Evidence map›Paper›PMID 37446520›Full record

ReviewNanomaterials (Basel, Switzerland)2023

Contrast Agents of Magnetic Resonance Imaging and Future Perspective.

Jie Lv, Shubham Roy, Miao Xie, Xiulan Yang, Bing Guo

Open access · goldAbstract readReview
In one paragraph

Review in Nanomaterials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.9field-weighted citation impact, top 8% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 30 citations in OpenAlex.

  1. Review
  2. Review
  3. Carbon Dots Meet MRI: Metal Doping for a Smart Contrast Agent Design.International journal of molecular sciences · 2026
    Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Nanotheranostics: A Cutting-edge Technology for Cancer Management.Current pharmaceutical biotechnology · 2025
    Review
  11. Contrast Agents Based on Human Serum Albumin and Nitroxides forInternational journal of molecular sciences · 2024
    Article
  12. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Jie LvSchool of Computer Science and Engineering, Yulin Normal University, Yulin 537000, China.
Shubham RoyShenzhen Key Laboratory of Flexible Printed Electronics Technology, School of Science, Harbin Institute of Technology, Shenzhen 518055, China.ORCID 0000-0001-5245-3229
Miao XieSchool of Computer Science and Engineering, Yulin Normal University, Yulin 537000, China.
Xiulan YangSchool of Computer Science and Engineering, Yulin Normal University, Yulin 537000, China.ORCID 0009-0000-8858-4087
Bing GuoShenzhen Key Laboratory of Flexible Printed Electronics Technology, School of Science, Harbin Institute of Technology, Shenzhen 518055, China.ORCID 0000-0001-7981-0644
Yulin Normal University · CNHarbin Institute of Technology · CN

Funding

General project of Guangdong Natural Science Foundation 2022A1515011781Guangdong Basic and Applied Basic Research Foundation 2021A1515110086JCYJ20210324132816039 JCYJ20210324132816039Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application ZDSYS20220527171407017the Science and Technology Innovation Commission of Shenzhen RCBS20200714114910141
6 · The paper itself

Abstract

In recent times, magnetic resonance imaging (MRI) has emerged as a highly promising modality for diagnosing severe diseases. Its exceptional spatiotemporal resolution and ease of use have established it as an indispensable clinical diagnostic tool. Nevertheless, there are instances where MRI encounters challenges related to low contrast, necessitating the use of contrast agents (CAs). Significant efforts have been made by scientists to enhance the precision of observing diseased body parts by leveraging the synergistic potential of MRI in conjunction with other imaging techniques and thereby modifying the CAs. In this work, our focus is on elucidating the rational designing approach of CAs and optimizing their compatibility for multimodal imaging and other intelligent applications. Additionally, we emphasize the importance of incorporating various artificial intelligence tools, such as machine learning and deep learning, to explore the future prospects of disease diagnosis using MRI. We also address the limitations associated with these techniques and propose reasonable remedies, with the aim of advancing MRI as a cutting-edge diagnostic tool for the future.

Indexed as

ChatGPTcontrast agentsdeep learningmachine learningMRI

Identifiers

PMID37446520
PMCPMC10343825
OpenAlexW4383262638

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.