Abstract
Abstract Transit times of a bolus through an organ can provide valuable information for researchers, technicians and clinicians. Therefore, an indicator is injected and the temporal propagation is monitored at two distinct locations. The transit time extracted from two indicator dilution curves can be used to calculate for example blood flow and thus provide the surgeon with important diagnostic information. However, the performance of methods to determine the transit time Δt cannot be assessed quantitatively due to the lack of a sufficient and trustworthy ground truth derived from in vivo measurements. Therefore, we propose a method to obtain an in silico generated dataset of differently subsampled indicator dilution curves with a ground truth of the transit time. This method allows variations on shape, sampling rate and noise while being accurate and easily configurable. COMSOL Multiphysics is used to simulate a laminar flow through a pipe containing blood analogue. The indicator is modelled as a rectangular function of concentration in a segment of the pipe. Afterwards, a flow is applied and the rectangular function will be diluted. Shape varying dilution curves are obtained by discrete-time measurement of the average dye concentration over different cross-sectional areas of the pipe. One dataset is obtained by duplicating one curve followed by subsampling, delaying and applying noise. Multiple indicator dilution curves were simulated, which are qualitatively matching in vivo measurements. The curves temporal resolution, delay and noise level can be chosen according to the requirements of the field of research. Various datasets, each containing two corresponding dilution curves with an existing ground truth transit time, are now available. With additional knowledge or assumptions regarding the detection-specific transfer function, realistic signal characteristics can be simulated. The accuracy of methods for the assessment of Δt can now be quantitatively compared and their sensitivity to noise evaluated.
Highlights
In the last two decades, optic-based medical systems were introduced to intraoperatively visualize vascular structures using flourescence angiography [1, 2]
Noise is unavoidable in data acquisition and applied to the dataset to match indicator dilution curves (IDCs) obtained from in vivo measurements
The noise level is described via the signal-to-noise ratio (SNR), as shown in Eq 3, where Psignal is the power of the signal and Pnoise is the power of the noise
Summary
In the last two decades, optic-based medical systems were introduced to intraoperatively visualize vascular structures using flourescence angiography [1, 2]. A current clinical routine for blood volume flow assessment involves the usage of an ultrasonic flowprobe. We propose a method to synthetically generate a highly adaptable dataset of two corresponding and differently subsampled IDCs with a ground truth of the transit time using an in silico model. These datasets can be used to enhance current methods ascertaining the transit time Δt, which temporal accuracies are not yet suitable for clinical studies [6]. The second part describes the post-processing steps to obtain the desired dataset of two corresponding IDCs with a ground truth of the transit time
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