Methods for determining the blood flow velocity in cerebral vessels using intraoperative Indocyanine Green fluorescence video angiography

Cimalla, Peter; Graf, Dirk; Duscha, P.; Meyer, Tobias; Kuß, Julia; Steinmeier, Ralf; Koch, Edmund; Morgenstern, Ute

doi:10.1007/978-3-540-89208-3_343

Cited by 6 publications

(7 citation statements)

References 2 publications

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“…Two methods for the determination of transit time τ (tau) from the time interval are implemented [3]. (i) Method of defined points: seven points at the same relative intensity were defined for each intensity curve.…”

Section: Algorithm For Quantification Of Cerebral Blood Flowmentioning

confidence: 99%

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Quantitative fluorescence angiography for neurosurgical interventions

Weichelt¹,

Duscha²,

Steinmeier³

et al. 2013

Biomedizinische Technik/Biomedical Engineering

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Present methods for quantitative measurement of cerebral perfusion during neurosurgical operations require additional technology for measurement, data acquisition, and processing. This study used conventional fluorescence video angiography--as an established method to visualize blood flow in brain vessels--enhanced by a quantifying perfusion software tool. For these purposes, the fluorescence dye indocyanine green is given intravenously, and after activation by a near-infrared light source the fluorescence signal is recorded. Video data are analyzed by software algorithms to allow quantification of the blood flow. Additionally, perfusion is measured intraoperatively by a reference system. Furthermore, comparing reference measurements using a flow phantom were performed to verify the quantitative blood flow results of the software and to validate the software algorithm. Analysis of intraoperative video data provides characteristic biological parameters. These parameters were implemented in the special flow phantom for experimental validation of the developed software algorithms. Furthermore, various factors that influence the determination of perfusion parameters were analyzed by means of mathematical simulation. Comparing patient measurement, phantom experiment, and computer simulation under certain conditions (variable frame rate, vessel diameter, etc.), the results of the software algorithms are within the range of parameter accuracy of the reference methods. Therefore, the software algorithm for calculating cortical perfusion parameters from video data presents a helpful intraoperative tool without complex additional measurement technology.

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Section: Algorithm For Quantification Of Cerebral Blood Flowmentioning

confidence: 99%

“…The temporal delay between the fluorescence signals locally observed at positions A and B corresponds to transit time[3].…”

mentioning

confidence: 99%

Quantitative fluorescence angiography for neurosurgical interventions

Weichelt¹,

Duscha²,

Steinmeier³

et al. 2013

Biomedizinische Technik/Biomedical Engineering

Self Cite

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“…The accuracy of the calculation ofV strongly depends on the errors in measuring s, d i , and t. The quantification of these errors and their error propagation is necessary and not sufficiently investigated yet (Cimalla et al, 2008;Weichelt et al, 2013). The accuracy in the measurement of the distance s (geodesic length of the vessel's centerline) is of a magnitude of 3% but shall not be discussed in this paper (Naber et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

“…Even though an in vitro flow phantom could provide a similar data set, in silico generated data sets are low cost, accurate, and easily configurable to the desired field of application (Kung et al, 2011). Subsequently, these data sets will be used to evaluate and enhance current methods ascertaining the transit time t, thereby increasing its temporal accuracy that is not yet suitable for clinical studies (Cimalla et al, 2008;Weichelt et al, 2013). Achieving a higher temporal accuracy by increasing the sampling rate of the measuring device is possible.…”

Section: Introductionmentioning

confidence: 99%

Transit Time Measurement in Indicator Dilution Curves: Overcoming the Missing Ground Truth and Quantifying the Error

2021

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The vascular function of a vessel can be qualitatively and intraoperatively checked by recording the blood dynamics inside the vessel via fluorescence angiography (FA). Although FA is the state of the art in proving the existence of blood flow during interventions such as bypass surgery, it still lacks a quantitative blood flow measurement that could decrease the recurrence rate and postsurgical mortality. Previous approaches show that the measured flow has a significant deviation compared to the gold standard reference (ultrasonic flow meter). In order to systematically address the possible sources of error, we investigated the error in transit time measurement of an indicator. Obtaining in vivo indicator dilution curves with a known ground truth is complex and often not possible. Further, the error in transit time measurement should be quantified and reduced. To tackle both issues, we first computed many diverse indicator dilution curves using an in silico simulation of the indicator's flow. Second, we post-processed these curves to mimic measured signals. Finally, we fitted mathematical models (parabola, gamma variate, local density random walk, and mono-exponential model) to re-continualize the obtained discrete indicator dilution curves and calculate the time delay of two analytical functions. This re-continualization showed an increase in the temporal accuracy up to a sub-sample accuracy. Thereby, the Local Density Random Walk (LDRW) model performed best using the cross-correlation of the first derivative of both indicator curves with a cutting of the data at 40% of the peak intensity. The error in frames depends on the noise level and is for a signal-to-noise ratio (SNR) of 20 dB and a sampling rate of fs = 60 Hz at fs-1·0.25(±0.18), so this error is smaller than the distance between two consecutive samples. The accurate determination of the transit time and the quantification of the error allow the calculation of the error propagation onto the flow measurement. Both can assist surgeons as an intraoperative quality check and thereby reduce the recurrence rate and post-surgical mortality.

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“…Vanderlei et al 2008), and intuitive and comprehensive open-source analysis software (e.g. Tarvainen et al 2009) suggest that this growth will continue well into the future.…”

Section: Introductionmentioning

confidence: 99%

A careful look at ECG sampling frequency and R-peak interpolation on short-term measures of heart rate variability

Ellis¹,

Zhu²,

Koenig³

et al. 2015

Physiol. Meas.

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As the literature on heart rate variability (HRV) continues to burgeon, so too do the challenges faced with comparing results across studies conducted under different recording conditions and analysis options. Two important methodological considerations are (1) what sampling frequency (SF) to use when digitizing the electrocardiogram (ECG), and (2) whether to interpolate an ECG to enhance the accuracy of R-peak detection. Although specific recommendations have been offered on both points, the evidence used to support them can be seen to possess a number of methodological limitations. The present study takes a new and careful look at how SF influences 24 widely used time- and frequency-domain measures of HRV through the use of a Monte Carlo-based analysis of false positive rates (FPRs) associated with two-sample tests on independent sets of healthy subjects. HRV values from the first sample were calculated at 1000 Hz, and HRV values from the second sample were calculated at progressively lower SFs (and either with or without R-peak interpolation). When R-peak interpolation was applied prior to HRV calculation, FPRs for all HRV measures remained very close to 0.05 (i.e. the theoretically expected value), even when the second sample had an SF well below 100 Hz. Without R-peak interpolation, all HRV measures held their expected FPR down to 125 Hz (and far lower, in the case of some measures). These results provide concrete insights into the statistical validity of comparing datasets obtained at (potentially) very different SFs; comparisons which are particularly relevant for the domains of meta-analysis and mobile health.

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Methods for determining the blood flow velocity in cerebral vessels using intraoperative Indocyanine Green fluorescence video angiography

Cited by 6 publications

References 2 publications

Quantitative fluorescence angiography for neurosurgical interventions

Quantitative fluorescence angiography for neurosurgical interventions

Transit Time Measurement in Indicator Dilution Curves: Overcoming the Missing Ground Truth and Quantifying the Error

A careful look at ECG sampling frequency and R-peak interpolation on short-term measures of heart rate variability

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