ArticleCancers2022
Prospectively Accelerated T2-Weighted Imaging of the Prostate by Combining Compressed SENSE and Deep Learning in Patients with Histologically Proven Prostate Cancer.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05820113 (Deep Learning Super Resolution Reconstruction for Fast and Motion Robust T2-weighted Prostate MRI), which is not on this map. Cited by 22 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Deep Learning Super Resolution Reconstruction for Fast and Motion Robust T2-weighted Prostate MRI
Who cites it
22 citing papers in PubMed, 36 citations in OpenAlex.
- Deep learning reconstruction in prostate MRI: technical principles, clinical applications, and future directions.Abdominal radiology (New York) · 2026Review
- AI-Assisted Compressed Sensing for Optimized Inner Ear Imaging: a Multi-Rater MRI Evaluation of CSAI T2-DRIVE.Clinical neuroradiology · 2026Article
- Accelerating prostate MRI: a practical review of modern acquisition strategies.Abdominal radiology (New York) · 2026Review
- Biomarkers for Precision Prognosis in Prostate Cancer: Imaging, Molecular, and Integrated Approaches.Cancers · 2026Review
- Cardiac MR function analysis with DL-based super resolution reconstruction: application in the clinical setting.The international journal of cardiovascular imaging · 2026Article
- Deep learning-enhanced super-resolution diffusion-weighted liver MRI: improved image quality, diagnostic performance, and acceleration.Insights into imaging · 2025Article
- 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
- Utility of Thin-slice Single-shot T2-weighted MR Imaging with Deep Learning Reconstruction as a Protocol for Evaluating Pancreatic Cystic Lesions.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Prostate MRI Using Deep Learning Reconstruction in Response to Cancer Screening Demands-A Systematic Review and Meta-Analysis.Journal of personalized medicine · 2025Review
- Deep learning super-resolution reconstruction for fast and high-quality cine cardiovascular magnetic resonance.European radiology · 2025Article
- Accelerated diffusion-weighted imaging of the prostate employing echo planar imaging with compressed SENSE based reconstruction.Scientific reports · 2025Article
- Article
- Rapid and motion-robust pediatric brain imaging: T2-weighted turbo-spin-echo PROPELLER acquisition with compressed sensing.Pediatric radiology · 2025Article
- Ultra-high gradient performance 3-Tesla MRI for super-fast and high-quality prostate imaging: initial experience.Insights into imaging · 2024Article
- Deep learning-accelerated T2WI of the prostate for transition zone lesion evaluation and extraprostatic extension assessment.Scientific reports · 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
- Deep-learning-based reconstruction of T2-weighted magnetic resonance imaging of the prostate accelerated by compressed sensing provides improved image quality at half the acquisition time.Quantitative imaging in medicine and surgery · 2024Article
- Deep learning denoising reconstruction for improved image quality in fetal cardiac cine MRI.Frontiers in cardiovascular medicine · 2024Article
- Development trends and knowledge framework in the application of magnetic resonance imaging in prostate cancer: a bibliometric analysis from 1984 to 2022.Quantitative imaging in medicine and surgery · 2023Article
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo assess the performance of prospectively accelerated and deep learning (DL) reconstructed T2-weighted (T2w) imaging in volunteers and patients with histologically proven prostate cancer (PCa).
methodsProspectively undersampled T2w datasets were acquired with acceleration factors of 1.7 (reference), 3.4 and 4.8 in 10 healthy volunteers and 23 patients with histologically proven PCa. Image reconstructions using compressed SENSE (C-SENSE) and a combination of C-SENSE and DL-based artificial intelligence (C-SENSE AI) were analyzed. Qualitative image comparison was performed using a 6-point Likert scale (overall image quality, noise, motion artifacts, lesion detection, diagnostic certainty); the T2 and PI-RADS scores were compared between the two reconstructions. Additionally, quantitative image parameters were assessed (apparent SNR, apparent CNR, lesion size, line profiles).
resultsAll C-SENSE AI-reconstructed images received a significantly higher qualitative rating compared to the C-SENSE standard images. Analysis of the quantitative parameters supported this finding, with significantly higher aSNR and aCNR. The line profiles demonstrated a significantly steeper signal change at the border of the prostatic lesion and the adjacent normal tissue in the C-SENSE AI-reconstructed images, whereas the T2 and PI-RADS scores as well as the lesion size did not differ.
conclusionIn this prospective study, we demonstrated the clinical feasibility of a novel C-SENSE AI reconstruction enabling a 58% acceleration in T2w imaging of the prostate while obtaining significantly better image quality.
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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.