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Introduction to CTIIP
If you had cancer, wouldn't you want your medical team to be able to share image data collected about your cancer, in its native format, no matter which discipline that data belonged to? The challenge to this ideal is that Today, medical technicians and doctors cannot directly compare image data from different disciplines are not currently directly comparable. You . For example, you cannot take an ultrasound of a tumor and compare its features to those on a slide containing cells of that same tumor, for example, let alone compare that tumor to the same kind in a mouse. Since image data from different disciplines are in different formats, comparing them means changing those native formats to something both can interpret, risking important changes to the data contained within.
Most cancer diagnoses are made based on images. You have to see a tumor, or compare images of it over time, to determine its level of threat. Ultrasounds, MRIs, and X-rays are all common types of images that radiologists use to collect information about a patient and perhaps cause a doctor to recommend a biopsy. Once that section of the tumor is under the microscope, pathologists learn more about it. Radiologists and pathologists represent different scientific disciplines. To gather even more information, a doctor may order a genetic panel. If that panel shows that the patient has a genetic anomaly, the doctor or a geneticist may search for clinical trials that match it, or turn to therapies that researchers have already proven effective for this combination of tumor and genetic anomaly through recent advances in precision medicine.
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Traditionally, the words used to describe how an image was acquired differ by the modality of the image, such as X-ray, CT scan, and MRI. This approach works well when image objects have very specific applications and when the domain from which the descriptive concepts are drawn is well understood and clearly defined.
In
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some
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cases,
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information
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about
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the
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acquisition
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that
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is
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relevant
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to
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the
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interpretation
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of
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the
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imaging
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may
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not
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be
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known
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to
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the
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modality.
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This
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is
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particularly
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the
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case in small animal preclinical research, where a myriad of factors that affect quantitative analysis need to be recorded, which would be overwhelming if required to be captured at the modality console user interface. Though it would be possible to add this information by post-processing of the acquired images,
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Supplement 187 of the DICOM standard, published in 2015, defines use cases and templates for the storage of information related to the acquisition of small animal images during preclinical research.
If we want to collect data that describes how the data about the animal was acquired, we need standard terminology.
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