Deep Learning Image Registration for X-Ray Stitching
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Solution Overview
Problem
Traditional image stitching methods in medical imaging, such as X-ray imaging, face challenges with lateral motion, non-rigid artifacts, low overlap, and varying radiation doses, leading to mis-registration and inaccuracies in stitching X-ray images.
Innovation Solution
A deep learning model, specifically a convolutional neural network (CNN), is trained to generate a transformation matrix based on image pairs with overlapping regions of interest (ROIs), using multiple loss functions and augmented training data sets to account for lateral motion, non-rigid artifacts, and dose variations, enabling accurate stitching of images with low overlap.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image stitching methods are used, then the stitching process is simple, but mis-registration and inaccuracies occur due to lateral motion, non-rigid artifacts, and low overlap
Solution Approach 1:
The patent replaces traditional mechanical/image-processing-based stitching methods with a deep learning model (convolutional neural network) that learns optimal transformation parameters from training data. The model substitutes complex algorithmic processing with trained neural network inference, achieving higher accuracy in registering images with lateral motion and non-rigid artifacts while maintaining automated operation.
Solution Approach 2:
The patent transforms the stitching problem from direct image manipulation to parameter prediction. The deep learning model outputs transformation parameters (translation, rotation, scaling) that are then applied to register images. This parameter-based approach allows the system to handle lateral motion and non-rigid artifacts by learning optimal parameter adjustments from augmented training data representing various motion conditions.
2Reliability
If multiple images are captured to ensure coverage, then complete anatomical coverage is achieved, but radiation dose to the patient increases
Solution Approach 1:
The patent applies partial action by capturing images with minimal overlap (reducing redundant radiation exposure) while the deep learning model compensates for the reduced overlap through learned transformation parameters. The system achieves complete anatomical coverage by intelligently registering partially overlapping images rather than requiring extensive overlap, thereby reducing the number of images needed and the associated radiation dose.
Solution Approach 2:
The deep learning model acts as an intermediary that processes and registers images with low overlap. Instead of requiring physical overlap between images to ensure coverage, the model learns from augmented training data (including cases with lateral motion and low overlap) to accurately align images, enabling complete anatomical coverage with fewer images and reduced radiation exposure.
3Measurement precision
If manual stitching is performed to handle complex cases, then accuracy is improved, but operator error and time consumption increase
Solution Approach 1:
The patent implements self-service by training the deep learning model to autonomously handle complex stitching cases including lateral motion, non-rigid artifacts, and low overlap. The model learns from augmented training data representing various challenging scenarios and automatically predicts transformation parameters without operator intervention. This eliminates manual stitching while maintaining high accuracy, reducing both operator error and processing time.
Solution Approach 2:
The patent applies preliminary action by pre-training the deep learning model on augmented training data that includes various motion conditions, artifact types, and overlap scenarios. This preliminary training enables the model to automatically handle complex cases during inference without requiring manual intervention, achieving both high accuracy and efficiency by preparing the system in advance for diverse stitching challenges.
Data Source
AI summary
Various methods and systems are provided for automatically registering and stitching images. In one example, a method includes entering a first image of a subject and a second image of the subject to a model trained to output a transformation matrix based on the first image and the second image, where the model is trained with a plurality of training data sets, each training data set including a pair of images, a mask indicating a region of interest (ROI), and associated ground truth, automatically stitching together the first image and the second image based on the transformation matrix to form a stitched image, and outputting the stitched image for display on a display device and/or storing the stitched image in memory.