e-book Measurement of Image Velocity

Free download. Book file PDF easily for everyone and every device. You can download and read online Measurement of Image Velocity file PDF Book only if you are registered here. And also you can download or read online all Book PDF file that related with Measurement of Image Velocity book. Happy reading Measurement of Image Velocity Bookeveryone. Download file Free Book PDF Measurement of Image Velocity at Complete PDF Library. This Book have some digital formats such us :paperbook, ebook, kindle, epub, fb2 and another formats. Here is The CompletePDF Book Library. It's free to register here to get Book file PDF Measurement of Image Velocity Pocket Guide.

Importantly, the PRF is not equivalent to frame-rate; to show the spectral velocity—time curves, the ultrasound system converts echo signals into velocity measurements by Fourier analyses and this procedure lowers the time resolution equivalent to frame-rate. The relationship between PRF and time resolution frame-rate of the spectral curve is non-linear.

By use of a phase-array probe transmitting at 2.


  1. Measuring Position and Velocity.
  2. Global Teams: How Top Multinationals Span Boundaries and Cultures with High-Speed Teamwork.
  3. PIV ( Particle Image Velocimetry ) | Overview & Principle.?

The pwTDI spectral curves show the spectrum of velocities obtained from the region between the two calipers. In images with an inferior signal to noise ratio the band will be broader and contain more falsely low-velocity measurements, because velocity measurements of noisy signals will tend to be lower. As for blood velocities, 20 the outer edge of the curves will show higher velocities if the gain setting during recording is set high, but no studies to date have explored the effect systematically in neonates.

Pitfalls in image acquisition and analysis of pulsed-wave tissue Doppler images. The upper panel shows small breaks in the spectral curve when the ultrasound system records 2D images and pulsed-wave tissue Doppler spectral curve simultaneously. Measurement 1 is the correct velocity 5. The only difference between the lower left and right panels is the gain setting during the off-line analysis.

It is customary to place the stationary sample area within the myocardial wall just apical to the AV-plane at end-diastole 15 , 21 , 22 Fig. As the sample area is stationary in the ultrasound sector and the AV-plane moves throughout the cardiac cycle, the spectral curve will in most parts of the cardiac cycle display velocities of myocardium proximal or distal to the AV-plane rather than the true AV-plane velocities. While the width and the out-of-plane depth of the sample area are fixed, the distance between the two calipers defining the length of the sample area can be set at acquisition, applying variable degrees of spatial smoothing.

A large distance between calipers will show velocities obtained from a larger area than a curve using a short distance between the calipers.

Share this:

The velocities can be measured either on the machine directly or offline. It is customary to lower the gain of the spectral curve to remove out-of-band noise and then measure the values at the peak of the curve band Fig. When recording the cTDI images, the ultrasound system estimates velocities for each pixel in the TDI sector in each frame. The number of beams per frame is the main determinant of frame-rate. The frame-rate is high in narrow sectors because narrow sectors contain few beams. The frame-rate will be lower in deep sector than in shallow sectors, but the sector width angle has higher impact on frame-rate than sector depth.

The user can increase frame-rate in a fixed-sized sector by reducing the beam density and hence reduce the lateral resolution. The output shows a color-coded velocity sector super-imposed on a gray-scale image with low frame-rate, similar to Doppler color flow mapping of blood. During off-line analyses, the software displays velocities from sample areas within the cTDI sector as velocity—time curves, similar to pwTDI velocities. Importantly, the curve will show the average velocity from all pixels within the sample area, more resembling a curve within the middle of the pwTDI band than the edge of the band usually assessed by pwTDI.

The echocardiographer can adjust the level of averaging between adjacent pixels laterally and radially in the cTDI sector during recording and the analyzer can adjust the length and width of the sample area during analysis. Averaging will cause spatial smoothing due to more velocities being used in the measurements, but at the risk of including pixels with false velocities from stationary echoes pericardium and stationary artefacts , fast-moving structures valve leaflets , and velocities from areas other than the AV plane.

In contrast to pwTDI, cTDI enables use of time-based averaging, both within a heartbeat by averaging consecutive velocity measurements and between heartbeats by averaging velocities assessed at the same time point in consecutive cardiac cycles. Averaging between consecutive heartbeats requires correct identification of the onset of heartbeats. Deformation imaging guidelines in adults recommend using the peak of the QRS complex as start of systole. Figure 6 shows the effects of time averaging on the velocity curves.

Notably, too much time averaging could cause the isovolumic phases and isovolumic velocities to disappear from the velocity curves. The use of averaging in neonatal TDI assessment awaits further clarification. Same loop as in upper panel of Fig. The left curve a shows the velocities without time smoothing and the right curve b shows the velocities with too much time smoothing. Too much and too little time smoothing make interpretation of the curves difficult.

Note that it is hard to identify the isovolumic contraction phase, especially in the right panel where the negative slope prior to the ejection phase is missing. Non-stationary sample areas improve repeatability and show velocity measurements different from stationary sample areas in adults. The two methods are therefore not interchangeable. Velocities by cTDI more closely resemble the true velocities in experimental settings.

Most repeatability indices assess random variation. Repeatability will therefore be better in analysis of images with large amount of averaging applied. However, averaging will tend to lower peak velocity measurements 27 and could therefore remove differences between the pathophysiological and normal clinical states.

Studies report different indices for repeatability, with their strengths and weaknesses, and the user must interpret repeatability indices in relation to the population studied. Few studies in neonates report test—retest repeatability. Most neonatal studies have assessed pwTDI repeatability by repeated assessment of velocity peaks and time intervals using the same spectral curve.

Inter-vendor variation has gained much attention for newer imaging modalities, but the inter-vendor agreement for tissue Doppler indices seems better than for conventional measurements of blood velocities and dimensions. In general, large peak systolic AV-plane velocities, accelerations, and displacements imply better myocardial function. This can be a result of better contractility or favorable loading conditions.

Large hearts have higher velocities and displacements than small hearts. Neonatal studies have often used end-diastolic septal length or length from apex to the lateral AV-plane hinge for normalization for size 38 , 39 and reported velocities and excursions normalized by LV length in neonates and children. We therefore suggest end-diastolic length of the ventricle wall as the most suitable index for normalization for size, assessed between the echo from the pericardium at the apex and the septal hinge of the mitral wall. Preload and afterload have impact on systolic velocities and deformation indices.

Changes in peak systolic velocities when adjusted for heart size or compared between hearts of similar size can imply changes in contractility, but importantly, the neonatologist must consider the loading condition when interpreting the values. Higher diastolic velocities can imply better myocardial relaxing function, but interpretation of diastolic velocities is more challenging because more factors influence the measurement. Restoring forces from the systolic contraction, active relaxation diastolic function , load venous pressure , and atrial contraction could influence diastolic velocity measurements.

The velocity curve could then show only one diastolic peak, as shown in the two last cardiac cycles in the upper panel of Fig.

Sidöversikt

Studies have approached this challenge differently, some by reporting only the largest diastolic velocity 19 , 38 and others by reporting one diastolic peak in case of fusion and two peaks if two diastolic peaks could be separated. Indices based on time intervals are independent of heart size and the angle of insonation, making comparisons between different heart sizes and during growth easier.

The first publication of MPI Tei index proposed this index as a relatively load-independent measure of contractility 17 by use of the time intervals from blood-Doppler velocity curves. Although studies have found significant associations between MPI and several pathological states, authors have questioned the use of MPI as an index of contractility, finding the index highly dependent on load and even questioned its ability to assess changes in contractility. Its use in neonates awaits further clarification.

Neonatal studies have also assessed the isovolumic time intervals. Recently, Kahr and colleagues showed that the time interval decreases with postnatal age in neonates. Table 1 shows important factors during acquisition and analysis of tissue Doppler velocities and time intervals. Apical four-chamber view is the most used view for velocity measurements. It is important to position the probe at the apex and have good signals from the region of interest AV-valve plane.

For pwTDI velocities, we recommend placing the stationary sample area just apical to the AV plane at end-diastole, acknowledging that the sample area will obtain velocities from atrial tissue at end-systole and velocities from ventricle myocardium at end-diastole. We suggest using a low gain setting to avoid oversaturated spectral images and freezing the 2D image during spectral image acquisition before recording several cardiac cycles.

Adaptive Tracking Control without Image Velocity Measurement

In the cTDI analyses, frame-rate will have impact on the time resolution of measurements. A high frame-rate will reduce the risk of missing true peak values between frames. A small sector, especially a narrow sector, will enable high frame-rates while maintaining a good lateral spatial resolution. If a larger sector is used, it is possible to obtain movement from right, septal, and left hinge points of the AV plane in a single recording. This reduces the burden of repeated image acquisition and enables the assessment of left- and right-cardiac function from the same image.

However, a large sector will reduce the time resolution frame-rate , the lateral spatial resolution few beams in the sector , or both. The frame-rate requirements are probably more critical for short-time intervals and for diastolic velocities than for long-time intervals, systolic velocities, and displacements. Acquisition of several cardiac cycles will enable direct comparisons between heartbeats and is a prerequisite for averaging between heartbeats. We suggest defining onset of systole at the peak of the QRS complex, in accordance with deformation imaging guidelines in adults.

For obtaining the cTDI curves during off-line analysis, studies have used different sizes for the sample area. The analyses should use averaging techniques with caution, especially for short timing intervals and diastolic peaks. Studies should describe in detail the use of averaging and neonatologists should use similar averaging settings for longitudinal assessment in clinical practice. Between-beat averaging techniques require regular rhythm and proper ECG recordings to ensure that the user uses the same start point for each heartbeat. Tracing of the cTDI sample area to keep it at the AV hinge during the cardiac cycle has impact on the measurements and repeatability in adults 29 but no study has yet systematically explored the difference between measurements from traced and stationary sample areas in neonates.

The systolic peak is the peak during the ejection phase. The peak in the left lateral wall occurs earlier in the ejection phase than the septal and right lateral peaks. With an increasing degree of fusion, the interpretation of the two peaks as separate values becomes more difficult. In case of complete fusion, the velocity curve will show only one diastolic peak.

Studies should report how they approach the challenge of fusion of diastolic velocities. Interpretation of diastolic velocities acquired in heartbeats with different degrees of velocity fusion could be difficult. In the isovolumic contraction phase, the peak velocity is usually positive white circle in Fig. The isovolumic acceleration is the slope of the velocity curve from baseline to the positive peak velocity during the isovolumic phase Fig. Measurements of displacements by cTDI assess motion distance relative to end-diastole.

The user can define onset of systole either by mechanical properties closure of the mitral valve or opening of the aortic valve or by electrical properties peak of the QRS complex. This will have an impact on the measurements and we recommend defining onset of systole at the peak of the QRS complex to standardize measurements.

If the peak of the QRS complex defines the onset of systole, there will be an early-systolic displacement away from the probe similar to systolic stretch by strain analysis before onset of mechanical movement towards the apex. When applying between-beat averaging or drift compensation for cTDI displacement analyses, it is important to ensure that the curves for each heartbeat start at the same point in the cardiac cycle and that the cardiac rhythm is regular. The closure of the aortic valve causes a notch in the displacement curves, especially for the left lateral and septal walls Fig.

Peak systolic displacement is the peak of the displacement curve prior to the notch, whereas the global displacement is the global peak of the curve; importantly, the systolic and global peaks are not interchangeable. Although the angle of insonation is less important for time indices than for velocities and displacements, the sonographer should ensure a good angle of insonation because time indices will always need to be interpreted together with the velocities and displacements. In the velocity curves, the isovolumic contraction and relaxation phases each show a positive and a negative velocity peak, the negative peaks on each side of the ejection phase and the positive peaks on each side of the filling phase 14 Fig.

A high frame-rate is a prerequisite for obtaining short-time intervals correctly, and if the user uses large amounts of smoothing, the identification of the isovolumic phases becomes difficult Fig. Time indices from the left lateral and right lateral walls assess left and right cardiac function, respectively. There are several studies describing reference ranges of tissue Doppler velocities and displacements from the apical four-chamber view in premature neonates 5 , 6 , 12 , 36 , 38 , 40 , 42 , 46 , 50 , 51 , 52 , 53 , 54 , 55 and term neonates.

In general, right cardiac velocities and displacement are higher than left cardiac indices. Measurements tend to increase over time, likely reflecting increasing heart size and improved function. Indices normalized for heart size tend to increase by maturation. Changes in right side measurements by maturation are often larger than changes in septal and left side measurements, possibly because changes in loading conditions have larger impact on right cardiac function.

Many studies in premature neonates show a transient decline in myocardial velocities during the first hours of life. Most studies of time intervals by tissue Doppler have used pwTDI. This is expected as flow Doppler measures event timings based on flow of blood while pwTDI measures event timings based on movement of muscle tissue.

The higher time resolution of TDI enables a more accurate assessment of the onset and cessation of the different events throughout the cardiac cycle. Tissue Doppler indices from apical four-chamber views often show differences between healthy and sick neonates. Observational studies comparing tissue Doppler indices against conventional indices often show better discriminating capabilities. Acute changes in load occur in closure of a patent ductus arteriosus PDA.

Use of indices derived from tissue Doppler velocity—time curves in neonates is feasible. They detect differences between neonates in normal and pathological conditions not diagnosed by conventional indices. Systolic velocities and displacements are well-established indices of cardiac function in neonates, while interpretation of tissue Doppler time intervals and diastolic velocities as markers of cardiac function is more challenging.

Standardization of acquisition and interpretation of indices will facilitate its use as a bedside tool in neonatal intensive care. Although less dependent on loading conditions than conventional indices, these new indices cannot assess the contractility per se. The neonatologist must take loading conditions and the interplay between the right and left side into account when interpreting measurements. In general, high preload and low afterload are associated with higher velocity and acceleration indices and shorter time intervals.

As for most diagnostic tools, studies have not yet shown that medical interventions based on abnormal TDI measurements lead to improvements in patient outcomes. Interventions should be based on evaluation of the combination of clinical state and available cardiac function indices, and not be solely based on one parameter.

Further research steps should focus on exploring the impact of pathophysiologic processes in neonatal hearts on these new indices, especially on how and when to apply therapeutic interventions guided by tissue Doppler indices. In the interim, we recommend that, when feasible, tissue Doppler indices of velocity and event timings should form part of a routine functional echocardiogram in the neonatal population.

Lang, R. Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Heart J. Imaging 16 , — Nestaas, E. Longitudinal strain and strain rate by tissue Doppler are more sensitive indices than fractional shortening for assessing the reduced myocardial function in asphyxiated neonates. Wei, Y. Left ventricular systolic function of newborns with asphyxia evaluated by tissue Doppler imaging.

Abdel-Hady, H. Myocardial dysfunction in neonatal sepsis: a tissue Doppler imaging study. James, A. Assessment of myocardial performance in preterm infants less than 29 weeks gestation during the transitional period. Early Hum. Hirose, A. Evolution of left ventricular function in the preterm infant. Malowitz et al. Right ventricular echocardiographic indices predict poor outcomes in infants with persistent pulmonary hypertension of the newborn.

Ferferieva, V. In other words, the finest detectable change in the angle improves with an increase in the number of streaks in a given image. The actual distance between RBC streaks can be greater than d s , making the actual number of streaks less than n s. Therefore, the sensitivity of the algorithm needs to accommodate the expected change in the blood velocity without being computationally expensive. This can be achieved by optimizing the angle step-size used for the Radon transform in order to detect the desirable change in velocity.

Where v 1 is new velocity and v 0 is initial velocity. Figure 6. Detection of fractional change in the velocity is dependent on the RBC streak angle and image dimensions. For a given image size an angle step-size value below the solid line is not resolvable. The size of angle steps used in the calculation of Radon transforms determines the precision of angle measurements.

This creates a trade-off between the achieved precision of angle measurements and the computational load. In the first implementation of the Radon transform to calculate streak angle Drew et al. Second, finer angle steps of 0. We refine the application of Radon transforms in a multi-step iterative manner to further optimize the balance between computational speed and precision of angle measurements.

The iterative Radon transform is started by first applying Radon transforms in sparse angle space and then finding the angle that gives the maximum variance in the image. In subsequent iterations, sets of angles are used that are less sparse than the preceding iteration and centered on the angle that yielded the maximum variance in the previous iterations Figure 7.

Figure 7. Iterative Radon transform with progressively finer angle steps on subsequent iterations leads to precise angle measurements.

Color-matched lines represent angles of the first five Radon transform iterations. Additional iterations continue with step-size and angular span values that are half of the preceding iteration. B Radon variance plot, showing the variance of the Radon transform at 40 different angles corresponding to 10 iterations. The variance values are represented with open circles and the first five iterations are color-matched with the lines shown in A.

Filled arrowheads of the corresponding colors in A and B represent the angle values for given iterations that were carried forward to the next iteration. Open arrowheads represent the angle values with the maximum variance for given iterations, but smaller than the one from the previous iterations, and therefore were not carried forward to the next iteration. C Algorithm outputs for the first five iterations showing the angle range, angle step-size, and final angle output at the end of the iteration.

The angles with the maximum variance for a given iteration are marked in bold. Angle value This was because the maximum variances for the angle values in those iterations were smaller than that for In this example, the iterative Radon transform offered a fold increase in the angle precision and was still 4. Thus, the first two iterations give an angle resolution of In the subsequent iterations, four Radon transforms with an angle step-size half that of the previous iteration are used. The number of iterations, i irt , required for achieving a given angle precision can be described as,.


  • International Handbook of Catholic Education: Challenges for School Systems in the 21st Century!
  • Web Application Development with Yii and PHP (2nd Edition).
  • Introduction.
  • Adaptive Tracking Control without Image Velocity Measurement;
  • Macroscopic Quantum Tunneling of the Magnetic Moment.
  • Sound Foundations Teacher Development.
  • Formation and Logic of Quantum Mechanics: Formation of Atomic Models.
  • Since each iteration performs four Radon transforms, the total number of Radon transforms n irt used in the iterative Radon transform algorithm is,. The vertical Sobel filter achieves this by removing low frequency changes in pixel luminance in the time dimension of the image. Removing the aggregate mean of the image by subtracting it from each pixel of the image whole-image demeaning and removing the mean value across time at each spatial point of the line temporal demeaning, vertical in our case has been proposed previously for pre-processing of the image before performing Radon transforms Drew et al.

    However, these methods do not effectively minimize the column-to-column variations in the luminance. Although subtle, these column-to-column variations in luminance Figure 8B lead to sub-optimal detection of streak angles. Application of Sobel filtering minimizes these variations and detected angles become more accurate Figure 8C. This error of 0. To further confirm the accuracy offered by Sobel filtering, we compared the differences in the measured angles from the actual angles on simulated line-scan images that were Sobel filtered versus vertically demeaned. We found that angles measured on Sobel filtered images are more accurate when compared with the same image sequences that were vertically demeaned Figure 8D.

    Figure 8. Effect of Sobel filtering on the accuracy of angle measurements when using the Radon transform. Resulting angle of B Vertical demeaning leads to the same angle measurement as shown in A. The orderly vertical bands highlighted by white arrows are the result of different mean luminance across columns of the original image.

    C Vertical Sobel filtering of the image shown in A gives D Sobel filtered images offer more accurate angle measurements when compared with vertically demeaned images. In line-scan images acquired for blood velocity measurements, the Sobel filter acts as a high-pass filter by enhancing RBC streak margins while suppressing small line-to-line changes in pixel luminance. This improves the accuracy and precision of measuring the angle, and in turn velocity.

    Sobel filtering can be applied to line-scan images which have both thick Figure 9 and thin Figure 10 RBC streaks. In line-scan images contaminated by brain movements that are due to transmitted pulsations from heartbeats Figure 9B , vertical demeaning fails to suppress time-varying artifacts Figure 9C and leads to incorrect RBC streak angle detection Figure 9E.

    Measurement of red cell velocity in microvessels using particle image velocimetry (PIV) - IOS Press

    Apart from suppressing artifacts, Sobel filtering also enhances the RBC streak edges Figure 9D leading to an angle measurement with the iterative Radon transform algorithm that matches the actual RBC streak angle and therefore velocity Figure 9F. We also compared the angles measured after Sobel filtering vs. Figure 9. Improvement in the angle measurement using Sobel filtering on an image with motion artifacts.

    The line-scan trajectory is shown as a red line. B Line-scan image from the microvessel. Continuous dark vertical bands are due to line-scan path going out of the vessel and the intermittent horizontal dark bands are related to brain motion due to transmitted heartbeat movements. C Vertically temporally demeaned image of B leads to incorrect angle and velocity estimation at some image segments see horizontal dashed yellow lines. Note the suppression of time-invariant vertical bands, but virtually unchanged time-varying horizontal bands.

    D Sobel filtered image of B leads to suppression of both types of artifacts and enhancement of RBC streak edges. Dashed yellow lines represent measured angles over image segments of lines. E,F Angle E and velocity F measurements obtained after vertical demeaning green traces and Sobel filtering black traces. Circles represent the dashed lines shown in C and D. G Distribution of measured angles after Vertical demeaning green or Sobel filtering black. A total of 35, contiguous line-scans were collected from the microvessel shown in A to create overlapping line image segments.

    Figure Application of Sobel filtering on an image with thin RBC streaks. B Line-scan image from the microvessel showing thin, near-horizontal RBC streaks. E,F Angle E and velocity F traces after vertical demeaning green traces and Sobel filtering black traces. Subtle difference in the values is the result of different filtering also see Figure 8.

    Sobel filtering preserves the streak angle information in the images with thin, relatively horizontal RBC streaks Figure As the streaks become more horizontal, the difference between true and measured angle becomes similar after both vertical demeaning and Sobel filtering see Figure 8D. Therefore, in the absence of time-varying artifacts, Sobel filtering provides little enhancement for images with very horizontal streaks. Blood velocity measurements can only be as precise as the angle step-size used in the Radon transform.

    But decreasing the step-size to improve precision results in additional Radon transforms and increased computation time. The iterative Radon transform method helps overcome this trade-off. Compared to the traditional non-iterative method, the method described here requires an order of magnitude fewer Radon transforms. Based on Equations 18, 20, and 21, the relationship between the total number of Radon transforms required by the traditional non-iterative method n trt and the iterative method n irt is,.

    Similarly, an angle precision of 0. The iterative Radon transform method overcomes the speed-precision trade-off by starting with sparse sampling of the angles and then sampling progressively finer angles in the subsequent iterations centered around the angle with the highest variance see Materials and Methods. As a result, there is an exponential increase in the computation speed for the iterative algorithm compared to the traditional non-iterative Radon transform methods.

    Results are based on Equations 18— The hybrid algorithm presented here performs pre-processing of the image followed by iterative application of Radon transforms. Pre-processing of the images with Sobel filtering has an advantage over vertical demeaning in that it removes the slow time-varying artifacts in addition to time-invariant artifacts.

    This is especially useful in large animal preparations where brain movement artifacts due to respiration and heartbeats are sometimes difficult to eliminate. Additionally, Sobel filtering also enhances the RBC streak margins. Other types of filtering using operators similar to Sobel, e. Sobel filtering results in an increased contrast at the plasma-RBC junctions.

    Measuring Velocity with a Camera

    Because the Sobel operator we use is oriented vertically, the margins of relatively horizontal streaks are enhanced more than vertical streaks. Completely vertical RBC streaks appear in cases of stagnant blood flow Kleinfeld et al. In these situations, any vertical operator including Sobel, vertical demeaning will suppress the streaks and therefore should be avoided. The detection of the correct angle using the iterative Radon transform is dependent on the SNR of the Sobel filtered image. The location of the interrogation windows in both images are same In the Standard FFT cross correlation, the interrogation windows are shifted in the advanced algorithms.

    Then the peak detection and displacement evaluation are applied to obtain the dominant displacement in each interrogation window. As the size of a pixel in flow and the time separation between two images are known, the velocity can be calculated. The size of a pixel in flow is determined by the simple velocity calibration.

    We can complete from measurement to analysis on the same software with Koncerto II dedicated PIV control and analysis software developed with Saika digital image. Various lasers and cameras high resolution camera, high speed camera can be selected according to the measurement object and control by Koncerto II is possible. Frame Straddling allows for all speed ranges. Moreover, with the latest PIV analysis algorithm, it is possible to accurately analyze a wide range of speeds from low speed to high speed at the same time.

    It is compatible with various types of cameras and laser equipments, making it possible to construct any type of PIV system. Stereo-PIV The stereo 3D-PIV system uses light sheets as well as the conventional two-dimensional PIV system, and it is possible to measure the three-dimensional velocity component in the light seat surface.

    It can be said that it is the most practical method for simultaneous multi-point three-dimensional measurement because high spatial resolution and high precision measurement can be performed easily. With the high sensitivity, high speed and high gradation of the latest high speed camera, it has become possible to apply it for high speed flow and wide area measurement.

    Online compatible with each company's high-speed camera. It is a range where the light intensity of the particle image seems to be strong enough to influence the speed measurement and is generally much thicker than DOF. Focus scanning Micro PIV enables measurement with high precision in microfluidics because MD can be made thin because it contains only a single velocity component in the MD area. In addition, since calibration is performed during maintenance, calibration by the user is unnecessary.