X-ray Micro-Computed Tomography for Nondestructive Three-Dimensional (3D) X-ray Histology

Air artifact suppression in phase contrast micro-CT using conditional generative adversarial networks

3D virtual histology of formalin-fixed and paraffin-embedded (FFPE) tissue by means of phase contrast micro-computed tomography (micro-CT) is an increasingly popular technique, as it allows the 3D architecture of the tissue to be addressed without the need of additional heavy ion based staining approaches. Therefore, it can be applied on archived standard FFPE tissue blocks. However, one of the major concerns of using phase contrast micro-CT in combination with FFPE tissue blocks is the trapped air within the tissue. While air inclusion within the FFPE tissue block does not strongly impact the workflow and quality of classical histology, it creates serious obstacles in 3D visualization of detailed morphology. In particular, the 3D analysis of structural features is challenging, due to a strong edge effect caused by the phase shift at the air-tissue/paraffin interface. Despite certain improvements in sample preparation to eliminate air inclusion, such as the use of negative pressure, it is not always possible to remove all trapped air, for example in soft tissues such as lung. Here, we present a novel workflow based on conditional generative adversarial networks (cGANs) to effectively replace these air artifact regions with generated tissue, which are influenced by the surrounding content. Our results show that this approach not only improves the visualization of the lung tissue but also eases the use of structural analysis on the air artifact-suppressed phase contrast micro-CT scans. In addition, we demonstrate the transferability of the generative model to FFPE specimens of porcine lung tissue.

Nanoscale Metal-Organic Frameworks and Nanoscale Coordination Polymers: From Synthesis to Cancer Therapy and Biomedical Imaging

Recently, there has been significant interest in nanoscale metal-organic frameworks (NMOFs) characterized by ordered crystal structures and nanoscale coordination polymers (NCPs) featuring amorphous structures. These structures arise from the coordination interactions between inorganic metal ions or clusters and organic ligands. Their advantages, such as the ability to tailor composition and structure, efficiently encapsulate diverse therapeutic or imaging agents within porous frameworks, inherent biodegradability, and surface functionalization capability, position them as promising carriers in the biomedical fields. This review provides an overview of the synthesis and surface modification strategies employed for NMOFs and NCPs, along with their applications in cancer treatment and biological imaging. Finally, future directions and challenges associated with the utilization of NMOFs and NCPs in cancer treatment and diagnosis are also discussed.

Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review

OBJECTIVE

To conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection.

DESIGN

We searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided.

RESULTS

Several studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel μ CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution μ CT, and the 3D microanatomy characterisation of human tuberculosis lung using μ CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented.

CONCLUSION

The literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of Mtb. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of bacilli and the other five used CT on human lung tissue scanned ex-vivo.

The potential of x-ray virtual histology in the diagnosis of skin tumors

BACKGROUND

Histopathological analysis represents the gold standard in clinical practice for diagnosing skin neoplasms. While the current diagnostic workflow has specialized in producing robust and accurate results, interpreting tissue architecture and malignant cellular morphology correctly remains one of the greatest challenges for pathologists. This paper aims to explore the prospect of applying x-ray virtual histology to human skin tumor excisions and correlating it with the histological validation.

MATERIALS AND METHODS

Seven skin biopsies containing intriguing melanoma types and pigmented skin lesions were scanned using x-ray Computed micro-Tomography (μCT) and then sectioned for conventional histology assessment.

RESULTS

The tissue microarchitecture reconstructed by μCT offers detailed insights into diagnosing the malignancy or benignity of the skin lesions. Three-dimensional reconstruction via x-ray virtual histology reveals infiltrative patterns in basal cell carcinoma and evaluated invasiveness in melanoma. The technology enables the identification of pagetoid distributions of neoplastic cells and the assessment of melanoma depth in three dimensions.

CONCLUSION

Although the proposed approach is not intended to replace conventional histology, the non-destructive nature of the sample and the clarity provided by virtual inspection demonstrate the promising impact of μCT as a valid support method prior to conventional histological sectioning. Indeed, μCT images can suggest the optimal sectioning position before using a microtome, as is commonly performed in histological practice. Moreover, the three-dimensional nature of the proposed approach paves the way for a more accurate assessment of significant prognostic factors in melanoma, such as Breslow thickness, by considering the whole micro-volume rather than a two-dimensional observation.

Virtual staining for histology by deep learning

In pathology and biomedical research, histology is the cornerstone method for tissue analysis. Currently, the histological workflow consumes plenty of chemicals, water, and time for staining procedures. Deep learning is now enabling digital replacement of parts of the histological staining procedure. In virtual staining, histological stains are created by training neural networks to produce stained images from an unstained tissue image, or through transferring information from one stain to another. These technical innovations provide more sustainable, rapid, and cost-effective alternatives to traditional histological pipelines, but their development is in an early phase and requires rigorous validation. In this review we cover the basic concepts of virtual staining for histology and provide future insights into the utilization of artificial intelligence (AI)-enabled virtual histology.

Correlative three-dimensional X-ray histology (3D-XRH) as a tool for quantifying mammalian placental structure

Mammalian placentas exhibit unparalleled structural diversity, despite sharing a common ancestor and principal functions. The bulk of structural studies in placental research has used two-dimensional (2D) histology sectioning, allowing significant advances in our understanding of mammalian placental structure. However, 2D histology sectioning may be limited if it does not provide accurate information of three-dimensional (3D) tissue architecture. Here, we propose correlative 3D X-ray histology (3D-XRH) as a tool with great potential for resolving mammalian placental structures. 3D-XRH involves scanning a formaldehyde-fixed, paraffin embedded (FFPE) tissue block with 3D X-ray microscopy (microCT) prior to histological sectioning to generate a 3D image volume of the embedded tissue piece. The subsequent 2D histology sections can then be correlated back into the microCT image volume to couple histology staining (or immunolabelling) with 3D tissue architecture. 3D-XRH is non-destructive and requires no additional sample preparation than standard FFPE histology sectioning, however the image volume provides 3D morphometric data and can be used to guide microtomy. As such, 3D-XRH introduces additional information to standard histological workflows with minimal effort or disruption. Using primary examples from porcine, bovine, equine, and canine placental samples, we demonstrate the application of 3D-XRH to quantifying placental structure as well as discussing the limitations and future directions of the methodology. The wealth of information derived from 2D histological sectioning in the biomedical, veterinary, and comparative reproductive sciences provides a rich foundation from which 3D-XRH can build on to advance the study of placental structure and function.

3D Virtual Histopathology by Phase-Contrast X-Ray Micro-CT for Follicular Thyroid Neoplasms

Histological analysis is the core of follicular thyroid carcinoma (FTC) classification. The histopathological criteria of capsular and vascular invasion define malignancy and aggressiveness of FTC. Analysis of multiple sections is cumbersome and as only a minute tissue fraction is analyzed during histopathology, under-sampling remains a problem. Application of an efficient tool for complete tissue imaging in 3D would speed-up diagnosis and increase accuracy. We show that X-ray propagation-based imaging (XPBI) of paraffin-embedded tissue blocks is a valuable complementary method for follicular thyroid carcinoma diagnosis and assessment. It enables a fast, non-destructive and accurate 3D virtual histology of the FTC resection specimen. We demonstrate that XPBI virtual slices can reliably evaluate capsular invasions. Then we discuss the accessible morphological information from XPBI and their significance for vascular invasion diagnosis. We show 3D morphological information that allow to discern vascular invasions. The results are validated by comparing XPBI images with clinically accepted histology slides revised by and under supervision of two experienced endocrine pathologists.

The importance of 3D fibre architecture in cancer and implications for biomaterial model design

The need for improved prediction of clinical response is driving the development of cancer models with enhanced physiological relevance. A new concept of 'precision biomaterials' is emerging, encompassing patient-mimetic biomaterial models that seek to accurately detect, treat and model cancer by faithfully recapitulating key microenvironmental characteristics. Despite recent advances allowing tissue-mimetic stiffness and molecular composition to be replicated in vitro, approaches for reproducing the 3D fibre architectures found in tumour extracellular matrix (ECM) remain relatively unexplored. Although the precise influences of patient-specific fibre architecture are unclear, we summarize the known roles of tumour fibre architecture, underlining their implications in cell-matrix interactions and ultimately clinical outcome. We then explore the challenges in reproducing tissue-specific 3D fibre architecture(s) in vitro, highlighting relevant biomaterial fabrication techniques and their benefits and limitations. Finally, we discuss imaging and image analysis techniques (focussing on collagen I-optimized approaches) that could hold the key to mapping tumour-specific ECM into high-fidelity biomaterial models. We anticipate that an interdisciplinary approach, combining materials science, cancer research and image analysis, will elucidate the role of 3D fibre architecture in tumour development, leading to the next generation of patient-mimetic models for mechanistic studies and drug discovery.

Chondrosarcoma evaluation using hematein-based x-ray staining and high-resolution 3D micro-CT: a feasibility study

BACKGROUND

Chondrosarcomas are rare malignant bone tumors diagnosed by analyzing radiological images and histology of tissue biopsies and evaluating features such as matrix calcification, cortical destruction, trabecular penetration, and tumor cell entrapment.

METHODS

We retrospectively analyzed 16 cartilaginous tumor tissue samples from three patients (51-, 54-, and 70-year-old) diagnosed with a dedifferentiated chondrosarcoma at the femur, a moderately differentiated chondrosarcoma in the pelvis, and a predominantly moderately differentiated chondrosarcoma at the scapula, respectively. We combined a hematein-based x-ray staining with high-resolution three-dimensional (3D) microscopic x-ray computed tomography (micro-CT) for nondestructive 3D tumor assessment and tumor margin evaluation.

RESULTS

We detected trabecular entrapment on 3D micro-CT images and followed bone destruction throughout the volume. In addition to staining cell nuclei, hematein-based staining also improved the visualization of the tumor matrix, allowing for the distinction between the tumor and the bone marrow cavity. The hematein-based staining did not interfere with further conventional histology. There was a 5.97 ± 7.17% difference between the relative tumor area measured using micro-CT and histopathology (p = 0.806) (Pearson correlation coefficient r = 0.92, p = 0.009). Signal intensity in the tumor matrix (4.85 ± 2.94) was significantly higher in the stained samples compared to the unstained counterparts (1.92 ± 0.11, p = 0.002).

CONCLUSIONS

Using nondestructive 3D micro-CT, the simultaneous visualization of radiological and histopathological features is feasible.

RELEVANCE STATEMENT

3D micro-CT data supports modern radiological and histopathological investigations of human bone tumor specimens. It has the potential for being an integrative part of clinical preoperative diagnostics.

KEY POINTS

• Matrix calcifications are a relevant diagnostic feature of bone tumors. • Micro-CT detects all clinically diagnostic relevant features of x-ray-stained chondrosarcoma. • Micro-CT has the potential to be an integrative part of clinical diagnostics.

Analysis of 3D pathology samples using weakly supervised AI

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.

Phase contrast micro-CT with adjustable in-slice spatial resolution at constant magnification

Objective.To report on a micro computed tomography (micro-CT) system capable of x-ray phase contrast imaging and of increasing spatial resolution at constant magnification.Approach.The micro-CT system implements the edge illumination (EI) method, which relies on two absorbing masks with periodically spaced transmitting apertures in the beam path; these split the beam into an array of beamlets and provide sensitivity to the beamlets' directionality, i.e. refraction. In EI, spatial resolution depends on the width of the beamlets rather than on the source/detector point spread function (PSF), meaning that resolution can be increased by decreasing the mask apertures, without changing the source/detector PSF or the magnification.Main results.We have designed a dedicated mask featuring multiple bands with differently sized apertures and used this to demonstrate that resolution is a tuneable parameter in our system, by showing that increasingly small apertures deliver increasingly detailed images. Phase contrast images of a bar pattern-based resolution phantom and a biological sample (a mouse embryo) were obtained at multiple resolutions.Significance.The new micro-CT system could find application in areas where phase contrast is already known to provide superior image quality, while the added tuneable resolution functionality could enable more sophisticated analyses in these applications, e.g. by scanning samples at multiple scales.

Multiscale and multimodal imaging for three-dimensional vascular and histomorphological organ structure analysis of the pancreas

Exocrine and endocrine pancreas are interconnected anatomically and functionally, with vasculature facilitating bidirectional communication. Our understanding of this network remains limited, largely due to two-dimensional histology and missing combination with three-dimensional imaging. In this study, a multiscale 3D-imaging process was used to analyze a porcine pancreas. Clinical computed tomography, digital volume tomography, micro-computed tomography and Synchrotron-based propagation-based imaging were applied consecutively. Fields of view correlated inversely with attainable resolution from a whole organism level down to capillary structures with a voxel edge length of 2.0 µm. Segmented vascular networks from 3D-imaging data were correlated with tissue sections stained by immunohistochemistry and revealed highly vascularized regions to be intra-islet capillaries of islets of Langerhans. Generated 3D-datasets allowed for three-dimensional qualitative and quantitative organ and vessel structure analysis. Beyond this study, the method shows potential for application across a wide range of patho-morphology analyses and might possibly provide microstructural blueprints for biotissue engineering.

The 3D Revolution in Cancer Discovery

SUMMARY

The transition from 2D to 3D spatial profiling marks a revolutionary era in cancer research, offering unprecedented potential to enhance cancer diagnosis and treatment. This commentary outlines the experimental and computational advancements and challenges in 3D spatial molecular profiling, underscoring the innovation needed in imaging tools, software, artificial intelligence, and machine learning to overcome implementation hurdles and harness the full potential of 3D analysis in the field.

Integrating X-ray phase-contrast imaging and histology for comparative evaluation of breast tissue malignancies in virtual histology analysis

Detecting breast tissue alterations is essential for cancer diagnosis. However, inherent bidimensionality limits histological procedures' effectiveness in identifying these changes. Our study applies a 3D virtual histology method based on X-ray phase-contrast microtomography (PhC μ CT), performed at a synchrotron facility, to investigate breast tissue samples including different types of lesions, namely intraductal papilloma, micropapillary intracystic carcinoma, and invasive lobular carcinoma. One-to-one comparisons of X-ray and histological images explore the clinical potential of 3D X-ray virtual histology. Results show that PhC μ CT technique provides high spatial resolution and soft tissue sensitivity, while being non-destructive, not requiring a dedicated sample processing and being compatible with conventional histology. PhC μ CT can enhance the visualization of morphological characteristics such as stromal tissue, fibrovascular core, terminal duct lobular unit, stromal/epithelium interface, basement membrane, and adipocytes. Despite not reaching the (sub) cellular level, the three-dimensionality of PhC μ CT images allows to depict in-depth alterations of the breast tissues, potentially revealing pathologically relevant details missed by a single histological section. Compared to serial sectioning, PhC μ CT allows the virtual investigation of the sample volume along any orientation, possibly guiding the pathologist in the choice of the most suitable cutting plane. Overall, PhC μ CT virtual histology holds great promise as a tool adding to conventional histology for improving efficiency, accessibility, and diagnostic accuracy of pathological evaluation.

A Systematic Review and Meta-Analysis of the Pathology Underlying Aneurysm Enhancement on Vessel Wall Imaging

Intracranial aneurysms are common, but only a minority rupture and cause subarachnoid haemorrhage, presenting a dilemma regarding which to treat. Vessel wall imaging (VWI) is a contrast-enhanced magnetic resonance imaging (MRI) technique used to identify unstable aneurysms. The pathological basis of MR enhancement of aneurysms is the subject of debate. This review synthesises the literature to determine the pathological basis of VWI enhancement. PubMed and Embase searches were performed for studies reporting VWI of intracranial aneurysms and their correlated histological analysis. The risk of bias was assessed. Calculations of interdependence, univariate and multivariate analysis were performed. Of 228 publications identified, 7 met the eligibility criteria. Individual aneurysm data were extracted for 72 out of a total of 81 aneurysms. Univariate analysis showed macrophage markers (CD68 and MPO, p = 0.001 and p = 0.002), endothelial cell markers (CD34 and CD31, p = 0.007 and p = 0.003), glycans (Alcian blue, p = 0.003) and wall thickness (p = 0.030) were positively associated with enhancement. Aneurysm enhancement therefore appears to be associated with inflammatory infiltrate and neovascularisation. However, all these markers are correlated with each other, and the literature is limited in terms of the numbers of aneurysms analysed and the parameters considered. The data are therefore insufficient to determine if these associations are independent of each other or of aneurysm size, wall thickness and rupture status. Thus, the cause of aneurysm-wall enhancement currently remains unknown.

Characterization of transient and progressive pulmonary fibrosis by spatially correlated phase contrast microCT, classical histopathology and atomic force microscopy

Pulmonary fibrosis (PF) is a severe and progressive condition in which the lung becomes scarred over time resulting in pulmonary function impairment. Classical histopathology remains an important tool for micro-structural tissue assessment in the diagnosis of PF. A novel workflow based on spatial correlated propagation-based phase-contrast micro computed tomography (PBI-microCT), atomic force microscopy (AFM) and histopathology was developed and applied to two different preclinical mouse models of PF - the commonly used and well characterized Bleomycin-induced PF and a novel mouse model for progressive PF caused by conditional Nedd4-2 KO. The aim was to integrate structural and mechanical features from hallmarks of fibrotic lung tissue remodeling. PBI-microCT was used to assess structural alteration in whole fixed and paraffin embedded lungs, allowing for identification of fibrotic foci within the 3D context of the entire organ and facilitating targeted microtome sectioning of planes of interest for subsequent histopathology. Subsequently, these sections of interest were subjected to AFM to assess changes in the local tissue stiffness of previously identified structures of interest. 3D whole organ analysis showed clear morphological differences in 3D tissue porosity between transient and progressive PF and control lungs. By integrating the results obtained from targeted AFM analysis, it was possible to discriminate between the Bleomycin model and the novel conditional Nedd4-2 KO model using agglomerative cluster analysis. As our workflow for 3D spatial correlation of PBI, targeted histopathology and subsequent AFM is tailored around the standard procedure of formalin-fixed paraffin-embedded (FFPE) tissue specimens, it may be a powerful tool for the comprehensive tissue assessment beyond the scope of PF and preclinical research.

Uptake and transport of micro/nanoplastics in terrestrial plants: Detection, mechanisms, and influencing factors

The pervasive dispersion of micro/nanoplastics in various environmental matrices has raised concerns regarding their potential intrusion into terrestrial ecosystems and, notably, plants. In this comprehensive review, we focus on the interaction between these minute plastic particles and plants. We delve into the current methodologies available for detecting micro/nanoplastics in plant tissues, assess the accumulation and distribution of these particles within roots, stems, and leaves, and elucidate the specific uptake and transport mechanisms, including endocytosis, apoplastic transport, crack-entry mode, and stomatal entry. Moreover, uptake and transport of micro/nanoplastics are complex processes influenced by multiple factors, including particle size, surface charge, mechanical properties, and physiological characteristics of plants, as well as external environmental conditions. In conclusion, this review paper provided valuable insights into the current understanding of these mechanisms, highlighting the complexity of the processes and the multitude of factors that can influence them. Further research in this area is warranted to fully comprehend the fate of micro/nanoplastics in plants and their implications for environmental sustainability.

Placental evolution from a three-dimensional and multiscale structural perspective

The placenta mediates physiological exchange between the mother and the fetus. In placental mammals, all placentas are descended from a single common ancestor and functions are conserved across species; however, the placenta exhibits radical structural diversity. The selective pressures behind this structural diversity are poorly understood. Traditionally, placental structures have largely been investigated by grouping them into qualitative categories. Assessing the placenta on this basis could be problematic when inferring the relative "efficiency" of a placental configuration to transfer nutrients from mother to fetus. We argue that only by considering placentas as three-dimensional (3D) biological structures, integrated across scales, can the evolutionary questions behind their enormous structural diversity be quantitatively determined. We review the current state of placental evolution from a structural perspective, detail where 3D imaging and computational modeling have been used to gain insight into placental function, and outline an experimental roadmap to answer evolutionary questions from a multiscale 3D structural perspective. Our approach aims to shed light on placental evolution, and can be transferred to evolutionary investigations in any organ system.

Engineering the future of 3D pathology

In recent years, technological advances in tissue preparation, high-throughput volumetric microscopy, and computational infrastructure have enabled rapid developments in nondestructive 3D pathology, in which high-resolution histologic datasets are obtained from thick tissue specimens, such as whole biopsies, without the need for physical sectioning onto glass slides. While 3D pathology generates massive datasets that are attractive for automated computational analysis, there is also a desire to use 3D pathology to improve the visual assessment of tissue histology. In this perspective, we discuss and provide examples of potential advantages of 3D pathology for the visual assessment of clinical specimens and the challenges of dealing with large 3D datasets (of individual or multiple specimens) that pathologists have not been trained to interpret. We discuss the need for artificial intelligence triaging algorithms and explainable analysis methods to assist pathologists or other domain experts in the interpretation of these novel, often complex, large datasets.

Optimal Agents for Visualizing Collagen Tissue Microarchitecture Using Contrast-Enhanced MicroCT

Micro-computed tomography (microCT) is a common tool for the visualization of the internal composition of organic tissues. Collagen comprises approximately 25-35% of the whole-body protein content in mammals, and the structure and arrangement of collagen fibers contribute significantly to the integrity of tissues. Collagen type I is also frequently used as a key structural component in tissue-engineered and bioprinted tissues. However, the imaging of collagenous tissues is limited by their inherently low X-ray attenuation, which makes them indistinguishable from most other soft tissues. An imaging contrast agent that selectively alters X-ray attenuation is thus essential to properly visualize collagenous tissue using a standard X-ray tube microCT scanner. This review compares various contrast-enhanced techniques reported in the literature for MicroCT visualization of collagen-based tissues. An ideal microCT contrast agent would meet the following criteria: (1) it diffuses through the tissue quickly; (2) it does not deform or impair the object being imaged; and (3) it provides sufficient image contrast for reliable visualization of the orientation of individual fibers within the collagen network. The relative benefits and disadvantages of each method are discussed. Lugol's solution (I3K), phosphotungstic acid (H3PW12O40), mercury(II) chloride (HgCl2), and Wells-Dawson polyoxometalates came closest to fitting the criteria. While none of the contrast agents discussed in the literature met all criteria, each one has advantages to consider in the context of specific lab capabilities and imaging priorities.

A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications

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.

A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications

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.

Spatial correlation of 2D hard-tissue histology with 3D microCT scans through 3D printed phantoms

Hard-tissue histology-the analysis of thin two-dimensional (2D) sections-is hampered by the opaque nature of most biological specimens, especially bone. Therefore, the cutting process cannot be assigned to regions of interest. In addition, the applied cutting-grinding method is characterized by significant material loss. As a result, relevant structures might be missed or destroyed, and 3D features can hardly be evaluated. Here, we present a novel workflow, based on conventual microCT scans of the specimen prior to the cutting process, to be used for the analysis of 3D structural features and for directing the sectioning process to the regions of interest. 3D printed fiducial markers, embedded together with the specimen in resin, are utilized to retrospectively register the obtained 2D histological images into the 3D anatomical context. This not only allows to identify the cutting position, but also enables the co-registration of the cell and extracellular matrix morphological analysis to local 3D information obtained from the microCT data. We have successfully applied our new approach to assess hard-tissue specimens of different species. After matching the predicted microCT cut plane with the histology image, we validated a high accuracy of the registration process by computing quality measures namely Jaccard and Dice similarity coefficients achieving an average score of 0.90 ± 0.04 and 0.95 ± 0.02, respectively. Thus, we believe that the novel, easy to implement correlative imaging approach holds great potential for improving the reliability and diagnostic power of classical hard-tissue histology.

The Value of Micro-CT in the Diagnosis of Lung Carcinoma: A Radio-Histopathological Perspective

Micro-computed tomography (micro-CT) is a relatively new imaging modality and the three-dimensional (3D) images obtained via micro-CT allow researchers to collect both quantitative and qualitative information on various types of samples. Micro-CT could potentially be used to examine human diseases and several studies have been published on this topic in the last decade. In this study, the potential uses of micro-CT in understanding and evaluating lung carcinoma and the relevant studies conducted on lung and other tumors are summarized. Currently, the resolution of benchtop laboratory micro-CT units has not reached the levels that can be obtained with light microscopy, and it is not possible to detect the histopathological features (e.g., tumor type, adenocarcinoma pattern, spread through air spaces) required for lung cancer management. However, its ability to provide 3D images in any plane of section, without disturbing the integrity of the specimen, suggests that it can be used as an auxiliary technique, especially in surgical margin examination, the evaluation of tumor invasion in the entire specimen, and calculation of primary and metastatic tumor volume. Along with future developments in micro-CT technology, it can be expected that the image resolution will gradually improve, the examination time will decrease, and the relevant software will be more user friendly. As a result of these developments, micro-CT may enter pathology laboratories as an auxiliary method in the pathological evaluation of lung tumors. However, the safety, performance, and cost effectiveness of micro-CT in the areas of possible clinical application should be investigated. If micro-CT passes all these tests, it may lead to the convergence of radiology and pathology applications performed independently in separate units today, and the birth of a new type of diagnostician who has equal knowledge of the histological and radiological features of tumors.

68Ga-bisphosphonates for the imaging of extraosseous calcification by positron emission tomography

Radiolabelled bisphosphonates (BPs) and [18F]NaF (18F-fluoride) are the two types of radiotracers available to image calcium mineral (e.g. bone), yet only [18F]NaF has been widely explored for the non-invasive molecular imaging of extraosseous calcification (EC) using positron emission tomography (PET) imaging. These two radiotracers bind calcium mineral deposits via different mechanisms, with BPs chelating to calcium ions and thus being non-selective, and [18F]NaF being selective for hydroxyapatite (HAp) which is the main component of bone mineral. Considering that the composition of EC has been reported to include a diverse range of non-HAp calcium minerals, we hypothesised that BPs may be more sensitive for imaging EC due to their ability to bind to both HAp and non-HAp deposits. We report a comparison between the 68Ga-labelled BP tracer [68Ga]Ga-THP-Pam and [18F]NaF for PET imaging in a rat model of EC that develops macro- and microcalcifications in several organs. Macrocalcifications were identified using preclinical computed tomography (CT) and microcalcifications were identified using µCT-based 3D X-ray histology (XRH) on isolated organs ex vivo. The morphological and mineral analysis of individual calcified deposits was performed using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). PET imaging and ex vivo analysis results demonstrated that while both radiotracers behave similarly for bone imaging, the BP-based radiotracer [68Ga]Ga-THP-Pam was able to detect EC more sensitively in several organs in which the mineral composition departs from that of HAp. Our results strongly suggest that BP-based PET radiotracers such as [68Ga]Ga-THP-Pam may have a particular advantage for the sensitive imaging and early detection of EC by being able to detect a wider array of relevant calcium minerals in vivo than [18F]NaF, and should be evaluated clinically for this purpose.

Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples

Human tissue consists of complex structures that display a diversity of morphologies, forming a tissue microenvironment that is, by nature, three-dimensional (3D). However, the current standard-of-care involves slicing 3D tissue specimens into two-dimensional (2D) sections and selecting a few for microscopic evaluation1,2, with concomitant risks of sampling bias and misdiagnosis3-6. To this end, there have been intense efforts to capture 3D tissue morphology and transition to 3D pathology, with the development of multiple high-resolution 3D imaging modalities7-18. However, these tools have had little translation to clinical practice as manual evaluation of such large data by pathologists is impractical and there is a lack of computational platforms that can efficiently process the 3D images and provide patient-level clinical insights. Here we present Modality-Agnostic Multiple instance learning for volumetric Block Analysis (MAMBA), a deep-learning-based platform for processing 3D tissue images from diverse imaging modalities and predicting patient outcomes. Archived prostate cancer specimens were imaged with open-top light-sheet microscopy12-14 or microcomputed tomography15,16 and the resulting 3D datasets were used to train risk-stratification networks based on 5-year biochemical recurrence outcomes via MAMBA. With the 3D block-based approach, MAMBA achieves an area under the receiver operating characteristic curve (AUC) of 0.86 and 0.74, superior to 2D traditional single-slice-based prognostication (AUC of 0.79 and 0.57), suggesting superior prognostication with 3D morphological features. Further analyses reveal that the incorporation of greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, suggesting that there is value in capturing larger extents of spatially heterogeneous 3D morphology. With the rapid growth and adoption of 3D spatial biology and pathology techniques by researchers and clinicians, MAMBA provides a general and efficient framework for 3D weakly supervised learning for clinical decision support and can help to reveal novel 3D morphological biomarkers for prognosis and therapeutic response.

Towards virtual histology with X-ray grating interferometry

Breast cancer is the most common type of cancer worldwide. Diagnosing breast cancer relies on clinical examination, imaging and biopsy. A core-needle biopsy enables a morphological and biochemical characterization of the cancer and is considered the gold standard for breast cancer diagnosis. A histopathological examination uses high-resolution microscopes with outstanding contrast in the 2D plane, but the spatial resolution in the third, Z-direction, is reduced. In the present paper, we propose two high-resolution table-top systems for phase-contrast X-ray tomography of soft-tissue samples. The first system implements a classical Talbot-Lau interferometer and allows to perform ex-vivo imaging of human breast samples with a voxel size of 5.57 μm. The second system with a comparable voxel size relies on a Sigray MAAST X-ray source with structured anode. For the first time, we demonstrate the applicability of the latter to perform X-ray imaging of human breast specimens with ductal carcinoma in-situ. We assessed image quality of both setups and compared it to histology. We showed that both setups made it possible to target internal features of breast specimens with better resolution and contrast than previously achieved, demonstrating that grating-based phase-contrast X-ray CT could be a complementary tool for clinical histopathology.

Correlative multiscale microCT-SBF-SEM imaging of resin-embedded tissue

Three-dimensional biological microscopy presents a trade-off between spatial resolution and field of view. Correlative approaches applying multiple imaging techniques to the same sample can therefore mitigate against these trade-offs. Here, we present a workflow for correlative microscopic X-ray microfocus computed tomography (microCT) and serial block face scanning electron microscopy (SBF-SEM) imaging of resin-embedded tissue, using mammalian placental tissue samples as an example. This correlative X-ray and electron microscopy (CXEM) workflow allows users to image the same sample at multiple resolutions, and target the region of interest (ROI) for SBF-SEM based on microCT. We detail the protocols associated with this workflow and demonstrate its application in multiscale imaging of horse placental villi and ROI selection in the labyrinthine zone of a mouse placenta. These examples demonstrate how the protocol may need to be adapted for tissues with different densities.

Volumetric Tissue Imaging of Surgical Tissue Specimens Using Micro-Computed Tomography: An Emerging Digital Pathology Modality for Nondestructive, Slide-Free Microscopy-Clinical Applications of Digital Pathology in 3 Dimensions

OBJECTIVES

Micro-computed tomography (micro-CT) is a novel, nondestructive, slide-free digital imaging modality that enables the acquisition of high-resolution, volumetric images of intact surgical tissue specimens. The aim of this systematic mapping review is to provide a comprehensive overview of the available literature on clinical applications of micro-CT tissue imaging and to assess its relevance and readiness for pathology practice.

METHODS

A computerized literature search was performed in the PubMed, Scopus, Web of Science, and CENTRAL databases. To gain insight into regulatory and financial considerations for performing and examining micro-CT imaging procedures in a clinical setting, additional searches were performed in medical device databases.

RESULTS

Our search identified 141 scientific articles published between 2000 and 2021 that described clinical applications of micro-CT tissue imaging. The number of relevant publications is progressively increasing, with the specialties of pulmonology, cardiology, otolaryngology, and oncology being most commonly concerned. The included studies were mostly performed in pathology departments. Current micro-CT devices have already been cleared for clinical use, and a Current Procedural Terminology (CPT) code exists for reimbursement of micro-CT imaging procedures.

CONCLUSIONS

Micro-CT tissue imaging enables accurate volumetric measurements and evaluations of entire surgical specimens at microscopic resolution across a wide range of clinical applications.

The Reconstruction of Human Fingerprints From High-Resolution Computed Tomography Data: Feasibility Study and Associated Ethical Issues

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Current Approaches for Image Fusion of Histological Data with Computed Tomography and Magnetic Resonance Imaging

Classical analysis of biological samples requires the destruction of the tissue's integrity by cutting or grinding it down to thin slices for (Immuno)-histochemical staining and microscopic analysis. Despite high specificity, encoded in the stained 2D section of the whole tissue, the structural information, especially 3D information, is limited. Computed tomography (CT) or magnetic resonance imaging (MRI) scans performed prior to sectioning in combination with image registration algorithms provide an opportunity to regain access to morphological characteristics as well as to relate histological findings to the 3D structure of the local tissue environment. This review provides a summary of prevalent literature addressing the problem of multimodal coregistration of hard- and soft-tissue in microscopy and tomography. Grouped according to the complexity of the dimensions, including image-to-volume (2D ⟶ 3D), image-to-image (2D ⟶ 2D), and volume-to-volume (3D ⟶ 3D), selected currently applied approaches are investigated by comparing the method accuracy with respect to the limiting resolution of the tomography. Correlation of multimodal imaging could position itself as a useful tool allowing for precise histological diagnostic and allow the a priori planning of tissue extraction like biopsies.

A high-resolution 3D atlas of the spectrum of tuberculous and COVID-19 lung lesions

Our current understanding of the spectrum of TB and COVID-19 lesions in the human lung is limited by a reliance on low-resolution imaging platforms that cannot provide accurate 3D representations of lesion types within the context of the whole lung. To characterize TB and COVID-19 lesions in 3D, we applied micro/nanocomputed tomography to surgically resected, postmortem, and paraffin-embedded human lung tissue. We define a spectrum of TB pathologies, including cavitary lesions, calcium deposits outside and inside necrotic granulomas and mycetomas, and vascular rearrangement. We identified an unusual spatial arrangement of vasculature within an entire COVID-19 lobe, and 3D segmentation of blood vessels revealed microangiopathy associated with hemorrhage. Notably, segmentation of pathological anomalies reveals hidden pathological structures that might otherwise be disregarded, demonstrating a powerful method to visualize pathologies in 3D in TB lung tissue and whole COVID-19 lobes. These findings provide unexpected new insight into the spatial organization of the spectrum of TB and COVID-19 lesions within the framework of the entire lung.

Giving volume to the elephant in the imaging room

Rudolf Virchow, the founder of cellular pathology, held that even when physical or chemical investigations yield the laws of physiology or medicine, the anatomist can still proudly state: This is the structure in which the law becomes manifest. In his words, "physiology presupposes anatomy." Pathological anatomy studies, at usual microstructural scales (approximately 1-100 μm), via light microscopic 2D histology, provided many insights into structure-function relationships of health and disease. For example, such studies established the progression of granulomas, bronchial erosions, microcavities, and destructive lung disease in tuberculosis. While histologic studies remain the cornerstone of such efforts, the advent of nano or micro-X-ray computed tomography (n/μCT) has now made it additionally possible to obtain 3D visualizations of soft and hard tissues, while preserving the tissue for additional investigations. This has applications for old as well as new diseases (Katsamenis et al, 2019; Tanabe & Hirai, 2021).

An optimized workflow for microCT imaging of formalin‐fixed and paraffin‐embedded ( FFPE ) early equine embryos

Here, we describe a workflow for high‐detail microCT imaging of formalin‐fixed and paraffin‐embedded (FFPE) equine embryos recovered on Day 34 of pregnancy (E34), a period just before placenta formation. The presented imaging methods are suitable for large animals' embryos with intention to study morphological and developmental aspects, but more generally can be adopted for all kinds of FFPE tissue specimens. Microscopic 3D imaging techniques such as microCT are important tools for detecting and studying normal embryogenesis and developmental disorders. To date, microCT imaging of vertebrate embryos was mostly done on embryos that have been stained with an X‐ray dense contrast agent. Here, we describe an alternative imaging procedure that allows to visualize embryo morphology and organ development in unstained FFPE embryos. Two aspects are critical for high‐quality data acquisition: (i) a proper sample mounting leaving as little as possible paraffin around the sample and (ii) an image filtering pipeline that improves signal‐to‐noise ratio in these inherently low‐contrast data sets. The presented workflow allows overview imaging of the whole embryo proper and can be used for determination of organ volumes and development. Furthermore, we show that high‐resolution interior tomographies can provide virtual histology information from selected regions of interest. In addition, we demonstrate that microCT scanned embryos remain intact during the scanning procedure allowing for a subsequent investigation by routine histology and/or immunohistochemistry. This makes the presented workflow applicable also to archival paraffin‐embedded material.

Nondestructive cellular-level 3D observation of mouse kidney using laboratory-based X-ray microscopy with paraffin-mediated contrast enhancement

For three-dimensional observation of unstained bio-specimens using X-ray microscopy with computed tomography (CT), one main problem has been low contrast in X-ray absorption. Here we introduce paraffin-mediated contrast enhancement to visualize biopsy samples of mouse kidney using a laboratory-based X-tray microscope. Unlike conventional heavy-atom staining, paraffin-mediated contrast enhancement uses solid paraffin as a negative contrast medium to replace water in the sample. The medium replacement from water to paraffin effectively lowers the absorption of low-energy X-rays by the medium, which eventually enhances the absorption contrast between the medium and tissue. In this work, paraffin-mediated contrast enhancement with 8 keV laboratory X-rays was used to visualize cylindrical renal biopsies with diameters of about 0.5 mm. As a result, reconstructed CT images from 19.4 h of data collection achieved cellular-level resolutions in all directions, which provided 3D structures of renal corpuscles from a normal mouse and from a disease model mouse. These two structures with and without disease allowed a volumetric analysis showing substantial volume differences in glomerular subregions. Notably, this nondestructive method presents CT opacities reflecting elemental composition and density of unstained tissues, thereby allowing more unbiased interpretation on their biological structures.

A multiscale X-ray phase-contrast tomography dataset of a whole human left lung

Technological advancements in X-ray imaging using bright and coherent synchrotron sources now allows the decoupling of sample size and resolution while maintaining high sensitivity to the microstructures of soft, partially dehydrated tissues. The continuous developments in multiscale X-ray imaging resulted in hierarchical phase-contrast tomography, a comprehensive approach to address the challenge of organ-scale (up to tens of centimeters) soft tissue imaging with resolution and sensitivity down to the cellular level. Using this technique, we imaged ex vivo an entire human left lung at an isotropic voxel size of 25.08 μm along with local zooms down to 6.05-6.5 μm and 2.45-2.5 μm in voxel size. The high tissue contrast offered by the fourth-generation synchrotron source at the European Synchrotron Radiation Facility reveals the complex multiscale anatomical constitution of the human lung from the macroscopic (centimeter) down to the microscopic (micrometer) scale. The dataset provides comprehensive organ-scale 3D information of the secondary pulmonary lobules and delineates the microstructure of lung nodules with unprecedented detail.

Patch-based artifact reduction for three-dimensional volume projection data of sparse-view micro-computed tomography

Micro-computed tomography (micro-CT) enables the non-destructive acquisition of three-dimensional (3D) morphological structures at the micrometer scale. Although it is expected to be used in pathology and histology to analyze the 3D microstructure of tissues, micro-CT imaging of tissue specimens requires a long scan time. A high-speed imaging method, sparse-view CT, can reduce the total scan time and radiation dose; however, it causes severe streak artifacts on tomographic images reconstructed with analytical algorithms due to insufficient sampling. In this paper, we propose an artifact reduction method for 3D volume projection data from sparse-view micro-CT. Specifically, we developed a patch-based lightweight fully convolutional network to estimate full-view 3D volume projection data from sparse-view 3D volume projection data. We evaluated the effectiveness of the proposed method using physically acquired datasets. The qualitative and quantitative results showed that the proposed method achieved high estimation accuracy and suppressed streak artifacts in the reconstructed images. In addition, we confirmed that the proposed method requires both short training and prediction times. Our study demonstrates that the proposed method has great potential for artifact reduction for 3D volume projection data under sparse-view conditions.

Advanced high resolution three-dimensional imaging to visualize the cerebral neurovascular network in stroke

As an important method to accurately and timely diagnose stroke and study physiological characteristics and pathological mechanism in it, imaging technology has gone through more than a century of iteration. The interaction of cells densely packed in the brain is three-dimensional (3D), but the flat images brought by traditional visualization methods show only a few cells and ignore connections outside the slices. The increased resolution allows for a more microscopic and underlying view. Today's intuitive 3D imagings of micron or even nanometer scale are showing its essentiality in stroke. In recent years, 3D imaging technology has gained rapid development. With the overhaul of imaging mediums and the innovation of imaging mode, the resolution has been significantly improved, endowing researchers with the capability of holistic observation of a large volume, real-time monitoring of tiny voxels, and quantitative measurement of spatial parameters. In this review, we will summarize the current methods of high-resolution 3D imaging applied in stroke.

From Macroscopy to Ultrastructure: An Integrative Approach to Pulmonary Pathology

Pathology and radiology are complimentary tools, and their joint application is often crucial in obtaining an accurate diagnosis in non-neoplastic pulmonary diseases. However, both come with significant limitations of their own: Computed Tomography (CT) can only visualize larger structures due to its inherent–relatively–poor resolution, while (histo) pathology is often limited due to small sample size and sampling error and only allows for a 2D investigation. An innovative approach of inflating whole lung specimens and subjecting these subsequently to CT and whole lung microCT allows for an accurate matching of CT-imaging and histopathology data of exactly the same areas. Systematic application of this approach allows for a more targeted assessment of localized disease extent and more specifically can be used to investigate early mechanisms of lung diseases on a morphological and molecular level. Therefore, this technique is suitable to selectively investigate changes in the large and small airways, as well as the pulmonary arteries, veins and capillaries in relation to the disease extent in the same lung specimen. In this perspective we provide an overview of the different strategies that are currently being used, as well as how this growing field could further evolve.

Three-dimensional label-free histological imaging of whole organs by microtomy-assisted autofluorescence tomography

Three-dimensional (3D) histology is vitally important to characterize disease-induced tissue heterogeneity at the individual cell level. However, it remains challenging for both high-throughput 3D imaging and volumetric reconstruction. Here we propose a label-free, cost-effective, and ready-to-use 3D histological imaging technique, termed microtomy-assisted autofluorescence tomography with ultraviolet excitation (MATE). With the combination of block-face imaging and serial microtome sectioning, MATE can achieve rapid and label-free imaging of paraffin-embedded whole organs at an acquisition speed of 1 cm3 per 4 h with a voxel resolution of 1.2 × 1.2 × 10 μm3. We demonstrate that MATE enables simultaneous visualization of cell nuclei, fiber tracts, and blood vessels in mouse/human brains without tissue staining or clearing. Moreover, diagnostic features, including nuclear size and packing density, can be quantitatively extracted with high accuracy. MATE is augmented to the current slide-based 2D histology, holding great promise to facilitate histopathological interpretation at the organelle level.

Virtual histology of an entire mouse brain from formalin fixation to paraffin embedding. Part 2: Volumetric strain fields and local contrast changes

BACKGROUND

Fixation and embedding of post mortem brain tissue is a pre-requisite for both gold-standard conventional histology and X-ray virtual histology. This process alters the morphology and density of the brain microanatomy.

NEW METHOD

To quantify these changes, we employed synchrotron radiation-based hard X-ray tomography with 3 μm voxel length to visualize the same mouse brain after fixation in 4% formalin, immersion in ethanol solutions (50%, 70%, 80%, 90%, and 100%), xylene, and finally after embedding in a paraffin block. The volumetric data were non-rigidly registered to the initial formalin-fixed state to align the microanatomy within the entire mouse brain.

RESULTS

Volumetric strain fields were used to characterize local shrinkage, which was found to depend on the anatomical region and distance to external surface. X-ray contrast was altered and enhanced by preparation-induced inter-tissue density changes. The preparation step can be selected to highlight specific anatomical features. For example, fiber tract contrast is amplified in 100% ethanol.

COMPARISON WITH EXISTING METHODS

Our method provides volumetric strain fields, unlike approaches based on feature-to-feature or volume measurements. Volumetric strain fields are produced by non-rigid registration, which is less labor-intensive and observer-dependent than volume change measurements based on manual segmentations. X-ray microtomography provides spatial resolution at least an order of magnitude higher than magnetic resonance microscopy, allowing for analysis of morphology and density changes within the brain's microanatomy.

CONCLUSION

Our approach belongs to three-dimensional virtual histology with isotropic micrometer spatial resolution and therefore complements atlases based on a combination of magnetic resonance microscopy and optical micrographs of serial histological sections.

Virtual histology of an entire mouse brain from formalin fixation to paraffin embedding. Part 1: Data acquisition, anatomical feature segmentation, tracking global volume and density changes

BACKGROUND

Micrometer-resolution neuroimaging with gold-standard conventional histology requires tissue fixation and embedding. The exchange of solvents for the creation of sectionable paraffin blocks modifies tissue density and generates non-uniform brain shrinkage.

NEW METHOD

We employed synchrotron radiation-based X-ray microtomography for slicing- and label-free virtual histology of the mouse brain at different stages of the standard preparation protocol from formalin fixation via ascending ethanol solutions and xylene to paraffin embedding. Segmentation of anatomical regions allowed us to quantify non-uniform tissue shrinkage. Global and local changes in X-ray absorption gave insight into contrast enhancement for virtual histology.

RESULTS

The volume of the entire mouse brain was 60%, 56%, and 40% of that in formalin for, respectively, 100% ethanol, xylene, and paraffin. The volume changes of anatomical regions such as the hippocampus, anterior commissure, and ventricles differ from the global volume change. X-ray absorption of the full brain decreased, while local absorption differences increased, resulting in enhanced contrast for virtual histology. These trends were also observed with laboratory microtomography measurements.

COMPARISON WITH EXISTING METHODS

Microtomography provided sub-10 μm spatial resolution with sufficient density resolution to resolve anatomical structures at each step of the embedding protocol. The spatial resolution of conventional computed tomography and magnetic resonance microscopy is an order of magnitude lower and both do not match the contrast of microtomography over the entire embedding protocol. Unlike feature-to-feature or total volume measurements, our approach allows for calculation of volume change based on segmentation.

CONCLUSION

We present isotropic micrometer-resolution imaging to quantify morphology and composition changes in a mouse brain during the standard histological preparation. The proposed method can be employed to identify the most appropriate embedding medium for anatomical feature visualization, to reveal the basis for the dramatic X-ray contrast enhancement observed in numerous embedded tissues, and to quantify morphological changes during tissue fixation and embedding.

Volumetric Imaging of Lung Tissue at Micrometer Resolution: Clinical Applications of Micro-CT for the Diagnosis of Pulmonary Diseases

Micro-computed tomography (micro-CT) is a promising novel medical imaging modality that allows for non-destructive volumetric imaging of surgical tissue specimens at high spatial resolution. The aim of this study is to provide a comprehensive assessment of the clinical applications of micro-CT for the tissue-based diagnosis of lung diseases. This scoping review was conducted in accordance with the PRISMA Extension for Scoping Reviews, aiming to include every clinical study reporting on micro-CT imaging of human lung tissues. A literature search yielded 570 candidate articles, out of which 37 were finally included in the review. Of the selected studies, 9 studies explored via micro-CT imaging the morphology and anatomy of normal human lung tissue; 21 studies investigated microanatomic pulmonary alterations due to obstructive or restrictive lung diseases, such as chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, and cystic fibrosis; and 7 studies examined the utility of micro-CT imaging in assessing lung cancer lesions (n = 4) or in transplantation-related pulmonary alterations (n = 3). The selected studies reported that micro-CT could successfully detect several lung diseases providing three-dimensional images of greater detail and resolution than routine optical slide microscopy, and could additionally provide valuable volumetric insight in both restrictive and obstructive lung diseases. In conclusion, micro-CT-based volumetric measurements and qualitative evaluations of pulmonary tissue structures can be utilized for the clinical management of a variety of lung diseases. With micro-CT devices becoming more accessible, the technology has the potential to establish itself as a core diagnostic imaging modality in pathology and to enable integrated histopathologic and radiologic assessment of lung cancer and other lung diseases.

Immunofluorescence-guided segmentation of three-dimensional features in micro-computed tomography datasets of human lung tissue

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.

Tutorial: methods for three-dimensional visualization of archival tissue material

Analysis of three-dimensional patient specimens is gaining increasing relevance for understanding the principles of tissue structure as well as the biology and mechanisms underlying disease. New technologies are improving our ability to visualize large volume of tissues with subcellular resolution. One resource often overlooked is archival tissue maintained for decades in hospitals and research archives around the world. Accessing the wealth of information stored within these samples requires the use of appropriate methods. This tutorial introduces the range of sample preparation and microscopy approaches available for three-dimensional visualization of archival tissue. We summarize key aspects of the relevant techniques and common issues encountered when using archival tissue, including registration and antibody penetration. We also discuss analysis pipelines required to process, visualize and analyze the data and criteria to guide decision-making. The methods outlined in this tutorial provide an important and sustainable avenue for validating three-dimensional tissue organization and mechanisms of disease.

3D cyclorama for digital unrolling and visualisation of deformed tubes

Colonic crypts are tubular glands that multiply through a symmetric branching process called crypt fission. During the early stages of colorectal cancer, the normal fission process is disturbed, leading to asymmetrical branching or budding. The challenging shapes of the budding crypts make it difficult to prepare paraffin sections for conventional histology, resulting in colonic cross sections with crypts that are only partially visible. To study crypt budding in situ and in three dimensions (3D), we employ X-ray micro-computed tomography to image intact colons, and a new method we developed (3D cyclorama) to digitally unroll them. Here, we present, verify and validate our '3D cyclorama' method that digitally unrolls deformed tubes of non-uniform thickness. It employs principles from electrostatics to reform the tube into a series of onion-like surfaces, which are mapped onto planar panoramic views. This enables the study of features extending over several layers of the tube's depth, demonstrated here by two case studies: (i) microvilli in the human placenta and (ii) 3D-printed adhesive films for drug delivery. Our 3D cyclorama method can provide novel insights into a wide spectrum of applications where digital unrolling or flattening is necessary, including long bones, teeth roots and ancient scrolls.

Integrative Computational Models of Lung Structure-Function Interactions

Anatomically based integrative models of the lung and their interaction with other key components of the respiratory system provide unique capabilities for investigating both normal and abnormal lung function. There is substantial regional variability in both structure and function within the normal lung, yet it remains capable of relatively efficient gas exchange by providing close matching of air delivery (ventilation) and blood delivery (perfusion) to regions of gas exchange tissue from the scale of the whole organ to the smallest continuous gas exchange units. This is despite remarkably different mechanisms of air and blood delivery, different fluid properties, and unique scale-dependent anatomical structures through which the blood and air are transported. This inherent heterogeneity can be exacerbated in the presence of disease or when the body is under stress. Current computational power and data availability allow for the construction of sophisticated data-driven integrative models that can mimic respiratory system structure, function, and response to intervention. Computational models do not have the same technical and ethical issues that can limit experimental studies and biomedical imaging, and if they are solidly grounded in physiology and physics they facilitate investigation of the underlying interaction between mechanisms that determine respiratory function and dysfunction, and to estimate otherwise difficult-to-access measures. © 2021 American Physiological Society. Compr Physiol 11:1501-1530, 2021.

Three-Dimensional Vessel Segmentation in Whole-Tissue and Whole-Block Imaging Using a Deep Neural Network: Proof-of-Concept Study

In the field of pathology, micro-computed tomography (micro-CT) has become an attractive imaging modality because it enables full analysis of the three-dimensional characteristics of a tissue sample or organ in a noninvasive manner. However, because of the complexity of the three-dimensional information, understanding would be improved by development of analytical methods and software such as those implemented for clinical CT. As the accurate identification of tissue components is critical for this purpose, we have developed a deep neural network (DNN) to analyze whole-tissue images (WTIs) and whole-block images (WBIs) of neoplastic cancer tissue using micro-CT. The aim of this study was to segment vessels from WTIs and WBIs in a volumetric segmentation method using DNN. To accelerate the segmentation process while retaining accuracy, a convolutional block in DNN was improved by introducing a residual inception block. Three colorectal tissue samples were collected and one WTI and 70 WBIs were acquired by a micro-CT scanner. The implemented segmentation method was then tested on the WTI and WBIs. As a proof-of-concept study, our method successfully segmented the vessels on all WTI and WBIs of the colorectal tissue sample. In addition, despite the large size of the images for analysis, all segmentation processes were completed in 10 minutes.