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It is now common for magnetic-resonance-imaging (MRI) based multi-site trials to include diffusion-weighted imaging (DWI) as part of the protocol. It is also common for these sites to possess MR scanners of different manufacturers, different software and hardware, and different software licenses. These differences mean that scanners may not be able to acquire data with the same number of gradient amplitude values and number of available gradient directions. Variability can also occur in achievable b-values and minimum echo times. The challenge of a multi-site study then, is to create a common protocol by understanding and then minimizing the effects of scanner variability and identifying reliable and accurate diffusion metrics. This study describes the effect of site, scanner vendor, field strength, and TE on two diffusion metrics: the first moment of the diffusion tensor field (mean diffusivity, MD), and the fractional anisotropy (FA) using two common analyses (region-of-interest and mean-bin value of whole brain histograms). The goal of the study was to identify sources of variability in diffusion-sensitized imaging and their influence on commonly reported metrics. The results demonstrate that the site, vendor, field strength, and echo time all contribute to variability in FA and MD, though to different extent. We conclude that characterization of the variability of DTI metrics due to site, vendor, field strength, and echo time is a worthwhile step in the construction of multi-center trials.

MRI-based multi-site trials now routinely include some form of diffusion-weighted imaging (DWI) in their protocol. These studies can include data originating from scanners built by different vendors, each with their own set of unique protocol restrictions, including restrictions on the number of available gradient directions, whether an externally-generated list of gradient directions can be used, and restrictions on the echo time (TE). One challenge of multi-site studies is to create a common imaging protocol that will result in a reliable and accurate set of diffusion metrics. The present study describes the effect of site, scanner vendor, field strength, and TE on two common metrics: the first moment of the diffusion tensor field (mean diffusivity, MD), and the fractional anisotropy (FA). We have shown in earlier work that ROI metrics and the mean of MD and FA histograms are not sufficiently sensitive for use in site characterization. Here we use the distance between whole brain histograms of FA and MD to investigate within- and between-site effects. We concluded that the variability of DTI metrics due to site, vendor, field strength, and echo time could influence the results in multi-center trials and that histogram distance is sensitive metrics for each of these variables.

In this paper, we propose and validate a fully automated pipeline for simultaneous skull-stripping and lateral ventricle segmentation using T1-weighted images. The pipeline is built upon a segmentation algorithm entitled fast multi-atlas likelihood-fusion (MALF) which utilizes multiple T1 atlases that have been pre-segmented into six whole-brain labels – the gray matter, the white matter, the cerebrospinal fluid, the lateral ventricles, the skull, and the background of the entire image. This algorithm, MALF, was designed for estimating brain anatomical structures in the framework of coordinate changes via large diffeomorphisms. In the proposed pipeline, we use a variant of MALF to estimate those six whole-brain labels in the test T1-weighted image. The three tissue labels (gray matter, white matter, and cerebrospinal fluid) and the lateral ventricles are then grouped together to form a binary brain mask to which we apply morphological smoothing so as to create the final mask for brain extraction. For computational purposes, all input images to MALF are down-sampled by a factor of two. In addition, small deformations are used for the changes of coordinates. This substantially reduces the computational complexity, hence we use the term “fast MALF”. The skull-stripping performance is qualitatively evaluated on a total of 486 brain scans from a longitudinal study on Alzheimer dementia. Quantitative error analysis is carried out on 36 scans for evaluating the accuracy of the pipeline in segmenting the lateral ventricle. The volumes of the automated lateral ventricle segmentations, obtained from the proposed pipeline, are compared across three different clinical groups. The ventricle volumes from our pipeline are found to be sensitive to the diagnosis.

In this paper, we propose a method to automatically segment the corticospinal tract (CST) in diffusion tensor images (DTIs) by incorporating the anatomical features from multi-modality images generated in DTI using multiple DTI atlases. The to-be-segmented test subject, and each atlas, is comprised of images with different modalities – the mean diffusivity, the fractional anisotropy, and the images representing the three elements of the primary eigenvector. Each atlas had a paired image containing the manually delineated segmentations of the three regions of interest - the left and right CST and the background surrounding the CST. We solve the problem via maximum a posteriori estimation using generative models. Each modality image is modeled as a conditional Gaussian mixture random field, conditioned on the atlas-label pair and the local change of coordinates for each label. The expectation-maximization algorithm is used to alternatively estimate the local optimal diffeomorphisms for each label and the maximizing segmentations. The algorithm is evaluated on six subjects with a wide range of pathology. We compare the proposed method with two state-of-the-art multi-atlas based label fusion methods, against which the method displayed a high level of accuracy.

Image distortions caused by eddy current and patient motion have been two major sources of the mis-registration issues in diffusion tensor imaging (DTI). Numerous registration methods have been proposed to correct them. However, quality control of DTI remains an important issue, because we rarely report how much mis-registration existed and how well they were corrected. In this paper, we propose a method for quantitative reporting of DTI data quality. This registration method minimizes a cost function based on mean square tensor fitting errors. Registration with twelve-parameter full affine transformation is used. From the registration result, distortion and motion parameters are estimated. Because the translation parameters involve both eddy-current-induced image translation and the patient motion, by analyzing the transformation model, we separate them by removing the contributions that are linearly correlated with diffusion gradients. We define the metrics measuring the amounts of distortion, rotation, translation. We tested our method on a database with 64 subjects and found the statistics of each of metrics. Finally we demonstrate that how these statistics can be used for assessing the data quality quantitatively in several examples.

The higher numerical aperture ArF scanning exposure tool was developed with completely new platform for further 100nm technology node. This new tool is designed and manufactured with wave front aberration controlled projection lens by using new phase measurement interferometer system, enhanced illuminator structure technology to optimize many various conditions in device production, higher power light source ArF excimer laser and higher speed stages in order to achieve enough required productivity of devices in this node. Not only for the run rate but for the finer accuracy in stage systems and focus control system were involved in this new platform. The actual imaging and accuracy performance will be shown and discussed.

The 193nm lithography is needed for manufacturing devices of finer than 180nm feature size rule. The each key technology in regard to 193nm wavelength, the light source, glass material, photo resist, mask, and so on, has been investigated until now. For ArF exposure too, the major concerns of 193nm wavelength are the projection unit and the illumination unit. The glass materials have been tested the quality and durability for exposure tools' life. Using enough good materials, the first solution of full field ArF projection lens was accomplished by all refractive type, and its performance was confirmed.