ArticleMagma (New York, N.Y.)2024
Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck.
Article in Magma (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 15 citations in OpenAlex.
- Enhanced image quality in head and neck MRI with PROPELLER and deep learning reconstruction.European journal of radiology open · 2026Article
- Deep Learning Reconstruction of Diffusion-weighted MRI Enables Shorter Examination Times While Maintaining Image Quality in Head and Neck Imaging.Clinical neuroradiology · 2026Article
- The role of diffusion-weighted MRI in biological image-guided radiation therapy: a roadmap.Physics in medicine and biology · 2026Review
- Assessment of deep learning reconstruction effects on detection and differentiation of liver metastasis from hepatic hemangioma in diffusion-weighted imaging.Japanese journal of radiology · 2026Article
- Quality of Head and Neck Diffusion-weighted MR Imaging Using a Combination of the Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction (PROPELLER) Sequence and Deep Learning Reconstruction.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Article
- Comparative evaluation of four reconstruction techniques for prostate T2-weighted MRI: Sensitivity encoding, compressed sensing, deep learning, and super-resolution.European journal of radiology open · 2025Article
- High Resolution TOF-MRA Using Compressed Sensing-based Deep Learning Image Reconstruction for the Visualization of Lenticulostriate Arteries: A Preliminary Study.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Dual-type deep learning-based image reconstruction for advanced denoising and super-resolution processing in head and neck T2-weighted imaging.Japanese journal of radiology · 2025Article
- Effects of Deep Learning-Based Reconstruction on the Quality of Accelerated Contrast-Enhanced Neck MRI.Korean journal of radiology · 2025Article
- Deep learning-based reconstruction for three-dimensional volumetric brain MRI: a qualitative and quantitative assessment.BMC medical imaging · 2025Article
- Advancing clinical MRI exams with artificial intelligence: Japan's contributions and future prospects.Japanese journal of radiology · 2025Review
- Enhancing the image quality of prostate diffusion-weighted imaging in patients with prostate cancer through model-based deep learning reconstruction.European journal of radiology open · 2024Article
- Evaluation of 3D T1-weighted spoiled gradient echo MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain.Neuroradiology · 2024Article
- Evaluation of T2W FLAIR MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain.Pediatric radiology · 2024Article
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Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo investigate the utility of deep learning (DL)-based image reconstruction using a model-based approach in head and neck diffusion-weighted imaging (DWI). MATERIALS AND
methodsWe retrospectively analyzed the cases of 41 patients who underwent head/neck DWI. The DWI in 25 patients demonstrated an untreated lesion. We performed qualitative and quantitative assessments in the DWI analyses with both deep learning (DL)- and conventional parallel imaging (PI)-based reconstructions. For the qualitative assessment, we visually evaluated the overall image quality, soft tissue conspicuity, degree of artifact(s), and lesion conspicuity based on a five-point system. In the quantitative assessment, we measured the signal-to-noise ratio (SNR) of the bilateral parotid glands, submandibular gland, the posterior muscle, and the lesion. We then calculated the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle.
resultsSignificant differences were observed in the qualitative analysis between the DWI with PI-based and DL-based reconstructions for all of the evaluation items (p < 0.001). In the quantitative analysis, significant differences in the SNR and CNR between the DWI with PI-based and DL-based reconstructions were observed for all of the evaluation items (p = 0.002 ~ p < 0.001). DISCUSSION: DL-based image reconstruction with the model-based technique effectively provided sufficient image quality in head/neck DWI.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.