Development and Implementation of Fully 3D Statistical Image Reconstruction Algorithms for Helical CT and Half-Ring PET Insert System

X-ray computed tomography: CT) and positron emission tomography: PET) have become widely used imaging modalities for screening, diagnosis, and image-guided treatment planning. Along with the increased clinical use are increased demands for high image quality with reduced ionizing radiation dose to the patient. Despite their significantly high computational cost, statistical iterative reconstruction algorithms are known to reconstruct high-quality images from noisy tomographic datasets. The overall goal of this work is to design statistical reconstruction software for clinical x-ray CT scanners, and for a novel PET system that utilizes high-resolution detectors within the field of view of a whole-body PET scanner. The complex choices involved in the development and implementation of image reconstruction algorithms are fundamentally linked to the ways in which the data is acquired, and they require detailed knowledge of the various sources of signal degradation. Both of the imaging modalities investigated in this work have their own set of challenges. However, by utilizing an underlying statistical model for the measured data, we are able to use a common framework for this class of tomographic problems. We first present the details of a new fully 3D regularized statistical reconstruction algorithm for multislice helical CT. To reduce the computation time, the algorithm was carefully parallelized by identifying and taking advantage of the specific symmetry found in helical CT. Some basic image quality measures were evaluated using measured phantom and clinical datasets, and they indicate that our algorithm achieves comparable or superior performance over the fast analytical methods considered in this work. Next, we present our fully 3D reconstruction efforts for a high-resolution half-ring PET insert. We found that this unusual geometry requires extensive redevelopment of existing reconstruction methods in PET. We redesigned the major components of the data modeling process and incorporated them into our reconstruction algorithms. The algorithms were tested using simulated Monte Carlo data and phantom data acquired by a PET insert prototype system. Overall, we have developed new, computationally efficient methods to perform fully 3D statistical reconstructions on clinically-sized datasets

Relevance of accurate Monte Carlo modeling in nuclear medical imaging

Monte Carlo techniques have become popular in different areas of medical physics with advantage of powerful computing systems. In particular, they have been extensively applied to simulate processes involving random behavior and to quantify physical parameters that are difficult or even impossible to calculate by experimental measurements. Recent nuclear medical imaging innovations such as single-photon emission computed tomography (SPECT), positron emission tomography (PET), and multiple emission tomography (MET) are ideal for Monte Carlo modeling techniques because of the stochastic nature of radiation emission, transport and detection processes. Factors which have contributed to the wider use include improved models of radiation transport processes, the practicality of application with the development of acceleration schemes and the improved speed of computers. This paper presents derivation and methodological basis for this approach and critically reviews their areas of application in nuclear imaging. An overview of existing simulation programs is provided and illustrated with examples of some useful features of such sophisticated tools in connection with common computing facilities and more powerful multiple-processor parallel processing systems. Current and future trends in the field are also discussed

Statistical X-Ray-Computed Tomography Image Reconstruction with Beam- Hardening Correction

This paper describes two statistical iterative reconstruction methods for X-ray CT. The rst method assumes a mono-energetic model for X-ray attenuation. We approximate the transmission Poisson likelihood by a quadratic cost function and exploit its convexity to derive a separable quadratic surrogate function that is easily minimized using parallelizable algorithms. Ordered subsets are used to accelerate convergence. We apply this mono-energetic algorithm (with edge-preserving regularization) to simulated thorax X-ray CT scans. A few iterations produce reconstructed images with lower noise than conventional FBP images at equivalent resolutions. The second method generalizes the physical model and accounts for the poly-energetic X-ray source spectrum and the measurement nonlinearities caused by energy-dependent attenuation. We assume the object consists of a given number of nonoverlapping tissue types. The attenuation coeÆcient of each tissue is the product of its unknown density and a known energy-dependent mass attenuation coeÆcient. We formulate a penalized-likelihood function for this polyenergetic model and develop an iterative algorithm for estimating the unknown densities in each voxel. Applying this method to simulated X-ray CT measurements of a phantom containing both bone and soft tissue yields images with signi cantly reduced beam hardening artifacts.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85939/1/Fessler165.pd

Fast Predictions of Variance Images for Fan-Beam Transmission Tomography With Quadratic Regularization

Accurate predictions of image variances can be useful for reconstruction algorithm analysis and for the design of regularization methods. Computing the predicted variance at every pixel using matrix-based approximations is impractical. Even most recently adopted methods that are based on local discrete Fourier approximations are impractical since they would require a forward and backprojection and two fast Fourier transform (FFT) calculations for every pixel, particularly for shift-variant systems like fan-beam tomography. This paper describes new "analytical" approaches to predicting the approximate variance maps of 2-D images that are reconstructed by penalized-likelihood estimation with quadratic regularization in fan-beam geometries. The simplest of the proposed analytical approaches requires computation equivalent to one backprojection and some summations, so it is computationally practical even for the data sizes in X-ray computed tomography (CT). Simulation results show that it gives accurate predictions of the variance maps. The parallel-beam geometry is a simple special case of the fan-beam analysis. The analysis is also applicable to 2-D positron emission tomography (PET).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86007/1/Fessler37.pd

Caracterización, mejora y diseño de escáneres PET preclínicos

El objetivo principal de esta tesis es contribuir a la mejora de la calidad de las imágenes de tomografía por emisión de positrones (PET) para la investigación preclínica con animales pequeños mediante el uso de simulaciones Monte Carlo, ya sea para el estudio de los problemas limitantes de la técnica en los escáneres existentes proporcionando métodos para compensarlos, ya sea para orientar el diseño de nuevos prototipos, analizando las ventajas y desventajas antes de tomar una decisión final. Los objetivos específicos son los siguientes: 1. Evaluar el rendimiento de los sistemas PET preclínicos disponibles en el Laboratorio de Imagen Médica, siguiendo, en la medida de lo posible, una metodología estándar para comparar los sistemas entre sí y con otros sistemas comerciales en las mismas condiciones [Vicente et al., 2006 , Goertzen et al., 2012, Vicente et al., 2010a]. 2. Estudiar las fuentes de error que limitan la calidad de las imágenes reconstruidas usando simulaciones Monte Carlo para investigar nuevos métodos y algoritmos para compensarlos [Vicente et al., 2012a, Abella et al., 2012, Vicente et al., 2010b]. 3. Utilizar simulaciones Monte Carlo para guiar el diseño de nuevos prototipos, realizando las modificaciones necesarias en el paquete de Monte Carlo empleado (peneloPET, [España et al., 2009]) y en los métodos de reconstrucción existentes (como GFIRST [Herraiz et al., 2011]) para adaptar los códigos existentes a la geometría no convencional de los nuevos diseños [Vicente et al., 2012b]. Todos los algoritmos desarrollados en el contexto de esta tesis no son exclusivos para un escáner en particular, sino que han sido diseñados para ser flexibles y fácilmente adaptables a diferentes arquitecturas que cumplan con ciertas condiciones en cada caso. Sin embargo, dado que este trabajo se beneficia del acceso a datos reales adquiridos por los escáneres disponibles en el Laboratorio de Imagen Médica, el desarrollo de los diferentes métodos se adaptan a la geometría particular de estos sistemas ([Wang et al., 2006b, Vaquero et al., 2005a]). Como consideración final, decir que una parte significativa de los resultados presentados en esta tesis, además de dar lugar a publicaciones científicas, se pretende que sean incorporados en el software de escáneres preclínicos de alta resolución fabricados por SEDECAL y distribuidos por todo el mundo en virtud de acuerdos de transferencia de tecnología con el Laboratorio de Imagen Médica y el Grupo de Física Nuclear (GFN). [ABSTRACT]The main goal of this thesis is to contribute to the improvement of the quality of positron emission tomography (PET) images for preclinical research with small animals by intensive use of Monte Carlo simulations, either for studying limiting problems in existing scanners providing methods to compensate them, either for guiding in the design of new prototypes, analyzing advantages and drawbacks before taking the final decision. Specific objectives are as follows: 1. To evaluate the performance of two of the small-animal PET systems available at the Medical Imaging Laboratory, following, as far as possible, a standard methodology in order to compare systems between them and with other commercial preclinical systems under similar conditions [Vicente et al., 2006 , Goertzen et al., 2012, Vicente et al., 2010a]. 2. To study the sources of error that limit the quality of reconstructed PET images using Monte Carlo simulations and to investigate new methods and algorithms to compensate for these errors [Vicente et al., 2011, Vicente et al., 2012a, Abella et al., 2012, Vicente et al., 2010b]. 3. To use Monte Carlo simulations for the design of new prototypes, performing the necessary modifications in the Monte Carlo package employed (peneloPET, [España et al., 2009]) and in the available reconstruction methods (as GFIRST [Herraiz et al., 2011]) in order to make them suitable to the non-conventional geometries of the new designs [Vicente et al., 2012b]. The algorithms developed in this thesis are not exclusive of any scanner in particular; they have been designed to be flexible and suitable for different architectures with only a few common constrains. However, since this work takes advantage of the access to real data collected by the specific systems available at the Medical Imaging Laboratory, the development and testing of the different methods were adapted to the particular geometry of these systems ([Wang et al., 2006b, Vaquero et al., 2005a]). As a final consideration, it is worth mentioning that significant part of the results presented in this thesis, besides giving rise to scientific publications, are intended to be incorporated into the preclinical high-resolution systems manufactured by SEDECAL and distributed worldwide under technology transfer agreements with the Medical Imaging Laboratory and the Nuclear Physics Group

Monte Carlo simulations for system modeling in emission tomography

Non-invasive diagnostic imaging can be performed with different technologies:X-ray radiography, computed radiography, direct radiography, mammography,Computed Tomography (CT), UltraSound (US), and Magnetic Resonance Imaging (MRI), which all give anatomical information, and also with functional MRI (fMRI), optical imaging, thermography, planar isotope imaging,Single Photon Emission Tomography (SPECT), Positron Emission Tomography (PET), and gamma camera PET which return functional information.Recent devices combine two modalities on the same gantry in order to achieve hardware fusion of anatomical and functional images. Given the demographic aging in Western Europe, there exists a large interest in what is popularly referred to as a GPS-tool for cancer, i.e. a diagnostic tool for oncology that detects small malignant lesions in a very early stadium and that can be used for disease staging. Therefore research in nuclear medicine has a social support and bearing. In nuclear medicine examinations, a radiopharmaceutical is injected in the patient, marked with a radionuclide emitting one single photon with an energy of 100-200 keV in SPECT and a positron emitting radionuclide in PET. The emission of a positron finally results in two annihilation photons of 511 keV. Those photons are detected, mostly using a scintillation crystal that generates optical photons which travel through a light guide before reaching the PhotoMultiplierTubes (PMTs). Those PMTs convert the optical photons to electrons, which are in their turn used to generate a position and energy encoding signal. In PET there is an electronic collimation to acquire directional information while this information is obtained by applying a lead collimator in SPECT. The acquired data is afterwards reconstructed to result in a threedimensional radioactive tracer distribution within the patient. Optimization,evaluation and (re)design of all elements in this detection chain is mostly done using simulations. Given the possibility of modeling different physical processes, the Monte Carlo method has also been applied in nuclear medicine to a wide range of problems that could not be addressed by experimental or analytical approaches

Incorporating accurate statistical modeling in PET: reconstruction for whole-body imaging

Tese de doutoramento em Biofísica, apresentada à Universidade de Lisboa através da Faculdade de Ciências, 2007The thesis is devoted to image reconstruction in 3D whole-body PET imaging. OSEM ( Ordered Subsets Expectation maximization ) is a statistical algorithm that assumes Poisson data. However, corrections for physical effects (attenuation, scattered and random coincidences) and detector efficiency remove the Poisson characteristics of these data. The Fourier Rebinning (FORE), that combines 3D imaging with fast 2D reconstructions, requires corrected data. Thus, if it will be used or whenever data are corrected prior to OSEM, the need to restore the Poisson-like characteristics is present. Restoring Poisson-like data, i.e., making the variance equal to the mean, was achieved through the use of weighted OSEM algorithms. One of them is the NECOSEM, relying on the NEC weighting transformation. The distinctive feature of this algorithm is the NEC multiplicative factor, defined as the ratio between the mean and the variance. With real clinical data this is critical, since there is only one value collected for each bin the data value itself. For simulated data, if we keep track of the values for these two statistical moments, the exact values for the NEC weights can be calculated. We have compared the performance of five different weighted algorithms (FORE+AWOSEM, FORE+NECOSEM, ANWOSEM3D, SPOSEM3D and NECOSEM3D) on the basis of tumor detectablity. The comparison was done for simulated and clinical data. In the former case an analytical simulator was used. This is the ideal situation, since all the weighting factors can be exactly determined. For comparing the performance of the algorithms, we used the Non-Prewhitening Matched Filter (NPWMF) numerical observer. With some knowledge obtained from the simulation study we proceeded to the reconstruction of clinical data. In that case, it was necessary to devise a strategy for estimating the NEC weighting factors. The comparison between reconstructed images was done by a physician largely familiar with whole-body PET imaging