A preclinical micro-computed tomography database including 3D whole body organ segmentations

Postmortem CT: Applications in Clinical and Forensic Medicine

Just as radiography has been used in forensic medicine since shortly after the discovery of x-rays in 1895, CT was introduced to postmortem investigation not long after its introduction to medicine in the 1970s. In recent decades, forensic radiology has declared itself as a new subspecialty capable of revolutionizing death investigation and research. A variety of postmortem imaging techniques have emerged. Postmortem CT (PMCT) is widely accepted around the world as a supplementary tool and, in specific cases and settings, an alternative to full autopsy. As its popularity grows, however, it is important for radiologists and pathologists to expand their understanding of the applications, benefits, and limitations of these techniques, as well as the unique nuances of postmortem imaging interpretation. This will ensure high-quality interpretations and avoid potential pitfalls that could result in premature or erroneous conclusions. The authors introduce the reader, particularly the radiologist, to the growing subspecialty of forensic imaging (focusing on imaging of the deceased), specifically focusing on PMCT and its applications in death investigation in both clinical and forensic settings. The authors also discuss the benefits and limitations of PMCT as well as important nuances of PMCT interpretation, emphasizing the similarities and differences between clinical and postmortem studies, the necessity of conducting thorough death investigations, and the importance of pursuing specialized education or training in postmortem imaging interpretation. Applications of more specialized imaging techniques to postmortem and forensic investigations are described, including developing research in this area. ©RSNA, 2025 Supplemental material is available for this article.

Sectional Anatomy with Micro-Computed Tomography and Magnetic Resonance Imaging Correlation of the Middle and Caudal Abdominal Regions in the Syrian Hamster (Mesocricetus auratus)

The abdomen is a key region in small animal veterinary practice, with the middle and caudal sections housing various organ systems that are susceptible to dysfunction, necessitating medical intervention or surgery. Sectional imaging techniques like CT and MRI are commonly used in small mammals, but no studies have focused on rodent abdomen. This study aimed to correlate micro-CT and MRI images of the middle and caudal abdominal regions with corresponding anatomical sections in Syrian hamsters (SHs), which are popular pets and experimental models. Ten healthy male SHs were used, and anatomical structures from frozen sections were compared with corresponding MCT and MRI images. Clinically relevant structures identified in anatomical sections were discernible on MCT and MRI scans. The key findings include the presence of glandular and non-glandular stomachs, the stomach and cecum primarily located on the left side, the absence of ampulla coli, sacculus rotundus, and cecal appendix, and sacculation of the colon, as well as the jejunum, mainly on the right side. The vesicular, coagulating, and prostate glands were also present, and the right kidney did not extend to the last thoracic vertebra. The results were similar to abdominal anatomical and radiologic studies in rats, mice, and guinea pigs, regardless of the rat's and mice's sacculated cecum and the guinea pig's glandular stomach. However, significant differences were observed compared to the rabbit abdomen's sectional anatomy and CT findings. This study highlights the diagnostic value of MCT and MRI in SHs and provides a valuable reference for interpreting cross-sectional abdominal images in SHs.

A robust deep learning approach for segmenting cortical and trabecular bone from 3D high resolution µCT scans of mouse bone

Recent advancements in deep learning have significantly enhanced the segmentation of high-resolution microcomputed tomography (µCT) bone scans. In this paper, we present the dual-branch attention-based hybrid network (DBAHNet), a deep learning architecture designed for automatically segmenting the cortical and trabecular compartments in 3D µCT scans of mouse tibiae. DBAHNet's hierarchical structure combines transformers and convolutional neural networks to capture long-range dependencies and local features for improved contextual representation. We trained DBAHNet on a limited dataset of 3D µCT scans of mouse tibiae and evaluated its performance on a diverse dataset collected from seven different research studies. This evaluation covered variations in resolutions, ages, mouse strains, drug treatments, surgical procedures, and mechanical loading. DBAHNet demonstrated excellent performance, achieving high accuracy, particularly in challenging scenarios with significantly altered bone morphology. The model's robustness and generalization capabilities were rigorously tested under diverse and unseen conditions, confirming its effectiveness in the automated segmentation of high-resolution µCT mouse tibia scans. Our findings highlight DBAHNet's potential to provide reliable and accurate 3D µCT mouse tibia segmentation, thereby enhancing and accelerating preclinical bone studies in drug development. The model and code are available at https://github.com/bigfahma/DBAHNet .

Robust Automated Mouse Micro-CT Segmentation Using Swin UNEt TRansformers

Image-guided mouse irradiation is essential to understand interventions involving radiation prior to human studies. Our objective is to employ Swin UNEt TRansformers (Swin UNETR) to segment native micro-CT and contrast-enhanced micro-CT scans and benchmark the results against 3D no-new-Net (nnU-Net). Swin UNETR reformulates mouse organ segmentation as a sequence-to-sequence prediction task using a hierarchical Swin Transformer encoder to extract features at five resolution levels, and it connects to a Fully Convolutional Neural Network (FCNN)-based decoder via skip connections. The models were trained and evaluated on open datasets, with data separation based on individual mice. Further evaluation on an external mouse dataset acquired on a different micro-CT with lower kVp and higher imaging noise was also employed to assess model robustness and generalizability. The results indicate that Swin UNETR consistently outperforms nnU-Net and AIMOS in terms of the average dice similarity coefficient (DSC) and the Hausdorff distance (HD95p), except in two mice for intestine contouring. This superior performance is especially evident in the external dataset, confirming the model's robustness to variations in imaging conditions, including noise and quality, and thereby positioning Swin UNETR as a highly generalizable and efficient tool for automated contouring in pre-clinical workflows.

Neutrophil Hitchhiking Enhances Liposomal Dexamethasone Therapy of Sepsis

Sepsis is characterized by a dysregulated immune response and is very difficult to treat. In the cecal ligation and puncture (CLP) mouse model, we show that nanomedicines can effectively alleviate systemic and local septic events by targeting neutrophils. Specifically, by decorating the surface of clinical-stage dexamethasone liposomes with cyclic arginine-glycine-aspartic acid (cRGD) peptides, we promote their engagement with neutrophils in the systemic circulation, leading to their prominent accumulation at primary and secondary sepsis sites. cRGD-targeted dexamethasone liposomes potently reduce immature circulating neutrophils and neutrophil extracellular traps in intestinal sepsis induction sites and the liver. Additionally, they mitigate inflammatory cytokines systemically and locally while preserving systemic IL-10 levels, contributing to lower IFN-γ/IL-10 ratios as compared to control liposomes and free dexamethasone. Our strategy addresses sepsis at the cellular level, illustrating the use of neutrophils both as a therapeutic target and as a chariot for drug delivery.

3D whole body preclinical micro-CT database of subcutaneous tumors in mice with annotations from 3 annotators

A pivotal animal model for development of anticancer molecules is mice with subcutaneous tumors, grown by injection of xenografted tumor cells, where micro-Computed Tomography (µCT) of the mice is used to analyze the efficacy of the anticancer molecule. Manual delineation of the tumor region is necessary for the analysis, which is time-consuming and inconsistent, highlighting the need for automatic segmentation (AS) tools. This study introduces a preclinical µCT database, comprising 452 whole-body scans from 223 individual mice with subcutaneous tumors, spanning ten diverse µCT datasets conducted between 2014 and 2020 on a preclinical PET/CT scanner, making it the hitherto largest dataset of its kind. Each tumor is annotated manually by three expert annotators, allowing for robust model development. Inter-annotator agreement was analyzed, and we report an overall annotation agreement of 0.903 ± 0.046 (mean ± std) Fleiss' Kappa and a mean deviation in volume estimation of 0.015 ± 0.010 cm3 (6.9% ± 4.7), which establishes a human baseline accuracy for delineation of subcutaneous tumors, while showing good inter-annotator agreement.

A Technique to Quantify Very Low Activities in Regions of Interest With a Collimatorless Detector

We present a new method to measure sub-microcurie activities of photon-emitting radionuclides in organs and lesions of small animals in vivo. Our technique, named the collimator-less likelihood fit, combines a very high sensitivity collimatorless detector with a Monte Carlo-based likelihood fit in order to estimate the activities in previously segmented regions of interest along with their uncertainties. This is done directly from the photon projections in our collimatorless detector and from the region of interest segmentation provided by an x-ray computed tomography scan. We have extensively validated our approach with 225Ac experimentally in spherical phantoms and mouse phantoms, and also numerically with simulations of a realistic mouse anatomy. Our method yields statistically unbiased results with uncertainties smaller than 20% for activities as low as ~111Bq (3nCi) and for exposures under 30 minutes. We demonstrate that our method yields more robust recovery coefficients when compared to SPECT imaging with a commercial pre-clinical scanner, specially at very low activities. Thus, our technique is complementary to traditional SPECT/CT imaging since it provides a more accurate and precise organ and tumor dosimetry, with a more limited spatial information. Finally, our technique is specially significant in extremely low-activity scenarios when SPECT/CT imaging is simply not viable.

Tricaprylin-based drug crystalline suspension for intramuscular long-acting delivery of entecavir with alleviated local inflammation

In order to ensure prolonged pharmacokinetic profile along with local tolerability at the injection site, tricaprylin-based drug crystalline suspension (TS) was designed and its local distribution, pharmacokinetics, and inflammatory response, were evaluated with conventional aqueous suspension (AS). As model drug particles, entecavir 3-palmitate (EV-P), an ester lipidic prodrug for entecavir (EV), was employed. The EV-P-loaded TS was prepared by ultra-sonication method. Prepared TS and conventional AS exhibited comparable morphology (rod or rectangular), median diameter (2.7 and 2.6 μm), crystallinity (melting point of 160-165°C), and in vitro dissolution profile. However, in vivo performances of drug microparticles were markedly different, depending on delivery vehicle. At AS-injected site, drug aggregates of up to 500 μm were formed upon intramuscular injection, and were surrounded with inflammatory cells and fibroblastic bands. In contrast, no distinct particle aggregation and adjacent granulation was observed at TS-injected site, with >4 weeks remaining of the oily vehicle in micro-computed tomographic observation. Surprisingly, TS exhibited markedly alleviated local inflammation compared to AS, endowing markedly lessened necrosis, fibrosis thickness, inflammatory area, and macrophage infiltration. The higher initial systemic exposure was observed with TS compared to AS, but TS provided prolonged delivery of EV for 3 weeks. Therefore, we suggest that the novel TS system can be a promising tool in designing parenteral long-acting delivery, with improved local tolerability.

Exploring Automated Contouring Across Institutional Boundaries: A Deep Learning Approach with Mouse Micro-CT Datasets

Image-guided mouse irradiation is essential to understand interventions involving radiation prior to human studies. Our objective is to employ Swin UNEt Transformers (Swin UNETR) to segment native micro-CT and contrast-enhanced micro-CT scans and benchmark the results against 3D no-new-Net (nnU-Net). Swin UNETR reformulates mouse organ segmentation as a sequence-to-sequence prediction task, using a hierarchical Swin Transformer encoder to extract features at 5 resolution levels, and connects to a Fully Convolutional Neural Network (FCNN)-based decoder via skip connections. The models were trained and evaluated on open datasets, with data separation based on individual mice. Further evaluation on an external mouse dataset acquired on a different micro-CT with lower kVp and higher imaging noise was also employed to assess model robustness and generalizability. Results indicate that Swin UNETR consistently outperforms nnU-Net and AIMOS in terms of average dice similarity coefficient (DSC) and Hausdorff distance (HD95p), except in two mice of intestine contouring. This superior performance is especially evident in the external dataset, confirming the model's robustness to variations in imaging conditions, including noise and quality, thereby positioning Swin UNETR as a highly generalizable and efficient tool for automated contouring in pre-clinical workflows.

3D visualization of the human anterior cruciate ligament combining micro-CT and histological analysis

PURPOSE

The study aimed to obtain a comprehensive 3D visualization of knee specimens, including the cruciate ligaments and corresponding femoral and tibial bone insertions using a non-destructive micro-CT method.

METHODS

Knee specimens were fixed in anatomical positions and chemically dehydrated before being scanned using micro-CT with a voxel size of 17.5 μm. RGBA (red, green, blue, alpha) transfer functions were applied to virtually colorize each structure. Following micro-CT scanning, the samples were rehydrated, decalcified, and trimmed based on micro-CT 3D reconstructions as references. Histological evaluations were performed on the trimmed samples. Histological and micro-CT images were registered to morphologically and densitometrically assess the 4-layer insertion of the ACL into the bone.

RESULTS

The output of the micro-CT images of the knee in extension and flexion allowed a clear differentiation of the morphologies of both soft and hard tissues, such as the ACL, femoral and tibial bones, and cartilage, and the subsequent creation of 3D composite models useful for accurately tracing the entire morphology of the ligament, including its fiber and bundle components, the trajectory between the femur and tibia, and the size, extension, and morphology of its insertions into the bones.

CONCLUSION

The implementation of the non-destructive micro-CT method allowed complete visualization of all the different components of the knee specimens. This allowed correlative imaging by micro-CT and histology, accurate planning of histological sections, and virtual anatomical and microstructural analysis. The micro-CT approach provided an unprecedented 3D level of detail, offering a viable means to study ACL anatomy.

Hepatocellular loss of mTOR aggravates tumor burden in nonalcoholic steatohepatitis-related HCC

Obesity and associated nonalcoholic steatohepatitis (NASH) are on the rise globally. NASH became an important driver of hepatocellular carcinoma (HCC) in recent years. Activation of the central metabolic regulator mTOR (mechanistic target of rapamycin) is frequently observed in HCCs. However, mTOR inhibition failed to improve the outcome of HCC therapies, demonstrating the need for a better understanding of the molecular and functional consequences of mTOR blockade. We established a murine NASH-driven HCC model based on long-term western diet feeding combined with hepatocellular mTOR-inactivation. We evaluated tumor load and whole-body fat percentage via µCT-scans, analyzed metabolic blood parameters and tissue proteome profiles. Additionally, we used a bioinformatic model to access liver and HCC mitochondrial metabolic functions. The tumor burden was massively increased via mTOR-knockout. Several signs argue for extensive metabolic reprogramming of glucose, fatty acid, bile acid and cholesterol metabolism. Kinetic modeling revealed reduced oxygen consumption in KO-tumors. NASH-derived HCC pathogenesis is driven by metabolic disturbances and should be considered separately from those caused by other etiologies. We conclude that mTOR functions as tumor suppressor in hepatocytes especially under long-term western diet feeding. However, some of the detrimental consequences of this diet are attenuated by mTOR blockade.

Assessment of the Extent of Intracerebral Hemorrhage Using 3D Modeling Technology

The second most common cause of stroke, accounting for 10% of hospital admissions, is intracerebral hemorrhage (ICH), and risk factors include diabetes, smoking, and hypertension. People with intracerebral bleeding experience symptoms that are related to the functions that are managed by the affected part of the brain. Having obtained 15 computed tomography (CT) scans from five patients with ICH, we decided to use three-dimensional (3D) modeling technology to estimate the bleeding volume. CT was performed on admission to hospital, and after one week and two weeks of treatment. We segmented the brain, ventricles, and hemorrhage using semi-automatic algorithms in Slicer 3D, then improved the obtained models in Blender. Moreover, the accuracy of the models was checked by comparing corresponding CT scans with 3D brain model cross-sections. The goal of the research was to examine the possibility of using 3D modeling technology to visualize intracerebral hemorrhage and assess its treatment.

Aligned Lovastatin-loaded Electrospun Nanofibers Regulate Collagen Organization and Reduce Scar Formation

Excessive scar formation caused by cutaneous injury leads to pruritus, pain, contracture, dyskinesia, and unpleasant appearance. Functional wound dressings are designed to accelerate wound healing and reduce scar formation. In this study, we fabricated aligned or random polycaprolactone/silk fibroin electrospun nanofiber membranes with or without lovastatin loading, and then evaluated their scar-inhibitory effects on wounds under a specific tension direction. The nanofiber membranes exhibited good controlled-release performance, mechanical properties, hydrophilicity, and biocompatibility. Furthermore, nanofibers' perpendicular placement to the tension direction of the wound most effectively reduced scar formation (the scar area decreased by 66.9%) and promoted skin regeneration in vivo. The mechanism was associated with its aligned nanofibers regulated collagen organization in the early stage of wound healing. Moreover, lovastatin-loaded nanofibers inhibited myofibroblast differentiation and migration. Both tension direction-perpendicular topographical cues and lovastatin synergistically inhibited mechanical transduction and fibrosis progression, further reducing scar formation. In summary, our study may provide an effective scar prevention strategy in which individualized dressings can be designed according to the local mechanical force direction of patients' wounds, and the addition of lovastatin can further inhibit scar formation. STATEMENT OF SIGNIFICANCE: In vivo, cells and collagen are always arranged parallel to the tension direction. However, the aligned topographic cues themselves promote myofibroblast differentiation and exacerbate scar formation. Electrospun nanofibers' perpendicular placement to the tension direction of the wound most effectively reduces scar formation and promotes skin regeneration in vivo. The mechanism is associated with its tension direction-perpendicular nanofibers reregulate collagen organization in the early stage of wound healing. In addition, tension direction-perpendicular topographical cue and lovastatin could inhibit mechanical transduction and fibrosis progression synergistically, further reducing scar formation. This study proves that combining topographical cues of wound dressing and drugs would be a promising therapy for clinical scar management.

Functional, morphological and molecular characteristics in a novel rat model of spinal sacral nerve injury-surgical approach, pathological process and clinical relevance

Spinal sacral nerve injury represents one of the most serious conditions associated with many diseases such as sacral fracture, tethered cord syndrome and sacral canal tumor. Spinal sacral nerve injury could cause bladder denervation and detrusor underactivity. There is limited clinical experience resolving spinal sacral nerve injury associated detrusor underactivity patients, and thus the treatment options are also scarce. In this study, we established a spinal sacral nerve injury animal model for deeper understanding and further researching of this disease. Forty 8 w (week) old Sprague Dawley rats were included and equally divided into sham (n = 20) and crush group (n = 20). Bilateral spinal sacral nerves of rats were crushed in crush group, and sham group received same procedure without nerve crush. Comprehensive evaluations at three time points (1 w, 4 w and 6 w) were performed to comprehend the nature process of this disease. According to urodynamic test, ultrasonography and retrograde urography, we could demonstrate severe bladder dysfunction after spinal sacral nerve injury along the observation period compared with sham group. These functional changes were further reflected by histological examination (hematoxylin-eosin and Masson's trichrome staining) of microstructure of nerves and bladders. Immunostaining of nerve/bladder revealed schwann cell death, axon degeneration and collagen remodeling of bladder. Polymerase Chain Reaction results revealed vigorous nerve inflammation and bladder fibrosis 1 week after injury and inflammation/fibrosis returned to normal at 4 w. The CatWalk gait analysis was performed and there was no obvious difference between two groups. In conclusion, we established a reliable and reproducible model for spinal sacral nerve injury, this model provided an approach to evaluate the treatment strategies and to understand the pathological process of spinal sacral nerve injuries. It allowed us to understand how nerve degeneration and bladder fibrosis changed following spinal sacral nerve injury and how recovery could be facilitated by therapeutic options for further research.

Deep learning-based segmentation of the thorax in mouse micro-CT scans

For image-guided small animal irradiations, the whole workflow of imaging, organ contouring, irradiation planning, and delivery is typically performed in a single session requiring continuous administration of anaesthetic agents. Automating contouring leads to a faster workflow, which limits exposure to anaesthesia and thereby, reducing its impact on experimental results and on animal wellbeing. Here, we trained the 2D and 3D U-Net architectures of no-new-Net (nnU-Net) for autocontouring of the thorax in mouse micro-CT images. We trained the models only on native CTs and evaluated their performance using an independent testing dataset (i.e., native CTs not included in the training and validation). Unlike previous studies, we also tested the model performance on an external dataset (i.e., contrast-enhanced CTs) to see how well they predict on CTs completely different from what they were trained on. We also assessed the interobserver variability using the generalized conformity index ([Formula: see text]) among three observers, providing a stronger human baseline for evaluating automated contours than previous studies. Lastly, we showed the benefit on the contouring time compared to manual contouring. The results show that 3D models of nnU-Net achieve superior segmentation accuracy and are more robust to unseen data than 2D models. For all target organs, the mean surface distance (MSD) and the Hausdorff distance (95p HD) of the best performing model for this task (nnU-Net 3d_fullres) are within 0.16 mm and 0.60 mm, respectively. These values are below the minimum required contouring accuracy of 1 mm for small animal irradiations, and improve significantly upon state-of-the-art 2D U-Net-based AIMOS method. Moreover, the conformity indices of the 3d_fullres model also compare favourably to the interobserver variability for all target organs, whereas the 2D models perform poorly in this regard. Importantly, the 3d_fullres model offers 98% reduction in contouring time.

Animal models in vaccinology: state of the art and future perspectives for an animal-free approach

Vaccine discovery and development is mainly driven by studies on immunogenicity and safety based on the appropriate animal models. In this review we will describe the importance of animal models in vaccinology, from research and development to pre-licensure and post-licensure commitments with particular emphasis on the advantages and limitations of each animal species. Finally, we will describe the most modern technologies, the new in vitro and ex vivo models and the new advances in the field which may drive into a new era of 'animal free' vaccinology.

Deep-Learning-Based Automatic Segmentation of Head and Neck Organs for Radiation Therapy in Dogs

Purpose: This study was conducted to develop a deep learning-based automatic segmentation (DLBAS) model of head and neck organs for radiotherapy (RT) in dogs, and to evaluate the feasibility for delineating the RT planning.

Materials and Methods: The segmentation indicated that there were potentially 15 organs at risk (OARs) in the head and neck of dogs. Post-contrast computed tomography (CT) was performed in 90 dogs. The training and validation sets comprised 80 CT data sets, including 20 test sets. The accuracy of the segmentation was assessed using both the Dice similarity coefficient (DSC) and the Hausdorff distance (HD), and by referencing the expert contours as the ground truth. An additional 10 clinical test sets with relatively large displacement or deformation of organs were selected for verification in cancer patients. To evaluate the applicability in cancer patients, and the impact of expert intervention, three methods–HA, DLBAS, and the readjustment of the predicted data obtained via the DLBAS of the clinical test sets (HA_DLBAS)–were compared.

Results: The DLBAS model (in the 20 test sets) showed reliable DSC and HD values; it also had a short contouring time of ~3 s. The average (mean ± standard deviation) DSC (0.83 ± 0.04) and HD (2.71 ± 1.01 mm) values were similar to those of previous human studies. The DLBAS was highly accurate and had no large displacement of head and neck organs. However, the DLBAS in the 10 clinical test sets showed lower DSC (0.78 ± 0.11) and higher HD (4.30 ± 3.69 mm) values than those of the test sets. The HA_DLBAS was comparable to both the HA (DSC: 0.85 ± 0.06 and HD: 2.74 ± 1.18 mm) and DLBAS presented better comparison metrics and decreased statistical deviations (DSC: 0.94 ± 0.03 and HD: 2.30 ± 0.41 mm). In addition, the contouring time of HA_DLBAS (30 min) was less than that of HA (80 min).

Conclusion: In conclusion, HA_DLBAS method and the proposed DLBAS was highly consistent and robust in its performance. Thus, DLBAS has great potential as a single or supportive tool to the key process in RT planning.

Advances in micro-CT imaging of small animals

PURPOSE

Micron-scale computed tomography (micro-CT) imaging is a ubiquitous, cost-effective, and non-invasive three-dimensional imaging modality. We review recent developments and applications of micro-CT for preclinical research.

METHODS

Based on a comprehensive review of recent micro-CT literature, we summarize features of state-of-the-art hardware and ongoing challenges and promising research directions in the field.

RESULTS

Representative features of commercially available micro-CT scanners and some new applications for both in vivo and ex vivo imaging are described. New advancements include spectral scanning using dual-energy micro-CT based on energy-integrating detectors or a new generation of photon-counting x-ray detectors (PCDs). Beyond two-material discrimination, PCDs enable quantitative differentiation of intrinsic tissues from one or more extrinsic contrast agents. When these extrinsic contrast agents are incorporated into a nanoparticle platform (e.g. liposomes), novel micro-CT imaging applications are possible such as combined therapy and diagnostic imaging in the field of cancer theranostics. Another major area of research in micro-CT is in x-ray phase contrast (XPC) imaging. XPC imaging opens CT to many new imaging applications because phase changes are more sensitive to density variations in soft tissues than standard absorption imaging. We further review the impact of deep learning on micro-CT. We feature several recent works which have successfully applied deep learning to micro-CT data, and we outline several challenges specific to micro-CT.

CONCLUSIONS

All of these advancements establish micro-CT imaging at the forefront of preclinical research, able to provide anatomical, functional, and even molecular information while serving as a testbench for translational research.

An Anatomical Thermal 3D Model in Preclinical Research: Combining CT and Thermal Images

Even though animal trials are a controversial topic, they provide knowledge about diseases and the course of infections in a medical context. To refine the detection of abnormalities that can cause pain and stress to the animal as early as possible, new processes must be developed. Due to its noninvasive nature, thermal imaging is increasingly used for severity assessment in animal-based research. Within a multimodal approach, thermal images combined with anatomical information could be used to simulate the inner temperature profile, thereby allowing the detection of deep-seated infections. This paper presents the generation of anatomical thermal 3D models, forming the underlying multimodal model in this simulation. These models combine anatomical 3D information based on computed tomography (CT) data with a registered thermal shell measured with infrared thermography. The process of generating these models consists of data acquisition (both thermal images and CT), camera calibration, image processing methods, and structure from motion (SfM), among others. Anatomical thermal 3D models were successfully generated using three anesthetized mice. Due to the image processing improvement, the process was also realized for areas with few features, which increases the transferability of the process. The result of this multimodal registration in 3D space can be viewed and analyzed within a visualization tool. Individual CT slices can be analyzed axially, sagittally, and coronally with the corresponding superficial skin temperature distribution. This is an important and successfully implemented milestone on the way to simulating the internal temperature profile. Using this temperature profile, deep-seated infections and inflammation can be detected in order to reduce animal suffering.

Deep-Learning Based Positron Range Correction of PET Images

Positron emission tomography (PET) is a molecular imaging technique that provides a 3D image of functional processes in the body in vivo. Some of the radionuclides proposed for PET imaging emit high-energy positrons, which travel some distance before they annihilate (positron range), creating significant blurring in the reconstructed images. Their large positron range compromises the achievable spatial resolution of the system, which is more significant when using high-resolution scanners designed for the imaging of small animals. In this work, we trained a deep neural network named Deep-PRC to correct PET images for positron range effects. Deep-PRC was trained with modeled cases using a realistic Monte Carlo simulation tool that considers the positron energy distribution and the materials and tissues it propagates into. Quantification of the reconstructed PET images corrected with Deep-PRC showed that it was able to restore the images by up to 95% without any significant noise increase. The proposed method, which is accessible via Github, can provide an accurate positron range correction in a few seconds for a typical PET acquisition.

Deep-Learning Based Positron Range Correction of PET Images

Positron emission tomography (PET) is a molecular imaging technique that provides a 3D image of functional processes in the body in vivo. Some of the radionuclides proposed for PET imaging emit high-energy positrons, which travel some distance before they annihilate (positron range), creating significant blurring in the reconstructed images. Their large positron range compromises the achievable spatial resolution of the system, which is more significant when using high-resolution scanners designed for the imaging of small animals. In this work, we trained a deep neural network named Deep-PRC to correct PET images for positron range effects. Deep-PRC was trained with modeled cases using a realistic Monte Carlo simulation tool that considers the positron energy distribution and the materials and tissues it propagates into. Quantification of the reconstructed PET images corrected with Deep-PRC showed that it was able to restore the images by up to 95% without any significant noise increase. The proposed method, which is accessible via Github, can provide an accurate positron range correction in a few seconds for a typical PET acquisition.

Deep learning-enabled multi-organ segmentation in whole-body mouse scans

Whole-body imaging of mice is a key source of information for research. Organ segmentation is a prerequisite for quantitative analysis but is a tedious and error-prone task if done manually. Here, we present a deep learning solution called AIMOS that automatically segments major organs (brain, lungs, heart, liver, kidneys, spleen, bladder, stomach, intestine) and the skeleton in less than a second, orders of magnitude faster than prior algorithms. AIMOS matches or exceeds the segmentation quality of state-of-the-art approaches and of human experts. We exemplify direct applicability for biomedical research for localizing cancer metastases. Furthermore, we show that expert annotations are subject to human error and bias. As a consequence, we show that at least two independently created annotations are needed to assess model performance. Importantly, AIMOS addresses the issue of human bias by identifying the regions where humans are most likely to disagree, and thereby localizes and quantifies this uncertainty for improved downstream analysis. In summary, AIMOS is a powerful open-source tool to increase scalability, reduce bias, and foster reproducibility in many areas of biomedical research.

X-ray microtomography as a new approach for imaging and analysis of tumor spheroids

Three-dimensional (3D) spheroids mimic important properties of tumors and may soon become a reasonable substitute for animal models and human tissue, eliminating numerous problems related to in vivo and ex vivo experiments and pre-clinical drug trials. Currently, various imaging methods including X-ray microtomography (micro-CT), exist but their spatial resolution is limited. Here, we visualized and provided a morphological analysis of spheroid cell cultures using micro-CT and compared it to that of confocal microscopy. An approach is proposed that can potentially open new diagnostic opportunities to determine the morphology of cancer cells cultured in 3D structures instead of using actual tumors. Spheroids were formed from human melanoma cell lines WM266-4 and WM115 seeded at different cell densities using the hanging drop method. Micro-CT analysis of spheroid showed that spheroid size and shape differed depending on the cell line, initial cell number, and duration of culture. The melanoma cell lines used in this study can successfully be cultured as 3D spheroids and used to substitute human and animal models in pre-clinical studies. The micro-CT allows for high-resolution visualization of the spheroids structure.

Non-invasive Imaging and Modeling of Liver Regeneration After Partial Hepatectomy

The liver has a unique regenerative capability upon injury or partial resection. The regeneration process comprises a complex interplay between parenchymal and non-parenchymal cells and is tightly regulated at different scales. Thus, we investigated liver regeneration using multi-scale methods by combining non-invasive imaging with immunohistochemical analyses. In this context, non-invasive imaging can provide quantitative data of processes involved in liver regeneration at organ and body scale. We quantitatively measured liver volume recovery after 70% partial hepatectomy (PHx) by micro computed tomography (μCT) and investigated changes in the density of CD68⁺ macrophages by fluorescence-mediated tomography (FMT) combined with μCT using a newly developed near-infrared fluorescent probe. In addition, angiogenesis and tissue-resident macrophages were analyzed by immunohistochemistry. Based on the results, a model describing liver regeneration and the interactions between different cell types was established. In vivo analysis of liver volume regeneration over 21 days after PHx by μCT imaging demonstrated that the liver volume rapidly increased after PHx reaching a maximum at day 14 and normalizing until day 21. An increase in CD68⁺ macrophage density in the liver was detected from day 4 to day 8 by combined FMT-μCT imaging, followed by a decline towards control levels between day 14 and day 21. Immunohistochemistry revealed the highest angiogenic activity at day 4 after PHx that continuously declined thereafter, whereas the density of tissue-resident CD169⁺ macrophages was not altered. The simulated time courses for volume recovery, angiogenesis and macrophage density reflect the experimental data describing liver regeneration after PHx at organ and tissue scale. In this context, our study highlights the importance of non-invasive imaging for acquiring quantitative organ scale data that enable modeling of liver regeneration.