ArticleMagnetic resonance in medicine2023
Motion compensated self supervised deep learning for highly accelerated 3D ultrashort Echo time pulmonary MRI.
Article in Magnetic resonance in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Respiratory Motion Management in Abdominal MRI: Revisiting the Gap Between Technical Advances and Clinical Translation.Magnetic resonance in medicine · 2026Review
- 3D Cartesian ultrashort double half-echo imaging of the lung parenchyma for water density imaging.Magnetic resonance in medicine · 2026Article
- Self-supervised learning for MRI reconstruction: a review and new perspective.Magma (New York, N.Y.) · 2025Review
- Repeatability-encouraging self-supervised learning reconstruction for quantitative MRI.Magnetic resonance in medicine · 2025Article
- Techniques for Respiratory Motion-Resolved Magnetic Resonance Imaging of the Chest in Children with Spinal or Chest Deformities: A Comprehensive Overview.Journal of clinical medicine · 2025Review
- Spectrally selective and interleaved water imaging and fat imaging (siWIFI).Magnetic resonance in medicine · 2025Article
- A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies.Biomedical engineering letters · 2024Review
- Stop moving: MR motion correction as an opportunity for artificial intelligence.Magma (New York, N.Y.) · 2024Review
- Comparison of weighting algorithms to mitigate respiratory motion in free-breathing neonatal pulmonary radial UTE-MRI.Biomedical physics & engineering express · 2024Article
- Article
- Machine Learning for the Design and the Simulation of Radiofrequency Magnetic Resonance Coils: Literature Review, Challenges, and Perspectives.Sensors (Basel, Switzerland) · 2024Review
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Authors and funding
2 authors.
Funding
Abstract
purposeTo investigate motion compensated, self-supervised, model based deep learning (MBDL) as a method to reconstruct free breathing, 3D pulmonary UTE acquisitions. THEORY AND
methodsA self-supervised eXtra dimension MBDL architecture (XD-MBDL) was developed that combined respiratory states to reconstruct a single high-quality 3D image. Non-rigid motion fields were incorporated into this architecture by estimating motion fields from a lower resolution motion resolved (XD-GRASP) reconstruction. Motion compensated XD-MBDL was evaluated on lung UTE datasets with and without contrast and compared to constrained reconstructions and variants of self-supervised MBDL that do not account for dynamic respiratory states or leverage motion correction.
resultsImages reconstructed using XD-MBDL demonstrate improved image quality as measured by apparent SNR (aSNR), contrast to noise ratio (CNR), and visual assessment relative to self-supervised MBDL approaches that do not account for dynamic respiratory states, XD-GRASP and a recently proposed motion compensated iterative reconstruction strategy (iMoCo). Additionally, XD-MBDL reduced reconstruction time relative to both XD-GRASP and iMoCo.
conclusionA method was developed to allow self-supervised MBDL to combine multiple respiratory states to reconstruct a single image. This method was combined with graphics processing unit (GPU)-based image registration to further improve reconstruction quality. This approach showed promising results reconstructing a user-selected respiratory phase from free breathing 3D pulmonary UTE acquisitions.
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Registered trials
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