ArticleNMR in biomedicine2022
Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning.
Article in NMR in biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- A Systematic Review and Identification of the Challenges of Deep Learning Techniques for Undersampled Magnetic Resonance Image Reconstruction.Sensors (Basel, Switzerland) · 2024Pooled it
- Denoising of ASL Data Using Deep Learning Priors Generated From Distribution Remapping.Magnetic resonance in medicine · 2026Article
- Article
- Enhancing Cerebral Blood Flow Quantification: A Comprehensive Review of Denoising, Artifact Correction, and Simulation in Arterial Spin Labeling MRI.Sensors (Basel, Switzerland) · 2026Review
- Adaptation of an arterial spin labeling sequence for the assessment of brain perfusion on a 0.35 T MR-Linac.Magma (New York, N.Y.) · 2026Article
- Joint learning of nonlinear representation and projection for fast constrained MRSI reconstruction.Magnetic resonance in medicine · 2025Article
- Improved accuracy for myocardial blood flow mapping with deep learning-enabled CMR arterial spin labeling (DeepMASL): Validation by microsphere in vivo.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
- Arterial spin labeled perfusion imaging with balanced steady-state free precession readout and radial sampling.Magnetic resonance imaging · 2023Article
- Parametric cerebral blood flow and arterial transit time mapping using a 3D convolutional neural network.Magnetic resonance in medicine · 2023Article
- Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges.Journal of digital imaging · 2023Review
- Recent Technical Developments in ASL: A Review of the State of the Art.Magnetic resonance in medicine · 2022Review
- Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends.NMR in biomedicine · 2022Review
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 at 1 institution in 1 country.
Funding
Abstract
Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. However, the clinical applications of ASL are currently limited by its low signal-to-noise ratio (SNR), limited spatial resolution, and long imaging time. In this work, we propose an unsupervised deep learning-based image denoising and reconstruction framework to improve the SNR and accelerate the imaging speed of high resolution ASL imaging. The unique feature of the proposed framework is that it does not require any prior training pairs but only the subject's own anatomical prior, such as T1-weighted images, as network input. The neural network was trained from scratch in the denoising or reconstruction process, with noisy images or sparely sampled k-space data as training labels. Performance of the proposed method was evaluated using in vivo experiment data obtained from 3 healthy subjects on a 3T MR scanner, using ASL images acquired with 44-min acquisition time as the ground truth. Both qualitative and quantitative analyses demonstrate the superior performance of the proposed txtc framework over the reference methods. In summary, our proposed unsupervised deep learning-based denoising and reconstruction framework can improve the image quality and accelerate the imaging speed of ASL imaging.
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.