1.
Duyn JH, Schenck J. Contributions to magnetic susceptibility of brain tissue. NMR
in Biomedicine. 2017;30:e3546.
2.
Hinoda T, Fushimi Y, Okada T, et al. Quantitative assessment of gadolinium
deposition in dentate nucleus using quantitative susceptibility mapping. Journal of
Magnetic Resonance Imaging. 2017;45:1352-1358.
3.
Oshima S, Fushimi Y, Okada T, et al. Brain MRI with Quantitative Susceptibility
Mapping: Relationship to CT Attenuation Values. Radiology. 2020:182934.
4.
Kim H-G, Park S, Rhee HY, et al. Quantitative susceptibility mapping to evaluate
the early stage of Alzheimer's disease. NeuroImage: Clinical. 2017;16:429-438.
5.
Zhang S, Nguyen TD, Rúa SMH, et al. Quantitative susceptibility mapping of timedependent susceptibility changes in multiple sclerosis lesions. American Journal of
Neuroradiology. 2019;40:987-993.
6.
Chen Q, Chen Y, Zhang Y, et al. Iron deposition in Parkinson’s disease by
quantitative susceptibility mapping. BMC neuroscience. 2019;20:23.
7.
Haller S, Vernooij MW, Kuijer JPA, Larsson E-M, Jäger HR, Barkhof F. Cerebral
microbleeds: imaging and clinical significance. Radiology. 2018;287:11-28.
8.
Ciraci S, Gumus K, Doganay S, et al. Diagnosis of intracranial calcification and
hemorrhage in pediatric patients: comparison of quantitative susceptibility mapping
and phase images of susceptibility-weighted imaging. Diagnostic and interventional
imaging. 2017;98:707-714.
9.
Sati P, Patil S, Inati S, et al. Rapid MR susceptibility imaging of the brain using
segmented 3D echo-planar imaging (3D EPI) and its clinical applications.
Magnetom FLASH. 2017;68:26-32.
16
10.
Sun H, Wilman AH. Quantitative susceptibility mapping using single‐shot echo‐
planar imaging. Magnetic resonance in medicine. 2015;73:1932-1938.
11.
De A, Sun H, Emery DJ, Butcher KS, Wilman AH. Rapid quantitative susceptibility
mapping of intracerebral hemorrhage. Journal of Magnetic Resonance Imaging.
2020;51:712-718.
12.
Poser BA, Koopmans PJ, Witzel T, Wald LL, Barth M. Three dimensional echoplanar imaging at 7 Tesla. Neuroimage. 2010;51:261-266.
13.
Olsson E, Wirestam R, Lind E. MRI-Based Quantification of Magnetic Susceptibility
in Gel Phantoms: Assessment of Measurement and Calculation Accuracy.
Radiology research and practice. 2018;2018.
14.
Gregoire SM, Chaudhary UJ, Brown MM, et al. The Microbleed Anatomical Rating
Scale (MARS): reliability of a tool to map brain microbleeds. Neurology.
2009;73:1759-1766.
15.
Hagiwara A, Fujita S, Ohno Y, Aoki S. Variability and Standardization of
Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics,
and Artificial Intelligence. Invest Radiol. 2020.
16.
Langkammer C, Bredies K, Poser BA, et al. Fast quantitative susceptibility
mapping using 3D EPI and total generalized variation. Neuroimage. 2015;111:622630.
17.
Lin PY, Chao TC, Wu ML. Quantitative susceptibility mapping of human brain at
3T: a multisite reproducibility study. American Journal of Neuroradiology.
2015;36:467-474.
17
18.
Deh K, Kawaji K, Bulk M, et al. Multicenter reproducibility of quantitative
susceptibility mapping in a gadolinium phantom using MEDI+ 0 automatic zero
referencing. Magnetic resonance in medicine. 2019;81:1229-1236.
19.
Deh K, Nguyen TD, Eskreis‐Winkler S, et al. Reproducibility of quantitative
susceptibility mapping in the brain at two field strengths from two vendors. Journal
of magnetic resonance imaging. 2015;42:1592-1600.
20.
Hinoda T, Fushimi Y, Okada T, et al. Quantitative susceptibility mapping at 3 T and
1.5 T: evaluation of consistency and reproducibility. Investigative radiology.
2015;50:522-530.
21.
Wei H, Zhang Y, Gibbs E, Chen NK, Wang N, Liu C. Joint 2D and 3D phase
processing for quantitative susceptibility mapping: application to 2D echo‐planar
imaging. NMR in Biomedicine. 2017;30:e3501.
22.
Langkammer C, Schweser F, Krebs N, et al. Quantitative susceptibility mapping
(QSM) as a means to measure brain iron? A post mortem validation study.
Neuroimage. 2012;62:1593-1599.
23.
Lancione M, Tosetti M, Donatelli G, Cosottini M, Costagli M. The impact of white
matter fiber orientation in single‐acquisition quantitative susceptibility mapping.
NMR in Biomedicine. 2017;30:e3798.
24.
Wharton S, Bowtell R. Effects of white matter microstructure on phase and
susceptibility maps. Magnetic resonance in medicine. 2015;73:1258-1269.
25.
Cheng A-L, Batool S, McCreary CR, et al. Susceptibility-weighted imaging is more
reliable than T2*-weighted gradient-recalled echo MRI for detecting microbleeds.
Stroke. 2013;44:2782-2786.
18
26.
Straub S, Schneider TM, Emmerich J, et al. Suitable reference tissues for
quantitative susceptibility mapping of the brain. Magnetic resonance in medicine.
2017;78:204-214.
27.
van Dooren M, Staals J, de Leeuw PW, Kroon AA, Henskens LH, van
Oostenbrugge RJ. Progression of brain microbleeds in essential hypertensive
patients: a 2-year follow-up study. American journal of hypertension. 2014;27:10451051.
28.
Poels MMF, Ikram MA, van der Lugt A, et al. Incidence of cerebral microbleeds in
the general population: the Rotterdam Scan Study. Stroke. 2011;42:656-661.
29.
Gregoire SM, Brown MM, Kallis C, J�ger HR, Yousry TA, Werring DJ. MRI
detection of new microbleeds in patients with ischemic stroke: five-year cohort
follow-up study. Stroke. 2010;41:184-186.
19
Table 1: Characteristics of study participants
Characteristic
Value
Healthy volunteers
No. of volunteers
40
Mean age ± SD (years)
59 ± 13
Sex
15
25
Patients
No. of patients
38
Mean age ± SD (years)
51 ± 20
Sex
18
20
Mean interval between QSM (range, days)
213 (13 – 365)
Underlying diseases
Intracranial neoplasm
15
Cavernoma
Aneurysm
Other vascular malformation
Infarction
Inflammatory pseudotumor
Microbleeds of unknown etiology
20
Table 2: ICC, R2, and Bland-Altman analyses between measured and theoretical
susceptibility values of gadolinium phantom
SKYRA
QSM
Sequence
R2
ICC (95%CI)
PRISMA
Mean
difference
R2
ICC (95%CI)
(P)
Mean
difference
(P)
(95%CI)
(95%CI)
0.997
0.996
-2 ppb
0.988
0.992
15 ppb
(0.987 – 0.999)
(< 0.001)
(-45 – 40)
(0.946 - 0.998)
(< 0.001)
(-67 – 97)
Single-TE
0.999
0.999
2 ppb
0.992
0.991
-4 ppb
GRE
(0.997 – 0.999)
(< 0.001)
(-24 – 20)
(0.960 - 0.998)
(< 0.001)
(-77 – 68)
Multi-TE
0.993
0.991
5 ppb
0.993
0.995
-2 ppb
GRE
(0.964 – 0.999)
(< 0.001)
(-63 – 74)
(0.964 - 0.999)
(< 0.001)
(-71 – 66)
3D-EPI
21
Table 3: Regional ICC calculation between QSM sequences
Region
3D-EPI to
3D-EPI to
Multi-TE to
Single-TE GRE
Multi-TE GRE
Single-TE GRE
ICC
95%CI
ICC
95%CI
ICC
95% CI
RGP
0.968
0.911 – 0.986
0.980
0.963 – 0.990
0.974
0.947 – 0.987
LGP
0.957
0.896 – 0.980
0.953
0.890 – 0.978
0.979
0.960 – 0.989
RP
0.957
0.910 – 0.978
0.951
0.785 – 0.982
0.969
0.939 – 0.984
LP
0.953
0.897 – 0.977
0.963
0.906 – 0.983
0.977
0.957 – 0.988
RRN
0.806
0.626 – 0.899
0.910
0.835 – 0.952
0.885
0.786 – 0.938
LRN
0.868
0.688 – 0.938
0.891
0.778 – 0.944
0.895
0.811 – 0.943
RSN
0.928
0.858 – 0.963
0.888
0.798 – 0.939
0.915
0.838 – 0.955
LSN
0.864
0.718 – 0.932
0.867
0.745 – 0.931
0.927
0.868 – 0.961
RDN
0.949
0.906 – 0.973
0.945
0.898 – 0.971
0.972
0.948 – 0.985
LDN
0.935
0.841 – 0.970
0.946
0.891 – 0.973
0.973
0.949 – 0.986
RCN
0.791
0.391 – 0.913
0.838
0.652 – 0.920
0.900
0.799 – 0.949
LCN
0.743
0.254 – 0.894
0.822
0.660 – 0.906
0.873
0.650 – 0.944
ROR
0.904
0.826 – 0.948
0.948
0.901 – 0.972
0.919
0.851 – 0.957
LOR
0.913
0.806 – 0.958
0.964
0.934 – 0.981
0.962
0.901 – 0.983
RIC
0.717
0.527 – 0.839
0.749
0.572 – 0.859
0.855
0.744 – 0.920
LIC
0.835
0.702 – 0.911
0.804
0.659 – 0.892
0.831
0.693 – 0.909
SPL
0.819
0.684 – 0.900
0.838
0.714 – 0.910
0.870
0.745 – 0.933
CPC
0.986
0.976 – 0.992
0.965
0.929 – 0.981
0.977
0.945 – 0.989
22
RGP: right globus pallidus, LGP: left globus pallidus, RP: right putamen, LP: left putamen,
RRN: right red nucleus, LRN: left red nucleus, RSN: right substantia nigra, LSN: left
substantia nigra, RDN: right dentate nucleus, LDN: left dentate nucleus, RCN: right
caudate nucleus, LCN: left caudate nucleus, ROR: right optic radiation, LOR: left optic
radiation, RIC: right internal capsule, LIC: left internal capsule, SPL: splenium of corpus
callosum, CPC: choroid plexus calcification.
23
Figure Legends
Figure 1: VOIs from healthy volunteers. VOIs are created semi-automatically based on
average normalized QSM images.
Figure 2: Flow diagram for the enrollment of study participants.
Figure 3: QSM of gadolinium phantom: 3D-EPI (a), single-TE GRE (b), and multi-TE GRE
(c) with gradually increased concentrations. ROIs are created as circular regions in the
center of the balloon to minimize the effect of adjacent streaking artifacts, with a control
ROI placed in the water region (d). Gadolinium concentrations of each phantom was as
follows: 0.25 (red), 0.5 (green), 0.75 (blue), 1.0 (yellow), 1.5 (light blue), 2.0 (orange), and
2.5 mmol/L (brown). An ROI was also placed on the water (purple, surrounding water).
Figure 4: Measured and theoretical susceptibility values of three QSMs are shown. The
regression line between the measured and theoretical value of 3D-EPI QSM shows
excellent linearity. Corresponding values of GRE QSMs (single-TE and multi-TE) are also
equivalent. Upper row: Skyra; lower row: Prisma.
Figure 5: Comparison of average QSMs among all volunteers generated from 3D-EPI,
single-TE GRE, and multi-TE GRE on the same slice. The intensities of most brain
structures are visually consistent.
24
Figure 6: Representative individual QSM images from healthy volunteers. Individual QSM
generated from 3D-EPI, single-TE GRE, and multi-TE GRE on the same slice.
Figure 7: Mean susceptibility values of VOIs are comparable between the 3 QSM
sequences. No significant difference in mean susceptibility was seen between image
sequences for QSM (ANOVA, P = 0.530). RGP: right globus pallidus, LGP: left globus
pallidus, RRN: right red nucleus, LRN: left red nucleus, RSN: right substantia nigra, LSN:
left substantia nigra, RP: right putamen, LP: left putamen, RDN: right dentate nucleus,
LDN: left dentate nucleus, RCN: right caudate nucleus, LCN: left caudate nucleus, SPL:
splenium of corpus callosum, ROR: right optic radiation, LOR: left optic radiation, RIC:
right internal capsule, LIC: left internal capsule.
Figure 8: Scatter plots and Bland-Altman plots of healthy volunteers. (a) The regression
line of 3D-EPI QSM in the study of healthy volunteers also demonstrates excellent linearity,
approaching the linearity between GRE QSMs. (b) Low estimated biases are depicted in
Bland-Altman plots (to single-TE GRE, mean difference 4 ppb, 95%CI -15 to 23 ppb; to
multi-TE GRE, mean difference 3 ppb, 95%CI -15 to 20 ppb), reflecting the low probability
of systematic error. As a reference, the plot between single-TE and multi-TE GRE QSM
showed a mean difference of -1 ppb (95%CI -16 to 14 ppb). Several outliers are present,
but the proportion is considerably small compared to the 680 measurement points in total.
Figure 9: Representative images of patients with cerebral microbleeds. 3D-EPI QSM (a-c)
and GRE-QSM (d-f) are shown. 3D- EPI and GRE were obtained on two different days.
Each paired image is obtained from different patients, showing paramagnetic spots
25
suggesting microbleeds in the left parietal lobe (a, d), right putamen (b, e), and cerebellum
(c, f). These microbleeds are visually equivalent on both 3D-EPI QSM and GRE-QSM.
Supplemental Figure 1: Outline of QSM reconstruction. (a) Raw-phase image (unfiltered)
from 3D-EPI or GRE acquisition. (b) Result of the Laplacian-based phase unwrapping. (c)
Corresponding magnitude image was used for creating (d) brain mask by using BET. (e)
Tissue phase map as the result of background phase removal (V-SHARP), of which
furtherly processed using the iLSQR algorithm to create (f) QSM.
26
Figure 1
Click here to access/download;Figure;Fig1.tif
Figure 2
Click here to access/download;Figure;Fig2.tif
Figure 3
Click here to access/download;Figure;Fig3.tif
Figure 4
Click here to access/download;Figure;Fig4.tif
Figure 5
Click here to access/download;Figure;Fig5.tif
Figure 6
Click here to access/download;Figure;Fig6.tif
Figure 7
Click here to access/download;Figure;Fig7.tif
Figure 8
Click here to access/download;Figure;Rev_Fig8_20200528.tiff
Figure 9
Click here to access/download;Figure;Fig9.tif
Supplemental Table 1a: Size of the volume of interest of healthy volunteers’ study (MNI
space)
No
VOI
Size (mm3)
Right Dentate Nucleus
1358.13
Left Dentate Nucleus
1439.05
Right Substantia Nigra
883.55
Left Substantia Nigra
833.98
Right Red Nucleus
213.60
Left Red Nucleus
188.81
Right Putamen
3061.80
Left Putamen
3252.07
Right Globus Pallidus
1502.47
10
Left Globus Pallidus
1469.66
11
Right Caudate Nucleus
1776.57
12
Left Caudate Nucleus
1633.69
13
Right Optic Radiation
310.55
14
Left Optic Radiation
396.58
15
Right Internal Capsule
679.43
16
Left Internal Capsule
737.02
17
Splenium
2708.24
Supplemental Table 1b: Size of the region of interest of choroid plexus calcification
(individual space).
No
Right Choroid Plexus Calcification [mm2]
Left Choroid Plexus Calcification [mm2]
10.53
11.34
12.15
22.68
22.68
24.3
29.97
15.39
8.91
29.16
16.2
14.58
10.53
13.77
14.58
13.77
42.93
46.17
10
12.96
13.77
11
14.58
12.96
12
6.48
7.29
13
17.82
11.34
14
17.01
11.34
15
51.03
50.22
16
11.34
20.25
17
8.1
10.53
18
5.67
8.91
19
7.29
8.91
20
26.73
19.44
21
30.78
12.96
22
8.91
16.2
23
11.34
10.53
24
17.01
53.46
25
68.85
22.68
26
17.01
21.06
27
17.82
12.96
28
22.68
29
18.63
Mean, 19.33 ± 14.18
19.44
Mean, 19.12 ± 12.11
Supplemental Table 2a: Frequency distribution of microbleeds observed in patients in
each QSM sequence
Inter-rater
3D-EPI
QSM
GRE QSM
No CMB
1 CMB
2-3 CMB
> 3 CMB
Sum
No CMB
595
609
596
610
1 CMB
44
41
51
48
2-3 CMB
21
17
22
18
> 3 CMB
15
15
Sum
601
617
46
42
22
18
15
684
684
Supplemental Table 2b: Frequency distribution of microbleeds observed in patients in
each rater
Intra-rater
RATER A
No CMB
RATER
1 CMB
2-3 CMB
> 3 CMB
Sum
EPI
GRE
EPI
GRE
EPI
GRE
EPI
GRE
EPI
GRE
No CMB
596
601
610
617
1 CMB
43
37
48
42
2-3 CMB
14
14
18
18
> 3 CMB
Sum
596
601
51
46
18
18
15
15
684
684
Supplemental Figure 1
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