INSPIRE: Integration of Non-linear Sliding Processes into Image REgistration

Lead Research Organisation: University of Oxford
Department Name: Engineering Science

Abstract

The purpose of this research project proposal is to design and investigate a new generation of nonlinear image registration methodologies using a novel constrained optimization approach based on the integration of sliding motion of organs. Though completely generic, the approach will be explored primarily for respiratory lung motion compensation in cancer CT imaging, where the sliding motion of the lungs interfacing to the pleura poses a particularly challenging problem in detecting disease and in monitoring of treatment or intervention. We believe that this kind of approach will find application in a wide range of clinical problems where respiratory motion is affecting imaged organs. For example, the slipping motion of masses in breast ultrasound could be tracked for image-guided biopsies. Similarly, liver tumours could be tracked for ultrasound-guided targeted drug delivery, compensating for the sliding of the liver due to the diaphragm contraction during the breathing cycle. One key and entirely novel idea to be developed in this project is to constrain registration to directional sliding motion along the lung surface, while compensating for deformations occurring within the lungs due to expansion or compression occurring at varying levels of respiration. Rather than regularizing the registration cost function locally, a more principled approach will be taken which embeds surfaces of the lungs, and also of nearby organs like the liver, to help drive the registration process in a physiologically plausible manner. Integrating surfaces in the registration will enable us to model - and hence recover - slipping motion of surfaces within an otherwise smooth motion field. Our hypothesis is that this integrated, constrained registration framework will be physiologically more grounded than current, state-of-the-art motion correction approaches which are largely ad-hoc. Consequently the proposed research will provide a significant step towards improved diagnosis and disease monitoring. We will demonstrate the benefit of our new registration methodology by applying it to respiratory motion correction in serial CT lung cancer imaging for patients with malignant pleural mesothelioma, who have been imaged using CT over the course of chemotherapy treatment.

Planned Impact

The direct beneficiaries of this research will be lung cancer patients. These benefits include many different aspects: Firstly, the research will contribute toward a significantly improved understanding of lung cancer and treatment response. Monitoring the disease, once detected, is essential for detecting and quantifying changes due to cancer growth or regression, as a response to therapy. The proposed image registration method will be a prerequisite for disease monitoring, and developing and evaluating therapeutic strategies. Secondly, the research will improve patient care by making imaging more robust with respect to patient motion and inconsistent breath holds, and delivering higher quality image data for analysis. Third, since the biophysically-grounded motion discontinuity extension to image registration is quite generic, the research should have impact widely across medical image analysis. To maximise the impact of this project we plan to work closely with our clinical collaborators at the Churchill Hospital, in particular Dr Fergus Gleeson, and will cross-link this research with ongoing research in the Oxford Cancer Imaging Centre, and the Wellcome Centre of Excellence in Medical Engineering at Oxford. This provides an excellent route for translation of the proposed research outcomes into the clinic. We will engage the wider clinical community and the NHS about the impact of these technologies in improving health care provision and reducing costs in the NHS and how they can be widely integrated into clinical workflow. We will submit articles to international journals such as IEEE Transactions on Medical Imaging, or Medical Image Analysis, and to international conferences such as MICCAI and IPMI, or national ones such as BMVA and MIUA. In addition to academic dissemination there will be a prospect of clinical dissemination, including staff groups from radiologists and oncologists. In addition, several companies (including Siemens Molecular Imaginf and GlaxSmithKline) have already expressed strong interest in this research. We will approach these and as well as other companies with a view to explore the potential for commercialization of the developed algorithms. For this we will involve Isis Innovation which is Oxford University's technology transfer company. Isis has a long experience in the commercialisation of intellectual property including patenting, licensing, spin-out companies. We will also involve them in developing an appropriate IP protection strategy for the algorithms developed. Finally, many of the concepts which will be explore in this project have applicability beyond the proposed application in lung imaging. For example, the modelling of discontinuous motion and deformation is crucial in many other medical imaging applications, e.g. in orthopaedic applications involving soft tissues and rigid bones. Similarly, the work on constrained registration will be applicable in many other scenarios.

Publications

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Heinrich H (2013) MRF-Based Deformable Registration and Ventilation Estimation of Lung CT in IEEE Transactions on Medical Imaging

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Heinrich M (2014) Biomedical Image Registration

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Heinrich MP (2014) Multispectral image registration based on local canonical correlation analysis. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

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Heinrich MP (2013) Edge- and detail-preserving sparse image representations for deformable registration of chest MRI and CT volumes. in Information processing in medical imaging : proceedings of the ... conference

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Heinrich MP (2011) Non-local shape descriptor: a new similarity metric for deformable multi-modal registration. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

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Heinrich MP (2012) Globally optimal deformable registration on a minimum spanning tree using dense displacement sampling. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

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Heinrich MP (2013) Towards realtime multimodal fusion for image-guided interventions using self-similarities. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

 
Description The purpose of this research project proposal was to design and investigate a new generation of nonlinear image registration methodologies using a novel constrained optimisation approach based on the integration of sliding motion of organs. Though completely generic, the approach has been explored primarily for respiratory lung motion compensation in cancer CT imaging, where the sliding motion of the lungs interfacing to the pleura poses a particularly challenging problem in detecting disease and in monitoring of treatment or intervention. Recently this kind of approach has also found application in compensating for liver motion in other imaging modalities, and is expected to be applicable to a wide range of clinical problems where respiratory motion is affecting imaged organs. It has also been the foundation for a recent EPSRC Healthcare Impact Partnership award on early detection for lung cancer.
Exploitation Route Close collaboration with local clinicians provides a clear translation of our research findings into clinical practice, with main beneficiaries being lung cancer patients. Parts of this research are being continued within the CRUK/EPSRC Cancer imaging Centre at Oxford. This is now continued as part fo a recent EPSRC Healthcare Impact Partnership award involving lung consults/chest physicians from Guy's and St. Thomas' Hospital trust, King's College Hospital, and the Royal Brompton National Heart and Lung Institute, and academic partners at Imperial College London, with Siemens Healthineers as the industry partner.
Sectors Digital/Communication/Information Technologies (including Software),Healthcare

URL https://kclpure.kcl.ac.uk/portal/julia.schnabel.html
 
Description Sliding motion problems in medical imaging, as investigated this grant have been further addressed by the scientific community based on our findings, and have received international recognition (see awards and recognition). It has also led to two further important awards - a Royal International Exchange award with Nagoya University in Japan, looking further at lung lymph node detection and involvement (see publications), and more recently an EPSRC Healthcare Impact Partnership award for early lung cancer detection, for which this award was the qualifying grant, and which is aiming to lay the base for a national lung cancer screening trial.
First Year Of Impact 2010
Sector Education,Healthcare
Impact Types Societal

 
Description Healthcare Impact Partnership
Amount £932,050 (GBP)
Funding ID EP/P023509/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 10/2017 
End 09/2020
 
Description MRC Industrial CASE studentship
Amount £104,565 (GBP)
Funding ID MR/N018028/1 
Organisation Medical Research Council (MRC) 
Sector Public
Country United Kingdom
Start 04/2017 
End 03/2021
 
Description RCs
Amount £69,121 (GBP)
Funding ID CASE with Siemens Molecular Imaging, Oxford 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 10/2012 
End 04/2016
 
Description Royal Society International Exchange
Amount £12,000 (GBP)
Organisation The Royal Society 
Sector Charity/Non Profit
Country United Kingdom
Start 03/2015 
End 02/2016
 
Description New strategies for physiologically realistic diffeomorphic image alignment 
Organisation Paul Sabatier University (University of Toulouse III)
Country France 
Sector Academic/University 
PI Contribution Bilateral research visits with the Institut de Mathématiques de Toulouse (Dr Laurent Risser), resulting in joint publications.
Collaborator Contribution Bilateral research visits with the Institut de Mathématiques de Toulouse (Dr Laurent Risser), resulting in joint publications.
Impact A range of publication have arisen with Dr Laurent Risser through this continued collaboration, which have been associated with this record.
Start Year 2012
 
Description Predictive deformation modelling in medical imaging 
Organisation Paul Sabatier University (University of Toulouse III)
Country France 
Sector Academic/University 
PI Contribution Bilateral research visits with the Institut de Mathématiques de Toulouse (Dr Laurent Risser).
Collaborator Contribution Bilateral research visits with the Institut de Mathématiques de Toulouse (Dr Laurent Risser).
Impact A range of publication have arisen with Dr Laurent Risser through this continued collaboration, which have been associated with this record.
Start Year 2014
 
Description Royal Society International Exchange: Image data science for lung cancer 
Organisation Nagoya University
Country Japan 
Sector Academic/University 
PI Contribution Prof Kensaku Mori and his research laboratory at Nagoya University have multiple exchanges with Prof Schnabel's laboratory.
Collaborator Contribution Prof Kensaku Mori and his research laboratory at Nagoya University have multiple exchanges with Prof Schnabel's laboratory.
Impact Under submission.
Start Year 2015
 
Description collaboration within the Cancer Imaging Centre in Oxford 
Organisation Oxford University Hospitals NHS Foundation Trust
Country United Kingdom 
Sector Academic/University 
PI Contribution We provided state-of-the-art software tools for our clinical partners and we helped analyse medical imaging data from a number of prospective and retrospective clinical trials.
Collaborator Contribution Our partners provided us with useful insight into the biological problem under investigation. They helped us formulate meaningful research questions and provided us with data from on-going clinical trials.
Impact The collaboration is multi-disciplinary, involving staff from Engineering, Oncology and Radiology.
Start Year 2011