A Semiautomatic Image Processing Tool to Measure Small Structures

in Magnetic Resonance Images of the Brain at 7 Tesla

Application to Hippocampus Subfields of Patients with Mild Cognitive Impairment

Alessandra Retico

, Graziella Donatelli

, Mauro Costagli

3,4

, Laura Biagi

, Maria Evelina Fantacci

1,5

Daniela Frosini

, Gloria Tognoni

, Mirco Cosottini

2,4

and Michela Tosetti

3,4

Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy

Dipartimento di Ricerca Traslazionale e delle Nuove Tecnologie in Medicina e Chirurgia, Università di Pisa, Pisa, Italy

IRCCS Fondazione Stella Maris, Pisa, Italy

Fondazione Imago7, Pisa, Italy

Dipartimento di Fisica, Università di Pisa, Pisa, Italy

Dipartimento di Medicina Clinica e Sperimentale, Università di Pisa, Pisa, Italy

Keywords: Image Processing Methods, Magnetic Resonance Imaging, Hippocampus, Mild Cognitive Impairment,

Alzheimer Disease.

Abstract: The current availability of Magnetic Resonance (MR) systems that operate at ultra high magnetic field (≥ 7

Tesla) allows the representation of anatomical structures at sub-millimeter resolution. Interestingly, small

structures of the brain, such as the subfields of the hippocampus, the inner structures of the basal ganglia

and of the brainstem become visible. Suitable software packages that allow analyzing and measuring such

small structures are not currently readily available. We developed a semi-automated procedure to measure

the thickness of the stratum radiatum and lacunosum-moleculare (SRLM) of the hippocampus. The change

in the thickness of this subfield of the hippocampal formation is supposed to have a role in the pathological

cognitive decline. Once we developed and validated the semiautomatic procedure on the 7T high-resolution

T2*-weighted images of a healthy volunteer, we carried out a preliminary study on a population affected by

Mild Cognitive Impairment to investigate the correlations of the SRLM thickness with the clinical scores of

the patients, e.g. the Mini-Mental State Examination score and the Free and Cued Selective Reminding Test.

1 INTRODUCTION

The use of ultra high-field (UHF) Magnetic

Resonance Imaging (MRI) systems, operating at

magnetic field strength of 7 Tesla and above, has

opened new perspectives in clinical research studies

(Kraff, 2015; van der Zwaag, 2015). Among the

established advantages of UHF is the high-resolution

structural imaging. Interestingly, small structures of

the brain, such as subfields of the hippocampus

(Thomas, 2008), become visible (Figure 1), but the

understanding of their potential role in the onset of

the Alzheimer’s Disease is still an open research

issue (Kerchner, 2012; Brown, 2014). Several

groups of researchers are working to make available

software packages to identify and measure the

hippocampal substructures with manual,

semiautomatic and automatic procedures (Van

Leemput, 2009; Wisse, 2012; Pipitone, 2014). Their

reliability and usefulness is still under evaluation by

the international scientific community. In particular,

none of the segmentation algorithms that have been

presented so far is able to measure an extremely thin

structure such as the stratum radiatum and

lacunosum-moleculare (SRLM) of the hippocampus.

Recent studies highlighted the relevance of this thin

hippocampal subfield in the early stages of

pathological cognitive decline, especially the mild

Alzheimer’s Disease. Cognitive decline can

represent a symptom of normal aging but also a

forerunner of dementia; within this spectrum of

disease, the Mild Cognitive Impairment (MCI),

defined as a cognitive decline greater than expected

for age and education that can evolve to dementia, is

an interesting transitional stage to investigate.

124

Retico, A., Donatelli, G., Costagli, M., Biagi, L., Fantacci, M., Frosini, D., Tognoni, G., Cosottini, M. and Tosetti, M.

A Semiautomatic Image Processing Tool to Measure Small Structures in Magnetic Resonance Images of the Brain at 7 Tesla - Application to Hippocampus Subﬁelds of Patients with Mild

Cognitive Impairment.

DOI: 10.5220/0005818001240128

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 2: BIOIMAGING, pages 124-128

ISBN: 978-989-758-170-0

Figure 1: High-resolution coronal image of the

hippocampus in vivo acquired at 7 T with 2D T2*-

weighted GRE sequence (TE=22 ms, TR=240 ms, in-

plane resolution 0.3×0.3 mm

, slice thickness = 2 mm). A

slice through the hippocampus body is visible, showing

detailed structures including cornu ammonis (CA), dentate

gyrus (DG), stratum radiatum and lacunosum-moleculare

(SRLM), subiculum (SUB) and parahippocampal gyrus

(PHG).The overall thickness of the SRLM was shown to

be lower in patients with mild Alzheimer’s Disease

compared to healthy subjects (Kerchner, 2010) and to

correlate with memory performance (Kerchner, 2012).

The aim of the present work is to develop a

semiautomatic procedure to measure the SRLM

thickness by using 7T high-resolution T2*-weighted

MR images. Once the algorithm is set up on a

sample image of a healthy volunteer, the

semiautomatic procedure is used to investigate the

relationship between SRLM thickness and clinical

scores in MCI patients.

2 METHODS

We propose a semiautomatic procedure to measure

the thickness of the SRLM, which imports some

basic ideas of the algorithm presented by Kerchner

et al (2012) with the same purpose, and extend it to

make the measure more reliable and reproducible.

2.1 The Semiautomatic Algorithm to

Measure the SRLM Width

First of all an oblique coronal slice prescribed

perpendicularly to the axis of the hippocampus,

where the SRLM is visible, is presented to the user,

who is asked to select the centre of the Region of

Interest (ROI) for the following steps (see Figure 2).

The ROI is zoomed to allow the user to modify the

image contrast and the brightness and to draw a line

by mouse clicking along the curved shape of the

SRLM. Then, the algorithm that estimates the

SRLM width automatically starts running. It

prompts the user intermediate graphs and figures,

which are useful to check in real time the quality and

the reliability of the estimated SRLM width obtained

at the end of the procedure. Going into the details of

the procedure, it consists in the following steps

(Figure 2):

1) the user defined line following the curve

shape of the SRLM is interpolated with a

spline;

2) the normal directions to the spline are

computed and the normal vectors are overlaid

to the original ROI and shown to the user;

3) the image intensity profiles along the normal

directions are computed and mounted in 2D

image, where the hippocampus appears as

unrolled along the SRLM;

4) the SRLM appears as a dark straight band in

this 2D image, which is finally squeezed

along the SRLM direction to obtain the

average of the image intensity across all

normal profiles;

5) as this averaged intensity profile shows a

Gaussian shape, a fit with a Gaussian

function is carried out, and its width (4 σ) is

retained as a measure of the SRLM thickness.

As shown in Figure 2, a first check on the quality

and reliability of the SRLM measure obtained

through this semiautomatic procedure can be done

directly by the user while running the software. For

the procedure to be correctly executed, the manually

delineated line should be placed in the middle of the

SRLM or close to either its upper or lower

boundary. Only in this case the hippocampus will be

correctly unrolled and the picture in Figure 2(d) will

contain straight bands. This correct alignment of the

profiles prevents their average to blur and the SRLM

width to be overestimated.

2.2 Reproducibility of the Measure

The reproducibility of the measure of the SRLM

provided by the semiautomatic procedure has to be

checked. Variability in repeated measures are indeed

induced by the choice of the points along the SRLM

profile operated by the end user, which is scarcely

reproducible from run to run. To this purpose one of

the user of the proposed semiautomatic tool was

asked to repeat ten times the same measurement for

both the left and right hippocampi of one subject.

By means of these measurements the variability

of the measurements has been estimated.

A Semiautomatic Image Processing Tool to Measure Small Structures in Magnetic Resonance Images of the Brain at 7 Tesla - Application

to Hippocampus Subﬁelds of Patients with Mild Cognitive Impairment

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Figure 2: Main steps of the semiautomatic procedure to estimate the thickness of the stratum radiatum and lacunosum-

moleculare (SRLM): a) the user selects a point in the region of interest (ROI); b) the ROI is enlarged to allow the user to

draw the SRLM profile by mouse clicking; c) these point are interpolated with a spline and the normal vectors to the

profiles are computed and shown; d) the image intensity profiles along the normal segments are combined in a linear image

(the “unrolled” SRLM); e) a subsample of the profiles are shown to the user for a visual check; f) the linearized SRLM

image (d) is squeezed along the SRLM direction, i.e. the profiles (e) are averaged and the SRLM thickness is derived from

the width of the Gaussian fit (4 σ).

3 SET UP AND VALIDATION OF

THE ALGORITHM

3.1 Practical Implementation of the

Algorithm on MRI Data

The algorithm has been implemented in Matlab

(R2009b, The MathWorks, Inc.), and its execution is

managed by a very basic GUI, which only aims to

allow the software usage by researchers with a

limited expertise in the Matlab environment.

To set up the algorithm and to fix all the free

parameters, the brain 7T-MR examination of a

healthy volunteer was considered. MR images were

acquired with a 7T MR950 scanner (GE Healthcare

Medical Systems, Milwaukee, WI, USA) equipped

with a 2ch-Tx/32ch-Rx head coil (Nova Medical,

Wilmington, MA USA).

The acquisition protocol included a high-

resolution 2D T2*-weighted sequence prescribed

perpendicular to the longitudinal axis of the

hippocampus and covering the hippocampal body

(gradient-recalled echo –GRE–, TE=22 ms, TR=240

ms, in-plane resolution 0.3×0.3 mm

, slice thickness

= 2 mm).

Data are converted from the DICOM to the

NIFTI format as a prerequisite to run our algorithm.

As we are only interested in the thickness on the

SRLM, regardless its contrast with respect to the

surrounding brain structures, we do not need to

normalize the MR image intensity.

The user selects points along the SRLM, which

are connected through spline interpolation. The

obtained user-defined profile is sampled so to

accommodate at least 15 normal segments, whose

length is set to 2.5 mm. This length has been

optimized on data to properly cover the SRLM

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thickness plus an additional margin, which is

necessary to obtain a stable Gaussian fitting.

3.2 Test of the Reproducibility of the

Measure of the SRLM Thickness

The thickness of the SRLM of the left and the right

hippocampi of the healthy volunteer has been

measured ten times. The variability in the

appearance of the linearized SRLM images obtained

at each repetition of the experiment is visible in

Figure 3, where only five out of the ten measures per

side are shown for demonstration purpose. Also the

Gaussian fits on the average profile are visible in the

figure.

We obtained the following measures of the

SRLM (average ± standard deviation [range of

values]): (1.61±0.10) mm [1.51–1.83 mm] for the

left hippocampus and (1.53±0.04) mm [1.47–1.61

mm] for the right hippocampus. The measure is

considered as highly reproducible, as the estimated

error is limited to few per cents of the measured

thickness, i.e. the 6% and the 3% for the left and

right hippocampus, respectively.

4 APPLICATION TO MCI

SUBJECTS

Once the semiautomatic algorithm has been

developed and validated on the 7T MR image of a

healthy volunteer, it has been used in a clinical study

involving subjects affected by MCI.

4.1 Data Sample

Ten MCI patients underwent a brain 7T-MR

examination including a high-resolution 2D T2*-

weighted sequence targeting hippocampus. All

patients underwent a neuropsychological battery

including the Mini-Mental State Examination

(MMSE) and the Free and Cued Selective

Reminding Test (giving great attention to the free

recall FCSRT-FR).

4.2 SRLM Measures on MCI Patients

and Correlation with Clinical Tests

An experienced neuroradiologist used the

semiautomatic image processing tool we developed

to delineate the SRLM profile on a coronal oblique

slice of the 7T T2*-weighted MRI of each MCI

patient. The thickness of the SRLM was estimated.

Figure 3: Test of the reproducibility of the SRLM

measure. Five repetitions of the algorithms for the left and

right hippocampi are shown (out of the 10 repetitions used

for the test): the user-defined SRLM profile slightly

changes from run to run leading to different “unrolled”

SRLM images, and slightly different averaged profile and

Gaussian fit.

This estimation has been done for both the right and

the left hippocampi.

The correlation between the measured SRLM

thickness and the numerical scores provided by the

neuropsychological tests were analysed according to

the Spearman’s rank test. Our preliminary results

showed that the average SRLM thickness (average

value between the left and right hippocampi)

correlated with MMSE score (r=0.60; p<0.1; n=10)

while the average SRLM thickness of the right

hippocampus correlated with the FCSRT-FR

(r=0.97; p<0.05; n=5).

5 CONCLUSIONS

We present in this paper a semiautomatic image

processing tool to measure the thickness of small

structures in anatomical images. The high-resolution

structural imaging that can be acquired with MRI at

UHF (7T) allows the visualization of very thin

anatomical structures. Suitable tools to measure their

size in a reproducible way might have a fundamental

role in clinical research studies.

As a case study we focused on the measure of

one of the hippocampal subfields, the SRLM, which

is supposed to be involved in early degenerative

changes related to pathological cognitive decline.

We developed the procedure on a representative 7T

T2*-weighted MR image of a healthy volunteer, and

tested it on a sample of subjects affected by MCI.

We found that the SRLM thickness correlated with

A Semiautomatic Image Processing Tool to Measure Small Structures in Magnetic Resonance Images of the Brain at 7 Tesla - Application

to Hippocampus Subﬁelds of Patients with Mild Cognitive Impairment

127

the MMSE and the FCSRT-FR neuropsychological

scores.

The choice of a semiautomatic approach instead

of a fully automated one was dictated by two main

reasons: 1) the definition of the structures of interest

varies according to international protocols still under

study by the community of neurologists

(Yushkevich, 2015); 2) a semiautomatic tool can at

this stage help defining eloquent structures of

interest for specific pathologies in exploratory

studies by the community of neuroradiologists and

neurologists. Then, once the target anatomy of

interest is fully delineated, software developers can

further improve the semiautomatic tools to make

them fully automatic.

The possibility to extract from 7T brain MRI

quantitative features related to the underlying

pathological condition of the MCI subjects can open

the way to the development and application of more

robust predictive models of early diagnosis of

Alzheimer's disease based on machine learning

techniques (Chincarini, 2011; Retico, 2015).

Among the limitations of the present work are:

the need to implement other segmentation strategies

to obtain the global extent of the anatomical

structure under evaluation (e.g. the entire

hippocampus) to account for its effect in the

statistical analysis; the 2D nature of the procedure,

which can be extended to 3D data, where available;

the need to substantiate the reproducibility test

results with more than one subject’s data and to

validate the algorithm reliability in an inter-rater

reliability test.

Finally, the algorithm proposed in this paper,

despite tailored to measure the SRLM thickness, can

be used to measure the thickness of other thin

anathomical structures represented in 7T MR

images. Moreover, as the proposed approach is only

based on the assumption of a Gaussian shape of the

image intensity profile, it can be extended with few

modifications to measure the thickness of different

anathomical thin structures appearing in images

acquired with other imaging modality.

ACKNOWLEDGEMENTS

This work has been partially founded by the Italian

Ministry of Health and the Tuscany Government

(RF-2009-1546281 Clinical impact of ultra high-

field MRI in neurodegenerative diseases diagnosis

PI: M. Cosottini) and by the National Institute for

Nuclear Physics (nextMR project).

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