Among the virtues of thin-client 3D advanced visualization are ease and economy of deployment across an enterprise. Above both, however, lies the ability to improve the quality of care. At Beebe Medical Center in Lewes, Delaware, which has extended access to its advanced visualization platform from the radiology department to the emergency department, critical care unit, and operating suite, thin-client advanced visualization is making a big difference.
Diagnostic radiologist Michael Ramjattansingh, MD, says that the technology played a decisive role recently in saving the life of a woman admitted to the emergency department with a complaint of chest pain that turned out to be caused by a rare, potentially fatal condition that no one expected to encounter. “She was bleeding into her chest; that much, the emergency-department team could determine in triage,” Ramjattansingh recalls.
Accordingly, a CT scan was performed, and it ruled out the possibility of pulmonary embolism. The test showed, however, that some sort of penetration of the thoracic aorta had occurred. Ramjattansingh says, “There was no sign of trauma, so this penetration was peculiar, since spontaneous rupture of the thoracic aorta is rare. There wasn’t any indication of aneurysm or significant chronic pathologic distension of a blood vessel, similar to what’s seen with abdominal aneurysm. In any event, our most worrisome issue here was that the source of the bleeding was not immediately apparent.”
A closer inspection of the CT image in 3D revealed that the patient had significant atherosclerosis and arterial plaque. “Using our thin-client advanced visualization system, we were able to focus down on that original CT dataset not only to locate the origin of the bleeding, but also to show us the morphology and shape of that point of origination,” Ramjattansingh says. “It turned out that one of the plaques on the vessel wall had become ulcerated. It eventually became so deeply ulcerated that it broke through the vessel wall. We were able to identify on the spot, and with great clarity, what was actually wrong,” he says, and the patient’s life was saved.
A Click Away
Beebe Medical Center is a 210-bed, not-for-profit seaside community hospital, founded in 1916 by two physician brothers. Its specialized service lines include cardiovascular, oncology, women's health, and orthopedic care.
The radiology department is made up of seven radiologists: Two (including Ramjattansingh) are cross-sectional body imagers, two are nuclear-medicine specialists, one is a neuroradiologist, and two are generalists. All are supported by approximately 80 technologists and other personnel. Their equipment includes PET/CT, CT, MRI, ultrasound, gamma-camera, fluoroscopy, radiography, mammography, and bone-densitometry systems.
Image interpretations are performed at six workstations, located either in a reading center in the hospital or at an outpatient imaging center across town. Each workstation is configured with three monitors (two black and white and one color). In addition, deployed throughout the hospital are a number of dedicated diagnostic-quality monitors for use by clinicians. These are found in the emergency department, the critical-care unit, and the surgery suites; plans call for eventually making online access to images available to the broader clinician community throughout the hospital and beyond its walls.
At Beebe Medical Center, using advanced visualization software at a workstation is as simple as clicking on a CT or MRI study. A drop-down menu then gives the user a choice of two advanced visualization software programs, one a thick-client package and the other a thin client.
The thick-client package was Beebe’s first. Installed in 2003, it permitted volume-rendered and 3D reconstructions of vascular work, such as CT angiograms (CTAs) and MR angiograms (MRAs). About a year ago, the thin-client enterprise visualization system from Visage Imaging, Carlsbad, California, was installed alongside the original.
“We make both systems available because doing so has been very helpful in transitioning from old to new,” Ramjattansingh explains. “The new system is far superior, but we’ve also got the familiar old one to fall back on during the learning process.”
The main difference between the two packages is that the new one is vastly quicker on the draw, he says. “With the old system, if you used it on a large dataset—anything above