ArticleQuantitative imaging in medicine and surgery2022
A miniature U-net for
Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Deep learning-based reconstruction: a reliability assessment in preoperative magnetic resonance imaging for primary rectal cancer.Quantitative imaging in medicine and surgery · 2024Article
- Image quality evaluation of diffusion-weighted imaging in bladder cancer: a comparison between integrated slice-specific dynamic shimming and single-shot echo-planar imaging.Quantitative imaging in medicine and surgery · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Deep learning-based magnetic resonance imaging (MRI) methods require in most cases a separate dataset with thousands of images for each anatomical site to train the network model. This paper proposes a miniature U-net method for Methods: The original U-net was tailored with fewer layers and channels, and the network was trained using the autocalibrating signal data with a mixing loss function involving magnitude loss and phase loss. The performance of the proposed method was measured using both phantom and Results: The proposed method alleviates aliasing artifacts and reduces noise with an acceleration factor of four for phantom and Conclusions: The proposed method introduces a miniature U-net to reconstruct the missing
Indexed as
Identifiers
What OpenQuestion holds
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.