Development of an In-Silico Research Framework for Accelerating the Translation of Quantitative Photon-Counting Spectral Imaging to the Clinic

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Personalising patient treatments and assessing treatment response are both tasks which could benefit greatly from molecular information. SPECT and PET offer molecular imaging but are expensive, have relatively poor spatial resolution and require specialist radio-pharmaceuticals and facilities. MRI can provide some molecular information, but these scanners are slow and many patients are unable to use MRI machines due to metal implants, pacemakers or claustrophobia. Ideally molecular imaging could be obtained from x-ray images, as these systems are fast, offer excellent spatial resolution and are suitable for almost all patient populations. Unfortunately, conventional x-ray machines are unable to provide molecular information, offer poor soft tissue contrast and deliver significant ionising radiation doses. All three of these problems are addressed in a new x-ray imaging technology, known as x-ray photon counting spectral imaging (x-CSI), which provides MRI comparable soft tissue contrast with CT spatial resolution and only 1 fifth of the radiation dose.x-CSI technology is just now entering clinical trials, with all major healthcare manufacturers working on developing their own system. Yet many important questions remain regarding how x-CSI can best be exploited for patient benefit. What are the best pixel sizes, sensor materials, signal correction schemes etc.? How should the spectral data be reconstructed? What clinical applications would benefit most from the added information? Computer simulations are normally used to answer these questions, however x-CSI simulations are significantly more complicated than conventional x-ray simulations due to the higher sensitivity to distortions from short range physics processes and consequently the more complicated electronics required. There are thus currently no tools capable of modelling an x-CSI scanner in enough detail to answer these questions fully. This project seeks to redress this by:1. Extending our existing simulation framework to better model short range physics processes that degrade x-CSI images and the novel electronics proposed to correct for them. We would also add 3D image reconstruction and image analysis tools so that imaging tasks used in treating cancer patients can be simulated2. Using the completed framework to optimise an x-CSI scanner for each of three different cancer related imaging tasks, considering a range of different cancer types as identified by our oncologist and radiologist collaborators3. Optimising a single general-purpose x-CSI scanner for performing all three clinical imaging tasks4. Comparing the general-purpose scanner in each imaging task with the scanner optimised for that task, quantifying any performance differencesThis work would provide both immediate and longer-term benefits to a range of stakeholders. By quantifying performance differences between a general-purpose and task optimised scanner for each clinical imaging task, this work will be able to determine whether a general-purpose scanner will be suitable in oncology, or whether task optimised x-CSI scanners are necessary. Combined with the optimised x-CSI scanner designs determined for the various oncology tasks, this information will both inform healthcare manufacturers seeking to adapt their scanners for oncology, and empower doctors with the information needed to argue for specialist scanners where these could affect clinical decisions. Longer term, publishing instructions for the simulation framework will allow more researchers to engage in x-CSI research by providing a low-cost source alternative to having a physical x-CSI scanner, unrestricted access to the data it generates and the ability to know the ground truth precisely at each stage of the imaging chain.This project would thus accelerate the translation of the x-CSI from the lab to the clinic and ensure that transfer occurs in a way which maximises patient benefit from this cutting-edge technology.