1st Joint ESMAC-GCMAS Meeting - Análise de Marcha

O-43
AUTOMATIC IDENTIFICATION OF MUSCLE INSERTION SITES IN
MR IMAGES USING ATLAS-BASED, NON-RIGID REGISTRATION
Scheys Lennart, M.Sc. 1,2 , Jonkers Ilse 2 , PhD, Loeckx Dirk, M.Sc. 1 , Spaepen Arthur, M.Sc.
Prof. 2 ; Suetens Paul, M.Sc. Prof. 1
1 Medical Image Computing - ESAT/PSI, U.Z. Gasthuisberg, Leuven, Belgium
2 Department of Biomedical Kinesiology, FABER/K.U.Leuven, Leuven, Belgium
Summary/conclusions
Atlas-based non-rigid image registration allows automatic identification of muscle attachments
that can be incorporated in subject-specific musculoskeletal models applicable to the
biomechanical analysis of gait.
Introduction
Gait analysis techniques have evolved from research instruments to an indispensable tool in the
management of patients suffering from a wide variety of medical conditions. Recent studies
show its added value over clinical examination data in selecting a patient-tailored surgical
intervention strategy [1]. More recent, newly ‘derived’ parameters, related to musculoskeletal
geometry (e.g. muscle length, muscle moment arms) as well as muscle–tendon dynamics (e.g.
force generating capacity of muscles), have been introduced in the clinical decision making
process. Consequently, generic biomechanical models used to date need to be accommodated
for inter-individual variability in musculoskeletal geometry [2], especially when bony
deformations and changes in muscle geometry are present.
Highly accurate, subject-specific musculoskeletal models can be build using information of
magnetic resonance (MR) images [3]. Since manual delineation of soft-tissue structures is
required, parameterization of the muscle geometry, with identification of muscle paths from
origin to insertion, is a time demanding step. Therefore, this approach is generally considered
to be too labour-intensive and high-priced.
This work presents the performance of a method to automatically identify muscle geometry
(attachments and muscle path) by atlas-based non-rigid image registration. This approach
decreases considerably the time required to build subject specific musculoskeletal models
based on medical images (MRI) while demonstrating an accuracy comparable to manual
delineation.
Statement of clinical significance
Automatic identification of muscle geometry based on medical imaging enhances the
feasibility to build subject specific musculoskeletal models for detailed biomechanical analysis
of gait deviations in patients.
Methods
The current work assumes that muscles can be modelled as a straight line running from origin
to insertion, describing the muscle’s lines of action. This approach is based on the muscle
model used in SIMM (Software for Interactive Musculoskeletal Modelling, Musculographics
Inc.). Therefore, the identification of the attachment and insertion sites of the muscle is a
crucial step in the process for which mostly a centroid approach is used.
Via non-rigid registration, muscle attachment and insertion sites are identified in T1 MR
images of a subject using the information of an atlas (i.e. a MR images from a non-pathologic
adult man (25y) with labelled muscle attachments). In a first step the geometrical relation
between both image volumes is determined by matching both image volumes using intensitybased
non-rigid image registration. The non-rigid deformation of the atlas image is modelled
by a B-spline deformation mesh [4]. The cost function uses mutual information [5] as a
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