Systems biology through mouse imaging centers: experience and new directions

Perceptions of Magnetic Resonance Imaging During Pregnancy: A Newfoundland and Labrador Perspective

OBJECTIVES

This study aimed to identify enablers and barriers to participation in MRI for clinical indications and scientific research, and to determine the perceptions of MRI performed during pregnancy.

METHODS

We conducted a survey of 156 pregnant people in Newfoundland and Labrador including sociodemographic information, obstetrical history, MRI history, and willingness to participate in an MRI. Categorical variables were analyzed using a Fisher exact test and open-ended questions were analyzed using thematic analysis.

RESULTS

In total, 80% of participants reported willingness to receive an MRI while pregnant for clinical indications compared to 24% for research. Only 10% reported prior knowledge about MRI during pregnancy and most participants (94%) wanted additional information from their physician before feeling comfortable with the procedure. Participants who knew someone with complications during pregnancy were more likely to be willing to participate in an MRI for research (uncorrected P < 0.05). Participants' positive perceptions towards MRI during pregnancy for clinical indications were that it was a necessary and useful procedure, while the negative perceptions identified MRI as unsafe. For research MRI, participants' positive perceptions included that it would add to the advancement of knowledge and the negative perceptions were that it was an unnecessary and risky procedure.

CONCLUSIONS

Strategies are needed to improve patient knowledge about the benefits and safety of MRI during pregnancy. The present study suggests recruitment for research should incorporate education on safety concerns and relative risk, personal stories about the benefits of MRI in diagnosing pregnancy complications and should highlight the contribution to advancing scientific knowledge.

Magnetic Resonance Imaging to Detect Structural Brain Changes in Huntington's Disease: A Review of Data from Mouse Models

Structural magnetic resonance imaging (MRI) is a powerful tool to visualize 3D neuroanatomy and assess pathology and disease progression in neurodegenerative disorders such as Huntington's disease (HD). The development of mouse models of HD that reproduce many of the psychiatric, motor and cognitive impairments observed in human HD has improved our understanding of the disease and provided opportunities for testing novel therapies. Similar to the clinical scenario, MRI of mouse models of HD demonstrates onset and progression of brain pathology. Here, we provided an overview of the articles that used structural MRI in mouse models of HD to date, highlighting the differences between studies and models and describing gaps in the current state of knowledge and recommendations for future studies.

A Deep Learning Approach for Segmentation, Classification, and Visualization of 3-D High-Frequency Ultrasound Images of Mouse Embryos

Segmentation and mutant classification of high-frequency ultrasound (HFU) mouse embryo brain ventricle (BV) and body images can provide valuable information for developmental biologists. However, manual segmentation and identification of BV and body requires substantial time and expertise. This article proposes an accurate, efficient and explainable deep learning pipeline for automatic segmentation and classification of the BV and body. For segmentation, a two-stage framework is implemented. The first stage produces a low-resolution segmentation map, which is then used to crop a region of interest (ROI) around the target object and serve as the probability map of the autocontext input for the second-stage fine-resolution refinement network. The segmentation then becomes tractable on high-resolution 3-D images without time-consuming sliding windows. The proposed segmentation method significantly reduces inference time (102.36-0.09 s/volume ≈ 1000× faster) while maintaining high accuracy comparable to previous sliding-window approaches. Based on the BV and body segmentation map, a volumetric convolutional neural network (CNN) is trained to perform a mutant classification task. Through backpropagating the gradients of the predictions to the input BV and body segmentation map, the trained classifier is found to largely focus on the region where the Engrailed-1 (En1) mutation phenotype is known to manifest itself. This suggests that gradient backpropagation of deep learning classifiers may provide a powerful tool for automatically detecting unknown phenotypes associated with a known genetic mutation.

DEEP MOUSE: AN END-TO-END AUTO-CONTEXT REFINEMENT FRAMEWORK FOR BRAIN VENTRICLE & BODY SEGMENTATION IN EMBRYONIC MICE ULTRASOUND VOLUMES

The segmentation of the brain ventricle (BV) and body in embryonic mice high-frequency ultrasound (HFU) volumes can provide useful information for biological researchers. However, manual segmentation of the BV and body requires substantial time and expertise. This work proposes a novel deep learning based end-to-end auto-context refinement framework, consisting of two stages. The first stage produces a low resolution segmentation of the BV and body simultaneously. The resulting probability map for each object (BV or body) is then used to crop a region of interest (ROI) around the target object in both the original image and the probability map to provide context to the refinement segmentation network. Joint training of the two stages provides significant improvement in Dice Similarity Coefficient (DSC) over using only the first stage (0.818 to 0.906 for the BV, and 0.919 to 0.934 for the body). The proposed method significantly reduces the inference time (102.36 to 0.09 s/volume ≈1000x faster) while slightly improves the segmentation accuracy over the previous methods using slide-window approaches.

Behavioral and neuroanatomical approaches in models of neurodevelopmental disorders: opportunities for translation

PURPOSE OF REVIEW

This review highlights the invaluable contribution of in-vivo rodent models in dissecting the underlying neurobiology for numerous neurodevelopmental disorders. Currently, models are routinely generated with precision genomics and characterized for research on neurodevelopmental disorders. In order to impact translation, outcome measures that are translationally relevant are essential. This review emphasizes the importance of accurate neurobehavioral and anatomical analyses.

RECENT FINDINGS

Numerous well validated assays for testing alterations across behavioral domains with sensitivity and throughput have become important tools for studying the effects of genetic mutations on neurodevelopment. Recent work has highlighted relationships and links between behavioral outcomes and various anatomical metrics from neuroimaging via magnetic resonance. These readouts are biological markers and outcome measures for translational research and will be have important roles for genetic or pharmacologic intervention strategies.

SUMMARY

Combinatorial approaches that leverage translationally relevant behavior and neuroanatomy can be used to develop a platform for assessment of cutting edge preclinical models. Reliable, robust behavioral phenotypes in preclinical model systems, with clustering of brain disease will lead to well informed, precise biochemical mechanistic hypotheses. Ultimately, these steadfast workhorse techniques will accelerate the progress of developing and testing targeted treatments for multiple neurodevelopmental disorders.

DEEP BV: A FULLY AUTOMATED SYSTEM FOR BRAIN VENTRICLE LOCALIZATION AND SEGMENTATION IN 3D ULTRASOUND IMAGES OF EMBRYONIC MICE

Volumetric analysis of brain ventricle (BV) structure is a key tool in the study of central nervous system development in embryonic mice. High-frequency ultrasound (HFU) is the only non-invasive, real-time modality available for rapid volumetric imaging of embryos in utero. However, manual segmentation of the BV from HFU volumes is tedious, time-consuming, and requires specialized expertise. In this paper, we propose a novel deep learning based BV segmentation system for whole-body HFU images of mouse embryos. Our fully automated system consists of two modules: localization and segmentation. It first applies a volumetric convolutional neural network on a 3D sliding window over the entire volume to identify a 3D bounding box containing the entire BV. It then employs a fully convolutional network to segment the detected bounding box into BV and background. The system achieves a Dice Similarity Coefficient (DSC) of 0.8956 for BV segmentation on an unseen 111 HFU volume test set surpassing the previous state-of-the-art method (DSC of 0.7119) by a margin of 25%.

Partitioning k-space for cylindrical three-dimensional rapid acquisition with relaxation enhancement imaging in the mouse brain

Three-dimensional rapid acquisition with relaxation enhancement (RARE) scans require the assignment of each phase encode step in two dimensions to an echo in the echo train. Although this assignment is frequently made across the entire Cartesian grid, collection of only the central cylinder of k-space by eliminating the corners in each phase encode dimension reduces the scan time by ~22% with negligible impact on image quality. The recipe for the assignment of echoes to grid points for such an acquisition is less straightforward than for the simple full Cartesian acquisition case, and has important implications for image quality. We explored several methods of partitioning k-space-exploiting angular symmetry in one extreme or emulating a cropped Cartesian acquisition in the other-and acquired three-dimensional RARE magnetic resonance imaging (MRI) scans of the ex vivo mouse brain. We evaluated each partitioning method for sensitivity to artifacts and then further considered strategies to minimize these through averaging or interleaving of echoes and by empirical phase correction. All scans were collected 16 at a time with multiple-mouse MRI. Although all schemes considered could be used to generate images, the results indicate that the emulation of a standard Cartesian echo assignment, by partitioning preferentially along one dimension within the cylinder, is more robust to artifacts. Samples at the periphery of the bore showed larger phase deviations and higher sensitivity to artifacts, but images of good quality could still be obtained with an optimized acquisition protocol. A protocol for high-resolution (40 μm) ex vivo images using this approach is presented, and has been used routinely with a success rate of 99% in over 1000 images.

Automated Computational Processing of 3-D MR Images of Mouse Brain for Phenotyping of Living Animals

Magnetic resonance (MR) imaging provides a method to obtain anatomical information from the brain in vivo that is not typically available by optical imaging because of this organ's opacity. MR is nondestructive and obtains deep tissue contrast with 100-µm³ voxel resolution or better. Manganese-enhanced MRI (MEMRI) may be used to observe axonal transport and localized neural activity in the living rodent and avian brain. Such enhancement enables researchers to investigate differences in functional circuitry or neuronal activity in images of brains of different animals. Moreover, once MR images of a number of animals are aligned into a single matrix, statistical analysis can be done comparing MR intensities between different multi-animal cohorts comprising individuals from different mouse strains or different transgenic animals, or at different time points after an experimental manipulation. Although preprocessing steps for such comparisons (including skull stripping and alignment) are automated for human imaging, no such automated processing has previously been readily available for mouse or other widely used experimental animals, and most investigators use in-house custom processing. This protocol describes a stepwise method to perform such preprocessing for mouse. © 2017 by John Wiley & Sons, Inc.

Nested Graph Cut for Automatic Segmentation of High-Frequency Ultrasound Images of the Mouse Embryo

We propose a fully automatic segmentation method called nested graph cut to segment images (2D or 3D) that contain multiple objects with a nested structure. Compared to other graph-cut-based methods developed for multiple regions, our method can work well for nested objects without requiring manual selection of initial seeds, even if different objects have similar intensity distributions and some object boundaries are missing. Promising results were obtained for separating the brain ventricles, the head, and the uterus region in the mouse-embryo head images obtained using high-frequency ultrasound imaging. The proposed method achieved mean Dice similarity coefficients of 0.87 ±0.04 and 0.89 ±0.06 for segmenting BVs and the head, respectively, compared to manual segmentation results by experts on 40 3D images over five gestation stages.

A deformable atlas of the laboratory mouse

PURPOSE

This paper presents a deformable mouse atlas of the laboratory mouse anatomy. This atlas is fully articulated and can be positioned into arbitrary body poses. The atlas can also adapt body weight by changing body length and fat amount.

PROCEDURES

A training set of 103 micro-CT images was used to construct the atlas. A cage-based deformation method was applied to realize the articulated pose change. The weight-related body deformation was learned from the training set using a linear regression method. A conditional Gaussian model and thin-plate spline mapping were used to deform the internal organs following the changes of pose and weight.

RESULTS

The atlas was deformed into different body poses and weights, and the deformation results were more realistic compared to the results achieved with other mouse atlases. The organ weights of this atlas matched well with the measurements of real mouse organ weights. This atlas can also be converted into voxelized images with labeled organs, pseudo CT images and tetrahedral mesh for phantom studies.

CONCLUSIONS

With the unique ability of articulated pose and weight changes, the deformable laboratory mouse atlas can become a valuable tool for preclinical image analysis.

Behavioral and Neuroanatomical Phenotypes in Mouse Models of Autism

In order to understand the consequences of the mutation on behavioral and biological phenotypes relevant to autism, mutations in many of the risk genes for autism spectrum disorder have been experimentally generated in mice. Here, we summarize behavioral outcomes and neuroanatomical abnormalities, with a focus on high-resolution magnetic resonance imaging of postmortem mouse brains. Results are described from multiple mouse models of autism spectrum disorder and comorbid syndromes, including the 15q11-13, 16p11.2, 22q11.2, Cntnap2, Engrailed2, Fragile X, Integrinβ3, MET, Neurexin1a, Neuroligin3, Reelin, Rett, Shank3, Slc6a4, tuberous sclerosis, and Williams syndrome models, and inbred strains with strong autism-relevant behavioral phenotypes, including BTBR and BALB. Concomitant behavioral and neuroanatomical abnormalities can strengthen the interpretation of results from a mouse model, and may elevate the usefulness of the model system for therapeutic discovery.

Pydpiper: a flexible toolkit for constructing novel registration pipelines

Using neuroimaging technologies to elucidate the relationship between genotype and phenotype and brain and behavior will be a key contribution to biomedical research in the twenty-first century. Among the many methods for analyzing neuroimaging data, image registration deserves particular attention due to its wide range of applications. Finding strategies to register together many images and analyze the differences between them can be a challenge, particularly given that different experimental designs require different registration strategies. Moreover, writing software that can handle different types of image registration pipelines in a flexible, reusable and extensible way can be challenging. In response to this challenge, we have created Pydpiper, a neuroimaging registration toolkit written in Python. Pydpiper is an open-source, freely available software package that provides multiple modules for various image registration applications. Pydpiper offers five key innovations. Specifically: (1) a robust file handling class that allows access to outputs from all stages of registration at any point in the pipeline; (2) the ability of the framework to eliminate duplicate stages; (3) reusable, easy to subclass modules; (4) a development toolkit written for non-developers; (5) four complete applications that run complex image registration pipelines “out-of-the-box.” In this paper, we will discuss both the general Pydpiper framework and the various ways in which component modules can be pieced together to easily create new registration pipelines. This will include a discussion of the core principles motivating code development and a comparison of Pydpiper with other available toolkits. We also provide a comprehensive, line-by-line example to orient users with limited programming knowledge and highlight some of the most useful features of Pydpiper. In addition, we will present the four current applications of the code.

Evaluation of coils for imaging histological slides: signal-to-noise ratio and filling factor

PURPOSE

To investigate the relative gain in sensitivity of five histology coils designed in-house to accommodate tissue sections of various sizes and compare with commercial mouse head coils.

METHODS

The coil set was tailored to house tissue sections ranging from 5 to1000 µm encased in either glass slides or coverslips.

RESULTS

Our simulations and experimental measurements demonstrated that although the sensitivity of this flat structure consistently underperforms relative to a birdcage head coil based on the gain expected from their respective filling factor ratios, our results demonstrate that it can still provide a remarkable gain in sensitivity. Our study also describes preparation protocols for freshly excised sections, as well as premounted tissue slides of both mouse and human specimens. Examples of the exceptional level of tissue detail and the near-perfect magnetic resonance imaging to light microscopic image coregistration are provided.

CONCLUSION

The increase in filling factor achieved by the histology radiofrequency (RF) probe overcomes the losses associated with electric leaks inherent to this structure, leading to a 6.7-fold improvement in performance for the smallest coil implemented. Alternatively, the largest histology coil design exhibited equal sensitivity to the mouse head coil while nearly doubling the RF planar area coverage.

Whole-brain mapping of behaviourally induced neural activation in mice

The ability to visualize behaviourally evoked neural activity patterns across the rodent brain is essential for understanding the distributed brain networks mediating particular behaviours. However, current imaging methods are limited in their spatial resolution and/or ability to obtain brain-wide coverage of functional activity. Here, we describe a new automated method for obtaining cellular-level, whole-brain maps of behaviourally induced neural activity in the mouse. This method combines the use of transgenic immediate-early gene reporter mice to visualize neural activity; serial two-photon tomography to image the entire brain at cellular resolution; advanced image processing algorithms to count the activated neurons and align the datasets to the Allen Mouse Brain Atlas; and statistical analysis to identify the network of activated brain regions evoked by behaviour. We demonstrate the use of this approach to determine the whole-brain networks activated during the retrieval of fear memories. Consistent with previous studies, we identified a large network of amygdalar, hippocampal, and neocortical brain regions implicated in fear memory retrieval. Our proposed methods can thus be used to map cellular networks involved in the expression of normal behaviours as well as to investigate in depth circuit dysfunction in mouse models of neurobiological disease.

Impact of a multiple mice holder on quantitation of high-throughput MicroPET imaging with and without Ct attenuation correction

PURPOSE

The aim of this study is to evaluate the impact of scanning multiple mice simultaneously on image quantitation, relative to single mouse scans on both a micro-positron emission tomography/computed tomography (microPET/CT) scanner (which utilizes CT-based attenuation correction to the PET reconstruction) and a dedicated microPET scanner using an inexpensive mouse holder "hotel."

METHODS

We developed a simple mouse holder made from common laboratory items that allows scanning multiple mice simultaneously. It is also compatible with different imaging modalities to allow multiple mice and multi-modality imaging. For this study, we used a radiotracer ((64)Cu-GB170) with a relatively long half-life (12.7 h), selected to allow scanning at times after tracer uptake reaches steady state. This also reduces the effect of decay between sequential imaging studies, although the standard decay corrections were performed. The imaging was also performed using a common tracer, 2-deoxy-2-[(18) F]fluoro-D-glucose (FDG), although the faster decay and faster pharmacokinetics of FDG may introduce greater biological variations due to differences in injection-to-scan timing. We first scanned cylindrical mouse phantoms (50 ml tubes) both in a groups of four at a time (multiple mice mode) and then individually (single mouse mode), using microPET/CT and microPET scanners to validate the process. Then, we imaged a first set of four mice with subcutaneous tumors (C2C12Ras) in both single- and multiple-mice imaging modes. Later, a second set of four normal mice were injected with FDG and scanned 1 h post-injection. Immediately after completion of the scans, ex vivo biodistribution studies were performed on all animals to provide a "gold-standard" to compare quantitative values obtained from PET. A semi-automatic threshold-based region of interest tool was used to minimize operator variability during image analysis.

RESULTS

Phantom studies showed less than 4.5 % relative error difference between the single- and multiple-mice imaging modes of PET imaging with CT-based attenuation correction and 18.4 % without CT-based attenuation correction. In vivo animal studies (n = 4) showed <5 % (for (64)Cu, p > 0.686) and <15 % (for FDG, p > 0.4 except for brain image data p = 0.029) relative mean difference with respect to percent injected dose per gram (%ID/gram) between the single- and multiple-mice microPET imaging mode when CT-based attenuation correction is performed. Without CT-based attenuation correction, we observed relative mean differences of about 11 % for (64)Cu and 15 % for FDG.

CONCLUSION

Our results confirmed the potential use of a microPET/CT scanner for multiple mice simultaneous imaging without significant sacrifice in quantitative accuracy as well as in image quality. Thus, the use of the mouse "hotel" is an aid to increasing instrument throughput on small animal scanners with minimal loss of quantitative accuracy.

A coming of age: advanced imaging technologies for characterising the developing mouse

The immense challenge of annotating the entire mouse genome has stimulated the development of cutting-edge imaging technologies in a drive for novel information. These techniques promise to improve understanding of the genes involved in embryo development, at least one third of which have been shown to be essential. Aligning advanced imaging technologies with biological needs will be fundamental to maximising the number of phenotypes discovered in the coming years. International efforts are underway to meet this challenge through an integrated and sophisticated approach to embryo phenotyping. We review rapid advances made in the imaging field over the past decade and provide a comprehensive examination of the relative merits of current and emerging techniques. The aim of this review is to provide a guide to state-of-the-art embryo imaging that will enable informed decisions as to which technology to use and fuel conversations between expert imaging laboratories, researchers, and core mouse production facilities.

Mapping registration sensitivity in MR mouse brain images

Nonlinear registration algorithms provide a way to estimate structural (brain) differences based on magnetic resonance images. Their ability to align images of different individuals and across modalities has been well-researched, but the bounds of their sensitivity with respect to the recovery of salient morphological differences between groups are unclear. Here we develop a novel approach to simulate deformations on MR brain images to evaluate the ability of two registration algorithms to extract structural differences corresponding to biologically plausible atrophy and expansion. We show that at a neuroanatomical level registration accuracy is influenced by the size and compactness of structures, but do so differently depending on how much change is simulated. The size of structures has a small influence on the recovered accuracy. There is a trend for larger structures to be recovered more accurately, which becomes only significant as the amount of simulated change is large. More compact structures can be recovered more accurately regardless of the amount of simulated change. Both tested algorithms underestimate the full extent of the simulated atrophy and expansion. Finally we show that when multiple comparisons are corrected for at a voxelwise level, a very low rate of false positives is obtained. More interesting is that true positive rates average around 40%, indicating that the simulated changes are not fully recovered. Simulation experiments were run using two fundamentally different registration algorithms and we identified the same results, suggesting that our findings are generalizable across different classes of nonlinear registration algorithms.

High-resolution MRI of early-stage mouse embryos

Both the availability of methods to manipulate genes and the completion of the mouse genome sequence have led to the generation of thousands of genetically modified mouse lines that provide a new platform for the study of mammalian development and developmental diseases. Phenotyping of mouse embryos has traditionally been performed on fixed embryos by the use of ex vivo histological, optical and high-resolution MRI techniques. Although potentially powerful, longitudinal imaging of individual animals is difficult or impossible with conventional optical methods because of the inaccessibility of mouse embryos inside the maternal uterus. To address this problem, we present a method of imaging the mouse embryo from stages as early as embryonic day (E)10.5, close to the onset of organogenesis in most physiological systems. This method uses a self-gated MRI protocol, combined with image registration, to obtain whole-embryo high-resolution (100 µm isotropic) three-dimensional images. Using this approach, we demonstrate high contrast in the cerebral vasculature, limbs, spine and central nervous system without the use of contrast agents. These results indicate the potential of MRI for the longitudinal imaging of developing mouse embryos in utero and for future applications in analyzing mutant mouse phenotypes.

Anaesthesia and physiological monitoring during in vivo imaging of laboratory rodents: considerations on experimental outcomes and animal welfare

The implementation of imaging technologies has dramatically increased the efficiency of preclinical studies, enabling a powerful, non-invasive and clinically translatable way for monitoring disease progression in real time and testing new therapies. The ability to image live animals is one of the most important advantages of these technologies. However, this also represents an important challenge as, in contrast to human studies, imaging of animals generally requires anaesthesia to restrain the animals and their gross motion. Anaesthetic agents have a profound effect on the physiology of the animal and may thereby confound the image data acquired. It is therefore necessary to select the appropriate anaesthetic regime and to implement suitable systems for monitoring anaesthetised animals during image acquisition. In addition, repeated anaesthesia required for longitudinal studies, the exposure of ionising radiations and the use of contrast agents and/or imaging biomarkers may also have consequences on the physiology of the animal and its response to anaesthesia, which need to be considered while monitoring the animals during imaging studies. We will review the anaesthesia protocols and monitoring systems commonly used during imaging of laboratory rodents. A variety of imaging modalities are used for imaging rodents, including magnetic resonance imaging, computed tomography, positron emission tomography, single photon emission computed tomography, high frequency ultrasound and optical imaging techniques such as bioluminescence and fluorescence imaging. While all these modalities are implemented for non-invasive in vivo imaging, there are certain differences in terms of animal handling and preparation, how the monitoring systems are implemented and, importantly, how the imaging procedures themselves can affect mammalian physiology. The most important and critical adverse effects of anaesthetic agents are depression of respiration, cardiovascular system disruption and thermoregulation. When anaesthetising rodents, one must carefully consider if these adverse effects occur at the therapeutic dose required for anaesthesia, if they are likely to affect the image acquisitions and, importantly, if they compromise the well-being of the animals. We will review how these challenges can be successfully addressed through an appropriate understanding of anaesthetic protocols and the implementation of adequate physiological monitoring systems.

A DTI tractography analysis of infralimbic and prelimbic connectivity in the mouse using high-throughput MRI

BACKGROUND

High throughput, brain-wide analysis of neural circuit connectivity is needed to understand brain function across species. Combining such tractography techniques with small animal models will allow more rapid integration of systems neuroscience with molecular genetic, behavioral, and cellular approaches.

METHODS

We collected DTI and T2 scans on 3 series of 6 fixed mouse brains ex vivo in a 9.4 Tesla magnet. The DTI analysis of ten mouse brains focused on comparing prelimbic (PL) and Infralimbic (IL) probabilistic tractography. To validate the DTI results a preliminary set of 24 additional mice were injected with BDA into the IL and PL. The DTI results and preliminary BDA results were also compared to previously published rat connectivity.

RESULTS

We focused our analyses on the connectivity of the mouse prelimbic (PL) vs. infralimbic (IL) cortices. We demonstrated that this DTI analysis is consistent across scanned mice, with prior analyses of rat IL/PL connectivity, and with mouse PL and IL projections using the BDA tracer.

CONCLUSIONS

High-throughput ex vivo DTI imaging in the mouse delineated both common and differential connectivity of the IL and PL cortex. The scanning methodology provided a balance of tissue contrast, signal-to-noise ratio, resolution and throughput. Our results are largely consistent with previously published anterograde staining techniques in rats, and the preliminary tracer study of the mouse IL and PL provided here.

Multiscale imaging and computational modeling of blood flow in the tumor vasculature

The evolution in our understanding of tumor angiogenesis has been the result of pioneering imaging and computational modeling studies spanning the endothelial cell, microvasculature and tissue levels. Many of these primary data on the tumor vasculature are in the form of images from pre-clinical tumor models that provide a wealth of qualitative and quantitative information in many dimensions and across different spatial scales. However, until recently, the visualization of changes in the tumor vasculature across spatial scales remained a challenge due to a lack of techniques for integrating micro- and macroscopic imaging data. Furthermore, the paucity of three-dimensional (3-D) tumor vascular data in conjunction with the challenges in obtaining such data from patients presents a serious hurdle for the development and validation of predictive, multiscale computational models of tumor angiogenesis. In this review, we discuss the development of multiscale models of tumor angiogenesis, new imaging techniques capable of reproducing the 3-D tumor vascular architecture with high fidelity, and the emergence of "image-based models" of tumor blood flow and molecular transport. Collectively, these developments are helping us gain a fundamental understanding of the cellular and molecular regulation of tumor angiogenesis that will benefit the development of new cancer therapies. Eventually, we expect this exciting integration of multiscale imaging and mathematical modeling to have widespread application beyond the tumor vasculature to other diseases involving a pathological vasculature, such as stroke and spinal cord injury.

Morphological studies of the murine heart based on probabilistic and statistical atlases

This study directly compares morphological features of the mouse heart in its end-relaxed state based on constructed morphometric maps and atlases using principal component analysis in C57BL/6J (n=8) and DBA (n=5) mice. In probabilistic atlases, a gradient probability exists for both strains in longitudinal locations from base to apex. Based on the statistical atlases, differences in size (49.8%), apical direction (15.6%), basal ventricular blood pool size (13.2%), and papillary muscle shape and position (17.2%) account for the most significant modes of shape variability for the left ventricle of the C57BL/6J mice. For DBA mice, differences in left ventricular size and direction (67.4%), basal size (15.7%), and position of papillary muscles (16.8%) account for significant variability.

Three-dimensional imaging of the mouse neurovasculature with magnetic resonance microscopy

Knowledge of the three-dimensional (3D) architecture of blood vessels in the brain is crucial because the progression of various neuropathologies ranging from Alzheimer's disease to brain tumors involves anomalous blood vessels. The challenges in obtaining such data from patients, in conjunction with development of mouse models of neuropathology, have made the murine brain indispensable for investigating disease induced neurovascular changes. Here we describe a novel method for “whole brain” 3D mapping of murine neurovasculature using magnetic resonance microscopy (μMRI). This approach preserves the vascular and white matter tract architecture, and can be combined with complementary MRI contrast mechanisms such as diffusion tensor imaging (DTI) to examine the interplay between the vasculature and white matter reorganization that often characterizes neuropathologies. Following validation with micro computed tomography (μCT) and optical microscopy, we demonstrate the utility of this method by: (i) combined 3D imaging of angiogenesis and white matter reorganization in both, invasive and non-invasive brain tumor models; (ii) characterizing the morphological heterogeneity of the vascular phenotype in the murine brain; and (iii) conducting “multi-scale” imaging of brain tumor angiogenesis, wherein we directly compared in vivo MRI blood volume measurements with ex vivo vasculature data.