# Dynamic imaging using a deep generative SToRM (Gen-SToRM) model

arXiv: https://arxiv.org/abs/2102.00034 · HTML: https://mlsanity.com/paper/2102.00034/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2021.

## Built on by (most cited first, 17 of 17)

- [Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction](https://mlsanity.com/paper/2301.00127/) (2022)
- [Implicit Neural Networks with Fourier-Feature Inputs for Free-breathing Cardiac MRI Reconstruction](https://mlsanity.com/paper/2305.06822/) (2023)
- [Unsupervised reconstruction of accelerated cardiac cine MRI using Neural Fields](https://mlsanity.com/paper/2307.14363/) (2023)
- [Dynamic imaging using Motion-Compensated SmooThness Regularization on Manifolds (MoCo-SToRM)](https://mlsanity.com/paper/2112.03380/) (2021)
- [Benchmarking Self-Supervised Learning Methods for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2502.14009/) (2025)
- [HyperSLICE: HyperBand optimized Spiral for Low-latency Interactive Cardiac Examination](https://mlsanity.com/paper/2302.02688/) (2023)
- [Multilinear Kernel Regression and Imputation via Manifold Learning](https://mlsanity.com/paper/2402.03648/) (2024)
- [Multi-dynamic deep image prior for cardiac MRI](https://mlsanity.com/paper/2412.04639/) (2024)
- [RED-PSM: Regularization by Denoising of Factorized Low Rank Models for Dynamic Imaging](https://mlsanity.com/paper/2304.03483/) (2023)
- [An unsupervised method for MRI recovery: Deep image prior with structured sparsity](https://mlsanity.com/paper/2501.01482/) (2025)
- [RSR-NF: Neural Field Regularization by Static Restoration Priors for Dynamic Imaging](https://mlsanity.com/paper/2503.10015/) (2025)
- [Dynamic imaging using motion-compensated smoothness regularization on manifolds (MoCo-SToRM)](https://mlsanity.com/paper/2111.10887/) (2021)
- [Joint alignment and reconstruction of multislice dynamic MRI using variational manifold learning](https://mlsanity.com/paper/2111.10889/) (2021)
- [Deep Image Prior using Stein's Unbiased Risk Estimator: SURE-DIP](https://mlsanity.com/paper/2111.10892/) (2021)
- [Deep Factor Model: A Novel Approach for Motion Compensated Multi-Dimensional MRI](https://mlsanity.com/paper/2304.00102/) (2023)
- [Multi-Linear Kernel Regression and Imputation in Data Manifolds](https://mlsanity.com/paper/2304.03041/) (2023)
- [Motion-compensated cardiac MRI using low-rank diffeomorphic flow (DMoCo)](https://mlsanity.com/paper/2505.03149/) (2025)

## Abstract (arXiv)

We introduce a generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The model assumes that the images in the dataset are non-linear mappings of low-dimensional latent vectors. We use the deep convolutional neural network (CNN) to represent the non-linear transformation. The parameters of the generator as well as the low-dimensional latent vectors are jointly estimated only from the undersampled measurements. This approach is different from traditional CNN approaches that require extensive fully sampled training data. We penalize the norm of the gradients of the non-linear mapping to constrain the manifold to be smooth, while temporal gradients of the latent vectors are penalized to obtain a smoothly varying time-series. The proposed scheme brings in the spatial regularization provided by the convolutional network. The main benefit of the proposed scheme is the improvement in image quality and the orders-of-magnitude reduction in memory demand compared to traditional manifold models. To minimize the computational complexity of the algorithm, we introduce an efficient progressive training-in-time approach and an approximate cost function. These approaches speed up the image reconstructions and offers better reconstruction performance.