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Katsamenis OL, Basford PJ, Robinson SK, Boardman RP, Konstantinopoulou E, Lackie PM, Page A, Ratnayaka JA, Goggin PM, Thomas GJ, Cox SJ, Sinclair I, Schneider P. A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications. Wellcome Open Res 2023; 8:366. [PMID: 37928208 PMCID: PMC10620852 DOI: 10.12688/wellcomeopenres.19666.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/28/2023] [Indexed: 11/07/2023] Open
Abstract
Background The University of Southampton, in collaboration with the University Hospital Southampton (UHS) NHS Foundation Trust and industrial partners, has been at the forefront of developing three-dimensional (3D) imaging workflows using X-ray microfocus computed tomography (μCT) -based technology. This article presents the outcomes of these endeavours and highlights the distinctive characteristics of a μCT facility tailored explicitly for 3D X-ray Histology, with a primary focus on applications in biomedical research and preclinical and clinical studies. Methods The UHS houses a unique 3D X-ray Histology (XRH) facility, offering a range of services to national and international clients. The facility employs specialised μCT equipment explicitly designed for histology applications, allowing whole-block XRH imaging of formalin-fixed and paraffin-embedded tissue specimens. It also enables correlative imaging by combining μCT imaging with other microscopy techniques, such as immunohistochemistry (IHC) and serial block-face scanning electron microscopy, as well as data visualisation, image quantification, and bespoke analysis. Results Over the past seven years, the XRH facility has successfully completed over 120 projects in collaboration with researchers from 60 affiliations, resulting in numerous published manuscripts and conference proceedings. The facility has streamlined the μCT imaging process, improving productivity and enabling efficient acquisition of 3D datasets. Discussion & Conclusions The 3D X-ray Histology (XRH) facility at UHS is a pioneering platform in the field of histology and biomedical imaging. To the best of our knowledge, it stands out as the world's first dedicated XRH facility, encompassing every aspect of the imaging process, from user support to data generation, analysis, training, archiving, and metadata generation. This article serves as a comprehensive guide for establishing similar XRH facilities, covering key aspects of facility setup and operation. Researchers and institutions interested in developing state-of-the-art histology and imaging facilities can utilise this resource to explore new frontiers in their research and discoveries.
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Affiliation(s)
- Orestis L. Katsamenis
- μ-VIS X-ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, England, SO17 1BJ, UK
- Institute for Life Sciences, University of Southampton, Southampton, UK
| | - Philip J. Basford
- μ-VIS X-ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, England, SO17 1BJ, UK
- Institute for Life Sciences, University of Southampton, Southampton, UK
- Computational Engineering and Design, Faculty of Engineering and Physical Sciences,, University of Southampton, Southampton, England, SO17 1BJ, UK
| | - Stephanie K. Robinson
- μ-VIS X-ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, England, SO17 1BJ, UK
| | - Richard P. Boardman
- μ-VIS X-ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, England, SO17 1BJ, UK
| | - Elena Konstantinopoulou
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, England, SO16 6YD, UK
| | - Peter M. Lackie
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, England, SO16 6YD, UK
- Biomedical Imaging Unit, Faculty of Medicine, University of Southampton, Southampton, England, SO16 6YD, UK
| | - Anton Page
- University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK
| | - J. Arjuna Ratnayaka
- Institute for Life Sciences, University of Southampton, Southampton, UK
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, England, SO16 6YD, UK
- Biomedical Imaging Unit, Faculty of Medicine, University of Southampton, Southampton, England, SO16 6YD, UK
- University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK
| | - Patricia M. Goggin
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, England, SO16 6YD, UK
- Biomedical Imaging Unit, Faculty of Medicine, University of Southampton, Southampton, England, SO16 6YD, UK
- University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK
| | - Gareth J. Thomas
- Institute for Life Sciences, University of Southampton, Southampton, UK
- University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK
- School of Cancer Sciences, Faculty of Medicine, University of Southampton, Southampton, England, SO16 6YD, UK
| | - Simon J. Cox
- Institute for Life Sciences, University of Southampton, Southampton, UK
- Computational Engineering and Design, Faculty of Engineering and Physical Sciences,, University of Southampton, Southampton, England, SO17 1BJ, UK
| | - Ian Sinclair
- μ-VIS X-ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, England, SO17 1BJ, UK
- Institute for Life Sciences, University of Southampton, Southampton, UK
| | - Philipp Schneider
- μ-VIS X-ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, England, SO17 1BJ, UK
- High-Performance Vision Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
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Donato S, Brombal L, Arana Peña LM, Arfelli F, Contillo A, Delogu P, Di Lillo F, Di Trapani V, Fanti V, Longo R, Oliva P, Rigon L, Stori L, Tromba G, Golosio B. Optimization of a customized simultaneous algebraic reconstruction technique algorithm for phase-contrast breast computed tomography. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac65d4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 04/08/2022] [Indexed: 12/22/2022]
Abstract
Abstract
Objective. To introduce the optimization of a customized GPU-based simultaneous algebraic reconstruction technique (cSART) in the field of phase-contrast breast computed tomography (bCT). The presented algorithm features a 3D bilateral regularization filter that can be tuned to yield optimal performance for clinical image visualization and tissues segmentation. Approach. Acquisitions of a dedicated test object and a breast specimen were performed at Elettra, the Italian synchrotron radiation (SR) facility (Trieste, Italy) using a large area CdTe single-photon counting detector. Tomographic images were obtained at 5 mGy of mean glandular dose, with a 32 keV monochromatic x-ray beam in the free-space propagation mode. Three independent algorithms parameters were optimized by using contrast-to-noise ratio (CNR), spatial resolution, and noise texture metrics. The results obtained with the cSART algorithm were compared with conventional SART and filtered back projection (FBP) reconstructions. Image segmentation was performed both with gray scale-based and supervised machine-learning approaches. Main results. Compared to conventional FBP reconstructions, results indicate that the proposed algorithm can yield images with a higher CNR (by 35% or more), retaining a high spatial resolution while preserving their textural properties. Alternatively, at the cost of an increased image ‘patchiness’, the cSART can be tuned to achieve a high-quality tissue segmentation, suggesting the possibility of performing an accurate glandularity estimation potentially of use in the realization of realistic 3D breast models starting from low radiation dose images. Significance. The study indicates that dedicated iterative reconstruction techniques could provide significant advantages in phase-contrast bCT imaging. The proposed algorithm offers great flexibility in terms of image reconstruction optimization, either toward diagnostic evaluation or image segmentation.
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Lawson MJ, Katsamenis OL, Chatelet D, Alzetani A, Larkin O, Haig I, Lackie P, Warner J, Schneider P. Immunofluorescence-guided segmentation of three-dimensional features in micro-computed tomography datasets of human lung tissue. ROYAL SOCIETY OPEN SCIENCE 2021; 8:211067. [PMID: 34737879 PMCID: PMC8564621 DOI: 10.1098/rsos.211067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 10/08/2021] [Indexed: 06/13/2023]
Abstract
Micro-computed tomography (µCT) provides non-destructive three-dimensional (3D) imaging of soft tissue microstructures. Specific features in µCT images can be identified using correlated two-dimensional (2D) histology images allowing manual segmentation. However, this is very time-consuming and requires specialist knowledge of the tissue and imaging modalities involved. Using a custom-designed µCT system optimized for imaging unstained formalin-fixed paraffin-embedded soft tissues, we imaged human lung tissue at isotropic voxel sizes less than 10 µm. Tissue sections were stained with haematoxylin and eosin or cytokeratin 18 in columnar airway epithelial cells using immunofluorescence (IF), as an exemplar of this workflow. Novel utilization of tissue autofluorescence allowed automatic alignment of 2D microscopy images to the 3D µCT data using scripted co-registration and automated image warping algorithms. Warped IF images, which were accurately aligned with the µCT datasets, allowed 3D segmentation of immunoreactive tissue microstructures in the human lung. Blood vessels were segmented semi-automatically using the co-registered µCT datasets. Correlating 2D IF and 3D µCT data enables accurate identification, localization and segmentation of features in fixed soft lung tissue. Our novel correlative imaging workflow provides faster and more automated 3D segmentation of µCT datasets. This is applicable to the huge range of formalin-fixed paraffin-embedded tissues held in biobanks and archives.
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Affiliation(s)
- Matthew J. Lawson
- School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Orestis L. Katsamenis
- μ-VIS X-ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, UK
| | - David Chatelet
- School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Aiman Alzetani
- School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Oliver Larkin
- Bioengineering Research Group, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, UK
| | - Ian Haig
- Nikon X-Tek Systems Ltd, Tring, UK
| | - Peter Lackie
- School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Jane Warner
- School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Philipp Schneider
- Bioengineering Research Group, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, UK
- High-Performance Vision Systems, Center for Vision, Automation and Control, AIT Austrian Institute of Technology, Vienna, Austria
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Porzionato A, Guidolin D, Emmi A, Boscolo-Berto R, Sarasin G, Rambaldo A, Macchi V, De Caro R. High-quality Digital 3D Reconstruction of Microscopic Findings in Forensic Pathology: The Terminal Pathway of a Heart Stab Wound. J Forensic Sci 2020; 65:2155-2159. [PMID: 32957166 DOI: 10.1111/1556-4029.14497] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 05/29/2020] [Accepted: 06/05/2020] [Indexed: 12/15/2022]
Abstract
High-quality digital three-dimensional (3D) reconstructions of microscopic findings have been used in anatomical and histopathologic research, but their use in forensic pathology may also be of interest. This paper presents an application of these methods to better characterize the pathway of a stab wound of the anterior surface of the heart in a case of suicide. A portion of the heart wall including the stab wound was serially sectioned for microscopic analysis along the full extent of the wound. Histologic sections were digitally acquired, and a 3D reconstruction was created with ImageJ software for 3D computer graphics. This showed a full-thickness wound path extending to the endocardial surface of the left ventricle, curvilinear in appearance. After correction for shrinkage, 3D reconstruction allowed estimation of the dimensions of the myocardial injury and comparison of the appearance of the wound with the suspected knife used. The curvilinear appearance was considered to reflect injury during myocardial contraction. Complete microscopic sectioning and 3D reconstruction may allow virtual sectioning through various orientations and also provide useful forensic information for selected injuries.
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Affiliation(s)
- Andrea Porzionato
- Section of Human Anatomy, Department of Neuroscience, University of Padova, Via Gabelli 65, Padova, 35127, Italy
| | - Diego Guidolin
- Section of Human Anatomy, Department of Neuroscience, University of Padova, Via Gabelli 65, Padova, 35127, Italy
| | - Aron Emmi
- Section of Human Anatomy, Department of Neuroscience, University of Padova, Via Gabelli 65, Padova, 35127, Italy
| | - Rafael Boscolo-Berto
- Section of Human Anatomy, Department of Neuroscience, University of Padova, Via Gabelli 65, Padova, 35127, Italy
| | - Gloria Sarasin
- Section of Human Anatomy, Department of Neuroscience, University of Padova, Via Gabelli 65, Padova, 35127, Italy
| | - Anna Rambaldo
- Section of Human Anatomy, Department of Neuroscience, University of Padova, Via Gabelli 65, Padova, 35127, Italy
| | - Veronica Macchi
- Section of Human Anatomy, Department of Neuroscience, University of Padova, Via Gabelli 65, Padova, 35127, Italy
| | - Raffaele De Caro
- Section of Human Anatomy, Department of Neuroscience, University of Padova, Via Gabelli 65, Padova, 35127, Italy
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Alhammadi AM, AlRatrout A, Bijeljic B, Blunt MJ. Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography. J Vis Exp 2018. [PMID: 30394374 PMCID: PMC6235578 DOI: 10.3791/57915] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
In situ wettability measurements in hydrocarbon reservoir rocks have only been possible recently. The purpose of this work is to present a protocol to characterize the complex wetting conditions of hydrocarbon reservoir rock using pore-scale three-dimensional X-ray imaging at subsurface conditions. In this work, heterogeneous carbonate reservoir rocks, extracted from a very large producing oil field, have been used to demonstrate the protocol. The rocks are saturated with brine and oil and aged over three weeks at subsurface conditions to replicate the wettability conditions that typically exist in hydrocarbon reservoirs (known as mixed-wettability). After the brine injection, high-resolution three-dimensional images (2 µm/voxel) are acquired and then processed and segmented. To calculate the distribution of the contact angle, which defines the wettability, the following steps are performed. First, fluid-fluid and fluid-rock surfaces are meshed. The surfaces are smoothed to remove voxel artefacts, and in situ contact angles are measured at the three-phase contact line throughout the whole image. The main advantage of this method is its ability to characterize in situ wettability accounting for pore-scale rock properties, such as rock surface roughness, rock chemical composition, and pore size. The in situ wettability is determined rapidly at hundreds of thousands of points. The method is limited by the segmentation accuracy and X-ray image resolution. This protocol could be used to characterize the wettability of other complex rocks saturated with different fluids and at different conditions for a variety of applications. For example, it could help in determining the optimal wettability that could yield an extra oil recovery (i.e., designing brine salinity accordingly to obtain higher oil recovery) and to find the most efficient wetting conditions to trap more CO2 in subsurface formations.
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Affiliation(s)
- Amer M Alhammadi
- Department of Earth Science and Engineering, Imperial College London;
| | - Ahmed AlRatrout
- Department of Earth Science and Engineering, Imperial College London
| | - Branko Bijeljic
- Department of Earth Science and Engineering, Imperial College London
| | - Martin J Blunt
- Department of Earth Science and Engineering, Imperial College London
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