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Research ArticleNeuroimaging Physics/Functional Neuroimaging/CT and MRI Technology

Whole-Brain Vascular Architecture Mapping Identifies Region-Specific Microvascular Profiles In Vivo

Anja Hohmann, Ke Zhang, Christoph M. Mooshage, Johann M.E. Jende, Lukas T. Rotkopf, Heinz-Peter Schlemmer, Martin Bendszus, Wolfgang Wick and Felix T. Kurz
American Journal of Neuroradiology September 2024, 45 (9) 1346-1354; DOI: https://doi.org/10.3174/ajnr.A8344
Anja Hohmann
aFrom the Department of Neurology (A.H., W.W.), Heidelberg University Hospital, Heidelberg, Germany
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Ke Zhang
bDepartment of Diagnostic and Interventional Radiology (K.Z.), Heidelberg University Hospital, Heidelberg, Germany
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Christoph M. Mooshage
cDepartment of Neuroradiology (C.M.M., J.M.E.J., M.B., F.T.K.), Heidelberg University Hospital, Heidelberg, Germany
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Johann M.E. Jende
cDepartment of Neuroradiology (C.M.M., J.M.E.J., M.B., F.T.K.), Heidelberg University Hospital, Heidelberg, Germany
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Lukas T. Rotkopf
dDivision of Radiology (L.T.R., H.-P.S., F.T.K.) German Cancer Research Center, Heidelberg, Germany
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Heinz-Peter Schlemmer
dDivision of Radiology (L.T.R., H.-P.S., F.T.K.) German Cancer Research Center, Heidelberg, Germany
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Martin Bendszus
cDepartment of Neuroradiology (C.M.M., J.M.E.J., M.B., F.T.K.), Heidelberg University Hospital, Heidelberg, Germany
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Wolfgang Wick
aFrom the Department of Neurology (A.H., W.W.), Heidelberg University Hospital, Heidelberg, Germany
eClinical Cooperation Unit Neurooncology (W.W.), German Cancer Research Center, Heidelberg, Germany
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Felix T. Kurz
cDepartment of Neuroradiology (C.M.M., J.M.E.J., M.B., F.T.K.), Heidelberg University Hospital, Heidelberg, Germany
dDivision of Radiology (L.T.R., H.-P.S., F.T.K.) German Cancer Research Center, Heidelberg, Germany
fDivision of Neuroradiology (F.T.K.), University Hospital Geneva, Geneva, Switzerland
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  • FIG 1.
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    FIG 1.

    Flow diagram showing the patient-selection protocol and criteria for inclusion and exclusion. The asterisk indicates that multiple scans per patient were used for further subanalyses on intrasubject test-retest reliability. VP-Shunt indicates ventriculoperitoneal shunt.

  • FIG 2.
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    FIG 2.

    Cerebral volumes of interest. Axial representation of VOIs on spatially normalized T1-weighted images displaying 6 atlas-based anatomic VOIs (female subject, 27 years of age; right-sided lesion) (A) and individually segmented gray and white matter VOIs (female subject, 38 years of age; left-sided lesion) (B). Only the hemisphere contralateral to the lesion was analyzed for each subject, with a total of 8 VOIs per subject.

  • FIG 3.
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    FIG 3.

    Color representations of distance map parameters I, VTI, and CBI. Parameters I, VTI, and CBI are shown as spatially normalized, averaged axial parametric maps. In MNI standard space, slice coordinates are as follows for all maps (from left to right, Z-axis): -40, -14, 0, 8, 24, 44. Maps of parameters I and VTI are closely correlated and depict differences between brain regions with predominantly venous outflow, eg, close to the transverse and rectus sinus, and predominantly arterial inflow, eg, in the insular cortex. Parameter CBI contrasts brain regions with increased capillary vascularization, such as the GP and hippocampus.

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    FIG 4.

    Parametric maps for CBV fraction, VIPS, and rCBV. Parameters BVF, VIPS, and rCBV are shown as spatially normalized, averaged axial parametric maps in MNI standard space as above. Because BVF identifies regions with increased blood volume, it is similar to rCBV with moderate correlations between both parameter values in the cGM and WM, respectively (Online Supplemental Data). The parameter VIPS is highly increased in the GP and thalamus (Table 2), as well as in parts of the WM along the pyramidal tracts.

  • FIG 5.
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    FIG 5.

    Color maps of VSI, microvessel density Q, and CGI. Parameters VSI, Q, and CGI are shown as spatially normalized, averaged axial parametric maps in MNI standard space as above. Parameters VSI and CGI indicate larger vessel calibers in the cGM compared with WM (Table 2). Microvessel density Q is increased in the lenticular nucleus (GP and putamen) and thalamus, compared with WM.

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    Table 1:

    Demographics and distribution of individual vascular risk factors within the cohort (n = 106)a

    Cohort
    Variable
     Age (mean) (range) (yr) 39.2 (SD, 12.5) (20–70)
     Sex (% female) 52.8
     BMI (mean) (range) (kg/m2)25.7 (SD, 4.2) (18.4–37.7)
    Vascular risk factors (No.) (% of 106)
     Obesity (BMI >30) 19 (17.9%)
     Arterial hypertension 16 (15.1%)
     Diabetes mellitus type 24 (3.8%)
     Smoking25 (23.6%)
    KPS (mean) (range)95.2 (SD, 6.8) (80–100)
    • ↵a Data are displayed as mean values (SD) and (range) except where otherwise indicated.

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    Table 2:

    VAM parameter value ranges within cerebral VOIsa

    Mean [95% CI]Cortical GMCNThalamusPutamenGPHippocampusAmygdalaWM
    Average mask size (voxel)38,3548331107103128793923436,322
    I−2.27−2.42−1.06−1.89−0.95−2.39−1.79−1.33
    [s−1][−2.45 to −2.08][−2.79 to −2.04][−1.37 to −0.75][−2.11 to −1.67][−1.23 to −0.67][−2.69 to −2.09][−2.31 to −1.26][−1.44 to −1.22]
    VTI−5.41−5.66−2.47−3.64−2.81−6.60−5.13−2.60
    [s−2][−5.96 to −4.86][−6.58 to −4.75][−.25 to −1.7][−4.24 to −3.04][−3.81 to −1.82][−7.5 to −5.7][−7.23 to −3.02][−2.84 to −2.35]
    CBI1.441.341.731.632.461.652.171.16
    [s−1][1.38–1.49][1.28–1.4][1.67–1.79][1.57–1.7][2.36–2.56][1.6–1.71][2.08–2.27][1.12–1.19]
    BVF20.4016.3918.2815.5215.6022.0523.9211.05
    [s−1][19.53–21.26][15.63–17.15][17.46–19.1][14.7–16.33][14.88–16.32][21.1–22.99][22.67–25.16][10.58–11.52]
    VIPS−3.97−2.820.02−2.236.68−5.39−7.17−3.19
    [10−1 s][−4.34 to −3.60][-4.25 to −1.38][−0.75–0.79][−2.84 to –1.61][4.91–8.44][−6.29 to –4.49][−8.89 to –5.46][−3.64 to −2.74]
    rCBV3.032.442.791.971.804.204.161.61
    [10−2][3.01–3.05][2.37–2.51][2.69–2.88][1.93–2.02][1.74–1.85][4.07–4.34][3.95–4.36][1.58–1.63]
    VSI37.3243.2029.1112.9310.9345.2045.2519.29
    [µm][36.43–38.22][40.2–46.2][27.31–30.91][12.53–13.32][10.13–11.73][43.3–47.1][42.37–48.13][18.85–19.74]
    Q4.253.364.504.915.903.813.904.23
    [10−2·ms−1/3][4.20–4.30][3.24–3.48][4.42–4.57][4.85–4.97][5.75–6.05][3.74–3.88][3.8–4][4.18–4.27]
    CGI5.806.195.104.143.146.105.684.25
    [ ][5.68–5.91][5.96–6.42][4.91–5.28][4.01–4.28][2.99–3.29][5.93–6.26][5.47–5.89][4.13–4.36]
    • Note:—[ ] indicates 95% confidence intervals.

    • a Data are presented as mean values with 95% confidence intervals for mean values.

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    Table 3:

    ICCs between 2 scans (n = 27)a

    ParameterICC95% CIFP
    I0.4895[0.3814–0.5844]2.9241<.0001
    VTI0.3623[0.2406–0.4727]2.1337<.0001
    CBI0.7297[0.6591–0.7873]6.5456<.0001
    BVF0.7771[0.7182–0.8250]7.9463<.0001
    VIPS0.5689[0.4713–0.6527]3.6274<.0001
    rCBV0.9274[0.9061–0.9440]26.4302<.0001
    VSI0.9163[0.892–0.9353]22.969<.0001
    Q0.853[0.8046–0.8890]13.3324<.0001
    CGI0.8109[0.7598–0.8520]9.5721<.0001
    • ↵a ICC estimates with 95% confidence intervals for each parameter and the F test for the hypothesis that ICC = 0. Mean values for 8 VOIs were compared between n = 27 individual subjects at 2 different time points (n = 216 values per time point).

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American Journal of Neuroradiology: 45 (9)
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Anja Hohmann, Ke Zhang, Christoph M. Mooshage, Johann M.E. Jende, Lukas T. Rotkopf, Heinz-Peter Schlemmer, Martin Bendszus, Wolfgang Wick, Felix T. Kurz
Whole-Brain Vascular Architecture Mapping Identifies Region-Specific Microvascular Profiles In Vivo
American Journal of Neuroradiology Sep 2024, 45 (9) 1346-1354; DOI: 10.3174/ajnr.A8344

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Whole-Brain Vascular Mapping
Anja Hohmann, Ke Zhang, Christoph M. Mooshage, Johann M.E. Jende, Lukas T. Rotkopf, Heinz-Peter Schlemmer, Martin Bendszus, Wolfgang Wick, Felix T. Kurz
American Journal of Neuroradiology Sep 2024, 45 (9) 1346-1354; DOI: 10.3174/ajnr.A8344
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