Mirada Medical | Date: 2010-11-24
Agency: GTR | Branch: Innovate UK | Program: | Phase: Smart - Proof of Concept | Award Amount: 100.00K | Year: 2012
Radiotherapy is a key method for treating cancer in which high energy radiation is directed at a tumour to disrupt DNA replication and thereby destroy the tumour. However in the process, it is inevitable that surrounding healthy tissue is also irradiated. It is therefore necessary to plan any therapy to maximise the tumour dose while minimising the dose to the healthy tissue. In this process, known as RT planning, teams of clinical experts spend a great deal of time developing a treatment plan prior to its administration. This process is very time consuming and can typically take some tens of hours, leading to high costs and limited throughput for the clinic. A major step in the planning process is the delineation of the tumour and surrounding healthy organs in a medical image scan of the patient, a task known as contouring. The resultant delineations, called RTstructures, are subsequently used to estimate the delivered dose and optimise the treatment plan. Typically, tumour and organ delineation is a laborous manual process. An image processing technique known as atlas-based contouring has been shown to be effective in speeding up this step. Here, expert delineations of healthy organs on an example scan, known as an atlas, are warped automatically onto the scan of the patient. However, it has been found that this method is effective only in cases where the anatomy of the patient is similar in appearance to the atlas. The aim of this proof-of-concept project is to make atlas-based contouring work accurately for most if not all patients. We will do this by developing innovative technology that can build and rapidly search large-scale databases of atlases containing thousands of example delineations representing the wide variability in human anatomy. In a process analogous to Web search of keywords, when applied to a new patient, the system will first retrieve the best matching case or cases from the database and use only those for the atlas-based contouring process.
Agency: GTR | Branch: Innovate UK | Program: | Phase: Collaborative Research & Development | Award Amount: 686.38K | Year: 2014
Lung cancer is one of the most common cancers with the highest mortality rate both in the UK and Worldwide. In 2010, some 42,026 new cases were diagnosed in the UK and 34,859 lung cancer deaths recorded. In 2008, 1.6 million new lung cancer cases and 1.4 million deaths were recorded worldwide. Against this background, this project addresses a hugely challenging and unmet need in stratifying patients with Pulmonary Nodules (PNs), small masses in the lung, in Chest Computed Tomography (CT) scans. Such findings might typically occur in one of two situations: as incidental findings on scans unrelated to lung cancer, e.g. investigations for pulmonary embolism, or in lung cancer screening. In either situation, the problem is the same: nodules are very common in Chest CT and so either require further investigation, if sufficiently suspicious, otherwise follow-up imaging after 6, 12 and 18 months. However, most nodule are not cancers. Therefore, this project will develop new image-based stratification techniques for patients with Chest CT nodules. The project has two broad objectives. First, to develop new image processing technologies to make it much more efficient to read multiple Chest studies. Second, develop new image processing techniques coupled with new protocols where necessary to radically improve both the sensitivity and especially specificity of imaging of the lung.
Mirada Medical | Date: 2011-06-03
Mirada Medical | Date: 2011-04-13
A method for estimating radiation exposure of a patient arising from at least one medical image study of that patient is described. The method comprises obtaining radiation exposure information relating to a plurality of procedures for which there exists a potential exposure of the patient to radiation, performing anatomical alignment of the obtained radiation exposure information to at least one reference image, estimating a radiation dose per procedure, and calculating an aggregated radiation dose based at least partly on the estimated radiation doses.