4D CT Hemodynamic Mapping with Synthetic-Data Neural Networks
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Solution Overview
Problem
Current methods for generating hemodynamic parametric maps from 4D computed tomography perfusion data suffer from low signal-to-noise ratio and errors due to bad registration and bolus superposition, leading to inaccurate hemodynamic parameter estimation.
Innovation Solution
Utilizing deep learning algorithms trained on synthetic data to estimate hemodynamic parameters by correcting image non-idealities in 4D computed tomography perfusion data, employing neural networks to determine residual impulse functions and derive parameters like blood flow, blood volume, and mean transit time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning algorithms trained on synthetic data are used to estimate hemodynamic parameters, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary training of deep learning algorithms using synthetic perfusion data before actual clinical use. This pre-training phase creates a ready-to-use computational model that can directly process real patient data without requiring complex real-time training, thus improving measurement precision while managing device complexity through advance preparation.
Solution Approach 2:
Synthetic perfusion data serves as an intermediary between theoretical models and real clinical data. The synthetic data, generated from known ground truth parameters, acts as a training mediator that enables the deep learning algorithm to learn accurate hemodynamic parameter estimation without directly requiring complex real-time processing of raw clinical data during actual use.
2Reliability
If deep learning algorithms are used to correct image non-idealities, then reliability is improved, but loss of time increases
Solution Approach 1:
The deep learning algorithm is pre-trained on extensive synthetic data that includes various types of image non-idealities and artifacts. This preliminary training enables the model to automatically correct these issues during inference without requiring complex real-time processing, thus improving reliability while minimizing additional processing time during actual clinical use.
3Measurement precision
If neural networks are used to determine residual impulse functions, then measurement precision is improved, but ease of operation decreases
Solution Approach 1:
The deep learning algorithm performs automatic feature extraction and parameter determination from the arterial and tissue signals without requiring manual intervention. The neural network self-optimizes during training and automatically applies learned patterns to determine residual impulse functions, improving measurement precision while maintaining ease of operation through automation rather than requiring complex user interactions.
Data Source
AI summary
Methods and systems are described herein for hemodynamic parameter estimation. In certain embodiments, a set of perfusion data is acquired for a region of interest using an imaging system. An artery signal is obtained from the set of perfusion data. A tissue signal is obtained from the set of perfusion data. The artery signal and the tissue signal are provided as inputs to one or more neural networks to determine one or more hemodynamic parameters for the region of interest. The one or more neural networks are trained using one or more synthetic data.