<p>The cross-sectional images of computed tomography (CT) are calculated from a series of projections using reconstruction methods. Recently introduced on clinical CT scanners, iterative reconstruction (IR) method enables potential patient dose reduction with significantly reduced image noise, but is limited by its "waxy" texture and nonlinear nature. To balance the advantages and disadvantages of IR, evaluations are needed with diagnostic accuracy as the endpoint. Moreover, evaluations need to take into consideration the type of the imaging task (detection and quantification), the properties of the task (lesion size, contrast, edge profile, etc.), and other acquisition and reconstruction parameters. </p><p>To evaluate detection tasks, the more acceptable method is observer studies, which involve image preparation, graphical user interface setup, manual detection and scoring, and statistical analyses. Because such evaluation can be time consuming, mathematical models have been proposed to efficiently predict observer performance in terms of a detectability index (d'). However, certain assumptions such as system linearity may need to be made, thus limiting the application of the models to potentially nonlinear IR. For evaluating quantification tasks, conventional method can also be time consuming as it usually involves experiments with anthropomorphic phantoms. A mathematical model similar to d' was therefore proposed for the prediction of volume quantification performance, named the estimability index (e'). However, this prior model was limited in its modeling of the task, modeling of the volume segmentation process, and assumption of system linearity.</p><p>To expand prior d' and e' models to the evaluations of IR performance, the first part of this dissertation developed an experimental methodology to characterize image noise and resolution in a manner that was relevant to nonlinear IR. Results showed that this method was efficient and meaningful in characterizing the system performance accounting for the non-linearity of IR at multiple contrast and noise levels. It was also shown that when certain criteria were met, the measurement error could be controlled to be less than 10% to allow challenging measuring conditions with low object contrast and high image noise.</p><p>The second part of this dissertation incorporated the noise and resolution characterizations developed in the first part into the d' calculations, and evaluated the performance of IR and conventional filtered backprojection (FBP) for detection tasks. Results showed that compared to FBP, IR required less dose to achieve a threshold performance accuracy level, therefore potentially reducing the required dose. The dose saving potential of IR was not constant, but dependent on the task properties, with subtle tasks (small size and low contrast) enabling more dose saving than conspicuous tasks. Results also showed that at a fixed dose level, IR allowed more subtle tasks to exceed a threshold performance level, demonstrating the overall superior performance of IR for detection tasks.</p><p>The third part of this dissertation evaluated IR performance in volume quantification tasks with conventional experimental method. The volume quantification performance of IR was measured using an anthropomorphic chest phantom and compared to FBP in terms of accuracy and precision. Results showed that across a wide range of dose and slice thickness, IR led to accuracy significantly different from that of FBP, highlighting the importance of calibrating or expanding current segmentation software to incorporate the image characteristics of IR. Results also showed that despite IR's great noise reduction in uniform regions, IR in general had quantification precision similar to that of FBP, possibly due to IR's diminished noise reduction at edges (such as nodule boundaries) and IR's loss of resolution at low dose levels. </p><p>The last part of this dissertation mathematically predicted IR performance in volume quantification tasks with an e' model that was extended in three respects, including the task modeling, the segmentation software modeling, and the characterizations of noise and resolution properties. Results showed that the extended e' model correlated with experimental precision across a range of image acquisition protocols, nodule sizes, and segmentation software. In addition, compared to experimental assessments of quantification performance, e' was significantly reduced in computational time, such that it can be easily employed in clinical studies to verify quantitative compliance and to optimize clinical protocols for CT volumetry.</p><p>The research in this dissertation has two important clinical implications. First, because d' values reflect the percent of detection accuracy and e' values reflect the quantification precision, this work provides a framework for evaluating IR with diagnostic accuracy as the endpoint. Second, because the calculations of d' and e' models are much more efficient compared to conventional observer studies, the clinical protocols with IR can be optimized in a timely fashion, and the compliance of clinical performance can be examined routinely.</p> / Dissertation

This thesis describes an automatic evaluation approach for estimating the quality of stereo displays and vision systems using image features. The method is inspired by the human visual system. Display of stereo images is widely used to enhance the viewing experience of three-dimensional (3D) visual displays and communication systems. Applications are numerous and range from entertainment to more specialized applications such as: 3D visualization and broadcasting, robot tele-operation, object recognition, body exploration, 3D teleconferencing, and therapeutic purposes. Consequently, perceived image quality is important for assessing the performance of 3D imaging applications. There is no doubt that subjective testing (i.e., asking human viewers to rank the quality of stereo images) is the most accurate method for quality evaluation. It reflects true human perception. However, these assessments are time consuming and expensive. Furthermore, they cannot be done in real time. Therefore, the goal of this research is to develop an objective quality evaluation methods computational models that can automatically predict perceived image quality) correlating well with subjective predictions that are required in the field of quality assessment. I believe that the perceived distortion and disparity of any stereoscopic display are strongly dependent on local features, such as edge (non-uniform) and non-edge (uniform) areas. Therefore, in this research, I propose a No-Reference (NR) objective quality assessment for coded stereoscopic images based on segmented local features of artifacts and disparity. Local feature information such as edge and non-edge area based relative disparity estimation, as well as the blockiness, blur, and the zero-crossing within the block of images, are evaluated in this method. A block-based edge dissimilarity approach is used for disparity estimation. I use the Toyama stereo images database to evaluate the performance and to compare it with other approaches both qualitatively and quantitatively.

<p>Image noise is a pervasive problem in medical imaging. It is a property endemic to all imaging modalities and one especially familiar in those modalities that employ ionizing radiation. Statistical uncertainty is a major limiting factor in the reduction of ionizing radiation dose; patient exposure must be minimized but high image quality must also be achieved to retain the clinical utility of medical images. One way to achieve the goal of radiation dose reduction is through the use of image post processing with noise reduction algorithms. By acquiring images at lower than normal exposure followed by algorithmic noise reduction, it is possible to restore image noise to near normal levels. However, many denoising algorithms degrade the integrity of other image quality components in the process. </p><p>In this dissertation, a new noise reduction algorithm is investigated: Correlated Polarity Noise Reduction (CPNR). CPNR is a novel noise reduction technique that uses a statistical approach to reduce noise variance while maintaining excellent resolution and a "normal" noise appearance. In this work, the algorithm is developed in detail with the introduction of several methods for improving polarity estimation accuracy and maintaining the normality of the residual noise intensity distribution. Several image quality characteristics are assessed in the production of this new algorithm including its effects on residual noise texture, residual noise magnitude distribution, resolution effects, and nonlinear distortion effects. An in-depth review of current linear methods for medical imaging system resolution analysis will be presented along with several newly discovered improvements to existing techniques. This is followed by the presentation of a new paradigm for quantifying the frequency response and distortion properties of nonlinear algorithms. Finally, the new CPNR algorithm is applied to computed tomography (CT) to assess its efficacy as a dose reduction tool in 3-D imaging.</p><p>It was found that the CPNR algorithm can be used to reduce x ray dose in projection radiography by a factor of at least two without objectionable degradation of image resolution. This is comparable to other nonlinear image denoising algorithms such as the bilateral filter and wavelet denoising. However, CPNR can accomplish this level of dose reduction with few edge effects and negligible nonlinear distortion of the anatomical signal as evidenced by the newly developed nonlinear assessment paradigm. In application to multi-detector CT, XCAT simulations showed that CPNR can be used to reduce noise variance by 40% with minimal blurring of anatomical structures under a filtered back-projection reconstruction paradigm. When an apodization filter was applied, only 33% noise variance reduction was achieved, but the edge-saving qualities were largely retained. In application to cone-beam CT for daily patient positioning in radiation therapy, up to 49% noise variance reduction was achieved with as little as 1% reduction in the task transfer function measured from reconstructed data at the cutoff frequency. </p><p>This work concludes that the CPNR paradigm shows promise as a viable noise reduction tool which can be used to maintain current standards of clinical image quality at almost half of normal radiation exposure This algorithm has favorable resolution and nonlinear distortion properties as measured using a newly developed set of metrics for nonlinear algorithm resolution and distortion assessment. Simulation studies and the initial application of CPNR to cone-beam CT data reveal that CPNR may be used to reduce CT dose by 40%-49% with minimal degradation of image resolution.</p> / Dissertation

This thesis describes an automatic evaluation approach for estimating the quality of stereo displays and vision systems using image features. The method is inspired by the human visual system. Display of stereo images is widely used to enhance the viewing experience of three-dimensional (3D) visual displays and communication systems. Applications are numerous and range from entertainment to more specialized applications such as: 3D visualization and broadcasting, robot tele-operation, object recognition, body exploration, 3D teleconferencing, and therapeutic purposes. Consequently, perceived image quality is important for assessing the performance of 3D imaging applications. There is no doubt that subjective testing (i.e., asking human viewers to rank the quality of stereo images) is the most accurate method for quality evaluation. It reflects true human perception. However, these assessments are time consuming and expensive. Furthermore, they cannot be done in real time. Therefore, the goal of this research is to develop an objective quality evaluation methods computational models that can automatically predict perceived image quality) correlating well with subjective predictions that are required in the field of quality assessment. I believe that the perceived distortion and disparity of any stereoscopic display are strongly dependent on local features, such as edge (non-uniform) and non-edge (uniform) areas. Therefore, in this research, I propose a No-Reference (NR) objective quality assessment for coded stereoscopic images based on segmented local features of artifacts and disparity. Local feature information such as edge and non-edge area based relative disparity estimation, as well as the blockiness, blur, and the zero-crossing within the block of images, are evaluated in this method. A block-based edge dissimilarity approach is used for disparity estimation. I use the Toyama stereo images database to evaluate the performance and to compare it with other approaches both qualitatively and quantitatively.

Since its introduction in 2004, the Structural Similarity (SSIM) index has gained widespread popularity as an image quality assessment measure. SSIM is currently recognized to be one of the most powerful methods of assessing the visual closeness of images. That being said, the Mean Squared Error (MSE), which performs very poorly from a perceptual point of view, still remains the most common optimization criterion in image processing applications because of its relative simplicity along with a number of other properties that are deemed important. In this thesis, some necessary tools to assist in the design of SSIM-optimal algorithms are developed. This work combines theoretical developments with experimental research and practical algorithms. The description of the mathematical properties of the SSIM index represents the principal theoretical achievement in this thesis. Indeed, it is demonstrated how the SSIM index can be transformed into a distance metric. Local convexity, quasi-convexity, symmetries and invariance properties are also proved. The study of the SSIM index is also generalized to a family of metrics called normalized (or M-relative) metrics. Various analytical techniques for different kinds of SSIM-based optimization are then devised. For example, the best approximation according to the SSIM is described for orthogonal and redundant basis sets. SSIM-geodesic paths with arclength parameterization are also traced between images. Finally, formulas for SSIM-optimal point estimators are obtained. On the experimental side of the research, the structural self-similarity of images is studied. This leads to the confirmation of the hypothesis that the main source of self-similarity of images lies in their regions of low variance. On the practical side, an implementation of local statistical tests on the image residual is proposed for the assessment of denoised images. Also, heuristic estimations of the SSIM index and the MSE are developed. The research performed in this thesis should lead to the development of state-of-the-art image denoising algorithms. A better comprehension of the mathematical properties of the SSIM index represents another step toward the replacement of the MSE with SSIM in image processing applications.

The focus of this thesis is the improvement of acquisition and processing of Mueller matrix polarimetry using a confocal scanning laser ophthalmoscope (CSLO) and the application of Mueller matrix polarimetry to image the retina. Stepper motors were incorporated into a CSLO to semi-automate Mueller matrix polarimetry and were used in retinal image acquisition. Success rates of Fourier transform based edge detection filters, designed to improve the registration of retinal images, were compared. The acquired polarimetry images were used to reassess 2 image quality enhancement techniques, Mueller matrix reconstruction (MMR) and Stokes vector reconstruction (SVR), focusing on the role of auto-contrasting or normalization within the techniques and the degree to which auto-contrasting or normalization is responsible for image quality improvement of the resulting images. Mueller matrix polarimetry was also applied to find the retardance image of a malaria infected retinal blood vessel imaged in a confocal scanning laser microscope (CSLM) to visualize hemozoin within the vessel. Image quality enhancement techniques were also applied and image quality improvement was quantified for this blood vessel. The semi-automation of Mueller matrix polarimetry yielded a significant reduction in experimental acquisition time (80%) and a non-significant reduction in registration time (44%). A larger sample size would give higher power and this result might become significant. The reduction in registration time was most likely due to less movement of the eye, particularly in terms of decreased rotation seen between registered images. Fourier transform edge detection methods increased the success rate of registration from 73.9% to 92.3%. Assessment of the 2 MMR images (max entropy and max signal-to-noise ratio (SNR)) showed that comparison to the best CSLO images (not auto-contrasted) yielded significant average image quality improvements of 158% and 4% when quantified with entropy and SNR, respectively. When compared to best auto-contrasted CSLO images, significant image quality improvements were 11% and 5% for entropy and SNR, respectively. Images constructed from auto-contrasted input images were of significantly higher quality than images reconstructed from original images. Of the 2 other images assessed (modified degree of polarization (DOPM) and the first element of the Stokes vector (S0)), DOPM and S0 yielded significant average image quality improvements quantified by entropy except for the DOPM image of the RNFL. SNR was not improved significantly when either SVR image was compared to the best CSLO images. Compared to the best auto-contrasted CSLO images, neither DOPM nor S0 improved average image quality significantly. This result might change with a larger number of participants. When MMR were applied to images of malaria infected retinal slides, image quality was improved by 19.7% and 15.3% in terms of entropy and SNR, respectively, when compared to the best CSLO image. The DOPM image yielded image quality improvements of 8.6% and -24.3% and the S0 image gave improvements of 9.5% and 9.4% in entropy and SNR, respectively. Although percent increase in image quality was reduced when images were compared to initial auto-contrasted CSLO images, the final image quality was improved when auto-contrasting occurred prior to polarimetry calculations for max SNR and max entropy images. Quantitative values of retardance could not be found due to physical constraints in the CSLM that did not allow for characterization of its polarization properties and vibrational noise. Mueller matrix polarimetry used to find the retardance image of a malaria infected retina sample did yield visualization of hemozoin within the vessel but only qualitatively. In conclusion, improvements in the acquisition and registration of CSLO images were successful in leading to considerably shorter experimentation and processing times. In terms of polarimetric image quality improvement techniques, when compared to the best CSLO image. A large proportion of the improvement was in fact due to partially or completely stretching the pixel values across the dynamic range of the images within the algorithm of each technique. However, in general the image quality was still improved by the Mueller matrix reconstruction techniques using both entropy and SNR to generate the CSLO retinal images and the CSLM imaged malaria infected sample. In the malaria sample, retinal blood vessel visualization was also qualitatively improved. The images yielded from Mueller matrix polarimetry applied to a malaria infected retinal sample localized hemozoin within the blood vessel, but a quantitative image of the phase retardance could not be achieved.

Indirect detection digital imaging systems used in medical imaging, compromises powder phosphor scintillators as X-ray to light converters. Powder phosphors should combine image quality and light output parameters in order to produce high quality diagnostic images, with the parallel dose reduction to the patient. Additionally, they must be characterized by short decay times in order to be used in digital breast tomosynthesis (DBT) and dual energy imaging (DE). The aim of the present PhD thesis is the investigation of the optimum powder phosphor scintillator for use in a CMOS based digital imaging system and the investigation of the combination of the digital imaging system with the optimum scintillator for low energy medical applications, like DBT. Scintillating screens, were prepared using the method of sedimentation, by Lu2SiO5:Ce, Gd2O2S:Eu and Gd2O2S:Tb powder phosphors. Their properties were evaluated by experimentally determining parameters related to optical signal intensity and distribution at the scintillator exit surface, characterizing medical image quality and the patient’s dose. By comparing the luminescence efficiency and image quality properties of Lu2SiO5:Ce, Gd2O2S:Eu and Gd2O2S:Tb, the scintillating screen with the optimum characteristics was defined and placed, in close contact with the CMOS photodiode. MTF and DQE of our CMOS sensor were found better at the whole spatial frequency range with previously published data for a passive CMOS sensor, while NNPS was comparable. The evaluated CMOS sensor is characterized by high spatial resolution and detection efficiency properties that make it suitable for DBT. Additionally, image quality is acceptable at low exposure levels, which is crucial in DBT and DE applications where high patient’s dose is a drawback for the establishment of these methods. / Τα ψηφιακά συστήματα ιατρικής απεικόνισης, έμμεσης ανίχνευσης, χρησιμοποιούν φωσφόρους σπινθηριστές ως μετατροπείς της ακτινοβολίας-Χ σε ορατό φως. Οι φώσφοροι σπινθηριστές πρέπει να συνδυάζουν χαρακτηριστικά ποιότητας εικόνας και απόδοσης σε φωταύγεια προκειμένου να παράγουν εικόνες υψηλής διαγνωστικής αξίας με τη παράλληλη ελάττωση της δόσης στον εξεταζόμενο. Επιπλέον πρέπει να έχουν μικρούς χρόνους απόσβεσης για χρήση σε συστήματα ψηφιακής τομοσύνθεσης (DBT) και απεικόνισης διπλής ενέργειας (DE). Σκοπός της παρούσας Διδακτορικής Διατριβής ήταν η διερεύνηση των βέλτιστων υλικών φωσφόρων σπινθηριστών για χρήση σε ολοκληρωμένο ψηφιακό απεικονιστικό σύστημα τύπου CMOS καθώς και η διερεύνηση των χαρακτηριστικών του συστήματος ψηφιακού ανιχνευτή/ βέλτιστου σπινθηριστή για ιατρικές εφαρμογές χαμηλών ενεργειών, όπως η DBT. Παρασκευάστηκαν, με τη μέθοδο της καθίζησης, φθορίζουσες οθόνες από υλικά σπινθηριστών όπως τα Lu2SiO5:Ce, Gd2O2S:Eu και Gd2O2S:Tb. Οι ιδιότητες τους μελετήθηκαν αξιολογώντας πειραματικά παραμέτρους οι οποίες εκφράζουν την ένταση και την κατανομή του παραγόμενου σήματος στην έξοδο του ανιχνευτή και σχετίζονται άμεσα με την ποιότητα της ιατρικής εικόνας αλλά και με τη δόση στον εξεταζόμενο. Στον ανιχνευτή τοποθετήθηκε, σε άμεση επαφή με τις φωτοδιόδους CMOS, φθορίζουσα οθόνη Gd2O2S:Tb, η οποία προσδιορίστηκε μέσω της σύγκρισης των ανωτέρω υλικών σε απόδοση φωταύγειας και ποιότητα εικόνας. Η απόδοση του CMOS ήταν καλύτερη εν συγκρίσει με δημοσιευμένα αποτελέσματα για αισθητήρα PPS CMOS, σε όλο το εύρος των χωρικών συχνοτήτων. Τα αποτελέσματα αυτά δείχνουν ότι ο υπό εξέταση ανιχνευτής τύπου CMOS έχει υψηλή διακριτική ικανότητα και ανιχνευτική αποδοτικότητα, κρατώντας παράλληλα χαμηλά επίπεδα θορύβου καθιστώντας τoν κατάλληλο για χρήση σε πρότυπο σύστημα (DBT). Επιπλέον βρέθηκε ότι η ποιότητα εικόνας δεν υποβαθμίζεται σε χαμηλά επίπεδα έκθεσης, στοιχείο που είναι σημαντικό για εφαρμογές (DBT) και (DE), όπου η αυξημένη δόση στον εξεταζόμενο είναι ανασταλτικός παράγοντας για τη καθιέρωση και ευρεία αποδοχή των συγκεκριμένων μεθόδων.

Mammography, either for screening or for the examination of a woman who displays symptoms of breast cancer, must be capable of revealing subtle differences in density and composition of breast parenchymal tissue, as well as the presence of any abnormalities. This is a challenging imaging task since connective tissue, glandular tissue, skin and fat must be simultaneously visualized and differentiated, while having very similar attenuation coefficients and thus low subject contrast. The detection of small low-contrast lesions is essential for screening mammography. Because the mammogram is a projected image of superimposed breast structures, it is generally more difficult to detect cancer in a dense breast pattern. This is attributed to minor differences in x-ray attenuation between lesions and the surrounding parenchymal. The visibility of small structures on a mammogram is restricted by the compromise between the image quality and the absorbed dose. This leads to a continuous drive for improved techniques, mainly with respect to improving the low contrast character of the technique. In the framework of this master thesis, the imaging performance of a wide range of mammographic spectra has been evaluated and their influence on imaging of inhomogeneities was studied. These spectra correspond to several combinations of anode (Mo, Rh, W) and filter (Mo, Rh, Nb, Zr Al, and Pd) materials and diameters (90–250 μm), and tube voltages (26-30 kVp), with 1 mm Be-window inherent filtration. A properly designed mathematical breast phantom was utilized, simulating water, containing two identical spherical inhomogeneities of air of various diameters. An earlier developed and validated Monte Carlo model (MASTOS), was utilized for the simulation studies. Utilizing the developed model, the influence of the factors affecting the x-ray spectrum (tube voltage, anode material, filter material) on the detectability of small low contrast lesions was investigated in terms of Contrast to Noise Ratio (CNR) and Contrast-Detail (CD) analysis. Results demonstrate that spectra mainly produced from W anodes and filtered with k-edge filters (Mo, Rh, Nb, Pd and Zr) provide improved imaging characteristics that is similar and in some cases better compared to commonly used Mo and Rh anodes filtered with k-edge filters, especially for inhomogeneities of larger diameters. However, in the case of inhomogeneities of smaller diameter softer spectra mainly produced from Mo anodes and spectra filtered with k-edge filters of low energy k-absorption edge provide better CNR. As far as the filter material is concerned, k-edge filters demonstrate improved imaging performance compared to Al filter. This is the reason that justifies the use of these filters in clinical mammographic procedure. Tube voltage has a limited effect on CNR for all inhomogeneity sizes. As a conclusion, the discriminability of low contrast details, in terms of CNR, depends on all the parameters affecting the x-ray spectrum, but especially on the combination of the anode material and filter. / Η μαστογραφία, είτε για απεικόνιση είτε για εξέταση γυναικών που παρουσιάζουν συμπτώματα καρκίνου του μαστού, πρέπει να είναι ικανή να αποκαλύπτει τόσο πολύ μικρές διαφορές στην πυκνότητα και σύσταση του μαστογραφικού παρεγχύματος, όσο και την παρουσία ανωμαλιών. Αυτή είναι μια αντικρουόμενη διαδικασία καθώς ο συνδετικός ιστός, ο αδενικός ιστός, το δέρμα και το λίπος πρέπει ταυτοχρόνως να απεικονιστούν και να διαφοροποιηθούν ενώ έχουν πολύ μικρές διαφορές στους συντελεστές εξασθένισης και γι’αυτό το λόγο χαμηλή αντίθεση θέματος. Η ανίχνευση μικρών χαμηλής αντίθεσης αλλοιώσεων είναι απαραίτητη για την απεικονιστική μαστογραφία. Επειδή το μαστογράφημα είναι μια προβολική εικόνα των δομών του μαστού, είναι γενικά πολύ δύσκολο να ανιχνευθεί ο καρκίνος σε ένα πυκνό μαστογραφικό παρέγχυμα. Αυτό αποδίδεται στις ασήμαντες διαφορές στην εξασθένιση των ακτινών χ μεταξύ των αλλοιώσεων και του περιβάλλοντος παρεγχύματος του μαστού. Η ανιχνευσιμότητα των μικρών δομών σε μια μαστογραφία περιορίζεται από τον συμβιβασμό ανάμεσα στην ποιότητας εικόνας και την απορροφούμενη δόση. Αυτό οδηγεί σε μια συνεχή προσπάθεια για βελτιωμένες τεχνικές, λαμβάνοντας υπόψη τα χαμηλής αντίθεσης χαρακτηριστικά της μαστογραφικής διαδικασίας. Στο πλαίσιο της παρούσας μεταπτυχιακής εργασίας αποτιμήθηκε η απεικονιστική ικανότητα ενός μεγάλου εύρους μαστογραφικών φασμάτων και μελετήθηκε η επίδρασή τους στην ανιχνευσιμότητα ανομοιογενειών. Τα φάσματα αυτά αντιστοιχούν σε διάφορους συνδυασμούς υλικών ανόδων (Mo, Rh, W), φίλτρων (Mo, Rh, Nb, Zr Al, and Pd) και υψηλών τάσεων με έμφυτο φιλτράρισμα 1mm παράθυρο Βe. Χρησιμοποιήθηκε ένα κατάλληλα σχεδιασμένο μαθηματικό ομοίωμα μαστού από νερό που περιέχει δύο πανομοιότυπες ανομοιογένειες αέρα διαφόρων διαμέτρων. Για τις μελέτες προσομοίωσης χρησιμοποιήθηκε ένα νωρίτερα ανεπτυγμένο και επικυρωμένο μοντέλο Monte Carlo, το MASTOS. Χρησιμοποιώντας το μοντέλο, διερευνήθηκε η επίδραση των παραγόντων που επηρεάζουν το μαστογραφικό φάσμα (υλικό ανόδου, υλικό φίλτρου, υψηλή τάση), στην ανιχνευσιμότητα μικρών χαμηλής αντίθεσης λεπτομερειών σε όρους αντίθεσης προς θόρυβο (CNR). Τα αποτελέσματα αποδεικνύουν ότι τα φάσματα που παράγονται από ανόδους W και φιλτράρονται με φίλτρα κ-αιχμής (Mo, Rh, Nb, Pd, Zr) δίνουν βελτιωμένα απεικονιστικά χαρακτηριστικά τα οποία είναι παρόμοια και σε μερικές περιπτώσεις καλύτερα συγκρινόμενα με φάσματα από ανόδους Mo και Rh φιλτραρισμένα με φίλτρα κ-αιχμής, ειδικά για τις μεγαλύτερες ανομοιογένειες. Παρολ’αυτά, στην περίπτωση των μικρότερων ανομοιογενειών τα μαλακά φάσματα που παράγονται από ανόδους Mo και φιλτράρονται με φίλτρα κ-αιχμής, με αιχμή απορρόφησης χαμηλής ενέργειας παρέχουν καλύτερο λόγο αντίθεσης προς θόρυβο (CNR). Λαμβάνοντας υπόψη το υλικό του φίλτρου, τα φίλτρα κ-αιχμής προσφέρουν καλύτερη απεικόνιση συγκρινόμενα με τα φίλτρα αλουμινίου (Al). Αυτός είναι ο λόγος που δικαιολογεί τη χρήση αυτών των φίλτρων στην κλινική μαστογραφική διαδικασία. Η υψηλή τάση έχει περιορισμένη επίδραση στον λόγο αντίθεσης προς θόρυβο για όλα τα μεγέθη ανομοιογενειών. Γενικά ως συμπέρασμα, η ανιχνευσιμότητα των χαμηλής αντίθεσης λεπτομερειών σε όρους αντίθεσης προς θόρυβο, εξαρτάται από όλους τους παράγοντες που επηρεάζουν το μαστογραφικό φάσμα, αλλά κυρίως από το υλικό της ανόδου και το υλικό του φίλτρου.

In X-Ray CT, there is always a desire to maintain the image quality while reducing the radiation dose. Recently several dose reduction approaches in both software and hardware have been developed to achieve the goal of making radiation as low as possible. Thus, the assessment of image quality becomes an important factor for routine quality control of medical X-Ray devices. In this work, task-based image quality measurements using model observers were used to evaluate the performance of X-Ray CT systems. To evaluate the dose reduction ability, detection tasks as well as combined detection and estimation tasks were considered. In detection tasks and combined detection and estimation tasks, the channelized Hotelling observer (CHO) and channelized scanning linear observer (CSLO) (with Dense Difference of Gauss channels) were employed respectively. They were used to evaluate the dose reduction capability of the iterative reconstruction algorithm developed by GE compared to the traditional reconstruction algorithm, filtered backprojection (FBP). Additionally, CHO and CSLO were also used for optimization of CT protocols. Our methods were also applied to Cardiac CT systems for temporal resolution evaluations. Two reconstruction algorithms, FBP and the motion correction algorithm, Snapshot Freeze (SSF), operated at two heart-beating rates with two reconstruction windows were quantitatively evaluated using task-based measurements. Finally, due to the huge demand of data acquisitions in the conventional channelized model observers, a proposed High-Dose-Signal-LOOL CHO/CSLO (HL-CHO/CSLO) that could efficiently reduce the data requirement has also been investigated in the pure detection, and combined detection and estimation task. In all studies, the practicality and the use of real data is emphasized. The results of all these studies demonstrate the usefulness of the task-based measurements of image quality in X-Ray CT imaging.

BACKGROUND: Although the Image Quality (IQ) indices calculated by objective Model observers contains more favourable characteristics compared to Figure Of Merits (FOM) derived from the more common subjective evaluations of modern digital diagnostic fluoroscopy units, like CDRAD or the Leeds test-objects, practical issues in form of limited access to unprocessed raw data and intricate laboratory measurements have made the conventional computational methods too inefficient and laborious. One approach of the Statistical Decision Variables (CDV) analysis, made available in the FluoroQuality software, overcome these limitations by calculating the SNR2rate from information entirely based on image frames directly obtained from the imaging system, operating in its usual clinical mode.      AIM: The overall aim of the project has been to make the proposed Model observer methodology readily available and verified for use in common IQ tests that takes place in a hospital based on simple measuring procedures with the default image enhancement techniques turned on. This includes conversion of FluoroQuality to MATLAB, assessment of its applicability on a modern digital unit by means of comparisons of measured SNR2rate with the expected linear response predicted by the classical Rose model, assessment of the methods limiting and optimized imaging conditions (with regard to both equipment and software parameters) and dose-efficiency measurements of the SNR2rate/Doserate Dose-to-information (DI) index including both routine quality control of the detector and equipment parameter analyses.      MATERIALS AND METHODS: A Siemens Axiom Artis Zee MP diagnostic fluoroscopy unit, a Diamentor transmission ionisation chamber and a small T20 solid state detector have been used for acquisition of image data and measurements of Air Kerma-area product rate (KAP-rate) and Entrance Surface Air Kerma rate (ESAK-rate without backscatter). Two sets of separate non-attached test-details, of aluminium and tissue equivalent materials respectively, and a Leeds test object were used as contrasting signals. Dose-efficiency measurements consisted of variation of 4 different parameters: Source-Object-Distance, Phantom PMMA thickness, Field size and Dose rate setting. In addition to these, dimensions of the test details as well as computational parameters of the software, like ROI size and number of frames, were included in the theoretical analyses.      RESULTS: FluoroQuality has successfully been converted to MATLAB and the method has been verified with SNR2rate in accordance with the Rose model with only small deviations observed in contrast analyses, most likely reflecting the methods sensitivity in observing non-linear effects. Useful guidelines for measurement procedures with regard to accuracy and precision have been derived from the studies. Results from measurements of the (squared) DI-indices indicates comparable precision (≤ 8%) with the highest performing visual evaluations but with higher accuracy and reproducibility. What still remains for the method to compete with subjective routine QC tests is to integrate the SNR2rate measurements in an efficient enough QA program.