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SELFIE: Self-Supervised Learning for Fast Dynamic Golden-Angle Radial MRI.

Melanie Schellenberg¹, Anthony Mekhanik¹, Victor Murray¹

¹Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

NMR in Biomedicine

|May 21, 2026

Summary

This summary is machine-generated.

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A new self-supervised learning method called SELFIE reconstructs dynamic MRI scans faster and with comparable quality to existing techniques. This approach for golden-angle radial MRI eliminates the need for reference scans, improving efficiency.

Area of Science:

Medical Imaging
Artificial Intelligence
Magnetic Resonance Imaging

Background:

Accelerated golden-angle radial MRI is crucial for dynamic imaging.
Compressed sensing (CS) and supervised deep learning (DL) have limitations in reconstruction speed and reference data requirements.

Purpose of the Study:

To introduce SELFIE (Self-supervised Learning for Fast dynamic golden-Angle radial MRI), a novel self-supervised reconstruction technique.
To enable fast and high-quality dynamic MRI reconstruction without requiring fully-sampled or CS training references.

Main Methods:

SELFIE utilizes slice-by-slice image-domain processing, avoiding k-space computations.
Self-supervision is achieved through variable temporal resolution and temporal sparsity inherent in golden-angle radial sampling.

Keywords:

dynamic MRI golden‐angle radial MRI reconstruction self‐supervised learning

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The method was validated on dynamic contrast-enhanced (DCE)-MRI and motion-resolved abdominal MRI datasets.

Main Results:

SELFIE demonstrated image quality and dynamic fidelity comparable to CS and supervised DL.
Reconstruction time was significantly reduced, with 3D time series reconstructed in seconds.
Reader studies showed SELFIE performed comparably to supervised DL and better than CS.

Conclusions:

SELFIE offers a fast, reference-free reconstruction framework for dynamic golden-angle radial MRI.
It presents a viable alternative to existing CS and supervised DL methods.
The technique achieves competitive performance and substantially improved reconstruction speed.