ArticleMagnetic resonance in medicine2019
Highly accelerated multishot echo planar imaging through synergistic machine learning and joint reconstruction.
Article in Magnetic resonance in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Three-dimensional high-isotropic-resolution MR fingerprinting optimized for 0.55 T.Magnetic resonance in medicine · 2025Article
- Leveraging Physics-Based Synthetic MR Images and Deep Transfer Learning for Artifact Reduction in Echo-Planar Imaging.AJNR. American journal of neuroradiology · 2025Article
- 3D distortion-free, reduced FOV diffusion-prepared gradient echo at 3 T.Magnetic resonance in medicine · 2025Article
- Blip-up blip-down circular EPI (BUDA-cEPI) for distortion-free dMRI with rapid unrolled deep learning reconstruction.Magnetic resonance imaging · 2025Article
- Self-supervised learning for denoising of multidimensional MRI data.Magnetic resonance in medicine · 2024Article
- Mesoscale Brain Mapping: Bridging Scales and Modalities in Neuroimaging - A Symposium Review.Neuroinformatics · 2024Review
- Clinical Evaluation of a 2-Minute Ultrafast Brain MR Protocol for Evaluation of Acute Pathology in the Emergency and Inpatient Settings.AJNR. American journal of neuroradiology · 2024Article
- Accelerated diffusion-weighted magnetic resonance imaging at 7 T: Joint reconstruction for shift-encoded navigator-based interleaved echo planar imaging (JETS-NAViEPI).Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Diffusion MRI data analysis assisted by deep learning synthesized anatomical images (DeepAnat).Medical image analysis · 2023Article
- 3D-EPI blip-up/down acquisition (BUDA) with CAIPI and joint Hankel structured low-rank reconstruction for rapid distortion-free high-resolutionMagnetic resonance in medicine · 2023Article
- Accelerated Diffusion-Weighted MR Image Reconstruction Using Deep Neural Networks.Journal of digital imaging · 2023Article
- Multi-band multi-shot diffusion MRI reconstruction with joint usage of structured low-rank constraints and explicit phase mapping.Magnetic resonance in medicine · 2023Article
- Deep Learning and Its Application to Function Approximation for MR in Medicine: An Overview.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022Review
- Emerging Techniques and Future Directions: Fast and Portable Magnetic Resonance Imaging.Magnetic resonance imaging clinics of North America · 2022Review
- Article
- Multi-band- and in-plane-accelerated diffusion MRI enabled by model-based deep learning in q-space and its extension to learning in the spherical harmonic domain.Magnetic resonance in medicine · 2022Article
- Learning a preconditioner to accelerate compressed sensing reconstructions in MRI.Magnetic resonance in medicine · 2022Article
- Scan-specific artifact reduction in k-space (SPARK) neural networks synergize with physics-based reconstruction to accelerate MRI.Magnetic resonance in medicine · 2022Article
- Distortion-Free Diffusion Imaging Using Self-Navigated Cartesian Echo-Planar Time Resolved Acquisition and Joint Magnitude and Phase Constrained Reconstruction.IEEE transactions on medical imaging · 2022Article
- Distortion-free, high-isotropic-resolution diffusion MRI with gSlider BUDA-EPI and multicoil dynamic BMagnetic resonance in medicine · 2021Article
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Authors and funding
11 authors.
Funding
Abstract
purposeTo introduce a combined machine learning (ML)- and physics-based image reconstruction framework that enables navigator-free, highly accelerated multishot echo planar imaging (msEPI) and demonstrate its application in high-resolution structural and diffusion imaging.
methodsSingle-shot EPI is an efficient encoding technique, but does not lend itself well to high-resolution imaging because of severe distortion artifacts and blurring. Although msEPI can mitigate these artifacts, high-quality msEPI has been elusive because of phase mismatch arising from shot-to-shot variations which preclude the combination of the multiple-shot data into a single image. We utilize deep learning to obtain an interim image with minimal artifacts, which permits estimation of image phase variations attributed to shot-to-shot changes. These variations are then included in a joint virtual coil sensitivity encoding (JVC-SENSE) reconstruction to utilize data from all shots and improve upon the ML solution.
resultsOur combined ML + physics approach enabled R
conclusionCombination of ML and JVC-SENSE enabled navigator-free msEPI at higher accelerations than previously possible while using fewer shots, with reduced vulnerability to poor generalizability and poor acceptance of end-to-end ML approaches.
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