ArticleMagnetic resonance in medicine2025
Learning-based motion artifact correction in the Z-spectral domain for chemical exchange saturation transfer MRI.
Article in Magnetic resonance in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Deep-learning saturation transfer magnetic resonance fingerprinting (ST-MRF) in patients with Parkinson's disease.NeuroImage · 2026Article
- Brain Tumor Imaging with Iopamidol CEST MRI: In Vivo Detection and Validation.Chemical & biomedical imaging · 2026Article
- Physics-informed optimization of saturation-transfer MRI protocols using non-differentiable Bloch models.Physics in medicine and biology · 2026Article
- Learning-based motion artifact correction in the Z-spectral domain for chemical exchange saturation transfer MRI.Magnetic resonance in medicine · 2025Article
- Innovative Value of Amide Proton Transfer Imaging in the Diagnosis and Treatment of Brain Gliomas: From Basic Principles to Clinical Applications.Technology in cancer research & treatmentReview
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
purposeTo develop and evaluate a physics-driven, saturation contrast-aware, deep-learning-based framework for motion artifact correction in CEST MRI.
methodsA neural network was designed to correct motion artifacts directly from a Z-spectrum frequency (Ω) domain rather than an image spatial domain. Motion artifacts were simulated by modeling 3D rigid-body motion and readout-related motion during k-space sampling. A saturation-contrast-specific loss function was added to preserve amide proton transfer (APT) contrast, as well as enforce image alignment between motion-corrected and ground-truth images. The proposed neural network was evaluated on simulation data and demonstrated in healthy volunteers and brain tumor patients.
resultsThe experimental results showed the effectiveness of motion artifact correction in the Z-spectrum frequency domain (MOCO
conclusionThe MOCO
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Registered trials
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