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August 14, 2024Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition0 citations

Performance Evaluation of Deep Learning-based Image Reconstruction for Head and Neck Imaging Protocol

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AKAmaresha Shridhar KonarJSJaemin ShinRPRamesh Paudyal

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Abstract

MRI has excellent extracranial soft-tissue contrast to detect tumors in the head and neck (HN) region. Technical challenges arise due to MRI related artifacts. In routine radiological practice, HN MR imaging protocols are optimized specifically to the subsites. We aimed to evaluate the performance of the HN imaging protocol that include qualitative T1w, T2w, and quantitative diffusion MRI powered by a novel deep learning (DL) based reconstruction (recon) using the ACR and QIBA diffusion phantoms. This phantom study showed that qualitative T1w and T2w images and multiple b-value DWI data powered with DL recon substantially improves the image quality.

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Cite This Study

Konar et al. (2024) studied this question.

synapsesocial.com/papers/68e5c52db6db64358755be16https://doi.org/10.58530/2023/4032
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Evaluation of deep learning-based reconstruction for qualitative and quantitative DW-MRI in head and neck cancers2024
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  3. 3Application of Deep Learning-based Reconstruction for Diffusion Kurtosis Imaging in Head and Neck Cancer2024
  4. 4The improvement of T2 weighted and diffusion weighted image quality in breast magnetic resonance imaging by deep learning reconstruction2025
  5. 5Quantitative Evaluation of Deep Learning Reconstruction of Diffusion-weighted MRI using a DWI Phantom2024