Typical approach to improve the performance of these imagers, particularly to increase the imaging speed, is to parallelize measurements by using 1D or 2D arrays of photodetectors. Several blood-flow imaging systems have been designed over the last few years. Scanning laser Doppler imagers [2
2. K. Wårdell, A. Jakobsson, and G.E. Nilsson, “Laser Doppler perfusion imaging by dynamic light scattering,” IEEE Trans. Biomed. Eng. 40, 309–316 (1993). [CrossRef] [PubMed]
3. T.J.H. Essex and P.O. Byrne, “A laser Doppler scanner for imaging blood flow in skin,” J. Biomed. Eng. 13, 189–193 (1991). [CrossRef] [PubMed]
], imagers based on speckle contrast analysis [4
4. H. Fujii, K. Nohira, Y. Yamamoto, H. Ikawa, and T. Ohura, “Evaluation of blood flow by laser speckle image sensing. Part 1,” Appl. Opt. 26, 5321–5325 (1987). [CrossRef] [PubMed]
5. J.D. Briers, G. Richards, and X.W. He, “Capillary blood flow monitoring using laser speckle contrast analysis (LASCA),” J. Biomed. Opt. 4, 164–175 (1999). [CrossRef]
] or laser speckle imagers (LSI) [6
6. K.R. Forrester, J. Tulip, C. Leonard, C. Stewart, and R.C. Bray, “A laser speckle imaging technique for measuring tissue perfusion,” IEEE Trans. Biomed. Eng. 51, 2074–2084 (2004). [CrossRef] [PubMed]
] were reported. A detailed review of these techniques can be found elsewhere [7
7. J.D. Briers, “Laser Doppler, speckle and related techniques for blood perfusion mapping and imaging,” Physiol. Meas. 22, R35–R66 (2001). [CrossRef]
]. However, there is still an important way to go for a widespread use in clinics, as existing instruments are either difficult to handle, slow, or not sufficiently accurate for measuring fast flow. Our goal was to develop a high-speed full-field laser Doppler imaging system that also should be reliable, objective, user- and patient-friendly.
To decrease the imaging time, a parallel detection scheme may be employed wherein the imaging speed increases by a factor proportional to the number of channels working in parallel. A 2D matrix of photodetectors is a suitable detection device for that purpose. Regarding photodetectors, there are presently four different technologies to produce photosensitive matrices: CCD, CID, simple PIN photodiode arrays, and finally CMOS. However not all of them can be employed for laser Doppler imaging. One limitation is imposed by the signal sampling frequency requirements. In laser Doppler blood flowmetry the measured frequencies are typically in the range of 0 to 20 kHz. The frame rate of CCD can hardly be faster than 1000 fps, which is not sufficient. Fast CCDs have been reported, however they are too much expensive for our application. CID technology allows for fast sub-frame rates however the sensitivity of these devices is better in the blue part of the spectrum, while laser Doppler blood flow measurements require red and near-infrared lasers. Besides the CID technology is very expensive. Use of an array of conventional PIN photodiodes is not attractive due to the insufficient packing density of the sensing elements that results in matrix being of a low resolution. However, the latest evolution of photodiode matrix technology, i.e. CMOS image sensors, possesses all the advantages for parallel detection of Doppler signals. These matrices have high resolution, good response in red, moderate response in near infrared, and they allow for fast sub-frame rates as pixels can be addressed randomly. Further, this technology is inexpensive and flexible as a result of the integration of photodetectors and associated electronics on the same chip.
In 2001 Serov et al. [8
8. A. Serov, W. Steenbergen, and F.F.M. de Mul, “Laser Doppler perfusion imaging with a complimentary metal oxide semiconductor image sensor,” Opt. Lett. 25, 300–302 (2002). [CrossRef]
] have demonstrated the usefulness of the CMOS image sensor technology using a non-integrating CMOS sensor for measurements of blood flow by means of laser Doppler technique. Recently, a new generation of such an imager has been designed and applied to measurements of perfusion on human skin resulting in flow images every 90 seconds for a 256×256 pixel region of interest [9
]. Both of these imaging systems were based on non-integrating CMOS image sensors. In this paper we describe a new laser Doppler imaging system that employs an integrating
CMOS image sensor. The advantage of the integrating versus the non-integrating detector, particularly for laser Doppler imaging, is explained in the following section.
2. Integrating vs. non-integrating detectors
There exist two concepts in CMOS image sensor technology for recording the signals deposited by the photons in the detector: non-integrating and integrating. In non-integrating detectors the photon flux is continuously converted into an electrical output signal. To obtain images, the detector array is read instantaneously by means of sequential photoelectrical scanning. Each pixel detects only the photons that are received during the time the pixel is sampled:
is the time to read out all N pixels of the frame (or sub-frame). Thus, during ∆t one pixel detects X photons:
is the total optical power received by N photodetectors.
In the integrating detector concept the total deposited energy is integrated via charges accumulated while the detector is being hit by photons. The charges are accumulated in a small capacitor, which at the end of the time frame has to be read out. The charge is then converted into an output signal that is linearly proportional to number of photons that have hit the detecting pixel. Either each pixel collects photons during the time the other pixels are read out (rolling shutter mode), or all pixels collect photons during the integration time and they are read out immediately thereafter (global shutter mode). The maximum integration time is equal to the time to read N pixels, Tint
. Therefore, the number of photons detected by one pixel of an integrating detector array is
For both systems the signal to noise ratio (SNR) is determined by the number of detected photons X
10. R.H. Webb and G.W. Hughes, “Detectors for scanning video imagers,” Appl. Opt. 32, 6227–6235 (1993). [CrossRef] [PubMed]
So the net advantage in the SNR for the integrating image sensor is
Above, we compared two imaging systems, one with an integrating detector array and one with a non-integrating (scanning) detector array. We have assumed equal detector noise for both imagers, which is not always true. For completeness, the influence of the temporal noise on the SNR of each imaging system should also be considered.
For both types of sensors the minimum noise floor consists of thermal noise (TN) and shot noise (SN).
〉 is the average photocurrent and 〈Idark
〉 is the average dark current in the
circuit; k is the Boltzmann constant, T is the temperature in degrees Kelvin, Bn
is the noise equivalent bandwidth, R is the load resistance, and q is the charge of an electron. The value of the load resistance is determined by the upper cutoff frequency required to pass the signal, fs
where C is the capacitance of the photodetector. The SNR is then
〉 is the mean-square value of the signal and M
is the average number of speckles
on a photodetector pixel [11
11. A.N Serov, W. Steenbergen, and F. de Mul, “Prediction of the photodetector signal generated by Doppler-induced speckle fluctuations: theory and some validations,” J. Opt. Soc. Am. A , 18, 622–639 (2001). [CrossRef]
In respect to the SNR, we first consider the class of non-integrating devices. In general, the noise bandwidth and the signal bandwidth are not the same. If the upper cutoff frequency is determined by a single RC time constant, then the signal bandwidth and the noise bandwidths are respectively
Thus for the non-integrating detector the SNR is
Second, for the integrating detector, the SNR is expressed as before (eq.(8a
)) except that the noise bandwidth is now defined as Bn
), where Tint
is the time interval between successive readouts of the diodes (the integration time). Therefore, to match the signal bandwidth the integration time is determined by
Now we find that the SNR of the integrating detector is
Thus, at the same photocurrent, the SNR of the integrating detector is about a factor of 1.5 better than for the non-integrating device.
Finally, from eqs.(5
5. J.D. Briers, G. Richards, and X.W. He, “Capillary blood flow monitoring using laser speckle contrast analysis (LASCA),” J. Biomed. Opt. 4, 164–175 (1999). [CrossRef]
) and (12
) we find that, compared to the non-integrating detector case, where only one pixel of the image is measured at a time, the SNR of the integrating detector array can be increased by a factor of up to
Another essential advantage of the integrating detector concept is the flexibility in selecting the integration time to always match the required signal bandwidth. Since both shot and thermal noises are distributed over a wide frequency range, reducing the noise bandwidth effectively reduces the noise in the measurement. Therefore the integration time can be used as an additional degree of freedom.
3. Experimental configuration; design considerations
Not any CMOS image sensor can be utilized for laser Doppler blood flow measurements. A limitation arises from the electronic architecture of a particular photosensor matrix. The main requirement here is a possibility to selectively read out the pixels from a predefined sub-frame at high-speed. Ideally, the sub-frames would be acquired at frame-rate of up to 40,000 frames per second as assumed for the maximum sampling frequency in laser Doppler flowmetry. Another important requirement is the spectral response of the sensor. For laser Doppler blood-flow measurements the source wavelength should be in the red to near-infrared range. Thus the spectral response of the detector should be optimized for this range. For our imager a digital CMOS camera based on the VCA1281 monochrome CMOS image sensor from Symagery (Canada) was utilized. This sensor operates in rolling shutter mode; it has a 1280H × 1024V resolution, a 7×7μm2 pixel size, a 40 MHz sampling rate, and an 8-bit ADC. The sensor has a specified flat spectral response in the range between 500 and 750 nm. The camera was connected to the host PC via a fast LVDS (Low-Voltage Differential Signaling) interface providing for a high-speed transfer of the obtained frames.
For the sample illumination we used a solid-state-diode-pumped laser of 250 mW output optical power emitting at 671 nm. The laser beam was coupled to a ∅1.5mm plastic optical fiber. A GRIN (gradient index) lens of ∅1.8mm was placed at the distal end of the fiber. This configuration produced a uniform illumination of the sample. The illuminated area was up to ∅170mm. The intensity profile of the illuminating beam is shown in Fig. 1b
(bottom). A slight increase of the intensity for higher pixel numbers is caused by the illuminating geometry – an angle of 9° between the illumination and observation directions. The high-frequency variations of the intensity are due to speckle effect.
The backscattered light was collected with an f=6 mm objective with an f-number of f#=1.2. The low f-number objective provided the system with the superior photon collection efficiency that becomes critical for short integration times (in the range of a few tens of milliseconds). The imager head was installed on the articulating arm system for providing an easy access to the object of interest, as shown in Fig. 1
. Typically, the imager head was placed at a distance of 150-250 mm from the measured surface.
Fig. 1. a) High-speed laser Doppler imaging system. The imager head was mounted on an articulating arm to simplify access to the measured objects. b) Block diagram of the laser Doppler imaging system modules (top); and the intensity profile of the illuminating beam (bottom).
In general, the geometry of the illuminating beam and the objective lens with respect to the sample influences the Doppler beat frequency response of the LDI system. For our system design we only found a minor influence (standard deviation less than 15%) for the imager head position within ±30° relatively to the vertical detection position. Typically, in optically dense biological tissues, such as skin, scattered photons loose their initial directions due to diffusion rapidly. Finally, the scattering angles as well as the directions of moving blood cells are random resulting in a less sensitive system response due to variations of the imaging geometry.
4. Data acquisition and signal processing
The signal sampling frequency is inversely proportional to the time to acquire one sub-frame. The sub-frame sampling rate of the sensor depends on its size and the pixel clock frequency. The clock frequency was fixed at 40 MHz for optimum performance speed/quality; the higher pixel-sampling rate increases the noise level. The size of the sampled sub-frame finally defines the signal sampling frequency of the imager. For 256×4 pixels sub-frame the frame sampling frequency was 30kHz, 256×6 pixels – 20 kHz, 256×8 pixels – 14 kHz, etc.
To obtain one flow map over a region of interest (ROI), which in our case was 256×256 pixels, the ROI must be subdivided in smaller regions (e.g. into 32 sub-frames of 256×8 pixels) and scanned electronically. From 32 to 512 sampled points were obtained for the acquired time-domain signal for each pixel of the sub-frame, thus the intensity fluctuation history was recorded for each pixels of the ROI.
Here the variable ν is the frequency of the intensity fluctuations induced by the Doppler shifted photons. We calculated the power density spectrum using an FFT algorithm applied to recorded signal variations at each sampled pixel of the ROI. Noise subtraction is performed upon the calculated spectra by setting a threshold level on the amplitude of the spectral components. This filtering is applied to reduce the white noise (e.g. thermal and readout noises) contribution to the signal. Thereafter the perfusion, concentration and speed maps are calculated and displayed on computer monitor.
The signal processing is performed with a general purpose PC CPU – AMD-64-3000+. The total imaging time (including data acquisition, processing and display) depends on the number of samples obtained for each pixel and the ROI size. For 256×256 pixel ROI the imaging time is 2.5 sec for 64 samples, 3.5 sec for 128 samples, 5.5 sec for 256 samples, and 10 sec for 512 samples.
5. Measurements and results
In this section we present the results of the measurements obtained with our new imager. First, we made some in vitro measurements to characterize the performance of the imaging system in terms of bandwidth, and the linearity of the imager response to velocity and concentration changes. Second, we demonstrate the performance of our imager in vivo and present typical flow-maps obtained by measuring microcirculation in human skin.
5.1. In vitro
To measure the imaging system bandwidth and the response linearity to concentration and velocity changes, a signal from a sine-modulated light emitting diode (LED) was measured at one pixel. The LED was connected to an analog output of a synthesized function generator (Stanford Research System, Model DS345).
Fig. 2. a) The M1/M0
(velocity) imager response as a function of the measured signal frequency. b) The √M0
(concentration) imager response as a function of the measured signal frequency. c) The √M0
(concentration) imager response as a function of the measured signal amplitude. d) The SNR of the system as a function of the integration time for measurements on finger and forearm skin; error bars represent standard error for each measured SNR.
In Fig. 2(b)
response of the imager as a function of the input signal frequency is shown. The ACRMS/DC
value is proportional to the square of the M0
value. The decay in the √M0
imager response is due to the non-zero integration time of the detectors. This dependence is very similar to the frequency response of a basic low pass filter RC-circuit with a time constant defined by eq.(9
) (see also eq.(11
)). A decay of a factor of 0.5 for an RC-circuit is typical at the high-frequency cut-off. For integrating sensors, the measured signal response near the cut-off frequency is even smaller being approximately of 0.7 of its maximum, see eqs.(9
In Fig. 2(c)
the imaging system √M0
response to the amplitude changes of the input signal is shown. The input signal frequency was fixed at 3000 Hz. The imager signal amplitude response shows expected linear dependence. At low amplitudes of the input signal the imager response demonstrates a nonlinearity caused by noise. The results shown in Fig. 2(d)
are discussed in the next section.
5.2. In vivo
Fig. 3. Flow-related maps obtained with the new imager on finger skin (ROI=256×256 pixels): a) perfusion map [Low=1500 a.u.; High=3000 a.u.]; b) blood concentration map [Low=150 a.u.; High=300 a.u.]; c) flow speed map [Low=500 a.u.; High=1500 a.u.]; d) image of the object. The imaging area is 5.5×5.5 cm2. The imaging time is 3.5 seconds in total.
shows the perfusion map images obtained during an artery occlusion experiment. The imager settings were the same as for the measurements described for Fig. 3
above. This example demonstrates the performance of the imager in the continuous imaging mode
. The images were taken subsequently every 3.5 seconds, 3.5 s being the imaging time. The selected maps are shown in the matrix of 4×3 images to see the perfusion changes before, during and after the occlusion. As expected, there is a decrease of the perfusion signal during the occlusion. After the occlusion is released the local perfusion raised above the initial value; this effect is known as reactive hyperemia, shortly after which the blood flow returned to the initial state.
Finally, we demonstrate one of the possible applications of the imager. We measured perfusion changes in skin after a stimulant cream was applied. This cream contains 1% benzyl nicotinate. This is a pharmaceutical/cosmetical substance that is used in creams against arthritis pain and a small amount of this cream on the skin stimulates blood flow within a few minutes. The effect of the cream is clearly seen on the images shown in Fig. 5
. The blood flow starts steadily increasing approximately 1.5 minutes after the cream was applied. The increased blood flow in the spot was also followed by reddening of the skin that was observed by eye. The effect is temporal lasting 1–2 hours.
Fig. 4. Artery occlusion experiment recording repeated perfusion images in real-time (ROI=256×256 pixels). Numbers show time (in seconds) when the images were obtained: 0–6 s, before occlusion; 16 s, occlusion on; 22–29 s, occlusion stopped blood flow; 35 s: occlusion is released; 38–45 s, post-occlusive hyperemia; 48–64 s, restored perfusion level. The imaging area is 5.5×5.5 cm2. Low=1500 a.u.; High=3000 a.u.
We measured the SNR of the instrument for measurements on finger and forearm skin. The SNR was measured and calculated according to SNR
as a function of different integration times of the sensor. The results are shown in Fig. 2(d)
. The noise signal was estimated from a M0
flow-map histogram obtained by imaging a statically scattering white Teflon object. The signal values were found from the M0
flow-map histogram obtained by imaging of finger and forearm skin. The SNR is increasing for longer integration times due to the decrease of the noise bandwidth, Bn
, (the integration time could also affect the signal bandwidth). The SNR for finger skin is approximately 1.5–2 times higher compared for SNR obtained on forearm skin; finger skin perfusion is known to be higher than that of forearm skin.
Fig. 5. Perfusion images obtained with the high-speed laser Doppler imager (ROI=256×256 pixels). The imaging area is 5.5×5.5 cm2. The effect of the stimulant cream (from Induchem AG, Switzerland) is the increased blood flow in the area where the cream was applied. The cream was applied on the skin of the inner side of the forearm. Images show the blood flow changes trough time: at 90, 97, 110, 124, 138, and 152 seconds after the cream was applied to the skin. The imaging time is c.a. 3.5 seconds per image. Low=500 a.u.; High=2500 a.u. (the red bar on the latest perfusion image is caused by an accident artifact during the sensor readout).
6. Conclusion and outlook
In this paper we described the design and performance of a new high-speed laser Doppler imaging system based on an integrating CMOS image sensor. The use of a 2D matrix of integrating photodetectors results in an increased SNR of the system compared to the analogous imagers based on non-integrating detectors. The bandwidth of the imager with integrating detectors can be adjusted to the bandwidth of the signal thus increasing the SNR of the measurement. The use of 2D matrix of integrating photodetectors is particularly important for the parallel detection modality.
We tested the imager in vitro and in vivo. The imager demonstrated a reliable performance and fast imaging speed: e.g. for a 256×256 pixel ROI the imaging time was 3.5 seconds for 128 points taken for the FFT. This time is essentially smaller compared to the imaging time of the scanning imagers. Current commercial systems, mainly due to their system design based on sequential mechanical scanning, need more than 5 minutes to obtain a flow-image of the same resolution. Here, we measured the changes of blood flow over an extended area of tissue of 5.5×5.5 cm2 virtually in real time. In principle, for this imager design, the imaging area can be up to 15×15cm2 for the illuminating laser power of 250 mW. High-resolution real-time laser Doppler imaging would provide physicians with additional functional information about microcirculation.
Considering that the medical doctors would use the imager, our main effort was to develop a reliable, objective, accurate, user- and patient-friendly high-speed imaging system to measure blood flow in various biological tissues. From our present results, we anticipate additional patient comfort resulting from measuring times of less than 5 s. Although the training and experience will still be needed in order for users to interpret the results, the measurements themselves could be implemented easily due to a superior instrument design in combination with simple, interactive software.
The new imaging modality demonstrated the paves for an accurate, inexpensive, and easy to use diagnosis for a widespread dissemination by laboratories and hospitals. The wide range of applications is one of the major challenges for a future application of the imager. High-resolution high-speed laser Doppler perfusion imaging is an innovative technique for diagnosis and assessing the treatment of diseases such as atherosclerosis, psoriasis, diabetes, skin cancer, allergies, cardiovascular diseases, skin irritation. The new technique could also be applied for burn assessment, wound healing, and plastic surgery.