ArticleInsights into imaging2022
Comparison of a deep learning-accelerated T2-weighted turbo spin echo sequence and its conventional counterpart for female pelvic MRI: reduced acquisition times and improved image quality.
Article in Insights into imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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Who cites it
12 citing papers in PubMed, 22 citations in OpenAlex.
- Deep learning reconstruction versus conventional parallel imaging for sagittal T2-weighted MRI in cervical cancer: a comparison of acceleration factors.Abdominal radiology (New York) · 2026Article
- Impact of deep-learning image reconstruction on multiplexed sensitivity encoding diffusion-weighted imaging in the female pelvis.Quantitative imaging in medicine and surgery · 2026Article
- Deep learning reconstruction of diffusion-weighted imaging with single-shot echo-planar imaging in endometrial cancer: a comparison with multi-shot echo-planar imaging.Abdominal radiology (New York) · 2025Article
- Optimizing bladder magnetic resonance imaging: accelerating scan time and improving image quality through deep learning.Abdominal radiology (New York) · 2025Article
- Ultrafast T2-weighted MR imaging of the urinary bladder using deep learning-accelerated HASTE at 3 Tesla.BMC medical imaging · 2025Article
- Feasibility/clinical utility of half-Fourier single-shot turbo spin echo imaging combined with deep learning reconstruction in gynecologic magnetic resonance imaging.Abdominal radiology (New York) · 2025Article
- Transforming Medical Imaging: The Role of Artificial Intelligence Integration in PACS for Enhanced Diagnostic Accuracy and Workflow EfficiencyCurrent medical imaging · 2025Review
- Recent trends in AI applications for pelvic MRI: a comprehensive review.La Radiologia medica · 2024Review
- Reduced field-of-view DWI based on deep learning reconstruction improving diagnostic accuracy of VI-RADS for evaluating muscle invasion.Insights into imaging · 2024Article
- Artificial intelligence in female pelvic oncology: tailoring applications to clinical needs.European radiology · 2024Article
- Bladder MRI with deep learning-based reconstruction: a prospective evaluation of muscle invasiveness using VI-RADS.Abdominal radiology (New York) · 2024Article
- Deep Learning-accelerated MRI in Body and Chest.Journal of computer assisted tomographyReview
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 2 countries.
Funding
Abstract
objectivesTo investigate the feasibility of a deep learning-accelerated T2-weighted turbo spin echo (TSE) sequence (T2
methodsIn total, 24 volunteers and 48 consecutive patients with benign uterine diseases were enrolled. Patients in the menstrual phase were excluded. T2
resultsT2
conclusionsCompared with standard TSE, deep learning-accelerated T2-weighted TSE is feasible to reduce acquisition time of female pelvic MRI with significant improvement of image quality.
Indexed as
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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.