ArticleIEEE transactions on medical imaging2021
Dynamic Imaging Using a Deep Generative SToRM (Gen-SToRM) Model.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- VQ-SToRM: Vector-Quantized Smoothness Regularization on Manifolds for Free-Breathing, Ungated Real-Time Cardiac MRI Reconstruction.Bioengineering (Basel, Switzerland) · 2026Article
- Multi-dynamic deep image prior for cardiac MRI.Magnetic resonance in medicine · 2025Article
- An unsupervised method for MRI recovery: deep image prior with structured sparsity.Magma (New York, N.Y.) · 2025Article
- Multifrequency Time-Dependent Deep Image Prior for Real-Time Free-Breathing Cardiac Imaging.NMR in biomedicine · 2025Article
- Deep-prior ODEs augment fluorescence imaging with chemical sensors.Nature communications · 2024Article
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- DEEP IMAGE PRIOR WITH STRUCTURED SPARSITY (DISCUS) FOR DYNAMIC MRI RECONSTRUCTION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- HyperSLICE: HyperBand optimized spiral for low-latency interactive cardiac examination.Magnetic resonance in medicine · 2024Article
- A low-rank deep image prior reconstruction for free-breathing ungated spiral functional CMR at 0.55 T and 1.5 T.Magma (New York, N.Y.) · 2023Article
- DEEP FACTOR MODEL: A NOVEL APPROACH FOR MOTION COMPENSATED MULTI-DIMENSIONAL MRI.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- Joint CardiacBioengineering (Basel, Switzerland) · 2023Article
- Variational Manifold Learning From Incomplete Data: Application to Multislice Dynamic MRI.IEEE transactions on medical imaging · 2022Article
- Dynamic imaging using motion-compensated smoothness regularization on manifolds (MoCo-SToRM).Physics in medicine and biology · 2022 · on this mapArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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.
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