# Deep Learning Assisted Outer Volume Removal for Highly-Accelerated Real-Time Dynamic MRI

arXiv: https://arxiv.org/abs/2505.00643 · HTML: https://mlsanity.com/paper/2505.00643/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 75 references that are in the map.
- First posted on arXiv 1 May 2025.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [MoDL: Model Based Deep Learning Architecture for Inverse Problems](https://mlsanity.com/paper/1712.02862/) (2017)
- [Learning a Variational Network for Reconstruction of Accelerated MRI Data](https://mlsanity.com/paper/1704.00447/) (2017)
- [A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction](https://mlsanity.com/paper/1704.02422/) (2017)
- [Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging](https://mlsanity.com/paper/2203.12215/) (2022)
- [Real-time Cardiovascular MR with Spatio-temporal Artifact Suppression using Deep Learning - Proof of Concept in Congenital Heart Disease](https://mlsanity.com/paper/1803.05192/) (2018)
- [Self-Supervised Physics-Based Deep Learning MRI Reconstruction Without Fully-Sampled Data](https://mlsanity.com/paper/1910.09116/) (2019)
- [Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI with Limited Data](https://mlsanity.com/paper/1904.01574/) (2019)
- [Multi-Mask Self-Supervised Learning for Physics-Guided Neural Networks in Highly Accelerated MRI](https://mlsanity.com/paper/2008.06029/) (2020)
- [20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction](https://mlsanity.com/paper/2105.05827/) (2021)
- [Accelerated respiratory-resolved 4D-MRI with separable spatio-temporal neural networks](https://mlsanity.com/paper/2211.05678/) (2022)
- [Direct Cardiac Segmentation from Undersampled K-space Using Transformers](https://mlsanity.com/paper/2406.00192/) (2024)
- [Off-resonance artifact correction for magnetic resonance imaging: a review](https://mlsanity.com/paper/2205.01028/) (2022)
- [Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI](https://mlsanity.com/paper/2402.15939/) (2024)

## Built on by (most cited first, 1 of 1)

- [Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors](https://mlsanity.com/paper/2608.18036/) (2026)

## Abstract (arXiv)

Real-time (RT) dynamic MRI plays a vital role in capturing rapid physiological processes, offering unique insights into organ motion and function. Among these applications, RT cine MRI is particularly important for functional assessment of the heart with high temporal resolution. RT imaging enables free-breathing, ungated imaging of cardiac motion, making it a crucial alternative for patients who cannot tolerate conventional breath-hold, ECG-gated acquisitions. However, achieving high acceleration rates in RT cine MRI is challenging due to aliasing artifacts from extra-cardiac tissues, particularly at high undersampling factors. In this study, we propose a novel outer volume removal (OVR) method to address this challenge by eliminating aliasing contributions from non-cardiac regions in a post-processing framework. Our approach estimates the outer volume signal for each timeframe using composite temporal images from time-interleaved undersampling patterns, which inherently contain pseudo-periodic ghosting artifacts. A deep learning (DL) model is trained to identify and remove these artifacts, producing a clean outer volume estimate that is subsequently subtracted from the corresponding k-space data. The final reconstruction is performed with a physics-driven DL (PD-DL) method trained using an OVR-specific loss function to restore high spatio-temporal resolution images. Experimental results show that the proposed method at high accelerations achieves image quality that is visually comparable to clinical baseline images, while outperforming conventional reconstruction techniques, both qualitatively and quantitatively. The proposed approach provides a practical and effective solution for artifact reduction in RT cine MRI without requiring acquisition modifications, offering a pathway to higher acceleration rates while preserving diagnostic quality.