DICOM Data Compression and Transfer Speed for Higher Enterprise Imaging Productivity

DICOM data compression and image quality are two factors that can greatly impact enterprise imaging productivity for healthcare institutions. DICOM data compression helps effectively manage medical images workflows at healthcare institutions and is a critical element to consider when designing enterprise imaging workflows. The cumulative amount of time lost to delays due to data compression and transfer speed can directly impact the level of revenue generated. An improvement in transfer speed results in higher productivity which translates to more revenue. For example, it takes 5 minutes to send a 1542 image CT study averaging 930.17 MB, assuming limited bandwidth around 12 Mbps. It will take 10 minutes for an uncompressed study to route vs. 6 minutes JPEG Lossless compressed, assuming compression is done as images are received from the modality with minimum overhead which causes a bit delay (6 minutes vs. 5 minutes). This 4 minutes improvement in turnaround time can be crucial and even life-saving in ER settings.

What Is Data Compression?

Data compression is the process of encoding, restructuring or modifying data in order to reduce its size and involves re-encoding information using fewer bits than the original representation.

There are two categories of data compression:
  • Lossy compression is a method of data compression in which the size of the file is reduced by eliminating data in the file. In doing so, image quality is sacrificed to decrease file size. Any data that the compression algorithm deems expendable is removed from the image, thereby reducing its size. The lossy compression technique does not restore the data in its original form, after decompression. The image reconstructed by lossy compression is visually similar to the original image, but not absolutely the same. Lossy compression transfers only relevant information during uncompression. With lossy compression, redundancy is eliminated.
  • Lossless compression is a compression technique that does not lose any data in the compression process. Lossless compression “packs” data into a smaller file size by using an internal shorthand to signify redundant data. After decompression, lossless compression restores and rebuilds the data to its original form; all of the original file data is intact after the file is uncompressed. The image reconstructed by lossless compression is exactly the same as the original image.
Lossless and lossy compression solutions can provide complete or partial data, depending on the specific clinician requirement. In a situation where some medical images only focus on the region of interest, applying lossless compression to the region of interest and lossy compression to the region of non interest
is a possible solution.
In many healthcare institutions, both lossless and lossy transfers are needed to ensure high productivity and image quality.

Data Compression And Image Quality

In addition to data compression, overall image quality can also impact productivity. When medical images are insufficient for screening or diagnostic purposes, it takes time to optimize those images properly to improve the quality and adequately solve the problem. When the attributes of compression and quality are combined, the speed of the digital file transfer correlates to the size of the compressed image. The data compression ratios achieved by lossless compression techniques are less than lossy encoding techniques, which makes lossless encoding techniques unsuitable in certain situations.
When selecting an enterprise imaging vendor, it’s important to evaluate medical image high-speed transfers and image quality through lossless and lossy compression.
The optimal combination of image compression and image quality can significantly improve daily productivity, as well as clinician, referrer, and patient satisfaction.

Vision Radiology Case Study

A few years ago, Dicom Systems partnered with Vision Radiology, whose core practice focuses on Emergency Teleradiology with a growing focus on non-emergent sub-specialty interpretations. Vision Radiology has been able to consistently provide high-quality service, scalability, and competitive turnaround times on case reads (~15 minutes). This case study explains how the Unifier platform added value to radiologic image interpretation and has substantially reduced the time and cost needed to deploy new sites onto the Vision network. The purpose of this whitepaper is to share results from testing average transmission time and transfer speed of a Dicom Systems router. Hardware and network speed were taken into consideration and three different hardware profiles running the same Dicom Systems software suite were tested.

Conclusion

The right compression can be a significant factor in transfer speed optimization and an important part of the enterprise imaging.