The 200TW ALLS laser system (30 fs, 5J) is used to accelerate electrons through laser wakefield and generate betatron emission in the 10keV range. Single shot phase contrast images of a series of nylon fibers with diameter ranging from 10μm to 400μm have been obtained in different geometries and are interpreted with a comprehensive model of x-ray propagation integrating the properties and geometries of the imaging beam line. A simple figure of merit, which can give indication on the interface sharpness of a phase object, is used to assess the quality of the imaging beam line.
KEYWORDS: Magnetic resonance imaging, Associative arrays, Medical imaging, Detection and tracking algorithms, Distance measurement, Principal component analysis, Brain, Neuroimaging, Image registration, Signal to noise ratio
Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of magnetic resonance imaging (MRI) scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are being used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. We present an improved version of the EigenBlockCD algorithm, named the EigenBlockCD-2. The EigenBlockCD-2 algorithm performs an initial global registration and identifies the changes between serial MR images of the brain. Blocks of pixels from a baseline scan are used to train local dictionaries to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between L1 and L2 norms as two possible similarity measures in the improved EigenBlockCD-2 algorithm. We show the advantages of the L2 norm over the L1 norm both theoretically and numerically. We also demonstrate the performance of the new EigenBlockCD-2 algorithm for detecting changes of MR images and compare our results with those provided in the recent literature. Experimental results with both simulated and real MRI scans show that our improved EigenBlockCD-2 algorithm outperforms the previous methods. It detects clinical changes while ignoring the changes due to the patient’s position and other acquisition artifacts.
KEYWORDS: Magnetic resonance imaging, Associative arrays, Distance measurement, Principal component analysis, Medical imaging, Image registration, Brain, Neuroimaging, Detection and tracking algorithms, Signal to noise ratio
Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of MRI scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. In this paper we present the Eigen-Block Change Detection algorithm (EigenBlockCD). It performs local registration and identifies the changes between consecutive MR images of the brain. Blocks of pixels from baseline scan are used to train local dictionaries that are then used to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between L1 and L2 norms as two possible similarity measures in the EigenBlockCD. We show the advantages of L2 norm over L1 norm theoretically and numerically. We also demonstrate the performance of the EigenBlockCD algorithm for detecting changes of MR images and compare our results with those provided in recent literature. Experimental results with both simulated and real MRI scans show that the EigenBlockCD outperforms the previous methods. It detects clinical changes while ignoring the changes due to patient's position and other acquisition artifacts.
The role of fluoroscopic imaging is critical for diagnostic and image guided therapy. However, fluoroscopic imaging
can require significant radiation leading to increased cancer risk and non-stochastic effects such as radiation burns.
Our purpose is to reduce the exposure and dose to the patient by an order of magnitude in these procedures by use of
the region of interest method. Method and Materials: Region of interest fluoroscopy (ROIF) uses a partial attenuator.
The central region of the image has full exposure while the image periphery, there to provide context only, has a
reduced exposure rate. ROIF using a static partial attenuator has been shown in our previous studies to reduce the
dose area product (DAP) to the patient by at least 2.5 times. Significantly greater reductions in DAP would require
improvements in flat panel detectors performance at low x-ray exposures or a different x-ray attenuation strategy.
Thus we have investigated a second, dynamic, approach. We have constructed an x-ray shutter system allowing a
normal x-ray exposure in the region of interest while reducing the number of x-ray exposures in the periphery
through the rapid introduction, positioning and removal of an x-ray attenuating shutter to block radiation only for
selected frames. This dynamic approach eliminates the DQE(0) loss associated with the use of static partial
attenuator applied to every frame thus permitting a greater reduction in DAP. Results: We have compared the two
methods by modeling and determined their fundamental limits.
We propose a novel neuro-fuzzy hybrid transformation model for deformable image registration in intra-operative image
guided procedures involving large soft tissue deformation. The hybrid model consists of two parts: a physics-based
model and a mathematical approximation model. The physics-based model is based on elastic solid mechanics to model
major deformation patterns of the central part of organs, and the mathematical approximation model depicts the
deformation of the residual part along organ boundary. A neuro-fuzzy technique is employed to seamlessly integrate the
two parts into a unified hybrid model. Its unique feature is to incorporate domain knowledge of soft tissue deformation
patterns and significantly reduce the number of transformation parameters. We demonstrate the effectiveness of our
hybrid model to register liver magnetic resonance (MR) images in human subject study. This technique has the potential
to significantly improve intra-operative image guidance in abdominal and thoracic procedures.
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