Factor analysis of dynamic structures (FADS) is a methodology of extracting time-activity curves (TACs) for corresponding different tissue types from noisy dynamic images. The challenges of FADS include long computation time and sensitivity to the initial guess, resulting in convergence to local minima far from the true solution. We propose a method of accelerating and stabilizing FADS application to sequences of dynamic PET images by adding preliminary cluster analysis of the time activity curves for individual voxels. We treat the temporal variation of individual voxel concentrations as a set of time-series and use a partial clustering analysis to identify the types of voxel TACs that are most functionally distinct from each other. These TACs provide a good initial guess for the temporal factors for subsequent FADS processing. Applying this approach to a set of single slices of dynamic 11C-PIB images of the brain allows identification of the arterial input function and two different tissue TACs that are likely to correspond to the specific and non-specific tracer binding-tissue types. These results enable us to perform direct classification of tissues based on their pharmacokinetic properties in dynamic PET without relying on a compartment-based kinetic model, without identification of the reference region, or without using any external methods of estimating the arterial input function, as needed in some techniques.
In this work we present a time-lapsed confocal microscopy image analysis technique for an automated gene expression study of multiple single living cells. Fluorescence Resonance Energy Transfer (FRET) is a technology by which molecule-to-molecule interactions are visualized. We analyzed a dynamic series of ~102 images obtained using confocal microscopy of fluorescence in yeast cells containing RNA reporters that give a FRET signal when the gene promoter is activated. For each time frame, separate images are available for three spectral channels and the integrated intensity snapshot of the system. A large number of time-lapsed frames must be analyzed to identify each cell individually across time and space, as it is moving in and out of the focal plane of the microscope. This makes it a difficult image processing problem. We have proposed an algorithm here, based on scale-space technique, which solves the problem satisfactorily. The algorithm has multiple directions for even further improvement. The ability to rapidly measure changes in gene expression simultaneously in many cells in a population will open the opportunity for real-time studies of the heterogeneity of genetic response in a living cell population and the interactions between cells that occur in a mixed population, such as the ones found in the organs and tissues of multicellular organisms.
Motion is a serious artifact in Cardiac nuclear imaging because the scanning operation takes a long time.
Since reconstruction algorithms assume consistent or stationary data the quality of resulting image is affected by
motion, sometimes significantly. Even after adoption of the gold standard MoCo(R) algorithm from Cedars-Sinai by
most vendors, heart motion remains a significant challenge. Also, any serious study in quantitative analysis
necessitates correction for motion artifacts. It is generally recognized that human eye is a very sensitive tool for
detecting motion. However, two reasons prevent such manual correction: (1) it is costly in terms of specialist's time,
and (2) no such tool for manual correction is available currently. Previously, at SPIE-MIC'11, we presented a simple
tool (SinoCor) that allows sinograms to be corrected manually or automatically. SinoCor performs correction of
sinograms containing inter-frame patient or respiratory motions using rigid-body dynamics. The software is capable
of detecting the patient motion and estimating the body-motion vector using scanning geometry parameters. SinoCor
applies appropriate geometrical correction to all the frames subsequent to the frame when the movement has occurred
in a manual or automated mode. For respiratory motion, it is capable of automatically smoothing small oscillatory
(frame-wise local) movements. Lower order image moments are used to represent a frame and the required rigid body
movement compensation is computed accordingly. Our current focus is on enhancement of SinoCor with the
capability to automatically detect and compensate for intra-frame motion that causes motion blur on the respective
frame. Intra-frame movements are expected in both patient and respiratory motions. For a controlled study we also
have developed a motion simulator. A stable version of SinoCor is available under license from Lawrence Berkeley
National Laboratory.
We present a simple method for correcting patient motion in SPECT. The targeted type of motion is a
momentary shift in patient's body position due to coughing, sneezing or a need to shift weight during a long scan.
When detected by the radiologist, this motion sometimes causes the scan data being discarded and the scan being
repeated, thus imposing extra costs and unnecessary health risks to the patients. We propose a partial solution to this
problem in the form of a graphical user interface based software tool SinoCor, integrated with the sinogram viewing
software that allows instant correction for the simplest types of motion. When used during the initial check of the scan
data, this tool allows the technologists to interactively detect the instances of motion and determine the parameters of
the motion by achieving consistent picture of the sinogram. Two types of motion are corrected by using the
algorithms: translational motion of the patient and small angle rotation about in-plane axes. All of the motion
corrections are performed at the sinogram level, after which the images may be reconstructed using
hospital's/organization's standard reconstruction software. SinoCor is platform independent, it requires no
modification of the acquisition protocol and other processing software, and it requires minimal personnel training. In
this article we describe the principal architecture of SinoCor software and illustrate its performance using both a
phantom and a patient scan data.
One of the recent efforts in development of cone-beam CT is aimed at the construction of a volumetric CT apparatus with distributed X-ray sources. This new concept in 3D CT requires a CT reconstruction algorithm designed for X-ray foci uniformly distributed on a surface
rather than on a curve. To research the properties of such algorithm an exact reconstruction formula is derived for a continuous distribution of sources on a surface of a sphere. The algorithm is implemented using finite number of focal spots for simulated phantom projection data. High resolution images were obtained for 100-400 focal spots for both noiseless and noisy input. The results exhibit a potential for CT image reconstruction from highly undersampled projection data.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.