Title: Principal Component Analysis in Medical Image Processing: A Diagnostic Aid
Funder: Health Education England

Abstract: Background A variety of Computer Aided Diagnosis (CAD) techniques have been developed to provide clincians with independent, objective assessments of medical image features. However, outside of a small number of applications CAD is yet to be used routinely in the clinic, particularly in the UK. There are likely to be many reasons for this, including a lack of sufficient validation and testing in CAD research and inadequate integration within the clinical workflow. CHAR(13) + CHAR(10)CHAR(13) + CHAR(10) The benefits of CAD will only be truly appreciated when adopted on a large scale. The conventional approach to CAD research has been to create isolated algorithms to meet particular problems in medical imaging. What is needed to facilitate wider use of CAD is a generalised, flexible toolkit for CAD development that can be easily adapted for different applications. CHAR(13) + CHAR(10)CHAR(13) + CHAR(10) Principal Component Analysis, in combination with image registration, is a powerful, well established method that has the potential to form the basis of such a solution. CHAR(13) + CHAR(10)CHAR(13) + CHAR(10) Aims and objectives The main aim of this work is to create a generalised image analysis toolkit, based on image registration and PCA, that is optimised and validated for different applications. Through the addition of classification algorithms it will be adapted for the classification of two types of Nuclear Medicine data: Dimercaptosuccinic acid (DMSA) images of the kidney and Ioflupane (DaTSCAN) images of the dopaminergic pathway. To demonstrate the wider applicability of the toolkit it will be adapted for the automatic measurement of low grade glioma volume in Magnetic Resonance Imaging (MRI) data. The impact of the DMSA classifier on clinical reporting will be assessed and the toolkit will be developed to fit within a flexible infrastructure to facilitate wider implementation. CHAR(13) + CHAR(10)CHAR(13) + CHAR(10) Research Plan An image registration and PCA framework has already been developed and used to generate principal components in a new way. A DMSA classifier, informed by component weights, has been incorporated. However, further development is required. Optimisation of the PCA toolkit will focus on the effects of using mixed databases of images to generate principal components. Optimisation of the DaTSCAN/DMSA classifiers and the glioma volume measurement function will be an iterative process, using large databases of test images to discover the most appropriate algorithm parameters. CHAR(13) + CHAR(10)CHAR(13) + CHAR(10) Validation of the classifiers will be based on phantom and clinical data. A bespoke DMSA sub-resolution sandwich phantom will be developed in partnership with University Hospitals Bristol. This will be complemented by a digital phantom which will be created through collaboration with University Hospital Southampton. DaTSCAN phantom images will be derived from scans of an commercial phantom. CHAR(13) + CHAR(10)CHAR(13) + CHAR(10) Gold standard clinical data will be generated through panel reporting of DMSA images and through clinical follow up of DaTSCAN patients. The Sheffield 3D Imaging Lab will provide gold standard glioma volume measurements, derived from semi-automatic segmentation processes. CHAR(13) + CHAR(10)CHAR(13) + CHAR(10) The toolkit will be split into different functions and placed within a flexible workflow environment. This will be implemented locally and the effects of the DMSA classifier on reporting variability will be measured. The toolkit will be installed at three pilot centres and further evaluated to develop a blueprint for wider CAD adoption. CHAR(13) + CHAR(10)CHAR(13) + CHAR(10) Benefits The toolkit will enable quality assured reporting, giving radiologists, referring clinicians and patients a greater confidence in the results of medical imaging tests. It will help to reduce variability in reporting for a range of applications and may potentially save money by speeding up reporting processes. In an era of ever increasing image volumes the toolkit offers a possible way forward for radiologists to better manage large datasets. By providing an independent measure of scan features the toolkit could be a useful training resource for clinicians.

Lay abstract: When patients are scanned the images are examined and reported by a trained clinician. Sometimes images can be difficult to interpret, and diagnostic opinions may vary, which may lead to different outcomes for patients depending on the reporter. This study will create software for automatic image analysis, applied to different applications. It will give clinicians an objective opinion on scan appearances, which is intended to increase confidence in diagnoses and improve reporting consistency. The software is intended to provide a level of quality assurance and may enable clinicians to better manage their workloads

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: GENERIC HEALTH RELEVANCE