# IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representation

arXiv: https://arxiv.org/abs/2106.15097 · HTML: https://mlsanity.com/paper/2106.15097/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 29 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network](https://mlsanity.com/paper/1803.01417/) (2018)
- [Multi-Contrast Super-Resolution MRI Through a Progressive Network](https://mlsanity.com/paper/1908.01612/) (2019)
- [MRI Super-Resolution with Ensemble Learning and Complementary Priors](https://mlsanity.com/paper/1907.03063/) (2019)
- [MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better](https://mlsanity.com/paper/2003.01217/) (2020)

## Built on by (most cited first, 30 of 42)

- [Neural Fields in Visual Computing and Beyond](https://mlsanity.com/paper/2111.11426/) (2021)
- [NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2209.14540/) (2022)
- [Implicit Neural Representation in Medical Imaging: A Comparative Survey](https://mlsanity.com/paper/2307.16142/) (2023)
- [An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation](https://mlsanity.com/paper/2110.14476/) (2021)
- [Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey](https://mlsanity.com/paper/2411.03688/) (2024)
- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations](https://mlsanity.com/paper/2303.15065/) (2023)
- [Diffeomorphic Image Registration with Neural Velocity Field](https://mlsanity.com/paper/2202.12498/) (2022)
- [Medical Image Registration via Neural Fields](https://mlsanity.com/paper/2206.03111/) (2022)
- [Implicit Neural Representation Learning for Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2112.10541/) (2021)
- [Implicit Neural Networks with Fourier-Feature Inputs for Free-breathing Cardiac MRI Reconstruction](https://mlsanity.com/paper/2305.06822/) (2023)
- [Spatial Attention-based Implicit Neural Representation for Arbitrary Reduction of MRI Slice Spacing](https://mlsanity.com/paper/2205.11346/) (2022)
- [Self-supervised arbitrary scale super-resolution framework for anisotropic MRI](https://mlsanity.com/paper/2305.01360/) (2023)
- [Fast and Explicit: Slice-to-Volume Reconstruction via 3D Gaussian Primitives with Analytic Point Spread Function Modeling](https://mlsanity.com/paper/2512.11624/) (2025)
- [Implicit U-Net for volumetric medical image segmentation](https://mlsanity.com/paper/2206.15217/) (2022)
- [Blaze3DM: Marry Triplane Representation with Diffusion for 3D Medical Inverse Problem Solving](https://mlsanity.com/paper/2405.15241/) (2024)
- [Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representation](https://mlsanity.com/paper/2409.16921/) (2024)
- [Meta-learning Slice-to-Volume Reconstruction in Fetal Brain MRI using Implicit Neural Representations](https://mlsanity.com/paper/2505.09565/) (2025)
- [Continuous Representation Methods, Theories, and Applications: An Overview and Perspectives](https://mlsanity.com/paper/2505.15222/) (2025)
- [Neural Computed Tomography](https://mlsanity.com/paper/2201.06574/) (2022)
- [K-Space Transformer for Undersampled MRI Reconstruction](https://mlsanity.com/paper/2206.06947/) (2022)
- [UlRe-NeRF: 3D Ultrasound Imaging through Neural Rendering with Ultrasound Reflection Direction Parameterization](https://mlsanity.com/paper/2408.00860/) (2024)
- [Continuous longitudinal fetus brain atlas construction via implicit neural representation](https://mlsanity.com/paper/2209.06413/) (2022)
- [Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks](https://mlsanity.com/paper/2210.08676/) (2022)
- [ST-NeRP: Spatial-Temporal Neural Representation Learning with Prior Embedding for Patient-specific Imaging Study](https://mlsanity.com/paper/2410.19283/) (2024)
- [Exploring Kernel Transformations for Implicit Neural Representations](https://mlsanity.com/paper/2504.04728/) (2025)
- [Enhance the Image: Super Resolution using Artificial Intelligence in MRI](https://mlsanity.com/paper/2406.13625/) (2024)
- [SeCo-INR: Semantically Conditioned Implicit Neural Representations for Improved Medical Image Super-Resolution](https://mlsanity.com/paper/2409.01013/) (2024)
- [Learning Two-factor Representation for Magnetic Resonance Image Super-resolution](https://mlsanity.com/paper/2409.09731/) (2024)
- [QFGN: A Quantum Approach to High-Fidelity Implicit Neural Representations](https://mlsanity.com/paper/2504.19053/) (2025)

## Abstract (arXiv)

For collecting high-quality high-resolution (HR) MR image, we propose a novel image reconstruction network named IREM, which is trained on multiple low-resolution (LR) MR images and achieve an arbitrary up-sampling rate for HR image reconstruction. In this work, we suppose the desired HR image as an implicit continuous function of the 3D image spatial coordinate and the thick-slice LR images as several sparse discrete samplings of this function. Then the super-resolution (SR) task is to learn the continuous volumetric function from a limited observations using an fully-connected neural network combined with Fourier feature positional encoding. By simply minimizing the error between the network prediction and the acquired LR image intensity across each imaging plane, IREM is trained to represent a continuous model of the observed tissue anatomy. Experimental results indicate that IREM succeeds in representing high frequency image feature, and in real scene data collection, IREM reduces scan time and achieves high-quality high-resolution MR imaging in terms of SNR and local image detail.