External Beam Therapy Dose Prediction with Latent Diffusion
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Solution Overview
Problem
Conventional radiation therapy plans rely heavily on manual adjustments by medical professionals, leading to inefficiencies and inaccuracies due to personal experience variations, and existing machine learning systems fail to adapt to diverse clinical conditions and instruments, limiting the effectiveness of radiation therapy planning.
Innovation Solution
An external beam radiation therapy dose prediction system using a latent diffusion model that integrates patient clinical data and prompts to automatically generate therapy plans, incorporating a storage unit and processor for training and generating radiation therapy plans based on medical images and dose distribution data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment by medical professionals is used for radiation therapy planning, then personal experience and expertise can be applied, but time consumption increases and accuracy varies due to individual experience differences
Solution Approach 1:
The patent uses a latent diffusion model to copy and replicate the expertise of experienced medical professionals by training on historical therapy plans and anatomical data. The model learns optimal dose distribution patterns from past successful treatments and automatically generates new therapy plans that replicate this expertise without requiring individual professional judgment for each case.
Solution Approach 2:
The patent replaces the manual mechanical process of dose calculation and plan adjustment with an automated AI-based system. The latent diffusion model computationally generates therapy plans by processing anatomical images and clinical data, substituting the manual workflow of medical physicists and dosimetrists with an automated neural network that operates continuously without fatigue or variability.
2Productivity
If machine learning systems are used for radiation therapy planning, then efficiency can be improved, but adaptability to different clinical conditions and instruments is limited
Solution Approach 1:
The patent implements a dynamic training approach where the latent diffusion model continuously learns from new clinical data, anatomical variations, and treatment outcomes. The model adapts its internal representations to accommodate different clinical scenarios, instrument types (LINAC, Proton, Carbon, Brachytherapy), and radiation sources by processing diverse input data through its neural network architecture.
Solution Approach 2:
The patent designs a universal latent diffusion model that can handle multiple clinical conditions and therapy types within a single framework. The model takes as input diverse anatomical data and clinical parameters, then generates appropriate therapy plans for different instruments and techniques (3D-CRT, IMRT, VMAT, proton, carbon, brachytherapy) without requiring separate specialized models for each modality.
3Reliability
If conventional manual therapy planning is used, then flexibility in handling complex cases is maintained, but consistency and reliability across different planners varies
Solution Approach 1:
The patent transforms the planning process by changing the fundamental parameters of how therapy plans are generated. Instead of relying on individual planner parameters (experience, skill level, fatigue), the system uses standardized input parameters (anatomical images, clinical data) and transforms them through the latent diffusion model to produce consistent output plans. This parameter transformation ensures reliability by eliminating human variability while maintaining flexibility through the model's learned patterns.
Data Source
AI summary
An external beam radiation therapy dose prediction system through a latent diffusion model is adapted to predict an external beam radiation therapy plan according to a clinical data of a patient and includes a storage unit adapted to store a training data and the clinical data and a processor signally connected to the storage unit. The training data includes a plurality of medical images, a plurality of dose distribution data, and a plurality of training prompts. The processor is adapted to input the training data to a latent diffusion training model to execute a latent diffusion model training and generate an external beam radiation therapy plan model and input the clinical data and at least one prompt to the external beam radiation therapy plan model to generate an external beam radiation therapy plan.