# Deblurring for Spiral Real-Time MRI Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2001.09427 · HTML: https://mlsanity.com/paper/2001.09427/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2020.

## Built on by (most cited first, 6 of 6)

- [Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance](https://mlsanity.com/paper/2203.11178/) (2022)
- [MRI Super-Resolution with Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2511.16854/) (2025)
- [Joint MR sequence optimization beats pure neural network approaches for spin-echo MRI super-resolution](https://mlsanity.com/paper/2305.07524/) (2023)
- [Attention-gated convolutional neural networks for off-resonance correction of spiral real-time MRI](https://mlsanity.com/paper/2102.07271/) (2021)
- [Multi-Weight Respecification of Scan-specific Learning for Parallel Imaging](https://mlsanity.com/paper/2204.01979/) (2022)
- [Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI](https://mlsanity.com/paper/2311.13177/) (2023)

## Abstract (arXiv)

Spiral acquisitions are preferred in real-time MRI because of their time efficiency. A fundamental limitation of spirals is image blurring due to off-resonance, which degrades image quality significantly at air-tissue boundaries. Here, we demonstrate a simple CNN-based deblurring method for spiral real-time MRI of human speech production. We show the CNN-based deblurring is capable of restoring blurred vocal tract tissue boundaries, without a need for exam-specific field maps. Deblurring performance is superior to a current auto-calibrated method, and slightly inferior to ideal reconstruction with perfect knowledge of the field maps.