1
|
Tapmeier TT, Howell JH, Zhao L, Papiez BW, Schnabel JA, Muschel RJ, Gal A. Evolving polarisation of infiltrating and alveolar macrophages in the lung during metastatic progression of melanoma suggests CCR1 as a therapeutic target. Oncogene 2022; 41:5032-5045. [PMID: 36241867 PMCID: PMC9652148 DOI: 10.1038/s41388-022-02488-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 09/16/2022] [Accepted: 09/26/2022] [Indexed: 12/30/2022]
Abstract
Metastatic tumour progression is facilitated by tumour associated macrophages (TAMs) that enforce pro-tumour mechanisms and suppress immunity. In pulmonary metastases, it is unclear whether TAMs comprise tissue resident or infiltrating, recruited macrophages; and the different expression patterns of these TAMs are not well established. Using the mouse melanoma B16F10 model of experimental pulmonary metastasis, we show that infiltrating macrophages (IM) change their gene expression from an early pro-inflammatory to a later tumour promoting profile as the lesions grow. In contrast, resident alveolar macrophages (AM) maintain expression of crucial pro-inflammatory/anti-tumour genes with time. During metastatic growth, the pool of macrophages, which initially contains mainly alveolar macrophages, increasingly consists of infiltrating macrophages potentially facilitating metastasis progression. Blocking chemokine receptor mediated macrophage infiltration in the lung revealed a prominent role for CCR2 in Ly6C+ pro-inflammatory monocyte/macrophage recruitment during metastasis progression, while inhibition of CCR2 signalling led to increased metastatic colony burden. CCR1 blockade, in contrast, suppressed late phase pro-tumour MR+Ly6C- monocyte/macrophage infiltration accompanied by expansion of the alveolar macrophage compartment and accumulation of NK cells, leading to reduced metastatic burden. These data indicate that IM has greater plasticity and higher phenotypic responsiveness to tumour challenge than AM. A considerable difference is also confirmed between CCR1 and CCR2 with regard to the recruited IM subsets, with CCR1 presenting a potential therapeutic target in pulmonary metastasis from melanoma.
Collapse
Affiliation(s)
- Thomas T. Tapmeier
- grid.4991.50000 0004 1936 8948CRUK/MRC Oxford Institute for Radiation Oncology, Department of Oncology, University of Oxford, Oxford, OX3 7DQ UK ,grid.1002.30000 0004 1936 7857Department of Obstetrics and Gynaecology, Monash University, Clayton, VIC 3168 Australia ,grid.452824.dThe Ritchie Centre, Hudson Institute of Medical Research, Clayton, VIC 3168 Australia
| | - Jake H. Howell
- grid.12477.370000000121073784School of Applied Sciences, University of Brighton, Brighton, BN2 4GJ UK
| | - Lei Zhao
- grid.440144.10000 0004 1803 8437Shandong Cancer Hospital and Institute, Shandong Cancer Hospital Affiliated to Shandong First Medical University, Jinan, 250117 China
| | - Bartlomiej W. Papiez
- Li Ka Shing Centre for Health Information and Discovery, Big Data Institute, Oxford, OX3 7LF UK
| | - Julia A. Schnabel
- grid.13097.3c0000 0001 2322 6764School of Biomedical Imaging and Imaging Sciences, King’s College London, London, SE1 7EU UK ,grid.4567.00000 0004 0483 2525Helmholtz Center Munich – German Center for Environmental Health, 85764 Neuherberg, Germany ,grid.6936.a0000000123222966Faculty of Informatics and Institute for Advanced Study, Technical University of Munich, 85748 Garching, Germany
| | - Ruth J. Muschel
- grid.4991.50000 0004 1936 8948CRUK/MRC Oxford Institute for Radiation Oncology, Department of Oncology, University of Oxford, Oxford, OX3 7DQ UK
| | - Annamaria Gal
- grid.4991.50000 0004 1936 8948CRUK/MRC Oxford Institute for Radiation Oncology, Department of Oncology, University of Oxford, Oxford, OX3 7DQ UK ,grid.12477.370000000121073784School of Applied Sciences, University of Brighton, Brighton, BN2 4GJ UK
| |
Collapse
|
2
|
Franklin JM, Irving B, Papiez BW, Kallehauge JF, Wang LM, Goldin RD, Harris AL, Anderson EM, Schnabel JA, Chappell MA, Brady M, Sharma RA, Gleeson FV. Tumour subregion analysis of colorectal liver metastases using semi-automated clustering based on DCE-MRI: Comparison with histological subregions and impact on pharmacokinetic parameter analysis. Eur J Radiol 2020; 126:108934. [PMID: 32217426 DOI: 10.1016/j.ejrad.2020.108934] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 01/21/2020] [Accepted: 03/01/2020] [Indexed: 12/29/2022]
Abstract
PURPOSE To use a novel segmentation methodology based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to define tumour subregions of liver metastases from colorectal cancer (CRC), to compare these with histology, and to use these to compare extracted pharmacokinetic (PK) parameters between tumour subregions. MATERIALS AND METHODS This ethically-approved prospective study recruited patients with CRC and ≥1 hepatic metastases scheduled for hepatic resection. Patients underwent DCE-MRI pre-metastasectomy. Histological sections of resection specimens were spatially matched to DCE-MRI acquisitions and used to define histological subregions of viable and non-viable tumour. A semi-automated voxel-wise image segmentation algorithm based on the DCE-MRI contrast-uptake curves was used to define imaging subregions of viable and non-viable tumour. Overlap of histologically-defined and imaging subregions was compared using the Dice similarity coefficient (DSC). DCE-MRI PK parameters were compared for the whole tumour and histology-defined and imaging-derived subregions. RESULTS Fourteen patients were included in the analysis. Direct histological comparison with imaging was possible in nine patients. Mean DSC for viable tumour subregions defined by imaging and histology was 0.738 (range 0.540-0.930). There were significant differences between Ktrans and kep for viable and non-viable subregions (p < 0.001) and between whole lesions and viable subregions (p < 0.001). CONCLUSION We demonstrate good concordance of viable tumour segmentation based on pre-operative DCE-MRI with a post-operative histological gold-standard. This can be used to extract viable tumour-specific values from quantitative image analysis, and could improve treatment response assessment in clinical practice.
Collapse
Affiliation(s)
- James M Franklin
- Institute of Medical Imaging and Visualisation, Bournemouth University, UK; Radiology Department, Royal Bournemouth and Christchurch Hospitals NS Foundation Trust, UK.
| | - Benjamin Irving
- Institute of Biomedical Engineering (Department of Engineering Science), University of Oxford, UK
| | - Bartlomiej W Papiez
- Institute of Biomedical Engineering (Department of Engineering Science), University of Oxford, UK
| | - Jesper F Kallehauge
- Institute of Biomedical Engineering (Department of Engineering Science), University of Oxford, UK
| | - Lai Mun Wang
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, UK
| | | | | | - Ewan M Anderson
- Radiology Department, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, UK
| | - Julia A Schnabel
- Institute of Biomedical Engineering (Department of Engineering Science), University of Oxford, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Michael A Chappell
- Institute of Biomedical Engineering (Department of Engineering Science), University of Oxford, UK
| | | | - Ricky A Sharma
- NIHR University College London Hospitals Biomedical Research Centre, UCL Cancer Institute, University College London, 72 Huntley Street, London, WC1E 6DD, UK
| | - Fergus V Gleeson
- Radiology Department, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, UK
| |
Collapse
|
3
|
Abstract
Recent developments in laser scanning microscopy have greatly extended its applicability in cancer imaging beyond the visualization of complex biology, and opened up the possibility of quantitative analysis of inherently dynamic biological processes. However, the physics of image acquisition intrinsically means that image quality is subject to a tradeoff between a number of imaging parameters, including resolution, signal-to-noise ratio, and acquisition speed. We address the problem of geometric distortion, in particular, jaggedness artefacts that are caused by the variable motion of the microscope laser, by using a combination of image processing techniques. Image restoration methods have already shown great potential for post-acquisition image analysis. The performance of our proposed image restoration technique was first quantitatively evaluated using phantom data with different textures, and then qualitatively assessed using in vivo biological imaging data. In both cases, the presented method, comprising a combination of image registration and filtering, is demonstrated to have substantial improvement over state-of-the-art microscopy acquisition methods.
Collapse
|
4
|
De Luca V, Banerjee J, Hallack A, Kondo S, Makhinya M, Nouri D, Royer L, Cifor A, Dardenne G, Goksel O, Gooding MJ, Klink C, Krupa A, Le Bras A, Marchal M, Moelker A, Niessen WJ, Papiez BW, Rothberg A, Schnabel J, van Walsum T, Harris E, Lediju Bell MA, Tanner C. Evaluation of 2D and 3D ultrasound tracking algorithms and impact on ultrasound-guided liver radiotherapy margins. Med Phys 2018; 45:4986-5003. [PMID: 30168159 DOI: 10.1002/mp.13152] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 07/26/2018] [Accepted: 07/27/2018] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Compensation for respiratory motion is important during abdominal cancer treatments. In this work we report the results of the 2015 MICCAI Challenge on Liver Ultrasound Tracking and extend the 2D results to relate them to clinical relevance in form of reducing treatment margins and hence sparing healthy tissues, while maintaining full duty cycle. METHODS We describe methodologies for estimating and temporally predicting respiratory liver motion from continuous ultrasound imaging, used during ultrasound-guided radiation therapy. Furthermore, we investigated the trade-off between tracking accuracy and runtime in combination with temporal prediction strategies and their impact on treatment margins. RESULTS Based on 2D ultrasound sequences from 39 volunteers, a mean tracking accuracy of 0.9 mm was achieved when combining the results from the 4 challenge submissions (1.2 to 3.3 mm). The two submissions for the 3D sequences from 14 volunteers provided mean accuracies of 1.7 and 1.8 mm. In combination with temporal prediction, using the faster (41 vs 228 ms) but less accurate (1.4 vs 0.9 mm) tracking method resulted in substantially reduced treatment margins (70% vs 39%) in contrast to mid-ventilation margins, as it avoided non-linear temporal prediction by keeping the treatment system latency low (150 vs 400 ms). Acceleration of the best tracking method would improve the margin reduction to 75%. CONCLUSIONS Liver motion estimation and prediction during free-breathing from 2D ultrasound images can substantially reduce the in-plane motion uncertainty and hence treatment margins. Employing an accurate tracking method while avoiding non-linear temporal prediction would be favorable. This approach has the potential to shorten treatment time compared to breath-hold and gated approaches, and increase treatment efficiency and safety.
Collapse
Affiliation(s)
- Valeria De Luca
- Computer Vision Laboratory, ETH Zurich, Zürich, Switzerland
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | | | - Andre Hallack
- Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | | | - Maxim Makhinya
- Computer Vision Laboratory, ETH Zurich, Zürich, Switzerland
| | | | - Lucas Royer
- Institut de Recherche Technologique b-com, Rennes, France
| | | | | | - Orcun Goksel
- Computer Vision Laboratory, ETH Zurich, Zürich, Switzerland
| | | | - Camiel Klink
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | | | | | - Maud Marchal
- Institut de Recherche Technologique b-com, Rennes, France
| | - Adriaan Moelker
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - Wiro J Niessen
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | | | | | - Julia Schnabel
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Theo van Walsum
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | | | - Muyinatu A Lediju Bell
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA
| | | |
Collapse
|
5
|
Kannan P, Kretzschmar WW, Winter H, Warren D, Bates R, Allen PD, Syed N, Irving B, Papiez BW, Kaeppler J, Markelc B, Kinchesh P, Gilchrist S, Smart S, Schnabel JA, Maughan T, Harris AL, Muschel RJ, Partridge M, Sharma RA, Kersemans V. Functional Parameters Derived from Magnetic Resonance Imaging Reflect Vascular Morphology in Preclinical Tumors and in Human Liver Metastases. Clin Cancer Res 2018; 24:4694-4704. [PMID: 29959141 PMCID: PMC6171743 DOI: 10.1158/1078-0432.ccr-18-0033] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Revised: 05/11/2018] [Accepted: 06/25/2018] [Indexed: 12/13/2022]
Abstract
Purpose: Tumor vessels influence the growth and response of tumors to therapy. Imaging vascular changes in vivo using dynamic contrast-enhanced MRI (DCE-MRI) has shown potential to guide clinical decision making for treatment. However, quantitative MR imaging biomarkers of vascular function have not been widely adopted, partly because their relationship to structural changes in vessels remains unclear. We aimed to elucidate the relationships between vessel function and morphology in vivo Experimental Design: Untreated preclinical tumors with different levels of vascularization were imaged sequentially using DCE-MRI and CT. Relationships between functional parameters from MR (iAUC, K trans, and BATfrac) and structural parameters from CT (vessel volume, radius, and tortuosity) were assessed using linear models. Tumors treated with anti-VEGFR2 antibody were then imaged to determine whether antiangiogenic therapy altered these relationships. Finally, functional-structural relationships were measured in 10 patients with liver metastases from colorectal cancer.Results: Functional parameters iAUC and K trans primarily reflected vessel volume in untreated preclinical tumors. The relationships varied spatially and with tumor vascularity, and were altered by antiangiogenic treatment. In human liver metastases, all three structural parameters were linearly correlated with iAUC and K trans For iAUC, structural parameters also modified each other's effect.Conclusions: Our findings suggest that MR imaging biomarkers of vascular function are linked to structural changes in tumor vessels and that antiangiogenic therapy can affect this link. Our work also demonstrates the feasibility of three-dimensional functional-structural validation of MR biomarkers in vivo to improve their biological interpretation and clinical utility. Clin Cancer Res; 24(19); 4694-704. ©2018 AACR.
Collapse
Affiliation(s)
- Pavitra Kannan
- CRUK and MRC Oxford Institute for Radiation Oncology Department of Oncology, University of Oxford, Oxford, United Kingdom.
| | - Warren W Kretzschmar
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Helen Winter
- CRUK and MRC Oxford Institute for Radiation Oncology Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Daniel Warren
- CRUK and MRC Oxford Institute for Radiation Oncology Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Russell Bates
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Philip D Allen
- CRUK and MRC Oxford Institute for Radiation Oncology Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Nigar Syed
- CRUK and MRC Oxford Institute for Radiation Oncology Department of Oncology, University of Oxford, Oxford, United Kingdom
- NHS, Department of Radiology, Churchill Hospital, Oxford, United Kingdom
| | - Benjamin Irving
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Bartlomiej W Papiez
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Jakob Kaeppler
- CRUK and MRC Oxford Institute for Radiation Oncology Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Bosjtan Markelc
- CRUK and MRC Oxford Institute for Radiation Oncology Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Paul Kinchesh
- CRUK and MRC Oxford Institute for Radiation Oncology Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Stuart Gilchrist
- CRUK and MRC Oxford Institute for Radiation Oncology Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Sean Smart
- CRUK and MRC Oxford Institute for Radiation Oncology Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Julia A Schnabel
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Tim Maughan
- CRUK and MRC Oxford Institute for Radiation Oncology Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Adrian L Harris
- CRUK and MRC Oxford Institute for Radiation Oncology Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Ruth J Muschel
- CRUK and MRC Oxford Institute for Radiation Oncology Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Mike Partridge
- CRUK and MRC Oxford Institute for Radiation Oncology Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Ricky A Sharma
- CRUK and MRC Oxford Institute for Radiation Oncology Department of Oncology, University of Oxford, Oxford, United Kingdom
- NIHR University College London Hospitals Biomedical Research Centre, University College London, London, United Kingdom
| | - Veerle Kersemans
- CRUK and MRC Oxford Institute for Radiation Oncology Department of Oncology, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
6
|
McGowan DR, Skwarski M, Papiez BW, Macpherson RE, Gleeson FV, Schnabel JA, Higgins GS, Fenwick JD. Whole tumor kinetics analysis of 18F-fluoromisonidazole dynamic PET scans of non-small cell lung cancer patients, and correlations with perfusion CT blood flow. EJNMMI Res 2018; 8:73. [PMID: 30069753 PMCID: PMC6070455 DOI: 10.1186/s13550-018-0430-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 07/23/2018] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND To determine the relative abilities of compartment models to describe time-courses of 18F-fluoromisonidazole (FMISO) tumor uptake in patients with advanced stage non-small cell lung cancer (NSCLC) imaged using dynamic positron emission tomography (dPET), and study correlations between values of the blood flow-related parameter K1 obtained from fits of the models and an independent blood flow measure obtained from perfusion CT (pCT). NSCLC patients had a 45-min dynamic FMISO PET/CT scan followed by two static PET/CT acquisitions at 2 and 4-h post-injection. Perfusion CT scanning was then performed consisting of a 45-s cine CT. Reversible and irreversible two-, three- and four-tissue compartment models were fitted to 30 time-activity-curves (TACs) obtained for 15 whole tumor structures in 9 patients, each imaged twice. Descriptions of the TACs provided by the models were compared using the Akaike and Bayesian information criteria (AIC and BIC) and leave-one-out cross-validation. The precision with which fitted model parameters estimated ground-truth uptake kinetics was determined using statistical simulation techniques. Blood flow from pCT was correlated with K1 from PET kinetic models in addition to FMISO uptake levels. RESULTS An irreversible three-tissue compartment model provided the best description of whole tumor FMISO uptake time-courses according to AIC, BIC, and cross-validation scores totaled across the TACs. The simulation study indicated that this model also provided more precise estimates of FMISO uptake kinetics than other two- and three-tissue models. The K1 values obtained from fits of the irreversible three-tissue model correlated strongly with independent blood flow measurements obtained from pCT (Pearson r coefficient = 0.81). The correlation from the irreversible three-tissue model (r = 0.81) was stronger than that from than K1 values obtained from fits of a two-tissue compartment model (r = 0.68), or FMISO uptake levels in static images taken at time-points from tracer injection through to 4 h later (maximum at 2 min, r = 0.70). CONCLUSIONS Time-courses of whole tumor FMISO uptake by advanced stage NSCLC are described best by an irreversible three-tissue compartment model. The K1 values obtained from fits of the irreversible three-tissue model correlated strongly with independent blood flow measurements obtained from perfusion CT (r = 0.81).
Collapse
Affiliation(s)
- Daniel R. McGowan
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ UK
- Radiation Physics and Protection, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Michael Skwarski
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ UK
| | - Bartlomiej W. Papiez
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Ruth E. Macpherson
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Fergus V. Gleeson
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ UK
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Julia A. Schnabel
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Geoff S. Higgins
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ UK
- Department of Oncology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - John D. Fenwick
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ UK
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| |
Collapse
|
7
|
Heinrich MP, Jenkinson M, Papiez BW, Glesson FV, Brady SM, Schnabel JA. Edge- and detail-preserving sparse image representations for deformable registration of chest MRI and CT volumes. Inf Process Med Imaging 2014; 23:463-74. [PMID: 24683991 DOI: 10.1007/978-3-642-38868-2_39] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
Deformable medical image registration requires the optimisation of a function with a large number of degrees of freedom. Commonly-used approaches to reduce the computational complexity, such as uniform B-splines and Gaussian image pyramids, introduce translation-invariant homogeneous smoothing, and may lead to less accurate registration in particular for motion fields with discontinuities. This paper introduces the concept of sparse image representation based on supervoxels, which are edge-preserving and therefore enable accurate modelling of sliding organ motions frequently seen in respiratory and cardiac scans. Previous shortcomings of using supervoxels in motion estimation, in particular inconsistent clustering in ambiguous regions, are overcome by employing multiple layers of supervoxels. Furthermore, we propose a new similarity criterion based on a binary shape representation of supervoxels, which improves the accuracy of single-modal registration and enables multimodal registration. We validate our findings based on the registration of two challenging clinical applications of volumetric deformable registration: motion estimation between inhale and exhale phase of CT scans for radiotherapy planning, and deformable multi-modal registration of diagnostic MRI and CT chest scans. The experiments demonstrate state-of-the-art registration accuracy, and require no additional anatomical knowledge with greatly reduced computational complexity.
Collapse
|
8
|
Papiez BW, Heinrich MP, Risser L, Schnabel JA. Complex lung motion estimation via adaptive bilateral filtering of the deformation field. Med Image Comput Comput Assist Interv 2014; 16:25-32. [PMID: 24505740 DOI: 10.1007/978-3-642-40760-4_4] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Estimation of physiologically plausible deformations is critical for several medical applications. For example, lung cancer diagnosis and treatment requires accurate image registration which preserves sliding motion in the pleural cavity, and the rigidity of chest bones. This paper addresses these challenges by introducing a novel approach for regularisation of non-linear transformations derived from a bilateral filter. For this purpose, the classic Gaussian kernel is replaced by a new kernel that smoothes the estimated deformation field with respect to the spatial position, intensity and deformation dissimilarity. The proposed regularisation is a spatially adaptive filter that is able to preserve discontinuity between the lungs and the pleura and reduces any rigid structures deformations in volumes. Moreover, the presented framework is fully automatic and no prior knowledge of the underlying anatomy is required. The performance of our novel regularisation technique is demonstrated on phantom data for a proof of concept as well as 3D inhale and exhale pairs of clinical CT lung volumes. The results of the quantitative evaluation exhibit a significant improvement when compared to the corresponding state-of-the-art method using classic Gaussian smoothing.
Collapse
Affiliation(s)
- Bartlomiej W Papiez
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK
| | - Mattias Paul Heinrich
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK
| | - Laurent Risser
- CNRS, Institut de Mathématiques de Toulouse (UMR5219), France
| | - Julia A Schnabel
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK
| |
Collapse
|