Proton radiotherapy is a cancer treatment which has the potential to offer greater cure rates and/or fewer serious side effects than conventional radiotherapy. Its availability in the UK is currently limited to a single low-energy fixed beamline for the treatment of ocular tumours, but a number of facilities designed to treat deep-seated tumours are in development. This thesis focusses on the quantitative use of x-ray computed tomography (CT) images in planning proton radiotherapy treatments. It arrives at several recommendations that can be used to inform clinical protocols for the acquisition of planning scans, and their subsequent use in treatment planning systems. The primary tool developed is a software CT scanner, which simulates images of an anthropomorphic virtual phantom, informed by measurements taken on a clinical scanner. The software is used to investigate the accuracy of the stoichiometric method for calibrating CT image pixel values to proton stopping power, with particular attention paid to the impact of beam hardening and photon starvation artefacts. The strength of the method adopted is in allowing comparison between CT-estimated and exactly-calculated proton stopping powers derived from the same physical data (specified in the phantom), leading to results that are difficult to obtain otherwise. A number of variations of the stoichiometric method are examined, identifying the best-performing calibration phantom and CT tube voltage (kVp). Improvements in accuracy are observed when using a second-pass beam hardening correction algorithm. Also presented is a method for identifying the proton paths where stopping power uncertainties are likely to be greatest. Estimates of the proton range uncertainties caused by CT artefacts and calibration errors are obtained for a range of realistic clinical scenarios. The current practice of including planning margins equivalent to 3.5% of the range is found to ensure coverage in all but the very worst of cases. Results herein suggest margins could be reduced to <2% if the best-performing protocol is followed; however, an analysis specific to the CT scanner and treatment site in question should be carried out before such a change is made in the clinic.

Les technologies CMOS représentent aujourd’hui plus de 90% du marché des capteurs d’images : elles permettent d’intégrer des systèmes intelligents dans une seule puce (SoC = System-On-Chip) et ouvrent la voie à l’intégration d’algorithmes de plus en plus complexes dans les dernières générations de capteurs. Des techniques telles que la reconstruction grande dynamique nécessitent d’acquérir plusieurs images avec un même capteur et de les recombiner. Ces nouvelles contraintes nécessitent d’augmenter drastiquement le débit d’images pour des capteurs de tailles conséquentes (Jusqu'à 30 Mpixels), ainsi que d’augmenter la résolution du convertisseur analogique numérique (jusqu’à 14 bits). Cela crée une demande forte en techniques de conversion analogique-numérique. Ces techniques doivent obéir en même temps aux contraintes de performance notamment la vitesse, la résolution, le faible bruit, la faible consommation et l'intégrabilité mais aussi aux contraintes de qualité d'image impactées directement par la chaine de conversion analogique-numérique en plus de la technologie du pixel. D'ici découle une double problématique pour le sujet:- Etudier et déterminer les limites atteignables en termes de performance sur les différents axes précités.- Gestion du fonctionnement massivement parallèle lié à la structure inhérente des capteurs d'image en vue d'avoir une qualité d'image irréprochable. / CMOS technologies represent nowadays more than 90% of image sensors market given their features namely the possibility of integrating entire intelligent systems on the same chip (SoC = System-On-Chip). Thereby, allowing the implementation of more and more complex algorithms in the new generations of image sensors.New techniques have emerged like high dynamic range reconstruction which requires the acquisition of several images to build up one, thus multiplying the frame rate.These new constraints require a drastic increase of image rate for sensors ofconsiderable size (Up to 30 Mpix and more). At the same time, the ADCresolution has to be increased to be able to extract more details (until 14 bits).With all these demanding specifications, analog-to-digital conversion capabilities have to be boosted as far as possible.These capabilities can be distinguished into two main research axes representing the pillars of the PhD work, namely:+ The study of the reachable limits in terms of performance: Speed, Resolution,Low Noise, Low power consumption and small design pitch.+ The management of the highly parallel operation linked to the structure of animage sensor. Solutions have to be found so as to avoid image artefacts andpreserve the image quality.