Despite significant increases in data volume, simultaneous implementation of a PACS allowed radiologists at the Groningen University Hospital in the Netherlands to increase productivity with the implementation of four- and 16-slice scanners.
Despite significant increases in data volume, simultaneous implementation of a PACS allowed radiologists at the Groningen University Hospital in the Netherlands to increase productivity with the implementation of four- and 16-slice scanners.
The expanding use of multislice CT not only increases CT data production dramatically, it also affects the way radiologists handle and diagnose images. The effect of the introduction of four- and 16-slice CT on PACS at a university hospital is described in a paper published in Academic Radiology (2004;11[6]:649-660).
"New acquisition devices and postprocessing techniques have an enormous impact on current PACS implementations," said Peter van Ooijen, Ph.D., of the radiology department at Groningen. "The functional requirements of PACS in radiology have to be reevaluated continuously."
It's not surprising that the hospital found the number of slices per study increasing with the introduction of the multislice scanners. But the number of patients also increased due to shorter scan times. This increase in data production ups demand on the capacity and capabilities of PACS, van Ooijen said.
Following the installation of the four-slice CT, Groningen Hospital experienced a 286% increase in number of series. Four months later, after a 16-slice CT was acquired, the number of series jumped 130%.
Patients seen during this time period grew by 54%, while the number of images per patient climbed from 175 to 450 (157%).
PACS enabled increased productivity for the facility through rapidly available images and the elimination of certain manual tasks, according to the authors. In this environment, the number of nondiagnosed studies decreased from about 100 to 120 to nearly zero after PACS implementation.
"One way to more effectively evaluate the large amounts of data is by using more advanced postprocessing tools," van Ooijen said. "To achieve this, postprocessing has to be available at every level of PACS."
PACS must be capable of receiving and storing the flood of data produced by the multislice scanners.
"The amount of data increases even more because 3D visualization and axial slice evaluation require different reconstruction protocols on the same raw data," van Ooijen said.
One data set, for example, is reconstructed both at 0.75-mm slice thickness for axial evaluation and at 1-mm thickness for 3D, because the noise level of the 0.75-mm scan becomes too high to obtain adequate 3D visualization.
Study Reaffirms Low Risk for csPCa with Biopsy Omission After Negative Prostate MRI
December 19th 2024In a new study involving nearly 600 biopsy-naïve men, researchers found that only 4 percent of those with negative prostate MRI had clinically significant prostate cancer after three years of active monitoring.
Study Examines Impact of Deep Learning on Fast MRI Protocols for Knee Pain
December 17th 2024Ten-minute and five-minute knee MRI exams with compressed sequences facilitated by deep learning offered nearly equivalent sensitivity and specificity as an 18-minute conventional MRI knee exam, according to research presented recently at the RSNA conference.
Can Radiomics Bolster Low-Dose CT Prognostic Assessment for High-Risk Lung Adenocarcinoma?
December 16th 2024A CT-based radiomic model offered over 10 percent higher specificity and positive predictive value for high-risk lung adenocarcinoma in comparison to a radiographic model, according to external validation testing in a recent study.