Analysis of Observer Performance in Unknown-Location Tasks for Tomographic Image Reconstruction

Our goal is to optimize regularized image reconstruction for emission tomography with respect to lesion detectability in the reconstructed images. We consider model observers whose decision variable is the maximum value of a local test statistic within a search area. Previous approaches have used simulations to evaluate the performance of such observers. We propose an alternative approach, where approximations of tail probabilities for the maximum of correlated Gaussian random fields facilitate analytical evaluation of detection performance. We illustrate how these approximations, which are reasonably accurate at low probability of false alarm operating points, can be used to optimize regularization with respect to lesion detectability.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85914/1/Fessler33.pd

Efficient Calculation of Resolution and Covariance for Penalized-Likelihood Reconstruction in Fully 3-D SPECT

Resolution and covariance predictors have been derived previously for penalized-likelihood estimators. These predictors can provide accurate approximations to the local resolution properties and covariance functions for tomographic systems given a good estimate of the mean measurements. Although these predictors may be evaluated iteratively, circulant approximations are often made for practical computation times. However, when numerous evaluations are made repeatedly (as in penalty design or calculation of variance images), these predictors still require large amounts of computing time. In Stayman and Fessler (2000), we discussed methods for precomputing a large portion of the predictor for shift-invariant system geometries. In this paper, we generalize the efficient procedure discussed in Stayman and Fessler (2000) to shift-variant single photon emission computed tomography (SPECT) systems. This generalization relies on a new attenuation approximation and several observations on the symmetries in SPECT systems. These new general procedures apply to both two-dimensional and fully three-dimensional (3-D) SPECT models, that may be either precomputed and stored, or written in procedural form. We demonstrate the high accuracy of the predictions based on these methods using a simulated anthropomorphic phantom and fully 3-D SPECT system. The evaluation of these predictors requires significantly less computation time than traditional prediction techniques, once the system geometry specific precomputations have been made.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85992/1/Fessler54.pd

Analysis of Observer Performance in Known-Location Tasks for Tomographic Image Reconstruction

We consider the task of detecting a statistically varying signal of known location on a statistically varying background in a reconstructed tomographic image. We analyze the performance of linear observer models in this task. We show that, if one chooses a suitable reconstruction method, a broad family of linear observers can exactly achieve the optimal detection performance attainable with any combination of a linear observer and linear reconstructor. This conclusion encompasses several well-known observer models from the literature, including models with a frequency-selective channel mechanism and certain types of internal noise. Interestingly, the "optimal" reconstruction methods are unregularized and in some cases quite unconventional. These results suggest that, for the purposes of designing regularized reconstruction methods that optimize lesion detectability, known-location tasks are of limited use.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85959/1/Fessler48.pd

Μελέτη ανιχνευσιμότητας Μονήρη Πνευμονικού Όζου σε εικόνα Αξονικής (CT) & Ποζιτρονικής (ΡΕΤ) τομογραφίας με χρήση μοντέλων προσομοίωσης

Σκοπός της διπλωματικής εργασίας αποτελεί η ανάπτυξη μιας μεθοδολογίας για τη δημιουργία ενός μοντέλου προσομοίωσης του Μονήρη Πνευμονικού Όζου (ΜΠΟ) σε εικόνες αξονικής τομογραφίας (CT) και τομογραφίας εκπομπής ποζιτρονίων (PET). Η ανάπτυξη του μοντέλου προσομοίωσης του ΜΠΟ υλοποιήθηκε με μεθόδους προσομοίωσης Monte Carlo (MC), λαμβάνοντας υπόψη τα εξωτερικά μορφολογικά γνωρίσματα, τα εσωτερικά χαρακτηριστικά και τo βαθμό πρόσληψης του ραδιοφαρμάκου (Standardized Uptake Value – SUV), όπως αυτά περιγράφονται στη διεθνή βιβλιογραφία. Με τη δημιουργία του μοντέλου σχηματίστηκαν ρεαλιστικές εικόνες διαφόρων τύπων προσομοιωμένων ΜΠΟ που ενσωματώθηκαν εντός πρωτογενών δεδομένων από 5 διαφορετικούς ασθενείς σε τομές εικόνας CT και PET. Τα δεδομένα που χρησιμοποιήθηκαν ελήφθησαν με το τομογράφο PET/CT του τμήματος Πυρηνικής Ιατρικής του ΙΙΒΕΑΑ και για την ανακατασκευή των εικόνων χρησιμοποιήθηκε το ανοιχτό λογισμικό STIR (http://stir.sourceforge.net). Για την τελική αξιολόγηση του μοντέλου πραγματοποιήθηκε μελέτη παρατηρητών από τρεις ανεξάρτητους ιατρούς. Συνολικά παράχθηκαν 80 προσομοιωμένοι ΜΠΟ, με το ποσοστό καλοήθειας και κακοήθειας να είναι 80% και 20% αντίστοιχα. Επίσης επιλέχθηκαν 20 αληθινά περιστατικά με διάγνωση ΜΠΟ, όπου τα 14 από αυτά ήταν καλοήθη και τα 6 κακοήθη. Μεταξύ των 120 περιστατικών που δημιουργήθηκαν, 25 επανελήφθησαν ώστε να διαπιστωθεί η συνέπεια των τριών παρατηρητών. Από τους παρατηρητές ζητήθηκε αρχικά να εντοπίσουν τον ΜΠΟ, στη συνέχεια να τον χαρακτηρίσουν ως προσομοιωμένο ή αληθινό και τέλος να τον ταξινομήσουν ως πιθανώς καλοήθη ή πιθανώς κακοήθη. Σύμφωνα με τα αποτελέσματα, περισσότεροι από 50% των προσομοιωμένων όζων δεν μπορούσαν να διαχωριστούν και χαρακτηρίστηκαν από τους παρατηρητές ως αληθινοί. Όσον αφορά την κατάταξη των όζων σε πιθανώς καλοήθη ή πιθανώς κακοήθη, η ταξινόμηση των προσομοιωμένων όζων ήταν σε συμφωνία με την ταξινόμηση των τριών ανεξάρτητων ιατρών. Αξίζει τέλος να αναφέρουμε ότι 28 περιστατικά χαρακτηρίστηκαν και από τους τρεις παρατηρητές ως αληθινά, όντας προσομοιωμένα.The purpose of this thesis is the development of a method for the modeling of Solitary Pulmonary Nodule (SPN) in Computed Tomography (CT) and Positron Emission Tomography (PET) images. The modeling of SPN was implemented by Monte Carlo simulation methods taking into consideration its morphological characteristics, internal features and Standardized Uptake Value (SUV) activity distribution. With this method, realistic images of various types of simulated SPNs were generated and embedded into raw data acquired from 5 different patients into CT and PET slices. The raw data were acquired using a clinical PET/CT scanner of the Nuclear Medicine Department of BBRFA, and for image reconstruction the software open STIR was used (http://stir.sourceforge.net). For the final validation of the model, an observer study from three independent medical experts was performed. A total of 80 simulated SPNs were produced and in this quantity, the percentage of benignity and malignancy was 80% and 20% respectively. Furthermore, 20 real cases with SPN were selected, 14 of them were benign and 6 malignant. Among the 120 cases, 25 of them were repeated in order to check the consistency of the observers. The reviewers were asked to localize the SPN, then to characterize the lesion as simulated or real and finally to classify it as probably benign or probably malignant. According to the results, more than 50% of the simulated lesions could not be differentiated from the real ones lesions and were designated as real. Regarding the classification of the nodules to probably benign or probably malignant, we noticed that the simulated class was consistent with the observers’ classification. Finally, it’s worth to be noted that, in 28 cases, all of the three observers designated the simulated lesion as real