Client-Server Volume Rendering via Pixel Mask Differential Updates
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Solution Overview
Problem
Current client-server architectures for interactive volume rendering face challenges in efficiently processing large medical imaging data, leading to high network bandwidth requirements and compromised diagnostic quality due to limitations in data compression and processing capabilities of typical local processors.
Innovation Solution
A system where a server loads a volume dataset, creates a low-resolution sub-sampled copy, and transmits it to the client, while generating and transmitting a pixel mask indicating differences between high and low-quality images, allowing the client to compute high-quality images locally based on the sub-sampled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a thin client architecture is used where the server renders all images and transmits them to the client, then the client hardware requirements are reduced, but the network bandwidth consumption increases significantly
Solution Approach 1:
The patent segments the image rendering process into two parts: the server performs initial rendering of the reference image, while the client performs differential rendering based on received difference data. This segmentation allows the client to use simpler hardware while reducing network bandwidth by transmitting only the differences rather than complete images.
Solution Approach 2:
The patent uses differential compression where the server transmits a copy of the difference between the reference image and the new image rather than the complete new image. The client reconstructs the new image by applying this difference copy to the stored reference image, significantly reducing network bandwidth requirements.
2Quantity of substance
If lossy compression algorithms are used to reduce data transmission, then network bandwidth usage decreases, but the diagnostic quality of medical images is compromised
Solution Approach 1:
The patent transmits only the difference data (a copy of changes) rather than complete images, enabling efficient compression. Since only the differences are transmitted and applied to the reference image kept at the client, the full diagnostic quality is preserved in the reconstructed images without requiring lossy compression of the entire image data.
Solution Approach 2:
The patent changes the parameter being transmitted from complete image data to differential data. This parameter transformation allows for more efficient compression and transmission while maintaining the ability to reconstruct full-quality images at the client side through the differential rendering process.
3Productivity
If multiple clients are connected to a server requiring interactive performance, then the system serves more users, but the network bandwidth becomes a significant bottleneck
Solution Approach 1:
Each client maintains a local copy of the reference image and receives only differential data from the server. This copying approach allows multiple clients to be served simultaneously with minimal network bandwidth consumption, as the bandwidth requirement per client is reduced to only the difference data rather than complete images.
Solution Approach 2:
The patent segments the image data into reference data (stored locally at each client) and difference data (transmitted over the network). This segmentation enables multiple clients to share the network bandwidth efficiently, as each client independently manages its own reference copy and only exchanges minimal difference information with the server.
Data Source
AI summary
In response to a request from a client, a server for volume rendering loads a volume dataset from a storage archive, creates a low resolution sub-sampled copy of such volume dataset and transmits it to a client. In response to subsequent requests from the client, the server for volume rendering renders a high quality image of the full resolution volume dataset and renders a low quality image of the sub-sampled copy of the volume dataset, then generates a pixel mask or a hybrid pixel mask indicative of a difference between the high quality image and the low quality image and transmits such pixel mask to the client. The client receives from the server the pixel mask and computes a high quality image based at least in part on the pixel mask and on a selective rendering of its local low resolution sub-sampled copy of the volume dataset.