Imaging informatics

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This example from the late 1990s shows how data from magnetic resonance imaging (MRI) could be transformed into a 3D rendering of the brain using informatics tools like the VOXEL-MAN program.

Imaging informatics (also known as radiology informatics or medical imaging informatics) involves the intersection between health informatics and bioinformatics that aims to improve the efficiency, accuracy, usability, and reliability of medical imaging services within the context of the healthcare environment.[1] "Notably, medical imaging informatics addresses not only the images themselves, but encompasses the associated data to understand the context of the imaging study; to document observations; and to correlate and reach new conclusions about a disease and the course of a medical problem."[2]

More specifically it is devoted to the study of how information about and contained within medical images is acquired, stored, exchanged, analyzed, and enhanced throughout the medical enterprise. Medical images must be in a standard, symbolic, and reproducible format for proper acquisition and storage. Computer-related transmission protocols like TCP/IP, HTTP, and DICOM as well as organized, sensical transaction diagrams are required for proper data exchange. And because of all those protocols and standards, software can be created and utilized to analyze, enhance, and even mine medical images for useful real-world healthcare data.[3]

As radiology is an inherently data-intensive and technology-driven specialty of medicine, radiologists have become leaders in imaging informatics. However, with the proliferation of digitized images to other fields such as cardiology, dermatology, surgery, gastroenterology, obstetrics, gynecology, and pathology, advances in imaging informatics are also being tested and applied in other areas of medicine.[4]

Diagnostic imaging modalities

Medical images created, studied, and mined in imaging informatics come from several modalities[5]:

Projection radiology utilizes X-rays to provide a grayscale image representing X-ray attenuation. The advantages of it include being fast, easy to perform, and inexpensive. Disadvantages include problems with low-contrast differentiations, image interpretation, and the use of ionizing radiation.

Fluorography utilizes a continuous low-power X-ray beam to provide a grayscale "movie" for real-time feedback. It has the advantage of capturing movement-based measurements for barium studies or placement of catheters. It's primary disadvantage is in the quality of the radiograph.

Computed tomography (CT) utilizes a moving collimated X-ray beam and a series of detectors to produce a digital image based on the X-ray attenuation of tissues. It has the advantage of providing finer resolutions, especially among tissues with similar densities. Disadvantages include high costs, the propensity for high-density artifacts, and the use of high doses of ionizing radiation.

Magnetic resonance imaging (MRI) utilizes a high-intensity magnetic field with controlled radiofrequency pulses to provide a grayscale image based on the magnetic properties of nuclei in the tissues of the body. Advantages include excellent soft tissue contrast and resolution, can image on any plane, and doesn't use ionizing radiation. Disadvantages include high costs, lengthy scan times, and an inability to show calcification.

Positron emission tomography (PET) utilizes X-ray or gamma-ray emitting radioisotopes placed into the body, their decay measured as pulses of light using photomultiplier tubes, which is then converted into a grayscale image. This method has the advantage of producing fine targeted measurements of chemical-physiologic tissue function. The high cost and need for a cyclotron to produce the radioisotopes pose challenges to some medical facilities.

Ultrasound utilizes high-frequency sound waves from a transducer, which also receives reflected sound and converts it to an electrical signal and then a grayscale image. Ultrasound has the benefit of being low-cost, safe, and useful for analyzing soft tissues like the kidneys, liver, and pancreas. Disadvantages include its operator dependence and its inability to render quality images in obese patients.

Visible light is used in some cases, though with limited practicality. It's most often used in the imaging of tissues in light microscopy and imaging the retina.

Application

Imaging informatics can help tackle problems and tasks such as the following[1]:

  • the creation and management of picture archiving and communication systems (PACS) and component systems
  • the embedding of medical images in electronic medical records
  • the development of radiology information systems (RIS)
  • the acquisition of images from medical devices
  • the development of image processing and enhancement software
  • the 3D visualization of medical imaging data
  • the integration of speech recognition into imaging apps for quicker reporting turnaround
  • the design of imaging facilities
  • the development of imaging vocabularies and ontologies
  • the mining of data from medical imaging databases
  • the development of DICOM, HL7, and other standards

Informatics

A portion of what imaging informatics does involves the technology surrounding the mentioned diagnostic imaging modalities, including transfer and storage of their digital output. PACS have began playing an important role in hospitals and other medical environments as early as the 1990s[6], helping to provide affordable digital storage and distribution of medical images from numerous modalities. Another important part of imaging informatics includes the development of software to view, enhance, and analyze output from those modalities. The RIS has played an important part in that for sure, though open-source image viewing and analysis tools like dcm4che2, OsiriX, and ITK have also broadened the scope and availability of informatics tools available to technicians and researchers. Of course, interoperability among the software, PACS, RIS, and even the hospital information system (HIS) are vital and benefited by protocols and standards like DICOM and HL7.

External links

Notes

This article reuses an element or two from the Wikipedia article.

References

  1. ↑ 1.0 1.1 Branstetter, B. (2007). "Basics of Imaging Informatics". Radiology 243 (3): 656–67. doi:10.1148/radiol.2433060243. PMID 17431128. 
  2. ↑ Bui, A.A.T.; Taira, R.K.; Kangerloo, H. (2009). "Chapter 1: Introduction". Medical Imaging Informatics. Springer. pp. 3–13. ISBN 9781441903853. https://books.google.com/books?id=3JClHj3SXjwC&pg=PA3. Retrieved 21 March 2020. 
  3. ↑ Langer, S.G. (2011). "Chapter 2: Informatics Constructs". Informatics in Medical Imaging. CRC Press. pp. 15–23. ISBN 9781439831243. https://books.google.com/books?id=JTYb3SZZraYC&pg=PA15. Retrieved 21 March 2020. 
  4. ↑ Horii, S.C. (2011). "Chapter 4: DICOM". Informatics in Medical Imaging. CRC Press. pp. 41–68. ISBN 9781439831243. https://books.google.com/books?id=JTYb3SZZraYC&pg=PA41. Retrieved 21 March 2020. 
  5. ↑ Andriole, Katherine P. (2009). "Chapter 1: Medical Imaging Modalities and Digital Images". Practical Imaging Informatics: Foundations and Applications for PACS Professionals. Springer. pp. 3–14. ISBN 9781441904850. https://books.google.com/books?id=Q6Hc0oMyiYYC&pg=PA3. Retrieved 21 March 2020. 
  6. ↑ Huang, H. K. (2010). "Chapter 1: Introduction". PACS and Imaging Informatics: Basic Principles and Applications. John Wiley & Sons. pp. 1–30. ISBN 9780470560518. https://books.google.com/books?id=Pjjkyae_55oC&pg=PA1. Retrieved 21 March 2020. 
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