ArticleJapanese journal of radiology2022
Impact of deep learning reconstruction on intracranial 1.5 T magnetic resonance angiography.
Article in Japanese journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed, 47 citations in OpenAlex.
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- Super-resolution deep learning reconstruction improves brain MRI quality and detection of metastases.Japanese journal of radiology · 2026Article
- Short-TR Acquisition Time-of-flight MR Angiography with Deep Learning Reconstruction: Technical Feasibility and Initial Clinical Evaluation in Moyamoya Disease.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Article
- Deep learning-enhanced zero echo time silent brain magnetic resonance imaging in infants without sedation.Pediatric radiology · 2026Article
- Super-Resolution Deep Learning Reconstruction for T2*-Weighted Images: Improvement in Microbleed Lesion Detection and Image Quality.Journal of imaging informatics in medicine · 2026Article
- Ultra-fast whole-brain T2-weighted imaging in 7 seconds using dual-type deep learning reconstruction with single-shot acquisition: clinical feasibility and comparison with conventional methods.Japanese journal of radiology · 2026Article
- Efficacy of a large language model in classifying branch-duct intraductal papillary mucinous neoplasms.Abdominal radiology (New York) · 2026Article
- Evaluation of AI diagnostic systems for breast ultrasound: comparative analysis with radiologists and the effect of AI assistance.Japanese journal of radiology · 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
- Accelerated intracranial time-of-flight MR angiography with image-based deep learning image enhancement reduces scan times and improves image quality at 3-T and 1.5-T.Neuroradiology · 2025Article
- Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Deep learning-based reconstruction for three-dimensional volumetric brain MRI: a qualitative and quantitative assessment.BMC medical imaging · 2025Article
- Robust resolution improvement of 3D UTE-MR angiogram of normal vasculatures using super-resolution convolutional neural network.Scientific reports · 2025Article
- Advancing clinical MRI exams with artificial intelligence: Japan's contributions and future prospects.Japanese journal of radiology · 2025Review
- Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature.Japanese journal of radiology · 2025Review
- Iterative Motion Correction Technique with Deep Learning Reconstruction for Brain MRI: A Volunteer and Patient Study.Journal of imaging informatics in medicine · 2024Article
- Recent trends in AI applications for pelvic MRI: a comprehensive review.La Radiologia medica · 2024Review
- Verification of image quality improvement by deep learning reconstruction to 1.5 T MRI in T2-weighted images of the prostate gland.Radiological physics and technology · 2024Article
- The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI.Japanese journal of radiology · 2024Review
- Deep Learning-Based High-Resolution Magnetic Resonance Angiography (MRA) Generation Model for 4D Time-Resolved Angiography with Interleaved Stochastic Trajectories (TWIST) MRA in Fast Stroke Imaging.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe purpose of this study was to evaluate whether deep learning reconstruction (DLR) improves the image quality of intracranial magnetic resonance angiography (MRA) at 1.5 T. MATERIALS AND
methodsIn this retrospective study, MRA images of 40 patients (21 males and 19 females; mean age, 65.8 ± 13.2 years) were reconstructed with and without the DLR technique (DLR image and non-DLR image, respectively). Quantitative image analysis was performed by placing regions of interest on the basilar artery and cerebrospinal fluid in the prepontine cistern. We calculated the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for analyses of the basilar artery. Two experienced radiologists evaluated the depiction of structures (the right internal carotid artery, right ophthalmic artery, basilar artery, and right superior cerebellar artery), artifacts, subjective noise and overall image quality in a qualitative image analysis. Scores were compared in the quantitative and qualitative image analyses between the DLR and non-DLR images using Wilcoxon signed-rank tests.
resultsThe SNR and CNR for the basilar artery were significantly higher for the DLR images than for the non-DLR images (p < 0.001). Qualitative image analysis scores (p < 0.003 and p < 0.005 for readers 1 and 2, respectively), excluding those for artifacts (p = 0.072-0.565), were also significantly higher for the DLR images than for the non-DLR images.
conclusionDLR enables the production of higher quality 1.5 T intracranial MRA images with improved visualization of arteries.
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