Dynamic Tomographic Image Reconstruction Using GPU Backprojection
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Solution Overview
Problem
Current digital mammography and tomosynthesis techniques face limitations due to the production of 2D images from 3D breast anatomy, leading to superimposed normal tissues that mask lesions, reducing sensitivity and specificity, and requiring lengthy computational times for image reconstruction.
Innovation Solution
A dynamic reconstruction and rendering (DRR) method using a Graphics Processing Unit (GPU) for real-time reconstruction of 3D tomographic images, incorporating a backprojection and filtering (BPF) algorithm that allows for on-demand image reconstruction and filtering, enabling sharper images and improved diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 2D images are produced from 3D breast anatomy using conventional mammography, then the imaging process is simple and fast, but normal tissues are superimposed masking lesions, reducing sensitivity and specificity
Solution Approach 1:
The patent transitions from 2D projection mammography to 3D tomographic imaging by acquiring multiple projection images at different angles and reconstructing them into 3D volumetric data. This dimensional change eliminates tissue superimposition and improves lesion detectability while maintaining reasonable system complexity through established tomosynthesis techniques.
2Measurement precision
If full 3D tomographic images are reconstructed using traditional methods, then complete volumetric data is obtained, but computational time is lengthy
Solution Approach 1:
The patent segments the 3D reconstruction process into two distinct passes: a first pass that generates low-resolution images for rapid ROI identification, and a second pass that performs high-resolution reconstruction only for the selected region of interest. This segmentation dramatically reduces computational time while preserving diagnostic accuracy for clinically relevant areas.
Solution Approach 2:
The patent applies local quality by concentrating computational resources on reconstructing high-resolution images only for the specific ROI identified in the first pass, rather than uniformly processing the entire 3D volume. This allows diagnostic-quality images to be generated rapidly for clinically important regions while minimizing overall computation time.
3Measurement precision
If high resolution images are reconstructed for the entire volume, then diagnostic quality is maximized, but computational burden and time increase significantly
Solution Approach 1:
The patent implements partial action by performing high-resolution reconstruction only for the ROI rather than the entire 3D volume. The first pass provides sufficient low-resolution quality for ROI selection, and the second pass applies full computational effort only where diagnostically necessary, optimizing the balance between image quality and processing efficiency.
4Ease of operation
If fixed slice spacing reconstruction is used, then the reconstruction process is simplified, but blurring occurs and diagnostic accuracy is reduced
Solution Approach 1:
The patent introduces dynamic adaptability by allowing the reconstruction process to adjust slice spacing and resolution based on the selected ROI and diagnostic requirements. Rather than using fixed slice spacing, the system dynamically optimizes reconstruction parameters for the specific clinical question, eliminating blurring while maintaining operational simplicity through automated parameter selection.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The DRR method provides faster reconstruction times, improved image quality, and enhanced diagnostic accuracy by allowing real-time adjustments to reconstruction parameters, reducing computational burden, and eliminating blurring from fixed slice spacing.
Implementation Method 1
reconstructing a 3D tomographic image from the set of projection images using a backprojecting and filtering (BPF) method in which the projection images are backprojected into a 3D tomographic image and then filtered
Implementation Method 2
Tomography is imaging by sections or sectioning an object into multiple images, and reconstructing the images to view an object of interest
Data Source
AI summary
A method of dynamically reconstructing three dimensional (3D) tomographic images from a set of projection images is disclosed. The method includes the steps of loading a set of projection images into a memory device, determining a reconstruction method for the set of projection images, reconstructing a 3D tomographic image from the set of projection images to be displayed to a user; and performing any post reconstruction processing on the 3D tomographic image.