Longitudinal Evaluation of Fatty Acid Metabolism in Normal and Spontaneously Hypertensive Rat Hearts with Dynamic MicroSPECT Imaging

The goal of this project is to develop radionuclide molecular imaging technologies using a clinical pinhole SPECT/CT scanner to quantify changes in cardiac metabolism using the spontaneously hypertensive rat (SHR) as a model of hypertensive-related pathophysiology. This paper quantitatively compares fatty acid metabolism in hearts of SHR and Wistar-Kyoto normal rats as a function of age and thereby tracks physiological changes associated with the onset and progression of heart failure in the SHR model. The fatty acid analog, 123I-labeled BMIPP, was used in longitudinal metabolic pinhole SPECT imaging studies performed every seven months for 21 months. The uniqueness of this project is the development of techniques for estimating the blood input function from projection data acquired by a slowly rotating camera that is imaging fast circulation and the quantification of the kinetics of 123I-BMIPP by fitting compartmental models to the blood and tissue time-activity curves

Estimation of the parameter covariance matrix for aone-compartment cardiac perfusion model estimated from a dynamic sequencereconstructed using map iterative reconstruction algorithms

In dynamic cardiac SPECT estimates of kinetic parameters ofa one-compartment perfusion model are usually obtained in a two stepprocess: 1) first a MAP iterative algorithm, which properly models thePoisson statistics and the physics of the data acquisition, reconstructsa sequence of dynamic reconstructions, 2) then kinetic parameters areestimated from time activity curves generated from the dynamicreconstructions. This paper provides a method for calculating thecovariance matrix of the kinetic parameters, which are determined usingweighted least squares fitting that incorporates the estimated varianceand covariance of the dynamic reconstructions. For each transaxial slicesets of sequential tomographic projections are reconstructed into asequence of transaxial reconstructions usingfor each reconstruction inthe time sequence an iterative MAP reconstruction to calculate themaximum a priori reconstructed estimate. Time-activity curves for a sumof activity in a blood region inside the left ventricle and a sum in acardiac tissue region are generated. Also, curves for the variance of thetwo estimates of the sum and for the covariance between the two ROIestimates are generated as a function of time at convergence using anexpression obtained from the fixed-point solution of the statisticalerror of the reconstruction. A one-compartment model is fit to the tissueactivity curves assuming a noisy blood input function to give weightedleast squares estimates of blood volume fraction, wash-in and wash-outrate constants specifying the kinetics of 99mTc-teboroxime for theleftventricular myocardium. Numerical methods are used to calculate thesecond derivative of the chi-square criterion to obtain estimates of thecovariance matrix for the weighted least square parameter estimates. Eventhough the method requires one matrix inverse for each time interval oftomographic acquisition, efficient estimates of the tissue kineticparameters in a dynamic cardiac SPECT study can be obtained with presentday desk-top computers

Measuring Regional Changes in the Diastolic Deformation of the Left Ventricle of SHR Rats Using microPET Technology and Hyperelastic Warping

The objective of this research was to assess applicability of a technique known as hyperelastic warping for the measurement of local strains in the left ventricle (LV) directly from microPET image data sets. The technique uses differences in image intensities between template (reference) and target (loaded) image data sets to generate a body force that deforms a finite element (FE) representation of the template so that it registers with the target images. For validation, the template image was defined as the end-systolic microPET image data set from a Wistar Kyoto (WKY) rat. The target image was created by mapping the template image using the deformation results obtained from a FE model of diastolic filling. Regression analysis revealed highly significant correlations between the simulated forward FE solution and image derived warping predictions for fiber stretch (R2 = 0.96), circumferential strain (R2 = 0.96), radial strain (R2 = 0.93), and longitudinal strain (R2 = 0.76) (p<0.001for all cases). The technology was applied to microPET image data of two spontaneously hypertensive rats (SHR) and a WKY control. Regional analysis revealed that, the lateral freewall in the SHR subjects showed the greatest deformation compared with the other wall segments. This work indicates that warping can accurately predict the strain distributions during diastole from the analysis of microPET data sets

Nonlinear edge preserving smoothing and segmentation of 4-D medical images via scale-space fingerprint analysis

Nonlinear edge preserving smoothing often is performed prior to medical image segmentation. The goal of the nonlinear smoothing is to improve the accuracy of the segmentation by preserving changes in image intensity at the boundaries of structures of interest, while smoothing random fluctuations due to noise in the interiors of the structures. Methods include median filtering and morphology operations such as gray scale erosion and dilation, as well as spatially varying smoothing driven by local contrast measures. Rather than irreversibly altering the image data prior to segmentation, the approach described here has the potential to unify nonlinear edge preserving smoothing with segmentation based on differential edge detection at multiple scales. The analysis of n-D image data is decomposed into independent 1-D problems that can be solved relatively quickly. Smoothing in various directions along 1-D profiles through n-D data is driven by a measure of local structure separation, rather than by a local contrast measure. Isolated edges are preserved independent of their contrast, given an adequate contrast to noise ratio. In addition, analytic expressions are obtained for the derivatives of the edge preserved 1-D profiles. Using these expressions, multidimensional edge detection operators such as the Laplacian or the second derivative in the direction of the image intensity gradient can be composed and used to segment n-D data. The smoothing and segmentation algorithms are applied to simulated 4-D medical image data

Nonlinear edge preserving smoothing and segmentation of 4-D medical images via scale-space fingerprint analysis

Nonlinear edge preserving smoothing often is performed prior to medical image segmentation. The goal of the nonlinear smoothing is to improve the accuracy of the segmentation by preserving changes in image intensity at the boundaries of structures of interest, while smoothing random fluctuations due to noise in the interiors of the structures. Methods include median filtering and morphology operations such as gray scale erosion and dilation, as well as spatially varying smoothing driven by local contrast measures. Rather than irreversibly altering the image data prior to segmentation, the approach described here has the potential to unify nonlinear edge preserving smoothing with segmentation based on differential edge detection at multiple scales. The analysis of n-D image data is decomposed into independent 1-D problems that can be solved relatively quickly. Smoothing in various directions along 1-D profiles through n-D data is driven by a measure of local structure separation, rather than by a local contrast measure. Isolated edges are preserved independent of their contrast, given an adequate contrast to noise ratio. In addition, analytic expressions are obtained for the derivatives of the edge preserved 1-D profiles. Using these expressions, multidimensional edge detection operators such as the Laplacian or the second derivative in the direction of the image intensity gradient can be composed and used to segment n-D data. The smoothing and segmentation algorithms are applied to simulated 4-D medical image data

Resolution of the spectral technique in kinetic modeling

Physiologic systems can be represented by compartmental models which describe the uptake of radio-labeled tracers from blood to tissue and their subsequent washout. Arterial and venous time-activity curves from isolated heart experiments are analyzed using spectral analysis, in which the impulse response function is represented by a sum of decaying exponentials. Resolution and uniqueness tests are conducted by synthesizing isolated heart data with predefined compartmental models, adding noise, and applying the spectral analysis technique. Venous time-activity curves are generated by convolving a typical arterial input function with the predefined spectrum. The coefficients of a set of decaying exponential basis functions are determined using a non-negative least squares algorithm, and results are compared with the predefined spectrum. The uniqueness of spectral method solutions is investigated by computing model covariance matrices, using error propagation and prior knowledge of noise distributions. Coupling between model parameters is illustrated with correlation matrices

Computationally efficient nonlinear edge preserving smoothing of n-D medical images via scale-space fingerprint analysis

Nonlinear edge preserving smoothing often is performed prior to medical image segmentation. The goal of the nonlinear smoothing is to improve the accuracy of the segmentation by preserving changes in image intensity at the boundaries of structures of interest, while smoothing random variations due to noise in the interiors of the structures. Methods include median filtering and morphology operations such as gray scale erosion and dilation, as well as spatially varying smoothing driven by local contrast measures. Rather than irreversibly altering the image data prior to segmentation, the approach described here has the potential to unify nonlinear edge preserving smoothing with segmentation based on differential edge detection at multiple scales. The analysis of n-D image data is decomposed into independent 1-D problems that can be solved quickly. Smoothing in various directions along 1-D profiles through the n-D data is driven by a measure of local structure separation, rather than by a local contrast measure. Isolated edges are preserved independent of their contrast, given an adequate contrast to noise ratio

Spectral analysis for evaluation of myocardial tracers for medical imaging

Kinetic analysis of dynamic tracer data is performed with the goal of evaluating myocardial radiotracers for cardiac nuclear medicine imaging. Data from experiments utilizing the isolated rabbit heart model are acquired by sampling the venous blood after introduction of a tracer of interest and a reference tracer. We have taken the approach that the kinetics are properly characterized by an impulse response function which describes the difference between the reference molecule (which does not leave the vasculature) and the molecule of interest which is transported across the capillary boundary and is made available to the cell. Using this formalism we can model the appearance of the tracer of interest in the venous output of the heart as a convolution of the appearance of the reference tracer with the impulse response. In this work we parameterize the impulse response function as the sum of a large number of exponential functions whose predetermined decay constants form a spectrum, and each is required only to have a nonnegative coefficient. This approach, called spectral analysis, has the advantage that it allows conventional compartmental analysis without prior knowledge of the number of compartments which the physiology may require or which the data will support