Value Of Parametric Images Research Articles

Diagnostic value of parametric imaging from contrast-enhanced ultrasound in retroperitoneal tumors

Objective To investigate the diagnostic value of parametric imaging of contrast-enhanced ultrasound (CEUS) in retroperitoneal tumors using SonoLiver software. Methods Sixty cases of retroperitoneal tumors were analyzed by CEUS with SonoLiver software. The color-mapped images of timedependent indexes, such as rise time(RT), time to peak(TTP), mean transit time(mTT), were abserved in the regions of interests(ROIS). The degree of the coincidence between pathologic diagnosis and parametric imaging diagnosis was carefully study by comparing parametric images with histopathological findings.Results 1 ) There was statistical significance of RT, TTP and mTT between the benign and malignant retroperitoneal tumors. 2) The time-dependent parametric images were obvious different between benign lesions and malignant lesions. The malignant lesions enjoyed a confused boundary and distribute disorderly,while the benign tumors had a clear distribution. There was a good coincidence degree between pathologic diagnosis and parametric imaging diagnosis(κ =0. 625, P =0. 000). Sensibility, specificity, the rate of misdiagnosis and missed diagnosis, the degree of accuracy respectively was 85.3 ％, 76.9 ％, 23.1 ％, 14.7 ％,81.7％ by the new diagnosis method. Conclusions Parametric imaging is a very useful way of evaluating the level of blood flow with the tumors and provides a new vision in the diagnosis of retroperitoneal tumors. It demonstrates retroperitoneal tumors clearly and objectively. Key words: Ultrasonography; Microbubbles ; Retroperitoneal neoplasms

Parametric images via dynamic 18F-fluorodeoxyglucose positron emission tomographic data acquisition in predicting midterm outcome of liver metastases secondary to gastrointestinal stromal tumours

(18)F-Fluorodeoxyglucose positron emission tomography (FDG PET) may underestimate viable tumour tissue in patients with gastrointestinal stromal tumours (GIST) treated with molecular targeted agents. The aim of the present study was to investigate the value of parametric images generated after dynamic data acquisition for the detection of active liver metastases. The analysis included 65 dynamic FDG PET studies in 34 patients with liver metastases from GIST who were treated with imatinib or sunitinib. Parametric images of intercept and slope were calculated by dedicated software using a voxel-based linear regression of time-activity data. Intercept images represent the tracer's distribution volume and the slope its overall metabolic turnover. All images were assessed visually and semi-quantitatively. Liver disease status was established 12 months after each PET study. Dichotomous variables of visual interpretation and various quantitative parameters were entered in a statistical model of linear discriminant analysis. Visual analysis of slope images was more sensitive than the standard 1-h FDG uptake evaluation (70.6 vs 51.0%, p = 0.016) in detecting cases with liver disease progression (n = 51). Specificity did not differ. Combination of all variables in the discriminant analysis model correctly classified 87.7% of cases as progressive or non-progressive disease. Sensitivity was raised to 88.2%. Parametric images of intercept and slope add a new dimension to the interpretation of FDG PET studies, by isolating visually and quantifying the perfusion and phosphorylation-dependent part of tracer uptake. In treated GIST patients, integration of this information with the 1-h uptake data achieves better characterization of hepatic lesions with respect to disease activity.

Improved Parametric Image Generation Using Spatial-Temporal Analysis of Dynamic PET Studies

The value of parametric images that represent both spatial distribution and quantification of the physiological parameters of tracer kinetics has long been recognized. However, the inherent high noise level of pixel kinetics of dynamic PET makes it unsuitable to generate parametric images of the microparameters of tracer kinetic model by conventional weighted nonlinear least squares (WNLS) fitting. Based on the concept that both spatial and temporal information should be integrated to improve parametric image quality, a nonlinear ridge regression with spatial constraint (NLRRSC) parametric imaging algorithm was proposed in this study. For NLRRSC, a term that penalizes local spatial variation of parameters was added to the cost function of WNLS fitting. The initial estimates and spatial constraint were estimated by component representation model (CRM) with cluster analysis. A hierarchical cluster with average linkage method was used to extract components. The ridge parameter was determined by linear ridge regression theory at each iteration, and a modified Gauss–Newton algorithm was used for minimizing the cost function. Results from a computer simulation showed that the percent mean square error of estimates obtained by NLRRSC can be decreased by 60–80% compared to that of WNLS. The parametric images estimated by NLRRSC are significantly better than the ones generated by WNLS. A highly correlated linear relationship was found between the ROI values calculated from the microparametric images generated by NLRRSC and estimates from ROI kinetic fitting. NLRRSC provided a reliable estimate of glucose metabolite uptake rate with a comparable image quality compared to Patlak analysis. In conclusion, NLRRSC is a reliable and robust parametric imaging algorithm for dynamic PET studies.