In an effort to improve the accuracy of transrectal ultrasound (TRUS)-guided needle biopsies of the prostate, it is
important to understand the non-rigid deformation of the prostate. To understand the deformation of the prostate when
an endorectal coil (ERC) is inserted, we develop an elastic registration framework to register prostate MR images with
and without ERC. Our registration framework uses robust point matching (RPM) to get the correspondence between the
surface landmarks in the source and target volumes followed by elastic body spline (EBS) registration based on the
corresponding landmark pairs. Together with the manual rigid alignment, we compared our registration framework
based on pure surface landmarks to the registration based on both surface and internal landmarks in the center of the
prostate. In addition, we assessed the impact of constraining the warping in the central zone of the prostate using a
Gaussian weighting function. Our results show that elastic surface-driven prostate registration is feasible, and that
internal landmarks further improve the registration in the central zone while they have little impact on the registration in
the peripheral zone of the prostate. Results varied case by case depending on the accuracy of the prostate segmentation
and the amount of warping present in each image pair. The most accurate results were obtained when using a Gaussian
weighting in the central zone to limit the EBS warping driven by surface points. This suggests that a Gaussian constrain
of the warping can effectively compensate for the limitations of the isotropic EBS deformation model, and for erroneous
warping inside the prostate created by inaccurate surface landmarks driving the EBS.
The use of electromagnetic (EM) tracking is an important guidance tool that can be used to aid procedures
requiring accurate localization such as needle injections or catheter guidance. Using EM tracking, the information
from different modalities can be easily combined using pre-procedural calibration information. These calibrations
are performed individually, per modality, allowing different imaging systems to be mixed and matched according
to the procedure at hand. In this work, a framework for the calibration of a 3D transesophageal echocardiography
probe to EM tracking is developed. The complete calibration framework includes three required steps: data
acquisition, needle segmentation, and calibration. Ultrasound (US) images of an EM tracked needle must be
acquired with the position of the needles in each volume subsequently extracted by segmentation. The calibration
transformation is determined through a registration between the segmented points and the recorded EM needle
positions. Additionally, the speed of sound is compensated for since calibration is performed in water that has a
different speed then is assumed by the US machine. A statistical validation framework has also been developed
to provide further information related to the accuracy and consistency of the calibration. Further validation of
the calibration showed an accuracy of 1.39 mm.
Live three dimensional (3D) transesophageal echocardiography (TEE) provides real-time imaging of cardiac
structure and function, and has been shown to be useful in interventional cardiac procedures. Its application in
catheter based cardiac procedures is, however, limited by its limited field of view (FOV). In order to mitigate
this limitation, we register pre-operative magnetic resonance (MR) images to live 3D TEE images. Conventional
multimodal image registration techniques that use mutual information (MI) as the similarity measure use
statistics from the entire image. In these cases, correct registration, however, may not coincide with the global
maximum of MI metric. In order to address this problem, we present an automated registration algorithm that
balances a combination global and local edge-based statistics. The weighted sum of global and local statistics is
computed as the similarity measure, where the weights are decided based on the strength of the local statistics.
Phantom validation experiments shows improved capture ranges when compared with conventional MI based
methods. The proposed method provided robust results with accuracy better than 3 mm (5°) in the range of
-10 to 12 mm (-6 to 3°), -14 to 12 mm (-6 to 6°) and -16 to 6 mm (-6 to 3°) in x-, y-, and z- axes respectively.
We believe that the proposed registration method has the potential for real time intra-operative image fusion
during percutaneous cardiac interventions.
Harmonic phase (HARP) MRI is used to measure myocardial motion and strain from tagged MR images. HARP MRI uses limited number of samples from the spectrum of the tagged images to reconstruct motion and strain. The HARP strain maps, however, suffer from artifacts that limit the accuracy of the computations and degrade the appearance of the strain maps. Causes of these, so called 'zebra', artifacts include image noise, Gibbs ringing, and interference from other Fourier spectral peaks. Computing derivatives of the HARP phase, which are needed to estimate strain, further accentuates these artifacts. Previous methods to reduce these artifacts include 1-D and 2-D nonlinear filtering of the HARP derivatives, and a 2-D linear filtering of unwrapped HARP phase. A common drawback among these methods is the lack of proper segmentation of the myocardium from the blood pool. Because of the lack of segmentation, the noisy phase values from the blood pool enter into the computation in the smoothed strain maps, which causes artifacts. In this work, we propose a smoothing method based on anisotropic diffusion that filters the HARP derivatives strictly within the myocardium without the need for prior segmentation. The information about tissue geometry and the strain distribution is used to restrict the smoothing to within the myocardium, thereby ensuring minimum distortion of the final strain map. Preliminary results demonstrate the ability of anisotropic diffusion for better artifact reduction and lesser strain distortion than the existing methods.
Analyzing the motion of the tongue surface provides valuable information about speech and swallowing. To analyze this motion, two-dimensional ultrasound images are acquired at video frame rates, and the tongue surface is automatically extracted and tracked. Further processing and statistical analysis of the extracted contours is made difficult by: 1) arbitrary spatial shifts and data loss resulting from ultrasound transducer positioning; 2) difference in tongue lengths over time for same utterance and across subjects; and 3) differences in the sampling locations. To address the above shortcombings, we used kriging to extrapolate and resample the tongue surface contours. Kriging was used becasue it does not lead to wild oscillations associated wiht traditional polynomial fitting. For our kriging implementation, we used the generalized covariance function and linear drift functions that are used in thin plate splines. Further, we designed a dedicated user interface called 'SURFACES' that exploits this extrapolation to visualize the contours as spatiotemporal surfaces. These spatiotemporal surfaces can be readily used for statistical comparison and visualization of tongue shapes for different utterances and swallows.
We have used fiber optic remote process to monitor processes at Kodak in the lab, in development or pilot, and in production. This talk will examine the use of near IR (NIR) diffuse reflectance spectroscopy as a process technology. Diffuse reflectance spectroscopy offers the capability of looking at powders, slurries, emulsions, and dispersions. Unlike attenuated total internal reflectance spectroscopy, diffuse reflectance offers the capability of interrogating both the liquid and solid phases of the material. This provides the ability to examine the physical state of the solid, such as particle size and morphology, even in a slurry, or in the presence of large amounts of solvent, in addition to the chemical quality of the solution. The use of the NIR spectral region provides the advantages of high signal-to-noise ratio, impressive photometric stability, and commercially available instrumentation, probes and optical fiber cable. Some representative examples will be presented to demonstrate the capabilities of diffuse reflectance spectroscopy for process monitoring with fiber optics.
Packetized video is likely to be one of the most significant users of bandwidth in future high- speed digital networks. In this paper we focus, in a unified manner, on the effects of packet losses, and the resulting error propagation, for a particular entropy-constrained subband coding (ECSBC) scheme employing hierarchical motion-compensated prediction (HMCP). We make use of the associated operational rate-distortion function to assess the quality of transmission that can be sustained under relatively low loss conditions representative of asynchronous transfer mode (ATM) networks. We show that the use of error-correcting codes helps in providing adequate protection for low-to-moderate packet loss. To capture the property of correlated loss in a network, a Markov chain model is used to represent packet losses.