Biomedical subjects

Arkadiusz Sitek

Publications and source records attributed to Arkadiusz Sitek.

3 recordsLinked to original sources

PubMed2002

Correction for ambiguous solutions in factor analysis using a penalized least squares objective.

Factor analysis is a powerful tool used for the analysis of dynamic studies. One of the major drawbacks of factor analysis of dynamic structures (FADS) is that the solution is not mathematically unique when only nonnegativity constraints are used to determine factors and factor coefficients. In this paper, a method to correct for ambiguous FADS solutions has been developed. A nonambiguous solution (to within certain scaling factors) is obtained by constructing and minimizing a new objective function. The most common objective function consists of a least squares term that when minimized with nonnegativity constraints, forces agreement between the applied factor model and the measured data. In our method, this objective function is modified by adding a term that penalizes multiple components in the images of the factor coefficients. Due to nonuniqueness effects, these factor coefficients consist of more than one physiological component. The technique was tested on computer simulations, an experimental canine cardiac study using 99mTc-teboroxime, and a patient planar 99mTc-MAG3 renal study. The results show that the technique works well in comparison to the truth in computer simulations and to region of interest (ROI) measurements in the experimental studies.

Algorithms↗

PubMed2002

Calculation of the left ventricular ejection fraction without edge detection: application to small hearts.

UNLABELLED: Quantitative gated SPECT (QGS) software has been reported to overestimate the left ventricular ejection fraction (LVEF) in patients with small hearts. This finding is caused by the inaccurate detection of the endocardial surface of the left ventricle (LV) due to low resolution and partial-volume effects. In this article we develop a method to calculate the LVEF from gated SPECT data without edge detection and compare it with the QGS method of calculating the LVEF. METHODS: The short-axis images were transformed to the prolate spheroid coordinate system, and detection of the layer of maximum counts (a surface area of maximum counts) was made. First, the volume enclosed by the layer of maximum counts (V(max)) was calculated; then the corresponding ejection fraction [(LVEF)(max)] was calculated. The LVEF was calculated by multiplying the (LVEF)(max) by a constant factor, which was determined from a series of calculations made using QGS on larger hearts. In computer simulations the end-diastolic left ventricular volume (EDV) and the targeted LVEF (tLVEF) were varied to produce LVs of different sizes. The LVs were modeled by 2 confocal hemiellipsoids with 7 different EDVs. The tLVEF was increased from 25% to 75%, in 5% step-size increments, for a total of 11 different ejection fractions. These datasets were then smoothed, creating a total of 77 smoothed sets. The smoothed images were processed by the QGS method and by our method. In patient studies, 58 patient datasets were processed by the QGS method and by our method. No attenuation correction was performed on these datasets. The patients were divided into 2 groups: 44 patients with large hearts (EDV > or = 80 mL) and 14 patients with small hearts (EDV < 80 mL). RESULTS: In computer simulations, the QGS method and our method performed well when imaging large EDVs (EDV > or = 80 mL). Our method derived better results than did the QGS method for small EDVs. In patient studies the LVEF calculated by our method matched well with the QGS LVEF in the 44 patients with large hearts. The correlation coefficient between them was found to be 0.957. Of the 14 patients with small hearts, the LVEFs of 5 patients were severely overestimated by the QGS method compared with the results obtained with our method. CONCLUSION: It is possible to calculate the LVEF without edge detection. Compared with QGS LVEF, our method gave better results for small LVs in computer simulations.

Algorithms↗

PubMedDate not supplied

Removal of liver activity contamination in teboroxime dynamic cardiac SPECT imaging with the use of factor analysis.

BACKGROUND: One of the major problems associated with technetium 99m teboroxime cardiac imaging is the high concentration of activity in the liver. In some cases it is impossible to diagnose defects on the inferior wall because of the finite resolution and scatter that cause images of the inferior wall and the liver to overlap. METHODS AND RESULTS: The least-squares factor analysis of dynamic structures method, with correction for non-unique solutions, was used to remove the liver activity from the image. The method was applied to dynamically acquired Tc-99m teboroxime data. The liver activity removal method was tested through use of computer simulations and tomographically acquired canine and patient cardiac studies. In all studies the least-squares factor analysis of dynamic structures method was able to extract the liver activity from the series of dynamic images, thereby making it possible to remove it quantitatively from the entire series. The method was used successfully to remove the liver activity that partially overlapped the inferior wall in normal hearts. The method tends to increase the contrast between defects and normal myocardial tissue in abnormal hearts. CONCLUSIONS: The method presented can be used to assist in diagnosis of cardiac disease when dynamically acquired teboroxime data are used. Because the contrast between the defect and normal myocardial tissue can be changed, the processed image cannot be used by itself to make an accurate diagnosis. However, with the liver activity removed, the image provides additional information that is very useful in the imaging of patients whose liver activity overlaps the inferior heart wall.

Animals↗