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2. Image Registration

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2. Image Registration ΠΑΝΕΠΙΣΤΗΜΙΟ ΠΑΤΡΩΝ ΔΙΑΤΜΗΜΑΤΙΚΟ ΠΡΟΓΡΑΜΜΑ ΜΕΤΑΠΤΥΧΙΑΚΩΝ ΣΠΟΥΔΩΝ ΣΤΗΝ ΙΑΤΡΙΚΗ ΦΥΣΙΚΗ ΑΞΙΟΛΟΓΗΣΗ ΑΛΓΟΡΙΘΜΩΝ ΑΝΤΙΣΤΟΙΧΙΣΗΣ ΕΙΚΟΝΑΣ ΣΤΗΝ ΥΠΟΛΟΓΙΣΤΙΚΗ ΤΟΜΟΓΡΑΦΙΑ ΠΟΛΛΑΠΛΩΝ ΑΝΙΧΝΕΥΤΩΝ ΘΩΡΑΚΑ ΔΗΜΗΤΡΙΟΥ ΔΗΜΗΤΡΙΟΣ ΜΕΤΑΠΤΥΧΙΑΚΗ ΔΙΠΛΩΜΑΤΙΚΗ ΕΡΓΑΣΙΑ 2015 UNIVERSITY OF PATRAS INTERDEPARTMENTAL PROGRAM OF POSTGRADUATE STUDIES IN MEDICAL PHYSICS EVALUATION OF IMAGE REGISTRATION ALGORITHMS IN THORAX MDCT DIMITRIOU DIMITRIOS MSc THESIS 2015 THREE MEMBER EXAMINING COMMITTEE Professor, Lena Costaridou (Supervisor) Associate Professor, Christina Kalogeropoulou Associate Professor, Ioannis Pratikakis ΤΡΙΜΕΛΗΣ ΕΞΕΤΑΣΤΙΚΗ ΕΠΙΤΡΟΠΗ Καθηγήτρια, Ελένη Κωσταρίδου (Επιβλέπουσα) Αναπληρώτρια Καθηγήτρια, Χριστίνα Καλογεροπούλου Αναπληρωτής Καθηγητής, Ιωάννης Πρατικάκης Ευχαριστίες Τις βαθύτερες ευχαριστίες μου στην επιβλέπουσα της παρούσας μεταπτυχιακής εργασίας κ. Κωσταρίδου Ελένη, Καθηγήτρια Ιατρικής Φυσικής του Πανεπιστήμιου Πατρών για την σημαντική ευκαιρία την οποία μου έδωσε να εργαστώ σε ένα τόσο ενδιαφέρον και σύγχρονο θέμα ιατρικής απεικόνισης. Η επιστημονική της καθοδήγηση και η ηθική της υποστήριξη συντέλεσε τα μέγιστα στην ολοκλήρωση της όλης ερευνητικής προσπάθειας. Ευχαριστώ θερμά τον κ. Πρατικάκη Ιωάννη, Αναπληρωτή Καθηγητή Ηλεκτρολόγων Μηχανικών & Μηχανικών Υπολογιστών του Δημοκρίτειου Πανεπιστημίου Θράκης, καθώς και την κ. Καλογεροπούλου Χριστίνα, Αναπληρώτρια Καθηγήτρια του τμήματος Ιατρικής Πανεπιστημίου Πατρών για τη συμμετοχή τους στη τριμελή επιτροπή. Ιδιαίτερα ευχαριστώ τον κ. Βλαχόπουλο Γεώργιο, υποψήφιο Διδάκτορα Ιατρικής Φυσικής για την πολύτιμη συνεισφορά του στο πειραματικό μέρος και στη γενικότερη επιστημονική καθοδήγηση και συνεισφορά του, καθώς επίσης και ηθική υποστήριξη για την ολοκλήρωση αυτής της ερευνητικής προσπάθειας. Abstract The follow-up of disease progression is one of the most challenging tasks in CT lung analysis. The main image analysis method behind the development of automated tools for image based quantification and estimation of disease progression is image registration. Alterations in lung field radiological appearance, caused by disease progression in the lung, as well as the elastic nature of lung tissue, challenge registration of disease affected lung fields. In this thesis, lesions associated to Interstitial Lung Disease (ILD) are considered. These lesions are characterized by diffuse lung appearance alterations. Specifically, the selection of the evaluation of registration algorithms suitable for ILD quantification is critical for a registration scheme. A registration scheme consists of the following basic components; the transformation (or deformation model), the cost function (or matching criteria or similarity metric) and the optimizer. In this study the effect of two registration optimizers, the Quasi-Newton (QS) and the Simultaneous Perturbation (SP), is assessed. A pilot clinical data set was analyzed consisting of 5 pairs of MDCT scans corresponding to 5 patients diagnosed with ILD secondary to connective tissue diseases, at two different instances, abstaining in time approximately two years. All patients were scanned with a 16-row multidetector CT (MDCT) scanner (GE Lightspeed 16, General Electric Medical Systems, Milwaukee, WI) at 120 kVp, rotation time of 0.5 s, automatic modulation of mA, collimation thickness of 16 × 0.625 mm, and slice thickness of 1.25 mm, using a protocol obtaining volumetric 3D data at full inspiration, in supine position. Each scan volume comprised of approximately 200–250 slices/patient. The mean volume CT dose index and the mean dose-length product, provided by CT application panel, were 11.2 mGy and 266.6 mGy cm, respectively. Assuming 0.017 mSv/mGy cm for a standard chest CT examination, the effective radiation dose for the volumetric chest CT protocol used was 4.5 mSv, complying with European Working Group for Guidelines on Quality Criteria in CT. It is proposed in literature that a spatial multiresolution procedure from coarse to fine image resolution can be used in the registration in order to improve speed, accuracy and robustness. The basic idea of multiresolution is that registration is first performed at a coarse scale where the images have much fewer pixels, which is fast and can help eliminate local optima. In that case, we talk about a “pyramid”. In this study we use a 4-level resolution pyramid, performing rigid transformation for the first three levels (for computational time considerations) and non-rigid transformation for the 4th level. The registration schemes were performed in the frame of elastix, which is an open source software based on the Insight Segmentation and Registration Toolkit (ITK). A total of 64 different registration schemes were generated by considering all possible combinations among: 2 different types of pyramids (Shrinking, which applies no smoothing, but only down-sampling by a factor of 2 in all three dimensions, and Gaussian Smoothing, which applies smoothing and down-sampling by a factor of 2 in all three dimensions), 4 different cost functions (Sum of Square Differences – SSD, Normalized Correlation Coefficient – NCC, Mutual Information – MI and Normalized Mutual Information – NMI), 4 different types of transforms for the first three levels (Euler, Similarity, Affine and 3rd order B-Splines) in order to obtain a coarse initial alignment, while in all cases for the 4th level, corresponding to the highest image resolution, the 3rd order B-Spline transform was selected for refinement purposes. Finally, 2 different types of optimizers were considered (Quasi-Newton (QS) and Simultaneous Perturbation (SP)) in order to compare their performances and evaluate their effect to registration accuracy. The registration schemes are evaluated using two distance metrics, the distance between corresponding points: (a) in ILD affected regions and (b) in normal lung parenchyma (NLP). Prior to evaluation, the registered lung volumes were prescreened for exclusion of schemes introducing folding regions. Irregular deformations were assessed in terms of the determinant of the Jacobian matrix of each voxel of registered volumes (deformation field). Registration schemes whose determinants of Jacobian matrices include negative values are excluded for subsequent analysis, since such areas correspond to singularities of deformation fields (foldings). In order to estimate registration distance error, 2 sets of landmark points were generated, corresponding to 2 different types of tissue in the lung fields: NLP and ILD affected tissue. In order to identify normal parenchyma lung tissue, an ILD region segmentation algorithm was applied to create binary masks corresponding to ILD affected and normal parenchyma regions. By considering the optimal schemes, 16 of 32 registration schemes were initially selected. These schemes obtained submillimeter registration accuracies in terms of average distance errors; 0.33 ± 0.01 mm for NLP and 0.33 ± 0.01 mm for ILD affected regions in the case of Quasi-Newton optimizer, 0.49 ± 0.09 mm for NPL and 0.48 ± 0.05 mm for ILD affected regions in the case of Simultaneous Perturbation. Best performance was achieved by the registration scheme using: the Gaussian smoothing pyramid, the Affine Transform for the first 3 resolution levels and the 3rd order B-Spline for the 4th resolution level, the Mutual Information Cost Function and the Quasi-Newton Optimizer. Non-parametric Wilcoxon signed-rank test indicates that there was statistical significant better performance in the case of Quasi-Newton compared to performances achieved using the Simultaneous Perturbation optimizer. Future efforts should include different tuning of the optimizer parameters, as well as regularization terms which prevent folding areas, in order to achieve higher registration accuracies. In addition, future steps should include constraints on full nonrigid schemes, that will not allow irregular deformations. Taking into account that registration is a fundamental subsystem of an integrated quantification and follow-up system, future efforts should also focus on integrate the quantification and registration system in order to create an integrated monitoring system of the ILD. Περίληψη Η παρακολούθηση της εξέλιξης της νόσου είναι μία από τις πιο απαιτητικές εργασίες στην ανάλυση της αξονικής τομογραφίας πνεύμονα. Η κύρια μέθοδος ανάλυσης εικόνας, πίσω από την ανάπτυξη αυτοματοποιημένων εργαλείων με υποβοήθηση εικόνας για την ποσοτικοποίηση και την εκτίμηση της εξέλιξης της νόσου, είναι η αντιστοίχιση εικόνας (image registration). Οι μεταβολές στο πνευμονικό πεδίο που προκαλούνται από διάχυτη εξέλιξη της νόσου, καθώς και η ελαστική φύση του πνευμονικού ιστού, δυσχεραίνουν την αντιστοίχιση (registration). Σε αυτή την εργασία μελετώνται περιοχές του πνεύμονα που έχουν προσβληθεί από διάμεση νόσο (ΔΝΠ-ILD). Αυτές οι περιοχές χαρακτηρίζονται από διάχυτες μεταβολές στην όψη του πνεύμονα. Η αξιολόγηση των αλγορίθμων αντιστοίχισης στην ποσοτικοποίηση της ΔΝΠ είναι κρίσιμη για την αξιόπιστη επιλογή ενός σχήματος αντιστοίχισης (registration scheme). Ένα σχήμα αντιστοίχισης εικόνας αποτελείται από τα παρακάτω: τον μετασχηματισμό, την συνάρτηση κόστους (cost function) και την μέθοδο βελτιστοποίησης (optimizer). Σε αυτή την εργασία εξετάζεται η επίδραση δύο ειδών μεθόδων βελτιστοποίησης αντιστοίχισης εικόνας, ο Quasi-Newton (QS) και ο Simultaneous Perturbation (SP). Χρησιμοποιήθηκαν πιλοτικά κλινικά δεδομένα που αποτελούνται από 5 ζεύγη λήψεων από αξονικό τομογράφο πολλαπλών τομών (MDCT) και αντιστοιχούν σε 5 ασθενείς (περίπου 5x250 τομές) οι οποίοι έχουν διαγνωσθεί
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