Evidence map›Paper›PMID 31377323›Full record

ArticleNeuroImage2019

Learning-based single-step quantitative susceptibility mapping reconstruction without brain extraction.

Hongjiang Wei, Steven Cao, Yuyao Zhang, Xiaojun Guan, Fuhua Yan, Kristen W Yeom, Chunlei Liu

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
7.2field-weighted citation impact, top 2% 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

18 citing papers in PubMed, 59 citations in OpenAlex.

  1. Review
  2. Article
  3. Reduced oxygen extraction fraction in deep cerebral veins associated with cognitive impairment in multiple sclerosis.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2024
    Article
  4. Article
  5. Article
  6. IncorporatingFrontiers in neuroscience · 2024
    Article
  7. Region Expansion of Background Field Removal with Local Spherical Harmonics Approximation for Whole-brain Quantitative Susceptibility Mapping.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023
    Article
  8. Article
  9. Article
  10. [A multi-channel input convolutional neural network for artifact reduction in quantitative susceptibility mapping].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2022
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
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

7 authors at 6 institutions in 2 countries.

Hongjiang WeiInstitute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. Electronic address: hongjiang.wei@sjtu.edu.cn.
Steven CaoDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.
Yuyao ZhangSchool of Information and Science and Technology, ShanghaiTech University, Shanghai, China.
Xiaojun GuanDepartment of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Fuhua YanDepartment of Radiology, Rui Jin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
Kristen W YeomDepartment of Radiology, Lucile Packard Children's Hospital, Stanford University, Palo Alto, CA, USA.
Chunlei LiuDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA. Electronic address: chunlei.liu@berkeley.edu.
University of California, Berkeley · USRuijin Hospital · CNSecond Affiliated Hospital of Zhejiang University · CNShanghai Jiao Tong University · CNShanghaiTech University · CNStanford University · US

Funding

MRI CORTICOGRAPHY: DEVELOPING NEXT GENERATION MICROSCALE HUMAN CORTEX MRI SCANNERU01EB025162 · NIBIB · UNIVERSITY OF CALIFORNIA BERKELEY · PI FEINBERG, DAVID ALAN, LIU, CHUNLEI · 2017 to 2021
$13.4M
Ultra-Resolution Imaging of Brain Circuitry and its Development in Mental HealthR01MH096979 · NIMH · UNIVERSITY OF CALIFORNIA BERKELEY · PI LIU, CHUNLEI · 2013 to 2017
$1.8M
NIBIB NIH HHS U01 EB025162NIMH NIH HHS R01 MH096979
6 · The paper itself

Abstract

Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from MRI gradient-echo phase signal and typically requires several processing steps. These steps involve phase unwrapping, brain volume extraction, background phase removal and solving an ill-posed inverse problem relating the tissue phase to the underlying susceptibility distribution. The resulting susceptibility map is known to suffer from inaccuracy near the edges of the brain tissues, in part due to imperfect brain extraction, edge erosion of the brain tissue and the lack of phase measurement outside the brain. This inaccuracy has thus hindered the application of QSM for measuring susceptibility of tissues near the brain edges, e.g., quantifying cortical layers and generating superficial venography. To address these challenges, we propose a learning-based QSM reconstruction method that directly estimates the magnetic susceptibility from total phase images without the need for brain extraction and background phase removal, referred to as autoQSM. The neural network has a modified U-net structure and is trained using QSM maps computed by a two-step QSM method. 209 healthy subjects with ages ranging from 11 to 82 years were employed for patch-wise network training. The network was validated on data dissimilar to the training data, e.g., in vivo mouse brain data and brains with lesions, which suggests that the network generalized and learned the underlying mathematical relationship between magnetic field perturbation and magnetic susceptibility. Quantitative and qualitative comparisons were performed between autoQSM and other two-step QSM methods. AutoQSM was able to recover magnetic susceptibility of anatomical structures near the edges of the brain including the veins covering the cortical surface, spinal cord and nerve tracts near the mouse brain boundaries. The advantages of high-quality maps, no need for brain volume extraction, and high reconstruction speed demonstrate autoQSM's potential for future applications.

Indexed as

Deep LearningAdolescentAdultAgedAged, 80 and overBrainChildFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedNeuroimagingYoung AdultDeep learningMagnetic resonance imagingMRINeural networkQSMQuantitative susceptibility mapping

Identifiers

PMID31377323
PMCPMC6819263
OpenAlexW2966280437

What OpenQuestion holds

Textmetadata
LicenceTDM
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.