Dynamic Image Preloading for Medical Imaging Speed
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Solution Overview
Problem
Medical imaging exams require rapid image availability and efficient navigation, but existing technologies face challenges with slow image transfer and limited local memory, leading to inefficiencies in image retrieval and processing.
Innovation Solution
A computing system monitors user behavior to predict image access patterns and preloads images likely to be needed next into local memory, using models based on characteristics like exam type, modality, and user roles, optimizing image processing and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If images are transferred from remote location to computing device, then image availability is improved, but transfer speed and network utilization are worsened
Solution Approach 1:
The system performs preliminary actions by monitoring user behavior patterns and preloading predicted images into local memory before they are actually requested. This advance preparation ensures images are available immediately when needed, eliminating transfer delays while maintaining reliable availability.
Solution Approach 2:
Local memory acts as an intermediary between remote image storage and the display system. Instead of directly transferring images from remote locations, the system uses local memory as a buffer to store predicted images, thereby reducing network dependency and improving access speed.
2Speed
If more images are stored in local memory, then image access speed is improved, but memory capacity is worsened
Solution Approach 1:
Instead of uniformly distributing memory resources or storing all possible images, the system applies local quality by selectively storing only those images predicted to be needed based on user behavior patterns. Each memory slot is allocated to images with specific characteristics (high prediction probability), optimizing the quality of stored content rather than quantity.
Solution Approach 2:
The system dynamically changes parameters by adjusting which images are stored in local memory based on real-time user behavior monitoring and prediction model updates. The selection criteria and prediction probabilities are continuously modified to optimize memory utilization and access speed.
3Measurement precision
If user behavior is monitored and analyzed, then image prediction accuracy is improved, but system complexity is worsened
Solution Approach 1:
The system performs self-service by automatically monitoring user behavior, analyzing patterns, and updating prediction models without external intervention. The behavior monitoring and prediction mechanisms are integrated into the existing image display system, allowing it to self-optimize based on observed usage patterns.
Solution Approach 2:
The system implements feedback loops where user behavior data is continuously collected, analyzed, and used to refine prediction models. This feedback mechanism improves prediction accuracy over time while the automated nature of the process minimizes the perceived complexity for users.
4Loss of time
If images are preloaded into local memory, then image retrieval time is improved, but network utilization is worsened
Solution Approach 1:
By performing preliminary preloading actions based on prediction models, the system transfers images to local memory in advance of actual requests. This reduces retrieval time to near-zero for predicted images while spreading network load over time rather than concentrating it during peak access periods.
Solution Approach 2:
The preloading strategy is dynamic rather than static, adjusting which images are preloaded based on real-time user behavior patterns and prediction probabilities. This dynamic approach optimizes the balance between retrieval time improvement and network utilization by preloading only the most likely candidates.
Data Source
AI summary
Provided herein are various systems and methods of adjusting images of an image series that are preloaded (and/or otherwise processed) in view of behavior data associated with viewing of other previous exams having similar characteristics (e.g., same modality) and/or by the same user.