Iterative CBCT Reconstruction for Adaptive Radiation Therapy
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Solution Overview
Problem
Current image-guided radiation therapy techniques face challenges in accurately positioning patients due to limitations in image quality, particularly in soft tissue discrimination, especially in abdominal and pelvic regions, leading to reliance on bony matches or surrogates and limited use of imaging for guiding radiation therapy.
Innovation Solution
The implementation of improved reconstruction techniques for CBCT scans, involving the generation of simulated projection data and comparison with actual CBCT images to determine residual volumes, which are then used to create accurate reconstructed volumes for better tumor segmentation and alignment, enabling more precise patient positioning and adaptive radiation therapy planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional CBCT reconstruction techniques are used, then patient positioning can be performed, but image quality is insufficient particularly for soft tissue discrimination
Solution Approach 1:
The system performs preliminary segmentation of the planning CT volume into anatomical structures (tumor, organs, bones) before the actual treatment session. These pre-segmented structures are then projected onto the CBCT images to guide alignment, eliminating the need to rely on poor soft tissue discrimination during positioning.
Solution Approach 2:
The invention creates a digital copy of the planning CT volume with pre-segmented anatomical structures. This digital phantom is projected onto the actual CBCT images to provide reference anatomy that compensates for the inferior soft tissue visualization in CBCT, enabling accurate positioning without requiring direct soft tissue discrimination.
2Productivity
If filtered back projection is used for CBCT reconstruction, then processing is relatively fast, but image quality remains insufficient for accurate tumor segmentation
Solution Approach 1:
The system performs tumor segmentation and anatomical structure identification in advance during the planning phase using high-quality planning CT images. This preliminary segmentation creates a digital reference that can be projected onto CBCT images for alignment, eliminating the need for real-time high-precision reconstruction during treatment positioning.
Solution Approach 2:
A digital phantom containing segmented tumor and organ structures is created from the planning CT and projected onto the CBCT images. This copying of anatomical information from the high-quality planning scan compensates for the inferior image quality of the CBCT reconstruction, enabling accurate tumor identification without requiring fast high-precision CBCT reconstruction.
3Ease of operation
If manual patient positioning is performed by comparing rough outlines, then simple techniques are used, but positioning accuracy is limited
Solution Approach 1:
The system projects a digital phantom containing segmented anatomical structures onto the CBCT images. This provides clear reference outlines and anatomical landmarks that are much more distinct than the rough outlines from conventional CBCT, enabling both simple automated matching and improved positioning accuracy without increasing operational complexity.
Solution Approach 2:
The projected digital phantom structures are displayed with distinct visual characteristics (differentiation of tumor, organs, and bones) that enhance contrast and visibility compared to conventional CBCT images. This visual enhancement makes anatomical structures easily distinguishable for both automated and manual positioning without complicating the operator's task.
Data Source
AI summary
Reconstruction of projection images of a CBCT scan is performed by generating simulated projection data, comparing the simulated projection data to the projection images of the CBCT scan, determining a residual volume based on the comparison, and using the residual volume to determine an accurate reconstructed volume. The reconstructed volume can be used to segment a tumor (and potentially one or more organs) and align the tumor to a planning volume (e.g., from a CT scan) to identify changes, such as shape of the tumor and proximity of the tumor to an organ. These changes can be used to update a radiation therapy procedure, such as by altering a radiation treatment plan and fine-tuning a patient position.