ArticleActa radiologica open2021
Reconstruction of cardiovascular black-blood T2-weighted image by deep learning algorithm: A comparison with intensity filter.
Article in Acta radiologica open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 17 citations in OpenAlex.
- Impact of Deep Learning-Based Reconstruction on the Accuracy and Precision of Cardiac Tissue Characterization.Diagnostics (Basel, Switzerland) · 2026Article
- Effectiveness of deep learning-based reconstruction for improvement of image quality and liver tumor detectability in the hepatobiliary phase of gadoxetic acid-enhanced magnetic resonance imaging.Abdominal radiology (New York) · 2024Article
- Evaluation of 3D T1-weighted spoiled gradient echo MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain.Neuroradiology · 2024Article
- Validation and Feasibility of Ultrafast Cervical Spine MRI Using a Deep Learning-Assisted 3D Iterative Image Enhancement System.Journal of multidisciplinary healthcare · 2024Article
- Deep learning-based magnetic resonance imaging reconstruction for improving the image quality of reduced-field-of-view diffusion-weighted imaging of the pancreas.World journal of radiology · 2023Article
- Reliability of respiratory-gated real-time two-dimensional cine incorporating deep learning reconstruction for the assessment of ventricular function in an adult population.The international journal of cardiovascular imaging · 2023Article
- Ultrafast deep learning super-resolution single-shot T2-weighted imaging for robust edema visualization in cardiovascular magnetic resonance.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic ResonanceArticle
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDeep learning-based methods have been used to denoise magnetic resonance imaging. PURPOSE: The purpose of this study was to evaluate a deep learning reconstruction (DL Recon) in cardiovascular black-blood T2-weighted images and compare with intensity filtered images. MATERIAL AND
methodsForty-five DL Recon images were compared with intensity filtered and the original images. For quantitative image analysis, the signal to noise ratio (SNR) of the septum, contrast ratio (CR) of the septum to lumen, and sharpness of the endocardial border were calculated in each image. For qualitative image quality assessment, a 4-point subjective scale was assigned to each image (1 = poor, 2 = fair, 3 = good, 4 = excellent).
resultsThe SNR and CR were significantly higher in the DL Recon images than in the intensity filtered and the original images (
conclusionsDL Recon reduced image noise while improving image contrast and sharpness in the cardiovascular black-blood T2-weight sequence.
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Registered trials
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