VNA

VNA

A vendor neutral archive (VNA) is a system that allows for the seamless integration and sharing of medical digital images between different departments, such as Radiology and Cardiology, without being tied to a specific vendor or proprietary format. It is typically used by major medical institutions to store imaging data alongside Picture Archiving and Communication Systems (PACS) using the DICOM standard.From: Computers and Society [2019], Radiomics and Radiogenomics [2019]

Predictive modeling, machine learning, and statistical issues

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Published in Ruijiang Li, Lei Xing, Sandy Napel, Daniel L. Rubin, Radiomics and Radiogenomics, 2019

Panagiotis Korfiatis, Timothy L. Kline, Zeynettin Akkus, Kenneth Philbrick, Bradley J. Erickson

Every major medical institution stores imaging data in the PACS or in a separate vendor-neutral archive (VNA), nearly always using digital image communications in medicine (DICOM) (2001). The radiology information system (RIS) is used for the text component of an imaging department, such as the order, the indication for examination, the interpretation, and billing information. The PACS provides an image viewer that enables quick image curation (typically by the radiologic technologist) and image viewing and measurement for examination interpretation (typically by a radiologist).

Research challenges and future directions towards medical data processing

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Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2022

Anusha Ampavathi, Vijaya Saradhi T

The algorithms used for ensuring the privacy preserving in medical data storage systems are represented in Figure 2. These approaches are ensuring the integrity and security of patients or individual’s medical data. This storage systems offer compliance, data resiliency and protection, scalability and Vendor-Neutral Archive (VNA). In general, it is the process of storing and securing the medical data against any attackers or hackers.

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Predictive modeling, machine learning, and statistical issues

Research challenges and future directions towards medical data processing