Purpose: To evaluate an efficient iterative deconvolution method (RSEMD) for improving the quantitative accuracy
of previously reconstructed breast images by commercial positron emission mammography (PEM) scanner.
Materials and Methods: The RSEMD method was tested on breast phantom data and clinical PEM imaging data.
Data acquisition was performed on a commercial Naviscan Flex Solo II PEM camera. This method was applied to
patient breast images previously reconstructed with Naviscan software (MLEM) to determine improvements in
resolution, signal to noise ratio (SNR) and contrast to noise ratio (CNR.)
Results: In all of the patients’ breast studies the post-processed images proved to have higher resolution and lower
noise as compared with images reconstructed by conventional methods. In general, the values of SNR reached a
plateau at around 6 iterations with an improvement factor of about 2 for post-processed Flex Solo II PEM images.
Improvements in image resolution after the application of RSEMD have also been demonstrated.
Conclusions: A rapidly converging, iterative deconvolution algorithm with a novel resolution subsets-based
approach RSEMD that operates on patient DICOM images has been used for quantitative improvement in breast
imaging. The RSEMD method can be applied to clinical PEM images to improve image quality to diagnostically
acceptable levels and will be crucial in order to facilitate diagnosis of tumor progression at the earliest stages. The
RSEMD method can be considered as an extended Richardson-Lucy algorithm with multiple resolution levels
(resolution subsets).
In this article we demonstrate the use of an automated technique to visualize lesions in the abdominal aorta, gathered from MRI imagery, and displayed as a stained pathology specimen. We have developed this technique in response to the suggestion from clinical colleagues that such a representation of the data is more understandable to the pathologist than presentation of axial MRI slices, even if the atheroma is of high contrast and very thick. This virtual dissection relies on an initial manual segmentation of the inner and outer walls of the vessel, which I achieved using a commercial cardiac analysis package. The algorithm consists of (1) decoding the file that describes the contours; (2) generating a center for each slice; (3) determining the appropriate posterior position on all slices; (4) interpolating along the (approximately circular) lumen and through all slices; (5) false color presentation of the wall thickness as a pathological stain.
KEYWORDS: Databases, Radiology, Information security, Control systems, Java, Computer security, Standards development, Image display, Data modeling, Binary data
Currently, Web-based access to mini-PACS or similar databases commonly utilizes either JavaScript, Java applets or ActiveX controls. Many sites do not permit applets or controls or other binary objects for fear of viruses or worms sent by malicious users. In addition, the typical CGI query mechanism requires several parameters to be sent with the http GET/POST request, which may identify the patient in some way; this in unacceptable for privacy protection. Also unacceptable are pages produced by server-side scripts which can be cached by the browser, since these may also contain sensitive information. We propose a simple mechanism for access to patient information, including images, which guarantees security of information, makes it impossible to bookmark the page, or to return to the page after some defined length of time. In addition, this mechanism is simple, therefore permitting rapid access without the need to initially download an interface such as an applet or control. In addition to image display, the design of the site allows the user to view and save movies of multi-phasic data, or to construct multi-frame datasets from entire series. These capabilities make the site attractive for research purposes such as teaching file preparation.
A method has been developed to utilize a 3D B0 fieldmap, with a multi-volume-of-interest segmentation map, to quantify and correct geometric distortions in echo-planar images. The purpose is to provide accurate co-registration of anatomical MRI to functional MRI time course sequences. A data structure capable of extracting and reporting the necessary information forms a central part of the solution. Images were obtained from a 1.5 Tesla scanner with an experimental y-gradient insert coil. Two 3D-gradient echo sequences supply the data needed to calculate the B0 map across the volume. Segmentation of the volume into brain/background produces the data needed for the phase unwrapping and volume(s) of interest generation, from which the global B0 variation map is obtained. Subsequent EPI acquisition yields the fMRI time- course information. Tests were carried out on a phantom and a human volunteer engaged in a motor task (finger-tapping). Strong distortions were measured, and subsequently corrected, particularly near the petrous bone/mastoid air cells and in the frontal and maxillary sinuses. Additionally, a strong eddy current resulting from the unshielded y-gradient was detected. The method facilitates geometric distortion correction through an imaging volume, containing multiple regions of interest within a slice, starting from a single starting point.
High demands are made of the gradient systems of MRI scanners during acquisition of echo-planar images. These may occasionally lead to spurious noise added to the received data in the form of short duration, high intensity 'spikes.' If these spikes are not caught prior to image formation, a severely degraded image may be formed. In the acquisition of functional MRI data this degradation may prohibit subsequent analysis of the data for identification of activated brain regions. We have devised several algorithms to isolate and remove these spurious spikes prior to the Fourier transform, and in one example show that subsequent fMRI analyses are possible. By contrast, the analysis is either impossible or produces differing results without this processing step.
Functional magnetic resonance imaging (fMRI) experiments are becoming recognized in a number of areas of neuroscience. Presenting useful information to the clinician in a reasonable time and in an understandable way is of paramount importance for the use of fMRI protocols in the clinical setting. We have developed a series of tools for fMRI analysis and presentation encapsulated by a commercially-available graphical-user interface which allows the user to immediately make use of fMRI data for multiple analyses. The application visualization system (AVS) was chosen to provide a graphical environment for the tools. A series of AVS modules were created to allow the user to perform several processing and analysis tasks using the serial fMRI image data as a starting point. Modules were developed to provide t-test analysis and cross-correlation analysis, in which the user is able to select a suitable idealized driving function which can be interactively modified to suit the given fMRI protocol. Both time and frequency analyses are possible on each pixel in the images. In addition, several co- registration methods have been developed to resolve problems arising from patient motion.
We present a method for reconstructing magnetic resonance (MR) images from data acquired using echo-planar imaging (EPI) techniques. All data were acquired from a commercial scanner, the 1.5 Tesla Picker Vista HPQ MR imaging system, equipped with a special, high- performance, gradient system. Blipped echo planar imaging (BEPI) was performed, with and without digitizer pausing during data acquisition. Both sinusoidal and trapezoidal readout gradients were programmed and tested. Samples obtained from the gradient amplifier current monitors were used to calculate the approximate position of every sample obtained in the spatial-frequency (k-space) plane. A convolution function with compact support int he k-space and good rolloff in the image domain was used to resample the data onto a lattice permitting the use of fast transformation methods to the image domain. Strong ghosting was observed in the resulting images due probably to static magnetic field variation, gradient asymmetry and echo asymmetry between data lines. A piecewise-linear function was used to model the introduced ghost and hence to remove pixels in the reconstructed image which were determined to be spurious. Initial results are promising.
KEYWORDS: Magnetic resonance imaging, Image segmentation, Image processing, Medical imaging, Machine vision, Computer vision technology, Magnetism, Image processing algorithms and systems, Tissues, Chemical elements
We present a solution method for adaptively smoothing magnetic resonance (MR) images while preserving discontinuities. We assume that the spatial behavior of MR data can be captured by a first order polynomial defined at every pixel. The formulation itself is similar to Leclerc's work on piecewise-smooth image segmentation, but we use the graduated non- convexity (GNC) algorithm as an optimizing tool for obtaining the solution. This requires initial values for polynomial coefficients of order greater than zero. These values are obtained by using ideas similar to that found in robust statistics. This initial step is also useful in determining the variance of the noise present in the input image. The variance is related to an important parameter (alpha) required by the GNC algorithm. Firstly, this replaces the heuristic nature of (alpha) with a quantity that can be estimated. Secondly, it is useful especially in situations where the variance of the noise is not uniform across the image. We present results on synthetic and MR images. Though the results of this paper are given using first order polynomials, the formulation can handle higher order polynomials.
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.