Prototype Lesion Tracking Clinical Workflow and Vendor Neutral Pre-Processing Platform for Medical Imaging
Prototype Lesion Tracking Clinical Workflow and Vendor Neutral Pre-Processing Platform for Medical Imaging
批准号:
720746
负责人:
金额:
$30.37万
依托单位国家:
英国
项目类别:
GRD Development of Prototype
财政年份:
2016
资助国家:
英国
项目状态:
已结题
起止时间:
2016 至 --
中文摘要
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英文摘要
50% of the UK population born after 1960 will be diagnosed with some form of cancer duringtheir lifetime. Medical imaging is used for diagnosis in 60% of all patient care episodes andplays a vital role in almost all cancer diagnosis, monitoring and treatment. The use of imagingthroughout the care chain, specifically in oncology, has increased dramatically over the lastdecade. Healthcare professionals involved are being overwhelmed by the quantity andcomplexity of data. In addition, radiology imaging databases are moving from PictureArchiving and Communication System (PACS) to Vendor Neutral Archive (VNA) systems.Reimbursement models are shifting dramatically across major markets, e.g. the US due to theAffordable Care Act. To address these issues, Blackford will develop a Lesion Trackingprototype integrating both our existing state-of-the-art registration and new, innovativesegmentation algorithms. Lesion Tracking will be a workflow for a Vendor Neutral Pre-Processing Platform (VNP3) that will allow broader market penetration for Blackford’s highlyadvanced image processing technology. It will also create a resource- and time-efficientmechanism for developing a suite of new, clinically-focused applications and allow improvedcollaboration with academics seeking to commercialise research. Lesion Tracking willimprove the efficiency of cancer diagnosis and monitoring which requires interplay betweenmany components, all geared towards accelerating radiologist assessment and referringclinician understanding. It will address shortfalls in current systems that are rare and/orincomplete. While some components are data management driven, some require a higherlevel of technical innovation, namely registration and semi-automatedsegmentation/measurement. We predict that the combination of these components will drive a50-70% increase in lesion tracking productivity.
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