Theme 1

Intervention planning and real-time computational update

Theme overview

This theme is concerned with development of the technologies for interventional planning, and for updating these plans intra-operatively, which ensure optimal delivery of treatments.

Aims and processes

Diagnostic imaging and sensing provide rich information for characterising patient anatomies and pathologies, and in turn a basis for optimising instrument entry points, energy delivery patterns, etc. in a patient-specific manner. The capabilities of different treatment modalities can thereby be maximised while minimising damage to healthy tissues. There is also growing recognition of the efficacy of combining these patient data with computational models that enable patient-specific prediction of treatment outcomes. Virtual placement of surgical devices (stents, needles, prostheses, etc.), prediction of physiological responses to energy inputs and other perturbations, and virtual comparison of treatment modalities all can lead to more reliable planning of interventions. Common tools for processing patient-specific diagnostic data to these ends are required, with interfaces for specialised visualisation and instrument customisation.

Following development of effective intervention plans, tools for updating these during procedures, based on intra-operative images and signals, are equally crucial. The deleterious effects of patient motion from various sources (viz. repositioning, respiration and cardiac motion, swelling, resection, etc.) are particularly relevant and pervasive confounders of precise treatment delivery according to pre-operative plans. Intra-operative data, efficient and robust registration tools for alignment with pre-operative data, and accurate physiological models may be combined to facilitate the required update. The need for real-time tools in this context exacerbates the difficulties.

Relation to other key themes

This theme both exploits and extends many of the technologies for patient characterisation and prediction of treatment outcomes afforded by the other themes: approaches to treatment planning and real-time update must be closely integrated with the requirements of specific therapies (T2), while exploiting and complementing the available interventional images and signals (T3); all of these technologies, in turn, feed development of synergistic clinical workflows, and their associated training and skills assessment needs (T4).

Alignment with the IGT sector

Image-based treatment planning and online computational updating are already key technologies throughout the IGT sector. T1 will support advances in the state-of-the-art in these areas, while seeking to promote translation of new technologies by linking researchers, clinicians, and industry.

Key collaborators

  • The University of Sheffield (Taylor, Frangi)
  • University College London (Barratt, Clarkson, Hawkes, McClelland, Ourselin, Stoyanov, Vercauteren)
  • King’s College London (Rhode, Rezavi)
  • Imperial College London (Yang, Lee)
  • University of Manchester (van Herk)
  • Cardiff University (Bordas)
  • University of East Anglia (Lapeer)

Theme Lead

taylor-zeike

Zeike Taylor

z.a.taylor@sheffield.ac.uk

Dr Taylor’s main area of research is soft tissue modelling and simulation, in particular with application to problems in interactive surgical simulation, and systems for therapy planning and guidance. His team has ongoing projects in mathematical modelling of soft tissues, numerical solution methods, image-based modelling, and applications of these in various clinical domains.