Automatic tracking of heart valves using time-resolved three-dimensional phase-contrast MRI (4D PC-MRI). Preliminary studies and feasibility evaluation

POLITECNICO DI MILANO

School of Industrial Engineering and Information

DEIB (Department of Electronics, Information and Bioengineering) Master in Biomedical Engineering

Automatic tracking of heart valves using time-resolved

three-dimensional phase-contrast MRI (4D PC-MRI)

Preliminary studies and feasibility evaluation

Advisor: Prof. Enrico Gianluca Caiani

Co-advisor: MSc. Jean-Paul Aben

Co-advisor: MSc. Marc Maussen

M.Sc. Thesis by:

Martina Stella, ID 852401

Acknowledgements

I would like to thank my foreign advisor, Mr. Aben, for the possibility he gave me to carry out my research project at Pie Medical Imaging and to join the R&D department of the company. My gratitude goes to my tutor, Marc Maussen, who daily helped me with huge patience and courtesy.

I am deeply grateful to my Italian advisor, Prof. Caiani. Despite the distance, his kindness, his precious suggestions and his continuous support were for me of primary importance for my academic formation. Beside being my advisor, I would like to consider him as an inspiring mentor.

A profound thanks goes to one of the most brilliant people I had the privilege to met, Alessandro. Thanks for all the advices and for having shared with me your knowledge and experience.

“Ciascuno cresce solo se sognato.”

Perci`o vorrei ringraziare i miei genitori per non aver mai smesso di credere in me, anche pi´u di me. Vorrei ringraziarli per avermi concesso tutta la fiducia per poter provare, cadere e sbagliare. Ma anche per avermi sempre aiutata a rialzarmi.

Un grazie va a mio pap`a, che tutti i giorni ha guardato l’alba come a un privilegio e non una condanna, e a mia mamma, per la sua incredibile indipendenza e passione per i viaggi. Un grazie di cuore va a mia zia che, in punta di piedi, sa essere sempre presente per gli altri; e al suo compagno, per aver adottato una nipote.

Questo traguardo `e stato per me possibile solo grazie a due persone: Maria ed Eleonora. Siete state le migliori compagne e guide che potessi trovare, scusate per il ritardo. Grazie a tutti coloro che hanno contribuito a rendere il Politecnico non solo un’universit`a ma un’esperienza indimenticabile, sentirsi parte di questo gruppo `e stato fondamentale. Infine, vorrei ringraziare la persona che negli ultimi tre anni ha avuto il coraggio di essere al mio fianco per crescere insieme. Tempo e distanza non sono nemici per chi osa sognare.

Contents

1 Introduction 1

1.1 Background . . . 2

1.1.1 Heart Anatomy and Blood Circulation . . . 2

1.1.2 Heart Valves . . . 4

1.2 Cardiovascular Diseases . . . 5

1.2.1 Vessel Morphology Alterations . . . 6

1.2.2 Valve Pathologies . . . 7

1.2.3 Complex Congenital Pathologies . . . 9

1.3 Clinical Utility . . . 10

1.4 Heart Valves Imaging Techniques . . . 12

1.4.1 Echocardiography . . . 12

1.4.2 Computed Tomography . . . 15

1.4.3 Positron Emission Tomography . . . 16

1.4.4 Cardiac Magnetic Resonance . . . 16

1.4.5 Intraoperative Imaging . . . 17

1.5 Aim of the Project . . . 18

2 State of the Art 21 2.1 Tracking Problem . . . 21

2.2 Region-based Tracking Approach . . . 22

2.2.1 Template Matching . . . 24

2.2.2 Image Registration . . . 26

2.3 Tracking Technique Adopted for Cardiac Images . . . 28

2.3.1 Retrospective Tracking . . . 30

2.3.2 Real-Time Tracking . . . 36

3 Materials and Methods 41 3.1 Data . . . 41

3.1.1 4D PC-MRI Principles and Methodology . . . 43

3.1.2 Scanner Parameters . . . 45

3.1.3 Available Dataset . . . 46

3.2 Pre-processing . . . 48

3.2.1 Scope of Pre-processing . . . 48

3.2.2 Preprocessing Methods Considered for this Project . . . 50

3.3 Tracking Algorithms . . . 67

3.3.1 Initialization Phase . . . 68

3.3.2 Template Matching Algorithm . . . 75

3.3.3 Non-rigid Image Registration Technique . . . 78

3.4.3 Current versus Proposed approach . . . 84

4 Results 87 4.1 Intra and Inter Method comparison . . . 87

4.1.1 Template Matching Tracking . . . 87

4.1.2 Non-rigid Registration Tracking . . . 90

4.1.3 Inter-methods comparison . . . 92

4.2 2D versus 3D comparison . . . 93

4.3 Current versus Proposed approach . . . 97

5 Discussions and Conclusion 105 5.1 Discussions . . . 105 5.2 Conclusions . . . 108 5.3 Future Developments . . . 109 Bibliography 111 A 2D vs 3D comparison 123 B Bland-Altman Plots 133

List of Figures

1 Grafici relativi all’analisi di Bland-Altman, nella sua forma non

parametrica, eseguita per il confronto tra metodo 2D e 3D. . . xxi

1.1 Data regarding CVDs mortality and NCDs distribution. . . 1

1.2 Sectional anatomy of the heart. . . 3

1.3 Four valves view. . . 4

1.4 2D Cine views depicting the heart valves. . . 6

1.5 Aortic stenosis. . . 7

1.6 Pathophysiology of ischaemic mitral regurgitation (MR). . . 8

1.7 Schematic of the bicuspid aortic valve. . . 9

1.8 Tetralogy of Fallot (ToF) major features. . . 10

1.9 Aortic flow. . . 11

1.10 The most common imaging echo views. . . 13

1.11 Illustration of the relation between scanning depth, line density, and spatial resolution of 3D imaging from a transthoracic (3D TTE; left) and transesophageal approach (3D TEE; right). . . 15

1.12 Visualization of cardiac valves by newer imaging methods. . . 18

1.13 MRI images quality comparison. . . 19

2.1 Illustration of template matching technique principle using the correlation as similarity metric. . . 24

2.2 Illustration of image registration principle. . . 26

2.3 Main image registration components. . . 27

2.4 4D PC-MRI for flow assessment through 4 heart valves. . . 31

2.5 User initialization phase. . . 33

2.6 Example of template matching approach adopted for MV annulus tracking. . . 34

2.7 Basis functions adopted by [Heyde et al., 2013]. . . 36

2.8 System overview adopted by [Voigt et al., 2015]. . . 37

2.9 AoVA point selection according to [Navkar et al., 2011]. . . 39

3.1 Principles of 2D PC-MRI and 4D PC-MRI. . . 44

3.2 Velocity encoding using bipolar gradients. . . 45

3.3 Procedures for 4D PC-MRI. . . 46

3.4 Phase unwrapping. . . 49

3.5 Eddy current correction. . . 50

3.6 Noise masking. . . 51

3.7 Expected result of the pre-processing phase on 4D PC-MRI data. 52 3.8 ROI obtained with the method based on standard deviation and accumulative matrix. . . 54

3.10 Application of the LEMS correction method to a synthetic phantom. 56 3.11 Features derived from locality measures: Local Phase Coherence

(LPC) and Haar wavelets mean value. . . 58

3.12 Result of method developed by Bock, J. in [Bock, 2012] applied to patient 2 dataset. . . 59

3.13 Figure illustrating the convention used in hybrid level-set method 61 3.14 Segmented heart obtained on 4D PC-MRI applying the hybrid level-set method proposed by [Zhang et al., 2008]. . . 63

3.15 2D view of the segmented volume obtained with the hybrid level-set method. . . 63

3.16 Result of morphological operations applied to the intermediate result after hybrid level-set approach computation. . . 64

3.17 Comparison of the two region growing methods for slices located at different positions (red is the one which requires the manual selection while green is the automatic one). . . 65

3.18 Result of the region growing operation applying two different methods. . . 65

3.19 Final result obtained with the hybrid level-set approach followed by morphological operations and region growing . . . 66

3.20 Flowchart representing the scheme adopted for cardiac valve track-ing. . . 67

3.21 Flowchart representing the initialization phase steps. . . 68

3.22 Aortic plane computed with CAAS MR 4D Flow software, Pie Medical Imaging, Maastricht, The Netherlands . . . 69

3.23 Longaxis views obtained rotating the plane perpendicular to SL valve view. . . 70

3.24 User interface for optimal longaxis view selection . . . 71

3.25 Longaxis plane displayed with user selected points. . . 72

3.26 Four chambers view with user selected points. . . 73

3.27 Longaxis plane displayed with kernel used for velocity vector com-putation. . . 73

3.28 Initialization result for mitral valve. . . 74

3.29 Flowchart representing the different steps which constitute the adopted template matching algorithm. . . 75

3.30 Figure representing the template volume moving inside the search-ing region. . . 77

3.31 Flowchart representing the algorithm steps to track the valvular plane using the non-rigid registration approach. . . 79

3.32 Representation of the mask inside the image volume. . . 80

4.1 Template size comparison. . . 88

4.1 Point tracked along the cardiac cycle for different template sizes. 89 4.2 Template matching method versions comparison. . . 90

4.3 Different mask versions used for the non-rigid registration, seen during the first 240 ms of the cardiac cycle. . . 91

4.4 Displacement graphs obtained with the two proposed techniques for each of the selected points. . . 92 4.5 Box-and-whisker plots of valve excursion per subject per method. 95

4.6 Comparison between 2D and 3D method to compute the valve excursion during the cardiac cycle. . . 96 4.6 Pie charts representing the percentages of visual scoring results. . 98 4.7 Comparison between aortic valve plane obtained with the actual

method (yellow circle) and the one tracked with the 3D method (transparent plane). . . 100 4.8 Comparison between mitral valve plane obtained with the actual

method (orange circle) and the one tracked with the 3D method (transparent plane). . . 101 4.9 Comparison between tricuspid valve plane obtained with the

ac-tual method (green circle) and the one tracked with the 3D method (transparent plane). . . 102 4.10 Comparison between pulmonary valve plane obtained with the

ac-tual method (blue circle) and the one tracked with the 3D method (transparent plane). . . 103 A.1 Comparison of displacement obtained with CAAS (2D approach)

and with a 3D method performed with the template matching technique for patient 1. . . 124 A.2 Comparison of displacement obtained with CAAS (2D approach)

and with a 3D method performed with the template matching technique for patient 2. . . 125 A.3 Comparison of displacement obtained with CAAS (2D approach)

and with a 3D method performed with the template matching technique for patient 3. . . 126 A.4 Comparison of displacement obtained with CAAS (2D approach)

and with a 3D method performed with the template matching technique for patient 4. . . 127 A.5 Comparison of displacement obtained with CAAS (2D approach)

and with a 3D method performed with the template matching technique for patient 5. . . 128 A.6 Comparison of displacement obtained with CAAS (2D approach)

and with a 3D method performed with the template matching technique for volunteer 1. . . 129 A.7 Comparison of displacement obtained with CAAS (2D approach)

and with a 3D method performed with the template matching technique for volunteer 2. . . 130 A.8 Comparison of displacement obtained with CAAS (2D approach)

and with a 3D method performed with the template matching technique for volunteer 3. . . 131 A.9 Comparison of displacement obtained with CAAS (2D approach)

and with a 3D method performed with the template matching technique for volunteer 4. . . 132 B.1 Bland-Altman plots for the mitral valve clustered according to

subject typologies. . . 134 B.2 Bland-Altman plots for the tricuspid valve clustered according to

List of Tables

1 Risultati della classificazione visiva. . . xxi 2.1 Literature overview summary AoV = aortic valve, MV = mitral valve,

TV = tricuspid valve, MA = mitral annulus, TA = tricuspid annulus, AoVA = aortic valve annulus, RV = right ventricle, LV = left ventricle, A-V = atrioventricular, TEE = transesophageal echocardiography, MRI = magnetic resonance images, RT3DTEE = real time three-dimensional transesophageal echocardiography, PC = phase contrast, NCC = normal-ized cross-correlation, PCC = principal component analysis . . . 30 3.1 Acquisition details for 4D PC-MRI with contrast agent. . . 47 3.2 Velocity and magnitude combination formula. Table from [Bock

et al., 2007] . . . 57 3.3 Maximum Valve Tissue Displacement. . . 76 4.1 Parameters adopted for the comparison between 2D and 3D

ap-proach for mitral and tricuspid valve. . . 93 4.2 Mean and Standard Deviation for the differences between point

displacement tracked with 3D and 2D approach, using both its basic and improved version. . . 94 4.3 Bland-Altman analysis results. . . 95 4.4 Visual scoring results. . . 97

List of Abbreviations

3CH Three chambers

4D P C − M RI 4D Phase Contrast MRI A − V Atrio-ventricular

AoV Aortic Valve CI Confidence Interval

CM R Cardiac Magnetic Resonance CT Computed tomography CV D Cardiovascular diseases IQR Interquartile range LA Long-axis

LoA Limits of Agreement LV Left ventricle M A Mitral annulus

M AP SE Mitral annulus peak systolic excursion M RI Magnetic Resonance Imaging

M V Mitral valve

N CC Normalized Cross Correlation pctl percentile

P D Plane displacement

P ET Positron emission tomography P V Pulmonary Valve

ROI Region of Interest

RP C Reproducibility coefficient RT 3DE Real-time 3D echocardiography

SAD Sum of Absolute Differences SL Semilunar

SSD Sum of squared differences

T AP SE Tricuspid annulus peak systolic excursion T AV I Transcatheter aortic valve implantation T EE Transesophageal echocardiography T T E Transthoracic echocardiogram T V Tricuspid valve

U S Ultrasounds V E Velocity encoded V HD Valvular Heart Diseases

Sommario

Introduzione Secondo l’Organizzazione Mondiale della Sanit`a, oggigiorno le malattie cardiovascolari costituiscono la prima causa di morte al mondo [Mendis et al., 2011]. Il loro impatto da un punto di vista socio-sanitario `e perci`o non trascurabile e la necessit`a di sviluppare applicazioni in grado di aiutare medici ed esperti evidente.

In particolare, si stima che nei paesi industrializzati l’incidenza delle valvulopatie (malattie che interessano le valvole cardiache) si assesti intorno al 2.5% [Iung and Vahanian, 2014]. Le valvole cardiache sono quattro, due situate nella parte destra del cuore (la valvola tricuspide e quella polmonare) e due in quella sinistra (la valvola mitrale e quella aortica). Le valvole semilunari, ovvero la polmonare e la aortica, separano i ventricoli dalle rispettive arterie mentre quelle atriovenicolari separano gli atri dai ventricoli.

Le patologie che possono colpire queste strutture cardiache sono differenti ed anche la loro eziologia pu`o variare profondamente.

Le principali sono:

ˆ la stenosi valvolare: per stenosi si intende il restringimento della valvola, attraverso cui passa il sangue prima di immettersi nel sistema arterioso. A causa di tale ostru-zione, il ventricolo `e costretto ad aumentare la propria pressione di spinta e come conseguenza diretta si ha l’ipertrofia (ingrossamento) della parete cardiaca.

ˆ l’insufficienza valvolare: `e una condizione in cui un difetto di chiusura delle val-vole fa s`ı che parte del sangue pompato dal ventricolo refluisca nell’atrio causando affaticamento e disturbi respiratori.

ˆ la valvola aortica bicuspide: la valvola aortica bicuspide `e una malformazione car-diaca congenita, che consiste nell’assenza di una cuspide. La valvola aortica, quindi, presenta due lembi valvolari anzich´e tre. Tale difetto, in genere, non crea problemi, purch´e sia associato al normale funzionamento (continenza) delle cuspidi rimanenti. Col tempo, pu`o tuttavia indurre una calcificazione prematura dei lembi valvolari, che diventano rigidi e si aprono con maggiore difficolt`a durante la contrazione sistolica. La valvola aortica bicuspide, quindi, pu`o predisporre ad una stenosi aortica o, meno frequentemente, a un’insufficienza aortica.

Negli ultimi anni, per diagnosticare queste patologie, si `e fatto uso di diverse tecniche di imaging medico al fine di identificare, curare e seguire lo sviluppo delle stesse.

L’ecocardiografia (US) rimane tutt’oggi la tecnica dominante, in tutte le sue diverse mo-dalit`a: 2D e 3D, M-mode e Doppler. Ognuna di esse ha lo scopo di evidenziare differenti aspetti delle valvole, come ad esempio la loro anatomia o la loro funzionalit`a. Tra i van-taggi vi sono la sicurezza dell’esame, la sua velocit`a, il suo basso costo e la capacit`a di fornire informazioni in tempo reale.

circa i tessuti cardiaci. Inoltre, una particolare tipologia di risonanza magnetica, la 4D PC-MRI, `e in grado di fornire informazioni sulla velocit`a del sangue nelle tre direzioni. Proprio quest’ultima `e di primaria importanza poich´e permette una pi`u corretta diagnosi delle patologie valvolari.

Questa tipologia di immagini `e stata perci`o scelta in questo progetto. Esso si prefigge lo scopo di eseguire un “tracking” tridimensionale e semi automatico del piano valvolare durante il ciclo cardiaco mediante l’utilizzo di questa sola modalit`a di imaging, e proprio quest’ultima caratteristica rende tale lavoro innovativo rispetto all’attuale stato dell’arte. L’impiego di questo genere di dati permetterebbe infatti di ridurre i tempi di acquisizione (e quindi i costi) legati alla necessit`a di acquisire sia immagini anatomiche (Cine) che im-magini contenenti informazioni sul flusso (4D-PC MRI). Inoltre, questa scelta eviterebbe la registrazione tra queste ultime due immagini, operazione che potrebbe introdurre ulte-riori errori. Non da ultimo, individuare il piano valvolare nello spazio permetterebbe di identificare anche gli spostamenti che avvengono fuori dal piano stesso.

Stato dell’Arte Come detto, l’interesse per le applicazioni atte a processare le immagini mediche `e cresciuto negli ultimi anni ed esse sempre pi`u costituiscono uno strumento di primaria importanza nell’iter diagnostico-terapeutico. In questo quadro, la necessit`a di un’accurata stima del flusso sanguigno attraverso le valvole ha spinto ad analizzare gli spostamenti del piano valvolare sia con un’identificazione manuale dei piani stessi che con metodi pi`u automatizzati.

La definizione del problema di “tracking”, comunque, non `e confinata all’ambito medico, ma indica pi`u in generale la stima della posizione di un oggetto che si muove in una sequenza di immagini. Il problema non `e banale, e per la sua risoluzione sono stati proposti diversi metodi. In questo lavoro si sono presi in considerazione due principali approcci, entrambi basati sull’utilizzo di alcune caratteristiche presenti nella regione dell’immagine contenente l’oggetto di interesse:

ˆ Template matching: questa tecnica `e costituita da tre principali componenti: l’imma-gine modello (template), l’immal’imma-gine in cui ci si aspetta di trovare una corrispondenza con l’immagine modello (immagine sorgente) e una metrica che determina la miglior posizione dell’immagine modello nell’immagine sorgente.

Una delle metriche pi`u utilizzate `e la cross-correlazione normalizzata (NCC). Infatti, nonostante sia sensibile a rotazioni e cambi nella scala, essa non lo `e rispetto ai cambi di luminosit`a.

ˆ Registrazione non-rigida: con il termine registrazione si intende il processo di alli-neamento di due o pi`u immagini riferite alla medesima scena. A seconda della regi-strazione necessaria, esistono diversi tipi di trasformazione possibili (rototraslazioni, trasformazioni affini, ecc.).

Nell’ambito delle immagini biomediche, la letteratura circa i diversi metodi adottati negli ultimi anni per la stima dei piani valvolari comprende molteplici approcci, ciascuno svi-luppato utilizzando differenti tipi di immagini.

All’interno di essi `e possibile identificare due gruppi principali: quelli che eseguono il trac-king in maniera retrospettiva e coloro che hanno provato ad individuare i piani valvolari in tempo reale.

tecniche di imaging (Cine MRI, 4D PC-MRI e US) sia diversi approcci (localizzazione manuale, template matching e registrazione non-rigida). Al secondo gruppo invece ap-partengono lavori eseguiti su immagini fluoroscopiche, 3D US e 2D Cine MRI, e sono caratterizzati da un calcolo real time delle strutture di interesse.

Il progetto qui presentato appartiene al primo gruppo, configurandosi quindi come un tracking semiautomatico e retrospettivo dei piani valvolari eseguito esclusivamente su dati 4D PC-MRI.

Materiali e Metodi Il set di dati utilizzato in questo lavoro `e costituito da 9 soggetti (5 pazienti e 4 volontari). Per ognuno di essi `e presente un set di immagini 4D PC-MRI composto da 30 fasi, un numero di slice variabile da 35 a 42 e un time step compreso tra 24 a 40 ms.

Ogni immagine `e costituita da 176 ×176 pixel, ognuno di essi caratterizzato da uno spacing uguale a 2.27 × 2.27 mm, mentre lo spessore di ogni slice `e di 3 mm.

Il tracking `e stato svolto in due principali fasi, utilizzando esclusivamente i dati 4D PC-MRI:

ˆ Pre-processing per l’identificazione della regione di interesse (ROI) ˆ Metodi per il tracking automatico del piano valvolare

Lo scopo della fase di pre-processing `e quello di identificare la regione di interesse, il cuore, e ridurre cos`ı il volume considerato e, di conseguenza, l’onere computazionale. A questo fine sono stati analizzate tre differenti opzioni, una basata sulla deviazione standard e sulla matrice di accumulazione, una ispirata al lavoro di [Bock, 2012] e infine un’ultima che sfrutta il cosiddetto metodo “hybrid level-set” [Zhang et al., 2008].

Quest’ultima `e stata adottata come soluzione definitiva per i positivi risultati ottenuti e il ridotto tempo computazionale. I parametri che questo metodo prende in considerazione sono tre: µ, che indica il limite inferiore di livello di grigio presente nell’oggetto di interes-se, α, che incoraggia i contorni a racchiudere le regioni con un livello di grigio maggiore di µ, e infine β, che invece attrae i contorni alle aree dell’immagine con un alto gradiente. Successivamente sono state applicate alcune operazioni morfologiche e un algoritmo di “region growing” per ottenere la regione d’interesse definitiva.

Il cuore del progetto, ovvero il tracking automatico dei piani valvolari, `e stato eseguito con due differenti metodi, accomunati dalla medesima fase di inizializzazione. Essi sono stati dapprima implementati, e successivamente `e stata testata la loro fattibilit`a e comparato il loro esito.

La fase di inizializzazione prevede la selezione da parte dell’utente di quattro punti che identifichino la valvola di interesse. Due di essi vengono indicati su un primo piano, pre-cedentemente selezionato dall’utente all’interno del volume, mentre i restanti punti sono scelti su un piano circa perpendicolare al primo, sempre individuato dall’utente.

Successivamente questi punti vengono utilizzati come input per gli algoritmi di tracking. Nel caso del tracking eseguito con la tecnica del template matching, la posizione di ogni punto viene stimata indipendentemente dalla posizione degli altri punti. Ad ogni iterazio-ne, attorno al punto, viene costruito un cubo che costituisce il modello la cui corrispondenza viene cercata nell’immagine sorgente attraverso l’uso della cross-correlazione normalizzata. Una volta calcolati tutti i punti, essi vengono utilizzati per individuare un piano, attraver-so il metodo dei minimi quadrati, il quale identifica il piano valvolare tracciato in quella fase cardiaca.

colati i parametri della trasformazione a cui sono soggetti i due volumi considerati e tali parametri vengono poi utilizzati per stimare la posizione dei punti selezionati alla fase successiva. Anche in questo caso avviene un fitting dei punti stessi per stimare il piano della valvola considerata.

La fase di valutazione dell’algoritmo si `e rivelata particolarmente impegnativa, data la non conoscenza dei valori reali dei piani valvolari, e a causa dell’impossibilit`a di fare riferimento ad una metodologia considerata come riferimento.

Nonostante ci`o, tre principali valutazioni sono state eseguite al fine di poter formulare un giudizio sull’output del progetto:

ˆ Comparazione intra e inter metodo

ˆ Comparazione tra metodo 2D implementato nel software CAAS MR 4D Flow e approccio 3D implementato con la tecnica del template matching, per gli stessi dati in input delle valvole atrioventricolari

ˆ Confronto tra approccio correntemente utilizzato (tracking 2D con immagini Cine) e metodo proposto in questo lavoro

Ognuna di tali valutazioni `e stata eseguita con diverse metodologie tra le quali un’analisi visiva, un confronto tra il grafico degli spostamenti, il calcolo della media e l’analisi com-parativa secondo Bland-Altman e infine una classificazione visiva.

Risultati L’utilizzo di diverse dimensioni per il template ha dimostrato che questo pa-rametro influenza l’esito del tracking stesso. Infatti, un template troppo piccolo (≈ 5 mm) si `e rivelato inadatto a contenere un numero sufficiente di dettagli che caratterizzano l’im-magine della regione considerata e perci`o questo influisce negativamente sul calcolo della NCC. Template di ≈ 10 mm e ≈ 15 mm si sono invece dimostrati pi`u adeguati. Inoltre, la versione dell’algoritmo che considera i punti usati come input due volte (in avanti e all’in-dietro nel tempo, ogni volta per tracciare met`a ciclo cardiaco) si `e dimostrata superiore e pi`u robusta.

Il tracking eseguito sfruttando la registrazione non rigida, invece, si `e rivelato infruttuoso. Quando si `e fatto uso di una maschera fissa, cio`e la cui posizione e il cui orientamento non venivano aggiornate ad ogni iterazione, dopo poche iterazioni il piano valvolare stimato si trovava gi`a al di fuori della maschera stessa.

L’utilizzo di una maschera che invece ad ogni iterazione aggiorna la sua posizione a seconda dei punti tracciati (maschera dinamica), sebbene risolva il precedente problema, calcola valori dell’escursione dei piani valvolari che sono irrealistici, sia se comparati con quelli ottenuti con un software commerciale, sia se confrontati con quelli riportati in letteratura. In merito alla comparazione tra l’approccio 2D (eseguito con il software CAAS, ma con dati 4D PC-MRI) e quello 3D, la media della differenza tra gli spostamenti calcolati con i due metodi `e uguale a 0.11 ± 1.56 mm per la valvola mitrale ed a 0.16 ± 2.18 mm per quella tricuspide. Una comparazione pi`u adeguata `e stata per`o effettuata attraverso l’uso del metodo di Bland-Altman, nella sua forma non parametrica, a causa della distribuzione non gaussiana dei dati, e quindi con mediana al posto di media e quantili al posto di deviazione standard. I relativi risultati sono stati riportati in Figura 1.

(a) Valvola Mitrale. (b) Valvola Tricuspide.

Figura 1: Grafici relativi all’analisi di Bland-Altman, nella sua forma non parametrica, eseguita per il confronto tra metodo 2D e 3D.

Nei due grafici `e possibile vedere come via sia un bias trascurabile (< 0.2 mm) nella comparazione tra i due metodi, per entrambe le valvole. Numerosi outlier, localizzati soprattutto sopra il limite superiore, sono invece presenti in entrambi i casi. Poich`e i dati non seguono una distribuzione normale, si `e proceduto utilizzando l’analisi di Bland-Altman nella sua forma non parametrica. La linea continua rappresenta la mediana della differenza, tra parentesi il p − value del t-test, mentre le linee tratteggiate rappresentano i limiti di concordanza (LoA) al 95%. Il LoA inferiore coincide con il 2.5mo percentile mentre quello superiore corrisponde al 97.5mo percentile.

Infine, due differenti osservatori (M.S. e M.M.) hanno indipendentemente classificato i piani ottenuti con l’algoritmo 3D, comparandoli a quelli ottenuti in 2D con l’impiego di immagini Cine MRI, e attribuendo per ogni valvola di ogni dataset un punteggio da 1 a 3 (1 = tracking con esito negativo, 2 = risultato mediocre e 3 = tracking con esito positivo). I risultati della valutazione visiva effettuata comparando il metodo proposto e quello attualmente disponibile in un software commerciale, sono stati riportati in Tabella 1.

AoV MV TV PV

Punteggio 1 2 3 1 2 3 1 2 3 1 2 3

Osservatore 1 6 1 7 5 2 4 2 1

Osservatore 2 3 4 6 1 1 3 3 3 3 1

Tabella 1: Risultati della classificazione visiva.

Punteggio 1: esito negativo, il processo di tracking fallisce completamente e non c’`e corre-lazione tra i movimenti della valvola e il piano stimato. Punteggio 2: mediocre, sia quando l’algoritmo `e capace di seguire i movimenti valvolari ma con un piccolo offset, sia quando l’algoritmo si rivela accurato durante la maggior parte del ciclo cardiaco ma non nella sua interezza. Punteggio 3: esito positivo, tracking accurato e coerente durante tutto il ciclo cardiaco. AoV = valvola aortica, MV = valvola mitrale, TV = valvola tricuspide e PV = valvola polmonare.

trac-solamente da parte dell’osservatore 2).

Il tracking della valvola polmonare ha dato invece esito negativo nella maggioranza dei casi.

Discussione e Conclusioni Dai risultati emerge chiaramente come il metodo che adot-ta la tecnica del template matching si configuri come superiore rispetto al tracking eseguito con il metodo della registrazione non-rigida. Quest’ultimo inoltre richiedeva ≈ 20 min per il calcolo dei parametri della trasformazione, a fronte dei ≈ 20 s necessari per l’esecuzione del tracking con l’approccio del template matching.

Rispetto alla miglior dimensione da utilizzare per il template stesso, si `e gi`a accennato a come un template troppo piccolo non contenga abbastanza caratteristiche, ma non `e stato possibile definire con precisione quale sia la dimensione pi`u appropriata senza eseguire alcuni test preliminari.

Dalla comparazione tra la stima dell’escursione della valvola eseguita con il metodo propo-sto e quella fatta con il software CAAS MR 4D Flow, sono emersi risultati promettenti per quanto riguarda l’adozione di un approccio 3D al posto del corrispettivo 2D. Il bias tra i due metodi non `e significativo, anche se vi sono ancora alcune misure al di sopra dei limiti di concordanza, sia nel caso della valvola mitrale, che in quello della valvola tricuspide (Figura 1).

Nonostante l’elevato numero di output classificati come “mediocri” evidenzi come sia anco-ra prematuro per il metodo proposto prendere il posto dell’attuale software, l’esito positivo del tracking stesso, in particolare per le valvole atrioventricolari e per quella aortica, inco-raggia a continuare questo progetto, implementando nuovi miglioramenti e accorgimenti gi`a adottati nel caso di un approccio in 2D. Tra questi, si pu`o qui menzionare la possibi-lit`a di memorizzare i diversi template calcolati ad ogni iterazione e dare ad essi un peso diverso in accordo alla fase corrispondente all’attuale iterazione. Inoltre, i progressi per quanto riguarda l’imaging medico, in particolare circa la risoluzione spaziale dei dati di 4D PC-MRI, si pensa possano portare miglioramenti al metodo proposto.

Abstract

Worldwide, cardiovascular disease (CVD) imposes a huge burden in terms of mortal-ity, morbidmortal-ity, disabilmortal-ity, functional decline, and healthcare cost. In light of the projected growth of the ageing population over the next several decades, the societal concern at-tributable to CVD will continue to rise. There is thus an enormous need to foster successful tools able to help physicians in the diagnose and treatment of such pathologies.

In this context, the flow measurements have been successfully used to quantify congeni-tal heart diseases, pulmonary arterial diseases, thoracic aortic diseases and valvular heart diseases. Indeed, the latter have been the focus of this work which aims at evaluating the feasibility of a 3D semi-automatic method for the valvular planes tracking.

The benefits of an accurate flow estimation have been largely demonstrated, and the nu-merous software applications developed in the last years to carry out this task with less and less user intervention have shown the need for more reliable and automatic tools. These reasons, together with the awareness of the limits of a manual valve displacement as-sessment in two dimensions, have driven to the accomplishment of this challenging project which aims at developing a three dimensional technique to semi automatically track the heart valves using only 4D PC-MRI.

On one hand, this approach could reduce the amount of data to be acquired, and con-sequently avoid the mapping operation between anatomical (Cine Images) and flow (4D PC-MRI) data, which can be a source of further errors. On the other hand, a semi au-tomatic method would reduce user dependency and the required time, allowing to adopt this technique in the clinical routine.

Moreover, the adoption of volumetric data can overcome the limits linked to a valve track-ing performed ustrack-ing two dimensional data, considertrack-ing also the out-of-plane valve excursion occurring during the cardiac cycle.

For these reasons, at the best of the author’s knowledge, this work positions itself as inno-vative for the kind of data adopted, where the focus of the project was to understand the feasibility of such proposed method and to evaluate its outcome with respect to the two dimensional approach currently available, on a population of both patients and volunteers. After a pre-processing step aimed at successfully identifying the ROI representing the heart position inside the acquired volume, using an hybrid level-set method, two approaches for the heart valves tracking have been implemented and tested.

The former, based on template matching technique, provided promising results and thus was further investigated and compared to the 2D approach. The latter, which made use of the non-rigid registration theory, was instead excluded due to its unsatisfying outcome and long computational time (≈ 20 min).

The proposed 3D method showed encouraging results with respect to its ability to track the valvular planes using only the magnitude data of 4D PC-MRI, especially referring to the aortic, mitral and tricuspid valve, and requiring a computational time < 20 s per

that the 3D approach is still premature and need further advances to replace the current standard.

However, the progresses in the 4D PC-MRI technique, together with the improvements that could be implemented in the presented method, encourage to continue the investiga-tions and developments of the proposed approach.

Chapter 1

Introduction

Cardiovascular diseases (CVDs) are of great clinical interest since, according to the World Health Organization [Mendis et al., 2011], they are the number one cause of death in the world (see Figure 1.1).

(a) Distribution of major causes of death including CVDs. (b) Distribution of global non com-municable diseases (NCDs) by cause of death, both sexes.

Figure 1.1: Data regarding CVDs mortality and NCDs distribution. Image by [Mendis et al., 2011]

Their initiation and evolution depends on many different variables, such as genetic predispositions, the vessel morphology and the blood hemodynamics. Thus, blood flow in the heart and its surrounding vessels, such as the aorta and pulmonary artery, have been the focus of clinical studies for decades.

severity, predict and monitor their progression, and support the corresponding treatment decision-making.

As Richter et al. explain in [Richter and Edelman, 2006], the accurate study and char-acterization of blood flow pattern and pathophysiology in the cardiac valves and in main vessels of the human anatomy plays a role of primary importance in the diagnosis and treatment of cardiovascular dysfunctions.

Thus, in recent decades, largely due to the advent of multidimensional flow imaging and computational fluid dynamics (CFD), the importance of improving our understanding of physiological and pathophysiological blood flow conditions is increasingly acknowledged. In the light of the above, this project aims to create a 3D algorithm which could auto-matically track the heart valves with a semi automatic initialization phase for an improved blood flow quantification. The purpose of this approach is to overcome the drawback of many current techniques that use a fixed imaging plane, causing the valve to move in and out of it, which can lead to poor visualization and inaccurate flow measurements.

This thesis is divided in five main chapters. The first one, which will be here presented, briefly recalls heart anatomy and function, introduces the CVDs problem and the clinical utility of the blood flow quantification, and explains the project aim.

The second one will present the state of the art regarding the tracking techniques, with a particular attention to the ones used in the biomedical imaging field, especially the two ones utilized for this project.

The core part of this dissertation, collected in “Materials and Methods” chapter follows. There, the available dataset will be described, the common algorithms initialization will be presented and a detailed description of the different tracking algorithms implemented will be showed. To conclude it, the evaluation criteria will be mentioned.

The fourth chapter will be dedicated to the results presentation.

At the end, results discussion and related conclusions arose from this project development will be shared.

1.1

Background

1.1.1

Heart Anatomy and Blood Circulation

The heart is a muscular organ about the size of a closed fist and weighs between about 280 to 340 grams in men and 230 to 280 grams in women [Gray and Lewis, 1918]. It is located in the chest behind the sternum (breastbone), in front of the trachea, oesophagus, and aorta and above the diaphragm.

It is cone-shaped, with the point of the cone pointing down to the left. Two-thirds of the heart lies in the left side of the chest, with the balance in the right side of the chest. The heart is composed of specialized cardiac muscle.

A double-walled sac called the pericardium encases the heart, which serves to protect the heart and anchor it inside the chest. Between the outer layer, the parietal pericardium, and the inner layer, the serous pericardium, runs pericardial fluid, which lubricates the heart during contractions and movements of the lungs and diaphragm.

The heart is characterized by a complicated configuration where the blood streams in two separate channels of limited space, crossing each other.

1.1. Background 3

Both heart halves consist of an atrium and ventricle, which are connected through a valve (see Figure 1.2). This is the tricuspid valve in the right ventricle (RV) and the mitral valve in the left ventricle (LV), respectively. The aorta (AO) is attached to the LV, separated through the aortic valve (AoV).

The crooked aorta is the largest artery in the body with a diameter of about 2,5-3,5 cm and 30-40 cm length. Its vessel sections are commonly divided into the aortic root, located at the AoV, the ascending aorta (AAo) behind the AoV, the aortic arch and the descending aorta (DAo).

The pulmonary artery (PA) is connected to the RV and separated through the pulmonary valve (PV). The main PA (also: pulmonary trunk), directly behind the PV, splits into the left (LPA) and right pulmonary artery (RPA), which reach to the left and right half of lung, respectively.

Figure 1.2: Sectional anatomy of the heart.

Anatomic overview of the heart with depiction of the systemic (red) and pulmonary circulation (blue). File licensed under Creative Commons Attribution.

The main purpose of the cardiovascular system is to control the blood flow to various parts of the body. A heart beat consists of two phases: systole and diastole.

During systole, oxygenated blood is pumped from the left ventricle into the aorta (see Fig-ure 1.2). The otherwise closed aortic valve is open at this point. Smaller arteries branch

off the aorta and supply the blood to all body regions. The blood is transported back to the right atrium through veins. This part of the circulatory system is called systemic circulation. Also during systole, deoxygenated blood is pumped from the right ventricle into the pulmonary artery through the opened pulmonary valve. The blood is enriched with oxygen in the left and right half of lung and then transported to the left atrium. This process is referred to as pulmonary circulation.

The ventricles are refilled during diastole with blood coming from the left and right atrium through the opened tricuspid and mitral valve, respectively. The process then repeats it-self.

The heart’s shape facilitates efficient blood supply to the lung. However, the blood flow leads to shear forces on the vessel walls, which causes a continuous remodelling of the heart morphology and vessel tissue.

1.1.2

Heart Valves

The four heart valves play a key role in heart sophisticated dynamics as they enable the blood to flow in a unidirectional way. They open and close over three billion times during a normal life. They also have the ability to allow between 1 and over 20 l of blood per minute to run through them during rest, exercise, or other physiological or pathological conditions. The aortic, pulmonary, mitral and tricuspid valves are positioned in a plane, the so-called “base” of the heart (Figure 1.3a). It is this area which was named by early French anatomists the “fibrous skeleton” of the heart. It consists of densely collagenous fibres and remains almost stationary in contrast to the dynamic movements of the my-ocardium, leaflets and arteries [Misfeld and Sievers, 2007].

The mitral and tricuspid atrioventricular valves (A-V) separate the atria from the

ventri-(a) View of the four heart valves from superior (R.C.A.=right coronary artery).

(b) Schematic drawing of the relationship to the right and left ventricle (S, summit of the left ventricle L.V.), R.C.A., right coro-nary artery; T.V., tricuspid valve; L.A., left atrium; R.A., right atrium; R.V., right ven-tricle.

Figure 1.3: Four valves view. Image by [Misfeld and Sievers, 2007]

1.2. Cardiovascular Diseases 5

cles, while the aortic and pulmonary semilunar (SL) valves separate the ventricles from the great arteries. A-V valves have leaflets and SL valves have cusps. There is a specialized support structure specific to A-V valves, while the distinct shape of SL valves creates a unique self-contained support structure within the arterial roots. In contrast to the aorta, the aortic root is made up of the fibrous valve annulus region and the arterial tissue within the sinuses of Valsalva.

The A-V valves are characterized by large asymmetric leaflets hinged to ring shaped an-nuli on the secured end and tethered to the ventricles by an elaborate apparatus made up of the chordae tendineae and papillary muscles on the mobile end. The fibrous skeleton of the heart is continuous with the annulus fibrosa that constitutes the interconnected fibrous cartilage-like support apparatus of the tricuspid, mitral, and aortic valves. The annulus fibrosa is connected to the muscle of the heart in a manner that is analogous to the attachment of tendon to skeletal muscle.

The pulmonary valve is separated from the other valves by a muscular sleeve and has a poorly defined, less substantial annulus structure.

The annuli of the A-V valves are ring-shaped; however, the annulus of the aortic valve is crown-shaped resulting in the “semilunar” shape of the individual cusps.

The mitral valve is composed of two leaflets, the anterior (or aortic) and posterior leaflets. The supporting tendinous cords (chordae tendineae) on the ventricular aspect of the valve leaflets insert into two well-defined papillary muscles that are continuous with the left ventricular myocardium. The posterior leaflet dominates the majority of the mitral valve annulus circumference, but the anterior leaflet is larger and makes up a greater area. Conversely, the tricuspid valve is composed of three leaflets, the anterior, posterior and septal leaflets, which attach to the ventricles via chordae tendineae to a large and variable number of seemingly unorganized papillary muscles within the trabecular right ventricle. The aortic valve is composed of three cusps, the left coronary, right coronary and non-coronary cusps, named for their relationship to the non-coronary arteries.

The pulmonary valve is situated anterior and leftward relative to the aortic valve, and the mirror “facing” cusps of the pulmonary valve are aligned in an orthogonal plane.

Human valve thickness varies by valve and valve region, but in all valves is normally less than 1 mm. The A-V valves are slightly thicker than the SL valves, and the left-sided valves are slightly thicker than the right-sided valves. The base and tip of the valves tend to be thicker, especially in the SL valves.

The anterior leaflet of the mitral valve is in direct continuity with the aortic valve un-like the tricuspid valve, which is separated by muscular tissue from the pulmonary valve. Despite the common functional requirements of all heart valves, each valve is structurally different, and there is emerging molecular evidence that individual cusps and leaflets main-tain distinct structural and biomechanical characteristics, potentially related to different intrinsic vulnerabilities to disease [Hinton and Yutzey, 2011].

An overview of all valves, as they are imaged in 2D Cine MRI, is depicted in Figure 1.4.

1.2

Cardiovascular Diseases

As stated before, cardiovascular diseases (CVDs) are the number one cause of death worldwide.

Besides the death toll itself, this represents an enormous cost factor for the health systems. For example, the direct and indirect cost of CVDs and stroke in the United States in 2011 were [Mozaffarian et al., 2015]:

Figure 1.4: 2D Cine views depicting the heart valves.

From top left: tricuspid, mitral, pulmonary and aortic valve. Green line represents the intersection between images. Image obtained with CAAS MR 4D Flow (Pie Medical Imaging, Maastricht, NL)

ˆ 46.4 billion USD for hypertension ˆ 33.6 billion USD for stroke ˆ 24.6 billion USD for other CVDs

In the European Union, instead, about 196 billion Euro are spent annually.

In the following, an overview of some selected CVDs, which are relevant for the further understanding of this thesis reasons, is given.

1.2.1

Vessel Morphology Alterations

This group of CVDs refers to changes of the vessel wall due to different causes, e.g., inflammatory processes.

ˆ Ectasia and Aneurysm: A pathologic vessel dilation up to 1.5 x the original vessel diameter is referred to as ectasia. Above a factor of 1.5 the term aneurysm is applied. Aneurysms bear the risk of rupture, which is, in most cases, for heart vessels. ˆ Stenosis and Coarctation: On the contrary, a pathologic narrowing the vessel

is named stenosis. If the aortic arch is affected, it is referred to as coarctation. A stenosis can cause increased flow velocities and raised pressure before the narrowed region. The severity is graded by the area percentage of the vessel’s cross-section that is blocked. Depending on whether the vessel is blocked equally from all sides or primarily from one side, a stenosis is concentric or eccentric. A potential cause for eccentric stenosis is plaque.

1.2. Cardiovascular Diseases 7

ˆ Aortic Dissection: A tear in the inner layer of the aortic wall allows blood to flow between the inner and outer wall layer, which causes their separation. The outer layer is widened pathologically and bears a high risk of rupture, which is fatal in most cases.

ˆ Pulmonary Hypertension: Pulmonary hypertension (PH) begins with an inflam-mation of pulmonary arteries’ (PA) vessel wall cells. Here, small PAs and capillaries in the lung are meant. These PAs become blocked, narrowed or even destroyed, which complicates blood transport. The (right) heart’s workload increases, resulting in raised blood pressure. In the long run, PA causes a weakening of the heart muscle and eventually its failing.

1.2.2

Valve Pathologies

Pathological valve alterations comprise morphological changes as well as malfunctions. They comprise stenosis (reduced orifice area), insufficiency (no proper closing, regurgita-tion), and altered morphology (bicuspid aortic valve (BAV)).

ˆ Stenosis: Aortic stenosis is defined as a condition in which opening of the aortic valve in systole is restricted (see Figure 1.5). This condition can be caused by a variety of disorders affecting the cusps or annulus. In infants, children, and young adults, the major causes of aortic stenosis are congenital malformation of the cusps or annulus and rheumatic disease. In patients over 60 years of age, the major causes are calcification of a bicuspid aortic valve and degeneration of the valve cusps or annulus.

Figure 1.5: Aortic stenosis.

The figure shows how aortic stenosis limits blood flow during systole. Image adapted from [Jin, 2014].

Mitral stenosis, due to restricted opening of the mitral valve in diastole, results in left ventricular inlet obstruction with a diastolic pressure gradient between the left atrium and the left ventricle. Rheumatic disease is the most common cause of mitral stenosis. Fusion of the commissures and thickening of the leaflets may result and the chordae tendineae may be affected as well.

Pulmonary valvular stenosis is usually a congenital anomaly that is well tolerated for many years and consists of fusion of the leaflets at the commissures, which pre-vents the leaflets from opening completely in systole. In severe pulmonary valvular stenosis, patients present with symptoms of chronic right ventricular failure.

ˆ Regurgitation: Aortic regurgitation may be due to abnormalities of the aortic cusps, a lesion of the annulus, or dilatation of the aortic root.

The major hemodynamic consequence of aortic regurgitation is an increase in end-diastolic volume in the left ventricle. The need for surgery is determined by the severity of symptoms, but more precise measurements of the degree of regurgitation and of ventricular function are also important in this regard.

Mitral regurgitation (see Figure 1.6) can be due to abnormalities of the mitral annu-lus, mitral leaflets, chordae tendineae, or papillary muscles. The major hemodynamic consequence of mitral regurgitation is an increase in the total stroke volume of the left ventricle. The need for surgery is determined by the severity of symptoms and by whether the ejection fraction falls toward 60%. A direct, non-invasive, quantitative assessment of the regurgitant volume can be accurately obtained with MR imaging. Pulmonary regurgitation may be caused by

– dilatation of the valve annulus secondary to pulmonary hypertension – endocarditis

– complications of surgical treatment for pulmonary stenosis – tetralogy of Fallot

– other conotruncal malformations

Figure 1.6: Pathophysiology of ischaemic mitral regurgitation (MR).

A normal mitral valve is shown in (a). Ischaemic MR, in which the leaflets cannot close effectively, is shown in (b). Image by [Levine, 2004].

The major hemodynamic consequence of pulmonary regurgitation is an increase in the end-diastolic volume of the right ventricle. MR imaging, with its multiplanar and 3D imaging capabilities accurately depicts right ventricular dilatation and hypertro-phy. It also allows direct measurement of regurgitant volume in the right ventricle and evaluation of right ventricular function.

Tricuspid regurgitation may result from endocarditis, rheumatic disease, dilatation of the right ventricle and the tricuspid annulus secondary to mitral valvulopathy,

1.2. Cardiovascular Diseases 9

etc. The major hemodynamic consequence of tricuspid regurgitation is an increase in the total stroke volume of the right ventricle.

ˆ Bicuspid Aortic Valve: The most common aortic valve malformation, with a prevalence of 1-2% [Fedak et al., 2002], is the bicuspid aortic valve (BAV). In this case, two of the normally three valvular leaflets are fused, as can bee see in Figure 1.7. In the majority of cases this is inherited, but it also can develop during the lifetime.

Figure 1.7: Schematic of the bicuspid aortic valve.

Bicuspid valve is typically made of 2 unequal-sized leaflets. The larger leaflet has a central raphe or ridge that results from fusion of the commissures. Image by [Siu and Silversides, 2010].

1.2.3

Complex Congenital Pathologies

This category refers to hereditary diseases that are often characterized by a multitude of conditions.

ˆ Tetralogy of Fallot: It is the most common, inherited, cyanotic heart disease. This complex condition consists of four components, also depicted in Figure 1.8:

– a ventricular septal defect (VSD) (a hole in the septum between the left and right ventricle), which causes a mixture of oxygenated and deoxygenated blood in the left ventricle

– due to a pulmonary valve stenosis, the mixed blood from both ventricles is preferably pumped through the aorta. This behaviour is called right-to-left shunt

– the constantly increased resistance due to the PV stenosis promotes the devel-opment of a right ventricular hypertrophy, an enlargement of the heart muscle – an overriding aorta names a special malpositioning where the aorta is directly

Figure 1.8: Tetralogy of Fallot (ToF) major features.

Figure showing the major components of ToF: ventricular wall defect, right ventricular hypertrophy and overriding aorta. Image by Mayo Foundation for Medical Education and Research.

ˆ The Marfan syndrome: It is a genetic disorder of the connective tissue. Marfan patients show an increased susceptibility to developing aneurysms due to “altered mechanical properties of the aortic wall related to the decreased aortic elasticity” [Geiger et al., 2012]. This strongly increases the risk of aortic dissection.

Finally, congestive heart failure (CHF) and congenital heart disease (CHD) are col-lective terms for pathologies that affect the sufficient blood supply or that are inherited, respectively.

1.3

Clinical Utility

Knowledge of normal and abnormal flow patterns in the human cardiovascular sys-tem increases the understanding of normal physiology and may help unravel the complex pathophysiological mechanisms leading to cardiovascular disease. Four-dimensional phase contrast magnetic resonance imaging (4D PC-MRI) has emerged as a suitable technique that enables visualization of in vivo blood flow patterns and quantification of parameters that could potentially be of prognostic value in the disease process [Kamphuis et al., 2017]. Moreover, it has been demonstrated ([Beerbaum et al., 2001]) that blood flow quantifica-tion is an integral part of the diagnostic work-up for patients with heart diseases.

For these reasons, CMR-based flow volume quantification is routinely used at many in-stitutions to estimate shunt flows, regurgitant flows, collateral flows, etc. These diagnostic tests are primarily based on 2D Cine PC-CMR. A large number of studies across different institutions and MR-systems have demonstrated that 4D PC-MRI permits flow volume

1.3. Clinical Utility 11

(a) Aortic trans-valvular flow during systole. (b) Aortic anatomy overlapped to aortic flow during systole.

Figure 1.9: Aortic flow.

Image obtained with CAAS MR 4D Flow (Pie Medical Imaging, Maastricht, NL).

quantification that is comparable to 2D Cine PC-CMR and has good scan-rescan repeata-bility.

Flow volume quantification with 4D PC-MRI has several advantages when compared to 2D Cine PC-CMR. 4D PC-MRI permits investigation of the internal consistency of the data by employing the “conservation of mass” principle (e.g. Qp/Qs within the same dataset).

Another advantage of 4D PC-MRI is the retrospective placement of analysis planes at any location within the acquisition volume. While standard 2D Cine PC techniques can easily be applied during a single breath hold, 4D PC-MRI on the other hand, offers the ability to retrospectively calculate blood flow through any planes of interest across the 3D volume. Despite longer scan times, 4D PC-MRI allows easy scan prescription (positioning of a sin-gle 3D volume) compared to the need to predetermine and accurately locate all relevant planes of 2D acquisitions. This may be especially advantageous in cases where multiple 2D Cine PC-CMR scans would be needed. In these situations, 4D PC-MRI may even be faster than prescribing and scanning a series of 2D Cine breath-held PC CMR acqui-sitions, enabling a reduced period of anaesthesia for younger children or of scan time in decompensated patients.

Further, the option of valve tracking may improve assessment of flow through heart valves. Compared to 2D Cine PC-CMR, 4D PC-MRI measures velocity in all spatial directions and has superior spatial coverage and may therefore also be better at capturing the peak velocity of a stenotic jet.

In addition to the flexible retrospective quantification of conventional flow parameters, 4D PC-MRI allows for the visualization of multidirectional flow features and alterations of these associated with cardiovascular disease [Dyverfeldt et al., 2015].

The intuitive flow visualizations that 4D PC-MRI offers have already found utility in sev-eral clinical studies. For example, time-resolved visualizations of blood flow have been used clinically to identify flow directionality and areas of flow acceleration in visceral ab-dominal blood flow. Finally, there are promising applications in complex congenital heart diseases.

1.4

Heart Valves Imaging Techniques

Imaging plays a fundamental role in the current diagnosis and treatment of valvular heart diseases (VHD) and in the preclinical and clinical research aiming at the develop-ment of novel pharmacologic or interventional therapies. Echocardiography remains the primary imaging technique to guide the management of VHD. However, the multifaceted and complex nature of VHD and the rapid development of transcatheter valve therapies has led to a spectacular increase in the use of cardiac magnetic resonance and cardiac com-puted tomography due to their recent advances. Thus, several new imaging techniques appear very promising in the future era of VHD [Pibarot et al., 2013].

Here the main imaging modalities currently adopted in clinical routine will be shortly presented.

1.4.1

Echocardiography

Transthoracic echocardiography (TTE) has been the clinical imaging modality of choice for assessing VHD since the 1980s, when it replaced invasive catheter-based measurements. It is safe, non-invasive and widely available, allowing direct visualisation of valve anatomy, function and hemodynamics whilst also facilitating measurement of the ventricular wall thickness, cavity dimensions and both systolic and diastolic function.

An echocardiogram uses sound waves called ultrasound to look at the size, shape, and motion of the heart. Ultrasound has a frequency above the range audible by humans (i.e., >20 000 Hz). For adult cardiac imaging, ultrasound waves in the range of 4-7 MHz are used (intravascular ultrasound uses frequencies as high as 30 MHz). These are created within the ultrasound probe by striking piezo-electric crystals with an electric pulse, which stimulates the crystals to release sound waves.

The central principle of ultrasound imaging is that, while most waves are absorbed by the body, those at interfaces between different tissue densities are reflected. In addition to emitting the ultrasound waves, the transducer detects the returning waves, processes the information, and displays it as characteristic images. Higher frequency ultrasound waves increase resolution, but decrease tissue penetration [Ashley and Niebauer, 2004].

Neither bone nor air is a good transmission medium for ultrasound waves; accordingly, specific windows (eg, apical, parasternal, subcostal, and suprasternal) are used to image the heart.

There are three basic “modes” used to image the heart: ˆ 2D or 3D imaging

ˆ M-mode imaging ˆ Doppler imaging

2D and 3D imaging 2D imaging constitutes the basis of echo imaging and allows struc-tures to be viewed moving in real time in a cross-section of the heart (two dimensions). The most common cross-sectional views are the parasternal long axis, the parasternal short axis, and the apical view (see Figure 1.10) Two-dimensional (2D) echocardiography pro-vides real-time imaging of heart structures throughout the cardiac cycle; more recently, 3Dimensional (3D) echocardiography has been developed. They both give valuable in-formation on a valve lesion’s functional pathology and the size and function of cardiac chambers. Although 3D echocardiography has been in clinical use for more than three

1.4. Heart Valves Imaging Techniques 13

(a) The first line illustrates the three planes (think of them as three plates of glass intersecting at 90o), the second line shows these three planes separated, and the third line shows the accompanying echo views. (a) Parasternal long axis; (b) parasternal short axis; (c) apical 4-chamber view. A-V: aortic valve; LA: left atrium; LV: left ventricle; RA: right atrium; RV: right ventricle. Image by [Ashley and Niebauer, 2004].

(b) Definition of transthoracic stan-dard 3D views based on direct com-parison of standard anatomic cut planes of the heart and corresponding 3D echocardiographic views. Copy-right John Wiley and Sons

Figure 1.10: The most common imaging echo views.

decades, recent innovations in ultrasound and computer technology have made real-time 3D echocardiography possible and easy to apply in daily patient care.

M-mode imaging The M-mode echo, which provides a 1D view, is used for fine mea-surements. Temporal and spatial resolutions are higher because the focus is on only one of the lines from the 2D scan sector.

Doppler Imaging Estimates of blood-flow velocity can be made by comparing the frequency change between the transmitted and reflected sound waves.

In cardiac ultrasound, Doppler is used in three ways:

ˆ continuous-wave (CW) Doppler CW Doppler is sensitive, but, because it mea-sures velocity along the entire length of the ultrasound beam and not at a specific depth, it does not localize velocity measurements of blood flow. It is used to estimate the severity of valve stenosis or regurgitation by assessing the shape or density of the output

ˆ pulsed-wave (PW) Doppler PW Doppler was developed because of the need to make localized velocity measurements of turbulent flow (it measures the blood-flow velocity within a small area at a specified tissue depth). It is used to assess ventricular in-flow patterns, intracardiac shunts, and to make precise measurements of blood flow at valve orifices

ˆ color-flow mapping(CFM) CFM uses measurements of the velocity and direction of blood flow to superimpose a color pattern onto a section of a 2D image. Tradition-ally, flow towards the transducer is red, flow away from the transducer is blue, and

higher velocities are shown in lighter shades. To aid observation of turbulent flow there is a threshold velocity, above which the color changes. This leads to a “mosaic” pattern at the site of turbulent flow and enables sensitive screening for regurgitant flow

Pulse-wave and continuous-wave Doppler and color flow imaging interrogation yield infor-mation on blood movement inside cardiac structures and on hemodynamics data that is not readily available using other imaging modalities. Among the hemodynamics quanti-ties that are recovered thanks to these techniques there are: pressure gradients, cardiac output, regurgitant volumes and fraction, pulmonary artery pressure, and an estimate of intracardiac pressures.

Instead, Tissue Doppler imaging (TDI) provides information about movement of cardiac structures. The relation between the dynamics of cardiac structures and the hemodynam-ics of the blood inside these structures provides information about cardiac diastolic and systolic function.

Transesophageal echocardiography (TEE) TEE offers superior visualization of pos-terior cardiac structures because of close proximity of the oesophagus to the posteromedial heart with lack of intervening lung and bone. This proximity permits use of high-frequency imaging transducers that afford superior spatial resolution [Buck and Thiele, 2015]. With its higher resolution, TEE allows for detailed evaluation of the valve morphology when transthoracic imaging is suboptimal, and detection of clots in the atria. A thin probe is passed down the oesophagus until it is at the same level of the heart. This position provides especially clear views (see Figure 1.11), particularly useful for imaging posterior cardiac structures. TEE is also used to guide interventional and surgical therapy.

Figure 1.11: Illustration of the relation between scanning depth, line density, and spatial resolution of 3D imaging from a transthoracic (3D TTE; left) and trans-esophageal approach (3D TEE; right).

In the example of 3D scanning of the mitral valve, the schematic illustrates that due to the di-vergence of imaging lines with increasing depth spatial resolution and, thus, resulting 3D image quality is higher at a lower depth of 4-5 cm, which is where the mitral valve is located from a TEE approach (right), compared to imaging the mitral valve at a higher depth of 9-10 cm from a TTE approach (left). Image by [Buck and Thiele, 2015].

1.4. Heart Valves Imaging Techniques 15

Stress Echocardiography Stress echocardiography is useful in assessing a valve le-sion’s hemodynamic severity and its impact on ventricular function as well as the effort tolerance of a patient.

Although underused in valve diseases, it is extremely valuable in many patients whose symptoms are discordant from the objective resting valvular data and in patients with low output states. Many patients may not be aware of their physical limitations since they adopt and adjust to slowly progressive valve lesions. Exercise testing and echocar-diographic evaluation of the hemodynamics at rest and exercise provide information on the effort tolerance of a patient as well as on the hemodynamic severity of the lesion. In patients with low output states, velocities and gradients across a stenotic valve may be low and not be indicative of the lesion’s true severity. Dobutamine stress echocardiography is useful in such scenarios: it provides information on the hemodynamic severity of the lesion and on the contractile reserve. Absence of contractile reserve is a pointer to the operative mortality and surgical outcome.

Echocardiography has many key advantages over other imaging modalities, thus so-lidifying its role in managing valvular disease for the foreseeable future. It is safe, fast, portable, and economical, and is capable of providing real-time information on valve struc-ture and function that can impact clinical decision making almost instantaneously. Echocardiography is continuously evolving and constantly being augmented by newer modalities, such as tissue harmonics, speckle tracking, tissue Doppler strain, and tissue characterization.

Whilst this combined approach is effective in the majority of patients, it leads to a wide spectrum of diagnostic categories and the potential for clinical confusion. Other potential limitations of echocardiography are also being increasingly acknowledged.

Firstly, acquisition of diagnostic acoustic windows can be impossible in certain patients and also perfect alignment of the Doppler probe with the direction of maximal blood flow through the valve could be difficult to achieve. In both circumstances, measurement errors will be introduced.

Secondly, echocardiography may have difficulty in measuring the left ventricular outflow tract (LVOT) diameter with accuracy. Indeed, echo often underestimates the LVOT di-ameter due to either calcification or its elliptical valve shape, and as the measurement is squared, even small errors become magnified substantially [Everett et al., 2016].

1.4.2

Computed Tomography

Since its introduction in 1972, CT, in which a focused rotating x-ray source is used to acquire cross-sectional anatomic data, has become an integral component of modern med-ical care. Recent advances in technology with 64- and 128-slice CT scanners have made it possible to study the beating heart in great detail. Cardiac CT can provide information on chamber size and myocardial mass, as well as morphologic details of the valve leaflets, chordae tendineae, and papillary muscles. In addition to evaluating cardiac valves, mod-ern CT scanners can assess coronary disease. Thus, CT can perform more comprehensive studies, providing information on coronary anatomy that echocardiography and MRI do not provide [de Ara´ujo Gon¸calves et al., 2015].

Indeed, calcium burden in the valves can be more accurately quantified on electrographi-cally gated non-contrast computed tomography.

Importantly, recent data has demonstrated that the aortic valve CT calcium score provides powerful prediction of disease progression and prognosis.

The disadvantages of cardiac CT include the lack of hemodynamic information, radiation, the need for contrast, and irregular heart rhythms [Everett et al., 2016].

1.4.3

Positron Emission Tomography

PET is a novel imaging technique, which allows the activity of specific disease processes to be measured in vivo. Recently, this technique has employed two tracers to measure in-flammation (18F-fluorodeoxyglucose (18F-FDG)) and calcification activity (18F-fluoride) in the valves of patients with stenosis [Everett et al., 2016].

Hybrid PET/CT scanners then allow the activity of these two key processes to be com-pared with the presence of established regions of macro calcification on CT.

18_{F-fluoride has been used as a bone tracer for 50 years binding to hydroxyapatite crystal}

and detecting regions of increased bone activity. In the vasculature, it binds preferentially to regions of newly developing microcalcification because the surface area of hydroxyapatite in these nanocrystalline regions is at its highest. By contrast in regions of macrocalcifica-tion, much of the hydroxyapatite is internalised and not available for binding. In valves stenosis, 18_{F-fluoride acts as a marker of calcification activity correlating with}

histolog-ical staining for alkaline phosphatase and osteocalcin and predicts where novel regions of macroscopic calcium are going to form. Tracer uptake increases with more advanced stenosis, offers powerful prediction of disease progression at 1 and 2 years, of small incre-mental value to computed tomography, and acts as an independent predictor of adverse clinical events.

This technique holds promise in better understanding the role of calcification in stenosis,

18_{F-fluoride PET may prove of clinical use in identifying patients likely to progress rapidly}

towards surgery and as a marker of disease activity and efficacy end point in clinical trials of novel therapies.

18_{F-FDG PET is widely used to image vascular inflammation. This PET tracer is a glucose}

analogue, which accumulates in metabolically active cells such as vascular macrophages. In stenosis,18_{F-FDG activity is higher in patients versus controls, demonstrating a modest}

correlation with severity of valvular disease.

1.4.4

Cardiac Magnetic Resonance

CMR, a relative newcomer to the area of cardiovascular imaging, has become a reliable technique for assessing cardiac structure and function over the past decade. One of this technique’s unique advantages is its ability to offer a complete 3D tomographic assessment of the ventricle and to acquire images in any orientation together with the unique possi-bility to obtain information on tissue characterization. The sources of image production for MRI are hydrogen nuclei, which are exceptionally abundant in the human body in the form of water. These hydrogen nuclei produce signals as they change orientation when exposed to a fluctuating magnetic field. As the hydrogen nuclei change alignment, they release radio waves, which are then converted to images.

Pulse sequences are a series of radiofrequency pulses that when applied to the magnetic field can bring out different tissue characteristics. Three types of imaging sequences used with CMR are needed to evaluate valvular heart disease.

The first goes by the term spin-echo or cine-MRI. This sequence produces multiple images that are evenly spaced throughout the cardiac cycle, and blood appears as black. Because blood appears as a contrast, this sequence is useful for assessing ventricular cavity sizes,