Thesis
Reference
Spatio-temporal sampling strategies and spiral imaging for translational cardiac MRI
DELATTRE, Bénédicte
Abstract
Magnetic Resonance Imaging is a reference tool to assess myocardial function and viability, the two key measurements in clinics. However, several technical challenges remain. This thesis focuses on the development of new strategies to provide an efficient characterization of the myocardium. Using tools provided by MR physics and image processing a translational
"bench-to-bedside" approach was adopted. Concerning the "bench", Manganese was studied as a contrast agent for myocardial viability assessment. A new cine sequence, "interleaved cine", was also developed to increase the time resolution and opens up the possibility of stress studies in mice on clinical scanners. In parallel, spiral imaging was applied to the
"bedside". In the context of real-time imaging, the proposed reconstruction method, k-t SPIRE, takes into account the temporal information of data which helps to resolve the undersampling artifacts and showed important improvements compared to the classical method both in numerical simulations and in-vivo.
DELATTRE, Bénédicte. Spatio-temporal sampling strategies and spiral imaging for translational cardiac MRI . Thèse de doctorat : Univ. Genève, 2011, no. Sc. 4302
URN : urn:nbn:ch:unige-150342
DOI : 10.13097/archive-ouverte/unige:15034
Available at:
http://archive-ouverte.unige.ch/unige:15034
Disclaimer: layout of this document may differ from the published version.
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Service de radiologie FACULT´ E DE M´ EDECINE Professeur J.-P. Vall´ee Groupe de Physique Appliqu´ee FACULT´ E DES SCIENCES
Professeur J.-P. Wolf
Spatio-temporal Sampling Strategies and Spiral Imaging for Translational Cardiac MRI
TH` ESE
pr´ esent´ ee ` a la Facult´ e des sciences de l’Universit´ e de Gen` eve pour obtenir le grade de Docteur ` es sciences, mention Physique
par
B´ en´ edicte Delattre de
Pr´evessin-Mo¨ens (France)
Th`ese N
o4302
GEN`EVE
Atelier de reproduction ReproMail 2011
Contents v
Acknowledgements ix
Abstracts xi
List of Figures xv
List of Tables xix
1 Introduction 1
1.1 Context . . . 1
1.2 MRI basis . . . 1
1.2.1 Signal detection . . . 2
1.2.2 Relaxation phenomenon . . . 4
1.2.3 Spatial encoding of information . . . 6
1.3 Cardiac MRI . . . 8
1.3.1 Sequences for function measurement . . . 8
1.3.2 Acquisition and reconstruction methods for rapid imaging . . . 9
1.3.3 Viability measurement . . . 12
1.3.4 Translational research . . . 15
1.4 Aim of the project . . . 15
I Small animal imaging (from bench...) 17
2 Manganese-enhanced MRI in mice 19 2.1 Manganese as a contrast agent . . . 19
2.2 Imaging myocardial viability in mice . . . 21
2.2.1 Material and methods . . . 21
2.2.2 Manganese optimal dose determination . . . 25
2.2.3 Myocardial function quantification . . . 29
2.2.4 Infarction quantification . . . 34
2.3 Manganese kinetics . . . 37
2.3.1 Animal groups . . . 37 v
2.3.2 Myocardial function quantification . . . 37
2.3.3 Kinetic curves . . . 38
2.3.4 Measurements of infarction extension . . . 41
2.4 Discussion . . . 44
2.4.1 Acute infarction . . . 44
2.4.2 Chronic infarction . . . 45
2.4.3 Manganese as a marker of cell viability. . . 45
3 Highly time-resolved functional imaging 47 3.1 Sequence presentation . . . 47
3.1.1 Sequence parameters . . . 48
3.1.2 Temporal regularization . . . 48
3.1.3 Validation experiments. . . 49
3.2 Mass and function measurements . . . 52
3.2.1 Animals . . . 52
3.2.2 Image analysis . . . 52
3.2.3 Results . . . 53
3.3 Going further with the image enhancement... . . 56
3.3.1 Model presentation. . . 56
3.3.2 Validation experiments. . . 58
3.3.3 Function and mass measurements. . . 60
3.4 Discussion . . . 60
II Spiral imaging (...to bedside) 61
4 Spiral Sequence 63 4.1 What is spiral ?. . . 634.1.1 Introduction . . . 63
4.1.2 What is spiral ? . . . 64
4.1.3 Spiral trajectory . . . 66
4.1.4 Specific advantages of spiral trajectory. . . 67
4.1.5 Eddy currents. . . 68
4.1.6 Sensibility to inhomogeneities . . . 69
4.1.7 Concomitant fields . . . 70
4.2 Designing the trajectory . . . 70
4.2.1 General solution . . . 70
4.2.2 Glover’s proposition to managek-space center . . . 73
4.2.3 Zhao’s adaptation to variable density spiral . . . 73
4.2.4 Comparison of the 3 propositions . . . 73
4.3 Coping with blurring in spiral images. . . 74
4.3.1 Measuring gradient deviations. . . 74
4.3.2 Correcting off-resonance effects . . . 76
4.3.3 Managing with concomitant fields . . . 80
4.3.4 Summary . . . 82
4.4 Spiral image reconstruction . . . 82
4.4.1 Gridding algorithm. . . 83
4.4.2 Spiral imaging and parallel imaging . . . 86
5 Spline-based image model for spiral reconstruction: SPIRE 87
5.1 Model assumptions and justification . . . 87
5.1.1 Spline-based image model . . . 88
5.1.2 Spline-based Image REconstruction: SPIRE . . . 90
5.2 Algorithm implementation. . . 90
5.3 Regularization . . . 92
5.3.1 Automatic parameter adjustment. . . 93
5.4 Evaluation on numercal Shepp-Logan phantom . . . 95
5.4.1 Methods . . . 95
5.4.2 Results . . . 97
5.5 MRI experiments . . . 102
5.5.1 Methods . . . 102
5.5.2 Results . . . 102
5.6 Discussion . . . 105
6 k−t SPIRE: time extension of spline-based image model applied to real-time 107 6.1 Spatio-temporal model . . . 107
6.2 Model fitting & implementation. . . 109
6.3 Evaluation on numerical phantom . . . 112
6.3.1 Methods . . . 112
6.3.2 Results . . . 114
6.4 k−tSPIRE for real-time cardiac imaging . . . 121
6.4.1 Methods . . . 121
6.4.2 Results . . . 121
6.4.3 Reconstruction artifacts . . . 127
6.5 Comparison with existing reconstruction methods. . . 131
7 Conclusions and perspectives 135 7.1 MEMRI and highly time-resolved cine . . . 135
7.2 Spiral imaging . . . 136
Bibliography 137
Appendix 151
Ce travail de th`ese, qui aura dur´e 3 ans au sein du service de radiologie de l’Hˆopital Cantonal Uni- versitaire de Gen`eve, a b´en´efici´e de l’interaction de nombreuses personnes, qui toutes ont eu une grande importance dans la r´ealisation de ce travail. J’aimerais ici leur adresser mes remerciements.
Je souhaite tout d’abord remercier le Prof. Jean-Paul Vall´ee qui m’a permis d’effectuer ce travail de th`ese sur un sujet passionnant, en me donnant la possibilit´e d’ˆetre encadr´ee par des personnes tr`es comp´etentes dans leur domaine. J’aimerais ´egalement remercier le Prof. Jean-Pierre Wolf qui a sans h´esit´e accept´e de co-diriger cette th`ese, en m’accordant une grande confiance lors de ce travail.
Je remercie le Prof. Christophe Becker pour m’avoir accueillie au sein de son service. Je tiens aussi
`a remercier le Prof. Dimitri Van De Ville, le Prof. Matthias St¨uber et le Prof. Sebastian Kozerke pour avoir accept´e de faire partie de mon jury de th`ese.
Il y a ensuite des personnes sans qui ce travail ne serait certainement pas ce qu’il est. Tout d’abord, je veux remercier le Dr Jean-No¨el Hyacinthe qui m’a suivie, ´epaul´ee, soutenue tout au long de ces trois ans. Son enthousiasme communicatif, sa disponibilit´e, ses remarques riches en questions pertinentes font de lui un excellent mentor grˆace `a qui j’ai beaucoup progress´e. Une grande partie de ce travail n’aurait ´egalement pas vue le jour sans les pr´ecieux conseils du Prof. Dimitri Van De Ville. Son expertise en traitement d’images a ´et´e un v´eritable atout. Tr`es p´edagogue, il a toujours su expliquer des concepts tout d’abord assez abstraits pour moi avec des images simples. Toujours de bonne humeur, il s’est rendu disponible `a chaque fois que j’en ai eu besoin, malgr´e un emploi du temps assez serr´e, et je lui en suis tr`es reconnaissante.
Je voudrais ´egalement remercier le Prof. Fran¸cois Mach pour m’avoir permis de collaborer avec son groupe pour toute la partie concernant les mod`eles murins, mes remerciements vont notamment au Dr Vincent Braunersreuther pour sa disponibilit´e lors de nos nombreuses exp´eriences.
Concernant la programmation de s´equence, je remercie le Dr Gunnar Kr¨uger pour m’avoir donn´e acc`es `a la s´equence spirale, ainsi que pour son aide sur quelques points sensibles du debuggage de la s´equence.
Je souhaiterais ´egalement remercier le Dr Magalie Viallon pour sa disponibilit´e et son efficacit´e concernant la r´esolution des probl`emes relatifs `a l’IRM ainsi que pour sa g´en´erosit´e concernant le partage de son exp´erience en IRM cardiaque.
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Mes prochains remerciements vont `a mes coll`egues de groupe. Un grand merci `a Lindsey qui a patiemment relu l’anglais de mes nombreuses “proses” et avec qui j’ai partag´e une cohabitation tr`es agr´eable dans le mˆeme espace de bureau. Je remercie St´ephany pour son avis pertinent du- rant les nombreuses discussions concernant les aspects biologiques de mon travail qui m’ont ´evit´e les dangereux raccourcis que j’avais tendance `a faire, mais aussi pour son regard ´eclair´e qui a bien souvent remis les morceaux de mon travail dans le bon ordre dans mon esprit. Merci ´egalement `a Jean-Luc, qui lors de mon arriv´ee dans le groupe, m’a appris la manipulation de l’IRM et form´ee aux examens cardiaques de souris ainsi qu’`a toutes les techniques relatives `a cette partie de mon travail.
Merci ´egalement `a Xavier, Karin et Frank pour les discussions enrichissantes que nous avons pu avoir.
Enfin, j’ai pu b´en´eficier d’un excellent cadre de travail grˆace au Dr Fran¸cois Lazeyras, qui m’a accueillie au sein des locaux du CIBM. Cet environnement o`u cohabitent des personnes de sp´ecial- it´es, mais aussi de personnalit´es, tr`es diff´erentes a souvent ´et´e le si`ege d’´emulations scientifiques, ou non, tr`es enrichissantes. Merci `a Suzanne, St´ephane, Tamara, Laura, Elda, Djano, Isik, C´esar, Jeff, Lorena, Vincent, Thomas, Rares, Michel, et `a tous ceux qui ont pass´e du temps dans l’open-space pour ces bons moments. Des remerciements particuliers vont `a Jonas pour son aide avec certains tests statistiques ainsi qu’avec quelques subtilit´es de LATEX.
Mais une th`ese est ´egalement une grande aventure humaine. Elle m’a permis de rencontrer des personnes pleines de coeur (ce qui a pu ˆetre v´erifi´e `a l’IRM !) qui ont ´et´e pr´esentes dans tous les moments importants, de la th`ese, mais aussi de la vie: Steph, Jean-No, Lindsey, Thomas, Frank, les mots sont un peu faibles pour exprimer mes sentiments, donc je me limiterai `a l’essentiel: merci d’ˆetre l`a.
Enfin, mes derniers remerciements, et non des moindres, vont `a mes parents. `A ma maman qui a support´e beaucoup trop de choses durant ces trois ans, mais qui a tout de mˆeme trouv´e les ressources pour me soutenir sur tous les plans durant ce marathon. `A mon papa, qui n’aura pas vu la fin de l’aventure, mais qui ´etait d´ej`a tr`es fier de moi lorsqu’elle a commenc´e... Ils m’ont toujours encourag´ee `a faire ce dont j’avais envie en me donnant la possibilit´e et les moyens de le faire. C’est une grande chance.
Gen`eve, janvier 2011 B´en´edicte Delattre
Currently, cardiovascular diseases are the leading cause of mortality worldwide. Overall, the ma- jority of deaths are due to coronary heart disease. Cardiac imaging has thus an important role in early diagnosis of the disease but also for the development of new treatments. Magnetic resonance imaging (MRI) is a reference tool to assess myocardial function and viability, the two key measure- ments in clinics. Despite the important developments in this domain during the last two decades, several technical challenges remain. This work focuses on the development of new techniques to provide an efficient characterization of the myocardium. Using tools provided by MR physics and image processing we adopted a translational “bench-to-bedside” approach, from mice to patients.
The first part of the thesis starts from the “bench” with the development of cardiac imaging in mice. Translational research can greatly benefit from mouse imaging on clinical scanners, never- theless it remains a challenge. In order to assess the myocardial viability of mice, we chose to use Manganese (Mn2+) as a contrast agent. Since this is an analog of Calcium (Ca2+), it constitutes a powerful tool for this application. A robust protocol to quantify myocardial infarction in mice was thus set up and we showed a high correlation between infarct volume evaluated with Mn2+- enhanced MRI and with the histologic reference method. The study of Mn2+ kinetics in infarction demonstrated a faster accumulation of the contrast agent in infarction in the acute phase compared to the chronic phase. Mn2+ kinetics provided thus an interesting tool to differentiate acute from chronic infarction. Moreover, important information concerning cardiac function can be derived from moving cine images, the tradeoff between spatial and temporal resolution is however strong on clinical scanners. We developed a new cine sequence, “interleaved cine”, to increase the final time resolution and reach values of the same order as dedicated scanners. The images provided by this sequence were then enhanced with a post processing algorithm that allowed the reduction of artifacts produced by the sequence itself. Finally, interleaved cine was successfully validated with mass and function measurements. This sequence opens up the possibility of stress studies in mice on clinical scanners.
The second part of the thesis is concerned with spiral imaging applied to the “bedside”. Spiral is probably the most relevant sampling scheme for cardiac imaging due to its inherent advantages such as an efficient coverage of k-space and inherent flow compensation. Several applications can thus benefit from it, a clinically relevant one being real-time imaging. However, the classical FFT (Fast Fourier Transform) image reconstruction method is not applicable to this trajectory and the strategy usually used in this case (gridding algorithm) does not perform well whenk-space is highly undersampled. To face this problem, we first developed an innovative spline-based reconstruction method, SPIRE, that was shown to be more robust to undersampling artifacts than gridding. This method was then extended to the temporal domain (k−tSPIRE) and applied to real-time imaging.
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Instead of considering that a set of data is acquired at the same time point as is the case in existing reconstruction methods, k−t SPIRE takes into account the temporal acquisition information of data which helps to resolve the undersampling artifacts. Compared to gridding associated with sliding window (the reference method), we demonstrated an important improvement of signal-to- noise ratio as well as a better preservation of edge sharpness in numerical simulations with k−t SPIRE. We also observed a better temporal definition of the heart motion in volunteer experiments.
As a future perspective, this method can be integrated with existing ones to further accelerate the acquisition. In particular, the combination with parallel imaging has to be investigated, but trendy regularization methods such as “compressed sensing” are also of great interest. k−tSPIRE is also suited to other applications such as perfusion imaging or hyperpolarized studies.
Les maladies cardiovasculaires sont actuellement la premi`ere cause de mortalit´e dans le monde, et les cardiopathies coronariennes repr´esentent la majeure cause de ces d´ec`es. L’imagerie cardiaque a donc un rˆole tr`es important dans le diagnostic pr´ecoce des cardiopathies ainsi que dans l’´evaluation et le d´eveloppement de nouveaux traitements. L’imagerie par r´esonance magn´etique (IRM) est un outil de r´ef´erence dans la caract´erisation de la fonction et de la viabilit´e myocardique, deux indices cl´es en pratique clinique. Malgr´e d’importants d´eveloppements dans le domaine de l’IRM ces vingt derni`eres ann´ees, plusieurs d´efis techniques restent `a relever. Ce travail est consacr´e au d´eveloppe- ment de nouvelles techniques permettant une meilleure compr´ehension de la pathophysiologie car- diaque. Pour ce faire, une approche translationnelle a ´et´e adopt´ee, utilisant les outils de la Physique de la R´esonance Magn´etique ainsi que du traitement d’images appliqu´es de la souris jusqu’au patient.
La premi`ere partie de cette th`ese a ´et´e d´edi´ee au d´eveloppement de l’imagerie cardiaque chez la souris. L’imagerie cardiaque des mod`eles murins sur des IRM cliniques peut apporter un grand b´en´efice `a la recherche translationnelle mais reste un challenge technique important. Concernant la mesure de la viabilit´e myocardique, nous avons choisi d’utiliser le Manganese (Mn2+) comme agent de contraste. En tant qu’analogue du Calcium (Ca2+) il constitue un puissant outil pour cette ap- plication. Un protocole robuste mis en place pour la quantification de l’infarctus chez la souris avec le Mn2+a ainsi permis de montrer une excellente corr´elation de la mesure du volume infarci avec la m´ethode histologique de r´ef´erence. L’´etude de la cin´etique du Mn2+ dans l’infarctus a montr´e une accumulation plus rapide de cet agent de contraste dans la phase aigue que dans la phase chronique.
La cin´etique du Mn2+ constitue donc un outil int´eressant pour la discrimination des infarctus aigus et chroniques. Par ailleurs, en plus des mesures de viabilit´e, d’importantes informations concer- nant l’´evaluation de la pathologie cardiaque sont donn´ees par la mesure de la fonction, d´eriv´ee des images cine. Cependant, le compromis entre r´esolution spatiale et temporelle est assez important sur les syst`emes cliniques. Nous avons donc d´evelopp´e une nouvelle s´equence appel´ee “interleaved cine” afin d’arriver `a une r´esolution temporelle dans le mˆeme ordre de grandeur que celle obtenue sur des syst`emes d´edi´es. Les images produites par cette s´equence ont ´egalement pu ˆetre am´elior´ees avec un algorithme de post-processing qui a permis de r´eduire les art´efacts produits par la s´equence elle-mˆeme. Enfin, la s´equence ”interleaved cine” a ´et´e valid´ee avec succ`es avec des mesures de masses et de fonction. Cette s´equence ouvre la voie `a des ´etudes de stress chez la souris sur scanners cliniques.
La seconde partie de cette th`ese a ´et´e consacr´ee `a l’imagerie spirale appliqu´ee au patient. La spirale est probablement le sch´ema d’acquisition le plus pertinent grˆace `a ses avantages intrins`eques que sont, entre autres, une couverture efficace de l’espace de Fourier, et une auto-compensation des flux. De nombreuses applications peuvent donc b´en´eficier de cette trajectoire, l’une d’entre elles
´etant l’imagerie cardiaque en temps r´eel qui est tr`es pertinente au niveau clinique. Cependant, la xiii
m´ethode de reconstruction classique, la FFT (transform´ee de Fourier rapide), ne s’applique plus avec cette trajectoire et la strat´egie habituellement utilis´ee (algorithme de gridding) ne donne pas de bons r´esultats lorsque l’espace de Fourier est sous-´echantillonn´e de mani`ere importante. Pour faire face `a ce probl`eme, nous avons tout d’abord d´evelopp´e une m´ethode de reconstruction bas´ee sur un mod`ele d’image interpol´ee avec des fonctions spline: SPIRE. Cette m´ethode s’est r´ev´el´ee plus robuste aux art´efacts de sous-´echantillonnage que le gridding. SPIRE a ensuite ´et´e ´etendue dans le domaine temporel (k−tSPIRE) et appliqu´ee `a l’imagerie temps r´eel. Au lieu de consid´erer qu’un ensemble de donn´ees a ´et´e acquise au mˆeme point dans le temps, comme cela est fait dans les m´ethodes de reconstruction existantes, k−t SPIRE prend en compte l’information temporelle concernant l’acquisition de chaque ´echantillon de signal, ce qui r´esout les art´efacts de sous-´echantillonnage.
Compar´e au gridding, associ´e avec la m´ethode de “sliding window” (m´ethode de r´ef´erence), nous avons d´emontr´e, lors des simulations num´eriques, une importante am´elioration du rapport signal- sur-bruit, mais ´egalement une meilleure conservation des bords des objets contenus dans l’image.
Nous avons ´egalement observ´e une meilleure d´efinition temporelle du mouvement cardiaque lors d’exp´eriences sur volontaires. Une perspective int´eressante serait l’int´egration de cette m´ethode avec les algorithmes existants pour acc´el´erer encore l’acquisition. En particulier, la combinaison avec l’imagerie parall`ele doit ˆetre investigu´ee mais aussi avec des m´ethodes de r´egularisation actuelles comme le “compressed sensing”. k−t SPIRE peut ´egalement s’av´erer particuli`erement adapt´ee `a d’autres applications comme l’imagerie de la perfusion cardiaque ainsi que dans des ´etudes utilisant l’hyperpolarisation.
1.1 Illustration of nuclear spins orientation in a magnetic field . . . 2
1.2 Illustration of radiofrequency excitation of the magnetization vector . . . 3
1.3 Illustration of longitudinal and transversal relaxation for myocardial tissue and blood at 3T . . . 4
1.4 Illustration of spin echo . . . 5
1.5 Illustration of slice selection with application of a gradient . . . 6
1.6 Illustration of relative importance of low and high frequencies ink-space sampling . 7 1.7 Scheme of the cine sequence . . . 8
1.8 Schematic representation of phased array coil . . . 9
1.9 Scheme of the Keyhole and BRISK methods. . . 10
1.10 Illustration of the UNFOLD andk−t BLAST methods . . . 11
1.11 Longitudinal magnetization relaxation in inversion recovery sequence. . . 14
2.1 Scheme of the experimental setup for MRI exams of mice . . . 22
2.2 Scheme of EDV and ESV evaluation . . . 24
2.3 Myocardial segmentation as defined by the American Heart Association . . . 25
2.4 Examples of dose response curves for low and high Mn2+ concentrations . . . 26
2.5 Synthesis of dose response curves for lvarying Mn2+ concentrations . . . 28
2.6 Scheme of SI after inversion pulse for different T1. . . 28
2.7 Example of cine images of systolic and diastolic phases in middle slice of the heart . 29 2.8 Examples of cine images before and after Mn2+ injection. . . 31
2.9 Wall thickening between diastolic and systolic phase for the different myocardial sectors 32 2.10 Left ventricular cavity and wall areas before and after Mn2+ injection . . . 33
2.11 T1-weighted PSIR short axis images and corresponding TTC staining for IR60 and sham mouse . . . 34
2.12 Contrast to noise ratio for control, sham and IR60 groups . . . 35
2.13 Segmentation method for infarction volume quantification . . . 35
2.14 Infarction volume quantification with MEMRI versus TTC staining. . . 36
2.15 Mean Mn2+ kinetics 24 hours and 8 days after reperfusion . . . 39
2.16 Slopes of Mn2+ wash-in kinetics 24 hours and 8 days after reperfusion . . . 40
2.17 Examples of PSIR images taken at representative time points during the MRI exam 24h and 8 days after surgery. . . 41
2.18 Schematic representation of infarct extension measurement . . . 41
2.19 Comparison of MR images and ex-vivo colorations . . . 42
2.20 Correlation between MEMRI and ex-vivo infarction extent measurements . . . 43 xv
3.1 Schematic representation of sequence design for interleaved cine . . . 48
3.2 Illustration of the ghosting artifacts corrupting the interleaved cine . . . 49
3.3 Validation experiment for interleaved cine . . . 50
3.4 Effect of threshold value on data term . . . 50
3.5 Cine image example with correspondingk-space. . . 51
3.6 Time course of the signal if one representativek-space line for basic and interleaved cine . . . 52
3.7 Example of interleaved cine for mouse with an infarction . . . 53
3.8 Example of the temporal regularization . . . 54
3.9 Correlation between MRI and ex-vivo mass measurements . . . 55
3.10 Ejection fraction for control mice and mice with an infarction . . . 55
3.11 Example of temporal profile of interleaved cine for several threshold values. . . 56
3.12 llustration of the proposed algorithm to remove the flickering artifact on numerical phantom. . . 57
3.13 Results of the denoising algorithm on the numerical phantom . . . 58
3.14 Example of temporal profile of interleaved cine for several threshold values. . . 59
4.1 Example of variable density spiral trajectory on a cartesian grid. . . 65
4.2 Constant-linear-velocity versus constant-angular-velocity spiral trajectory . . . 67
4.3 Examples of spiral trajectories,k-space values and gradient wavefroms . . . 71
4.4 Comparison of different trajectory designs, effect on the slew-rate overshooting . . . 72
4.5 Synthetic scheme of the time-segmented reconstruction algorithm . . . 77
4.6 Synthetic scheme of the frequency-segmented reconstruction algorithm . . . 78
4.7 Voronoi diagram for density compensation . . . 84
4.8 Illustration of the gridding method in image domain . . . 85
4.9 Simulated aliasing artifact behavior of Cartesian and spiral imaging . . . 86
5.1 Illustration of weaknesses of gridding reconstruction . . . 88
5.2 B-spline functions of different degree . . . 89
5.3 Spline interpolation of 1-D signal . . . 89
5.4 Scheme of the automatic setting of parameterµ . . . 94
5.5 Shepp-Logan analytic phantom . . . 95
5.6 Trajectories used in Shepp-Logan experiments. . . 96
5.7 Normalization areas on Shepp-Logan phantom . . . 97
5.8 Shepp-Logan reconstruction with gridding and SPIRE . . . 99
5.9 Shepp-Logan reconstruction with gridding and SPIRE in the case of noisy data . . . 101
5.10 Comparison of SPIRE and gridding reconstruction of a phantom . . . 103
5.11 Comparison of SPIRE and gridding reconstruction of a volunteer heart. . . 104
6.1 k−tspace representation of spiral trajectory . . . 108
6.2 Temporal interpolation with spline functions . . . 111
6.3 Numerical phantom reference for k-t reconstruction. . . 112
6.4 Spiral trajectory with golden ratio angle rotation . . . 113
6.5 Scheme of the sliding window technique . . . 114
6.6 Temporal profiles of pixels on diagonal for all methods . . . 115
6.7 SNR for the different reconstruction methods . . . 116
6.8 Sequence of steps performed to determine the edge SNR . . . 117
6.9 Gradient of the angular average profile for the different reconstructions. . . 118
6.10 Edge SNR for the different reconstructions. . . 119
6.11 Edge SNR for the different reconstructions. . . 120
6.12 Images of apex and basis slices reconstructed with gridding andk−tSPIRE compared with cine sequence . . . 122
6.13 Gridding andk−tSPIRE images for apex slice. . . 123
6.14 Temporal profiles of apex slice for all reconstructions . . . 124
6.15 Selection of temporal profiles of apex slices. . . 125
6.16 Reconstructed frames around the systole for reference sequence andk−tSPIRE . . 125
6.17 Comparison of temporal profiles and gradient of CINE, gridding andk−tSPIRE for apex slice . . . 126
6.18 Comparison of temporal profiles and gradient of CINE, gridding andk−tSPIRE for basis slice . . . 127
6.19 PSF for gridding andk−tSPIRE . . . 128
6.20 Temporal profile of the PSF for gridding andk−t SPIRE . . . 129
6.21 Effect of the matrix size and sampling strategy on the grid-like artifact. . . 130
6.22 Effect of the matrix size on the grid-like artifact on real data . . . 131
6.23 Effect of temporal assumption on the sample acquisition . . . 132
2.1 Mn2+ injection parameters for dose determination study . . . 27 2.2 EDV, ESV, EF and heart rate for IR60, sham and control groups measured before
and after Mn injection . . . 30 2.3 Wall thickening between diastolic and systolic phase for IR60, sham and control
groups, before and after Mn2+injection . . . 33 2.4 Global function parameters measured 24 hours and 8 days after reperfusion . . . 38 2.5 Manganese entry slopes and mean correlation coefficient for acute and chronic time
points . . . 39 2.6 Linear regression between infarction extent measurements with MEMRI and ex-vivo
methods . . . 43 3.1 Evaluation of the flickering artifact reduction with the two proposed denoising al-
groithms. . . 59 4.1 Comparison of proposed methods efficiency for deblurring images . . . 82 5.1 Gradient of the regularization term for different spline degreeαand derivative orderγ 94 5.2 Parameters used for the different experiment on Shepp-Logan numerical phantom. . 95 5.3 SNR for reconstruction of Shepp-Logan phantom with gridding and SPIRE . . . 98 5.4 SNR for reconstruction of Shepp-Logan phantom in the case of noisy data, with
gridding and SPIRE . . . 100 6.1 Detailed calculation times for sp5 on 3 interleaves on matrix size N=64 and N=128 131
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Introduction 1
1.1 Context
Cardiovascular diseases include disorders of the heart and the blood vessels. This is currently the leading cause of mortality, representing one third of deaths worldwide. Overall, an estimated 42%
of deaths were due to coronary heart disease (CHD) in 2004 [1]. Governments are increasing public awareness about the risk factors of CHD, such as smoking, diabetes, high blood pressure and inac- tivity [2], however, more than ever before, there is a real need to face this pandemic with efficient tools and treatments.
In this context, imaging tools are of particular importance for diagnostic purposes, since an early an accurate assessment of the disease is essential to optimize patient management and treatment decisions. Imaging has also a major role in fundamental research as well as treatment development [3]. Magnetic Resonance Imaging (MRI) has emerged as an important imaging technique to assess patients with CHD, with the advantage of using non-ionizing radiation [4]. It has already demon- strated relevant diagnostic and prognostic information in many forms of heart disease [5] but still holds challenges.
1.2 MRI basis
This introductory part succinctly presents the theoretical notions of Nuclear Magnetic Resonance (NMR) and MRI that are needed for the comprehension of the following chapters. The interested reader can find more detailed information in references [6–8].
Before MRI emerged, it was already known from NMR that the angular momentum, or spin, of a hydrogen nucleus placed in a magnetic field precesses about that field at the Larmor (or resonant) frequency [9]:
ω=γB0, (1.1)
1
whereγis the gyromagnetic ratio specific for a given nucleus, for Hydrogen1H in waterγ= 2.68·108 rad/(s·T). The resonance frequency may be shifted from the Larmor frequency depending on the nuclei environment, which makes NMR specifically sensitive to the different components of a sample.
This is referred to as chemical shift. The idea of Lauterbur [10] and Mansfield [11] was to add to the static magnetic field B0, spatially varying magnetic fields (commonly referred to as gradients) to encode the position of the object of interest. Resonant frequency will then vary proportionally to the gradient added and thus contain its location information. This was the key concept leading to MRI. We will see in the next sections how signal frequency measurement can lead to an image of tissues.
In biological tissues, there is a natural abundance of hydrogen. This is the reason why clinical MRI focuses on the signal provided by this nucleus. However, other nuclei, present in smaller quantity in the human body, also have magnetic properties, such as13C,19F,31P and23Na. When a biological tissue is placed in a magnetic field, the proton spins will rotate around an axis aligned along the field direction. The signal measured in MRI comes from the difference between the number of spins that are in the low energy level (with a parallel alignment to the magnetic field) and those that are in the high energy level (with an anti-parallel alignment). The portion of spins in the low energy state is slightly higher than the one in high energy state and is given by Boltzmann equilibrium. This depends on the factor [7]:
spin excess ≈N~ω0
2kT, (1.2)
where N is the number of protons in the sample, ω0 is the Larmor frequency, k the Boltzmann constant and ~ = 2πh with h Planck’s constant. The spin excess will be more important with an increasing magnetic field, see figure 1.1. For example at 3T, the MR signal is given by only 10 nuclear spins on 106 protons.
Figure 1.1— Illustration of nuclear spin orientation in a magnetic field. Spins with a parallel alignment to the magnetic field are in the low energy level and are slightly in excess compared to the one in the high energy level, with an anti-parallel alignment to the field. This fraction excess is greater for a higher magnetic field.
1.2.1 Signal detection
In MRI the signal is produced by tipping the magnetization vector −→M0 (the resultant sum of all angular momentum vectors) away from the static magnetic fieldB0 with a radiofrequency fieldB1
(rf pulse) set at the Larmor frequency. If −→M0 is aligned alongz and the B1 field is applied along x, the magnetization vector then rotates aroundxaxis of an angleα(the flip angle). The varying magnetic flux produced by the transverse magnetization−M→through a nearby conductor loop induces a current in this conductor that can be measured. Figure1.2illustrate this process in a frame that is rotating at the Larmor frequency (referred to as the rotating frame). The corresponding induced elecrtomotive force (emf) is given by:
emf =−d dt
Z M~(~r, t)·B~receive(~r)d~r, (1.3)
whereBreceive(~r)is the received field produced by the detection coil at all point where magnetization is non-zero. The dependance of the emf on the applied B1 field is implicitly contained in the magnetization M~. Signal is proportional to the magnetization which is in turn proportional to the spin densityρ. After some simplifications it can thus be expressed as (see [7]):
s(t) = Z
ρ(~r)ei(ω0t+φ(~r,t))d~r, (1.4) whereφ(t)is the accumulated phase expressed as:
φ(~r, t) = Z t
0
ω(~r, t′)dt′. (1.5)
In the presence of only a static magnetic field B0,ω =ω0.
Figure 1.2 — Illustration of radiofrequency excitation of the magnetization vector −→
M0 in the rotating frame. An rf pulse (B1 field) is applied alongxaxis and tips−→
M0 atπ/2angle fromz axis. A conductor loop measures the signal produced.
Noise in MRI
Noise in MRI has several origins, however in an ideal experiment some sources of noise such as digitization noise or pseudo-random ghosting due to moving spins can be neglected. The main source of noise derives thus from random fluctuations in the receive coil electronics and the sample.
The variance of this noise can be expressed as:
var(emfnoise)≡σthermal2 (~k) = 4kT ·R·BW, (1.6) where R is the effective resistance of the coil load by the body, and BW is the bandwidth of the detecting system. The bandwidth is the main noise contribution since the temperature and resistance of the coils and bodies are not variable. The effective resistanceR can also be expressed as the sum of the contributions of the body and coil load as well as electronic noise:
Ref f ective =Rbody+Rcoil+Relectronics. (1.7) The noise expressed in1.6is expected to have equal power components at all frequencies within the readout bandwidth, so is call a “white” noise.
However, another definition of noise will be used in this work and is derived in an image processing point of view. Indeed, the difference between a processed image compared to one reference image can also be defined as “noise”. This noise is therefore independent of the physical alterations of the signal and reflects only an error introduced by a specific post-processing process.
1.2.2 Relaxation phenomenon
When radiofrequency excitation stops, the magnetization vector tends to return to its original posi- tion alongB0field due to interactions between the spins and their surroundings. This phenomenon is called spin-lattice relaxation and is governed by a specific relaxation timeT1. Another relaxation effect is given by the interaction between the spins themselves that causes a dephasing resulting in a reduction of transverse magnetization. This so-called spin-spin relaxation has a specifcT2relaxation time. These relaxation phenomenon are expressed by the Bloch equations:
Mx(t) = e−t/T2 Mx(0) cos(ω0t) +My(0) sin(ω0t)
, (1.8)
My(t) = e−t/T2 My(0) cos(ω0t)−Mx(0) sin(ω0t)
, (1.9)
Mz(t) = Mz(0)e−t/T1+M0 1−e−t/T1
. (1.10)
Figure1.3illustrates longitudinal (T1) and transversal (T2) relaxation for the myocardial tissue and the blood at 3T (T1= 1471 ms,T2= 47 ms andT1= 1932 ms,T2= 275 ms respectively [12]).
0 1000 2000 3000 4000 5000
0 0.2 0.4 0.6 0.8 1
Mz
t (ms)
0 50 100 150 200 250 300
0 0.2 0.4 0.6 0.8 1
Mxy
t (ms) Myocardium
Blood
Myocardium Blood
Figure 1.3— Illustration of longitudinal and transversal relaxation for myocardial tissue and blood at 3T (T1andT2 values were taken from [12]).
T1and T2 parameters play an important role in contrast depending on the timing of the signal acquisition. In real conditions the transverse relaxation is also altered by interactions with local magnetic field inhomogeneities, this is called theT2∗relaxation, withT2∗<T2. Figure1.4shows the signal measured after a 90◦pulse, after the application of the rf pulse, the magnetization is decaying due toT2∗ relaxation. This signal is called free induction decay (FID). If we add after the 90◦pulse a 180◦ rf excitation, the spins are refocussed and produce a signal called the echo. This sequence called “spin echo”, can be repeated over the time to produce an image. If we define TE the time between the 90◦excitation pulse and the maximum amplitude in the signal echo, and TR the time separating two consecutive repetitions, the transverse magnetization (which represents the acquired signal intensity) is given by:
Mxy(T E) =M0 1−e−T R/T1
e−T E/T2. (1.11)
Figure 1.4— Illustration of spin echo. (a) FID signal measured after the 90◦pulse, spin refocalization after the 180◦ pulse and echo formation, schematic representation of 2 spins with different rotating frequencies in the transverse plane to illustrate the refocalizaiton process. (b) Repetition of the spin echo, definition of TE and TR.
1.2.3 Spatial encoding of information
As seen above, ignoring relaxation effects, signal is related to the spin density image by the relation 1.4where the precessing frequencyωdepends on the static magnetic field. To spatially discriminate the signal of protons, the position of each spin along one direction, for examplez, can be encoded with a spatially varying magnetic field along that direction. The proton’s precessing frequency now depends on that gradientG:
ω(z, t) =ω0+ωG(z, t), (1.12)
ωG(z, t) =γzG(t). (1.13)
Figure1.5 illustrates this principle in the case of a gradient applied along the field directionz.
Gradients can then be applied inxandy directions to encode spatially the spins in the transverse plane. If we definek(t)as:
~k(t) =γ− Z t
0
G(t~ ′)dt′, (1.14)
the signal is thus expressed as:
s(~k) =Z
ρ(~r)e−i2π~k·~r. (1.15)
Figure 1.5— Illustration of slice selection with application of a gradient. (a) Precession frequency of spins in all the volume is the same, (b) after application of a gradient inz direction, each spin rotates at different frequency alongz.
Relation1.15 describes the signal as the Fourier transform of the spin densityρ. The domain of spatial frequency of the signal is referred to as the k-space. Figure 1.6 illustrates the relative importance of low spatial frequencies and high spatial frequencies in the image composition. Most of the energy of the image is contained in the center ofk-space whereas the details of the image are encoded into the high frequencies.
Usually,k-space is sampled line-by-line in order to be reconstructed with the Fast Fourier Trans- form (FFT) algorithm. Knowing the relative importance of low and high frequencies in the image formation, other sampling strategies can be more advantageous like radial sampling or spiral sam- pling that spend more time sampling low frequencies than high ones. The straightforward recon- struction with FFT is therefore no longer possible since points are not placed on the Cartesian grid, but has to be performed with other methods. An illustration can be found in section4.1.2, p. 65.
Part of this thesis is devoted to the development and validation of such a trajectory, however, in this introductory part, we will only focus on Cartesian sampling since it is the more widely used in clinical routine.
Figure 1.6— Illustration of relative importance of low and high frequencies ink-space sampling. Upper, full k-space sampling and its corresponding image; middle, sampling of the center of k-space gives the intensity information of the image; lower, sampling of high frequencies only gives the edges of the image and not the main contrast.
1.3 Cardiac MRI
1.3.1 Sequences for function measurement
Signal acquisition performed line-by-line, takes a certain time to sample the entire k-space. The acquisition duration is often not compatible with requirements of imaging moving objects. In cardiac imaging, one of the strategies is to segment the k-space sampling and to trigger the acquisition onto a physiological information like the R-wave of the ECG. Figure 1.7 represents the complete acquisition of k-space segmented over several heart beats. With this technique it is possible to acquire consecutive packets of data during each R-R cycle which give temporal information to the resulting images, as it is illustrated in the lower image of figure 1.7. The reconstruction of all the collectedk-space informations give a movie of the beating heart, referred to as “cine” [13]. To avoid the respiratory motion artifacts into the final images, we often perform this sequence under patient breathold. Another technique if the patient is unable to retain his respiration during 10 to 20 seconds is to perform the acquisition with a respiratory navigator in order to reconstruct only data that were acquired at the same phase of the respiratory cycle [14].
Figure 1.7— Schematic representation ofk-space sampling in cine sequence. (a) Acquisition of one phase of the cardiac cycle; (b) the acquisition is performed for each cardiac phase and is repeated until the entire k-space is sampled.
The cine sequence is the standard sequence for the evaluation of myocardial function [15, 16]
and is considered as diagnostic. Contraction deficit due to a myocardial infarction, for example, is visible when the movie is played and measurements such as left ventricular cavity volume or wall thickening between diastole and systole are performed on the images and are considered as diagnos- tically relevant by a consensus of scientists and healthcare professionals [17].
However, this sequence is limited in presence of arrhythmia in patients. In this case, one alter- native is to perform real-time imaging [18]. This last option is however non-trivial and needs some
acceleration methods as described in section1.3.2to maintain the acquisition time sufficiently short to depict the cardiac motion, typically less than 200 ms in normal human heart.
1.3.2 Acquisition and reconstruction methods for rapid imaging
Parallel imaging
Phased-array coils are composed of several coil elements that together provide a signal comparable to the one obtained with a single surface coil but extended to a larger FOV, see figure 1.8. They, were first used to improve image SNR [19]. Indeed, combining the images obtained with each separate coil results in an homogeneous image as if it was acquire with a larger coil but with a noise that is reduced [8]. However, the potential of parallel imaging to accelerate acquisition was quickly recognized when Sodickson et al. proposed the SMASH method [20], nearly followed by Pruessmann et al. [21] with the SENSE algorithm. Then a large number of methods were proposed but nowadays, the two most widely used parallel imaging techniques are still SENSE and GRAPPA [22] (derived from SMASH).
Figure 1.8— Schematic representation of phased array coil. Left, one coil element (in blue) covers a small part of the subject, right, several elements composing a phased array coil cover a larger FOV.
Acquisition time depends linearly on the number of phase encoding lines. Acquiring a fraction 1/Rof the complete k-space lines will reduce the acquisition time by a factorR. While GRAPPA resolves the problem of missing data in k-space, SENSE solves the problem in the image domain.
We will not detail the algorithms here (for more information see [22] and [21]) but we note that some features of parallel imaging methods are a reduced overall SNR and a non-uniform noise over the image [8]. Parallel imaging is therefore most useful in applications where SNR of images is important such as perfusion imaging or angiography [23].
Acceleration methods by sharing information along acquisition time
In addition to parallel imaging, other methods use the intrinsic spatio-temporal properties of the object of interest to accelerate the acquisition. In cardiac imaging, the temporal variation ofk-space signal is more important at the center ofk-space than at edges. Here the idea was to sample high frequencies less often than low frequencies.
One of the first method exploiting that property was the keyhole method [24,25]. As illustrated in part a of figure1.9, a reference scan is first acquired to have a complete spatial resolution of the object and the subsequent acquisitions only contain a reduced number of k-space line (the keyhole views) located around the k-space center. BRISK [26] is an extension of keyhole method that relies on the same principle but that spreads the acquisition of the high resolution image over several phases (see figure1.9, b).
Figure 1.9 — Scheme of the Keyhole (a) and BRISK (b) methods. By combining informations acquired over several phases the completek-space can be recovered.
Acceleration method by filtering of x−f space
The UNFOLD method was proposed by Madore et al. [27] and uses some of the principles of parallel imaging however without phased array coils. Indeed, like in parallel imaging, only a fraction1/Rof k-space line is acquired but the recovery of missing information is done by using the temporal infor- mation instead of the signal provided by the different coil elements. Whereas UNFOLD is limited to 2-fold acceleration (or 4-fold in particular conditions), the method presented by Tsao et al. [28], referred to as k−tBLAST, offers larger acceleration factors. For example an 8 times acceleration factor could be reached in cardiac application [29]. Figure1.10illustrates the underlying principle of these methods.
If we consider a fully sampledk-space and observe one line profile over time (parta of figure 1.10), we notice that one part of the field of view is nearly stationary (chest wall, liver, ...) whereas the other part moves periodically (heart). So, when we plot the Fourier transform of the profile into the time direction we end up with an area with a relatively thin bandwidth (the no-moving structures) and an area with a broad bandwidth (corresponding to myocardium).
Now, ifk−tspace is undersampled by acquiring for example only odd lines at odd time points and even lines at even time points, we will end up with an aliasing artifact in the corresponding images with structures lying on top of each other (partbof figure1.10). Correspondingly, thex−f space (x refers to a spatial dimension that can be either x or y axis) exhibits replications of the main temporal spectra. However, the replications are not overlapping and are located such that a simple filtering at edges ofx−f space is sufficient to recover the main information of the data, and thus to eliminate aliasing of the image. This corresponds to the UNFOLD method.
Figure 1.10— From left to right, scheme of thek−tspace sampling (to save visibility only a selection of line are plotted), one image of the time serie, white line profile in function of time denoted asy−tspace, result of the Fourier transform in temporal direction,y−f space. (a) represents the case of a fullk-space sampling, (b) represents the 2-fold undersampling with an alternation of line acquired in function of time, (c) represents a 5-fold undersampledk-space, also with an alternation of line acquired in function of time.
When going further with the undersampling (part c of figure 1.10), the replications are now overlapping in x−f space and a simple filtering is therefore not possible. However, with the help of training data, one can recover the global shape of the main temporal spectra and extract it in x−f space in order to remove the aliasing artifacts. The determination of this specific filter is the particularity of the k−tBLAST method. Following equation1.15, the signal can be expressed slightly differently with a matrix formulation:
s=Eρx−f, (1.16)
where s is the acquired signal in k−t space, ρx−f is the image in x−f space and E is the transformation between signal and image. The imageρx−f can be initialized with non-zero values, it is therefore expressed as:
ρx−f = ¯ρx−f+W q, (1.17)
whereW is a weighting matrix andqis a solution of the following constrained minimization problem:
arg minks−EW qk22 (1.18)
k−tBLAST algorithm is then given by the following regularized minimization problem:
arg minks−Eρ¯x−f−EW qk22+λkqk22 (1.19) These two methods can be combined with parallel imaging to further increase the acceleration factor, UNFOLD was coupled with SMASH in [30] and also with SENSE (referred to as TSENSE) in [31] whereask−tBLAST was enlarged tok−tSENSE to benefit from the signal given by multiple coils [28].
1.3.3 Viability measurement
Even if cine imaging gives a good evaluation of myocardial anatomy and function, a complementary tool is necessary to accurately evaluate the extent of myocardial diseases such as infarction [32].
Indeed, T1 of infarcted tissue was shown to vary slightly when compared to normal myocardium [33], but the difference is difficult to highlight with classical sequences. Thus the use of contrast agents was introduced in order to enhance the contrast between different areas, for example infarction and viable myocardium.
Contrast agents
The most widely used contrast agents are derived from the Gd3+ ion. This paramagnetic ion re- duces dramatically theT1andT2of its surroundings but is nevertheless highly toxic in its ionic form [34]. All approved contrast agents are thus chelates of Gd3+ that have different pharmacological properties. There are two broad categories of chelates, the macrocyclic molecules, where Gd3+ is caged into the ligand, and the linear molecules. Two examples of contrast agents routinely used in clinics are Gd-DOTA (Dotarem, cyclic molecule) and Gd-DTPA (MagnevistR , linear molecule)R [35]. However, one severe adverse effect of Gd3+ chelate administration in patients is nephrogenic systemic fibrosis (NFS). This potentially fatal complication is more likely to occur in patients with a high degree renal impairment. It has been shown that the administration of some linear molecule chelates induced NFS whereas it was not the case with cyclic molecule chelates [35].
The use of Gd3+ chelates for infarction visualization mainly relies on the fact that this extra- cellular contrast agent accumulates in the interstitial space that is enlarged in infarction but not in viable tissue, it can also be trapped in scar tissue due to high concentration of collagen fibers producing a signal enhancement due to the increased transit time in this area. Other types of Gd3+
chelates were specifically designed for other purposes, one example is intravascular agent that was used to assess microvascular flow [36].
Even if Gd3+ chelates are the most routinely used in clinics, they have some limited specificity that can be overcome with other types of paramagnetic ions. Indeed, intracellular contrast agents have a great potential for molecular imaging. Mn2+ was quickly recognized as an efficient agent to assess cellular viability in ischemia-induced injuries (in heart but also in brain [37]) and, after being validated for hepatic dysfunction assessment [38], its chelate Mn-DPDP was even recognized as a viability marker in patients with a myocardial infarction [39]. A part of this thesis was devoted to the investigation of Mn2+-enhanced infarction quantification.
Super-paramagnetic iron oxide particules (SPIO) have been used to label specific cells such as macrophages in the context of tumor staging or lymph node detection [40]. The in vivo labeling of macrophages with SPIO was even able to show their mobilization to myocardial infarction [41].
Compared to Gd3+ and Mn2+ that modify the T1 relaxation of the surrounding tissue, here the contrast mechanism is different since the iron acts on the T2∗ relaxation by creating local field inhomogeneities.
T1-weighted sequence
T1 contrast between viable myocardium and infarction is usually measured with sequences that are very sensitive to T1 variations [16]. The most commonly used is the inversion-recovery sequence, where the magnetization is first inverted with a 180◦rf pulse. The time between the inversion and the signal acquisition is called the inversion time T Iand is usually set to the time corresponding to a null signal in the viable myocardium, since this was shown to give the best contrast [42]. Figure 1.11 shows the signal recovery of infarction and viable myocardium signal intensity. The contrast between these two areas depends mainly on theT Ichosen, we observe that infarction first appears as a hypointense signal compared to viable myocardium for smallT I, whereas this contrast is inverted for longer T I. A major improvement of this sequence is the used of phase information to recover the sign of the longitudinal magnetization, this sequence is referred to as Phase-Sensitive Inversion Recovery, PSIR [43]. With this sequence, infarcted myocardium always appear as an hyperintense signal compared to normal myocardium and the contrast has the advantage of being less dependent of the choice ofT I. This is illustrated in the lower part of figure1.11.
Figure 1.11 — Longitudinal magnetization relaxation in inversion recovery sequence for infarction (MI) and viable myocardium (normal). (a) Acquired signal is proportional to the absolute value of longitudinal magnetization, depending on the timing of the acquisition the contrast between MI and viable can be either negative, null (around 220 ms) or positive. (b) With PSIR sequence the original sign of the magnetization is recovered, the contrast is less dependent of theT Ichosen and is exclusively positive.
1.3.4 Translational research
Improvement of Human health is often done by translating the “bench” discoveries into the clinical context, or ”bedside”. This “from bench-to-bedside” view of research, also referred to as “transla- tional research” is clearly a two ways relation. Indeed, clinics can benefit from drug discoveries or tool improvements and the new observations made in patients or existing clinical “gold standards”
then guide the fundamental research.
Translational research is the main motivation of developing small animal imaging onto clinical MR scanners. In fact, tools developed on rodent experiments can be directly applied to human studies since the same system is used. Similarly, recent developments in sequence design giving access to advanced tools in clinics can directly be used in the context of fundamental research without having to implement them on the experimental system. Another motivation is the limited availability of dedicated scanners to most institutions, that makes small animal imaging on clinical systems a more widely considered alternative [44].
1.4 Aim of the project
Nowadays, an important part of MRI research is devoted to the development of molecular imag- ing, nevertheless, complete cardiac assessment still consists of two main measurements. One is function assessment to detect the contraction abnormalities, the other one is viability assessment, giving insight into the precise extent and location of ischemic myocardial injuries. Several technical challenges remain both in functional and in viability imaging. In a translational approach to the problem, the aim of this work was to develop new techniques to provide an efficient characterization of the myocardium using tools provided by MR physics and image processing, from mice to patients.
The manuscript is divided into two main parts. The first one presents techniques developed for small animal viability and function imaging. The second part of is dedicated to spiral imaging and its application to real-time in humans.
Small animal imaging (from bench...)
17
Manganese-
enhanced MRI
in mice 2
Part of this chapter has been published in: Delattre et al., “Myocardial infarction quantification with Manganese-Enahnced MRI (MEMRI) in mice using a 3T clinical scanner” [45]
The two following chapters aim at presenting the challenges of cardiac imaging of mice on a clinical 3T scanner. Function parameters measurements such as end-diastolic, end-systolic volumes (EDV and ESV respectively) and ejection fraction (EF) as well as tissue characterization are needed to evaluate infarction extension. The choice to perform small animal studies on a clinical scanner is motivated by a translational research approach as it was presented in chapter1(p15).
2.1 Manganese as a contrast agent
In cardiac magnetic resonance imaging (MRI), extracellular contrast agents such as Gadolinium (Gd3+) chelates, are now routinely used in clinical practice, as well as in research protocols to as- sess myocardial perfusion or interstitial space remodeling [46–48]. Gd3+ is paramagnetic and thus shortens the T1 of the surrounding environment. It therefore enhances the contrast between two areas of interest that were initially difficult to discriminate with conventional sequences, as it is the case for infarcted and viable myocardial tissues for example.
The main limitation of Gd3+-based contrast agents is that the usually used chelates are non- specific (see chapter1p12). Even if they have proven their accuracy to depict the infarction volume, the measurement of the hyperintensity related to the presence of Gd3+ stays an indirect method of viability assessment and is restricted to models where the region of interest corresponds to an extracellular space. This is not the case in stunning for example where cells are not contracting but with a membrane still intact. This limitation is the main motivation for the use of more specific contrast agents in cardiac MRI.
Intracellular MR contrast agents can provide additional information on the cellular ions ex- changes. Manganese ion (Mn2+) was quickly recognized as an efficient MR contrast agent as it
19
induces a strong T1 shortening effect [49] to surrounding tissues. It was then successfully applied to activity detection in the brain and in the heart [37], and recently to pancreaticβ-cells, where the loss of function is involved in diabetes pathologies [50]. Even if all the transportation mechanisms are not known [51, 52], it is admitted that Mn2+ enters cardiomyocytes mainly by L-type voltage dependant Calcium (Ca2+) channels and stays into the cells for hours [53]. As an analog of the Ca2+ ion, Mn2+ should therefore has the potential to assess Ca2+ homeostasis in-vivo, generating an important interest for researchers [54]. Indeed, Ca2+ cycling is of vital importance to cardiac cell function and plays an important role in ventricular dysfunction such as heart failure [55]. In opposition to clinically used Gd3+-based chelate (Gd-DOTA or Gd-DTPA [35]), T1 shortenning induced signal is therefore depicting viable cells or indeed cells where Ca2+ influx is present.
The potential ofM n2+to depict cardiomyocyctes activity has been shown in presence of dobu- tamine and diltiazem (which are known to respectively increase and decrease Ca2+ influx into the heart). T1-weighted images showed respectively an increased and a decreased Mn2+-induced sig- nal intensity (SI) with the addition of those drugs compared with Mn2+ only injection [56]. In the context of cardiac pathologies, a reduced Mn2+accumulation has also been observed in stunned car- diomyocytes [57] as well as in the zone adjacent to a myocardial infarct [54]. Manganese-Enhanced MRI (MEMRI) has also been successfully used to assess myocardial infarction in various animal models from pig to rat [53, 58–61]. In all these studies, it was shown that Mn2+ was retained into viable cells allowing an accurate visualization of the infarction area. These results tend to support the hypothesis that Mn2+is a good marker of cell viability, however the role of perfusion and protein binding into Mn2+ distribution in viable and infarcted compartments are not fully understood.
Only a few studies, however, investigated the possible use of MEMRI for myocardial infarction assessment in mice [54, 62]. These studies used a model of permanent coronary occlusion where infarct size determined by triphenyltetrazolium chloride (TTC) at 7 days was linearly correlated to the infarct size measured from MEMRI [62]. However, a lower SI, suggesting a decreased Mn2+
accumulation was also observed in the peri-infarct area where ischemic tissue may also be present [54]. The type of coronary occlusion, as well as the timing of examination after the induction of the myocardial injury, may also impact MEMRI experiments. Gadolinium chelates (Gd-DTPA or Gd- DTPA-BMA) were largely used for assessment of myocardial infarction in mouse models [46,63,64].
However, it revealed some disadvantages over Mn2+contrast enhancement that are presented below:
Timing: The time window during which assessment is possible is relatively short with Gd3+
(contrast agent is visible during approximately 1 hour [63]) compared with Mn2+ where ions can stay in mitochondria for several hours [54]. Performing infarct quantification too early with Gd3+ can lead to an overestimation of the infarct zone that restrains again this time window between 20 and 60 min after injection [63–65]. From this point of view, Mn2+ allows more flexibility.
Accuracy: Mn2+ makes the viable part of myocardium appear bright (with the usual sequences used) which allows an easier segmentation of the myocardium and the infarct pattern than with Gd3+. In the latter case, parameters of the sequence are often chosen to null myocardium signal prior to contrast agent addition in order to maximize the contrast between viable myocardium and enhanced infarction [66]. As the Gd3+ induced SI decreases during the acquisition, care must be taken to adapt the TI according to this decrease otherwise the size of the non-viable zone will appear to decrease with time [53]. The accuracy of infarction delineation is thus hardly dependent on the timing of imaging while it is less the case for Mn2+.
