Experts release consensus statement on abdominal ultrasound in dogs & cats
The American College of Veterinary Radiology (ACVR) and the European College of Veterinary Diagnostic Imaging (ECVDI) have issued a consensus statement to standardize abdominal ultrasound exams in dogs and cats. It was published as an open-access article in the November/December Veterinary Radiology & Ultrasound issue.
Dr. Gabi Seiler, a North Carolina State University professor, was chair of the joint committee that drafted the consensus statement. She intends to use the standard in the clinic to guide ultrasound examinations and documentation, as well as handouts for ultrasound students and attendees of continuing education courses.
"It was helpful to have a committee of many radiologists with different backgrounds and different work environments, from academia to private practice and teleultrasonography," Dr. Seiler said in an announcement about the consensus statement.
Dr. Seiler explained that every suggestion was taken into account, even if it was not mentioned in the consensus.
"Every comment and suggestion was discussed by the committee—even if not included because our consensus opinion differed," she further stated.
The consensus statement provides clear, illustrated guidelines for acquiring views, video clips, and measurements for a complete abdominal ultrasound. The guidelines include the following points:
• Each organ or system image is illustrated.
• Tables that list the still images and video clips to be acquired.
• Documentation, patient preparation, and equipment recommendations.
According to the ACVR, ultrasound is a modality that is easily accessible to veterinarians, technologists, and others. These guidelines can ensure consistent image quality across practitioners and aid in interpretation.
The ACVR and ECVDI envision this consensus statement as a teaching tool in practice, academia, and continuing education. The specialty colleges want to promote high-quality ultrasound imaging for the benefit of animal owners and patients, as well as to maximize the information that can be derived from the documented images.