Aqueous Humor Outflow Structure and Function Imaging At the Bench and Bedside: A Review.

Anterior segment glaucoma clinical care and research has recently gained new focus because of novel imaging modalities and the advent of angle-based surgical treatments. Traditional investigation drawn to the trabecular meshwork now emphasizes the entire conventional aqueous humor outflow (AHO) pathway from the anterior chamber to the episcleral vein. AHO investigation can be divided into structural and functional assessments using different methods. The historical basis for studying the anterior segment of the eye and AHO in glaucoma is discussed. Structural studies of AHO are reviewed and include traditional pathological approaches to modern tools such as multi-model two-photon microscopy and optical coherence tomography. Functional assessment focuses on visualizing AHO itself through a variety of non-real-time and real-time techniques such as aqueous angiography. Implications of distal outflow resistance and segmental AHO are discussed with an emphasis on melding bench-side research to viable clinical applications. Through the development of an improved structure: function relationship for AHO in the anterior segment of the normal and diseased eye, a better understanding of the eye with improved therapeutics may be developed

Immersive Visualization for Enhanced Computational Fluid Dynamics Analysis

Modern biomedical computer simulations produce spatiotemporal results that are often viewed at a single point in time on standard 2D displays. An immersive visualization environment (IVE) with 3D stereoscopic capability can mitigate some shortcomings of 2D displays via improved depth cues and active movement to further appreciate the spatial localization of imaging data with temporal computational fluid dynamics (CFD) results. We present a semi-automatic workflow for the import, processing, rendering, and stereoscopic visualization of high resolution, patient-specific imaging data, and CFD results in an IVE. Versatility of the workflow is highlighted with current clinical sequelae known to be influenced by adverse hemodynamics to illustrate potential clinical utility

SPECT Imaging of Pulmonary Blood Flow in a Rat

Small animal imaging is experiencing rapid development due to its importance in providing high-throughput phenotypic data for functional genomics studies. We have developed a single photon emission computed tomography (SPECT) system to image the pulmonary perfusion distribution in the rat. A standard gamma camera, equipped with a pinhole collimator, was used to acquire SPECT projection images at 40 sec/view of the rat thorax following injection of Tc99m labeled albumin that accumulated in the rat\u27s lungs. A voxel-driven, ordered-subset expectation maximization reconstruction was implemented. Following SPECT imaging, the rat was imaged using micro-CT with Feldkamp conebeam reconstruction. The two reconstructed image volumes were fused to provide a structure/function image of the rat thorax. Reconstruction accuracy and performance were evaluated using numerical simulations and actual imaging of an experimental phantom consisting of Tc99m filled chambers with known diameters and count rates. Full-width half-maximum diameter measurement errors decreased with increasing chamber diameter, ranging from \u3c 6% down to 0.1%. Errors in the ratio of count rate estimates between tubes were also diameter dependent but still relatively small. This preliminary study suggests that SPECT will be useful for imaging and quantifying the pulmonary blood flow distribution and the distribution of Tc99m labeled ligands in the lungs of small laboratory animals

Challenges and opportunities for quantifying roots and rhizosphere interactions through imaging and image analysis

The morphology of roots and root systems influences the efficiency by which plants acquire nutrients and water, anchor themselves and provide stability to the surrounding soil. Plant genotype and the biotic and abiotic environment significantly influence root morphology, growth and ultimately crop yield. The challenge for researchers interested in phenotyping root systems is, therefore, not just to measure roots and link their phenotype to the plant genotype, but also to understand how the growth of roots is influenced by their environment. This review discusses progress in quantifying root system parameters (e.g. in terms of size, shape and dynamics) using imaging and image analysis technologies and also discusses their potential for providing a better understanding of root:soil interactions. Significant progress has been made in image acquisition techniques, however trade-offs exist between sample throughput, sample size, image resolution and information gained. All of these factors impact on downstream image analysis processes. While there have been significant advances in computation power, limitations still exist in statistical processes involved in image analysis. Utilizing and combining different imaging systems, integrating measurements and image analysis where possible, and amalgamating data will allow researchers to gain a better understanding of root:soil interactions

Imaging spontaneous imbibition in full Darcy‐scale samples at pore‐scale resolution by fast X‐ray tomography

Spontaneous imbibition is a process occurring in a porous medium which describes wetting phase replacing nonwetting phase spontaneously due to capillary forces. This process is conventionally investigated by standardized, well-established spontaneous imbibition tests. In these tests, for instance, a rock sample is surrounded by wetting fluid. The following cumulative production of nonwetting phase versus time is used as a qualitative measure for wettability. However, these test results are difficult to interpret, because many rocks do not show a homogeneous but a mixed wettability in which the wetting preference of a rock varies from location to location. Moreover, during the test the flow regime typically changes from countercurrent to cocurrent flow and no phase pressure or pressure drop can be recorded. To help interpretation, we complement Darcy-scale production curves with X-ray imaging to describe the differences in imbibition processes between water-wet and mixed-wet systems. We found that the formation of a spontaneous imbibition front occurs only for water-wet systems; mixed-wet systems show localized imbibition events only. The asymmetry of the front depends on the occurrence of preferred production sites, which influences interpretation. Fluid layers on the outside of mixed-wet samples increase connectivity of the drained phase and the effect of buoyancy on spontaneous imbibition. The wider implication of our study is the demonstration of the capability of benchtop laboratory equipment to image a full Darcy-scale experiment while at the same time obtaining pore-scale information, resolving the natural length and time scale of the underlying processes

Feature Representation Analysis of Deep Convolutional Neural Network using Two-stage Feature Transfer -An Application for Diffuse Lung Disease Classification-

Transfer learning is a machine learning technique designed to improve generalization performance by using pre-trained parameters obtained from other learning tasks. For image recognition tasks, many previous studies have reported that, when transfer learning is applied to deep neural networks, performance improves, despite having limited training data. This paper proposes a two-stage feature transfer learning method focusing on the recognition of textural medical images. During the proposed method, a model is successively trained with massive amounts of natural images, some textural images, and the target images. We applied this method to the classification task of textural X-ray computed tomography images of diffuse lung diseases. In our experiment, the two-stage feature transfer achieves the best performance compared to a from-scratch learning and a conventional single-stage feature transfer. We also investigated the robustness of the target dataset, based on size. Two-stage feature transfer shows better robustness than the other two learning methods. Moreover, we analyzed the feature representations obtained from DLDs imagery inputs for each feature transfer models using a visualization method. We showed that the two-stage feature transfer obtains both edge and textural features of DLDs, which does not occur in conventional single-stage feature transfer models.Comment: Preprint of the journal article to be published in IPSJ TOM-51. Notice for the use of this material The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). This material is published on this web site with the agreement of the author (s) and the IPS

In vitro ovarian tumor growth and treatment response dynamics visualized with time-lapse OCT imaging

In vitro three-dimensional models for metastatic ovarian cancer have been useful for recapitulating the human disease. These spheroidal tumor cultures, however, can grow in excess of 1 mm in diameter, which are difficult to visualize without suitable imaging technology. Optical coherence tomography (OCT) is an ideal live imaging method for non-perturbatively visualizing these complex systems. OCT enabled detailed observations of the model at both nodular and cellular levels, revealing growth dynamics not previously observed. The development ofa time-lapse OCT system, capable of automated, multidimensional acquisition, further provided insights into the growth and chemotherapeutic response of ovarian cancer.© 2009 Optical Society of America