Evidence map›Paper›PMID 38453416›Full record

ArticleAJNR. American journal of neuroradiology2024

Visualizing the Habenula Using 3T High-Resolution MP2RAGE and QSM: A Preliminary Study.

BingYang Bian, Lin Hou, YaTing Chai, YueLuan Jiang, XingChen Pan, Yang Sun, HongChao Wang, DongDong Qiu, ZeChen Yu, Hua Zhao and 3 more

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

Article in AJNR. American journal of neuroradiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 18% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. [Research progress on quantitative magnetic susceptibility imaging reconstruction method based on improved U-network model].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025
    Review
  3. Article
  4. Biomarkers for Epilepsy Deep Brain Stimulation.Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society · 2025
    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

13 authors at 3 institutions in 2 countries.

BingYang BianFrom the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China.ORCID 0009-0005-5512-8639
Lin HouFrom the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China.ORCID 0009-0007-5013-2078
YaTing ChaiFrom the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China.
YueLuan JiangMR Scientific Marketing, Diagnostic Imaging (Y.J.), Siemens Healthineers Ltd, Beijing, China.ORCID 0009-0004-5680-8102
XingChen PanFrom the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China.ORCID 0000-0002-3489-188X
Yang SunFrom the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China.
HongChao WangFrom the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China.
DongDong QiuFrom the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China.
ZeChen YuSiemens Healthineers Digital Technology (Shanghai) Co Ltd (Z.Y.), Shanghai, China.
Hua ZhaoDepartment of Physiology (H. Zhao), College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China.
HuiMao ZhangFrom the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China.
FanYang MengFrom the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China.
Lei ZhangFrom the Department of Radiology (B.B., L.H., Y.C., X.P., Y.S., H.W., D.Q., H. Zhang, F.M., L.Z.), Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Radiology and Technology Innovation Center of Jilin Province, Jilin Provincial International Joint Research Center of Medical Artificial Intelligence, The First Hospital of Jilin University, Changchun, Jilin, China zlei99@jlu.edu.cn.ORCID 0000-0002-5567-7647
Jilin University · CNDigital China Health (China) · CNSiemens Healthcare (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background and purposeThe habenula is a key node in the regulation of emotion-related behavior. Accurate visualization of the habenula and its reliable quantitative analysis is vital for the assessment of psychiatric disorders. To obtain high-contrast habenula images and allow them to be compatible with clinical applications, this preliminary study compared 3T MP2RAGE and quantitative susceptibility mapping with MPRAGE by evaluating the habenula segmentation performance. MATERIALS AND

methodsTen healthy volunteers were scanned twice with 3T MPRAGE and MP2RAGE and once with quantitative susceptibility mapping. Image quality and visibility of habenula anatomic features were analyzed by 3 radiologists using a 5-point scale. Contrast assessments of the habenula and thalamus were also performed. The reproducibility of the habenula volume from MPRAGE and MP2RAGE was evaluated by manual segmentation and the Multiple Automatically Generated Template brain segmentation algorithm (MAGeTbrain). T1 values and susceptibility were measured in the whole habenula and habenula geometric subregion using MP2RAGE T1-mapping and quantitative susceptibility mapping.

resultsThe 3T MP2RAGE and quantitative susceptibility mapping demonstrated clear boundaries and anatomic features of the habenula compared with MPRAGE, with a higher SNR and contrast-to-noise ratio (all

conclusionsWe identified an optimized sequence combination (3T MP2RAGE combined with quantitative susceptibility mapping) that may be useful for enhancing habenula visualization and yielding more reliable quantitative data.

Indexed as

HabenulaAlgorithmsBrainHealthy VolunteersHumansMagnetic Resonance ImagingReproducibility of Results

Identifiers

PMID38453416
PMCPMC11288573
OpenAlexW4392545536

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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.