Motion is a massive barrier in cancer imaging hampering developments in cancer research and treatment. My research work has comprised the development of accurate, thus complex and realistic but still computationally efficient, models of organ motion, and has established a solid foundation to reliable quantitative cancer image analysis. The developed image analysis framework stands as an essential tool:
- for monitoring changes of lung tumours during treatment, especially when a tumour is located close to chest boundaries, preserving discontinuities (i.e. sliding motion) when multiple organs move independently;
- to enable meaningful analysis of a wide range of dynamic contrast-enhanced imaging sequences (e.g. dynamic contrast-enhanced Magnetic Resonance Imaging, perfusion Computed Tomography), opening to clinical researchers a new opportunity for reliable tumour heterogeneity assessment, leading to a better understanding of functional tumour microenvironment (e.g. tumour metabolism), with possible improvements in the future to patient stratification;
- for analysis of ex-vivo lung imaging data, exploring metastatic cancer colonies in small rodents, significantly expanding the usability of an in vivo microscopy techniques by reducing the high rate of abortive experiments caused by motion artefacts.