Scatter and Blurring Compensation in Inhomogeneous Media Using a Postprocessing Method

An efficient postprocessing method to compensate for the scattering and blurring effects in inhomogeneous medium in SPECT is proposed. A two-dimensional point spread function (2D-PSF) was estimated in the image domain to model the combination of these two physical effects. This 2D-PSF in the inhomogeneous medium is fitted with an asymmetric Gaussian function based on Monte Carlo simulation results. An efficient further blurring and deconvolution method was used to restore images from the spatially variant 2D-PSF kernel. The compensation is performed using a computer-simulated NCAT phantom and a flanged Jaszczak experimental phantom. The preliminary results demonstrate an improvement in image quality and quantity accuracy with increased image contrast (25% increase compared to uncompensated image) and decreased error (40% decrease compared to uncompensated image). This method also offers an alternative to compensate for scatter and blurring in a more time efficient manner compared to the popular iterative methods. The execution time for this efficient postprocessing method is only a few minutes, which is within the clinically acceptable range

Doctor of Philosophy

dissertationImage-based biomechanics, particularly numerical modeling using subject-specific data obtained via imaging, has proven useful for elucidating several biomechanical processes, such as prediction of deformation due to external loads, applicable to both normal function and pathophysiology of various organs. As the field evolves towards applications that stretch the limits of imaging hardware and acquisition time, the information traditionally expected as input for numerical routines often becomes incomplete or ambiguous, and requires specific acquisition and processing strategies to ensure physical accuracy and compatibility with predictive mathematical modeling. These strategies, often derivatives or specializations of traditional mechanics, effectively extend the nominal capability of medical imaging hardware providing subject-specific information coupled with the option of using the results for predictive numerical simulations. This research deals with the development of tools for extracting mechanical measurements from a finite set of imaging data and finite element analysis in the context of constructing structural atlases of the heart, understanding the biomechanics of the venous vasculature, and right ventricular failure. The tools include: (1) application of Hyperelastic Warping image registration to displacement-encoded MRI for reconstructing absolute displacement fields, (2) combination of imaging and a material parameter identification approach to measure morphology, deformation, and mechanical properties of vascular tissue, and (3) extrapolation of diffusion tensor MRI acquired at a single time point for the prediction the structural changes across the cardiac cycle with mechanical simulations. Selected tools were then applied to evaluate structural changes in a reversible animal model for right ventricular failure due to pressure overload

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities

Acceleration of GATE Monte Carlo simulations

Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography are forms of medical imaging that produce functional images that reflect biological processes. They are based on the tracer principle. A biologically active substance, a pharmaceutical, is selected so that its spatial and temporal distribution in the body reflects a certain body function or metabolism. In order to form images of the distribution, the pharmaceutical is labeled with gamma-ray-emitting or positron-emitting radionuclides (radiopharmaceuticals or tracers). After administration of the tracer to a patient, an external position-sensitive gamma-ray camera can detect the emitted radiation to form a stack of images of the radionuclide distribution after a reconstruction process. Monte Carlo methods are numerical methods that use random numbers to compute quantities of interest. This is normally done by creating a random variable whose expected value is the desired quantity. One then simulates and tabulates the random variable and uses its sample mean and variance to construct probabilistic estimates. It represents an attempt to model nature through direct simulation of the essential dynamics of the system in question. Monte Carlo modeling is the method of choice for all applications where measurements are not feasible or where analytic models are not available due to the complex nature of the problem. In addition, such modeling is a practical approach in nuclear medical imaging in several important application fields: detector design, quantification, correction methods for image degradations, detection tasks etc. Several powerful dedicated Monte Carlo simulators for PET and/or SPECT are available. However, they are often not detailed nor flexible enough to enable realistic simulations of emission tomography detector geometries while also modeling time dependent processes such as decay, tracer kinetics, patient and bed motion, dead time or detector orbits. Our Monte Carlo simulator of choice, GEANT4 Application for Tomographic Emission (GATE), was specifically designed to address all these issues. The flexibility of GATE comes at a price however. The simulation of a simple prototype SPECT detector may be feasible within hours in GATE but an acquisition with a realistic phantom may take years to complete on a single CPU. In this dissertation we therefore focus on the Achilles’ heel of GATE: efficiency. Acceleration of GATE simulations can only be achieved through a combination of efficient data analysis, dedicated variance reduction techniques, fast navigation algorithms and parallelization. In the first part of this dissertation we consider the improvement of the analysis capabilities of GATE. The static analysis module in GATE is both inflexible and incapable of storing more detail without introducing a large computational overhead. However, the design and validation of the acceleration techniques in this dissertation requires a flexible, detailed and computationally efficient analysis module. To this end, we develop a new analysis framework capable of analyzing any process, from the decay of isotopes to particle interactions and detections in any detector element for any type of phantom. The evaluation of our framework consists of the assessment of spurious activity in 124I-Bexxar PET and of contamination in 131I-Bexxar SPECT. In the case of PET we describe how our framework can detect spurious coincidences generated by non-pure isotopes, even with realistic phantoms. We show that optimized energy thresholds, which can readily be applied in the clinic, can now be derived in order to minimize the contamination. We also show that the spurious activity itself is not spatially uniform. Therefore standard reconstruction and correction techniques are not adequate. In the case of SPECT we describe how it is now possible to classify detections into geometric detections, phantom scatter, penetration through the collimator, collimator scatter and backscatter in the end parts. We show that standard correction algorithms such as triple energy window correction cannot correct for septal penetration. We demonstrate that 124I PET with optimized energy thresholds offer better image quality than 131I SPECT when using standard reconstruction techniques. In the second part of this dissertation we focus on improving the efficiency of GATE with a variance reduction technique called Geometrical Importance Sampling (GIS). We describe how only 0.02% of all emitted photons can reach the crystal surface of a SPECT detector head with a low energy high resolution collimator. A lot of computing power is therefore wasted by tracking photons that will not contribute to the result. A twofold strategy is used to solve this problem: GIS employs Russian Roulette to discard those photons that will not likely contribute to the result. Photons in more important regions on the other hand are split into several photons with reduced weight to increase their survival chance. We show that this technique introduces branches into the particle history. We describe how this can be taken into account by a particle history tree that is used for the analysis of the results. The evaluation of GIS consists of energy spectra validation, spatial resolution and sensitivity for low and medium energy isotopes. We show that GIS reaches acceleration factors between 5 and 13 over analog GATE simulations for the isotopes in the study. It is a general acceleration technique that can be used for any isotope, phantom and detector combination. Although GIS is useful as a safe and accurate acceleration technique, it cannot deliver clinically acceptable simulation times. The main reason lies in its inability to force photons in a specific direction. In the third part of this dissertation we solve this problem for 99mTc SPECT simulations. Our approach is twofold. Firstly, we introduce two variance reduction techniques: forced detection (FD) and convolution-based forced detection (CFD) with multiple projection sampling (MPS). FD and CFD force copies of photons at decay and at every interaction point to be transported through the phantom in a direction sampled within a solid angle toward the SPECT detector head at all SPECT angles simultaneously. We describe how a weight must be assigned to each photon in order to compensate for the forced direction and non-absorption at emission and scatter. We show how the weights are calculated from the total and differential Compton and Rayleigh cross sections per electron with incorporation of Hubbell’s atomic form factor. In the case of FD all detector interactions are modeled by Monte Carlo, while in the case of CFD the detector is modeled analytically. Secondly, we describe the design of an FD and CFD specialized navigator to accelerate the slow tracking algorithms in GEANT4. The validation study shows that both FD and CFD closely match the analog GATE simulations and that we can obtain an acceleration factor between 3 (FD) and 6 (CFD) orders of magnitude over analog simulations. This allows for the simulation of a realistic acquisition with a torso phantom within 130 seconds. In the fourth part of this dissertation we exploit the intrinsic parallel nature of Monte Carlo simulations. We show how Monte Carlo simulations should scale linearly as a function of the number of processing nodes but that this is usually not achieved due to job setup time, output handling and cluster overhead. We describe how our approach is based on two steps: job distribution and output data handling. The job distribution is based on a time-domain partitioning scheme that retains all experimental parameters and that guarantees the statistical independence of each subsimulation. We also reduce the job setup time by the introduction of a parameterized collimator model for SPECT simulations. We reduce the data output handling time by a chain-based output merger. The scalability study is based on a set of simulations on a 70 CPU cluster and shows an acceleration factor of approximately 66 on 70 CPUs for both PET and SPECT.We also show that our method of parallelization does not introduce any approximations and that it can be readily combined with any of the previous acceleration techniques described above

Automatic video segmentation employing object/camera modeling techniques

Practically established video compression and storage techniques still process video sequences as rectangular images without further semantic structure. However, humans watching a video sequence immediately recognize acting objects as semantic units. This semantic object separation is currently not reflected in the technical system, making it difficult to manipulate the video at the object level. The realization of object-based manipulation will introduce many new possibilities for working with videos like composing new scenes from pre-existing video objects or enabling user-interaction with the scene. Moreover, object-based video compression, as defined in the MPEG-4 standard, can provide high compression ratios because the foreground objects can be sent independently from the background. In the case that the scene background is static, the background views can even be combined into a large panoramic sprite image, from which the current camera view is extracted. This results in a higher compression ratio since the sprite image for each scene only has to be sent once. A prerequisite for employing object-based video processing is automatic (or at least user-assisted semi-automatic) segmentation of the input video into semantic units, the video objects. This segmentation is a difficult problem because the computer does not have the vast amount of pre-knowledge that humans subconsciously use for object detection. Thus, even the simple definition of the desired output of a segmentation system is difficult. The subject of this thesis is to provide algorithms for segmentation that are applicable to common video material and that are computationally efficient. The thesis is conceptually separated into three parts. In Part I, an automatic segmentation system for general video content is described in detail. Part II introduces object models as a tool to incorporate userdefined knowledge about the objects to be extracted into the segmentation process. Part III concentrates on the modeling of camera motion in order to relate the observed camera motion to real-world camera parameters. The segmentation system that is described in Part I is based on a background-subtraction technique. The pure background image that is required for this technique is synthesized from the input video itself. Sequences that contain rotational camera motion can also be processed since the camera motion is estimated and the input images are aligned into a panoramic scene-background. This approach is fully compatible to the MPEG-4 video-encoding framework, such that the segmentation system can be easily combined with an object-based MPEG-4 video codec. After an introduction to the theory of projective geometry in Chapter 2, which is required for the derivation of camera-motion models, the estimation of camera motion is discussed in Chapters 3 and 4. It is important that the camera-motion estimation is not influenced by foreground object motion. At the same time, the estimation should provide accurate motion parameters such that all input frames can be combined seamlessly into a background image. The core motion estimation is based on a feature-based approach where the motion parameters are determined with a robust-estimation algorithm (RANSAC) in order to distinguish the camera motion from simultaneously visible object motion. Our experiments showed that the robustness of the original RANSAC algorithm in practice does not reach the theoretically predicted performance. An analysis of the problem has revealed that this is caused by numerical instabilities that can be significantly reduced by a modification that we describe in Chapter 4. The synthetization of static-background images is discussed in Chapter 5. In particular, we present a new algorithm for the removal of the foreground objects from the background image such that a pure scene background remains. The proposed algorithm is optimized to synthesize the background even for difficult scenes in which the background is only visible for short periods of time. The problem is solved by clustering the image content for each region over time, such that each cluster comprises static content. Furthermore, it is exploited that the times, in which foreground objects appear in an image region, are similar to the corresponding times of neighboring image areas. The reconstructed background could be used directly as the sprite image in an MPEG-4 video coder. However, we have discovered that the counterintuitive approach of splitting the background into several independent parts can reduce the overall amount of data. In the case of general camera motion, the construction of a single sprite image is even impossible. In Chapter 6, a multi-sprite partitioning algorithm is presented, which separates the video sequence into a number of segments, for which independent sprites are synthesized. The partitioning is computed in such a way that the total area of the resulting sprites is minimized, while simultaneously satisfying additional constraints. These include a limited sprite-buffer size at the decoder, and the restriction that the image resolution in the sprite should never fall below the input-image resolution. The described multisprite approach is fully compatible to the MPEG-4 standard, but provides three advantages. First, any arbitrary rotational camera motion can be processed. Second, the coding-cost for transmitting the sprite images is lower, and finally, the quality of the decoded sprite images is better than in previously proposed sprite-generation algorithms. Segmentation masks for the foreground objects are computed with a change-detection algorithm that compares the pure background image with the input images. A special effect that occurs in the change detection is the problem of image misregistration. Since the change detection compares co-located image pixels in the camera-motion compensated images, a small error in the motion estimation can introduce segmentation errors because non-corresponding pixels are compared. We approach this problem in Chapter 7 by integrating risk-maps into the segmentation algorithm that identify pixels for which misregistration would probably result in errors. For these image areas, the change-detection algorithm is modified to disregard the difference values for the pixels marked in the risk-map. This modification significantly reduces the number of false object detections in fine-textured image areas. The algorithmic building-blocks described above can be combined into a segmentation system in various ways, depending on whether camera motion has to be considered or whether real-time execution is required. These different systems and example applications are discussed in Chapter 8. Part II of the thesis extends the described segmentation system to consider object models in the analysis. Object models allow the user to specify which objects should be extracted from the video. In Chapters 9 and 10, a graph-based object model is presented in which the features of the main object regions are summarized in the graph nodes, and the spatial relations between these regions are expressed with the graph edges. The segmentation algorithm is extended by an object-detection algorithm that searches the input image for the user-defined object model. We provide two objectdetection algorithms. The first one is specific for cartoon sequences and uses an efficient sub-graph matching algorithm, whereas the second processes natural video sequences. With the object-model extension, the segmentation system can be controlled to extract individual objects, even if the input sequence comprises many objects. Chapter 11 proposes an alternative approach to incorporate object models into a segmentation algorithm. The chapter describes a semi-automatic segmentation algorithm, in which the user coarsely marks the object and the computer refines this to the exact object boundary. Afterwards, the object is tracked automatically through the sequence. In this algorithm, the object model is defined as the texture along the object contour. This texture is extracted in the first frame and then used during the object tracking to localize the original object. The core of the algorithm uses a graph representation of the image and a newly developed algorithm for computing shortest circular-paths in planar graphs. The proposed algorithm is faster than the currently known algorithms for this problem, and it can also be applied to many alternative problems like shape matching. Part III of the thesis elaborates on different techniques to derive information about the physical 3-D world from the camera motion. In the segmentation system, we employ camera-motion estimation, but the obtained parameters have no direct physical meaning. Chapter 12 discusses an extension to the camera-motion estimation to factorize the motion parameters into physically meaningful parameters (rotation angles, focal-length) using camera autocalibration techniques. The speciality of the algorithm is that it can process camera motion that spans several sprites by employing the above multi-sprite technique. Consequently, the algorithm can be applied to arbitrary rotational camera motion. For the analysis of video sequences, it is often required to determine and follow the position of the objects. Clearly, the object position in image coordinates provides little information if the viewing direction of the camera is not known. Chapter 13 provides a new algorithm to deduce the transformation between the image coordinates and the real-world coordinates for the special application of sport-video analysis. In sport videos, the camera view can be derived from markings on the playing field. For this reason, we employ a model of the playing field that describes the arrangement of lines. After detecting significant lines in the input image, a combinatorial search is carried out to establish correspondences between lines in the input image and lines in the model. The algorithm requires no information about the specific color of the playing field and it is very robust to occlusions or poor lighting conditions. Moreover, the algorithm is generic in the sense that it can be applied to any type of sport by simply exchanging the model of the playing field. In Chapter 14, we again consider panoramic background images and particularly focus ib their visualization. Apart from the planar backgroundsprites discussed previously, a frequently-used visualization technique for panoramic images are projections onto a cylinder surface which is unwrapped into a rectangular image. However, the disadvantage of this approach is that the viewer has no good orientation in the panoramic image because he looks into all directions at the same time. In order to provide a more intuitive presentation of wide-angle views, we have developed a visualization technique specialized for the case of indoor environments. We present an algorithm to determine the 3-D shape of the room in which the image was captured, or, more generally, to compute a complete floor plan if several panoramic images captured in each of the rooms are provided. Based on the obtained 3-D geometry, a graphical model of the rooms is constructed, where the walls are displayed with textures that are extracted from the panoramic images. This representation enables to conduct virtual walk-throughs in the reconstructed room and therefore, provides a better orientation for the user. Summarizing, we can conclude that all segmentation techniques employ some definition of foreground objects. These definitions are either explicit, using object models like in Part II of this thesis, or they are implicitly defined like in the background synthetization in Part I. The results of this thesis show that implicit descriptions, which extract their definition from video content, work well when the sequence is long enough to extract this information reliably. However, high-level semantics are difficult to integrate into the segmentation approaches that are based on implicit models. Intead, those semantics should be added as postprocessing steps. On the other hand, explicit object models apply semantic pre-knowledge at early stages of the segmentation. Moreover, they can be applied to short video sequences or even still pictures since no background model has to be extracted from the video. The definition of a general object-modeling technique that is widely applicable and that also enables an accurate segmentation remains an important yet challenging problem for further research

Image Restoration

This book represents a sample of recent contributions of researchers all around the world in the field of image restoration. The book consists of 15 chapters organized in three main sections (Theory, Applications, Interdisciplinarity). Topics cover some different aspects of the theory of image restoration, but this book is also an occasion to highlight some new topics of research related to the emergence of some original imaging devices. From this arise some real challenging problems related to image reconstruction/restoration that open the way to some new fundamental scientific questions closely related with the world we interact with