical imaging services. We developed the Quantitative Neuroradiology Initiative (QNI), as a model framework for the technical and clinical validation necessary to embed automated segmentation and other image quantification software into the clinical neuroradiology workflow. We have 2 exemplar tools: a) brain volume, commercialised through a startup company (Brainminer) and b) Quantitative Neuroradiology report for epilepsy patients with hippocampal sclerosis, which has been successfully introduced into the clinical routine. Our position has been further strengthened by a strategic partnership with Siemens Healthineers to accelerate the adoption of quantitative neuroradiological innovations into healthcare practice. Based on this experience, the aim of this proposal is to develop a platform for clinical Quantitative Neuroradiological MR Reports for PD patients. To establish such a report, a time-efficient MR protocol must be developed to ensure maximal patient compliance, and minimal head motion related artefacts. Current QSM, NM-sensitive and 3D-T1 sequences require relatively long acquisition times, resulting in image degradation in a large proportion of clinical scans. Novel acceleration and image-enhancing AI techniques have the potential to significantly reduce acquisition times, and therefore improve image quality by reducing (and ideally eliminating) motion artefacts. In addition, QSM and NM-sensitive imaging usually require complex processing and analysis that is usually only possible in the research setting. This initiative will bring these advanced techniques into clinical pipelines for the benefit of patients. Over a period of 18 months, we will enrol a total of 70 subjects: a) 20 patients with early PD, b) 20 patients with late PD, and c) 20 age-matched healthy volunteers. In preparation for the main part of the study, imaging protocol development and testing will be performed on 10 healthy volunteers. Scanning will be performed on a 3T scanner equipped with the latest software platform which includes cutting-edge acceleration methods, as well as machine learning-based image reconstruction technology. This study will deliver time-efficient, quantitative sequences and the corresponding automated analysis pipelines for all 3 biomarkers. In combination with the acquired reference data, these will form the basis for the prototype platform for a PD-relevant automated clinical Quantitative Neuroradiological MR Report.