Transforming Tissue Differentiation via Quantum Digital Tomosynthesis

This application is about improving an existing medical imaging technique which is used during cancer surgery to distinguish between healthy and non-healthy tissue. The improvements will rely on the application of 'quantum technology'.

Pathology is the study and diagnosis of disease through examination of surgically removed organs, tissues (biopsy samples) and fluids. When a cancerous tumour is excised (taken out) the surgeon needs to be certain that all the diseased tissue has been removed, and therefore they also remove some surrounding tissue around the edge of the tumour (the 'margins'). The surgeon needs to be sure these margins are free of cancer and can be described as 'clear or negative'. Clear margins suggest all the cancer has been removed and is not able to spread, giving the best outcome for the patient.

So, a highly sensitive method of differentiating between healthy and unhealthy soft tissue is vital, and also between soft and hard tissues (bones). The establishment of these 'clear tissue margins' is best done whilst surgery is ongoing -- so the technique also needs to give accurate 3D images quickly and not take up much room in a busy operating theatre.

Currently this is done via 'pathology cabinets' which give 2D or 3D images - but are often are slow (several minutes) and bulky (similar to a filing cabinet). The need is for more accurate differentiation of the boundaries between the tumour and healthy tissue, enabling surgeons to make confident real-time decisions during operations. The equipment also needs to be cost-effective, have a small footprint in the operating theatre and give accurate, easily understandable images.

This grant would be used to build a prototype of a new type of pathology cabinet -- using quantum technology applied to both key parts of the system (the X-ray source & detector), plus new software to produce high-resolution material discriminating images (which are also better suited for the training of machine learning and application of Artificial Intelligence).

The resulting images would give better differentiation between cancerous and healthy tissue, enabling surgeons to confidently remove the minimum amount of healthy tissue whilst being sure of clear margins. This will benefit healthcare providers in terms of better patient care, reduced workflow and costs, and most importantly, improve outcomes for patients in terms of reduced risk of more than one operation and a reduced chance of cancer spreading from positive margins left after initial surgery.