Thoracic imaging for small animals has emerged as an important tool for monitoring pulmonary disease progression
and therapy response in genetically engineered animals. Micro-CT is becoming the standard thoracic
imaging modality in small animal imaging because it can produce high-resolution images of the lung parenchyma,
vasculature, and airways. Segmentation, measurement, and visualization of the airway tree is an important step
in pulmonary image analysis. However, manual analysis of the airway tree in micro-CT images can be extremely
time-consuming since a typical dataset is usually on the order of several gigabytes in size. Automated and
semi-automated tools for micro-CT airway analysis are desirable. In this paper, we propose an automatic airway
segmentation method for in vivo micro-CT images of the murine lung and validate our method by comparing
the automatic results to manual tracing. Our method is based primarily on grayscale morphology. The results
show good visual matches between manually segmented and automatically segmented trees. The average true
positive volume fraction compared to manual analysis is 91.61%. The overall runtime for the automatic method
is on the order of 30 minutes per volume compared to several hours to a few days for manual analysis.
Identifying the three-dimensional content of non-small cell lung cancer tumors is a vital step in the pursuit of
understanding cancer growth, development and response to treatment. The majority of non-small cell lung cancer
tumors are histologically heterogeneous, and consist of the malignant tumor cells, necrotic tumor cells, fibroblastic
stromal tissue, and inflammation. Geometric and tissue density heterogeneity are utilized in computed tomography (CT)
representations of lung tumors for distinguishing between malignant and benign nodules. However, the correlation
between radiolographical heterogeneity and corresponding histological content has been limited. In this study, a
multimodality dataset of human lung cancer is established, enabling the direct comparison between histologically
identified tissue content and micro-CT representation. Registration of these two datasets is achieved through the
incorporation of a large scale, serial microscopy dataset. This dataset serves as the basis for the rigid and non-rigid
registrations required to align the radiological and histological data. The resulting comprehensive, three-dimensional
dataset includes radio-density, color and cellular content of a given lung tumor. Using the registered datasets, neural
network classification is applied to determine a statistical separation between cancerous and non-cancerous tumor
regions in micro-CT.
Micro-CT, a technique for imaging small objects at high resolution using micro focused x-rays, is becoming widely available for small animal imaging. With the growing number of mouse models of pulmonary pathology, there is great interest in following disease progression and evaluating the alteration in longitudinal studies. Along with the high resolution associated with micro CT comes increased scanning times, and hence minimization of motion artifacts is required. We propose a new technique for imaging mouse lungs in vivo by inducing an intermittent iso-pressure breath hold (IIBH) with a fixed level of positive airway pressure during image acquisition, to decrease motion artifacts and increase image resolution and quality.
Mechanical ventilation of the respiratory system for such a setup consists of three phases, 1) tidal breathing (hyperventilated), 2) a breath hold during a fixed level of applied positive airway pressure, 3) periodic deep sighs. Image acquisition is triggered over the stable segment of the IIBH period.
Comparison of images acquired from the same mouse lung using three imaging techniques (normal breathing / no gating, normal breathing with gating at End Inspiration (EI) and finally the IIBH technique) demonstrated substantial improvements in resolution and quality when using the IIBH gating. Using IIBH triggering the total image acquisition time increased from 15 minutes to 35 minutes, although total x-ray exposure time and hence animal dosage remains the same. This technique is an important step in providing high quality lung imaging of the mouse in vivo, and will provide a good foundation for future longitudinal studies.
The development of multi-modality image analysis has gained increasing popularity over recent years. Multi-modality image databases are being developed to benefit patient clinical care, research and education. The incorporation of histopathology in these multi-modality datasets is complicated by the large differences in image quality, content and spatial association. We have developed a novel system, the large-scale image microtome array (LIMA), to bridge the gap between non-structurally destructive and destructive imaging such that reliable registration and incorporation of three-dimensional (3D) histopathology can be achieved. We have developed registration algorithms to align the micro-CT, LIMA and histopathology data to a common coordinate system. Using this multi-modality image dataset we have developed a classification algorithm to identify on a pixel basis, the tissue types present. The output from the classification processing is a 3D color coded map of tissue distributions. The resulting complete dataset provides an abundance of valuable information relating to the tissue sample including density, anatomical structure, color, texture and cellular information in three dimensions. In this study we have chosen to use normal and diseased lung tissue, however the flexibility of the image acquisition and subsequent processing algorithms makes it applicable to any soft organ tissue.
Micro-CT will have a profound influence on the accumulation of anatomical and physiological phenotypic changes in natural and transgenetic mouse models. Longitudinal studies will be greatly facilitated, allowing for a more complete and accurate description of events if in-vivo studies are accomplished. The purpose of the ongoing project is to establish a feasible and reproducible setup for in-vivo mouse lung micro-computed tomography (μCT). We seek to use in-vivo respiratory-gated μCT to follow mouse models of lung disease with subsequent recovery of the mouse. Methodologies for optimizing scanning parameters and gating for the in-vivo mouse lung are presented. A Scireq flexiVent ventilated the gas-anesthetized mice at 60 breaths/minute, 30 cm H20 PEEP, 30 ml/kg tidal volume and provided a respiratory signal to gate a Skyscan 1076 μCT. Physiologic monitoring allowed the control of vital functions and quality of anesthesia, e.g. via ECG monitoring. In contrary to longer exposure times with ex-vivo scans, scan times for in-vivo were reduced using 35μm pixel size, 158ms exposure time and 18μm pixel size, 316ms exposure time to reduce motion artifacts. Gating via spontaneous breathing was also tested. Optimal contrast resolution was achieved at 50kVp, 200μA, applying an aluminum filter (0.5mm). There were minimal non-cardiac related motion artifacts. Both 35μm and 1μm voxel size images were suitable for evaluation of the airway lumen and parenchymal density. Total scan times were 30 and 65 minutes respectively. The mice recovered following scanning protocols. In-vivo lung scanning with recovery of the mouse delivered reasonable image quality for longitudinal studies, e.g. mouse asthma models. After examining 10 mice, we conclude μCT is a feasible tool evaluating mouse models of lung pathology in longitudinal studies with increasing anatomic detail available for evaluation as one moves from in-vivo to ex-vivo studies. Further developments include automated bronchial tree segmentation and airway wall thickness measurement tools. Improvements in Hounsfield unit calibration have to be performed when the interest of the study lies in determining and quantifying parenchymal changes and rely on estimating partial volume contributions of underlying structures to voxel densities.
Mouse models are important for pulmonary research to gain insight into structure and function in normal and diseased states, thereby extending knowledge of human disease conditions. The flexibility of human disease induction into mice, due to their similar genome, along with their short gestation cycle makes mouse models highly suitable as investigative tools. Advancements in non-invasive imaging technology, with the development of micro-computed tomography (μ-CT), have aided representation of disease states in these small pulmonary system models. The generation ofμCT 3D airway reconstructions has to date provided a means to examine structural changes associated with disease. The degree of accuracy ofμCT is uncertain. Consequently, the reliability of quantitative measurements is questionable. We have developed a method of sectioning and imaging the whole mouse lung using the Large Image Microscope Array (LIMA) as the gold standard for comparison. Fixed normal mouse lungs were embedded in agarose and 250μm sections of tissue were removed while the remaining tissue block was imaged with a stereomicroscope. A complete dataset of the mouse lung was acquired in this fashion. Following planar image registration, the airways were manually segmented using an in-house built software program PASS. Amira was then used render the 3D isosurface from the segmentations. The resulting 3D model of the normal mouse airway tree developed from pathology images was then quantitatively assessed and used as the standard to compare the accuracy of structural measurements obtained from μ-CT.
Noninvasive imaging of the reporter gene expression based on bioluminescence is playing an important role in the areas of cancer biology, cell biology, and gene therapy. The central problem for the bioluminescence tomography (BLT) we are developing is to reconstruct the underlying bioluminescent source distribution in a small animal using a modality fusion approach. To solve this inversion problem, a mathematical model of the mouse is built from a CT/micro-CT scan, which enables the assignment of optical parameters to various regions in the model. This optical geometrical model is used in the Monte Carlo simulation to calculate the flux distribution on the animal body surface, as a key part of the BLT process. The model development necessitates approximations in surface simplification, and so on. It leads to the model mismatches of different kinds. To overcome such discrepancies, instead of developing a mathematical model, segmented CT images are directly used in our simulation software. While the simulation code is executed, those images that are relevant are assessed according to the location of the propagating photon. Depending upon the segmentation rules including the pixel value range, appropriate optical parameters are selected for statistical sampling of the free path and weight of the photon. In this paper, we report luminescence experiments using a physical mouse phantom to evaluate this image-guided simulation procedure, which suggest both the feasibility and some advantages of this technique over the existing methods.
Stereomicroscopy is an important method for use in image acquisition because it provides a 3D image of an object when other microscopic techniques can only provide the image in 2D. One challenge that is being faced with this type of imaging is determining the top surface of a sample that has otherwise indistinguishable surface and planar characteristics. We have developed a system that creates oblique illumination and in conjunction with image processing, the top surface can be viewed. The BFST consists of the Leica MZ12 stereomicroscope with a unique attached lighting source. The lighting source consists of eight light emitting diodes (LED's) that are separated by 45-degree angles. Each LED in this system illuminates with a 20-degree viewing angle once per cycle with a shadow over the rest of the sample. Subsequently, eight segmented images are taken per cycle. After the images are captured they are stacked through image addition to achieve the full field of view, and the surface is then easily identified. Image processing techniques, such as skeletonization can be used for further enhancement and measurement. With the use of BFST, advances can be made in detecting surface features from metals to tissue samples, such as in the analytical assessment of pulmonary emphysema using the technique of mean linear intercept.
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