cerebral artery segmentation based on magnetization-prepared … · we employed the dice...

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Cerebral artery segmentation based on magnetization-prepared two rapid acquisition gradient

echo multi-contrast images in 7 Tesla magnetic resonance imaging

Uk-Su Choia, b

, Hirokazu Kawaguchic, 1

, and Ikuhiro Kidaa, b, *

a Center for Information and Neural Networks, National Institute of Information and Communications

Technology, Japan

b Graduate School of Frontier Biosciences, Osaka University, Japan

c Siemens Healthcare K.K., Japan

*Corresponding author

Ikuhiro Kida, Ph.D.

Center for Information and Neural Networks,

National Institute of Information and Communications Technology

Email: [email protected]

1 Present address: Division of Advanced Multidisciplinary Research, Tokyo Medical and Dental

University,

.CC-BY-NC-ND 4.0 International license(which was not certified by peer review) is the author/funder. It is made available under aThe copyright holder for this preprintthis version posted December 12, 2019. . https://doi.org/10.1101/2019.12.12.870840doi: bioRxiv preprint

https://doi.org/10.1101/2019.12.12.870840

http://creativecommons.org/licenses/by-nc-nd/4.0/

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Abstract

Cerebral artery segmentation plays an important role in the direct visualization of the human brain to

obtain vascular system information. At ultra-high field magnetic resonance imaging, hyperintensity of

the cerebral arteries in T1 weighted (T1w) images could be segmented from brain tissues such as gray

and white matter. In this study, we propose an automated method to segment the cerebral arteries

using multi-contrast images including T1w images of a magnetization-prepared two rapid acquisition

gradient echoes (MP2RAGE) sequence at 7 T. The proposed method employed a seed-based region-

growing strategy with the following procedures. (1) Two seed regions were defined by Frangi filtering

applied to T1w images and by a simple calculation from multi-contrast images, (2) the two seed

regions were combined, (3) the combined seed regions were expanded using a region growing

algorithm to acquire the cerebral arteries. Time-of-flight (TOF) images were obtained as a reference

to evaluate the proposed method. We successfully performed vessel segmentations from T1w

MP2RAGE images, which mostly overlapped with the segmentations from the TOF images. As large

arteries can affect the normalization of anatomical images to the standard coordinate space in

functional and structural studies, we also investigated the effect of the cerebral arteries on spatial

transformation using vessel segmentation by the proposed method. As a result, the T1w image

removing the cerebral arteries showed better agreement with the standard atlas compared with the

T1w image containing the arteries. Thus, because the proposed method using MP2RAGE images can

obtain brain tissue anatomical information as well as cerebral artery information without need for

additional acquisitions such as of the TOF sequence, it is useful and time saving for medical diagnosis

and functional and structural studies.


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1. Introduction

The visualization of the cerebral vasculature plays an important role in the diagnosis of

vascular diseases such as hemorrhage or aneurysms (Stapf et al., 2006; Matsushige et al., 2016) but

also to differentiate between anatomical areas in different brain regions (Neumann et al., 2016).

Uncertain segmentation of brain tissues and blood vessels can lead to misidentifying the localization

of the electroencephalographic signal source and lead to anatomical confounding in certain cortical

regions with large vessels in studies of brain function and of cortical characteristics, such as cortical

thickness(Fiederer et al., 2016; Viviani, 2016). Therefore, accurate segmentation of blood vessels is

essential for neuroimaging research and medical image analysis.

Time-of-flight (TOF) magnetic resonance angiography (MRA) has been widely used as a

non-invasive technique to contrast between blood and brain tissues (Lévy et al., 1994). The

unsaturated blood flow of cerebral arteries on the TOF image is brighter than the saturated

background of other brain tissues. As at ultra-high fields (UHFs), the longitudinal relaxation times

(T1) are extended both for blood and brain tissues, leading to enhanced contrast between them,

utilization of UHFs for vessel imaging is more efficient (Park et al., 2018). However, there is less

anatomical information such as gray matter (GM) and white matter (WM) in TOF images due to

background suppression.

Magnetization-prepared rapid gradient echo (MPRAGE) is a candidate for the visualization

of blood vessels indicating high signal intensity of arterial blood vessels while the background shows

intermediate signal intensities, resulting in high vessel-to-background contrast and is potentially

suitable for MRA display (Penumetcha et al., 2008, Wrede et al 2014). Although the T1 weighted

(T1w) image in the MPRAGE sequence provides good anatomical information and differentiation of

GM and WM, it is difficult to automatically segment the high signal arteries from the intermediate

signal tissues in this image. The enhanced contrast between cerebral arteries and brain tissues in the

T1w image at UHF can help automatically segment cerebral arteries from the brain (Maderwald et al.,

2008; Van de Moortele et al., 2009; Fiederer et al., 2016; Gulban et al., 2018).

Magnetization prepared two rapid acquisition gradient echoes (MP2RAGE) can obtain three

different images, a T1 map (T1) and a uniform T1w image with background noise (UNI) and without

background noise (UNIDEN) derived by two images at different inversion times, i.e., the first and

second inversion gradient echo images (INV1 and INV2, respectively) (Marques et al., 2010). The

MP2RAGE sequence has an advantage over the MPRAGE sequence for signal homogeneity in the

T1w image (UNI) because the inhomogeneity is cancelled out when the T1w image in the MP2RAGE

sequence is calculated from two inversion gradient echo images. As multi-contrast images in the

MP2RAGE sequence are useful to segment brain tissues such as GM, WM, and cerebrospinal fluid

(CSF) (Choi et al., 2019), the MP2RAGE sequence may also help segment the cerebral arteries.

In this study, we propose an automated method to segment the cerebral arteries using multi-


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contrast images in the MP2RAGE sequence at 7 T. The proposed method is based on our brain tissue

segmentation (MP2rage based RApid SEgmentation; MP2RASE) (Choi et al., 2019) combined with a

seed-based approach and a region-growing algorithm. The proposed method using the MP2RAGE

sequence was evaluated using vessel segmentation with the TOF sequence as a reference. In addition,

we investigated the effect of cerebral arteries on the spatial transformation of T1w images to a

standard coordinate space, i.e., the Montreal Neurological Institute (MNI) space, which is ordinarily

used in the process of functional and structural MR imaging (MRI) studies.

2. Materials and Methods

2.1 Subjects

Four volunteers (four men, aged 34–49 years) without history of neurological disease or any other

medical condition participated in this study after providing written informed consent. All experiments

were approved by the Ethics and Safety Committees of National Institute of Information and

Communications Technology.

2.2 MRI acquisition

The experiments were performed on a 7-T investigational MRI scanner (MAGNETOM 7T; Siemens

Healthineers, Erlangen, Germany) with a 32-channel head coil (Nova Medical, Wilmington, MA).

The MP2RAGE was acquired using a research sequence from Siemens Healthineers and using the

following parameters: repetition time = 5000 ms, echo time = 3.43 ms, inversion times = 800 ms/2600

ms, flip angles = 4°/5°, matrix = 368 × 368 × 256, voxel size = 0.7 × 0.7 × 0.7 mm3, GRAPPA

acceleration factor = 3, and scan time = 9 min 27 s (Choi et al., 2019). To evaluate the proposed

method, we additionally acquired 3D TOF images with the following parameters: voxel resolution =

0.3 x 0.3 x 0.4 mm3, repetition time = 20 ms, echo time = 4.47 ms, flip angle = 18°, matrix = 704 ×

704 × 192, GRAPPA acceleration factor = 4, and scan time = 12 min 19 s (Wrede et al., 2014;

Matsushige et al., 2016). The acquisition region of the TOF sequence included large cerebral arteries

such as the anterior cerebral artery (ACA), middle cerebral artery (MCA), and internal carotid artery

(ICA).

2.3 Vessel segmentation using MP2RAGE images

The image processing pipeline in the proposed method is shown in Fig. 1. All MP2RAGE images

were stripped to remove non-brain structures using the BET toolbox in FSL 5.0.10 (FMRIB, Oxford,

UK) during the brain segmentation procedure. After removing non-brain structures, we normalized

the intensities of all images for the brain tissue segmentation using a feature-scaling method described

in Eq. 1 because MP2RAGE images have different intensity scales. Sraw indicated raw intensities, min

(Sraw) indicated the minimum intensity, and max (Sraw) indicated the maximum intensity of each


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MP2RAGE image.

��

�� (1)

First, we employed a Frangi filter (Frangi et al., 1998), which has been used for vessel segmentation

in TOF (Hsu et al., 2019) and MPRAGE (Fiederer et al., 2016; Oliveira et al., 2016; ), to construct a

binary vessel mask (Seed 1 in Fig. 1) from the normalized UNI (nUNI) image (Kroon, 2009). The

filtering was performed with the following parameters; α = 0.5, β = 0.5, γ = 5.0 (Antiga, 2007). The

filtered image was binarized with a threshold of a 10% robust range of non-zero voxels and masked

out by a WM binary mask, which was segmented using our previous method (Choi et al., 2019).

Second, a binary vessel mask (Seed 2 in Fig. 1) was constructed using three normalized contrast

images (nINV1, nINV2, and nT1) and a CSF binary mask, which was also segmented using our

previous method (Choi et al., 2019). The binary vessel masks, i.e., seed 1 and seed 2, were combined

for the region-growing procedure. Finally, the vessel mask was obtained by expanding the voxels of

the combined binary vessels masks by a region growing procedure using our in-house MATLAB code

including a recursive region growing algorithm (Daniel, 2011).

2.4 Vessel segmentation using TOF images

The intensities of the TOF images were normalized using the feature-scaling method described in Eq.

1. The vessels were segmented from the normalized TOF images using a Frangi filter with the

following parameters; α = 0.5, β = 0.5, γ = 5.0. The filtered images were binarized with a threshold of

a 25% robust range of non-zero voxels to construct the TOF binary vessel mask.

2.5 Comparison between vessel segmentations using MP2RAGE and TOF images

We employed the Dice coefficient to evaluate the similarity of the vessel segmentations of MP2RAGE

images with those of TOF images as a reference. Before evaluating the similarity, the two datasets

were coregistered because the dimensions between TOF and MP2RAGE images differ

(Supplementary Fig. 1). TOF images were initially registered to the UNIDEN of the MP2RAGE

sequence to acquire the transformation matrix using FLIRT in FSL. The matrix was applied to binary

vessel masks from TOF images with nearest-neighbor interpolation. Next, the pre-registered vessel

masks from the TOF images were registered to vessel masks from MP2RAGE for fine spatial

transformation using the non-linear registration method of Advanced Normalization Tools (ANTs)

(Avants et al., 2011) with nearest-neighbor interpolation. After registration, we defined two fields of

view (FOVs) for the evaluation with the Dice coefficient. One FOV was the acquisition region of the

TOF sequence, which was fully covered by the MP2RAGE image (Supplementary Fig. 1). Another


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FOV was manually defined including large arteries such as the ACA, MCA, and ICA as local FOV

regions. The vessel segmentations within two FOVs were evaluated to calculate the Dice coefficient

with Eq. 2.

�� |�� |

|�� |�|�� | 100 (2)

2.5. Spatial normalization to the standard space

Cerebral arteries in the UNIDEN T1w image were firstly removed based on vessel segmentation by

the proposed method using an in-house script. The T1w images with and without cerebral arteries

were spatially normalized to the MNI152 template in FSL using an in-house script including ANTs.

3. Results

3.1 Vessel segmentation using MP2RAGE images

Two binary vessel masks were constructed by independent processes: Frangi filtering of the nUNI

image and a simple calculation of MP2RAGE images. The masks partially overlapped, indicating that

the two seed regions represented independent parts of the cerebral arteries (Fig. 2). Most voxels in the

arteries were extracted as a vessel mask by Frangi filtering (Fig. 2a) but some voxels in relatively

larger arteries were not obtained. Conversely, the voxels, which were not detected by Frangi filtering,

were represented as a vessel mask using a simple calculation of MP2RAGE images (Fig. 2b).

Application of a region-growing algorithm for a combination of seed 1 and seed 2 extracted most

voxels in the arteries as a vessel mask (Fig. 2c), which shown in 3D reconstruction view of the

cerebral arteries (Fig. 3). Large arteries such as the MCA, ACA, and ICA were well segmented.

3.2 Comparison between vessel segmentations of MP2RAGE and TOF images

Figure 4 shows a comparison of vessel segmentations from MP2RAGE images by the proposed

method and from TOF images by Frangi filtering in 3D reconstruction view. Most arteries extracted

from the MP2RAGE images by the proposed method showed good agreement with the ones extracted

from the TOF images. Larger arteries such as the MCA, ACA, and ICA well overlapped, but

peripheral branches of arteries were mismatched between vessel segmentations of MP2RAGE and

TOF images. We evaluated the proposed method by comparison to TOF vessel segmentation using the

Dice coefficient for whole FOV regions, which overlapped between the two sequences, and the local

FOV regions including larger arteries (Fig. 5). The Dice coefficients with vessel segmentation from

TOF images were approximately 40% for the whole FOV regions and 55% for local regions when

vessels in the T1w MP2RAGE image were segmented by Frangi filtering (seed 1), which was the

same method as that used in TOF segmentation. The coefficients by simple calculation from the

MP2RAGE images (seed 2) for both regions were almost the same as those obtained with Frangi


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filtering. After combining the two seeds, the similarities of vessel segmentation dramatically

improved from 40 to 57% for the whole FOV regions and from 55 to 73% for the local FOV regions.

The Dice coefficients showed a lower similarity for the whole FOV region because this approach may

not be able to properly handle the small branches of large cerebral arteries.

3.3 Influence of cerebral arteries on normalization to standard coordinate space

We examined the influence of the cerebral arteries on the spatial transformation of T1w images to a

standard coordinate space, i.e., the MNI space, which is ordinarily used in the process of functional

MRI and voxel-based morphometry (VBM). Figure 6 shows the spatial transformation in the T1w

image with and without cerebral arteries, segmented by the proposed method. By removing the MCA

and ACA from the T1w image, the GM intensities in the frontal orbital cortex and cingulate cortex

recovered when the T1w image was normalized to the standard coordinate space. Thus, the T1w

image after normalization excluding the cerebral arteries is very consistent with the MNI template in

FSL.

Discussion

We developed a novel method to automatically segment cerebral arteries with a region

growing algorithm for two combined seed masks from T1w MP2RAGE sequence images at 7 T. The

two seed masks were defined by Frangi filtering of the T1w image and by a simple calculation of

multi-contrast images in the MP2RAGE sequence. The seed mask of the region growing algorithm,

which has been widely used as a segmentation method for medical images (Malek et al., 2012), is

important to define core voxels of arteries for the robust region growth of cerebral arteries. Most of

the seed voxels were defined by Frangi filtering, which has been employed to segment blood vessels

in the retina (Oliveira et al., 2016) and the brain (Fiederer et al., 2016; Hsu et al., 2019). The filtering

can be useful to differentiate blood vessels from brain tissues in high contrast images, i.e., TOF

images but not with less suppressed tissue images such as T1w image (i.e., UNI) in the MP2RAGE

sequence. The advantage of multiple contrasts for segmentation in MP2RAGE images has been

described in previous studies (Wang et al., 2018; Choi et al., 2019). Therefore, as more seed voxels

might be needed for segmentation, we additionally defined the seed voxels using a simple calculation

of multi-contrast images from the MP2RAGE sequence, which provided better results.

As the major cerebral arteries are located in frontal parts of the brain such as the anterior

cingulate, orbitofrontal, and insular cortices, which are core brain regions in cognitive studies

(Bechara et al., 2000; Bush et al., 2000; Mutschler et al., 2009), the large cerebral arteries might

influence the standard space transformation of T1w images. Better agreement with the MNI atlas was

achieved when the T1w image was transformed to exclude the cerebral arteries using vessels mask

extracted by the proposed method compared with the agreement of T1w images that contained the


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cerebral arteries (Fig. 6), which led to more precise transformation evaluation. Precise transformation

information could be important to assess the thickness of the cerebral cortex and volume using

methods such as VBM (Kotikalapudi et al., 2019) and to obtain accurate location of neuronal

activation such as olfactory functional MRI study (Gottfried et al., 2002). Especially, as recent UHF

studies have focused on fine brain structures and brain function at the submillimeter scale without

spatial smoothing, functional images with high spatial resolution can be affected because cerebral

arteries have the potential to induce errors of structural transformation in those cortical areas, which

can induce mis-localization of specific brain function.

The proposed method has limitations to be addressed in further studies. The proposed

method could reliably detect major cerebral arteries such as the MCA, ACA, and ICA but failed to

segment small peripheral vessels possibly due to less contrast with the surrounding brain tissues

because of limited resolution, slow blood flow rate, and impaired image quality involving distortion

or inhomogeneity. B1 mapping of the SA2RAGE sequence (Eggenschwiler et al., 2012) could

possibly improve the inhomogeneity of the T1w image at UHFs. The enhanced contrast to noise ratio

between GM and WM could also help improve the segmentation by changing the acquisition MR

parameters of the MP2RAGE sequence such as the inversion times and flip angles (Tanner et al., 2012;

Marques and Gruetter, 2013). Although increased spatial resolution could be another possibility to

measure peripheral arteries, a low signal to noise ratio and longer scan time comprise trade-offs for

the MP2RAGE sequence.

We employed vessel segmentation of TOF images with Frangi filtering as reference to

compare with the proposed method based on the Dice coefficient. The quality of vessel segmentation

in both TOF and the proposed method depends on the parameter and threshold of Frangi filtering,

which also depends on the quality of the TOF (Phellan et al., 2017). In fact, parts of major arteries

were not segmented using TOF images with Frangi filtering. Further evaluation of the proposed

method will be needed by a quantitative verification with the different acquisition and analysis

parameters (Frangi filtering and threshold), which may lead to further improvement.

In conclusion, we proposed a novel method to automatically segment cerebral arteries using

MP2RAGE images at 7 T. As we previously developed a method to segment brain tissues using the

MP2RAGE sequence (MP2RASE) (Choi et al., 2019), together, a single MP2RAGE sequence

acquisition could provide automated brain tissue masks of GM, WM, and CSF as well as of blood

vessels. In addition, the proposed method could be used to derive accurate spatial transformation

information with reference to the standard coordinate space by removing the influences of cerebral

arteries from T1w images. Thus, because the proposed method of vessel segmentation using

MP2RAGE sequence can obtain brain tissue anatomical information as well as cerebral artery

information without any additional acquisitions such as TOF sequence, it is useful and time saving for

medical diagnosis and functional and structural studies.


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Acknowledgements

We would like to thank Dr. Kober for the development of MP2RAGE sequence and technical supports.

This work was supported by the JSPS KAKENHI [grant number JP18K07701, JP18H04084, and

JP19H03537] from the Japanese Ministry of Education, Culture, Sports, Science and Technology (IK).

Declarations of interests

HK was employed by Siemens Healthcare K.K. Siemens Healthcare K.K. did not have any additional

role in the study design, data collection and analysis. UC and IK have no conflicts of interest.

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Abbreviations:

ACA: anterior cerebral artery

CSF: cerebrospinal fluid

FOV: field of view

GM: gray matter

ICA: internal carotid artery

INV1: first inversion gradient echo image

INV2: second inversion gradient echo image

MCA: middle cerebral artery

MNI: Montreal Neurological Institute

MRA: magnetic resonance angiography

MPRAGE: Magnetization-prepared rapid gradient-echo

MP2RAGE: magnetization-prepared two rapid acquisition gradient echoes

TOF: time-of-flight

UHF: ultra-high field

UNI: uniform T1w image with background noise

UNIDEN: uniform T1w image without background noises


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VBM: voxel-based morphometry

WM: white matter


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Figure caption

Figure 1. Image processing pipeline of the proposed method.

Dotted squared images were constructed from the brain tissue segmentation method (Choi et al.,

2019). CSF: cerebral spinal fluid, nINV1: normalized 1st inversion time image, nINV2: normalized 2nd

inversion time image, nT1: normalized T1 map, nUNI: normalized uniform T1w image, WM: white

matter.

Figure 2. Visual presentation of segmentation at major stages. (a) segmentation by Frangi filtering

applied to the T1w image, (b) segmentation by a simple calculation of multi-contrast images, (c)

segmentation in the final result of the proposed method. Segmentation masks were overlaid to the

T1w image, which indicated a sagittal view of regions around the anterior cerebral artery.

Figure 3. 3D visualization of vessel segmentation by the proposed method in all subjects.

Figure 4. Comparison of cerebral artery segmentation between the results from MP2RAGE and

TOF images in all subjects. Red color: overlapped segmentation between two methods, Green color:

segmentation only from MP2RAGE images, Blue color: segmentation only from TOF images.

Figure 5. Similarity between vessel segmentations at major stages from MP2RAGE and TOF

images. Seed 1: segmentation by Frangi filtering, Seed 2: segmentation by a simple calculation of

MP2RAGE images. The Dice values show vessel segmentation from the whole FOV region (whole)

and from large vessel FOV regions (local) in all subjects.

Figure 6. Effect of the cerebral arteries on normalization to the MNI coordinates.


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(a) Normalized T1w image with cerebral arteries, (b) Normalized T1w image without cerebral arteries,

(c) MNI template. The 1st, 2nd, and 3rd columns from the left side indicate the axial view of the

orbitofrontal cortex and insular cortex and the sagittal view of the cingulate cortex, respectively. The

MCA in the 1st and 2nd columns and the ACA in the 3rd column are shown in (a) but not in (b).

Supplementary Figure 1. MP2RAGE and TOF images. (a) uniform T1w image without

background noise from the MP2RAGE sequence, (b) TOF image.


https://doi.org/10.1101/2019.12.12.870840



https://doi.org/10.1101/2019.12.12.870840


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