Cloud-Based Radiation Therapy Planning System
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Solution Overview
Problem
Current radiation therapy treatment planning systems are time-consuming and inefficient due to communication delays and computationally intensive processes, limiting patient throughput and quality of treatment plans, especially in small clinics where large GPU servers are impractical.
Innovation Solution
A cloud-based treatment planning system that distributes computational tasks across a user device, a hospital relay server, and an external GPU cluster server, allowing for real-time communication and processing of patient information to determine radiation dose calculations, enabling efficient and high-quality treatment planning on mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If treatment planning is performed using conventional local stationary machines with GPU hardware, then computational power is sufficient for treatment planning, but device size and cost become too large for mobile devices and small clinics
Solution Approach 1:
The system divides the treatment planning computational tasks into segments: mobile device handles user interface and data input, hospital server handles data management and coordination, and cloud GPU server handles intensive radiation dose calculations. This segmentation allows each component to be optimized independently, enabling high computational power to be accessed remotely without requiring large local hardware.
Solution Approach 2:
The hospital server acts as an intermediary between the mobile device and the cloud GPU server, managing data transmission and coordination. This intermediary layer enables the mobile device to access remote computational resources without direct connection complexity, solving the problem of accessing high computational power without local hardware overhead.
2Productivity
If multiple GPU cards are used to increase computational power, then treatment planning speed improves, but cost and device size increase making them impractical for small clinics
Solution Approach 1:
Instead of each clinic owning expensive GPU hardware, the system creates a virtual copy of high-performance computational capabilities through cloud infrastructure. Small clinics can access the same computational power as large centers by connecting to the shared cloud GPU server, eliminating the need to purchase and maintain expensive local hardware while maintaining treatment planning speed.
3Loss of time
If treatment planning is performed locally to reduce communication delays, then computational autonomy is improved, but computational time and resource requirements increase significantly
Solution Approach 1:
The system segments computational tasks by complexity: simple data input and display operations are performed locally on the mobile device to eliminate communication delays for these functions, while intensive radiation dose calculations are performed remotely on the cloud GPU server where appropriate computational power is available, optimizing the overall process by placing each task where it can be executed most efficiently.
Data Source
AI summary
A cloud-based radiation therapy treatment planning system (TPS). The TPS changes the clinical workflow of the radiotherapy treatment planning process, by increasing the mobility and computational power of the treatment planning software and hardware architecture. The system is divided into computational components. A user device includes a light and flexible user interface, while the server side entails a hospital relay server, and/or a graphics processing unit cluster server. The TPS computational architecture enables the computational power of a GPU-cluster server on a tablet, laptop, or smartphone.