TABLE 6 Key attributes commonly used in Q/R service class operations"

Patient level

Patient name Patient ID Study level

Study instance UID Study ID Study Date Study Time Accession number Series level

Series instance UID Series ID Modality Image level

SOP instance UID Image number

"UID; unique identifier; SOP, service-object pair.

client process at a display workstation), therefore, can perform a hierarchical query and retrieve operation to obtain a desired image or set of images within any level of the information model.

A list of key attributes, or search keys, that are commonly used in a hierarchical searching algorithm supporting the Q/R service class operations is given in Table 6.

Study Root Q/R Information Model

The Study Root Q/R information model is similar to the patient root Q/R information model, except the top level of the hierarchy is the Study level (see Table 5).

Patient/Study Only Q/R Information Model

The patient/study only Q/R information model is similar to the patient root Q/R information model, except that it does not support the Series and Image levels (see Table 5).

8 PACS Research Applications

Image processing applied to medical research has made many clinical diagnosis protocols and treatment plans more efficient and accurate. For example, a sophisticated nodule detection algorithm applied to digital mammogram images can aid in the early detection of breast cancer. However, image processing applications usually require significant implementation and evaluation effort before they can be accepted for clinical use. The common necessities during the implementation and evaluation of these applications are image data and the workstations that allow the display and manipulation of the images. For this purpose, PACS can serve as a powerful tool that provides (a) numerous sample images of statistical significance for testing and debugging the image processing algorithm, and (b) display workstations with built-in image manipulation functions in support of clinical evaluation.

9 Summary

PACS provide a means to acquire, store, and manage medical images that are produced by a wide variety of imaging equipment. These images can be used for clinical review, diagnosis, and medical imaging related research. The introduction of DICOM provides an industry standard for interconnecting multivendor medical imaging equipment and PACS components, allowing communication of images and exchange of information among these individual computer systems.

Digital image archiving provides online access to patients' historical images, which facilitates the clinical practice of radiologists and physicians. The archived images can be used as a resource to provide enormous image data in support of medical imaging related research. When an image archive system is placed in a clinical environment supporting a PACS, reliability and timely access to images become dominant factors in determining whether the archive system is operated satisfactorily. The sophisticated image management software implemented in the archive system therefore plays an important role in providing reliable yet efficient operations that support the archive, retrieval, and distribution of images for the entire PACS.

Several aspects must be considered when implementing a medical image archive system to support a PACS for clinical use:

(a) Data integrity, which promises no loss of medical images and their relevant data

(b) System efficiency, which provides timely access to both current and historical images

(c) Scalability for future expansion of the archive system

(d) System availability, which provides nearly 100% system uptime for the archive, retrieval, and distribution of images within the entire PACS


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