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J J Sychra

Publications and source records attributed to J J Sychra.

At least 19 recordsLinked to original sources
PubMed1997

Cerebral perfusion SPECT imaging in epileptic and nonepileptic seizures.

Patients with epileptic and nonepileptic seizures are commonly encountered in clinical practice, and they can pose a difficult diagnostic problem. We present two cases that show the difficult task of differentiating between true epileptic and nonepileptic or psychogenic seizures in some patients. The clinical presentations were complex and the use of video-monitored EEG alone was insufficient to make definitive diagnoses. Ictal and interictal Tc-99m HMPAO brain perfusion SPECT imaging examinations were used to help establish the correct diagnoses. This report describes the advantage of using the brain perfusion SPECT imaging examination. The injection of stabilized Tc-99m HMPAO during an ictal event followed by appropriate medical therapy provides a method of obtaining a reasonable image of relative perfusion (activity) during the seizure. These images can then be compared with interictal examinations and an epileptic or nonepileptic focus may be localized. The Tc-99m HMPAO brain perfusion SPECT imaging study was helpful in establishing the correct diagnosis in both cases.

Adult↗
PubMed1996

Compton scatter correction in case of multiple crosstalks in SPECT imaging.

A strategy for Compton scatter correction in brain SPECT images was proposed recently. It assumes that two radioisotopes are used and that a significant portion of photons of one radioisotope (for example, Tc99m) spills over into the low energy acquisition window of the other radioisotope (for example, Tl201). We are extending this approach to cases of several radioisotopes with mutual, multiple and significant photon spillover. In the example above, one may correct not only the Tl201 image but also the Tc99m image corrupted by the Compton scatter originating from the small component of high energy Tl201 photons. The proposed extension is applicable to other anatomical domains (cardiac imaging).

Humans↗
PubMed1995

MR image classification algorithms for neurosurgical and stereotactic radiosurgical trajectory planning and volumetric analysis.

Neurosurgical and stereotactic radiosurgical trajectory planning involves a phase of image data acquisition followed by localization of tissues to be avoided or crossed during the procedure. Success of the procedure is often assessed during follow-up by performing volumetric analysis of tissues of interest. Both localization and volumetric analysis can be facilitated by classifying the input data into different tissue types. This study compares classification results based on four different input image data: (1) MR data obtained with one TR and TE parameter combination, (2) MR data acquired with eight combinations of TR and TE parameters, (3) input data compressed into four principal components, and (4) the input data (3) together with first- and second-order texture features. The algorithms (a) to reduce the dimensionality using principal components, (b) to generate texture features, (c) to determine feature usefulness in classification using the Wilk's lambda criterion, and (d) to classify the input set are described. Classification results are found to be poor for input data 1 and much improved (qualitatively and quantatively) for input data 2. Input data 3 produces classification results (quantitatively and qualitatively) similar to that of 2. Input data 4 is found to produce quantitative results similar to those of 2 and 3, but the qualitative results show enhanced classification of some tissues and distortion of others.

Algorithms↗
PubMed1995

'Morphing' class filter: an interactive tool for continuous adjustment of tissue type related contrast.

The proposed class filter increases tissue type related contrast in MR images of brain. During the first phase of the filtering process tissue type classes are defined. This is done by operator intervention, or by a semiautomatic process based either on a supervised or unsupervised classifier, respectively. During the second phase a pixel intensity transform makes pixels of the same tissue class appear 'more similar' while the pixel intensities of different classes will become 'more different', in effect increasing the tissue type related contrast. For example, normal mixture cluster analysis is performed on an MR image set obtained by varying pulse sequence (PS) parameters and provides unsupervised definition of classes while taking advantage of much greater information content of the whole image set in comparison to that of a single image. The algorithm permits continuous transition ('morphing') between the original image and the tissue classification image that has been calculated from the input image set by simple sliding cursor-bar on the computer screen under the physician's control. Consequently, the resulting images do not require retraining of the physician who is already familiar with the appearance of standard MR images and they make mental integration of information from a large input image set possible and easier.

Brain↗
PubMed1995

Postoperative cognitive and single photon emission computed tomography assessment of patients with resection of perioperative high-risk arteriovenous malformations.

We studied the outcome of 10 patients who had undergone high-risk surgery for an arteriovenous malformation at our institution between November 1991 and November 1993. All of the lesions were located in the dominant (left) hemisphere. Perioperative risk was assessed by the location of the lesion in functionally eloquent cortex (seven patients) or deep structures (two patients) or the lesion's large volume (two patients). Our patients included six women and four men, and their ages ranged from 22 to 53 years (mean, 35.8). Our follow-up study included the evaluation of neurological sequelae but mainly emphasized the study of cognitive deficits (seven major functional clusters), the incidence of depression and behavioral changes, and the assessment of regional cerebral blood flow with single photon emission computed tomography. Six patients returned to a seemingly "normal" daily life with some minor deficits postoperatively, three developed contralateral hemiparesis, and one had disabling cognitive deficits. Our comprehensive cognitive assessment, in particular, showed that although patients might appear "normal" on a routine neurological examination, most patients showed a mild deficit in at least one cognitive function and three were severely impaired. In addition, the single photon emission computed tomographic studies pointed out hypoperfusion in more extensive regions than the surgical defects shown by magnetic resonance imaging or computed tomographic studies. These single photon emission computed tomography images helped to explain some of the cognitive and behavioral changes better than the anatomic studies. This information will make it possible for the physician to offer continuing supportive care for the patient in postoperative transition to normal life activities.

Adult↗
PubMed1995

A strategy for Compton scatter correction in brain SPECT by orthogonal image expansions assisted by neural networks.

The simultaneous dual energy acquisition is often required to accurate co-registration of brain images of both energy windows. However, the reconstructed images in the lower energy window are usually distorted by a significant Compton scatter component originating from the higher energy photons. We are proposing two methods for calculating the Compton scatter correction. Both of them utilize sets of TC99m and associated Compton scatter images in TI(201) energy window that represent a patient population and calculate the sought Compton image estimate 'by comparison' of the given new Tc99m image with the Tc99m set. The first method is based on the principal component image expansion and the second one utilizes in addition learning neural networks. Means to measure the accuracy of results are proposed as well. The proposed methods can be modified for application to other anatomical domains.

Brain↗
PubMed1994

The accuracy of SPECT brain activation images: propagation of registration errors.

Functional single photon emission computed tomography (SPECT) images of brain activation are based on a comparison of base line and activation images. The correctness of the functional images depends, among other factors, on the accurate spatial registration (alignment) of the base line and activation image data. The relationship between the registration errors and the errors of the resulting functional images is studied. It is shown that misregistration errors as small as a shift by 1/8 pixel or rotation by 1 degree result in 5%-10% errors of the pixel values of functional SPECT images of regional blood flow (the ratio and the relative difference images).

Biometry↗
PubMed1994

Synthetic images by subspace transforms. I. Principal components images and related filters.

The principal component (PC) approach offers compressions of an image sequence into fewer images and noise suppressing filters. Multiple MR images of the same tomographic slice obtained with different acquisition parameters (i.e., with different TR, TE, and flip angles), time sequences of images in nuclear medicine, and cardiac ultrasound image sequences are examples of such input image sets. In this paper noise relationships of original and linearly transformed image sequences in general, and specifically of original, PC, and PC-filtered images are discussed. As the spinoff, it introduces locally weighted PC transforms and filters, nonlinear PC's, and a single-image based filter for suppression of noise. Examples illustrate increased perceptibility of anatomical/functional structures in PC images and PC-filtered images, including extraction of physiological functional information by PC loading curves. Generally, the more correlated the original images are, the more effective is the PC approach.

Diagnostic Imaging↗
PubMed1994

The accuracy of SPECT brain activation images: propagation of registration errors.

Functional single photon emission computed tomography (SPECT) images of brain activation are based on a comparison of base line and activation images. The correctness of the functional images depends, among other factors, on the accurate spatial registration (alignment) of the base line and activation image data. The relationship between the registration errors and the errors of the resulting functional images is studied. It is shown that misregistration errors as small as a shift by 1/8 pixel or rotation by 1 degree result in 5%-10% errors of the pixel values of functional SPECT images of regional blood flow (the ratio and the relative difference images).

Brain↗
PubMed1994

Language-activated single-photon emission tomography imaging in the evaluation of language lateralization--evidence from a case of crossed aphasia: case report.

We report a right-handed patient who developed a nonfluent aphasia after surgery for a right parietal arteriovenous malformation. Resting brain single-photon emission tomography displayed decreased regional cerebral blood flow only in the right hemisphere, with spared regional cerebral blood flow in the left hemisphere. Single-photon emission tomography performed after a language activation task (Boston Naming Task) showed a consistent area of increased regional cerebral blood flow in the right inferior and posterior frontal lobe, supporting a right hemisphere dominance for language. These results suggest a potential role for this noninvasive study in the evaluation of language lateralization.

Adult↗
PubMed1980

Hierarchic classification of multiple types of urothelial cells by computer.

A two-level hierarchic classification of 915 urothelial cell images from the urinary sediment was performed by computer using the TICAS programs. The purpose of the analysis was to determine whether adequate discrimination could be obtained among several classes of cells, such as degenerated (DG), multinucleated (MN) and mononucleated well-preserved (WP) ones. The first level in the classification hierarchy showed that the three classes of cells could be identified by computer and that the identification of the group of WP cells was particularly satisfactory, the analysis of WP cells, using training and object sets, documented once again that the identification of diagnostically significant subgroups could be achieved with a very small misclassification error, which was less than 1% for benign (NEG) and malignant (POS) cells. In view of the prior successful application of the classification of images of WP cells to establish patient profiles, the results of the hierarchic classification support the concept of automated analysis of cells in the urinary sediment by computer.

Computers↗
PubMed1980

Computer identification of degenerated urothelial cells.

Computer algorithms were developed to identify very poorly preserved, degenerated urothelial cells from the urinary sediment. Cell images were analyzed by two methods: the first one based on nucleus-finding algorithms (determination of nuclear boundaries) and the second based on the analysis of histograms of optical density. Discrimination between benign and malignant degenerated cells could be accomplished by both methods, although the misclassification of cells rose from +/- 10% by the first approach to +/- 15% by the second approach. Most important, however, useful algorithms were obtained and incorporated into the TICAS file for the identification of this large group of urothelial cells as a significant step in the automated analysis of the cells in thr urinary sediment.

Cells↗
PubMed1980

Computer identification of multinucleated urothelial cells.

Computer analysis of multinucleated urothelial cells from the urinary sediment is reported. The cell images were analyzed by two methods: the first based on nucleus-finding algorithms (determination of nuclear boundaries) and the second based on algorithms derived from the histograms of optical density. Both approaches resulted in good discrimination between benign and malignant cells, although the misclassification rate rose slightly from +/- 7% by the first approach to +/- 10% by the second approach. Algorithms identifying multinucleated cells are now a part of the TICAS file as an important step in automated analysis of cells in the urinary sediment.

Cell Nucleus↗
PubMed1980

Cytomorphometric markers for uterine cancer in intermediate cells.

Precise microphotometric assessment of intermediate cells from patients with normal cervical cytology and from patients with dysplasia or carcinoma in situ shows the existence of small but consistent differences. Marker features for the presence of premalignant and malignant disease can be extracted from the cell images of "normal"-appearing intermediate cells. The marker features and their diagnostic classification potential are described.

Automation↗
PubMed1979

Discriminant analysis on cells from developing squamous cancer of the respiratory tract.

Cytologic preparations made from the tracheobronchial tree taken by the Schreiber catheter have been scanned by three color microphotometry. The digitized cell images were processed by the analytical cytodiagnostic programs of the TICAS system. Cells were sorted into two control groups and five groups of increasing atypia ranging from normal epithelium to invasive squamous cell carcinoma. Standard statistical tests, including Wilk's Lambda, Rao's V, and the Kruskal-Wallis tests are performed on these subsets of cell image features. This study demonstrates that discriminant analyses permit differentiation between normal cells and those from marked atypia or carcinoma and that the classification achieves a high degree of agreement with visual assignment.

Animals↗
PubMed1979

Differentiation by TICAS analysis of cell populations of tracheal aspirates from hamsters with squamous-cell carcinoma.

Individual cells from the tracheal aspirates of hamsters exposed to benzo-a-pyrene were scanned at .5 mum in three colors. Features relating to size, shape, and color were extracted and calculated by computer. The single cells were then classified by these features into separate populations with varying degrees of atypia, extending up to frank cancer cells. A high degree of accuracy was attained in classification by these methods.

Animals↗