Quantitative Scoring of Muscle Involvement in MRI of

Neuromuscular Diseases

M. E. Fantacci

1,2

, G. Astrea

, R. Battini

, A. Retico

, C. Sottocornola

1,2

and M. Tosetti

Physics Department, Pisa University, Largo Pontecorvo 3, Pisa, Italy

Pisa Section of INFN, Pisa, Italy

Stella Maris Scientific Institute, Calambrone, Pisa, Italy

Keywords: Muscle MRI, Neuromuscular Diseases, Muscle Segmentation.

Abstract: An automated method to evaluate the fat infiltration in tissues has been developed and applied to images of

the human leg. The final aim is to obtain a quantitative evaluation of fat infiltration percentage and to relate

it to the grade of muscle impairment in subjects affected by Neuro-Muscular Diseases (NMD). Through a

muscle segmentation algorithm on structural T1-weighted magnetic resonance images (MRIs), the estimated

non-muscle percentage (eNMP) in the segmented muscle area has been evaluated in healthy subjects as a

reference value. A semi-automated procedure allows extending the algorithm to MRIs of NMD patients. A

strong correlation has been demonstrated between the eNMP index and the disease severity.

1 INTRODUCTION

Magnetic Resonance Imaging (MRI) is an extremely

useful imaging method to carry out reliable and non-

invasive clinical assessment and follow up of

subjects affected by NeuroMuscular Diseases

(NMD).

In particular, as MRI is able to detect muscle

involvement and to reveal the severity of disease, it

is already used also in pediatric protocols for

diagnostic study of NMD (Mercuri, 2002).

However, the interpretation of muscle MRI data

and the assessment of NMD severity is currently

carried out only visually by expert neurologists;

software tools for automated quantitative analysis of

muscle MRI have not yet been developed and

provided in clinical diagnostic protocols.

Standardization procedures and quantitative methods

could be very useful instruments to optimize the

muscle MRI diagnostic performance.

Recently the muscle fat fraction (MFF) has been

evaluated (Gaeta, 2011; Gaeta, 2012), relying on the

dual-echo dual-flip-angle spoiled gradient-recalled

acquisition in the steady state (SPGR) magnetic

resonance (MR) imaging technique. The muscle

biopsy has been used in that case as reference

standard.

A quantitative assessment has been made with

Dixon method (Dixon, 1984) to highlight significant

increase in fat fraction in longitudinal studies

(Willis, 2013).

Quantitative 3-point Dixon method has been

compared to qualitative radiological scoring,

concluding that the qualitative method overestimates

the fat fraction (Wokke, 2013). In all cases, manual

segmentation of each single muscle, which is an

extremely time consuming task and hardly

reproducible across different experts, is needed to

compute the fat faction in each ROI. Attempts to

automatize the segmentation of skeletal muscles on

Dixon images have recently been introduced, e.g.

the automated computation of the fat fraction

thought the extraction of subcutaneous adipose

tissue with parametric deformable models

(Makrogiannis, 2012) and the segmentation of calf

muscles through Random Walks with shape prior

information (Baudin, 2012).

In this paper we present a method for automatic

quantitative analysis of T1-weighted Spin Echo

(T1w SE) MR images of the leg, based on

algorithms for tissues segmentation and histograms

analysis. This method has been assessed on the

images of healthy volunteers with the extraction of

information used as reference standard in the

analysis of the images of NMD patients in order to

obtain a quantification of their fatty infiltrations and

to study its correlation with the grade of muscle

impairment in the NMD pathology.

100

Fantacci M., Astrea G., Battini R., Retico A., Sottocornola C. and Tosetti M..

Quantitative Scoring of Muscle Involvement in MRI of Neuromuscular Diseases.

DOI: 10.5220/0005255801000105

In Proceedings of the International Conference on Bioimaging (BIOIMAGING-2015), pages 100-105

ISBN: 978-989-758-072-7

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

2 MATERIALS AND METHODS

2.1 Subjects

Sixteen NMD patients and ten healthy subjects (HS)

were considered for this study.

Patients age ranges between 1 to 54 years. In our

Institute clinical studies follow the ethical guidelines

of our local ethics committee. Informed written

parental consent was obtained before the enrollment

in the study. As expected, any side effects from

muscle MRI examination has not observed. This

examination is now routinely performed and does

not require the use of anesthetic in young children

after the 6 or 7 years of age. Sometimes muscle MRI

has been performed in children less 6 yrs of age

following general anaesthesia when brain MRI has

been also required for diagnostic investigation.

The Medical Research Council (MRC) scale was

used to assess the weakness in lower limbs. Patients

were stratified in the five classes listed below

according to MRC scores and maximal functional

motor achievement: class 1 asymptomatic, class 2

mild symptomatic, class 3 moderate symptomatic,

class 4 severely symptomatic, class 5 non-

ambulatory. All patients except one were

ambulatory.

The MRI exams of NMD patients were acquired

between 2011 and 2012 at the MR laboratory of

IRCCS Stella Maris Institute (Pisa, Italy) with a

1.5T scanner MR Signa GE Medical Systems HdxT

with a whole body TX-RX coil. The standard MR

protocol consisted of a 2D axial T

1-weighted Spin

Echo sequence with acquisition matrix of 256 x 256,

FOV = 44 cm x 44 cm, TE = 14 ms and TR = 540

ms. The resulting images have an in-plane resolution

of 1.72 mm x 1.72 mm and 5 mm of slice thickness.

Only the scan of the proximal third of the thigh

of each subject was taken into account for analysis.

All images were qualitatively assessed for the

presence/absence of fat infiltration by a pediatric

neurologist expert in muscle MRI, using the Mercuri

grading (Mercuri, 2002).

The MRI scans of the ten healthy volunteers

(healthy subjects, HS) have been acquired with the

same acquisition protocol to obtain the reference

standard for the eNMP (estimated Non Muscle

Percentage) index in the healthy condition.

2.2 Software Tools

MRI data were analyzed with the medical image

processing and visualization tool MeVisLab (MeVis

Medical Solutions AG and Fraunhofer MEVIS in

Bremen, Germany, http://www.mevislab.de/). It

consists in an image-processing environment with a

special focus on visualization and analysis of

diagnostic images. It is structured in a modular

framework, where algorithms for segmentation,

registration and quantitative image analysis can be

implemented. It is based on Python programming

language and modular expandable C++ image

processing libraries. The Insight Toolkit (ITK) and

Visualization Toolkit (VTK) software are integrated;

they are open-source, freely available software

systems that support computer graphics, image

processing, modeling techniques and advanced

visualization applications.

2.3 Characterization of Healthy

Subjects

The MRI data set of the ten healthy subjects was

studied in order to make an automatic analysis of

anatomical features such as geometry and signal

intensity of muscle and fat tissues.

We introduced an original index, the eNMP

index, to take into account the fraction of non-

muscle tissues, which are present within the muscle

area. More in details, considering as muscle area the

geometric area defined on the MR image excluding

the bone and the subcutaneous fat by means of a

segmentation procedure, it is evident that in this area

blood vessels, nerves, fat and connective tissue are

still included. By the eNMP index, the percentage of

any tissues different from muscle included in the

delineated muscle area is taken into account. To the

best of our knowledge this reference standard

obtained in the analysis of healthy subjects has not

previously been reported in the literature.

The basic idea of the whole analysis is to take

advantage of the different signal intensity of muscle

and fat in MR images. An example of thigh MR

image is shown in Figure 1, where the main muscle

districts, the subcutaneous fat and the femur bone

are clearly visible.

2.3.1 Image Histogram Analysis

The analysis has been performed on a particular 2D

image of the thigh, i.e. a slice selected by the child

neurologist, where all the muscle sectors are clearly

visible (at about half thigh length). It starts with a

multiple Gaussian fit of the histogram of the image

intensity values. Assuming that voxels values which

are respectively part of muscle and fat tissue follow

Gaussian distributions, a process of curve fitting has

been executed in each histogram by using a least

QuantitativeScoringofMuscleInvolvementinMRIofNeuromuscularDiseases

101

squares fit method to find the mean and standard

deviation values of subcutaneous fat and muscle

distributions.

Figure 1: An axial T1-weighted MR image through the

middle thigh of a healthy subject acquired with a 1.5 T

MRI scanner.

The ratio between the fitted subcutaneous fat and

muscle peaks (



and

) has been computed for all

subjects and averaged, thus generating a standard

reference measure of the ratio (R = 

/



) of the

intensity values of fat and muscle.

The ratio between the fitted subcutaneous fat and

muscle peaks (



and

) has been computed for all

subject and averaged, thus generating a standard

reference measure of the ratio (R = 

/



) of the

intensity values of fat and muscle.

2.3.2 Muscle Area Segmentation

In the following step an automatic segmentation

method has been performed in order to isolate

muscle tissue from femur bone and subcutaneous

fat; the objective was to quantify the percentage of

voxels of non-muscular tissue as blood vessels,

nerves and connective tissue present in muscular

districts present in muscular district.

The automatic segmentation algorithm has been

developed and implemented using a

CSOIsoGenerator (Contour Segmentation Objects)

module algorithm of the MeVisLab software

package. The CSO library provides data structures

and modules for an interactive or an automatic

generation of contours in voxel images.

Furthermore, these contours can be analyzed,

maintained, grouped and converted into a voxel

image or a set of markers. The module

CSOIsoGenerator generates iso-contours for a whole

image at a fixed iso value: the input image is

scanned by a marching-square algorithm that always

produces closed CSOs. The border is treated as

being always lower that the iso value and the CSOs

can be interpolated by a linear interpolation scheme

and/or smoothed by applying a spline

approximation. A Creation mode lets the user adjust

which CSOs on one slice should be kept; if the mode

is set to All, all CSOs are kept. If it is set to Largest,

only the largest CSO (measured in number of seed

points) on a slice is kept of the generating contours.

In our case, two different ISO contours are

needed to isolate the muscle tissue from neighboring

bone and subcutaneous fat. Within the segmented

muscle area, also non-muscle components are

present, including fat, connective tissue, nerves and

blood vessels.

2.3.3 ENMP Index Evaluation

Allowing that the muscle tissue shows intensity

values are in the 



± 3 range, where 



is the

fitted central of the muscle peak in the intensity

histogram of each subject and  is the standard

deviation according to our Gaussian distribution

assumption.

The eNMP has thus been computed (1) as the

ratio of non-muscle components in the segmented

muscle area:

eNMP 

∑





|







|

∑

















∀ ∈  (1)

where n

is the number of voxels with intensity I

and

i runs on all voxels belonging to the mask M, i.e. the

muscle mask identified with the iso-contour based

segmentation algorithm.

The eNMP index has been evaluated for all HC

subjects and its average value constitutes the

reference standard for the healthy population.

2.4 Analysis of NMD Patients

The evaluation of the eNMP has been carried out

also on NMD subjects with the final aim to correlate

it with the grade of muscle impairment in the NMD

pathology.

In this case it is unfortunately very difficult to

discriminate the subcutaneous fat from the fat

infiltrated in the muscle with an automated

segmentation procedure, as shown in the two

example of Figure 2. For this reason, an expert child

neurologist was asked to interactively draw the

manual subcutaneous fat delineation contour, thus

defining the muscle masks also in case of NMD

subjects. Once verified the validity of the method, in

the near future it may be possible an automatic

segmentation of the muscle area by means of a

three-dimensional image analysis based on

innovative image processing methods developed for

very noisy image segmentation as, for example

BIOIMAGING2015-InternationalConferenceonBioimaging

102

virtual ants based models (Cerello, 2010). Taking

into account the known ratio R between the intensity

values of fat and muscle in our images, and

measuring trough a Gaussian fit the average value of

the fat (

) in the MRI image of a NMD patient, the

corresponding 



has be obtained. The eNMP index

has thus been evaluated also for NMD subjects and

is available for correlation with the NMD severity.

Figure 2: Two examples of axial image of thighs in NMD

patients. On the left a NMD patient with a low-grade

MRC score (mild symptomatic) and mild involvement of

thigh muscles in the posterior compartment. On the right a

second patient with a high-grade MRC score (severely

symptomatic) and massive fat infiltration of all the thigh's

muscles with only minimal preservation.

3 RESULTS

To compute the eNMP index, first of all the 

, 





and 

values were obtained trough a multiple

Gaussian fit on the image histogram of the ten

healthy subjects. The values obtained are reported in

Table 1, where also the ratio R = 

/



is shown

together with the estimated error on these quantities.

The average ratio results R

= 3.4 ± 0.3. This

quantity has been used as a reference standard in the

analysis of NMD patients. The eNMP index

computed for each HS is also reported in Table 1. It

can be noticed that the eNMP indices computed on

HS never exceeded the threshold of 7%.

Table 1: The 

, 

, 

and 

values obtained through a

Gaussian fit on the image histogram for each HS are

reported. The ratio R=

/

is also shown. The last

column reports the eNMP index obtained of each HS.

ID 







eNMP

HS1

174.7 27.2 601.0 93.7 3.4±0.8 6.3%

HS2

162.3 25.8 548.0 91.4 3.4±0.8 5.1%

HS3

165.2 14.3 550.1 121.0 3.3±0.9 3.5%

HS4

146.6 25.0 470.6 109.5 3.2±0.9 6.2%

HS5

170.1 24.0 571.9 125.5 3.4±0.9 6.2%

HS6

177.3 30.6 615.8 89.6 3.5±0.8 6.6%

HS7

187.0 42.8 694.0 108.3 3.7±1.0 4.4%

HS8

164.7 30.4 542.0 114.1 3.3±0.9 6.5%

HS9

182.5 32.4 670.1 107.8 3.7±0.9 4.2%

HS10

189.6 28.4 669.4 87.5 3.5±0.7 6.3%

In the segmentation step to isolate muscle tissue

from femur bone and subcutaneous fat the input

image has been scanned by a marching-square

algorithm that produces closed contour segmentation

objects. The largest contour defines the boundary

between the muscle and the surrounding

subcutaneous fat, whereas the smaller defines the

boundary between the muscle and the femur bone, as

shown in Figure 3.

Figure 3: Example of automatic segmentation of femur

bone and muscular district in a HC subject: the iso-contour

generator module provides closed contours at fixed iso-

values.

The muscle peak in the histograms of the images

of NMD patients is far less evident, when not

completely absent, whereas the fat peak is higher

due to fat replacement in muscular area. Only the 

and 

values have thus been extracted through a

Gaussian fit from the histogram of the images of

NMD subjects and are reported in Table 2. The

corresponding 

and 

expected values have been

obtained for each NMD subject according to the

average coefficient R

estimated in the analysis of

HC subjects. The so obtained eNMF indices are also

reported for the NMD subjects in Table 2. It can be

noticed that the eNMP indices obtained on NMD

subjects are generally higher with respect to the HC

subjects. The eNMP indices we computed on NMD

subjects have also shown a positive correlation with

the grade of muscle impairment in NMD subjects as

well as it was evaluated from a clinical point of view

by means of the MRC scale and from an imaging

point of view with a visually grading (Mercuri,

2002). In particular, a strong correlation has been

found between the visually scored muscle

involvement (Mercuri grading) and the quantitative

eNMP index estimated on MR images. In Figure 4

the scatter plot of the Mercuri grading versus the

eNMP index is reported.

As the correlation between muscle involvement

and disease severity is known not to be a linear

function, with small advance of fat infiltration being

QuantitativeScoringofMuscleInvolvementinMRIofNeuromuscularDiseases

103

weightier in the first categories with respect to the

higher ones (i.e. in those patients mild symptomatic

respect to those severely affected), the Spearman

rank correlation coefficient has been estimated,

obtaining



= 0.97 (p<10

-5

). This preliminary result

is very interesting from an analytical point of view

and seem in agreement with clinical experience, that

shows some patients with a high degree of fat

infiltration but with a more of less stable clinical

progression. This trend can also be related to the

different pathogenetic mechanisms underneath the

different diseases.

Table 2: The 

and 

values obtained through a Gaussian

fit on the image histogram for each NMD subject are

reported. The last column reports the eNMP index

obtained for NMD subjects.

ID 



eNMP

NMD1 590.8 61.9 6.4%

NMD2 678.0 62.3 18.6%

NMD3 680.4 51.6 35.6%

NMD4 591.5 50.3 26.2%

NMD5 544.5 74.0 29.0%

NMD6 674.3 68.3 41.7%

NMD7 585.8 37.3 48.6%

NMD8 629.2 34.4 60.2%

NMD9 719.4 50.9 18.8%

NMD10 658.2 67.5 35.7%

NMD11 698.3 64.0 4.7%

NMD12 751.7 42.6 96.6%

NMD13 735.5 34.9 34.7%

NMD14 581.0 59.4 9.2%

NMD15 650.2 79.3 13.1%

NMD16 599.4 61.0 28.6%

4 CONCLUSIONS

A totally automatic analysis of MR images of the

thigh of healthy subjects has been carried out,

allowing the extraction of a reference standard

useful for a semi-automatic evaluation of muscle

involvement in NMD patients. Besides the strong

correlation obtained between the Mercuri grading

and the eNMP index, lower values of the eNMP

involvement scores were attributable to patients with

neurogenic alterations at muscle biopsy, correlated

to less altered muscle structures, while higher eNMP

values were found for the dystrophic patients, in

correlation with their massive muscle architecture

disruption.

In view of realizing a fully automated

system of quantitative analysis, the few

morphological differences between subcutaneous fat

and fat infiltration in muscle may render ineffective

the standard procedures of segmentation and the

analysis of the two-dimensional MR images may not

be sufficient for the automatic segmentation and

quantitative analysis of the muscles in the

pathological subjects. May therefore be necessary

reformatting the volumes and making a three-

dimensional analysis of three-dimensional images.

Moreover, may have to be used innovative methods

for pattern recognition such as the use of virtual ants

and active contours methods driven by local/global

histogram statistics. Then, such pattern recognition

module devoted to the segmentation and 3D

reconstruction of muscles area may be followed by a

feature extraction tool which will be able to identify

the characteristics which can indicate the presence of

the pathology, both geometrical (i.e. muscular

shapes and volumes) than related to the local signal

intensities (i.e. presence of fibrosis or fat

infiltration). In the last step these features will be

used to train an artificial intelligence based

classifier. Overall, these preliminary results show

that a quantitative analysis of muscle MRI could be

a promising instrument to quantify the patterns of fat

infiltration in neuromuscular patients.

Figure 4: Scatter plot of the Mercuri grading versus the

eNMP index obtained for each NMD patient.

REFERENCES

Baudin, P.Y. et al., 2012. Prior Knowledge, Random

Walks and Human Skeletal Muscle Segmentation.

Lecture Notes in Computer Science 7510:569-576.

Cerello, P. et al., 2010. 3-D object segmentation using ant

colonies. Pattern Recognition 43(4):1476-1490.

Dixon, W.T., 1984. Simple proton spectroscopic imaging.

Radiology 153:189-194.

Gaeta, M. et al., 2011. Muscle Fat Fraction in

Neuromuscular Disorders: Dual-Echo Dual-Flip-

BIOIMAGING2015-InternationalConferenceonBioimaging

104

Angle Spoiled Gradient-Recalled MR Imaging

Technique for Quantification-A Feasibility Study.

Radiology 259(2):487-494.

Gaeta, M. et al., 2012. Muscle fat-fraction and mapping in

Duchenne muscular dystrophy: evaluation of disease

distribution and correlation with clinical assessments.

Skeletal Radiol 41:955–961.

Makrogiannis, S. et al., 2012. Automated quantification of

muscle and fat in the thigh from water-, fat-, and

nonsuppressed MR images. J Magn Reson Imaging

35(5):1152-61.

Mercuri, E. et al., 2002. Clinical and imaging findings in

six cases of congenital muscular dystrophy with rigid

spine syndrome linked to chromosome 1p (RSMD1).

Neuromuscul Disord 12:631-638.

Willis, T.A. et al., 2013. Quantitative Muscle MRI as an

Assessment Tool for Monitoring Disease Progression

in LGMD2I: A Multicentre Longitudinal Study. PLOS

ONE 8/8: e70993.

Wokke B.H. et al., 2013. Comparison of dixon and T1-

weighted MR methods to assess the degree of fat

infiltration in duchenne muscular dystrophy patients. J

Magn Reson Imaging 38(3):619-624.

QuantitativeScoringofMuscleInvolvementinMRIofNeuromuscularDiseases

105