A fully automated application was developed and used for the registration of T1-weighted
magnetic resonance images (MRIs) for Alzheimer patients. Two methods for image
registration were implemented and compared: affine and nonlinear registration. Nonlinear
registration uses continuum-mechanics-based elastic deformation. The affine registration
algorithm is linear and is generated by an amplitude-modulated phase-only filter. The
nonlinear registration method uses an elastic transformation generated by Navier-Stokes
continuum-mechanics models. The validation method to quantitatively compare the
performance of the affine and nonlinear registration algorithms uses root-mean-square error
and three-dimensional volume rendering.
One of the major purposes of National Ignition Facility at Lawrence Livermore National Laboratory is to accurately focus 192 high energy laser beams on a nanoscale (mm) fusion target at the precise location and time. The automatic alignment system developed for NIF is used to align the beams in order to achieve the required focusing effect. However, if a distorted image is inadvertently created by a faulty camera shutter or some other opto-mechanical malfunction, the resulting image termed "off-normal" must be detected and rejected before further alignment processing occurs. Thus the off-normal processor acts as a preprocessor to automatic alignment image processing. In this work, we discuss the development of an "off-normal" pre-processor capable of rapidly detecting the off-normal images and performing the rejection. Wide variety of off-normal images for each loop is used to develop the criterion for rejections accurately.
Alignment of laser beams based on video images is a crucial task necessary to automate operation of the 192 beams at the National Ignition Facility (NIF). The final optics assembly (FOA) is the optical element that aligns the beam into the target chamber. This work presents an algorithm for determining the position of a corner cube alignment image in the final optics assembly. The improved algorithm was compared to the existing FOA algorithm on 900 noise-simulated images. While the existing FOA algorithm based on correlation with a synthetic template has a radial standard deviation of 1 pixel, the new algorithm based on classical matched filtering (CMF) and polynomial fit to the correlation peak improves the radial standard deviation performance to less than 0.3 pixels. In the new algorithm the templates are designed from real data stored during a year of actual operation.
The purpose of the automatic alignment algorithm at the National Ignition Facility (NIF) is to determine the position of a laser beam based on the position of beam features from video images. The position information obtained is used to command motors and attenuators to adjust the beam lines to the desired position, which facilitates the alignment of all 192 beams. One of the goals of the algorithm development effort is to ascertain the performance, reliability, and uncertainty of the position measurement. This paper describes a method of evaluating the performance of algorithms using Monte Carlo simulation. In particular we show the application of this technique to the LM1_LM3 algorithm, which determines the position of a series of two beam light sources. The performance of the algorithm was evaluated for an ensemble of over 900 simulated images with varying image intensities and noise counts, as well as varying diffraction noise amplitude and frequency. The performance of the algorithm on the image data set had a tolerance well beneath the 0.5-pixel system requirement.
An algorithm for determining the position of the KDP back-reflection image was developed. It was compared to a centroid-based algorithm. While the algorithm based on centroiding exhibited a radial standard deviation of 9 pixels, the newly proposed algorithm based on classical matched filtering (CMF) and a Gaussian fit to correlation peak provided a radial standard deviation of less than 1 pixel. The speed of the peak detection was improved from an average of 5.5 seconds for Gaussian fit to 0.022 seconds by using a polynomial fit. The performance was enhanced even further by utilizing a composite amplitude modulated phase only filter; producing a radial standard deviation of 0.27 pixels. The proposed technique was evaluated on 900+ images with varying degrees of noise and image amplitude as well as real National Ignition Facility (NIF) images.
The alignment of high energy laser beams for potential fusion experiments demand high precision and accuracy by the underlying positioning algorithms. This paper discusses the feasibility of employing on-line optimal position estimators in the form of model-based processors to achieve the desired results. Here we discuss the modeling, development, implementation and processing of model-based processors applied to both simulated and actual beam line data.