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Research ArticleNEUROIMAGING PHYSICS/FUNCTIONAL NEUROIMAGING/CT AND MRI TECHNOLOGY

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, HuiMao Zhang, FanYang Meng and Lei Zhang
American Journal of Neuroradiology March 2024, DOI: https://doi.org/10.3174/ajnr.A8156
BingYang Bian
aFrom 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
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Lin Hou
aFrom 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
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YaTing Chai
aFrom 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
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YueLuan Jiang
bMR Scientific Marketing, Diagnostic Imaging (Y.J.), Siemens Healthineers Ltd, Beijing, China
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XingChen Pan
aFrom 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
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Yang Sun
aFrom 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
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HongChao Wang
aFrom 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
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DongDong Qiu
aFrom 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
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ZeChen Yu
cSiemens Healthineers Digital Technology (Shanghai) Co Ltd (Z.Y.), Shanghai, China
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Hua Zhao
dDepartment of Physiology (H. Zhao), College of Basic Medical Sciences, Jilin University, Changchun, Jilin, China
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HuiMao Zhang
aFrom 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
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FanYang Meng
aFrom 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
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Lei Zhang
aFrom 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
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    FIG 1.

    Representative MPRAGE, MP2RAGE-UNI, and T1-mapping images of the Hb from a healthy man 30 years of age (black arrow). Visual contrast between the Hb and the thalamus in UNI is better than that in MPRAGE.

  • FIG 2.
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    FIG 2.

    The Hb ROI was chosen on the basis of the largest axial image on MP2RAGE-UNI and MPRAGE, and the thalamus ROI was defined as twice the size of the Hb ROI, which is located on the anterolateral side of the Hb ROI (first row). The CNR (middle row) and SNR (third row) of UNI is statistically higher than those of MPRAGE (paired samples t test). Radio indicates radiologist.

  • FIG 3.
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    FIG 3.

    Segmentations from 5 atlases are propagated via image registration to 21 selected template images before being propagated using image registration to each UNI and MPRAGE. This feature creates a large number (5 atlases × 21 templates = 105) of candidate segmentations that are fused using majority vote. This has 2 advantages: reducing atlas bias and averaging registration errors. Also, because the procedure is based on image registration rather than on contrast-based segmentation, it allows the neuroanatomic variability of the subjects to refine each individual subject’s final segmentation. Reg_result indicates registration result.

  • FIG 4.
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    FIG 4.

    ROI on the T1 mapping from the left-to-right Hb of a representative participant. A, Anterior (red) and posterior (green) subregions within the habenula. B, Medial (pink) and lateral (yellow) subregions within the Hb. C and D, The T1 values of the posterior halves of the Hb are higher than those in the anterior halves, and the medial halves are higher than those in the lateral halves.

  • FIG 5.
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    FIG 5.

    Bland-Altman plot showing the volume differences of the right Hb (green dots) and left Hb (red dots) between the MAGeTbrain and manual segmentations in the first scan for MPR2RAGE-UNI and MPRAGE. Radio indicates radiologist.

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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, HuiMao Zhang, FanYang Meng, Lei Zhang
American Journal of Neuroradiology Mar 2024, DOI: 10.3174/ajnr.A8156
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Cite this article
BingYang Bian, Lin Hou, YaTing Chai, YueLuan Jiang, XingChen Pan, Yang Sun, HongChao Wang, DongDong Qiu, ZeChen Yu, Hua Zhao, HuiMao Zhang, FanYang Meng, Lei Zhang
Visualizing the Habenula Using 3T High-Resolution MP2RAGE and QSM: A Preliminary Study
American Journal of Neuroradiology Mar 2024, DOI: 10.3174/ajnr.A8156

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