MiniPACS tames volumetric data set explosion

Article

Using a miniPACS has proved an effective method to archive CT volumetric data sets and to deliver them to radiologists.

Using a miniPACS has proved an effective method to archive CT volumetric data sets and to deliver them to radiologists.

Dr. Kyung Won Lee and colleagues from South Korea moved the volumetric data set from 16-slice CT scanners to a miniPACS with 271-GB online and 680-GB nearline storages. A thicker slice data set was stored in the main PACS.

They studied a two-week period to determine the impact of storage needs of each data set type: volumetric, thick axial, standardized 3D images routinely produced by technologists, 3D images added by radiologists, and scan planning. They also analyzed the storage need of each PACS over a five-month period.

For the 867 CT exams performed during the two-week period, the percentage data volumes of volumetric, thick axial, standardized 3D, additional 3D, and scan planning data sets decreased linearly: 74.4%, 15.9%, 7%, 2.3%, 0.5%, respectively.

Over the five-month period, 278 GB of CT data (8976 exams) were stored in the main PACS, and 738 GB of volumetric data sets (6193 exams) were stored in the miniPACS. The volumetric data sets formed 33% of total data for all modalities (2.2 TB) in the main PACS and miniPACS.

At the end of this period, volumetric data sets of 1892 and 5162 exams were kept online and nearline, respectively.

Recent Videos
What New Research Reveals About the Impact of AI and DBT Screening: An Interview with Manisha Bahl, MD
Can AI Assessment of Longitudinal MRI Scans Improve Prediction for Pediatric Glioma Recurrence?
A Closer Look at MRI-Guided Adaptive Radiotherapy for Monitoring and Treating Glioblastomas
Incorporating CT Colonography into Radiology Practice
What New Research Reveals About Computed Tomography and Radiation-Induced Cancer Risk
What New Interventional Radiology Research Reveals About Treatment for Breast Cancer Liver Metastases
New Mammography Studies Assess Image-Based AI Risk Models and Breast Arterial Calcification Detection
Can Deep Learning Provide a CT-Less Alternative for Attenuation Compensation with SPECT MPI?
Employing AI in Detecting Subdural Hematomas on Head CTs: An Interview with Jeremy Heit, MD, PhD
Pertinent Insights into the Imaging of Patients with Marfan Syndrome
Related Content
© 2025 MJH Life Sciences

All rights reserved.