Contextual Deep Network for Cross-Modality Medical Image Synthesis
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Solution Overview
Problem
Medical image analysis algorithms trained on one modality often fail to work effectively on data from different modalities due to modality heterogeneity, making it impractical to collect large datasets for each modality, and existing cross-modal synthesis approaches are typically tailored to specific applications or based on heuristics.
Innovation Solution
A contextual deep network (CtDN) is used to synthesize medical images in a target domain from a source domain by modeling hidden nodes as products of feature responses and contextual responses, allowing for the capture of dependencies between voxel intensities and spatial locations, enabling effective cross-domain synthesis across any pair of imaging modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If algorithms are trained on data from one imaging modality, then they achieve good performance on that modality, but they fail to work effectively on data from different modalities due to modality heterogeneity
Solution Approach 1:
The patent introduces a cross-domain adaptation layer as an intermediary component between the source domain (training modality) and target domain (testing modality). This adaptation layer learns to transform features from the source domain to the target domain, enabling algorithms trained on one modality to effectively process data from another modality. The intermediary resolves the contradiction by maintaining training performance while enabling cross-modality generalization.
Solution Approach 2:
The patent employs parameter adaptation techniques where the model parameters are adjusted to account for domain differences. By changing parameters dynamically based on the input domain or using domain-specific parameter sets, the system maintains high performance across different imaging modalities without requiring complete retraining for each modality.
2Reliability
If large amounts of training data are collected from each imaging modality, then algorithms can be trained to handle modality heterogeneity, but the process becomes time consuming and expensive
Solution Approach 1:
The patent uses synthetic data generation techniques to create virtual training data that replicates the characteristics of target domain modalities. Instead of collecting actual multi-modal data, the system generates synthetic images or augmented data that preserve the essential features needed for training, significantly reducing the time and cost of data collection while maintaining training effectiveness.
Solution Approach 2:
The patent performs preliminary domain adaptation training on easily obtainable source domain data, preparing the model in advance to handle target domain data. This preliminary action allows the system to be pre-configured for cross-modality performance, reducing the need for extensive real-time data collection and retraining when new modalities are encountered.
3Adaptability or versatility
If existing cross-modal synthesis approaches are used, then some synthesis capability is achieved, but they are typically tailored to specific applications or based on heuristics rather than being generalized
Solution Approach 1:
The patent develops a universal cross-domain adaptation framework that can handle multiple imaging modalities and applications through a single unified model architecture. Rather than creating separate specialized systems for each modality pair, the system uses a general-purpose adapter that learns domain transformations across different modalities, reducing overall system complexity while maintaining broad versatility.
Solution Approach 2:
The patent segments the cross-modal synthesis task into separate learnable components: a base model that processes source domain data and an adaptation layer that handles domain-specific transformations. This segmentation allows the system to maintain a simple base architecture while adding complexity only where needed for specific modality pairs, making the overall system more manageable and easier to deploy across different applications.
Data Source
AI summary
A method and apparatus for cross-domain medical image synthesis is disclosed. A source domain medical image is received. A synthesized target domain medical image is generated using a trained contextual deep network (CtDN) to predict intensities of voxels of the target domain medical image based on intensities and contextual information of voxels in the source domain medical image. The contextual deep network is a multi-layer network in which hidden nodes of at least one layer of the contextual deep network are modeled as products of intensity responses and contextual response.