The Digital Imaging and Communication in Medicine (DICOM) Standard is used globally to store, exchange, and transmit medical images. The DICOM Standard incorporates protocols for imaging techniques such as radiography, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and radiation therapy.
Medical imaging techniques produce very large amounts of data, especially from CT, MRI and PET modalities. As a result, storage and communications of electronic image data are prohibitive without the use of compression. JPEG 2000 image compression is used by the DICOM standard for storage and transmission of medical images. The cost and feasibility of accessing large image data sets over low or various bandwidths are further addressed by use of another DICOM standard, called JPIP, to enable efficient streaming of the JPEG 2000 compressed image data.
There has been growing trend to migrate from on-premise PACS to a cloud-based PACS. A recent article by Applied Radiology said, “As the digital-imaging realm is embraced across the healthcare enterprise, the swift transition from terabytes to petabytes of data has put radiology on the brink of information overload. Cloud computing offers the imaging department of the future the tools to manage data much more intelligently.”
A typical clinical trial goes through multiple phases and can take up to eight years. Clinical endpoints or outcomes are used to determine whether the therapy is safe and effective. Once a patient reaches the endpoint, he or she is generally excluded from further experimental interaction. Trials that rely solely on clinical endpoints are very costly as they have long durations and tend to need large numbers of patients.
In contrast to clinical endpoints, surrogate endpoints have been shown to cut down the time required to confirm whether a drug has clinical benefits. Imaging biomarkers (a characteristic that is objectively measured by an imaging technique, which is used as an indicator of pharmacological response to a therapy) and surrogate endpoints have shown to facilitate the use of small group sizes, obtaining quick results with good statistical power.