X-ray Computed Tomography (CT) imaging has been playing a central role in clinical practice since it was invented in 1972. However, the traditional x-ray CT technique fails to distinguish different materials with similar density, especially for biological tissues. The lack of a quantitative imaging representation has constrained the application of CT technique from a broadening application such as personal or precision medicine. Therefore, my major thesis statement is to develop novel material-specific CT imaging techniques for molecular imaging in biological bodies. To achieve the goal, comprehensive studies were conducted to investigate three different techniques: x-ray fluorescence molecular imaging, material identification (specification) from photon counting CT, and photon counting CT data distortion correction approach based on deep learning. X-ray fluorescence molecular imaging (XFMI) has shown great promise as a low-cost molecular imaging modality for clinical and pre-clinical applications with high sensitivity. In this study, the effects of excitation beam spectrum on the molecular sensitivity of XFMI were experimentally investigated, by quantitatively deriving minimum detectable concentration (MDC) under a fixed surface entrance dose of 200 mR at three different excitation beam spectra. The result shows that the MDC can be readily increased by a factor of 5.26 via excitation spectrum optimization. Furthermore, a numerical model was developed and validated by the experimental data (≥0.976). The numerical model can be used to optimize XFMI system configurations to further improve the molecular sensitivity. Findings from this investigation could find applications for in vivo pre-clinical small-animal XFMI in the future. PCCT is an emerging technique that has the ability to distinguish photon energy and generate much richer image data that contains x-ray spectral information compared to conventional CT. In this study, a physics model was developed based on x-ray matter interaction physics to calculate the effective atomic number () and effective electron density () from PCCT image data for material identification. As the validation of the physics model, the and were calculated under various energy conditions for many materials. The relative standard deviations are mostly less than 1% (161 out of 168) shows that the developed model obtains good accuracy and robustness to energy conditions. To study the feasibility of applying the model with PCCT image data for material identification, both PCCT system numerical simulation and physical experiment were conducted. The result shows different materials can be clearly identified in the − map (with relative error ≤8.8%). The model has the value to serve as a material identification scheme for PCCT system for practical use in the future. As PCCT appears to be a significant breakthrough in CT imaging field, there exists severe data distortion problem in PCCT, which greatly limits the application of PCCT in practice. Lately, deep learning (DL) neural network has demonstrated tremendous success in medical imaging field. In this study, a deep learning neural network based PCCT data distortion correction method was proposed. When applying the algorithm to process the test dataset data, the accuracy of the PCCT data can be greatly improved (RMSE improved 73.7%). Compared with traditional data correction approaches such as maximum likelihood, the deep learning approach demonstrate superiority in terms of RMSE, SSIM, PSNR, and most importantly, runtime (4053.21 sec vs. 1.98 sec). The proposed method has the potential to facilitate the PCCT studies and applications in practice. / Doctor of Philosophy / X-ray Computed Tomography (CT) has played a central role in clinical imaging since it was invented in 1972. It has distinguishing characteristics of being able to generate three dimensional images with comprehensive inner structural information in fast speed (less than one second). However, traditional CT imaging lacks of material-specific capability due to the mechanism of image formation, which makes it cannot be used for molecular imaging. Molecular imaging plays a central role in present and future biomedical research and clinical diagnosis and treatment. For example, imaging of biological processes and molecular markers can provide unprecedented rich information, which has huge potentials for individualized therapies, novel drug design, earlier diagnosis, and personalized medicine. Therefore there exists a pressing need to enable the traditional CT imaging technique with material-specific capability for molecular imaging purpose. This dissertation conducted comprehensive study to separately investigate three different techniques: x-ray fluorescence molecular imaging, material identification (specification) from photon counting CT, and photon counting CT data distortion correction approach based on deep learning. X-ray fluorescence molecular imaging utilizes fluorescence signal to achieve molecular imaging in CT; Material identification can be achieved based on the rich image data from PCCT; The deep learning based correction method is an efficient approach for PCCT data distortion correction, and furthermore can boost its performance on material identification. With those techniques, the material-specific capability of CT can be greatly enhanced and the molecular imaging can be approached in biological bodies.

Research on photon counting detectors (PCDs) is focused on semiconductor materials, and silicon is a strong candidate to use in PCDs for photon counting computer tomography (CT). In a silicon detector, a significant portion of the counts is due to Compton scattering events. Since only part of the incident photon energy is deposited in a Compton interaction, Compton interactions lead to a loss of spectral information. By using Compton coincidence detection, i.e., combining information from multiple Compton events caused by the same incident photon, it is possible to obtain more spectral information from Compton scattered photons, increasing the energy resolution of the detector. The goal of this thesis is to develop and evaluate a method for Compton coincidence detection for photon counting CT. In this thesis, a method for Compton coincidence detection based on Compton kinematics and a χ2 test is presented and compared to a previously developed method based on maximum likelihood estimation. The χ2 method utilised the connection between the energy before vs after a Compton interaction, and the scattering angle. The possible scattering angles due to deposited energy in each interaction were called the energy angles. The spatial angles between the interaction positions in the detector were calculated and compared to the energy angles through a χ2 test in order to find the correct order of interaction and the incident photon energy. The χ2 method correctly identified the interaction order of 85.8% of simulated interaction chains ending in photoelectric effect and 64.1% of simulated interaction chains containing only Compton interactions. The energy estimation was 100% correct for all chains ending in photoelectric effect, since all of the incident energy was deposited in the detector. For chains of only Comptoninteractions, the energy was estimated with an RMS error of 21.2 keV. Combining the results from chains ending in a photoelectric interaction and chains of only Comptoninteractions, the total RMS error of the energy estimation was 11.5 keV. / Datortomografi (CT) är en viktig del av dagens sjukvård, och fotonräknande detektorer för CT är på väg från forskning till klinisk användning. Forskningen inom fotonräknande detektorer fokuserar på att använda halvledande material, och kisel är en stark kandidat till att användas för fotonräknande detektorer. I en kiseldetektor interagerar en betydande andel av fotonerna genom Compton-spridning. Då endast en del av fotonenergin deponeras i detektorn när en Compton-interaktion sker leder det till en förlust av spektral information. Genom att kombinera information från flera Compton-interaktioner som orsakats av samma infallande foton, så kallad sammanfallsdetektering, är det möjligtatt erhålla en ökad mängd spektral information från Compton-spridna fotoner. Målet med detta examensarbete är att utveckla och utvärdera en metod för sammanfallsdetektering för att erhålla spektral information från Compton-spridda fotoner i en detektortill fotonr¨aknande CT. I detta arbete presenteras en metod baserad på kinematiken bakom en Compton-interaktion och ett χ2-test. Metoden jämförs sedan med en tidigare utvecklad metod baserad på maximum likelihood-uppskattning. χ2-metoden utnyttjade sambandet mellan deponerad energi i en Compton-interaktion och möjliga spridningsvinklar, här kallade energivinklar. De spatiella vinklarna mellan interaktionerna i detektorn mättes och jämfördes genom ett χ2-test för att hitta interaktionsordningen och den infallande energin. χ2-metoden identifierade interaktionsordningen korrekt för 85.5% av alla simulerade interaktionskedjor som slutade i fotoelektrisk effekt och 64.1% av alla simulerade interaktionskedjorsom endast innehöll Compton-interaktioner. Uppskattningen av infallande energi var 100% korrekt för alla interaktionskedjor som slutade med en fotoelektrisk interaktion,eftersom all infallande energi deponerats i detektorn. För kejdor som endast bestod av Compton-interaktioner uppskattades den infallande energin med ett RMS-fel på 21.2 keV. Genom att kombinera resultaten från kedjor som slutade med en fotoelektrisk interaktion och resultaten från kejdor som endast bestod av Compton-interaktioner blev det totala RMS-felet för energi-uppskattningen 11.5 keV.

Ma thèse de doctorat s’inscrit à l’interface entre la physique expérimentale et la biologie. Ce travail a été développé au sein de l’équipe imXgam du CPPM, qui a construit un prototype de micro-tomographie pour le suivi non-invasif du petit animal, équipé d’une caméra à pixels hybrides XPAD3 fonctionnant en mode comptage de rayons X. Le comptage de rayons X rendu possible par la technologie des pixels hybrides, permet de s’affranchir du bruit électronique et d’augmenter ainsi la détectabilité des tissus faiblement contrastés. Elle présente de plus la capacité d'appliquer à chaque pixel un seuil de détection en énergie permettant d’accéder à l’information spectrale des rayons X détectés et ouvre la voie au développement d’une méthode d’imagerie spectrale dite au K-edge, qui permet de différencier des agents de contraste particuliers. La caméra XPAD3 développée avec un capteur en Si présente une efficacité de détection qui limite son utilisation pour l’imagerie du vivant. Une caméra XPAD3 avec une meilleure efficacité au delà de 25 keV a été assemblée avec des capteurs en CdTe. Dans un premier temps, nous avons effectué une comparaison des caméras XPAD3/Si et XPAD3/CdTe en imagerie d’absorption standard et en imagerie au K-edge. Nous avons ensuite, en collaboration avec des biologistes de l’IBDM, assuré le suivi quantitatif et in vivo sur plusieurs mois, du développement de tumeurs hépatiques chez un modèle spécifique de souris et de l’efficacité d’un traitement ciblant les cellules tumorales. Enfin, nous avons développé un protocole d’acquisition spectrale à faible dose pour réaliser une tomographie spectrale in vivo d’un foie de souris en exploitant le K-edge du baryum. / My PhD thesis is at the interface between experimental physics and biology. This work has been developed within the imXgam team at CPPM, which has built a micro-computed tomography prototype for the non-invasive longitudinal monitoring of small animal, equipped with the XPAD3 hybrid pixel camera that operates in X-ray photon counting mode. X-ray photon counting that has been made possible by hybrid pixels, allows to free images from the electronic noise and thus to increase detectability of weakly contrasted tissues. Moreover, it provides the possibility to set an energy threshold for each pixel that allows to accessing spectral information on the detected X-rays and paving the way to the development of a spectral imaging modality also named K-edge imaging, which allows to differentiate selected contrast agents. Actually, the XPAD3 camera developed with a Si sensor presents a low detective efficiency that limits its use for biomedical imaging. A XPAD3 camera with a better efficiency above 25 keV has been assembled with high-Z CdTe sensors. Firstly, we have performed a comparison of XPAD3/Si and XPAD3/CdTe cameras for standard absorption CT and K-edge imaging. Then, in collaboration with a team of biologists from IBDM, we have carried out the quantitative and in vivo follow-up of hepatic tumour development in a specific mouse model over several months, and of the effectiveness of a treatment targeting these tumour cells. Finally, we have developed a protocol for low dose acquisition of spectral data to realize an in vivo spectral tomography of a mouse liver using the barium spectral signature.

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