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A Total Hip Replacement Toolbox: from CT-Scan to Patient-Specific

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A Total Hip Replacement Toolbox: from CT-Scan to Patient-Specific A Total Hip Replacement Toolbox: From CT-Scan to Patient-Specific FE Analysis Diogo Moreira Campos Ferreira de Almeida Promotors: Prof B. Verhegghe, PhD, Prof M. De Beule, PhD Prof J. Folgado, PhD, Prof R. Ruben, PhD Doctoral thesis submitted in order to obtain the academic degrees of Doctor of Biomedical Engineering (Ghent University) and Doutor em Engenharia Biomédica (Universidade de Lisboa) Department of Electronics and Information Systems Head of Department: Prof R. Van de Walle, PhD Faculty of Engineering and Architecture Department of Bioengineering Head of Department: Prof J. P. Conde, PhD Instituto Superior Técnico Academic year 2016 - 2017 ISBN 978-90-8578-980-2 NUR 954 Wettelijk depot: D/2017/10.500/15 Supervisors: Prof. dr. ir. Rui B. Ruben Prof. dr. ir. Benedict Verhegghe Prof. dr. ir. Jo~aoFolgado Prof. dr. ir. Matthieu de Beule Research institutions: Institute Biomedical Technology Biofluid, Tissue and Solid Mechanics for Medical Applications (bioMMeda) Ghent University De Pintelaan 185 - Blok B B{9000 Gent Belgium Institute of Mechanical Engineering (IDMEC-IST) Associated Laboratory for Energy,Transports and Aeronautics (laeta) University of Lisbon Av. Rovisco Pais, 1 1049-001 Lisbon Portugal Members of the exam committee: Chairman: Prof. dr. ir. Rik Van de Walle Faculty of Engineering and Architecture, UGent, Belgium Secretaries: Prof. dr. ir. Jan Belis Faculty of Engineering and Architecture, UGent, Belgium Prof. dr. ir. Paulo Rui Fernandes Instituto Superior T´ecnico, ULisbon, Portugal Other members: Dr. ir. Toon Huysmans Department of Physics, Universiteit Antwerpen, Belgium Prof. dr. ir. Jef Vandemeulebroucke Department of Electronics and Informatics, Vrije Universiteit Brussel, Belgium Prof. dr. ir. Rui B. Ruben Center for Rapid and Sustainable Product Development, Polytechnic Institute of Leiria, Portugal Prof. dr. ir. Benedict Verhegghe Faculty of Engineering and Architecture, UGent, Belgium Prof. dr. ir. Jo~aoFolgado Instituto Superior T´ecnico, ULisbon, Portugal Prof. dr. ir. Matthieu de Beule Faculty of Engineering and Architecture, UGent, Portugal Resumo A artroplastia total da anca ´euma interven¸c~aocir´urgica que consiste em sub- stituir a articula¸c~aoda anca por uma articula¸c~aoprost´etica. Ainda que seja considerada uma das interven¸c~oescir´urgicasmais bem sucedidas, seguras e eficazes, complica¸c~oesrelacionadas com o seu design e posicionamento do im- plante ainda s~aofrequentes. A aus^enciade pontos de refer^enciaque definam o alinhamento do f´emur e da p´elvisdificulta o posicionamento do implante na cirurgia. Todos os anos, cerca de um milh~aode pacientes em todo o mundo s~aosubmetidos a uma artroplastia da anca, restaurando a funcionalidade da mesma. Devido ao envelhecimento da popula¸c~ao,a quantidade de artroplastias tem tend^enciaa aumentar, sendo que uma interven¸c~aode revis~aodepende da quantidade de tecido ´osseoainda existente e por isso os seus riscos e custos s~ao consideravelmente maiores. Desta forma, o desenvolvimento de uma metodologia que automaticamente recria computacionalmente a articula¸c~aode um paciente espec´ıfico com um conjunto de ferramentas que possibilitam o estudo e optimiza¸c~aodo posiciona- mento dos diferentes implantes no f´emur ´eo principal foco do trabalho aqui apresentado. Assim, um planeamento da interven¸c~aocir´urgicabaseado no processamento e modela¸c~aocomputacional de imagens m´edicasdo paciente, usualmente TACs, permitir´aao cirurgi~ao,numa fase pr´e-cir´urgica,obter in- forma¸c~aoquantitativa acerca do desempenho de diferentes implantes de uma forma r´apidae simples e portanto melhorar o planeamento da interven¸c~ao, minimizando a influ^enciada sua experi^enciae per´ıciae consequentemente re- duzindo a dura¸c~aoe o risco de complica¸c~oes na cirurgia. De forma a criar um conjunto de ferramentas que possibilite a um uti- lizador inexperiente um planeamento de uma artroplastia da anca sem que lhe seja necess´arioter um conhecimento profundo sobre os m´etodos utiliza- dos para o efeito, tentou maximizar-se o n´ıvel de automatiza¸c~aodas t´ecnicas aqui apresentadas. Para tal, utilizou-se o pyFormex, um programa baseado na linguagem Python que permite transforma¸c~oescom modelos tridimension- ais. As imagens m´edicasutilizadas s~aoresultantes de TACs realizados a uma popula¸c~aoheterog´eneade pacientes, disponibilizadas pela cl´ınica m´edicaQuad- rantes, em Lisboa, e pelo hospital universit´ariode Ghent, na B´elgica.As ima- gens diferem principalmente na sua resolu¸c~ao:enquanto que a dist^anciaentre as fatias das imagens provenientes do hospital de Ghent ´ede 0.625 mm, a res- olu¸c~aodas provenientes da cl´ınica Quandrantes ´ede 5 a 7 mm, isto ´e,cerca de dez vezes inferior. A metodologia ser´aapresentada em duas fases distintas: a segmenta¸c~aoautom´aticado osso do f´emur a partir de imagens m´edicase a extra¸c~aoautom´aticade pontos de refer^enciae eixos anat´omicosque permite a optimiza¸c~aodo posicionamento e orienta¸c~aodo implante. i A segmenta¸c~aodo f´emur foi conseguida atrav´esde uma adapta¸c~aode m´e- todos de an´aliseestat´ısticaao processamento de imagem m´edicadenominada por Active Shape Models (ASM). A quantifica¸c~aoda vari^anciade objectos ´edescrita por um modelo estat´ıstico,conhecido por Statistical Shape Model (SSM). De forma a construir o SSM, uma malha de elementos de superf´ıcie param´etricosdefinidos por equa¸c~oesde Lagrange foi utilizada para descrever a geometria do f´emur. De forma a remover todas as varia¸c~oestranslacionais, rotacionais e de tamanho, foi utilizada uma an´alisede Procrustes. A modela¸c~ao estat´ısticae consequente redu¸c~aode dimens~aodo problema foram conseguidas atrav´esde uma An´alisede Componentes Principais (ACP). Um ASM requer tamb´emuma forma de relacionar a geometria do f´emur com a informa¸c~aocontida nas imagens m´edicas. Neste contexto, criou-se um modelo estat´ıstico da apar^enciado f´emur, conhecido por Local Appearance Model (LAM). O LAM ´ebaseado no gradiente da imagem normalizado ao longo das normais `asuperf´ıciedo objecto em localiza¸c~oesespec´ıficasna geometria do mesmo. A sua constru¸c~aobaseia-se num conjunto de imagens previamente segmentadas, na qual se analisam os perfis do gradiente ao longo da fronteira do objecto e se identificam comportamentos que correspondem `afronteira da geometria do objecto. Uma ACP sobre cada um destes pontos resulta no LAM que ´eposteriormente usada para segmentar novas imagens. O processo de treino do SSM e do LAM foi efectuado com 30 imagens m´edicasdo hospital de Ghent. Cada uma dessas 30 imagens foi segmentada e adaptada a uma malha param´etrica,a partir da qual se construiu o ASM. A segmenta¸c~aode uma nova imagem come¸capor estimar a localiza¸c~aodo f´emur no dom´ıniodessa imagem atrav´esum m´etodo inovador baseado num algoritmo de Iterative Closest Point (ICP) com constrangimentos na forma das n´uvens de pontos. O algoritmo rapidamente estima a posi¸c~aoe a orienta¸c~ao do f´emur para que o processo iterativo de segmenta¸c~aocomece. Neste, a cada itera¸c~ao,o algoritmo compara o perfil do gradiente do modelo na imagem e do ASM e altera-se de forma a minimizar as diferen¸casentre ambos. Ap´os,existe uma correc¸c~aode valores desenquadrados e possivelmente errados e a itera¸c~ao termina com uma actualiza¸c~aoda malha param´etrica. A segmenta¸c~aoda cavidade medular ´ebaseada na sua facilidade de iden- tifica¸c~aono dom´ıniodo f´emur devido ao seu preenchimento com medula que difere do valor do osso cortical que rodeia a cavidade na escala de cinzentos da imagem. Foi proposto um sistema de coordenadas que uniformizasse as opera¸c~oes com toda a popula¸c~aode f´emures que possibilitou que se localizassem as co- ordenadas das proemin^enciasou eixos anat´omicasmais relevantes do f´emur. Com esta informa¸c~ao,desenvolveu-se um algoritmo em conjunto com cirurg~oes ortop´edicosque optimiza o posicionamento e o dimensionamento do implante para um f´emur espec´ıfico.Paralelamente, modelaram-se alguns implantes com- ercialmente dispon´ıveis de forma a que se possam comparar os diferentes designs ou materiais constituintes dos mesmos. Ap´oso acoplamento do implante escolhido ao f´emur segmentado, foi im- plementado um m´etodo de gera¸c~aode uma malha de elementos finitos (EF) que combina duas t´ecnicasde forma a conseguir uma malha de elementos hexa´edricoshomog´eneae autom´atica: existe uma primeira abordagem grid- based para o interior do dom´ıniouma agordagem baseada na t´ecnica receding front na zona mais externa que evita elementos distorcidos na superf´ıciede ii contacto. O processo de treino do SSM provou ser preciso, compacto e generaliz´avel. O processo de adapta¸c~aodas nuvens de pontos da segmentac~ao`amalha para- m´etricateve valores de erro m´ediode 0:8895±0:0179 mm, na ordem do tamanho do voxel que limita a precis~aoda segmenta¸c~aoinicial. Os primeiros quatro principais componentes da ACP `ageometria do f´emur representam mais de 95% da varia¸c~aototal, pelo que existiu uma grande redu¸c~aode dimensionalidade. Por fim, a valida¸c~aocruzada apresentou um erro m´ediode 1.42 mm quando usados apenas 5 componentes principais mas que diminuia `amedida que estes aumentavam. O m´etodo de inicializa¸c~aodo ASM tem resultados muito similares `ainicial- iza¸c~aomanual em apenas 30 segundos, o que levou a que o processo iterativo de segmenta¸c~aofosse mais eficiente. O m´etodo de segmenta¸c~aofoi validado por compara¸c~aocom a segmenta¸c~aomanual de 10 f´emures. De notar que por vezes a fronteira do f´emur n~aose encontra bem vis´ıvel na imagem pelo que a segmenta¸c~aomanual ´etamb´emsuscept´ıvel de erro. O m´etodo apresentou um erro m´edio de 1:014 ± 0:474 mm e uma dist^anciade Hausdorff m´ediade 4:336 ± 0:861 mm.
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