CardioSynth: Parameter-driven cardiac MRI generation via oriented bounding boxes
Year: 2026
Type: article
Abstract: BACKGROUND AND OBJECTIVE: Cardiac magnetic resonance imaging provides detailed anatomical information but is costly and not feasible for routine monitoring. Accurate control of cardiac substructure areas is essential for studying development, adaptation, and disease progression. This work introduces a framework for synthetic cardiac imaging that enables parameter-driven area modifications using oriented bounding box representations while preserving anatomical plausibility. METHODS: We propose a three-stage framework that integrates: (1) Encoding each cardiac substructure as an oriented bounding box to enable structured representation and easier shape manipulation. (2) A progressive label modification algorithm to apply parameter-driven area changes while maintaining anatomical consistency. (3) A bounding-box-to-segmentation model for reconstructing detailed masks and (4) A diffusion-based segmentation-to-image synthesis model for generating realistic cardiac magnetic resonance images. The oriented bounding box encoding serves as the foundation for controlled anatomical transformations, while the subsequent models ensure structural plausibility and image fidelity. RESULTS: Experiments show that oriented bounding box encoding enables more accurate control of cardiac substructure area modifications than conventional approaches. Both increments and decrements exhibit a systematic deviation of about 5%, which is effectively corrected by applying a 5% calibrated input, reducing mean errors to within ±1%. Generated images remain anatomically plausible and structurally consistent. CONCLUSIONS: The proposed framework enables parameter-driven cardiac MRI synthesis with precise substructure area control. By combining oriented bounding box encoding, progressive modification, and diffusion modeling, it achieves anatomically consistent results while reducing reliance on repeated scans, supporting applications in longitudinal monitoring and progression studies.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,350
OpenAlex ID: W7131108323