WeChat Mini Program
Old Version Features

Repeat It Without Me: Crowdsourcing the T1 Mapping Common Ground Via the ISMRM Reproducibility Challenge

Magnetic Resonance in Medicine(2024)

Polytech Montreal | Univ Southern Calif | Ingham Inst Appl Med Res | Univ Padua | Univ Nacl Autonoma Mexico | Philips Res Hamburg | Stanford Univ | McGill Univ | Department of Diagnostic and Interventional Imaging | Philips Canada | Univ Wisconsin Madison | UCL | Case Western Reserve Univ | Univ Gothenburg | Univ Trento | Imperial Coll London | Philips Healthcare | Univ Calif Los Angeles | Douglas Brain Imaging Ctr | Ctr Invest Matemat AC | Univ Texas MD Anderson Canc Ctr | Univ British Columbia

Cited 3|Views28
Abstract
Purpose T-1 mapping is a widely used quantitative MRI technique, but its tissue-specific values remain inconsistent across protocols, sites, and vendors. The ISMRM Reproducible Research and Quantitative MR study groups jointly launched a challenge to assess the reproducibility of a well-established inversion-recovery T-1 mapping technique, using acquisition details from a seminal T-1 mapping paper on a standardized phantom and in human brains. Methods The challenge used the acquisition protocol from Barral et al. (2010). Researchers collected T-1 mapping data on the ISMRM/NIST phantom and/or in human brains. Data submission, pipeline development, and analysis were conducted using open-source platforms. Intersubmission and intrasubmission comparisons were performed. Results Eighteen submissions (39 phantom and 56 human datasets) on scanners by three MRI vendors were collected at 3 T (except one, at 0.35 T). The mean coefficient of variation was 6.1% for intersubmission phantom measurements, and 2.9% for intrasubmission measurements. For humans, the intersubmission/intrasubmission coefficient of variation was 5.9/3.2% in the genu and 16/6.9% in the cortex. An interactive dashboard for data visualization was also eveloped: https://rrsg2020.dashboards.neurolibre.org. Conclusion The T-1 intersubmission variability was twice as high as the intrasubmission variability in both phantoms and human brains, indicating that the acquisition details in the original paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T-1 measures across independent research groups, bringing us one step closer to a practical clinical baseline of T-1 variations in vivo.
More
Translated text
Key words
inversion recovery,open data,quantitative MRI,reproducibility,T-1 mapping
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined