Jacqueline Thiesse

In vivo micro-CT lung imaging via a computer-controlled intermittent iso-pressure breath hold (IIBH) technique

Respiratory research with mice using micro-computed tomography (micro-CT) has been predominantly hindered by the limited resolution and signal-to-noise ratio (SNR) as a result of respiratory motion artefacts. In this study, we develop a novel technique for capturing the lung microstructure in vivo using micro-CT, through a computer-controlled intermittent iso-pressure breath hold (IIBH), to reduce respiratory motion, increasing resolution and SNR of reconstructed images. We compare four gating techniques, i.e. no gating, late expiratory (LE) gating, late inspiratory (LI) gating and finally intermittent iso-pressure breath hold (IIBH) gating. Quantitatively, we compare several common image analysis methods used to extract valuable physiologic and anatomic information from the respiratory system, and show that the IIBH technique produces the most representative and repeatable results.

Lung structure phenotype variation in inbred mouse strains revealed through in vivo micro-CT imaging

Within pulmonary research, the development of mouse models has provided insight into disease development, progression, and treatment. Structural phenotypes of the lung in healthy inbred mouse strains are necessary for comparison to disease models. To date, progress in the assessment of lung function in these small animals using whole lung function tests has been made. However, assessment of in vivo lung structure of inbred mouse strains has yet to be well defined. Therefore, the link between the structure and function phenotypes is still unclear. With advancements in small animal imaging it is now possible to investigate lung structures such as the central and peripheral airways, whole lung, and lobar volumes of mice in vivo, through the use of micro-CT imaging. In this study, we performed in vivo micro-CT imaging of the C57BL/6, A/J, and BALB/c mouse strains using the intermittent iso-pressure breath hold (IIBH) technique. The resulting high-resolution images were used to extract lung structure phenotypes. The three-dimensional lobar structures and airways were defined and a meaningful mouse airway nomenclature was developed. In addition, using these techniques we have uncovered significant differences in the airway structures between inbred mouse strains in vivo.

Longitudinal assessment of lung cancer progression in the mouse using in vivo micro-CT imaging

PURPOSE Small animal micro-CT imaging is being used increasingly in preclinical biomedical research to provide phenotypic descriptions of genomic models. Most of this imaging is coincident with animal death and is used to show the extent of disease as an end point. Longitudinal imaging overcomes the limitation of single time-point imaging because it enables tracking of the natural history of disease and provides qualitative and, where possible, quantitative assessments of the effects of an intervention. The pulmonary system is affected by many disease conditions, such as lung cancer, chronic obstructive pulmonary disease, asthma, and granulomatous disorders. Noninvasive imaging can accurately assess the lung phenotype within the living animal, evaluating not only global lung measures, but also regional pathology. However, imaging the lung in the living animal is complicated by rapid respiratory motion, which leads to image based artifacts. Furthermore, no standard mouse lung imaging protocols exist for longitudinal assessment, with each group needing to develop their own systematic approach. METHODS In this article, the authors present an outline for performing longitudinal breath-hold gated micro-CT imaging for the assessment of lung nodules in a mouse model of lung cancer. The authors describe modifications to the previously published intermittent isopressure breath-hold technique including a new animal preparation and anesthesia protocol, implementation of a ring artifact reduction, variable scanner geometry, and polynomial beam hardening correction. In addition, the authors describe a multitime-point data set registration and tumor labeling and tracking strategy. RESULTS In vivo micro-CT data sets were acquired at months 2, 3, and 4 posturethane administration in cancer mice (n = 5) and simultaneously in control mice (n = 3). 137 unique lung nodules were identified from the cancer mice while no nodules were detected in the control mice. A total of 411 nodules were segmented and labeled over the three time-points. Lung nodule metrics including RECIST, Ortho, WHO, and 3D volume were determined and extracted. A tumor incidence rate of 30.44 +/- 1.93 SEM for n = 5 was found with identification of nodules as small as 0.11 mm (RECIST) and as large as 1.66 mm (RECIST). In addition, the tumor growth and doubling rate between months 2-3 and 3-4 were calculated. Here, the growth rate was slightly higher in the second period based on the 3D volume data (0.12 +/- 0.13 to 0.13 +/- 0.17 microl) but significantly less based on the linear diameter metrics [RECIST (0.33 +/- 0.19 to 0.17 +/- 0.18 mm); Ortho (0.24 +/- 0.15 to 0.16 +/- 0.15 mm)], indicating the need to understand how each metric is obtained and how to correctly interpret change in tumor size. CONCLUSIONS In conclusion, micro-CT imaging provides a unique platform for in vivo longitudinal assessment of pulmonary lung cancer progression and potentially tracking of therapies at very high resolutions. The ability to evaluate the same subject over time provides for a sensitive assay that can be carried out on a smaller sample size. When integrated with image processing and analysis routines as detailed in this study, the data acquired from micro-CT imaging can now provide a very powerful assessment of pulmonary disease outcomes.

Large image microscope array for the compilation of multimodality whole organ image databases

Three‐dimensional, structural and functional digital image databases have many applications in education, research, and clinical medicine. However, to date, apart from cryosectioning, there have been no reliable means to obtain whole‐organ, spatially conserving histology. Our aim was to generate a system capable of acquiring high‐resolution images, featuring microscopic detail that could still be spatially correlated to the whole organ. To fulfill these objectives required the construction of a system physically capable of creating very fine whole‐organ sections and collecting high‐magnification and resolution digital images. We therefore designed a large image microscope array (LIMA) to serially section and image entire unembedded organs while maintaining the structural integrity of the tissue. The LIMA consists of several integrated components: a novel large‐blade vibrating microtome, a 1.3 megapixel peltier cooled charge‐coupled device camera, a high‐magnification microscope, and a three axis gantry above the microtome. A custom control program was developed to automate the entire sectioning and automated raster‐scan imaging sequence. The system is capable of sectioning unembedded soft tissue down to a thickness of 40 μm at specimen dimensions of 200 × 300 mm to a total depth of 350 mm. The LIMA system has been tested on fixed lung from sheep and mice, resulting in large high‐quality image data sets, with minimal distinguishable disturbance in the delicate alveolar structures. Anat Rec 290:1377‐1387, 2007.

New imaging approaches for understanding lung cancer response to treatment.

The survival rate for lung cancer patients has barely improved over the past 30 years. New evaluation benchmarks for cancer response are needed to test therapy agents in a cost‐effective and timely manner. From recent work, it is evident that primary lung cancers are very complex structures containing not only cancerous cells but also fibrotic and inflammatory cells and necrotic tissue. A greater understanding of the three‐dimensional structure of primary lung cancer is emerging, allowing for the first time an appreciation of how this biomass is represented in medical imaging data. It is only through a greater understanding of the lung cancer biomass that we can define rational and early‐response measures, including specific cellular responses such as cancer cell death or growth inhibition. In doing so, we can define response metrics that will shorten new drug discovery times and reduce costs, allowing for the evaluation of many more agents with therapeutic potential.

Dynamic in vivo Alveolar Morphology Using a Novel Laser Scanning Confocal Microscope

Understanding the structure and function of alveoli in vivo is crucial for understanding the normal and diseased lung. In this study we image the alveoli of mice in vivo, using a custom fiber optic catheter based laser scanning confocal microscope. Images obtained using this system are analyzed with an automated software application for alveolar size, wall thickness and alveolar number. Results show that direct dynamic visualization of alveoli and surrounding structures is possible in vivo, with high resolution. Early results indicate a high heterogeneity in alveolar structure in vivo, as opposed to an ordered uniform structure. Using the techniques presented in this study there is great promise for advancing our knowledge of the functional unit of the lung, the alveoli; for alveolar mechanics, cell traffic and 3D structural visualization.

Three-dimensional murine airway segmentation in micro-CT images

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.

Methods of in-vivo mouse lung micro-CT

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.

Three-dimensional histopathology of lung cancer with multimodality image registration

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.

Three-dimensional visual truth of the normal airway tree for use as a quantitative comparison to micro-CT reconstructions

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.