AUTOMATED APPROACH FOR WHOLE BRAIN

INFARCTION CORE DELINEATION

Using Non-contrast and Computed Tomography Angiography

Petr Maule

, Jana Klečková

and Vladimír Rohan

Department of Computer Science and Engineering, University of West Bohemia, Pilsen, Czech Republic

Department of Neurology, The University Hospital in Pilsen, Pilsen, Czech Republic

Keywords: Automated Infarction Core Segmentation, Brain Ischemia, Perfusion Blood Volume, Volumetric Maps,

Acute Stroke.

Abstract: This article proposes automated approach for whole brain infarction core delineation while using only non-

contrast computed tomography and computed tomography angiography. The main aim is to provide

additional information measuring infarction core volume while exceeding certain level is contraindication of

early recanalization. Process of generation of Perfusion Blood Volume maps is described first followed by

description of process of infarction core delineation. Verification of correctness is based on comparison

against follow-up examinations. Discussion and future works summarizes weaknesses of the method and

steps for improvement.

1 INTRODUCTION

Acute stroke is the third leading cause of death and

first leading cause of disability in population over 60

years old. When we use computed tomography the

best localization and stoke visualization can be

reached by perfusion examination (CTP). This kind

of examination has limitations depending on the

device and settings restrictions. Radiation dose is

another factor which must be taken into account.

CTP is generally limited in width of the acquired

area. Early recanalization is a treatment of choice in

acute stage of ischemic stroke. This kind of

treatment can be used at those patients who did not

exceed certain level of infarction core. Evaluation of

actual infarction core tissue volume is not possible

from CTP in many cases because of limited acquired

width while the volume must be summed over all the

brain tissue. Our study is concerned in the detection

of the necrotic tissue and computation of its volume.

To cover whole area of the brain we use different

examinations – computed tomography angiography

(CTA) and non-contrast computed tomography

(NCCT). This article deals with assumption that

there exists certain level of density increase between

CTA and NCCT (due to contrast material) where all

voxels with lower increase are considered as the

necrotic core. Study (Wintermark, 2006) present

optimal level in range 2.0 ml/100g to 2.3 ml/100g

using CTP method.

The method proposed here tries to find the way

for fully automated detection and delineation of the

infarction core. It is based on studies (Hamberg,

1996) and (Hunter, 2003). To ensure objectivity of

results we compare findings with follow-up non-

contrast CT examination where the final infarction

core area has significant density decrease compared

to the non-contrast CT acquired at the time of patient

admission.

Following sections describe each step of the

processing and the final section summarizes results

and mentions our future plans.

2 INPUT EXAMINATIONS

Our study contained examinations of 32 patients

with acute ischemia. For each patient we had 2 pairs

of examinations. One pair acquired at the time of

admission and the follow-up was acquired one day

after. Each pair consisted of non-contrast CT

examination and CT angiography.

All examinations were performed using a dual-

source CT (Somatom Definition, Siemens

433

Maule P., Kle

cková J. and Rohan V..

AUTOMATED APPROACH FOR WHOLE BRAIN INFARCTION CORE DELINEATION - Using Non-contrast and Computed Tomography Angiography.

DOI: 10.5220/0003651704250429

In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (KDIR-2011), pages 425-429

ISBN: 978-989-8425-79-9

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

Healthcare, Forchheim, Germany). First, an

unenhanced brain CT using a spiral technique with

the following parameters was performed in all

patients: collimation 2× (32 × 0.6 mm) with

simultaneous acquisition of 64 slices by means of a

z-flying focal spot (double z-sampling),

reconstruction slice width 6 mm without overlap,

and—in addition—0.75 mm with a reconstruction

increment of 0.5 mm. A medium-smooth head

kernel (H25) was used for all reconstructions.

All CT angiography, ranging from the aortic arch

to the vertex of the head, was performed in a dual-

energy (DE) mode using 140-kV tube voltage for

measurement system A and 80-kV tube voltage for

measurement system B. Collimation was again 2×

(32 × 0.6 mm) with simultaneous acquisition of 64

slices by means of a z-flying focal spot (double z-

sampling). The examinations were performed after

application of an iodine contrast medium (60 ml) of

400 mg/ml at a flow of 4 ml/s with subsequent saline

flush using 50 ml of saline solution.

For each examination, we reconstructed two

image data sets, one at 140 kV and one at 80 kV. A

medium-smooth head kernel (H25) was used for all

dual-energy reconstructions.

From all these examinations we have chosen 18

patients who had significant findings on the follow-

up non contrast examination. From this group we

have chosen 6 patients because of infarction core

location in a white matter where the method has

better results (see Discussion). Examinations of

those six patients underwent following processing.

3 METHOD DESCRIPTION

3.1 Overview

We have developed prototype software processing

input examinations resulting in binary volumetric

maps where each voxel represents information

1=infarction core, 0=non infarction core. Whole

process can be described by these parts:

 Registration

 Segmentation

 Subtraction

 Infarction core delineation

Method requires a pair of examinations - NCCT

and CTA. First these examinations are registered to

each other. After this step segmentation follows by

removing non-brain areas and large vessels. The

same way we process both examinations and

afterwards we subtract non-contrast examination

from angiography thus we get values of density

enhancement caused by the contrast material in

Hounsfield's units. Infarction core delineation

follows using a threshold value. The aim of our

study is to find the best threshold value which will

lead to best fit with the findings of the follow-up

findings. The best threshold value is found by ROC

analysis described later.

3.2 Registration

Method requires a pair of examinations - NCCT and

CTA. First these examinations are registered to each

other. We use open source software ITK (Yoo,

2002) for registration process. First we convert all

source examinations to 2 mm slice thickness to

avoid memory complexity problems of using 1 mm

or less of slice thickness. Reconstructions in 2 mm

slice thickness are generated also by the ITK

software.

We use rigid registration with Mattes Mutual

Information image to image metric, multi resolution

pyramidal approach and versor rigid transformation

optimizer with stopping criteria of 200 iterations.

Result of the registration is angiography

examination registered to non-contrast examination

thus voxels of both examinations correspond to each

other.

3.3 Segmentation

Segmentation step just removes “non-important”

areas like skull bones, large vessels and other non-

brain areas like eyes, ears, etc. from both NCCT and

CTA examination. Large vessels are removed by

thresholding leaving just voxels with density

between 20-80 HU.

3.4 Subtraction

Simple subtraction on voxel by voxel basis does not

provide satisfactory results because of high ratio of

noise. Denoising pre-processing is required despite

of missing information about this step in literature.

Denoising process is crucial step and have high

influence on detection of infarction core. We tried

denoising by a method of averaging neighborhood

area. The method computes average density for all

voxels in a cuboid area with the voxel as the center

of the area and dimensions m, n, o where m, n, o are

dimensions along axes x, y and z. All voxels get

new density equal to the average density of the area.

Subtraction follows after the denoising process

(Figure 1). It is based on voxel by voxel basis.

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434

3.5 Infarction Core Delineation

The goal of this step is to automatically find the area

corresponding to the necrotic tissue area. The

procedure starts with the subtracted volume where

voxels contain information about local density

increase caused by the contrast material. Infarction

core can be characterized by a low density increase –

let's call separating threshold as τ. All values below

the threshold are considered to be infarction core

and all voxels above the threshold are marked as

non-infarction core. Not all voxels having the value

below the threshold are in fact infarction core. We

may assume that infarction core is the largest

continuous area formed by voxels with values below

the threshold. Thus we can find all the continuous

areas, measure their dimensions as a count of voxels

of the group and keep just the largest group.

Nevertheless the process of finding infarction

core is not that simple. Large groups of voxels

below the threshold can be found also at the bottom

parts of brain and head, including under-brain areas

which can be present because of non-perfect brain

Figure 1: Subtraction on voxel by voxel basis, perfused

blood volume map.

area segmentation. To get rid of these groups we

calculate average density of each group and all

groups having average density below -2 we remove

from detection of infarction core.

Having the largest continuous area of pixel below

the threshold τ we can mark all voxels belonging to

this group as those belonging to the infarction core

area (Figure 2). All other voxels outside this group

we can mark as non-infarction core.

3.6 Follow-up Examinations

We processed follow-up examinations in same way

using only non-contrast follow-up examination

(NCCT2) and non-contrast examination acquired at

the time of patient admission (NCCT1). After pre-

processing we made subtraction and manually we

found threshold for infarction core. NCCT2 has

significant density decrease in areas of real

infarction core and we can mark real infarction core

as shown on Figure 3. Follow-up findings were

confirmed by clinician. We can use these findings

for our method correctness verification and also for

tuning parameters to produce best match. We found

best parameters using ROC analysis separately for

each patient.

Figure 2: Method Result - Infarction Core Delineation

(White Area).

4 ROC ANALYSIS

We have already described method of processing

examinations and also way of preparing data for

verification and tuning parameters process. We

made ROC analysis for each patient separately. We

set ROC analysis variables to dimensions of the

considered area (for pre-processing) and threshold

value for infarction core delineation. The variable

for threshold value is for ROC analysis in

Hounsfield units. We limited values range as

follows:

 Dimensions m, n, o – we set m=n with range (2-

8 mm) and range of o (2-14 mm), dimensions

AUTOMATED APPROACH FOR WHOLE BRAIN INFARCTION CORE DELINEATION - Using Non-contrast and

Computed Tomography Angiography

435

are taken symmetrically, so whole considered

area has size of 2*m, 2*n, 2*o.

 Threshold values range from 0 to 5 HU with

step 0.5 HU.

We use Matthews correlation coefficient to measure

similarity between our method results and follow-up

findings. ROC analysis outputs for all patients

separately is summarized in Table 1.

5 ABSOLUTE VOLUME

EVALUATION

Figure 3: Follow-up examination with marked infarction

core (white).

To convert threshold values (relative density

increase in Hounsfiled's units) to absolute numbers

in mL/100g (column PBV in Table 1) we use

equations from (Hamberg, 1996). First we compute

correction factor CF using Equation 1.

10.85*

Hct

−

(1)

Hct

means large-vessel hematocrit. We use value

0.4 which comes as average value (for man and

woman). Next value which we must find is ΔHU

Blood

which means an average density increase in large

vessels. We need to find this value in an automatic

way. First we find all brain tissue voxels with

densities in range from 40 HU to 50 HU in NCCT1.

For all these voxels we find corresponding voxels in

angiography examinations, but only those having

increases more than 300 HU aiming to select only

increases of the large vessels. Finally we take

median from all these increases and we call it as

ΔHU

Blood

. Having ΔHU

Blood

value, we can use

Equation 2:

100

1.05

Blood

CBV CF

(2)

where ΔHU is density increase in any place of the

map as described in the part of our method

description. Constant 1.05 is density of brain tissue

in g/100ml (Sabatini, 1991). If we put ΔHU value of

the threshold we get absolute threshold value in

mL/100g.

Table 1: ROC Analysis Results, m, o,= dimensions of area

used for denoising, τ = infarction core threshold, PBV =

threshold τ in absolute values, Matthews = correlation

coefficient.

Patient

m [mm] o [mm] τ [HU]

PBV

[mL/

100g]

Mat-

thews

1 8 8 4,5

1,15 0,48

2 5 8 5

1,64 0,42

3 5 11 2

0,5 0,37

4 4 8 4,5

1,17 0,28

5 3 8 4,5

1,4 0,23

6 2 14 3,5

1,17 0,16

6 RESULTS

We processed examinations of 6 patients resulting in

volumetric maps with additional information which

voxels correspond to infarction core and which not.

We made an ROC analysis for each patient

separately to find the best tuning of parameters and

thus getting the best match against follow-up

infarction core findings. The best match is supposed

to reflect high ratio of similarity expressed by

Matthew's coefficient.

We had group of 32 patient where only 16 of

them had significant finding on follow-up

examinations. From these 16 patients our method

correctly determined infarction core only at 6

patients with precision from 16% to 48%.

Results are not yet sufficient enough for method

to be used in clinical practice. But the method seems

like a good starting point for automated infarction

core delineation.

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436

7 DISCUSSION

Although results are not sufficient there are many

new ideas which can lead to provide better results in

future. The main point is that the method provide

satisfactory results in the white brain matter. White

matter can be generally characterized by a lower

density increase due to injected contrast material

than in gray matter. White matter ischemic areas

have even lower increase and such areas is easier to

find by thresholding with threshold below the

normal increase for the white matter.

Ischemia in the grey matter is another point.

Ischemic area are optically well visible on our

pefusion maps but automatic process of infarction

core delineation fails to mark them. The reason is

that there is significant density decrease in cortical

gray matter areas compared to non-ischemic cortical

gray matter areas but the decrease is not enough to

fall down below normal values for the white matter.

Thus making threshold value higher above the

normal white matter increase leads to marking larger

area containg also normal (non ischemic) areas of

the white matter.

Having threshold values lower then normal

density increase of white matter leads to selection of

smaller infarction core areas than on the follow-up

findings. That is why Matthew's coefficient gives the

highest values at relatively low level and ROC

analysis does not find better combination of

parameters to produce better match.

8 FUTURE WORK

We believe that we can improve our results by

introduction of symmetry as mentioned in (Hunter,

2003). Symmetry information can lead to detect

ischemic areas also in cortical gray matter despite of

any threshold used by current method. The

technique of symmetry can provide information like

local density decrease compared to the other side.

We may put this decrease some kind of weight and

use it as another criterion available when deciding

whether the voxel belongs to the infarction core.

ACKNOWLEDGEMENTS

The work presented in this paper is supported by

The Czech Science Foundation project 106/09/0770

dealing with brain perfusion modelling.

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Computed Tomography Angiography

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