Videodensitometric Methods for Cardiac Output Measurements

Abstract

Cardiac output is often measured by indicator dilution techniques, usually based on dye or cold saline injections. Developments of more stable ultrasound contrast agents (UCA) are leading to new noninvasive indicator dilution methods. However, several problems concerning the interpretation of dilution curves as detected by ultrasound transducers have arisen. This paper presents a method for blood flow measurements based on UCA dilution. Dilution curves are determined by real-time densitometric analysis of the video output of an ultrasound scanner and are automatically fitted by the Local Density Random Walk model. A new fitting algorithm based on multiple linear regression is developed. Calibration, that is, the relation between videodensity and UCA concentration, is modelled by in vitro experimentation. The flow measurement system is validated by in vitro perfusion of SonoVue contrast agent. The results show an accurate dilution curve fit and flow estimation with determination coefficient larger than 0.95 and 0.99, respectively

Identification of ultrasound contrast agent dilution systems for ejection fraction measurements

“…Usually the SNR of measured IDCs is very low (SNR = 20 log(A max /N ) 15 dB, where A max and N are the IDC maximum amplitude and the noise amplitude respectively [18], [19]) and the definition of the right samples to estimate C i and C i+1 is critical. A better approach is the use of an IDC model interpolation.…”

“…In fact, if two boluses of mass equal to αM and βM are injected (α, β ∈ R), the detected IDC, as from all the IDC models [18], [19], [21], [34], [35], equals αC(t) + βC(t). Therefore, once the LA and LV ultrasound intensity curves are transformed into IDCs by compensating for all the nonlinear effects, they can be used as inputs of a deconvolution algorithm for the estimation of the LV dilution-system impulse response [39].…”

“…Due to the broad-band characteristic, the noise power spectrum S nn (ω) can be approximated by a constant N 2 (white noise) [18]. As a result, (11) can be expressed as given in (12):…”

“…The input IDC f (t) was generated according to the local density random walk (LDRW) model, which was introduced by Sheppard and Savage in 1951 [53]. The model is solution of the diffusion equation [35] and shows the best least square estimation of the IDC [18], [54]- [58]. White noise was added for SNR equal to 10 dB, 15 dB, 20 dB, and ∞.…”

“…The acoustic backscatter is related to the contrast concentration and can be used to generate IDCs. The UCA IDCs usually are interpreted by means of indicator dilution models, such as gamma variate, lognormal, compartmental, or random walk models, which are directly fitted to the curves [18], [19], [21], [34], [35].…”