Performance evaluation of image co-registration methods in photoacoustic mesoscopy of the vasculature
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Objective. The formation of functional vasculature in solid tumours enables delivery of oxygen and nutrients, and is vital for effective treatment with chemotherapeutic agents. Longitudinal characterisation of vascular networks can be enabled using mesoscopic photoacoustic imaging, but requires accurate image co-registration to precisely assess local changes across disease development or in response to therapy. Co-registration in photoacoustic imaging is challenging due to the complex nature of the generated signal, including the sparsity of data, artefacts related to the illumination/detection geometry, scan-to-scan technical variability, and biological variability, such as transient changes in perfusion. To better inform the choice of co-registration algorithms, we compared five open-source methods, in physiological and pathological tissues, with the aim of aligning evolving vascular networks in tumours imaged over growth at different time-points. Approach. Co-registration techniques were applied to 3D vascular images acquired with photoacoustic mesoscopy from murine ears and breast cancer patient-derived xenografts, at a fixed time-point and longitudinally. Images were pre-processed and segmented using an unsupervised generative adversarial network. To compare co-registration quality in different settings, pairs of fixed and moving intensity images and/or segmentations were fed into five methods split into the following categories: affine intensity-based using (1) mutual information (MI) or (2) normalised cross-correlation (NCC) as optimisation metrics, affine shape-based using (3) NCC applied to distance-transformed segmentations or (4) iterative closest point algorithm, and deformable weakly supervised deep learning-based using (5) LocalNet co-registration. Percent-changes in Dice coefficients, surface distances, MI, structural similarity index measure and target registration errors were evaluated. Main results. Co-registration using MI or NCC provided similar alignment performance, better than shape-based methods. LocalNet provided accurate co-registration of substructures by optimising subfield deformation throughout the volumes, outperforming other methods, especially in the longitudinal breast cancer xenograft dataset by minimising target registration errors. Significance. We showed the feasibility of co-registering repeatedly or longitudinally imaged vascular networks in photoacoustic mesoscopy, taking a step towards longitudinal quantitative characterisation of these complex structures. These tools open new outlooks for monitoring tumour angiogenesis at the meso-scale and for quantifying treatment-induced co-localised alterations in the vasculature.
Date Issued
2024-11-07
Date Acceptance
2024-09-25
Citation
Physics in Medicine & Biology, 2024, 69 (21)
ISSN
0031-9155
Publisher
IOP Publishing
Journal / Book Title
Physics in Medicine and Biology
Volume
69
Issue
21
Copyright Statement
© 2024 The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd Original Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/39321985
Subjects
computational methodologies
deep learning
image co-registration
medical image analysis
oncology
photoacoustic imaging
vasculature
Publication Status
Published
Coverage Spatial
England
Article Number
215007
Date Publish Online
2024-10-17