Mirada Medical | Date: 2017-07-26
A method and apparatus for delineating an object within a volumetric medical image. The method comprises obtaining auto-generated contour data for the object within the volumetric medical image, the auto-generated contour data defining a set of auto-generated contours forming a delineation structure for the object, selecting a subset of auto-generated contours for manual editing, and identifying the selected subset of auto-generated contours to a user. In some examples, the method further comprises presenting at least the selected subset of auto-generated contours to the user, receiving user feedback for at least one auto-generated contour, deriving a full set of contours forming a revised delineation structure for the object based at least partly on an interpolation of the auto-generated contour(s) for which user feedback was received, and storing contour data defining the derived full set of contours forming the revised delineation structure for the object within at least one data storage device.
Mirada Medical | Date: 2014-06-10
A method of generating image alignment data for medical images. The method comprises determining prospective image pair registration connections, determining registration weighting values for the determined prospective image pair registration connections based at least partly on intra-image attribute data, determining optimal registration paths for image pairs based at least partly on the registration weighting values for the prospective image pair registration connections, and generating image alignment data for the determined optimal registration paths.
Mirada Medical | Date: 2011-06-03
A method, medical imaging workstation (300) and a hybrid medical imaging scanner (400) are provided for the analysis of images obtained during medical scans. The extent of a first region of interest (ROI-1) in a first scan image (120) is defined. A second region (ROI-2), in a second scan image (130), is identified. The second region (ROI-2) in the second scan image (130) corresponds to the first region of interest (ROI-1) in the first scan image. Each of the spatial locations of the second region (ROI-2) is classified, in order to identify the spatial locations of the second region (ROI-2) that comprise at least one tissue type. The invention may improve the recognition of lesions in medical scan images, and may reduce the incidence of false positives.
Mirada Medical | Date: 2013-05-29
A method and system are provided for creating and simultaneously displaying medical scan images, from each of first (780) and second (530) medical scan datasets, obtained by scanning a 3-dimensional (3-D) object with different scanning modalities. A first image (410) is derived from the first (780) dataset, the first image (410) lying in a first plane corresponding to an acquisition plane of the first (780) dataset. A second image (420) is obtained from the second dataset (530), the second image also lying in the first plane. The second image may be obtained by re-slicing the second data set. One or both medical scan datasets may be multivolume datasets. The invention may improve viewing resolution and/or speed, when viewing a multi-series MRI scan together with a CT and/or a PET scan.
Mirada Medical | Date: 2012-09-28
A method, medical imaging workstation (1000) and hybrid medical imaging scanner (1100) are provided for defining a region of interest (RoI) for display on at least two medical scan images. When displaying a first medical scan image (740), input data defining a RoI on the image is captured, and stored as at least a first region representation (760). The RoI is displayed on a second medical scan image (750), based on the first region representation (760). Changes to the RoI on the second medical scan image (750) are used to update the first region representation (760). There may be separate region representations (760, 770) associated with each of several medical scan images. The invention may improve the definition of a region of interest, by allowing editing on each of multiple image displays (820, 830, 880) to feed through to all medical scan images.
Mirada Medical | Date: 2012-09-14
A signal processing method that includes inputting sample values of a signal and considering the signal to have a plurality of portions. For each portion, a predetermined function is fitted to the sample values of that portion of the signal by calculating values of coefficients for that predetermined function. At least one statistical information function is evaluated for the signal to determine statistical information about the signal and the calculated coefficient values are used so that the form of the statistical information function has been determined for the predetermined function used to fit the signal portion and further includes using the statistical information obtained about the signal to process the signal.
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.
Agency: GTR | Branch: Innovate UK | Program: | Phase: European | Award Amount: 287.89K | Year: 2015
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.