Related Experiment Video
Updated: Sep 28, 2025
Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph
Published on: February 21, 2025
Cardiac CT motion artifact grading via semi-automatic labeling and vessel tracking using synthetic image-augmented
Yongshun Xu1, Asif Sushmit2, Qing Lyu2
1Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, USA.
Insights
Motion artifacts in cardiac CT imaging degrade image quality. This study introduces semi-automatic and automatic methods for grading these artifacts, improving diagnostic accuracy and reducing the need for extensive clinical data.
Area of Science:
- Medical Imaging
- Cardiovascular Disease Evaluation
- Artificial Intelligence in Healthcare
Background:
- Cardiac CT is crucial for diagnosing cardiovascular diseases.
- Patient and organ motion during scanning causes artifacts, reducing image quality and diagnostic value.
- Objective grading of motion artifacts is essential for reliable cardiac CT analysis.
Purpose of the Study:
- To develop and validate effective methods for grading motion artifacts in cardiac CT angiography (CCTA).
- To implement semi-automatic labeling and vessel tracking for image quality assessment.
- To train a neural network for fully-automatic motion artifact grading, enhanced by synthetic data.
Main Methods:
- Semi-automatic CCTA image quality grading using vessel tracking algorithms.
- Development of a neural network model for fully-automatic motion artifact grading.
- Utilizing XCAT simulation tools to generate synthetic CT data for training.
Main Results:
- Semi-automatic grading scores closely align with expert readers (within 0.85 points on a 5-point scale).
- Synthetic data supplementation significantly improved the neural network's scoring performance.
- Mean square error for right coronary artery motion grading reduced by 36% with synthetic data.
Conclusions:
- Proposed semi-automatic and automatic methods effectively grade motion artifacts in cardiac CT.
- Synthetic data augmentation enhances the performance of automated grading systems.
- This approach can potentially reduce the volume of clinical data required for training AI models.
Abstract:
Cardiac CT provides critical information for the evaluation of cardiovascular diseases. However, involuntary patient motion and physiological movement of the organs during CT scanning cause motion blur in the reconstructed CT images, degrading both cardiac CT image quality and its diagnostic value. In this paper, we propose and demonstrate an effective and efficient method for CT coronary angiography image quality grading via semi-automatic labeling and vessel tracking. These algorithms produce scores that accord with those of expert readers to within 0.85 points on a 5-point scale. We also train a neural network model to perform fully-automatic motion artifact grading. We demonstrate, using XCAT simulation tools to generate realistic phantom CT data, that supplementing clinical data with synthetic data improves the scoring performance of this network. With respect to ground truth scores assigned by expert operators, the mean square error of grading motion of the right coronary artery is reduced by 36% by synthetic data supplementation. This demonstrates that augmentation of clinical training data with realistically synthesized images can potentially reduce the number of clinical studies needed to train the network.
More Related Videos
Related Concept Videos
01:28Imaging Studies for Cardiovascular System V: CT
01:25Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT