Multiple Fields Of View Research Articles

Design and implementation of algorithms for focus automation in digital imaging time‐lapse microscopy

Digital time-lapse microscopy using Nomarski-DIC requires that an autofocusing system adapt to changes in cell shape, size, and position while contending with drift, noise, and hysteresis in the microscope and imager. We have designed and implemented an autofocusing system that tracks subjects under dynamic conditions and maintains focus within a threshold of discriminability. With the use of proven and novel algorithms for autofocusing in Nomarski, we performed "virtual" experiments on recorded image stacks to simulate drift and sudden displacements and test the search algorithm response. We found that combining a simple [1, -1] contrast function with an adaptive "warmer-colder" focusing algorithm yields a reasonable compromise between focusing precision and noise tolerance. This method was implemented to record growth kinetics of yeast cells in single and multiple fields of view over several hours. We have implemented a robust digital autofocus that maintains focus on optically complex samples imaged at high resolution. The tolerance of this system of drift and vibration suggests that it is a practical system for time-lapse imaging in many biological applications.

Multiple-scattering lidar retrieval method: tests on Monte Carlo simulations and comparisons with in situ measurements.

A multiple-field-of-view (MFOV) lidar measurement and solution technique has been developed to exploit the retrievable particle extinction and size information contained in the multiple-scattering contributions to aerosol lidar returns. We describe the proposed solution algorithm. The primary retrieved parameters are the extinction coefficient at the lidar wavelength and the effective particle diameter from which secondary products such as the extinction at other wavelengths and the liquid-water content (LWC) of liquid-phase clouds can be derived. The solutions are compared with true values in a series of Monte Carlo simulations and with in-cloud measurements. Good agreement is obtained for the simulations. For the field experiment, the retrieved effective droplet diameter and LWC for the available seven cases studied are on average 15% and 35% (worst case) smaller than the measured data, respectively. In the latter case, the analysis shows that the differences cannot be attributed solely to lidar inversion errors. Despite the limited penetration depth (150-300 m) of the lidar pulses, the results of the studied cases indicate that the retrieved lidar solutions remain statistically representative of measurements performed over the full cloud extent. Long-term MFOV lidar monitoring could thus become a practical and economical option for cloud statistical studies but more experimentation on more varied cloud conditions, especially for LWC, is still needed.

Multiple field of view MR fluoroscopy.

This work describes a real-time imaging and visualization technique that allows multiple field of view (FOV) imaging. A stream of images from a single receiver channel can be reconstructed at multiple FOVs within each image frame. Alternately, or in addition, when multiple receiver channels are available, image streams from each channel can be independently reconstructed at multiple FOVs. The implementation described here provides for real-time visualization of the placement of guidewires and catheters on a dynamic roadmap during interventional procedures. The loopless catheter antenna, an electrically active intravascular probe, was used for MR signal reception. In 2D projection images, the catheter and surrounding structures within its diameter of sensitivity appear as bright signal. The simplicity of the resulting images allows very-narrow-FOV imaging to decrease imaging time. Very-narrow-FOV images are acquired on MR receiver channels that collect guidewire or catheter data. These very-narrow-FOV images provide very high frame rate continuous, real-time imaging of the interventional devices (25 fps). Large-FOV images are formed from receiver channels that collect anatomical data from standard imaging surface coils, and simultaneously provide a dynamic, frequently updated roadmap. These multiple-FOV images are displayed together, improving visualization of interventional device placement.

Retrieval of droplet-size density distribution from multiple-field-of-view cross-polarized lidar signals: theory and experimental validation

Multiple-field-of-view (MFOV) secondary-polarization lidar signals are used to calculate the particle-size density distribution (PSD) at the base of a cloud. At the cloud base, multiple scattering is weak and single backscattering is predominant by many orders of magnitude. Because secondary polarization is a direct measure of multiple scattering, it is therefore advantageous to use secondary polarization. A mathematical relation among the PSD, the lidar fields of view, the scattering angles, and the angular depolarization is derived to facilitate use of secondary polarization. The model is supported by experimental MFOV lidar measurements carried out in a controlled environment, and its limitations and restrictions are discussed.

Estimation of cloud droplet size density distribution from multiple-field-of-view lidar returns

A simple model based on diffraction and geometrical optics relates the field-of-view dependent lidar signal returns from clouds to the size density distribution of their water droplets. The relation is written in a matrix form. The apparent size density distribution is obtained via matrix inversion with a constraint technique. The perturbation caused by the geometrical optics is interpreted in terms of small-particle diffraction. The apparent size density distribution is corrected for multiple scattering a posteriori. The correction is established by calculating the relative con- tribution of the different scattering orders to the broadening of the scat- tering phase function. The validity of the model is supported by Monte Carlo simulations and by experimental data performed under controlled conditions. Finally the inversion technique is applied to multiple-field-of- view (MFOV) lidar measurements from real clouds. © 1997 Society of Photo- Optical Instrumentation Engineers. (S0091-3286(97)02712-8)