Error is inevitable in any process requiring data input. It is especially prevalent when entering the details of human subjects during medical scans. We present a method to accommodate errors introduced during scans, allowing corrected and de-identified results to be produced while satisfying the conditions of truth and correctness.
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In this talk we'll discuss the evolution of free and open source software applied to medical imaging, particularly PET and research applications. Cloud computing and web services are becoming more common in server-based applications, while there is an ever-increasing number of desktop applications for local computing. The DICOM standard is rapidly developing its DICOMweb REST API, which has quickly been adopted by several major imaging projects. Programming languages and software distribution techniques also reflect the way in which we develop and use software in 2017.
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In this series of three, three-hour workshops, we will be learning techniques to address common data processing needs in medical imaging, with particular emphasis on PET research imaging using free software. We’ll learn how to use software to reduce manual work so we can concentrate on the actual science.
In each session we’ll use readily-available free software and use it on supplied example data. We will concentrate on improving efficiency and accuracy by using good practices and frequently-used techniques that can be applied to any image processing workflow
New this year: Pre-configured Windows machines will be available that contain all the workshop's software and data, pre-installed and ready to run. You'll access these remotely using Microsoft Remote Desktop which is available for Windows or Mac. If you're comfortable with installing a variety of software, you can install everything on your own laptop.
Day One: The first session will focus on viewing and understanding the two parts of medical image files: the image data, and the metadata, or image header, that contains information about the patient, the scan, and the image data itself.
Day Two: The second session will involve modifying images: editing the header, anonymisation, and converting the image to another format. We’ll also set up a PACS node on our computers to receive images from a hospital network.
Day Three: In the final session we’ll look at tools to automate the processing of large numbers of images. We will also set up a PACS server that can be used to store and distribute images within your workgroup.
Learn these skills and you’ll be the hero of your lab!