The typical radiology reporting workflow involves the radiologist first looking at one or more relevant prior studies before interpreting the current study. To improve workflow efficiency, PACS systems can display relevant prior imaging studies, typically based on a study's anatomy as indicated in the Body Part Examined field of the DICOM header. The content of the Body Part Examined field can be very generic. For instance, an imaging study to exclude pancreatitis and another one to exclude renal stones will both have “abdomen” in their body part field, making it difficult to differentiate them. To improve prior study matching and support better study filtering, in this paper, we present a rule-based approach to determine specific body parts contained in the free-text DICOM Study Description field. Algorithms were trained using a production dataset of 1200 randomly selected unique study descriptions and validated against a test dataset of 404 study descriptions. Our validation resulted in 99.94% accuracy. The proposed technique suggests that a rule-based approach can be used for domain specific body part extraction from DICOM headers.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 firstname.lastname@example.org
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 email@example.com