This PDF file contains the front matter associated with SPIE Proceedings Volume 7370, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

We propose a cancer diagnostics method using 3D reconstruction of fluorescence based optical imaging data. The system was tested with analytical simulations. Phantom measurements will be acquired and compared with the simulations.

Unambiguous FRET detection in living cells is often challenging. Here we describe how the advantages of fluorescence lifetime sensing with FLIM, fluorophore selection, and critical environmental controls provide better FRET detection.

Here we describe the cell- and molecular-biological concepts to utilise excitable primary isolated cells, namely cardiomyocytes, for optical high content screens. This starts with an optimised culture of human adult cardiomyocytes, allowing culture with diminished dedifferentiation for one week. To allow fluorescence based molecular imaging genetically encoded biosensors need to be expressed in the cardiomyocytes. For transduction of end-differentiated primary cells such as neurons or cardiomyocytes, a viral gene transfer is necessary. Several viral systems were balanced against each other and an adenoviral system proofed to be efficient. This adenoviral transduction was used to express the calcium sensors YC3.6 and TN-XL in cardiomyocytes. Example measurements of calcium transients were performed by wide-field video imaging. We discuss the potential application of these cellular and molecular tools in basic research, cardiac safety screens and personalised diagnostics.

Fluorescence Lifetime Imaging Microscopy (FLIM) is a molecular imaging technique that is useful for biological studies in living cells and tissues [1, 2]. When high-intensity light sources such as lasers are used for fluorescence excitation, it is important to ensure that live-cell systems remain viable and do not become significantly stressed. Error analysis helps to achieve precision in lifetime determination with low-light live-cell imaging [3-5]. We have combined error analysis and Monte Carlo simulations to develop a temporal approach to enhance the precision of time-gated FLIM. This approach can involve both optimal gating and curve fitting. We have compared the precision associated with various lifetime determination techniques, and then searched parameter space in order to find optimal gating conditions in terms of minimal achievable relative standard deviation. Precision and accuracy were investigated via Monte Carlo simulations that included Poisson noise. The results were validated with fluorescence lifetime standards and fluorescent beads. Because time-gated FLIM produces images for each gating, another way to improve precision in low-light FLIM is to utilize spatial information from the gated images to remove noise. Total variation (TV) models are commonly used denoising algorithms [6, 7]. We have considered several TV denoising models to improve the precision of lifetime determination with low-light FLIM. These methods remove electronic-related noise from FLIM images and hence can increase the precision with which the lifetimes are determined. Since the temporal and spatial methods apply to different dimensions, we assume that they work independently and their precision improvements are additive. We test this assumption when the pixel-to-pixel variation due to noise in one image is high enough to cause possible unexpected nonlinear effects. We demonstrate that the precision improvements from the temporal and spatial techniques are independent and additive in a regime pertinent to livecell FLIM studies.

One of the major challenges in biomedical imaging is the extraction of quantified information from the acquired images. Light and tissue interaction leads to the acquisition of images that present inconsistent intensity profiles and thus the accurate identification of the regions of interest is a rather complicated process. On the other hand, the complex geometries and the tangent objects that very often are present in the acquired images, lead to either false detections or to the merging, shrinkage or expansion of the regions of interest. In this paper an algorithm, which is based on alternating sequential filtering and watershed transformation, is proposed for the segmentation of biomedical images. This algorithm has been tested over two applications, each one based on different acquisition system, and the results illustrate its accuracy in segmenting the regions of interest.

We demonstrate the feasibility of using a dual-modality fluorescence and x-ray computed tomography (CT) system for quantitative molecular imaging with phantom studies. A CCD based non-contact FT system, which can take measurements from multiple views was built. High-resolution X-Ray CT was used to obtain structural information from the phantom. A 3.6 mm diameter fluorescence inclusion was deeply embedded in the heterogeneous optical background. The results demonstrated that the fluorophore concentration can only be obtained accurately when guided by the a priori information provided by the x-ray CT.

A novel hybrid imaging system for simultaneous X-ray and Fluorescence Tomography is presented, capitalizing on 360°-projection free-space fluorescence tomography. The system is implemented within a commercial micro-CT scanner allowing reconstructions with a resolution of 95μm. Acquired data sets are intrinsically coregistered in the same coordinate system and can be used to correctly localize reconstructed fluorescence distributions with morphological features. More importantly, the micro-CT data, automatically segmented into different organ and tissue segments can be used to guide the fluorescence reconstruction algorithm and reduce the ill coditioning of the inverse problem. We showcase the use of the system and the improvements in image quality for lesions in brain and lung.