Miyoung Lee

Comparison of echocardiography with dual-source computed tomography for assessment of left ventricular volume in healthy Beagles

OBJECTIVE To compare echocardiographic measurements of left ventricular (LV) volume obtained via a modified Simpson or Teichholz method with those obtained via dual-source CT (DSCT). ANIMALS 7 healthy Beagles. PROCEDURES Each dog was anesthetized for DSCT; LV volume was determined from contrast-enhanced images of the LV lumen during all phases of contraction. Echocardiography was performed with dogs awake and anesthetized. End-diastolic volume (EDV), end-systolic volume (ESV), stroke volume, and ejection fraction were measured via a modified Simpson method and Teichholz method. Each dog was anesthetized twice with a 1-week interval between anesthetic sessions. RESULTS Results obtained while dogs were anesthetized revealed that the modified Simpson method underestimated LV volume (mean ± SD EDV, 24.82 ± 2.38 mL; ESV, 12.24 ± 1.77 mL), compared with that estimated by the Teichholz method (EDV, 32.57 ± 2.85 mL; ESV, 14.87 ± 2.09 mL) or DSCT (EDV, 34.14 ± 1.57 mL; ESV, 16.71 ± 0.76 mL). Ejection fraction (modified Simpson method, 48.53% ± 4.24%; Teichholz method, 54.33% ± 4.26%; DSCT, 51.00% ± 2.71%) differed significantly among the 3 methods. Echocardiographic results obtained while dogs were awake revealed that EDV, ESV, and stroke volume differed significantly between the modified Simpson and Teichholz methods. CONCLUSIONS AND CLINICAL RELEVANCE LV volume determined via the Teichholz method was more similar to that determined via DSCT than was the LV volume determined via the modified Simpson method. The modified Simpson method underestimated LV volume, compared with that obtained via the Teichholz method, in both anesthetized and awake dogs.

An iterative method for flattering the ridge in the Ridge regression

In many cases of multiple regression in an undefined system, some independent variables may not be orthogonal to each other. In such cases, the systems either are not solvable or induce incorrect results which could vary with the data used. Such problems are often overcome by using the Ridge regression method. This article proposes an alternative way of getting an exact least square estimator by using an iterative method. We prove the solvability of the proposed algorithm and demonstrate that our method outperforms traditional approaches.