ArticleMagnetic resonance in medicine2022
Scan-specific artifact reduction in k-space (SPARK) neural networks synergize with physics-based reconstruction to accelerate MRI.
Article in Magnetic resonance in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review and Identification of the Challenges of Deep Learning Techniques for Undersampled Magnetic Resonance Image Reconstruction.Sensors (Basel, Switzerland) · 2024Pooled it
- Accelerating Stroke MRI With Diffusion Probabilistic Models Through Large-Scale Pre-Training and Target-Specific Fine-Tuning.Magnetic resonance in medicine · 2026Article
- Bilevel Optimized Implicit Neural Representation for Scan-Specific Accelerated MRI Reconstruction.IEEE transactions on medical imaging · 2026Article
- Radon-Guided Wavelet-Domain Attention U-Net for Periodic Artifact Suppression in Brain MRI.Journal of imaging · 2026Article
- Insights on Scan-Specific Deep-Learning Strategies for Brain MRI Parallel Imaging Reconstruction.NMR in biomedicine · 2025Article
- Scan-Specific Self-Supervised Bayesian Deep Non-Linear Inversion for Undersampled MRI Reconstruction.IEEE transactions on medical imaging · 2024Article
- Enhanced Partial Fourier MRI With Zero-Shot Deep Untrained Priors.IEEE access : practical innovations, open solutions · 2024Article
- A Hybrid Residual Attention Convolutional Neural Network for Compressed Sensing Magnetic Resonance Image Reconstruction.Diagnostics (Basel, Switzerland) · 2023Article
- De-Aliasing and Accelerated Sparse Magnetic Resonance Image Reconstruction Using Fully Dense CNN with Attention Gates.Bioengineering (Basel, Switzerland) · 2022Article
- Wave-Encoded Model-Based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction.Bioengineering (Basel, Switzerland) · 2022Article
- Residual RAKI: A hybrid linear and non-linear approach for scan-specific k-space deep learning.NeuroImage · 2022Article
- A review and experimental evaluation of deep learning methods for MRI reconstruction.The journal of machine learning for biomedical imaging · 2022Article
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Authors and funding
6 authors.
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Abstract
purposeTo develop a scan-specific model that estimates and corrects k-space errors made when reconstructing accelerated MRI data.
methodsScan-specific artifact reduction in k-space (SPARK) trains a convolutional-neural-network to estimate and correct k-space errors made by an input reconstruction technique by back-propagating from the mean-squared-error loss between an auto-calibration signal (ACS) and the input technique's reconstructed ACS. First, SPARK is applied to generalized autocalibrating partially parallel acquisitions (GRAPPA) and demonstrates improved robustness over other scan-specific models, such as robust artificial-neural-networks for k-space interpolation (RAKI) and residual-RAKI. Subsequent experiments demonstrate that SPARK synergizes with residual-RAKI to improve reconstruction performance. SPARK also improves reconstruction quality when applied to advanced acquisition and reconstruction techniques like 2D virtual coil (VC-) GRAPPA, 2D LORAKS, 3D GRAPPA without an integrated ACS region, and 2D/3D wave-encoded imaging.
resultsSPARK yields SSIM improvement and 1.5 - 2× root mean squared error (RMSE) reduction when applied to GRAPPA and improves robustness to ACS size for various acceleration rates in comparison to other scan-specific techniques. When applied to advanced reconstruction techniques such as residual-RAKI, 2D VC-GRAPPA and LORAKS, SPARK achieves up to 20% RMSE improvement. SPARK with 3D GRAPPA also improves RMSE performance by ~2×, SSIM performance, and perceived image quality without a fully sampled ACS region. Finally, SPARK synergizes with non-Cartesian, 2D and 3D wave-encoding imaging by reducing RMSE between 20% and 25% and providing qualitative improvements.
conclusionSPARK synergizes with physics-based acquisition and reconstruction techniques to improve accelerated MRI by training scan-specific models to estimate and correct reconstruction errors in k-space.
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