Performance enhancement of a web-based picture archiving and communication system using commercial off-the-shelf server clusters.
Yan-Lin LiuCheng-Ting ShihYuan-Jen ChangShu-Jun ChangJay WuPublished in: BioMed research international (2014)
The rapid development of picture archiving and communication systems (PACSs) thoroughly changes the way of medical informatics communication and management. However, as the scale of a hospital's operations increases, the large amount of digital images transferred in the network inevitably decreases system efficiency. In this study, a server cluster consisting of two server nodes was constructed. Network load balancing (NLB), distributed file system (DFS), and structured query language (SQL) duplication services were installed. A total of 1 to 16 workstations were used to transfer computed radiography (CR), computed tomography (CT), and magnetic resonance (MR) images simultaneously to simulate the clinical situation. The average transmission rate (ATR) was analyzed between the cluster and noncluster servers. In the download scenario, the ATRs of CR, CT, and MR images increased by 44.3%, 56.6%, and 100.9%, respectively, when using the server cluster, whereas the ATRs increased by 23.0%, 39.2%, and 24.9% in the upload scenario. In the mix scenario, the transmission performance increased by 45.2% when using eight computer units. The fault tolerance mechanisms of the server cluster maintained the system availability and image integrity. The server cluster can improve the transmission efficiency while maintaining high reliability and continuous availability in a healthcare environment.
Keyphrases
- healthcare
- contrast enhanced
- deep learning
- computed tomography
- magnetic resonance
- protein protein
- image quality
- convolutional neural network
- magnetic resonance imaging
- dual energy
- positron emission tomography
- optical coherence tomography
- primary care
- small molecule
- squamous cell carcinoma
- mental health
- emergency department
- artificial intelligence
- acute care
- neural network
- rectal cancer
- electronic health record
- big data
- health information
- affordable care act