In order to perform quantitative 3D positron tomography, it is essential that an accurate means of correcting for the effects of Compton scattered photons be developed. The two main approaches to compensate for scattered radiation rely on energy considerations or on filtering operations. Energy based scatter correction methods exploit the reduced energy of scattered photons to differentiate them from unscattered photons. Filtered scatter correction methods require the measurement of scatter point spread functions to be used for convolution with the acquired emission data set. Neither approach has demonstrated sufficient accuracy to be applied in a clinical environment. In this thesis, I have developed the theoretical framework for generating the scatter point spread functions for the general case of any source position within any nonuniform attenuation object. This calculation is based on a first principles approach using the Klein-Nishina differential cross section for Compton scattering to describe the angular distribution of scatter annihilation photons. The attenuation correction factors from transmission scans are included within the theory as inputs describing the distribution of matter in the object being imaged. The theory has been tested by comparison with experimental scatter profiles of point sources which are either centered, or off-center in water-filled cylinders. Monte Carlo simulations have been used to identify the detector energy threshold where the single scatter assumption employed by the theory is most satisfied. The validity of a mean scatter position assumption, used in the development of the theory, is tested using analytic calculations of a non-uniform attenuation phantom. The physical effects most responsible for determining the shape of the scatter profiles, as well as the assumptions employed by several common scatter correction methods, are revealed using the analytic scatter correction theory. / Thesis / Master of Science (MS)

The current cardiac computed tomography (CT) technology is mainly limited by motion blurring and radiation dose. The conceptual multi-source interior CT scheme has provided a potential solution to reduce motion artifacts and radiation exposure. This dissertation work conducted multi-facet investigations on a novel multi-source interior CT architecture (G. Cao, et. al, IEEE Access, 2014;2:1263-71) which employs distributed stationary multi-beam Carbon-nanotube (CNT) X-ray sources and simultaneously operates multiple source-detector chains to improve temporal resolution. The collimation based interior CT is integrated in each imaging chain, to suppress radiation dose. The central thesis statement is: Compared to conventional CT design, this distributed source array based multi-source interior CT architecture shall provide ultra-fast CT scan of region-of-interest (ROI) inside body with comparable image quality at lower radiation dose. Comprehensive studies were conducted to separately investigate three critical aspects of multi-source interior CT: interior CT mode, X-ray scattering, and scatter correction methods. First, a single CNT X-ray source based interior micro-CT was constructed to serve as a down-scaled experimental verification platform for interior CT mode. Interior CT mode demonstrated comparable contrast-noise-ratio (CNR) and image structural similarity to the standard global CT mode, while inducing a significant radiation dose reduction (< 83.9%). Second, the data acquisition of multi-source interior CT was demonstrated at clinical geometry, via numerical simulation and physical experiments. The simultaneously operated source-detector chains induced significant X-ray forward / cross scattering and thus caused severe CNR reduction (< 68.5%) and CT number error (< 1122 HU). To address the scatter artifacts, a stationary beam-stopper-array (BSA) based and a source-trigger-sequence (STS) based scatter correction methods were proposed to enable the online scatter measurement / correction with further radiation dose reduction (< 50%). Moreover, a deterministic physics model was also developed to iteratively remove the scatter-artifacts in the multi-source interior CT, without the need for modifications in imaging hardware or protocols. The three proposed scatter correction methods improved CNR (< 94.0%) and suppressed CT number error (< 48 HU). With the dedicated scatter correction methods, the multi-source interior CT could provide ROI-oriented imaging with acceptable image quality at significantly reduced radiation dose. / Ph. D. / Cardiac computed tomography (CT) technology enables a non-invasive imaging examination of patients’ cardiovascular system, and thus it has been widely applied in the fields of the diagnosis, treatment, and scientific research of cardiovascular diseases (CVD). The image quality of the current cardiac CT is frequently degraded by rapid cardiac motion and X-ray scattering, and the potential radiation harm has also raised public concern. The above limitations could be theoretically overcome by a recently proposed conceptual stationary multi-beam X-ray sources based interior CT (i.e. multi-source interior CT) system architecture. This dissertation conducted a comprehensive investigation on the actual image quality and radiation dose of this conceptual CT system. The experiments demonstrated that a significant radiation dose reduction could be achieved in multi-source interior CT. The image quality of multi-source interior CT could be maintained with the in-housedeveloped scatter correction methods.

Excessive imaging doses from repeated scans and poor image quality mainly due to scatter contamination are the two bottlenecks of cone-beam CT (CBCT) imaging. This study investigates a method that combines measurement-based scatter correction and a compressed sensing (CS)-based iterative reconstruction algorithm to generate scatter-free images from low-dose data. Scatter distribution is estimated by interpolating/extrapolating measured scatter samples inside blocked areas. CS-based iterative reconstruction is finally carried out on the under-sampled data to obtain scatter-free and low-dose CBCT images. In the tabletop phantom studies, with only 25% dose of a conventional CBCT scan, our method reduces the overall CT number error from over 220 HU to less than 25 HU, and increases the image contrast by a factor of 2.1 in the selected ROIs. Dual-energy CT (DECT) is another important application of CBCT. DECT shows promise in differentiating materials that are indistinguishable in single-energy CT and facilitates accurate diagnosis. A general problem of DECT is that decomposition is sensitive to noise in the two sets of projection data, resulting in severely degraded qualities of decomposed images. The first study of DECT is focused on the linear decomposition method. In this study, a combined method of iterative reconstruction and decomposition is proposed. The noise on the two initial CT images from separate scans becomes well correlated, which avoids noise accumulation during the decomposition process. To fully explore the benefits of DECT on beam-hardening correction and to reduce the computation cost, the second study is focused on an iterative decomposition method with a non-linear decomposition model for noise suppression in DECT. Phantom results show that our methods achieve superior performance on DECT imaging, with respect to noise reduction and spatial resolution.

The images in positron emission tomography (PET) represent three dimensional dynamic distributions of biologically interesting molecules labelled with positron emitting radionuclides (radiotracers). Spatial localisation of the radio-tracers is achieved by detecting in coincidence two collinear photons which are emitted when the positron annihilates with an ordinary electron. In order to obtain quantitatively accurate images in PET, it is necessary to correct for the effects of photon attenuation within the subject being imaged. These corrections can be obtained using singles-mode photon transmission scanning. Although suitable for small animal PET, these scans are subject to high amounts of contamination from scattered photons. Currently, no accurate correction exists to account for scatter in these data. The primary purpose of this work was to implement and validate an analytical scatter correction for PET transmission scanning. In order to isolate the effects of scatter, we developed a simulation tool which was validated using experimental transmission data. We then presented an analytical scatter correction for singles-mode transmission data in PET. We compared our scatter correction data with the previously validated simulation data for uniform and non-uniform phantoms and for two different transmission source radionuclides. Our scatter calculation correctly predicted the contribution from scattered photons to the simulated data for all phantoms and both transmission sources. We then applied our scatter correction as part of an iterative reconstruction algorithm for simulated and experimental PET transmission data for uniform and non-uniform phantoms. We also tested our reconstruction and scatter correction procedure using transmission data for several animal studies (mice, rats and primates). For all studies considered, we found that the average reconstructed linear attenuation coefficients for water or soft-tissue regions of interest agreed with expected values to within 4%. Using a 2.2 GHz processor, the scatter correction required between 6 to 27 minutes of CPU time (without any code optimisation) depending on the phantom size and source used. This extra calculation time does not seem unreasonable considering that, without scatter corrections, errors in the reconstructed attenuation coefficients were between 18 to 45% depending on the phantom size and transmission source used.

The images in positron emission tomography (PET) represent three dimensional dynamic distributions of biologically interesting molecules labelled with positron emitting radionuclides (radiotracers). Spatial localisation of the radio-tracers is achieved by detecting in coincidence two collinear photons which are emitted when the positron annihilates with an ordinary electron. In order to obtain quantitatively accurate images in PET, it is necessary to correct for the effects of photon attenuation within the subject being imaged. These corrections can be obtained using singles-mode photon transmission scanning. Although suitable for small animal PET, these scans are subject to high amounts of contamination from scattered photons. Currently, no accurate correction exists to account for scatter in these data. The primary purpose of this work was to implement and validate an analytical scatter correction for PET transmission scanning. In order to isolate the effects of scatter, we developed a simulation tool which was validated using experimental transmission data. We then presented an analytical scatter correction for singles-mode transmission data in PET. We compared our scatter correction data with the previously validated simulation data for uniform and non-uniform phantoms and for two different transmission source radionuclides. Our scatter calculation correctly predicted the contribution from scattered photons to the simulated data for all phantoms and both transmission sources. We then applied our scatter correction as part of an iterative reconstruction algorithm for simulated and experimental PET transmission data for uniform and non-uniform phantoms. We also tested our reconstruction and scatter correction procedure using transmission data for several animal studies (mice, rats and primates). For all studies considered, we found that the average reconstructed linear attenuation coefficients for water or soft-tissue regions of interest agreed with expected values to within 4%. Using a 2.2 GHz processor, the scatter correction required between 6 to 27 minutes of CPU time (without any code optimisation) depending on the phantom size and source used. This extra calculation time does not seem unreasonable considering that, without scatter corrections, errors in the reconstructed attenuation coefficients were between 18 to 45% depending on the phantom size and transmission source used.

The images in positron emission tomography (PET) represent three dimensional dynamic distributions of biologically interesting molecules labelled with positron emitting radionuclides (radiotracers). Spatial localisation of the radio-tracers is achieved by detecting in coincidence two collinear photons which are emitted when the positron annihilates with an ordinary electron. In order to obtain quantitatively accurate images in PET, it is necessary to correct for the effects of photon attenuation within the subject being imaged. These corrections can be obtained using singles-mode photon transmission scanning. Although suitable for small animal PET, these scans are subject to high amounts of contamination from scattered photons. Currently, no accurate correction exists to account for scatter in these data. The primary purpose of this work was to implement and validate an analytical scatter correction for PET transmission scanning. In order to isolate the effects of scatter, we developed a simulation tool which was validated using experimental transmission data. We then presented an analytical scatter correction for singles-mode transmission data in PET. We compared our scatter correction data with the previously validated simulation data for uniform and non-uniform phantoms and for two different transmission source radionuclides. Our scatter calculation correctly predicted the contribution from scattered photons to the simulated data for all phantoms and both transmission sources. We then applied our scatter correction as part of an iterative reconstruction algorithm for simulated and experimental PET transmission data for uniform and non-uniform phantoms. We also tested our reconstruction and scatter correction procedure using transmission data for several animal studies (mice, rats and primates). For all studies considered, we found that the average reconstructed linear attenuation coefficients for water or soft-tissue regions of interest agreed with expected values to within 4%. Using a 2.2 GHz processor, the scatter correction required between 6 to 27 minutes of CPU time (without any code optimisation) depending on the phantom size and source used. This extra calculation time does not seem unreasonable considering that, without scatter corrections, errors in the reconstructed attenuation coefficients were between 18 to 45% depending on the phantom size and transmission source used. / Science, Faculty of / Physics and Astronomy, Department of / Graduate

The RTT80 cone beam x-ray computed tomography system, developed by Rapiscan Systems Ltd, uses switched x-ray sources and fixed offset detector rings to remove the time consuming mechanical rotations of earlier imaging systems. This system produces three-dimensional images in real time. A Geant4 Monte Carlo simulation has been developed to investigate scattered radiation in the uncollimated detector machine, showing high levels of scatter behind highly attenuating objects. A new scatter correction method is proposed which estimates scatter to each detector, in each projection, from 1cm³ voxels of the computerised object. The scatter distributions from different materials are pre-determined using a Geant4 Monte Carlo simulation. The intensity of scatter from each voxel is based on measured data. The method is applied to two simulated test objects, a water box simulated with a monoenergetic input spectrum and a test suitcase simulated with a polyenergetic spectrum. The test suitcase is broken down into separate components to analyse the method further. The results show that the method performs well for low attenuating objects, but the results are sensitive to the intensity values. However, the method provides a good basis for a scatter correction method.

Positron emission tomography (PET) is a nuclear medicine imaging techniquethat uses radiotracers to visualize processes like metabolism and perfusion. Theradiotracer emits positrons, which collide with shell electrons of the atomsthat make up the surrounding tissue. Such a collision produces two gammarayphotons, emitted roughly 180 degrees apart [1]. PET captures thesephotons using a cylindrical arrangement of detectors. When two photons aredetected simultaneously by different detectors, it registers as a line of response(LOR). These LORs are then pre-processed into a sinogram. A mathematicalreconstruction method is used to computationally recover the 3D distribution ofthe radiotracer (activity map) from the sinogram. However, genuine LORs can becorrupted by false LORs that come from scattering, random events, and spuriousevents. Mitigating these in reconstruction algorithms is essential for improvingPET imaging accuracy and reliability.This paper explores the theoretical foundation of the Time of Flight (TOF) SingleScatter Simulation (SSS) model by Watson (2007) [2]. It also includes a Pythonimplementation of the MATLAB code associated with [2]. The model modelsCompton scattering to accurately estimate scattered photons in PET.Incorporating TOF data into the SSS model improves estimation accuracy, albeitat the cost of increased computational time. To expedite computations, thealgorithm was simplified by restricting operations to a subset of rings anddetectors and by pre-processing images through cropping and downscaling.Interpolation fills in missing data, ensuring complete estimation.The outcome of this project is a Python implementation that exhibited a strongcorrelation with the estimates obtained using the MATLAB implementation. Anotable issue arose during the comparison between the main components ofthe SSS algorithm in Python and MATLAB. The Euclidean norm between theresults from these two implementations was significant, indicating that they wereon different scales. Nevertheless, both implementations accurately predictedthe scatter in the same locations and relative magnitudes, despite the scalediscrepancy. Investigation into the discrepancy’s cause is ongoing, but theproject demonstrates the feasibility of implementing the TOF SSS algorithm inPython.

En radiothérapie, la tomodensitométrie (CT) fournit l’information anatomique du patient utile au calcul de dose durant la planification de traitement. Afin de considérer la composition hétérogène des tissus, des techniques de calcul telles que la méthode Monte Carlo sont nécessaires pour calculer la dose de manière exacte. L’importation des images CT dans un tel calcul exige que chaque voxel exprimé en unité Hounsfield (HU) soit converti en une valeur physique telle que la densité électronique (ED). Cette conversion est habituellement effectuée à l’aide d’une courbe d’étalonnage HU-ED. Une anomalie ou artefact qui apparaît dans une image CT avant l’étalonnage est susceptible d’assigner un mauvais tissu à un voxel. Ces erreurs peuvent causer une perte cruciale de fiabilité du calcul de dose. Ce travail vise à attribuer une valeur exacte aux voxels d’images CT afin d’assurer la fiabilité des calculs de dose durant la planification de traitement en radiothérapie. Pour y parvenir, une étude est réalisée sur les artefacts qui sont reproduits par simulation Monte Carlo. Pour réduire le temps de calcul, les simulations sont parallélisées et transposées sur un superordinateur. Une étude de sensibilité des nombres HU en présence d’artefacts est ensuite réalisée par une analyse statistique des histogrammes. À l’origine de nombreux artefacts, le durcissement de faisceau est étudié davantage. Une revue sur l’état de l’art en matière de correction du durcissement de faisceau est présentée suivi d’une démonstration explicite d’une correction empirique. / Computed tomography (CT) is widely used in radiotherapy to acquire patient-specific data for an accurate dose calculation in radiotherapy treatment planning. To consider the composition of heterogeneous tissues, calculation techniques such as Monte Carlo method are needed to compute an exact dose distribution. To use CT images with dose calculation algorithms, all voxel values, expressed in Hounsfield unit (HU), must be converted into relevant physical parameters such as the electron density (ED). This conversion is typically accomplished by means of a HU-ED calibration curve. Any discrepancy (or artifact) that appears in the reconstructed CT image prior to calibration is susceptible to yield wrongly-assigned tissues. Such tissue misassignment may crucially decrease the reliability of dose calculation. The aim of this work is to assign exact physical values to CT image voxels to insure the reliability of dose calculation in radiotherapy treatment planning. To achieve this, origins of CT artifacts are first studied using Monte Carlo simulations. Such simulations require a lot of computational time and were parallelized to run efficiently on a supercomputer. An sensitivity study on HU uncertainties due to CT artifacts is then performed using statistical analysis of the image histograms. Beam hardening effect appears to be the origin of several artifacts and is specifically addressed. Finally, a review on the state of the art in beam hardening correction is presented and an empirical correction is exposed in detail.

A quantitative evaluation of single photon emission computed tomography (SPECT) and planar scintigraphic imaging may be valuable for both diagnostic and therapeutic purposes. For an accurate quantification it is usually necessary to correct for attenuation and scatter and in some cases also for septal penetration. For planar imaging a background correction for the contribution from over- and underlying tissues is needed. In this work a few correction methods have been evaluated and further developed. Much of the work relies on the Monte Carlo method as a tool for evaluation and optimisation. A method for quantifying the activity of I-125 labelled antibodies in a tumour inoculated in the flank of a mouse, based on planar scintigraphic imaging with a pin-hole collimator, has been developed and two different methods for background subtraction have been compared. The activity estimates of the tumours were compared with measurements in vitro. The major part of this work is attributed to SPECT. A method for attenuation and scatter correction of brain SPECT based on computed tomography (CT) images of the same patient has been developed, using an attenuation map calculated from the CT image volume. The attenuation map is utilised not only for attenuation correction, but also for scatter correction with transmission dependent convolution subtraction (TDCS). A registration method based on fiducial markers, placed on three chosen points during the SPECT examination, was evaluated. The scatter correction method, TDCS, was then optimised for regional cerebral blood flow (rCBF) SPECT with Tc-99m, and was also compared with a related method, convolution scatter subtraction (CSS). TDCS has been claimed to be an iterative technique. This requires however some modifications of the method, which have been demonstrated and evaluated for a simulation with a point source. When the Monte Carlo method is used for evaluation of corrections for septal penetration, it is important that interactions in the collimator are taken into account. A new version of the Monte Carlo program SIMIND with this capability has been evaluated by comparing measured and simulated images and energy spectra. This code was later used for the evaluation of a few different methods for correction of scatter and septal penetration of I-123 brain SPECT. The methods were CSS, TDCS and a method where correction for scatter and septal penetration are included in the iterative reconstruction. This study shows that quantitative accuracy in I-123 brain SPECT benefits from separate modelling of scatter and septal penetration.