CT Liver Scan Coregistration for HCC Lesion Detection
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Solution Overview
Problem
Existing methods for diagnosing and monitoring hepatocellular carcinoma (HCC) in CT scans are inadequate due to the reliance on single-scan analysis, which fails to account for varying enhancement patterns, and require precise alignment of multiple CT scans to accurately detect and predict HCC, especially when considering liver lesions and internal organ movements.
Innovation Solution
A computer-implemented method involving coregistration of CT scans using liver masks to align and integrate information from multiple scans, combined with machine-learning models trained on processed datasets to enhance detection and prediction of HCC, including steps like obtaining liver masks, cropping, and filtering to improve alignment and lesion identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple CT scan images are acquired at different time points to capture different enhancement patterns, then the detection accuracy of HCC is improved, but the alignment precision deteriorates due to organ movement and shape changes
Solution Approach 1:
The patent introduces an intermediary registration process that uses anatomical landmarks and transformation matrices as mediators to align multiple CT scans. The system establishes correspondence between images by identifying matching anatomical features and computing spatial transformations, thereby resolving the misalignment caused by organ movement while preserving the temporal information needed for HCC detection
Solution Approach 2:
The patent applies preliminary image registration and normalization procedures before performing HCC analysis. By pre-aligning the CT scans using rigid and deformable registration techniques, the system prepares the data in advance to minimize the impact of organ movement, ensuring that subsequent detection algorithms work with properly aligned images
2Manufacturing precision
If manual alignment of multiple CT scans is performed to achieve perfect alignment, then the alignment precision is improved, but the operational complexity and time consumption increase
Solution Approach 1:
The patent implements automated image registration algorithms that perform alignment without requiring manual intervention. The system uses computational methods including intensity-based registration, feature matching, and optimization algorithms to automatically compute transformation parameters, thereby achieving high alignment precision while eliminating the need for time-consuming manual adjustment
Solution Approach 2:
The patent replaces manual mechanical alignment operations with computational algorithms. Instead of requiring operators to visually adjust and align images manually, the system uses digital image processing techniques including convolutional neural networks and optimization algorithms to automatically compute and apply spatial transformations, substituting mechanical/manual operations with automated computational processes
3Reliability
If examination of multiple CT scan phases is conducted to account for different enhancement patterns, then the diagnostic reliability is improved, but the processing complexity increases
Solution Approach 1:
The patent segments the analysis process into distinct phases corresponding to different contrast enhancement time points. By processing each phase separately through dedicated neural network models and then integrating the results, the system manages the complexity of multi-phase analysis while maintaining high diagnostic reliability through specialized processing for each phase's unique characteristics
Solution Approach 2:
The patent develops a universal processing framework that handles multiple CT scan phases through a common architectural structure. The system uses multi-functional neural networks that can process different phase types (arterial, venous, delayed) using the same underlying algorithms and data structures, thereby reducing processing complexity through standardized approaches while maintaining the ability to capture phase-specific enhancement patterns
Data Source
AI summary
The present invention relates to systems and methods of processing CT scan images comprising a liver of a subject to detect and/or predict hepatocellular carcinoma (HCC) in the subject.