IMPPACT

IMPPACT (Image-based multi-scale physiological planning for ablation cancer treatment) will develop an intervention planning and monitoring application for Radiofrequency Ablation (RFA) of malignant liver tumours. RFA is a minimally invasive form to treat cancer without open surgery, by placing a needle inside the malignancy and destroying it through intensive heating. Though the advantages of this approach are obvious, the intervention is currently hard to plan, almost impossible to monitor or assess, and therefore is not the first choice for treatment.

IMPPACT will develop a physiological model of the liver and simulate the intervention's result, accounting for patient specific physiological factors. Gaps in the understanding of particular aspects of the RFA treatment will be closed by multi-scale studies on cells and animals. New findings will be evaluated microscopically and transformed into macroscopic equations. The long-established bio-heat equation will be extended to incorporate multiple scales. Validation will be performed at multiple levels. Images from ongoing patient treatment will be used to cross check validity for human physiology. Final validation will be performed at macroscopic level through visual comparison of simulation and treatment results gathered in animal studies and during patient treatment.

This extensive validation together with a user-centred software design approach will guarantee suitability of the solution for clinical practice. The consortium consists of two Hospitals, three Universities, one Research Institute and one industrial SME. The final project deliverables will be the patient specific intervention planning system and an augmented reality training simulator for the RFA intervention.

For further information, please visit:
http://imppact.icg.tugraz.at/

Project co-ordinator:
Fraunhofer Gesellschaft zur Förderung der angewandten Forschung e.V. (Germany)

Partners:

  • NUMA Engineering Services Ltd (Ireland)
  • Universität Leipzig (Germany)
  • Chancellor, Masters and Scholars of the University of Oxford (United Kingdom)
  • Medizinische Universität Graz (Austria)
  • TKK - Teknillinen korkeakoulu (Finland)
  • Technische Universität Graz (Austria)

Timetable: from 09/2008 - to 08/2011

Total cost: € 4.550.000

EC funding: € 3.460.000

Programme Acronym: FP7-ICT

Subprogramme Area: Virtual physiological human

Contract type: Collaborative project (generic)


Related news article:

Most Popular Now

500 Patient Images per Second Shared thr…

The image exchange portal, widely known in the NHS as the IEP, is now being used to share as many as 500 images each second - including x-rays, CT, MRI...

Is Your Marketing Effective for an NHS C…

How can you make sure you get the right message across to an NHS chief information officer, or chief nursing information officer? Replay this webinar with Professor Natasha Phillips, former...

We could Soon Use AI to Detect Brain Tum…

A new paper in Biology Methods and Protocols, published by Oxford University Press, shows that scientists can train artificial intelligence (AI) models to distinguish brain tumors from healthy tissue. AI...

Welcome Evo, Generative AI for the Genom…

Brian Hie runs the Laboratory of Evolutionary Design at Stanford, where he works at the crossroads of artificial intelligence and biology. Not long ago, Hie pondered a provocative question: If...

Telehealth Significantly Boosts Treatmen…

New research reveals a dramatic improvement in diagnosing and curing people living with hepatitis C in rural communities using both telemedicine and support from peers with lived experience in drug...

AI can Predict Study Results Better than…

Large language models, a type of AI that analyses text, can predict the results of proposed neuroscience studies more accurately than human experts, finds a new study led by UCL...

Using AI to Treat Infections more Accura…

New research from the Centres for Antimicrobial Optimisation Network (CAMO-Net) at the University of Liverpool has shown that using artificial intelligence (AI) can improve how we treat urinary tract infections...

Research Study Shows the Cost-Effectiven…

Earlier research showed that primary care clinicians using AI-ECG tools identified more unknown cases of a weak heart pump, also called low ejection fraction, than without AI. New study findings...

New Guidance for Ensuring AI Safety in C…

As artificial intelligence (AI) becomes more prevalent in health care, organizations and clinicians must take steps to ensure its safe implementation and use in real-world clinical settings, according to an...

Remote Telemedicine Tool Found Highly Ac…

Collecting images of suspicious-looking skin growths and sending them off-site for specialists to analyze is as accurate in identifying skin cancers as having a dermatologist examine them in person, a...

Philips Aims to Advance Cardiac MRI Tech…

Royal Philips (NYSE: PHG, AEX: PHIA) and Mayo Clinic announced a research collaboration aimed at advancing MRI for cardiac applications. Through this investigation, Philips and Mayo Clinic will look to...

Deep Learning Model Accurately Diagnoses…

Using just one inhalation lung CT scan, a deep learning model can accurately diagnose and stage chronic obstructive pulmonary disease (COPD), according to a study published today in Radiology: Cardiothoracic...