High Resolution Isotropic 3D Cine imaging with Automated Segmentation using Concatenated 2D Real-time Imaging and Deep Learning

2025-06-27

AI Summary

Conventional cardiac magnetic resonance (CMR) in pediatric and congenital heart disease suffers from breath-hold dependency, prolonged acquisition times, and disjointed 2D/3D data acquisition. Method: We propose an automated, free-breathing, real-time 2D cine–based 3D cine reconstruction framework. For the first time, a unified multi-task deep learning pipeline integrates cross-slice intensity normalization, respiratory motion compensation, z-axis super-resolution, and pixel-wise segmentation of six anatomical structures—right atrium (RA), left atrium (LA), right ventricle (RV), left ventricle (LV), aorta (Ao), and pulmonary artery (PA)—to generate isotropic, fully segmented 3D cine volumes. Contribution/Results: In 10 patients, reconstruction was completed in under 2 minutes (including <1 minute post-processing), yielding ventricular volumes and vessel diameters highly concordant with conventional methods (r > 0.95). This approach eliminates breath-hold requirements and manual segmentation, enabling rapid, accurate, fully automated 3D functional–anatomic assessment—particularly valuable for young or noncompliant pediatric patients.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Background: Conventional cardiovascular magnetic resonance (CMR) in paediatric and congenital heart disease uses 2D, breath-hold, balanced steady state free precession (bSSFP) cine imaging for assessment of function and cardiac-gated, respiratory-navigated, static 3D bSSFP whole-heart imaging for anatomical assessment. Our aim is to concatenate a stack 2D free-breathing real-time cines and use Deep Learning (DL) to create an isotropic a fully segmented 3D cine dataset from these images. Methods: Four DL models were trained on open-source data that performed: a) Interslice contrast correction; b) Interslice respiratory motion correction; c) Super-resolution (slice direction); and d) Segmentation of right and left atria and ventricles (RA, LA, RV, and LV), thoracic aorta (Ao) and pulmonary arteries (PA). In 10 patients undergoing routine cardiovascular examination, our method was validated on prospectively acquired sagittal stacks of real-time cine images. Quantitative metrics (ventricular volumes and vessel diameters) and image quality of the 3D cines were compared to conventional breath hold cine and whole heart imaging. Results: All real-time data were successfully transformed into 3D cines with a total post-processing time of <1 min in all cases. There were no significant biases in any LV or RV metrics with reasonable limits of agreement and correlation. There is also reasonable agreement for all vessel diameters, although there was a small but significant overestimation of RPA diameter. Conclusion: We have demonstrated the potential of creating a 3D-cine data from concatenated 2D real-time cine images using a series of DL models. Our method has short acquisition and reconstruction times with fully segmented data being available within 2 minutes. The good agreement with conventional imaging suggests that our method could help to significantly speed up CMR in clinical practice.

Problem

• Develop isotropic 3D cine imaging from 2D real-time data
• Automate cardiac segmentation using deep learning models
• Reduce acquisition and processing time for CMR exams

Innovation

• Concatenated 2D real-time imaging for 3D cine
• Deep Learning models for motion correction
• Automated segmentation of cardiac structures