Simulating single-coil MRI from the responses of multiple coils
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Summary
This work converts the information-rich measurements of parallel and phased-array MRI into noisier data that a corresponding single-coil scanner could have taken, and replaces the responses from multiple receivers with a linear combination that emulates the response from only a single, aggregate receiver.
- Type
- article
- Published
- 2018-11-19
- Cited by
- 25
- References
- 12
- Access
- Open access
- OpenAlex
- https://openalex.org/W2900783926
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:53753657
Keywords
Interpolation (computer graphics), Convergence (economics), Context (archaeology), Representation (politics), Range (aeronautics)
References
- Die Orthogonalinvarianten quadratischer Formen von unendlichvielen Variabelen
- The NMR phased array
- An introduction to coil array design for parallel MRI
- Magnitude least squares optimization for parallel radio frequency excitation design demonstrated at 7 Tesla with eight channels
- ESPIRiT — An Eigenvalue Approach to Autocalibrating Parallel MRI: Where SENSE meets GRAPPA
- Decoding by linear programming
- Image quality assessment: from error visibility to structural similarity
- fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
- SciPy 1.0: fundamental algorithms for scientific computing in Python
- SciPy: Open Source Scientific Tools for Python
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- fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
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- Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction
- Adversarial Robust Training of Deep Learning MRI Reconstruction Models
- PUERT: Probabilistic Under-Sampling and Explicable Reconstruction Network for CS-MRI
- Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoising
- The Complex-valued PD-net for MRI reconstruction of knee images
- On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Disease
- CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction
- Dual states based reinforcement learning for fast MR scan and image reconstruction
- Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction
- Spatial Frequency Adaptive Spatiotemporal Learning for Accelerating CMR Reconstruction
- Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
- Jointly Modeling Inter- & Intra-Modality Dependencies for Multi-modal Learning
- Anatomical Token Uncertainty for Transformer-Guided Active MRI Acquisition
- Physics-Informed Deep Unrolled Network for Portable MR Image Reconstruction
- Learned Half-Quadratic Splitting Network for MR Image Reconstruction
- CDF-Net: Cross-Domain Fusion Network for Accelerated MRI Reconstruction
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