Training deep learning based dynamic MR image reconstruction using synthetic fractals

TL;DR

This study demonstrates that deep learning models trained on synthetic fractal data can effectively reconstruct real-time cardiac MRI images, offering a privacy-preserving alternative to using actual clinical datasets.

Contribution

The paper introduces a novel approach of using synthetic fractal data for training deep learning models in dynamic MRI reconstruction, reducing reliance on clinical data.

Findings

Fractal-trained models perform comparably to models trained on real cardiac MRI data.

Both fractal-trained and cardiac-trained models outperform traditional methods like compressed sensing.

Ventricular volume and function measurements from fractal-trained models are clinically acceptable.

Abstract

Purpose: To investigate whether synthetically generated fractal data can be used to train deep learning (DL) models for dynamic MRI reconstruction, thereby avoiding the privacy, licensing, and availability limitations associated with cardiac MR training datasets. Methods: A training dataset was generated using quaternion Julia fractals to produce 2D+time images. Multi-coil MRI acquisition was simulated to generate paired fully sampled and radially undersampled k-space data. A 3D UNet deep artefact suppression model was trained using these fractal data (F-DL) and compared with an identical model trained on cardiac MRI data (CMR-DL). Both models were evaluated on prospectively acquired radial real-time cardiac MRI from 10 patients. Reconstructions were compared against compressed sensing(CS) and low-rank deep image prior (LR-DIP). All reconstrctuions were ranked for image quality, while ventricular volumes and ejection fraction were compared with reference breath-hold cine MRI. Results: There was no significant difference in qualitative ranking between F-DL and CMR-DL (p=0.9), while both outperformed CS and LR-DIP (p<0.001). Ventricular volumes and function derived from F-DL were similar to CMR-DL, showing no significant bias and accptable limits of agreement compared to reference cine imaging. However, LR-DIP had a signifcant bias (p=0.016) and wider lmits of agreement. Conclusion: DL models trained using synthetic fractal data can reconstruct real-time cardiac MRI with image quality and clinical measurements comparable to models trained on true cardiac MRI data. Fractal training data provide an open, scalable alternative to clinical datasets and may enable development of more generalisable DL reconstruction models for dynamic MRI.