Patient-specific multi-parametric computational model of lower limb muscle function from PET/MRI studies

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Thesis

Reference

Patient-specific multi-parametric computational model of lower limb muscle function from PET/MRI studies

GARCIA JUAN, David

Abstract

Le but de la présente étude est d'étudier et de développer un modèle multidimensionnel des muscles des membres inférieurs spécifique pour le patient. Ce modèle combine des données anatomiques tridimensionnelles et des informations dynamiques fonctionnelles concernant la déformation musculaire, acquises à l'aide de l'imagerie PET et MRI.

GARCIA JUAN, David. Patient-specific multi-parametric computational model of lower limb muscle function from PET/MRI studies . Thèse de doctorat : Univ. Genève, 2017, no.

Sc. 5127

URN : urn:nbn:ch:unige-1002299

DOI : 10.13097/archive-ouverte/unige:100229

Available at:

http://archive-ouverte.unige.ch/unige:100229

Disclaimer: layout of this document may differ from the published version.

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UNIVERSITE DE GENEVE

Département d’Informatique FACULTE DES SCIENCES Professeur José Rolim

Institut de Science FACULTE D’ECONOMIE

de Service Informationnel ET DE MANAGEMENT

Professeur Nadia Magnenat Thalmann

Patient-specific multi-parametric computational model of lower limb muscle function from PET/MRI studies

THÈSE

présentée à la Faculté des Sciences de l'Université de Genève pour obtenir le grade de Docteur de Sciences, mention Interdisciplinaire

par

David García Juan

de

Alicante (Espagne) Thèse N

^o

5127

GENÈVE

Atelier d’impression ReproMail

2017

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A cknowledgments

First of all, I would like to thank my thesis director Prof. Nadia Magnenat Thalmann for her support and supervision throughout this Ph.D., and my thesis co-director Prof. José Rolim.

Many thanks to the jury member Prof. Eric Stindel (University Hospital of Brest) for his time and effort in reviewing the manuscript.

I would like to thank all my colleagues of the University Hospitals of Geneva, Division of Nuclear Medicine, not only for their scientific and clinical collaboration, but also for their daily support and friendship. Special thanks to Dr. Bénédicte Delattre for the invaluable help and countless hours spent together during the imaging protocol implementation. All the Osirix development team: Dr. Antoine Rosset, M. Benoit Deville and M. Alessandro Volz for the rich discussions about software development and image analysis, thanks to them I learned how to be a better software engineer. Finally, I would like to thank all my laboratory colleagues, Dr. Abofazi Mehranian, Dr. Hossein Arabi, Dr. Giulia Didomenicantonio, Dr. Jan Perhac, Mr. Thomas Strgar, Dr. Nicolas Karakasanis, Dr. Fotis Kotasidis and Dr. Tien Wu for their supportive constant presence. Special thanks to Dr. Sophie Zawadynsky for her constant care.

This work would not have been possible without the support of the Marie Curie ITN project

”MultiScaleHuman” funded by the EU’s Seventh Framework Programme. In addition to the financial support, the MultiScaleHuman project offered me the great opportunity to work in an interdisciplinary and international outstanding consortium, which gave me the possibility to become a real researcher. In particular, special credits go to all the MultiScaleHuman ESRs and ERs, for their scientific collaboration as well as friendship and good time spent together, I learned from all of them. I would also like to credit my colleagues from the MiraLAB: Dr.

Andra Chincisan, Dr. Matthias Becker and Dr. Hon Fai Choi. I am also grateful to all the scientists in charge of the MultiScaleHuman partners who welcomed and assisted me during my secondments. Many thanks to the volunteers that took part in the studies.

My deepest gratitude to Prof. Osman Ratib, his door was always opened not only for scientific supervision, but also for any kind of advice during all these years. You were a real mentor for me.

I would like to thank to all my friends in Spain, Geneva and all over the world for their sincere support and interest in my work. Being surrounded by people who cares about you makes you

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stronger, and I got stronger than ever thanks to them.

Finally, I would like to thank to my family and specially my parents, my grandparents, my sister, my brother in law and my nephews for destroying the distance and let me feel their love no matter where I am. If I am grateful for something in my life, is for the luck to have such a wonderful family.

Some words on a sheet of paper cannot express the gratitude that I feel for all the things that Sara did for me. I would never had finished this PhD thesis without her and I will never forget her help and support. This work is also yours, you know it.

David García Juan

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A bstract

The purpose of the present study is to investigate and develop a patient-specific multi-dimensional model of the lower limb muscles that combines three-dimensional anatomical data and functional dynamic information about muscle deformation from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images.

Muculoskeletal disorders (MSDs) are a group of pathologies that affect different parts of the musculoskeletal system like bones, muscles or tendons. Due to the strong relationship between the current rising of world population aging and MSDs spreading, these pathologies will represent in the forthcoming years a big economical burden and a significant challenge to the health systems of developed countries. Among MSDs, Osteoarthritis (OA), and most particularly Knee osteoarthritis (KOA), is one of the most widely diffused MSDs over the world population. OA is caused by the inflammation and by a partial or total loss of the cartilage in the joints, causing stiffness in the affected joint and producing severe pain in the subject suffering the disease, up to a complete incapability to perform daily common tasks.

Quadriceps muscles are strongly correlated with knee function, and their weakness or impairment can lead to an abnormal knee function that could result in the development or progression of KOA. The investigation of muscle function or recovery has been historically addressed with manifold techniques. Self-reported questionaries and performance studies are still currently employed for preliminary evaluation of patient status or recovery. Other techniques, such as biopsy, Electromiography (EMG) or Maximum Voluntary Isometric Contraction (MVIC), have been employed to provide valuable information that have extended our knowledge about skeletal muscles behavior. However, more quantitative data are required to accurately evaluate muscle function from a scientific point of view. Imaging techniques like MRI and PET, capable to perform in- vivo studies of the body and acquire anatomical and metabolic muscle features in a non-invasive way, have changed our understanding about skeletal muscles and pushed further our knowledge about their function.

Muscle Cross-sectional area (CSA) and Muscle Volume (MV) have been extensively reported in literature to have a strong correlation with muscle strength, a strictly linked characteristic to muscle function that has been extensively exploited to evaluate muscle impairment or recovery after surgery. Up to date, MRI studies assessing muscle CSA and MV have addressed the assessment of these anatomical muscle parameters using a static approach. Extracting these features exclusively from static images implies missing the dynamic information, which could complete

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our understanding of muscle behavior when performing a specific task or exercise. Current dynamic studies assessing muscle deformation of the skeletal muscles are limited to the investigation of two-dimensional images in a single plane. This limitation does not allow performing three-dimensional reconstructions of the muscle that could completely characterize it. PET imaging has also been demonstrated to be a valuable tool capable to provide meaningful information about muscle activation.

The multidimensional model developed in this study allows extracting anatomical and functional muscle features such as CSA and MV leveraging on a novel dynamic approach. The creation of three-dimensional reconstructions of the complete skeletal muscle while performing dynamic tasks allows exploring the dynamic evolution of muscular anatomical characteristics, up to now widely assessed only in static studies, up to the best of our knowledge. By combining this information to metabolic data from PET studies, a unique dataset will be inferred, allowing to address the study of MSDs from a new perspective with respect to the conventionally adopted approaches in the musculoskeletal research field, thus potentially leading to new therapies and improved surgical techniques.

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R ésumé

Le but de la présente étude est d’étudier et de développer un modèle multidimensionnel des muscles des membres inférieurs spécifique pour le patient. Ce modèle combine des données anatomiques tridimensionnelles et des informations dynamiques fonctionnelles concernant la déformation musculaire, acquises à l’aide de l’imagerie PET et MRI.

Les troubles musculo-squelettiques (TMS) représentent un groupe de pathologies qui peuvent affecter des différentes parties du système musculo-squelettique chez l’humain, telles que les os, les muscles ou les tendons. En raison de la forte relation existante entre le vieillissement croissant de la population mondiale et la propagation des TMS, ces pathologies représenteront un lourd fardeau économique ainsi qu’un défi majeur pour les systèmes de santé des Pays développés dans les années à venir. Parmi les TMS, l’ostéoarthrite (OA), et plus en particulier l’ostéoarthrite du genou (OAG), est l’un des TMS les plus diffus dans la population mondiale. L’OA est causée par l’inflammation et par la perte partielle ou totale de cartilage dans les articulations, qui est cause de rigidité dans l’articulation affectée et de douleur sévère chez le sujet souffrant de cette pathologie, qui amènent jusqu’à une incapacité totale d’effectuer des tâches de vie quotidienne.

Les muscles du quadriceps sont fortement corrélés avec le fonctionnement du genou. Leur fai- blesse ou altération peut entraîner une fonction anormale du genou, qui pourrait, à son tour, déterminer le développement ou la progression de l’OAG. L’étude du fonctionnement et de la récupération musculaire a été historiquement abordée à l’aide de plusieurs techniques. Les questionnaires d’auto-évaluation et les études sur la performance musculaire sont encore utilisés au présent pour l’évaluation préliminaire du statut ou du rétablissement du patient. D’autres techniques, telles que la biopsie, l’électromyographie ou la contraction isométrique volontaire maxi- male (CIVM), ont permis d’obtenir des informations précieuses à l’extension des connaissances relatives au fonctionnement des muscles squelettiques. Cependant, d’autres données quantita- tives sont nécessaires pour pouvoir évaluer avec précision la fonction musculaire d’un point de vue scientifique. Les techniques d’imagerie telles que l’IRM et le PET, qui permettent de mener des études in vivo chez l’humain et de quantifier certaines caractéristiques anatomiques et mé- taboliques des organes internes de manière non invasive, ont changé notre compréhension des muscles squelettiques et amélioré notre connaissance de leur fonctionnement.

La surface transversale (STM) et le volume musculaires (VM) ont été largement décrits en litté- rature comme caractérisés par une forte corrélation avec la force musculaire, une caractéristique strictement liée au fonctionnement musculaire qui a été largement investiguée afin d’évaluer

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l’affaiblissement ou la récupération musculaires après chirurgie. Au présent, les études IRM concernant la STM et le VM ont abordé l’évaluation de ces paramètres musculaires anatomiques en utilisant une approche statique. L’extraction de ces données à partir exclusivement des images statiques implique de rater l’information dynamique, ce qui pourrait nous donner une compré- hension plus complète du comportement musculaire lors d’une tâche ou d’un exercice spécifique.

Les études dynamiques actuelles évaluant la déformation des muscles squelettiques sont limités à l’étude d’images bidimensionnelles dans un seul plan. Cette limitation ne permet pas d’effectuer des reconstructions tridimensionnelles du muscle, qui pourrait le caractériser de façon plus complète. Egalement, l’imagerie PET a prouvée comme un outil précieux capable de fournir des informations significatives sur l’activation musculaire.

Le modèle multidimensionnel développé dans cette étude permet d’extraire des caractéristiques musculaires anatomiques et fonctionnelles, telles que la ST et le VM, grâce à une nouvelle approche dynamique. La création de reconstructions tridimensionnelles du muscle squelettique complet lors de tâches dynamiques permet d’explorer, au mieux de nos connaissances, l’évolu- tion dynamique des caractéristiques anatomiques musculaires, qui avaient, jusqu’à maintenant, largement été évaluées uniquement dans des études statiques. De la combinaison de cette information fonctionnelle et des données métaboliques dérivées des études PET, un ensemble unique de données sera déduit, qui permettra d’aborder l’étude des TMS à partir d’une nouvelle perspective par rapport aux approches adoptées conventionnellement dans le domaine de la recherche musculo-squelettique. Cela pourrait conduire à de nouvelles thérapies ainsi qu’à l’amélioration des techniques chirurgicales.

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C ontents

Acknowledgments i

Abstract iii

Résumé v

Table of Content vii

List of Figures xi

List of Tables 1

1 Introduction 2

1.1 Research context and Motivations . . . 3

1.2 Objectives and contributions . . . 5

1.3 Thesis outline . . . 7

2 Musculoskeletal imaging techniques overview 9 2.1 Introduction . . . 10

2.2 Medical imaging overview . . . 10

2.2.1 Magnetic Resonance Imaging (MRI) . . . 10

2.2.1.1 The Larmor Frequency . . . 11

2.2.1.2 Longitudinal relaxation T1 . . . 13

2.2.1.3 Transverse relaxation T2 . . . 14

2.2.2 Positron Emission Tomographic (PET) . . . 15

2.2.2.1 Photon interaction with matter . . . 16

2.2.2.2 Positron Annihilation . . . 16

2.2.2.3 Radioactivity quantification methods . . . 17

2.2.2.4 PET data acquisition . . . 18

2.2.2.5 Types of coincidence events . . . 18

2.2.2.6 Attenuation and attenuation correction . . . 20

2.2.3 Hybrid Imaging . . . 20

2.2.3.1 PET/MRI . . . 21

2.3 Conclusion . . . 23

3 Overview of manual and semi automatic segmentation muscle segmentation tools. 25 3.1 Introduction . . . 26

3.1.1 ITK-Snap . . . 26

3.1.2 Seg3d . . . 28

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CONTENTS

3.1.3 Osirix imaging platform . . . 28

3.1.3.1 Image segmentation and rendering . . . 29

3.1.3.2 iSix . . . 31

3.1.3.3 DMFramework . . . 32

3.2 Medical imaging file formats . . . 32

3.2.1 DICOM Standard format . . . 32

3.2.1.1 Data structures . . . 33

3.2.1.1.1 File header . . . 33

3.2.1.1.2 Data set . . . 33

3.2.1.1.3 Data Elements . . . 34

3.2.2 MetaImage imaging format . . . 34

3.3 Osirix skeletal muscle segmentation plugin . . . 37

3.3.1 Plugin description . . . 38

3.3.2 Plugin GUI . . . 38

3.3.3 Plugin outcomes . . . 41

4 Assessment of skeletal muscle function with PET and MRI studies 45 4.1 Introduction . . . 46

4.2 Assessment of skeletal muscle function . . . 47

4.2.1 Anatomical properties of skeletal muscles . . . 47

4.3 Assessment of muscle function with MRI . . . 49

4.3.1 Muscle function assessment with static MRI techniques . . . 51

4.3.1.1 Conclusion . . . 53

4.3.2 Muscle function assessment with dynamic MRI techniques . . . 55

4.3.2.1 CINE-PC MRI . . . 56

4.3.2.2 Real-time MRI . . . 57

4.3.2.3 Other dynamic MRI techniques . . . 59

4.3.2.4 Muscle volume estimation from CSA . . . 59

4.3.2.4.1 The Cavalieri algorithm . . . 62

4.3.2.4.2 The truncated cone algorithm . . . 62

4.3.2.4.3 The cubic-spline interpolation algorithm . . . 62

4.3.2.4.4 The Deformation of parametric specific object (DPSO) algorithm . . . 62

4.3.2.5 Conclusion . . . 63

4.4 Assessment of muscle function with PET/MRI . . . 65

5 PET/MRI imaging protocol for skeletal muscle function assessment 69 5.1 Introduction . . . 70

5.2 MRI sequences definition . . . 70

5.2.1 Static MRI sequences . . . 71

5.2.2 Dynamic MRI sequence . . . 73

5.3 Imaging protocol . . . 75

5.3.1 MRI protocol . . . 76

5.3.1.1 Patients and controls . . . 76

5.3.1.2 MRI acquisition . . . 77

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CONTENTS

5.3.1.3 Exercise definition and execution . . . 80

5.3.1.4 Guiding device . . . 80

5.3.2 PET/MRI protocol . . . 83

5.4 Osirix plugin for merging of separated stacks of images . . . 84

5.4.1 Plugin motivation . . . 85

5.4.2 Plugin GUI . . . 86

5.4.3 Plugin outcome . . . 89

6 Generation of patient-specific functional muscle models of lower limb 93 6.1 Introduction . . . 94

6.2 Four-dimensional skeletal muscle models generation from dynamic MRI images . . 94

6.2.1 Segmentation of dynamic MRI images through the exploitation of static MRI muscle segmentation . . . 94

6.2.2 Generation of missing data from dynamic MRI segmented contours . . . 96

6.3 Osirix plugin for generation of patient-specific skeletal muscle function models . . 97

6.3.1 Plugin description . . . 97

6.3.2 Plugin GUI . . . 100

6.3.3 Plugin outcomes . . . 102

6.4 Skeletal muscle function assessment by anatomical 4D skeletal muscle models . . . 109

6.4.1 3D/4D muscle CSA characterization . . . 109

6.4.2 Dynamic MV studies . . . 113

7 Conclusion 117 7.1 Contributions . . . 118

7.2 Conclusions . . . 119

7.3 Future steps and possible improvements. . . 120

7.3.1 Use-case study with PET/MRI protocol . . . 121

7.3.2 Application proposal: Football players quadriceps assessment . . . 121

Relevant medical imaging techniques for musculoskeletal assessment 125 A Medical imaging techniques for musculoskeletal assessment 125 A.1 Ultrasonography (US) . . . 125

A.2 Planar X-Ray radiography . . . 126

A.3 Computed Tomography (CT) . . . 126

A.4 PET/CT . . . 129

List of Publications 130

Bibliography 131

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CONTENTS

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L ist of F igures

1.1 Proportion of occupational diseases. . . 3

2.1 NMV of a tissue when is placed inside a electromagnetic field B0 . . . 12

2.2 Parallel and anti-parallel protons precessing . . . 13

2.3 NMV orientation recovery . . . 15

2.4 Positron/electron annihilation phenomenon . . . 17

2.5 PET types of coincidences . . . 19

2.6 Annihilation estimation through photons coincidence detection . . . 19

3.1 ITK-Snap in use . . . 27

3.2 Snakes semi-automatic segmentation algorithm . . . 28

3.3 Seg3D main workspace . . . 29

3.4 Osirix Volume Rendering example . . . 30

3.5 iSix in use . . . 31

3.6 Segmentation plugin GUI. . . 39

3.7 Segmentation Plugin Import controls. . . 40

3.8 Segmentation Plugin visualization Control Area . . . 40

3.9 Segmentation Plugin Visualization Area . . . 41

3.10 Skeletal muscle 2D ROI . . . 41

3.11 Segmentation MPR reconstruction . . . 42

3.12 ROIs 3D volume rendering . . . 42

3.13 3D reconstruction of segmentation ROIs . . . 43

4.1 Soleus tractography with DWI . . . 48

4.2 Muscles and bones three-dimensional meshes of the lower limb . . . 50

4.3 MV and ACSA relationship at different proximal-to-distal levels of thigh muscles . 52 4.4 CSA and MVIC correlation . . . 53

4.5 Knee flexion torque vs Semitendinosus MV . . . 54

4.6 Relationship between elbow torque and muscle volume . . . 54

4.7 Tissue displacement assessment with CINE-PC MRI . . . 57

4.8 CINE-PC MRI anatomical and velocity images . . . 58

4.9 Comparison of real time MRI and cine PC MRI velocity measurements in the bi- ceps brachii. . . 60

4.10 DENSE MRI images of elbow displacement maps . . . 61

4.11 DPSO algorithm steps . . . 63

4.12 Muscle activation heterogeneity . . . 66

4.13 ¹¹C-acetate PET/Computed Tomography (CT) 3 weeks after hip arthroplasty . . . . 67

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LIST OF FIGURES

5.1 Dixon In-Phase, water and fat images and T2 TSE and STIR images . . . 72

5.2 Dixon water sequence without surface coil . . . 73

5.3 T1 TSE vs Dixon water image . . . 74

5.4 Water, in-phase, out-of-phase and fat images provided by mDixon sequence . . . . 75

5.5 Real time bTFE dynamic images of lower limb . . . 76

5.6 MRI patient setup . . . 78

5.7 MRI protocol . . . 79

5.8 Table setup and surface coil support system. . . 79

5.9 Flexion/Extension exercise for quadriceps activation. . . 81

5.10 MRI-compatible guiding device . . . 82

5.11 PET/MRI protocol . . . 83

5.12 Image separation issue . . . 85

5.13 Image Stitching Plugin modal window . . . 86

5.14 Image Stitching plugin GUI. . . 86

5.15 Image Stitching Plugin series selection . . . 87

5.16 Image Stitching Plugin Configuration area . . . 88

5.17 Dicom tags example . . . 89

5.18 Example of MRI merged series from separated stacks of images . . . 90

6.1 4D data model generation diagram . . . 95

6.2 4D model generation (1) . . . 98

6.3 4D model generation (2) . . . 99

6.4 Segmented static ROIs extension procedure . . . 100

6.5 Segmented dynamic ROIs images adjustment . . . 101

6.6 4D Model Plugin GUI display . . . 102

6.7 4D Model Plugin ROIs selection . . . 103

6.8 4D Model plugin GUI. . . 104

6.9 4D Model Plugin ROIs Control Area . . . 104

6.10 4D Model Plugin Control Area . . . 105

6.11 4D Model Plugin 3D models list . . . 105

6.12 4D Model Plugin ROIS export feature . . . 106

6.13 4D model Plugin 2D dynamic ROIs exploration . . . 106

6.14 4D Model Plugin 2D ROIs overlapping . . . 107

6.15 3D models functional analysis . . . 108

6.16 Static CSAs vs dynamic CSAs distribution comparison . . . 110

6.17 Muscle CSA spatial distribution and evolution in time . . . 111

6.18 Inter-subject CSA distribution comparison . . . 112

6.19 CSA evolution in time at different space locations . . . 114

7.1 Multimodal 3D VR issue . . . 123

7.2 Orthogonal MPR of multi-parametric PET/MRI images . . . 124

7.3 Volume rendering of PET/MRI data . . . 124

A.1 First CT design . . . 127

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L ist of T ables

2.1 T1 values of different human tissues atB0=1 Tesla . . . 14

2.2 Approximate T2 values of several human tissues. . . 15

2.3 Imaging modalities comparison . . . 23

4.1 Quadriceps MV comparison . . . 55

4.2 Dynamic MRI techniques comparison . . . 59

4.3 Relative and absolute volumes of the quadriceps muscles . . . 64

5.1 MRI acquisition subjects characteristics . . . 77

6.1 Muscle volume comparison . . . 115

A.1 HU units of common materials and tissues . . . 128

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C hapter 1

I ntroduction

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1.1 Research context and Motivations

MSDs are a group of diseases and impairments that affect different elements of the musculoskeletal system. This set of diseases can impact on all genders and races and cause pain in the affected part of the musculoskeletal apparatus, loss of physical autonomy and incapacity of the individual to carry out certain tasks. The class of disorders classified as MSDs is quite ample and ranges from back pain to rheumatoid arthritis, also including different types of tendinitis and even gout.

Global population is ageing more and more as a result of the changed balance among mortality and fertility trends . The number of elder people is increasing at a faster rate than the global population is growing, resulting tripled during the last 50 years. This increase will most likely result in an augmentation of MSDs incidence, due to their strong relationship with ageing. This scenario will make MSDs a big burden to be tackled not only by the national healthcare systems, but also by the economies of the developed countries. According to the European Occupational Disease Statistics (EODS), MSDs are the most frequent occupational diseases in the European Union (EU), being characterized by more than a 38% occurrence among all occupational diseases (Fig. 1.1) [88] and having an enormous impact in terms of work absence due to disabled individuals still in their working age.

Figure 1.1: MSDs suppose more than a 38% of all occupational diseases.Image from [88], ©European Agency for Safety and Health at Work 2010, used with permission.

Among all MSDs, OA is one of the most spread over the population. OA is a degenerative joint disease caused by inflammation and partial or total loss of the cartilage in the joints that reduces the friction between the bones. When this cartilage disappears or becomes inflammated, the mentioned friction between bones can produce stiffness and joint pain up to the disability of the individual who suffers it to perform certain tasks. KOA is one of the most extended forms of arthritis not only among the elderly, but also affecting an increasing number of young subjects.

A correct function of the knee joint strongly depends on the strength and stability provided by the the lower-limb muscles. In particular, quadriceps muscles are the principal contributors to

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Chapter 1. Introduction

knee joint stability and their weakness or pathological condition can cause a deficient neuro- muscular control and in consequence, an abnormal knee function. Quadriceps weakness has been demonstrated to be strongly linked with the development and progression of KOA [76], nevertheless, it is still not clear if quadriceps weakness is a cause or an effect of KOA. On one side, some studies have described the insurgence of quadriceps weakness before KOA symp- toms are developed by the subject [83], on the other hand other reports have detected quadriceps weakness showing up after the apparition of knee pain or KOA [76].

Self-reported questionaries and performance tests are commonly employed in the evaluation of KOA patients [94]. In self-reported studies the patient is asked about his perception about daily functional abilities by answering standard questionnaires, such as the the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36), the Western Ontario and McMaster Univer- sities Osteoarthritis Index (WOMAC) or the Knee and Osteoarthritis Outcomes Score (KOOS).

However, these questionaries are extremely dependent on subject’s perception who can over- or underestimate his real functional status . The patient’s physical status can also be evaluated by means of performance studies, such as the Six-Minute Walk Time (6MWT) and the Timed Up and Go (TUG) tests [38], where the patient is requested to perform specific tasks and results are scaled later on for evaluation of muscular function [95], thus providing with more quantitative data about subject functional status.

EMG, biopsy and the MVIC index are techniques frequently employed in musculoskeletal assessment, capable to provide researchers and clinicians with more objective criteria not only about patient’s functional status but also about the condition of an single muscle or a set of muscles [68, 75].

Medical imaging techniques can provide with new possibilities and potentially new approaches for the assessment of muscle anatomical insights and function [90, 78, 77]. They are non- invasive and provide with complete anatomical and functional data, furthermore, powerful post- processing tools can be applied to infer new and significant parameters about muscle functional status. These techniques have become more and more used in the last years, complementing or substituting classical methods. Among all the different techniques currently available in medical imaging, MRI is proving as a powerful technology offering a wide range of possibilities. MRI employes a high magnetic field to produce anatomical images based on tissue characteristics and concentrations of protons in water molecules. This technique, which offers the major advantage of being less invasive than other imaging techniques using ionizing radiation, has made a major progress in the recent years offering the best method for analyzing soft tissues and organs in the human body. The latest generation of MRI scanners also allows for the acquisition of dynamic images and high-resolution 3D volumes for better assessment of functional parameters of different organs and parts of the body. On the other hand, muscle activity extracted from metabolic PET images can provide meaningful information about muscle function that extends conclusions extracted from MRI function studies [22, 60]. Ultimately, the use of these complementary imaging technologies can lead to a better characterization of muscle function.

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1.2 Objectives and contributions

In this study, we aim to explore the possibilities offered by a new hybrid PET-MR scanner (Phillips Ingenuity TF PET/MRI), allowing to acquire both modalities in a single session when imaging the lower limb muscles. MRI allows obtaining morphological data of the whole body or part of it, and PET inferring information about the biological paths and the metabolic behavior of the target organ. State-of-the-art hybrid PET/MRI devices enable the acquisition of fully reg- istered anatomical (MRI) and metabolic (PET) image datasets. Our final goal will be to produce a patient-specific model of the muscle that integrates the structural properties and the functional parameters derived from specific images analysis.

The work presented in this dissertation has been developed within the framework of the Multi- scale Biological modalities for physiological human articulation (MultiScaleHuman) project. This project is a multi-centric research project funded under the European Marie-Curie program with two main objectives: the development of novel visualization/interaction tools to study musculoskeletal systems in motion and bring further structural and functional comprehension of the human body and merging data types of multiple scales acquired from different imaging modalities. Our contributions to the overall project was focusing on the MRI functional imaging aspect with the aim to develop a novel patient-specific model of muscle function from static and dynamic MRI images. A secondary objective of our work was the integration of the created data with metabolic PET images to evaluate the usefulness of new state-of-the-art hybrid PET/ MRI devices in the assessment of models of muscle functional performance and behavior.

It must be remarked the close link of this research project with work of Dr. Matthias Becker [16], developed also within the framework of the MultiScaleHuman project . Muscle segmentation is an important step prior to the generation of the patient-specific functional muscle model presented in this PhD thesis, but is not one of its objectives. Therefore, a close collaboration was promoted to include the toolkit for efficient extraction of musculoskeletal structures from multi- channel MRI images (DMFramework) developed by Dr. Becker within the image processing pipeline developed in the presented project.

1.2 Objectives and contributions

Our aim is to develop a multidimensional model of the skeletal muscles from PET/MRI studies to improve tools nowadays employed in the assessment of muscle function. Our primary goal is to leverage on MRI technology to study anatomical features like CSA and MV of the skeletal muscles from a new perspective, thus creating novel data not yet available that could push further current knowledge about muscle function. This objective is proposed for the following reasons:

– Muscle CSA and muscle volume have been extensively reported in literature to have a strong correlation with muscle strength [35, 10] , which is, in turn, a strong descriptor of muscle function [59, 81, 69].

– MRI is considered, nowadays, the preferred technology to assess MV and CSA when muscle function is evaluated. Its unique capability to differentiate soft tissue, its lack of radiation and a high level of reproducibility have led clinicians and researchers to define this medical

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Chapter 1. Introduction

imaging technique as the reference imaging modality in musculoskeletal studies [86, 74, 10].

– Studies assessing muscle CSA and MV have addressed the assessment of these anatomical muscle parameters using a static perspective. Therefore, even if these features were extracted from high-resolution MRI images [86, 74], the information from dynamic MRI techniques about muscle behavior is missing.

– Dynamic MRI techniques have been employed to study deformation of skeletal muscles in a single plane [77, 30, 101, 26, 32]. Thus, lacking the three-dimensional information required to fully characterize muscle behavior during exercise.

– PET imaging has been demonstrated to provide meaningful information about muscle activation [22, 60]. Providing complementary information to MRI function studies.

To pursue this objective, we propose a comprehensive patient-specific oriented image analysis and visualization pipeline where all the aspects from image acquisition to the display of the results are addressed:

– A specific MRI protocol was developed to acquire in a single scanning session the required anatomical and functional images to characterize skeletal muscle function.

– With the aim to stimulate quadriceps muscles, which have a strong correlation with the onset and evolution of OA, a specific flexion/extension exercise was developed to be performed within a scanner bore.

– Different devices were developed in-house to address the practical needs imposed by the implemented imaging protocol.

– Pre- and post-processing techniques and tools were implemented and provided in the form of Osirix plugins, with the purpose of providing with an imaging pipeline capable to create patient-specific multidimensional models of muscle function fully integrable in the clinical practice.

The resulting work provides a multidimensional muscle model that provides clinicians and researchers with a new way to explore muscle function and that complements conventional anatomical studies by providing more quantitative data of muscle function. Our work covers all the consequent steps from the development of a specific imaging protocol, allowing for the acquisition of required anatomical and metabolic images and parameters extraction, to data integration and multi-parametric display. Emphasis was put in the development of a 3D interpolation technique of the muscle shape from 2D dynamic contours, to overcome current technical MRI limitations that do not allow acquiring three-dimensional dynamic data sets, by computing three- dimensional estimates of the muscle shape over time and, hence, providing a four-dimensional (space and time) model of muscle behavior.

The different MRI imaging sequences that were developed specifically for evaluation of muscular anatomy and function are part of this work and have been published in the early stage of the project [53], being one of the contributions of this PhD project that is applicable in clinical practice. We would like to emphasize that the scope of this PhD research plan as a whole is a contribution not only to the medical imaging and visualization field but also to the research

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1.3 Thesis outline

concerning functional assessment of the musculoskeletal apparatus, where we are introducing an innovative approach for quantitative analysis of muscle behavior not available so far. This achievement benefit from the interdisciplinary approach of the study for developing, optimizing, and creating innovative methods based on existing technology that was never used before with the goal here pursued. The main results of this work will be of benefit for medical imaging, orthopedics surgery and computer science applicable to medical informatics.

1.3 Thesis outline

The present manuscript is organized as follows :

– In chapter 1, the medical and research context of this project is introduced. The objective of this thesis is presented and the main tasks carried out to reach it are listed.

– Chapter 2 offers an overview of the state-of-the-art about PET and MRI imaging techniques.

New hybrid imaging scanners and the additional possibilities that they bring to musculoskeletal studies are also introduced.

– Chapter 3 addresses the current state-of-the art about skeletal muscle function assessment and how medical imaging, and in particular PET and MRI, have been employed so far in the study of muscle function.

– In chapter 4, we describe the in-house developed imaging protocol to acquire the particular sets of images required for our study and related practical issues are addressed.

– In chapter 5, the pre- and post-processing tools developed for the integration of muscle segmentation tools and the four-dimensional interpolation algorithm for the generation of three- dimensional muscle reconstructions from a limited set of dynamic contours are detailed. Pre- liminary results provided by the proposed image processing pipeline are presented as an example of possible output data.

– In chapter 6, results obtained from our research work are presented and discussed, potential medical applications of our work are suggested and possible future improvements to be explored are proposed.

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C hapter 2

M usculoskeletal imaging techniques overview

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Chapter 2. Musculoskeletal imaging techniques overview

2.1 Introduction

Conventional techniques applied in musculoskeletal assessment such as biopsy, MVIC or EMG suffer of several drawbacks, which were overcame by the introduction of medical imaging techniques. Issues as the limited access to the target muscle or a high level of variability can be overwhelmed by imaging techniques, which provide images capable to offer quantitative information of musculoskeletal structures and to infer reproducible conclusions.

The apparition of medical imaging with the discovery of x-rays by Wilhem Conrad in 1895 started a new era in medicine. The possibility to observe in-vivo the inner human body without physical injury or discomfort for the patient let researchers and clinicians improve their understanding of human organs and their regulatory biological processes. Since then, the medical imaging field experimented a constant evolution that led to the development of several imaging techniques leveraging on different kind of tissue properties, such as the attenuation to a certain radiation (x- ray CT), the penetrability to ultrasound waves (Ultrasonography (US)), the density of hydrogen protons in the tissue and their behavior within an electromagnetic field (MRI), or the interaction of the tissue with injected radio-labeled molecules into the body (PET). These techniques can be used to target different diseases and characterize the inner body from different points of view, either addressing the visualization of the anatomical structures (MRI,CT,US) or their metabolic behavior (PET). The latest technical revolution in the field of medical imaging was the apparition of novel hybrid devices such as PET/MRI or PET/CT that combine different imaging modalities and acquire complementary morphological and functional images simultaneously.

In this study, we employed a hybrid PET/MRI scanner (Philips Ingenuity TF PET/MRI) for image acquisition and specific protocols were designed for musculoskeletal imaging. In this chapter, a brief overview of the imaging techniques we leveraged on for our research work is provided. For completeness, an additional description of other techniques that were extensively used in literature and in clinical practice to investigate the musculoskeletal apparatus is provided in Appendix A.

2.2 Medical imaging overview

The regulating principles for image production and acquisition in MRI and PET techniques are described, and PET/MRI hybrid imaging is introduced.

2.2.1 Magnetic Resonance Imaging (MRI)

MRI imaging technique allows creating three-dimensional images of the inner human body, leveraging on the application of a strong magnetic field, several thousand times higher than the Earth’s magnetic field, and of radio waves that induce a response in the hydrogen atoms of the imaged body, thus generating electric signals that can be opportunely detected and processed.

The intensity of the detected signals depends on the amount of hydrogen atoms in the different tissues within the body. MRI was first introduced in 1972 by R. Damadian and is nowadays

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2.2 Medical imaging overview

recognized all as an invaluable tool for research and clinical diagnostics worldwide.

The radio signals induced by radio-frequency stimulation induce a certain precession of the hydrogen protons that subsequently decay at different speed depending on the tissue density of protons. This is the reason why, different tissues in the human body generate radio signals with different magnitudes. The variation between magnitudes is exploited to create images of the diverse anatomical structures in the body. As well, since differences in concentration of hydrogen protons between different tissues result in different signals, this also allow differentiating between normal tissue and pathological lesions or tumors.

Among the many advantages that MRI offers with respect to other imaging techniques, non- invasiveness, the absence of ionization radiation administered to the imaged subject and outstanding soft tissue differentiation stand as the most relevant. MRI provides the best differentiation of soft tissue by depicting differences in molecular composition of the tissues and their content in protons and water. This has made a significant quantum leap, since all other imaging techniques leveraging on x-ray can only infer differences in tissue densities. However, MRI also shows a few drawbacks. In particular, the use of radio waves and strong magnetic fields during the imaging process limit the range of subjects that can effectively undergo an MRI: people with non MRI-compatible pacemakers or metallic implants can not undergo this scan. Also, MRI technology is expensive and relatively complex to use, with a high number of imaging parameters to be set-up to image at best a specific type of tissue, thus requiring good knowledge of the technique itself and its characteristics.

While the first MRI machines were built using large resistive magnets , the introduction of super conducting magnets [65] capable to induce higher electromagnetic fields (up to 20 Tesla) represented a major step that lead to MRI imaging as it is known now. Common MRI medical devices produce magnetic fields between 0.1-4 Tesla and the latest state-of-the-art machines can produce stable magnetic fields up to 9 Tesla. Due to the need of an extremely homogenous magnetic field inside the bore of the scanner (of the order of 10 ppm), the bore must be magnetically shielded against any kind of external magnetic field or electromagnetic radiation.

2.2.1.1 The Larmor Frequency

Hydrogen atoms are present in any biological tissue in a proportion near 10²²/cm³. Without the influence of a magnetic field, protons inside hydrogen atoms nuclei precess with an arbitrary phase and direction, acting as small magnets with a magnetic moment of random orientation and the same energy as their neighbor hydrogen atoms. Due to the high number of hydrogen atoms present in biological tissues, there is always a complementary pair to each hydrogen atom with equivalent energy but opposite orientation of the magnetic moment, thus resulting in a neutral magnetic field in the absence of an applied external field.

If a strong magnetic field B₀ is applied, hydrogen atoms start precessing aligned with respect to the external magnetic field. Some will be aligned along the anti-parallel and some will align along the parallel direction with respect B₀in a proportion of almost 50:50 percent. However, a

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slightly majority of protons (7 ppm) result to be aligned in the parallel direction to the external magnetic field, which is the direction of lower energy. Because of the slight difference between the number of protons at each energy state, a small magnetization is present along the positive z-axis, which is defined to be the axis along B₀ direction. This magnetization is named Net magnetization vector (NMV) (Mz) and results to be proportional to the proton density, to the intensity of the external magnetic fieldB0and to the square of the gyromagnetic ratiog.

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Figure 2.1: NMV of a tissue when is placed inside a electromagnetic field B0 (1). This magnetization is result of the small differences in population between the lower and higher proton energy levels (2). Image from [33]

The difference between the two energy levels is defined by the equation:

∆E=h⋅γ⋅B0 (2.1)

Wherehis the Planck’s constant (h=6.626⋅10⁻³⁴Js).

As stated in 2.1, the number of spins in the low-energy state is related to B0 and it gets higher the stronger is B0. Thus, resulting in an increased difference in the number of protons (spin excess) between the two energy levels and in the consequent tissue magnetization. In summary, the stronger is B₀the higher will result the spin excess and the tissue magnetization.

Inside an electromagnetic fieldB0of 1 Tesla, the required energy to make electrons jump from the lower to the higher energy level is∆E=2.821⋅10⁻²⁶J. This energy can be provided by means of an electromagnetic radiation of frequency f and energyε. We talk about ”resonance” whenεand

∆Eare equal and protons jump from their lower energy state to the higher one. f =42.58MHzis called Larmor frequency and is defined as the frequency at which protons precess inside a tissue placed inside an electromagnetic fieldB₀of 1 T.

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ε=h⋅f =∆E (2.2)

f =γ⋅B0 (2.3)

Hydrogen nuclei contain a single proton and are extremely abundant in the human body. If a tissue rich in hydrogen is placed inside an external magnetic field, it gets magnetized following the described process. Since almost all tissues in the human body are rich in hydrogen but each tissue is characterized by a different hydrogen concentration, it is possible to induce the Nuclear Magnetic Resonance (NMR) process and measure it, this being the basic principle for MRI imaging.

2.2.1.2 Longitudinal relaxation T1

When a biological tissue is placed inside an external electromagnetic field B₀, hydrogen atoms precess lined up along B0 axes a result of the NMR process. However, protons do not precess in phase mainly due to small inhomogeneities in the magnetic field. Due to this, the net tissue magnetization is aligned parallel toB0but does not precess around it.

If a Radio Frequency (RF) pulse is applied across B0at the Larmor frequency, hydrogen protons start precessing in phase, thus the NMV starts precessing. If this pulse continues in time, some of the protons in the lower energy state will absorb energy from the RF signal and will jump to the higher energy state, inducing the NMV to move towards its transverse plane. If the RF is applied during a sufficient duration of time, it can cause a 360º rotation of the NMV across the transversal plane, but typically a 90º rotation angle is applied by means of a so called ”90-degree pulse”.

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Figure 2.2: Precessing of protons aligned parallel (spin up) and anti-parallel (spin down) toB₀(1). When a 90º pulse at Larmor Frequency is applied in the x direction, protons start precessing in phase andMzis

"shifted" to the xy planeMxy. Image from [33]

Once the RF pulse is over, the system goes back to its equilibrium state, thus the NMV gradually

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Tissue T1 (ms)

Blood 1000

Cerebrospinal fluid 2000

Fat 250

Gray brain matter 900

Liver 500

Muscle 850

White brain matter 800

Water 3000

Table 2.1:T1 values of different human tissues atB₀=1 Tesla

gets back to the z-axis. This recovering process can take from some milliseconds even up to several seconds and it is described by the equation:

Mz,t=Mz,eq⋅ (1−exp(t/T1)) (2.4) Where T1 is called relaxation time and represents the time needed to a complete relaxation when 1/e of the excited protons has not yet relaxed . T1 depends not only on the magnetic field, but also on the specific tissue characteristics (Table 2.1).

During the relaxation process, hydrogen atoms loose energy, emitting a RF signal (Free-Induction Decay (FID)) that is measured with a conductive coil. The measured signal is then processed to create the characteristic MRI grey-scale anatomical images, the intensity of the different body tissues in the image depending on the tissue hydrogen protons density, in turn linked to water density in the tissue.

If we need to perform different experiments, it is mandatory to wait several T1s until the system has recovered its equilibrium state in order to avoid undesired interactions between experiments.

2.2.1.3 Transverse relaxation T2

When a 90-degree RF pulse is applied to a tissue placed inside a magnetic field, its NMV starts precessing at the Larmor frequency. This precession results in a magnetization vectorMxywith the same magnitude asMzand precessing at the Larmor Frequency. This magnetization induces an electrical signal FID when a coil is placed along the xy plane. When the RF pulse is inter- rupted, the hydrogen protons precess again out of phase, thus inducing a decay of the FID signal, besides theMzrecovery that is characterized by the Longitudinal Relaxation time T1.

The process by which the hydrogen protons loose their coherence when the RF pulse ends is called spin-spin relaxation and is linked with the magnetic coupling of adjacent spins. This process can be mathematically described by the equation 2.5:

Mxy,t=M_xy,0⋅exp(−t/T2) (2.5)

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Figure 2.3: After the 90º pulse, protons precessing starts logging the coherence (1) and NMV recovers its orientation along the z axis (2). Image from [33].

Equation 2.5 describes the sinusoidal signal with exponentially decaying amplitude that is induced in the receiving coil. This signal oscillates with the Lamor frequency. The two relaxation processes that these parameters are characteristic of always happen simultaneously, but it has to be mentioned that T2 is always shorter than T1, as reported in Table 2.1 and Table 2.2.

Tissue T2 (ms)

Blood 180

Cerebrospinal fluid 250

Fat 80

Gray brain matter 100

Liver 40

Muscle 45

White brain matter 90

Water 2500

Table 2.2:Approximate T2 values of several human tissues.

2.2.2 Positron Emission Tomographic (PET)

PET imaging is a nuclear medicine imaging technique that is based on the introduction into the human body of molecules labeled with a positron-emitting isotope (radio-labeled molecules).

The peculiar difference of this technique with respect to the other existing nuclear imaging techniques is that positron emission generates two gamma rays emitted at 180° apart, which allows for a better three dimensional localization of the point of emission inside the imaged body.

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2.2.2.1 Photon interaction with matter

Compton scatter and photoelectric absorption are the two main interactions that photons undergo in the human tissue.

Compton scatter is originated when an emitted photon interacts with an electron present in the tissue causing an energy exchange that increases the electron kinetic energy and changes the direction and the energy of the photon. Changes in the direction of the photon can lead not only to errors in the estimation of the Line Of Response (LOR) by the scanner and miscalculations in tissue radioactivity distribution, but also the involved photon experiments a decrease in its energy, described by Eq.2.6:

E^′= E

1+ (E/m₀c²)(1−cosθ) (2.6) In photoelectric absorption the photon is absorbed by an atom during his travel across the tissue and an electron from the atom is expelled. The probability of photoelectric absorption increases with the atomic number of the absorber atom and decreases with the photon energy.

The probability for a photon to persist while passing through a certain material is measured by the linear attenuation coefficient µ. A photon with initial incident intensity I₀ that will travel through a material with a linear attenuation coefficient µ for a distance x, will have a final intensityI(x):

I(x) =I₀e

−

x

∫

0

µ(x)dx

(2.7)

This equation is of crucial importance in PET imaging since it enables anticipating and correcting for the attenuation that the emitted photons will undergo while moving through the tissue along a certain LOR, thus enabling quantitative measurements of radiation distribution within the human body.

2.2.2.2 Positron Annihilation

When a positron is emitted during the radionuclide decay process, it travels through the tissue interacting with the atoms found along its path. Such interaction causes the excitation and ionization of the atoms and, on the other hand, makes the kinetic energy of the positron to be moderated to thermal energies on a very short distance. At this point, the positron may inter- act with an electron to form an electron-positron system called positronium. The positronium rapidly collapses and matter and antimatter annihilate resulting in a pure energy emission in the form of two 511-KeV photons at opposite directions.

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Figure 2.4: Positron/electron annihilation phenomenon. Two photons are created with same energy (511 keV) and moving in opposite directions.

2.2.2.3 Radioactivity quantification methods

PET data require to be quantified in order to provide images whose pixel values represent the real radioactivity concentration inside the body. Thus, several corrections for attenuation, scatter, random events and calibrations need to be done to translate detector counts to radioactivity concentration values. Depending on the type of quantitative or semi-quantitative parameters that we want to extract, different scanning configurations or calculations must be adopted. Two acquisition settings are possible: a single static acquisition can be acquired at a certain time after injection (static acquisition mode), or the full time-course of radioactivity can be collected (dynamic acquisition mode).

Dynamic acquisition mode allows for quantitative evaluation of PET images. As an example, compartmental models are a class of mathematical models that allow to quantitatively describing the distribution and uptake of a tracer within the body. They require as input data the arterial blood radioactivity as a function of time, as well as radioactivity evolution in time of the target region.

On the other hand, the Standardized Uptake Value (SUV) offers a well-known example of index allowing a semi-quantitative evaluation of static PET images. Its extensive use in the clinical practice is partially due to its computational ease. SUV normalizes the injected dose and the body size and the injected dose (2.8).

SUV= Radioactivity concentration

(Injected dose)/(Body mass) (2.8)

Thus, the SUV index results to be independent from the amount of radioactivity injected in the

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body. Body mass allows for comparison of data from a large range of patients, by compensat- ing for the fact that larger bodies have more competing tissue than small bodies. SUV can be displayed as map images where each voxel is associated to the SUV at a specific location. Thus, Region of Interests (ROIs) can be defined and different statistics and parameters extracted, such as mean, maximum and minimum regional SUV.

2.2.2.4 PET data acquisition

When two gamma rays are detected within a short time window and in opposite directions in the scanner detector ring, they are defined as coincident.

Not all couples of photons detected in coincidence actually belong to a single event occurred on the LOR. In particular, scattered and random coincidences represent two types of events that are experimentally detected in coincidence but do not represent true coincidences. These event types will be detailed in the next section.

In order to improve the localization of a coincidence event, physical collimators can be used (septa made of lead) to prevent photons emitted from locations outside the plane of imaging to reach the detector. When a coincidence event is assigned to a LOR connecting the two fired detectors without need for physical collimators, the procedure is identified as electronic collima- tion.

2.2.2.5 Types of coincidence events

Four possible coincidence events can be detected during a PET scan: true, scattered, random and multiple.

True coincidenceshappen when the two photons generated by the same annihilation process reach the detectors within a short enough time window. No photon experiences any interaction with the surrounding matter and no other event is detected within the same time-window.

Scattered coincidences occur when at least one of the emitted photons undergoes one or more Compton scattering before the detection, thus changing its direction and being assigned to the wrong LOR. This kind of coincidences add background noise to true coincidences, decreasing the image contrast and causing an overestimation of isotopes concentration and a decrease of the signal-to-noise ratio. Scattered coincidences depend on the volume and attenuation characteristics of the traversed material and on the geometry of the scanner. In order to minimize these type of coincidence events, shielding can be implemented in front and behind the detector ring to limit the number of photons that hit the detector without coming from a proper annihilation process. These shields are called septa.

Random coincidencesarise when photons originated from different annihilation events are detected within the same time-window, thus they are considered as coming from the same annihilation event. The number of random coincidences for each specific LOR depend on the rate of prompt events measured by the detectors for that specific LOR. They rapidly increase with the activity

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inside the target region, and depend as scattered coincidences on volume and attenuation characteristics of the tissue and on detector geometry. As scattered events, random coincidences also cause overestimation of isotopes concentration and the addition of undesired statistical noise.

Figure 2.5:Types of coincidences in PET. Scattered and random coincidences can cause the estimation of a wrong LOR providing a false true coincidence. Image from [4].

The rate at which random coincidences will be measured by two detectors A and B within the detector is given by equation 2.9:

RR=2τRARB (2.9)

Being RA and RB the detection rates of detectors A and B and 2tthe timing window size. In particular, the size of the time window is crucial for the detection of annihilation events and estimation of isotope distribution., It has to be large enough to allow the detection of true events but, on the other hand, small enough to be able to discard the maximum possible number of random events.

Figure 2.6: Annihilation estimation through photons coincidence detection.Photons travel along the LOR and hit the detector, two detections inside the defined time-window are considered as photons originated from positron annihilation. Image from [5].

Finally,multiple coincidencesoccurs when more than two photons are detected in different detec-

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tors within the same time window. When this situation occurs, it is not possible to determine the LOR where the annihilation took place, so the coincidence is discarded.

2.2.2.6 Attenuation and attenuation correction

Attenuation is the loss of true coincidence events due to scatter or absorption of one or both photons generated during the annihilation process outside the Field of view (FOV). In PET imaging, each of the photons emitted inside the human body undergoes different attenuation effects that depend both on the type of tissue traversed and the length of its path along the LOR.

Attenuation finally affects PET images, if not opportunely corrected. Image artifacts are generated, making the actual radioactivity distribution visualization not accurate. In particular:

activity at body surface edges appears increased due to relative lack of attenuation at the sur- faces compared to inner structures; the appearance of areas with high activity are distorted due to the wide variety of attenuation effects occurring in different directions to activities originat- ing from this areas themselves; regions characterized by low attenuation may show increased activities with respect to actual ones.

An appropriate attenuation correction can restore image quantitative accuracy and remove related image artifacts. Such correction is based on the evaluation of the attenuation factor for each LOR, defined as the probability for a photon emitted along the specific LOR to survive until detection. The reciprocal factor constitute the attenuation correction factor for the LOR and can be applied to estimate the number of actual counts that would have been obtained in absence of attenuation effects.

Attenuation correction can be mathematically inferred, otherwise experimentally obtained from a transmission scan. A reference or a blank scanner is first measured, then either a set of ring sources or a set of rotating rod sources, is placed inside the detector ring and a transmission scan is acquired to measure the attenuation correction factors for all LORs in the scanner as the ratio between the blank sinograms and the transmission sinograms. The introduction of hybrid PET/CT scans allows using X-ray from the scan as transmission data. An advantage is that short CT scanning time offers a lower noise.

2.2.3 Hybrid Imaging

Technical innovations have led to the development of multimodal, or hybrid, imaging devices that combine different imaging methods presented in previous sections in a simultaneous or sequential way. The underlying idea of hybrid imaging devices is to combine functional and metabolic information (PET, or Single-photon Positron Emission Tomography (SPECT)) together with anatomical and morphological characterization (CT or MRI). The first hybrid device applied for clinical use was the combination of SPECT with CT. Nowadays PET/CT plays the role of an important diagnostic tool in clinical routine. Concurrently, the concepts for combining PET with MRI were explored, but only recently a first generation of hybrid PET/MRI scanners has been developed and tested in clinical applications. The first clinical data obtained from the

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emerging PET/MRI imaging technique are showing their potential and defining their role in clinical routine, and it may supersede PET/CT in many applications and practical aspects in the future.

During the last decades a growing interest arose around hybrid, or multi-modal, imaging techniques combining two different imaging devices in one scanner, e.g. a CT and a PET scanner. An increasing amount of studies have demonstrated that multimodal imaging can provide unique and important information in diagnostic and follow-up treatment for different types of pathol- ogy. Such hybrid devices open a whole new scope of diagnostic perspectives and investigative capabilities that goes far beyond the traditional anatomical evaluation of the human body. The ability to obtain functional and metabolic parameters concurrent with anatomical data for the whole human body or for a single organ, has not only opened new perspectives for diagnosis and follow-up of diseases, but also for a better understanding of physiological and biological functions of different parts of the human body.

2.2.3.1 PET/MRI

Despite the clinical success of PET/CT, there are some debated points regarding the use of CT as morphological imaging technique within a multimodal imaging device. In particular, CT adds a significant contribution to the final amount of radiation dose that the patient receives during the examination, and moreover, it provides relatively poor soft-tissues discrimination, even when the scan is acquired with contrast media. The desire to overcome these limitations, supported by the great success of hybrid PET/CT imaging in the clinical and medical research environment, encouraged the development of another multimodal imaging techniques, such as PET/MRI. It is interesting to observe how the idea of combining PET and MRI arose around the same time that PET/CT was conceptualized. PET/MRI was firstly applied to small animal imaging studies in the early 90s, with a major difficulty represented by the interference between the high magnetic field of MRI and the sensitive electronic components of the PET scanners [91]. After this first approach, the first clinical device for human brain PET/MRI imaging was introduced in 2006 using solid state PET detectors that are less sensitive to interference from magnetic field than conventional photomultipliers used in PET scanners. MRI does not show the same limitations as for CT, since it does not involve any ionizing radiation and provides superior soft-tissue imaging compared to CT, in particular if innovative and specific MRI contrast agents are used [47]. This fact, in particular, indicates MRI as a natural and excellent alternative to CT when imaging the brain.

Another great advantage of MRI with respect to CT is that offers a much broader variety of data acquisition techniques than CT that may adapt to a high variety of clinical needs. MRI allows for the use of contrast agents that that have less toxicity than contrast agents used for CT and enables an additional enhancement of soft-tissue contrast. Moreover, MRI allows for advanced functional techniques that are not feasible by CT, such as diffusion and perfusion imaging, and for other methods using the dynamics of contrast agents to evaluate physiological parameters such as flow, perfusion and diffusion, which may complement and enhance PET functional information. MRI also provides spectroscopy, that allows for better evaluation of tissue composition

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and allows for detection of organ-specific abnormalities and pathologies by quantifying ratios of concentration of specific molecules. There are different conceivable options for combining PET and MRI systems. The easiest method, adopted in the earliest devices available for clinical use, is to place the two scanners in series in a manner analogous to PET/CT devices. Nowadays, technical improvements of this configuration, coupled with the excellent results reached by software fusion in many situations (in particular, for brain and heart imaging) brought this sequential multimodal technique approach closer to an ideal simultaneous configuration. In practice, a full integration of the PET system into the MRI gantry is preferred. Such configuration shows many advantages compared to the PET/CT systems adopted in the clinical environment, and to the sequential PET/MRI configuration as well. With a sequential scan, synchronous data acquisition is not feasible. Any temporal separation of the two study components increases the likelihood for image misregistration, and affects attenuation correction, caused by artifacts due to patient movement as well as to the physiological movements occurring internally in the human body, such as gastric emptying or bladder filling, which can compromise the accuracy of tissue activity quantitation [39]. Therefore, fully integrated PET/MRI scanners that may provide accurate temporal correlation of dynamically acquired data-sets from the two imaging modalities remain the final goal for the healthcare industry. The main problems in the development of a fully integrated PET/MRI device are:

– The impossibility for the Photomultiplier (PMT)-based PET detectors to work within or near the magnetic field generated by the MRI scanner. The PET system, and in particular the various hardware components of the PET PMT-based detector, can reduce the MRI performance by degrading the homogeneity of the MRI main magnetic field and of the radio-frequency field as well. This interference may cause artifacts in the MRI images. Moreover, the variable MRI gradients may induce eddy currents in conductive materials of the PET detector, which can distort the effective gradient field. On the other hand, the high magnetic field used in the MRI system excludes the use of PMTs used in traditional PET scanners, since electrons in the vacuum tube of the PMTs are deflected by the interaction with the strong MRI magnetic field (Lorentz force). Despite this, a physical integration of PET and MRI devices in a single gantry became possible as innovative solid-state photo-detectors that are insensitive to the external magnetic field, became available (e.g. Geiger-mode avalanche photodiodes or silicon PMTs [102]).

– Metallic objects (such as surface coils) used to acquire higher quality MRI images interfere with gamma rays from PET, producing attenuation effects. Surface coil arrangements are needed for better MRI image quality, but they contain several metal parts, that may cause artifacts in PET images, with subsequent need for adequate corrections. Currently, system manufactures are attempting to reduce metallic content both in the detector and in the surface coils, in order to minimize this problem.

– The necessity to adapt the ”slow” PET acquisition protocols to the ”faster ” segmental MRI examination of specific body parts. Traditionally, MRI exams have been limited to portions of the human body, due to long acquisition times. However, recent developments have allowed