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1. PAM: Photoacoustic Microscopy

http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png | the object end of the PAM system

Mechanism

After a target absorbs the laser pulse energy incident on it, the target will generate an ultrasound wave pulse due to thermoelastic expansion. By detecting the ultrasound pulses, we can reconstruct an image of the target. This technique can get both structural and functional images based on the absorption of the tissue to the laser pulse energy.

System

http://lh4.ggpht.com/_dHk4QtJVXYY/Sj67GkVXSiI/AAAAAAAAF1o/bGA3bObHXVA/s640/photoacoustic%20experiment%20flow%20chart.jpg | block diagram of the photoacoustic experiment system

Results

Attach:inkspotpa.jpg Δ | Photoacoustic signal by ink spot Attach:peak.jpg Δ | peak value variation of successive PA signals

http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7Q7D3P4WI/AAAAAAAAF14/mHNeIyljCZ8/s720/indi.jpg|Individual Display of Thirty-One successive PA Signals Generated by a Black Ink Spot

Problems

Noise Caused by Quantization and Laser Source

This experiment was meant to detect the photoacoustic signals' change during the resting and excited states of a a lobster nerve cord. By processing the signals I got from a black ink spot and a phantom object, I found that the variation in the amplitude of the photoacoustic signals caused by the quantization of the oscilloscope and the instability of the laser source was too large to accurately compensated. Since the variation in the amplitude of the photoacoustic signals caused by the action potential's propagation along the nerve cord is presumed to be a very small percentage of the resting state signal, we are not able to go on with this experiment unless we can figure out a way to deal with the noises.

Memory Length Limitation

Generally an action potential of a lobster nerve lasts about five milliseconds. The central frequency of the ultrasound transducer I used in the setup is 25 megahertz. Even if we use the maximum memory length of the oscilloscope (LeCroy WaveJet 324) which is 500,000 points, we are only able to achieve a maximum sampling rate of 100 megahertz. This sampling rate is some distance away from the desired sampling rate. I used zero-padding in the frequency domain in the MATLAB code to deal with this issue. For the laser trigger signal which is recorded and used to compensation the variation in the PA signal caused by laser pulses, we used a 1.9 megahertz low pass filter since the 1 nanosecond width of the laser pulse render it impossible to record the pulse directly using 25 megahertz sampling rate. All these efforts have corrected the signal to some extent, but are still far away from telling the change in photoacoustic signals caused by action potential propagation.

2. OCT: Optical Coherence Tomography

Began working on SD-OCT from June, 2009. Research progress will be uploaded here regularly.

Attach:OCT.jpg Δ An OCT image of my left thumb finger nail. Done on July 17, 2009.

Attach:fingertip.jpg Δ An OCT image of my fore finger tip. Done on Sept. 6, 2009.

An OCT fingertip movie Δ. Done on Sept. 12, 2009.

Ways to improve image quality:

Background Subtraction.

In my setup, the signal is carried by a laser pulse. When FFT is performed, the profile of the laser pulse will cause a fixed pattern noise. This fixed pattern noise will dramatically deteriorate the image quality. In order to reduce the fixed pattern noise, a couple of methods were tried out.

Method 1: blocking the sample arm, get one frame of the reference arm only as the background frame, then have it subtracted from the data frame before further processing. This method generated an image shown as below: Attach:Frame2.png Δ

Method 2: instead of recording a separate background frame, I added all the A line data together as a background frame assuming that the information of the sample are displayed randomly in the same pixel for different A lines for a certain frame. Perform interpolation and Fourier transform to this quasi background frame, then divide it from a processed frame data to remove the theoretically multiplicative noise. The reconstructed image is shown below: Attach:bgdivision.jpg Δ

Method 3: Instead of performing division, just use the quasi background as the subtrahend and the raw data as the minuend. This yields an image as follows: Attach:bgsubtractinRawDat.jpg Δ It makes no noticeable difference if I perform the subtraction after FFT both the background and the raw data.

Method 4: As we can see, the noise removing effect is still not that good for method 3. The fixed pattern noise is obvious in the depth range of (0.2, 0.6). In order to remove this noise, I used the processed data (meaning: data that has already undergone interpolation, FFT and A line compression) as the starting point. Notice that a large lateral region is unused because the sample is not placed there. So I use the A line data in those regions to get the distribution of fixed pattern noise, then expand the averaged A line column to a 2D matrix having the same dimension as a frame data. Subtraction of these two matrices will yield a much cleaner image, as given below: Attach:5th.jpg Δ

A mouse femur SD-OCT scanning movie using method 4 can be downloaded here: mouse femur scanning Δ (Oct/12/09)

Yet other background subtraction methods will be tried out to further improve image quality.

Spectrometer Calibration

This topic is discussed in detail in my group meeting presentation 6. The wavelength independent spectrometer calibration is realized by both LabVIEW and MATLAB. The theory, the programming tips, the simulation and the final calibration test results are discussed in my presentation and in a separate report Δ.

Depth and Lateral Axis's Scaling

For depth, use translation stages to manually set the height of a gold mirror in the sample arm, then run the program and see where will the mirror's image shows up. With a couple of positions, it is very easy to scale the depth.

For lateral dimension, first use a caliper to measure the width of a wrench, then put the wrench on the sample holder. Generate an image and see how it shows up in the image. By doing so, it is straightforward to calibrate the lateral dimension. This material is covered in the 7th presentation of me for the group meeting.

Lateral Scanning Range Control

The CCD camera for the spectrometer is working in free run mode. An Agilent function generator is sending triggers for frame grabber. The lateral scanning range is dependent upon the camera integration time, the triangular waveform's amplitude and frequency. Experimentally, a 6mm lateral scanning range is achieved by using 7V PP amplitude at 5Hz. The integration time is approximately 36 micrometers. Details may be discussed in coming presentations.

A Line Number Compression

For a high resolution imaging modality, to compress A line number from 2048 to 512 is a decent choice to enhance contrast and reduce white noise. I used addition to compress A lines, which is to say: the ith to (i+4)th A lines are added together and then averaged to generate the i^,th A line in the new image. When no compression is used: Attach:noadd.jpg Δ When compression is used: Attach:add.jpg Δ Compare the two images above, you can tell that the A line compressed image has better contrast and the features of the femur sample have a better display in the latter one.

temporary data download: data Δ data2 Δ

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temporary data download: data Δ

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temporary data download: data Δ data2 Δ

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Compare the two images above, you can tell that the A line compressed image has better contrast and the features of the femur sample have a better display in the latter one.

to:

Compare the two images above, you can tell that the A line compressed image has better contrast and the features of the femur sample have a better display in the latter one.

temporary data download: data Δ

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Depth and Lateral Axes Scaling

For depth, use translation stages to manually set the height of a gold mirror in the sample arm, then run the program and see where will the mirror's image show up. With a couple of positions, it is very easy to scale the depth.

to:

Depth and Lateral Axis's Scaling

For depth, use translation stages to manually set the height of a gold mirror in the sample arm, then run the program and see where will the mirror's image shows up. With a couple of positions, it is very easy to scale the depth.

October 13, 2009, at 12:56 AM EST by 68.44.40.84 -

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For a high resolution imaging modality, to compress A line number from 2048 to 512 is a decent choice to enhance contrast and reduce white noise. I used addition to compress A lines, which is to say: the ith to (i+4)th A lines are added together and then averaged to generate the i^,th A line in the new image.

to:

For a high resolution imaging modality, to compress A line number from 2048 to 512 is a decent choice to enhance contrast and reduce white noise. I used addition to compress A lines, which is to say: the ith to (i+4)th A lines are added together and then averaged to generate the i^,th A line in the new image. When no compression is used: Attach:noadd.jpg Δ When compression is used: Attach:add.jpg Δ Compare the two images above, you can tell that the A line compressed image has better contrast and the features of the femur sample have a better display in the latter one.

October 13, 2009, at 12:35 AM EST by 68.44.40.84 -

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The CCD camera for the spectrometer is working in free run mode. An Agilent function generator is sending triggers for frame grabber. The lateral scanning range is dependent upon the camera integration time, the triangular waveform's amplitude and frequency. Experimentally, a 6mm lateral scanning range is achieved by using 7V PP amplitude at 5Hz. The integration time is approximately 36 micrometers. Details may be discussed in coming presentations.

to:

The CCD camera for the spectrometer is working in free run mode. An Agilent function generator is sending triggers for frame grabber. The lateral scanning range is dependent upon the camera integration time, the triangular waveform's amplitude and frequency. Experimentally, a 6mm lateral scanning range is achieved by using 7V PP amplitude at 5Hz. The integration time is approximately 36 micrometers. Details may be discussed in coming presentations.

A Line Number Compression

For a high resolution imaging modality, to compress A line number from 2048 to 512 is a decent choice to enhance contrast and reduce white noise. I used addition to compress A lines, which is to say: the ith to (i+4)th A lines are added together and then averaged to generate the i^,th A line in the new image.

October 13, 2009, at 12:26 AM EST by 68.44.40.84 -

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For lateral dimension, first use a caliper to measure the width of a wrench, then put the wrench on the sample holder. Generate an image and see how it shows up in the image. By doing so, it is straightforward to calibrate the lateral dimension. This material is covered in the 7th presentation of me for the group meeting.

to:

For lateral dimension, first use a caliper to measure the width of a wrench, then put the wrench on the sample holder. Generate an image and see how it shows up in the image. By doing so, it is straightforward to calibrate the lateral dimension. This material is covered in the 7th presentation of me for the group meeting.

Lateral Scanning Range Control

The CCD camera for the spectrometer is working in free run mode. An Agilent function generator is sending triggers for frame grabber. The lateral scanning range is dependent upon the camera integration time, the triangular waveform's amplitude and frequency. Experimentally, a 6mm lateral scanning range is achieved by using 7V PP amplitude at 5Hz. The integration time is approximately 36 micrometers. Details may be discussed in coming presentations.

October 13, 2009, at 12:20 AM EST by 68.44.40.84 -

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Attach:fingertip.jpg Δ An OCT image of my fore finger tip. Done on Sept. 6, 2009.

An OCT fingertip movie Δ. Done on Sept. 12, 2009.

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A mouse femur SD-OCT scanning movie can be downloaded here: mouse femur scanning Δ (Oct/12/09)

to:

A mouse femur SD-OCT scanning movie using method 4 can be downloaded here: mouse femur scanning Δ (Oct/12/09)

Yet other background subtraction methods will be tried out to further improve image quality.

Spectrometer Calibration

This topic is discussed in detail in my group meeting presentation 6. The wavelength independent spectrometer calibration is realized by both LabVIEW and MATLAB. The theory, the programming tips, the simulation and the final calibration test results are discussed in my presentation and in a separate report Δ.

Depth and Lateral Axes Scaling

For depth, use translation stages to manually set the height of a gold mirror in the sample arm, then run the program and see where will the mirror's image show up. With a couple of positions, it is very easy to scale the depth.

For lateral dimension, first use a caliper to measure the width of a wrench, then put the wrench on the sample holder. Generate an image and see how it shows up in the image. By doing so, it is straightforward to calibrate the lateral dimension. This material is covered in the 7th presentation of me for the group meeting.

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Method 4: As we can see, the noise removing effect is still not that good for method 3. The fixed pattern noise is obvious in the depth range of (0.2, 0.6). In order to remove this noise, I used the processed data (meaning: data that has already undergone interpolation, FFT and A line compression) as the starting point. Notice that a large lateral region is unused because the sample is not placed there. So I use the A line data in those regions to get the distribution of fixed pattern noise, then expand the averaged A line column to a 2D matrix having the same dimension as a frame data. Subtraction of these two matrices will yield a much cleaner image, as given below: Attach:5th.jpg Δ

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Method 3:

to:

Method 3: Instead of performing division, just use the quasi background as the subtrahend and the raw data as the minuend. This yields an image as follows: Attach:bgsubtractinRawDat.jpg Δ It makes no noticeable difference if I perform the subtraction after FFT both the background and the raw data.

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Firstly, blocking the sample arm, get one frame of the reference arm only as the background frame, then have it subtracted from the data frame before further processing. This method generated an image shown as below:

to:

Method 1: blocking the sample arm, get one frame of the reference arm only as the background frame, then have it subtracted from the data frame before further processing. This method generated an image shown as below:

Added lines 46-50:

Method 2: instead of recording a separate background frame, I added all the A line data together as a background frame assuming that the information of the sample are displayed randomly in the same pixel for different A lines for a certain frame. Perform interpolation and Fourier transform to this quasi background frame, then divide it from a processed frame data to remove the theoretically multiplicative noise. The reconstructed image is shown below: Attach:bgdivision.jpg Δ

Method 3:

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Ways to improve image quality:

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[Ways to improve image quality:]

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+[Ways to improve image quality:+]

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Ways to improve image quality:

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[Ways to improve image quality:]

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Ways to improve image quality:

Background Subtraction.

In my setup, the signal is carried by a laser pulse. When FFT is performed, the profile of the laser pulse will cause a fixed pattern noise. This fixed pattern noise will dramatically deteriorate the image quality. In order to reduce the fixed pattern noise, a couple of methods were tried out.

Firstly, blocking the sample arm, get one frame of the reference arm only as the background frame, then have it subtracted from the data frame before further processing. This method generated an image shown as below: Attach:Frame2.png Δ

October 12, 2009, at 10:59 PM EST by 68.44.40.84 -

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A mouse femur SD-OCT scanning movie can be downloaded here: mouse femur scanning Δ (Oct/12/09)

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A mouse femur SD-OCT scanning movie can be downloaded here: mouse femur scanning Δ (Oct/12/09)

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A mouse femur SD-OCT scanning movie can be downloaded here: mouse femur scanning Δ (Oct/12/09)

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A mouse femur SD-OCT scanning movie can be downloaded here: mouse femur scanning Δ (Oct/12/09)

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A mouse femur SD-OCT scanning movie can be downloaded here: Attach:femur.avi Δ

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A mouse femur SD-OCT scanning movie can be downloaded here: mouse femur scanning Δ (Oct/12/09)

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A mouse femur SD-OCT scanning movie can be downloaded here: Attach:mouse Δ femur image.avi

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A mouse femur SD-OCT scanning movie can be downloaded here: Attach:femur.avi Δ

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An OCT image of my left thumb finger nail. Done on July 17, 2009.

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An OCT image of my left thumb finger nail. Done on July 17, 2009.

A mouse femur SD-OCT scanning movie can be downloaded here: Attach:mouse Δ femur image.avi

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An OCT image of my left thumb finger tip. Done on July 17, 2009.

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An OCT image of my left thumb finger nail. Done on July 17, 2009.

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Attach:OCT.jpg Δ Attach:inkspotpa.jpg Δ

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Attach:OCT.jpg Δ An OCT image of my left thumb finger tip. Done on July 17, 2009.

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Attach: OCT.jpg

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Attach:OCT.jpg Δ Attach:inkspotpa.jpg Δ

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Began working on SD-OCT from June, 2009. Research progress will be uploaded here regularly.

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Began working on SD-OCT from June, 2009. Research progress will be uploaded here regularly.

Attach: OCT.jpg

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Attach:inkspotpa.jpg Δ | Photoacoustic signal by ink spot Attach:peak.jpg Δ | peak value variation of successive PA signals

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Attach:inkspotpa.jpg Δ | Photoacoustic signal by ink spot Attach:peak.jpg Δ | peak value variation of successive PA signals

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7Q7D3P4WI/AAAAAAAAF14/mHNeIyljCZ8/s720/indi.jpgIndividual Display of Thirty-One successive PA Signals Generated by a Black Ink Spot

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7Q7D3P4WI/AAAAAAAAF14/mHNeIyljCZ8/s720/indi.jpg|Individual Display of Thirty-One successive PA Signals Generated by a Black Ink Spot

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Attach:inkspotpa.jpg Δ | Photoacoustic signal by ink spot Attach:peak.jpg Δ | peak value variation of successive PA signals

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7Q7D3P4WI/AAAAAAAAF14/mHNeIyljCZ8/s720/indi.jpgIndividual Display of Thirty-One successive Photoacoustic Signals Generated by an Black Ink Spot

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7Q7D3P4WI/AAAAAAAAF14/mHNeIyljCZ8/s720/indi.jpgIndividual Display of Thirty-One successive PA Signals Generated by a Black Ink Spot

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7QccEKDZI/AAAAAAAAF10/hXa03nki-EQ/peak%20value%20varitation.jpgpeak value variation of successive PA signals

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7QccEKDZI/AAAAAAAAF10/hXa03nki-EQ/peak%20value%20varitation.jpg|peak value variation of successive PA signals

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Attach:inkspotpa.jpg Δ | Photoacoustic signal by ink spot % rfloat width=300px% http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7QccEKDZI/AAAAAAAAF10/hXa03nki-EQ/peak%20value%20varitation.jpg | peak value variation of successive PA signals

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Attach:inkspotpa.jpg Δ | Photoacoustic signal by ink spot
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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7OvSE-NBI/AAAAAAAAF1s/vq3wC716ZlI/inkspotpa.jpg | Photoacoustic signal by ink spot

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7OvSE-NBI/AAAAAAAAF1s/vq3wC716ZlI/inkspotpa.jpg | Photoacoustic signal by ink spot

to:

http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7OvSE-NBI/AAAAAAAAF1s/vq3wC716ZlI/inkspotpa.jpg | Photoacoustic signal by ink spot

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7OvSE-NBI/AAAAAAAAF1s/vq3wC716ZlI/inkspotpa.jpg | Photoacoustic signal by ink spot

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7OvSE-NBI/AAAAAAAAF1s/vq3wC716ZlI/inkspotpa.jpg | Photoacoustic signal by ink spot

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7OvSE-NBI/AAAAAAAAF1s/vq3wC716ZlI/inkspotpa.jpg | Photoacoustic signal by ink spot

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2. OCT: Optical Coherence Tomography

to:

2. OCT: Optical Coherence Tomography

Began working on SD-OCT from June, 2009. Research progress will be uploaded here regularly.

June 21, 2009, at 09:22 PM EST by 68.44.40.84 -

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This experiment was meant to detect the photoacoustic signals' change during the resting and excited states of a a lobster nerve cord. By processing the signals I got from a black ink spot and a phantom object, I found that the variation in the amplitude of the photoacoustic signals caused by the quantization of the oscilloscope and the instability of the laser source was too large to accurately compensated. Since the variation in the amplitude of the photoacoustic signals caused by the action potential's propagation along the nerve cord is presumed to be a very small percentage of the resting state signal, we are not able to go on with this experiment unless we can figure out a way to deal with the noises.

to:

This experiment was meant to detect the photoacoustic signals' change during the resting and excited states of a a lobster nerve cord. By processing the signals I got from a black ink spot and a phantom object, I found that the variation in the amplitude of the photoacoustic signals caused by the quantization of the oscilloscope and the instability of the laser source was too large to accurately compensated. Since the variation in the amplitude of the photoacoustic signals caused by the action potential's propagation along the nerve cord is presumed to be a very small percentage of the resting state signal, we are not able to go on with this experiment unless we can figure out a way to deal with the noises.

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Generally an action potential of a lobster nerve lasts about five milliseconds. The central frequency of the ultrasound transducer I used in the setup is 25 megahertz. Even if we use the maximum memory length of the oscilloscope (LeCroy WaveJet 324) which is 500,000 points, we are only able to achieve a maximum sampling rate of 100 megahertz. This sampling rate is some distance away from the desired sampling rate. I used zero-padding in the frequency domain in the MATLAB code to deal with this issue. For the laser trigger signal which is recorded and used to compensation the variation in the PA signal caused by laser pulses, we used a 1.9 megahertz low pass filter since the 1 nanosecond width of the laser pulse render it impossible to record the pulse directly using 25 megahertz sampling rate. All these efforts have corrected the signal to some extent, but are still far away from telling the change in photoacoustic signals caused by action potential propagation.

to:

Generally an action potential of a lobster nerve lasts about five milliseconds. The central frequency of the ultrasound transducer I used in the setup is 25 megahertz. Even if we use the maximum memory length of the oscilloscope (LeCroy WaveJet 324) which is 500,000 points, we are only able to achieve a maximum sampling rate of 100 megahertz. This sampling rate is some distance away from the desired sampling rate. I used zero-padding in the frequency domain in the MATLAB code to deal with this issue. For the laser trigger signal which is recorded and used to compensation the variation in the PA signal caused by laser pulses, we used a 1.9 megahertz low pass filter since the 1 nanosecond width of the laser pulse render it impossible to record the pulse directly using 25 megahertz sampling rate. All these efforts have corrected the signal to some extent, but are still far away from telling the change in photoacoustic signals caused by action potential propagation.

June 21, 2009, at 09:20 PM EST by 68.44.40.84 -

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Noise Caused by Quantization and Laser Source

This experiment was meant to detect the photoacoustic signals' change during the resting and excited states of a a lobster nerve cord. By processing the signals I got from a black ink spot and a phantom object, I found that the variation in the amplitude of the photoacoustic signals caused by the quantization of the oscilloscope and the instability of the laser source was too large to accurately compensated. Since the variation in the amplitude of the photoacoustic signals caused by the action potential's propagation along the nerve cord is presumed to be a very small percentage of the resting state signal, we are not able to go on with this experiment unless we can figure out a way to deal with the noises.

Memory Length Limitation

Generally an action potential of a lobster nerve lasts about five milliseconds. The central frequency of the ultrasound transducer I used in the setup is 25 megahertz. Even if we use the maximum memory length of the oscilloscope (LeCroy WaveJet 324) which is 500,000 points, we are only able to achieve a maximum sampling rate of 100 megahertz. This sampling rate is some distance away from the desired sampling rate. I used zero-padding in the frequency domain in the MATLAB code to deal with this issue. For the laser trigger signal which is recorded and used to compensation the variation in the PA signal caused by laser pulses, we used a 1.9 megahertz low pass filter since the 1 nanosecond width of the laser pulse render it impossible to record the pulse directly using 25 megahertz sampling rate. All these efforts have corrected the signal to some extent, but are still far away from telling the change in photoacoustic signals caused by action potential propagation.

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Problems

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Problems

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aa

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Problems

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7Q7D3P4WI/AAAAAAAAF14/mHNeIyljCZ8/s720/indi.jpg|Individual Display of Thirty-One successive Photoacoustic Signals Generated by an Black Ink Spot

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7Q7D3P4WI/AAAAAAAAF14/mHNeIyljCZ8/s720/indi.jpgIndividual Display of Thirty-One successive Photoacoustic Signals Generated by an Black Ink Spot

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7Q7D3P4WI/AAAAAAAAF14/mHNeIyljCZ8/s720/indi.jpg | Individual Display of Thirty-One successive Photoacoustic Signals Generated by an Black Ink Spot

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7Q7D3P4WI/AAAAAAAAF14/mHNeIyljCZ8/s720/indi.jpg|Individual Display of Thirty-One successive Photoacoustic Signals Generated by an Black Ink Spot

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7QccEKDZI/AAAAAAAAF10/hXa03nki-EQ/peak%20value%20varitation.jpg | peak value variation of successive PA signals\\

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7QccEKDZI/AAAAAAAAF10/hXa03nki-EQ/peak%20value%20varitation.jpg | peak value variation of successive PA signals

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7Q7D3P4WI/AAAAAAAAF14/mHNeIyljCZ8/s720/indi.jpg | Individual Display of Thirty-One successive Photoacoustic Signals Generated by an Black Ink Spot
aa

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7Q7D3P4WI/AAAAAAAAF14/mHNeIyljCZ8/s720/indi.jpg | Individual Display of Thirty-One successive Photoacoustic Signals Generated by an Black Ink Spot

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7OvSE-NBI/AAAAAAAAF1s/vq3wC716ZlI/inkspotpa.jpg | Photoacoustic signal by ink spot % width=150px % http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7QccEKDZI/AAAAAAAAF10/hXa03nki-EQ/peak%20value%20varitation.jpg | peak value variation of successive PA signals\\

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7OvSE-NBI/AAAAAAAAF1s/vq3wC716ZlI/inkspotpa.jpg | Photoacoustic signal by ink spot http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7QccEKDZI/AAAAAAAAF10/hXa03nki-EQ/peak%20value%20varitation.jpg | peak value variation of successive PA signals\\

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7OvSE-NBI/AAAAAAAAF1s/vq3wC716ZlI/inkspotpa.jpg | Photoacoustic signal by ink spot

http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7QccEKDZI/AAAAAAAAF10/hXa03nki-EQ/peak%20value%20varitation.jpg | peak value variation of successive PA signals\\

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7OvSE-NBI/AAAAAAAAF1s/vq3wC716ZlI/inkspotpa.jpg | Photoacoustic signal by ink spot % width=150px % http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7QccEKDZI/AAAAAAAAF10/hXa03nki-EQ/peak%20value%20varitation.jpg | peak value variation of successive PA signals\\

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7OvSE-NBI/AAAAAAAAF1s/vq3wC716ZlI/inkspotpa.jpg | Photoacoustic signal by ink spot

http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7QccEKDZI/AAAAAAAAF10/hXa03nki-EQ/peak%20value%20varitation.jpg | peak value variation of successive PA signals\\

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7OvSE-NBI/AAAAAAAAF1s/vq3wC716ZlI/inkspotpa.jpg | Photoacoustic signal by ink spot

http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7QccEKDZI/AAAAAAAAF10/hXa03nki-EQ/peak%20value%20varitation.jpg | peak value variation of successive PA signals\\

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7OvSE-NBI/AAAAAAAAF1s/vq3wC716ZlI/inkspotpa.jpg | Photoacoustic signal by ink spot

http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7QccEKDZI/AAAAAAAAF10/hXa03nki-EQ/peak%20value%20varitation.jpg | peak value variation of successive PA signals\\

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7OvSE-NBI/AAAAAAAAF1s/vq3wC716ZlI/inkspotpa.jpg | Photoacoustic signal by ink spot

http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7QccEKDZI/AAAAAAAAF10/hXa03nki-EQ/peak%20value%20varitation.jpg | peak value variation of successive PA signals\\

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7OvSE-NBI/AAAAAAAAF1s/vq3wC716ZlI/inkspotpa.jpg | Photoacoustic signal by ink spot

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7OvSE-NBI/AAAAAAAAF1s/vq3wC716ZlI/inkspotpa.jpg | Photoacoustic signal by ink spot

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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7OvSE-NBI/AAAAAAAAF1s/vq3wC716ZlI/inkspotpa.jpg | Photoacoustic signal by ink spot

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% width=300px%http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7OvSE-NBI/AAAAAAAAF1s/vq3wC716ZlI/inkspotpa.jpg | Photoacoustic signal by ink spot
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http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7OvSE-NBI/AAAAAAAAF1s/vq3wC716ZlI/inkspotpa.jpg | Photoacoustic signal by ink spot
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Results

http://lh6.ggpht.com/_dHk4QtJVXYY/Sj7OvSE-NBI/AAAAAAAAF1s/vq3wC716ZlI/inkspotpa.jpg | Photoacoustic signal by ink spot

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http://lh4.ggpht.com/_dHk4QtJVXYY/Sj67GkVXSiI/AAAAAAAAF1o/bGA3bObHXVA/s640/photoacoustic%20experiment%20flow%20chart.jpg

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http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png | the object end of the PAM system

to:

http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png | the object end of the PAM system

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http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png | the object end of the PAM system

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http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png | the object end of the PAM system

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After a target absorbs the laser pulse energy incident on it, the target will generate an ultrasound wave pulse due to thermoelastic expansion. By detecting the ultrasound pulses, we can reconstruct an image of the target. This technique can get both structural and functional images based on the absorption of the tissue to the laser pulse energy.\\\

to:

After a target absorbs the laser pulse energy incident on it, the target will generate an ultrasound wave pulse due to thermoelastic expansion. By detecting the ultrasound pulses, we can reconstruct an image of the target. This technique can get both structural and functional images based on the absorption of the tissue to the laser pulse energy.\\

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System

aaa

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Mechanism

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http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png | the object end of the PAM system

to:

http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png | the object end of the PAM system

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http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png | the object end of the PAM system

to:

http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png | the object end of the PAM system

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http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png | the object end of the PAM system

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http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png | the object end of the PAM system

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http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png | the object end of the PAM system

to:

http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png | the object end of the PAM system

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http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png | the object end of the PAM system

to:

http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png | the object end of the PAM system

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http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png | the object end of the PAM system

to:

http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png | the object end of the PAM system

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http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png' | the object end of the PAM system

to:

http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png | the object end of the PAM system

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http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.pngobject end of the PAM system | the object end of the PAM system

to:

http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png' | the object end of the PAM system

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http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.pngobject end of the PAM system

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http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.pngobject end of the PAM system | the object end of the PAM system

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After a target absorbs the laser pulse energy incident on it, the target will generate an ultrasound wave pulse due to thermoelastic expansion. By detecting the ultrasound pulses, we can reconstruct an image of the target. This technique can get both structural and functional images based on the absorption of the tissue to the laser pulse energy.

to:

After a target absorbs the laser pulse energy incident on it, the target will generate an ultrasound wave pulse due to thermoelastic expansion. By detecting the ultrasound pulses, we can reconstruct an image of the target. This technique can get both structural and functional images based on the absorption of the tissue to the laser pulse energy.\\\

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http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png

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http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.pngobject end of the PAM system

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http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png ->After a target absorbs the laser pulse energy incident on it, the target will generate an ultrasound wave pulse due to thermoelastic expansion. By detecting the ultrasound pulses, we can reconstruct an image of the target. This technique can get both structural and functional images based on the absorption of the tissue to the laser pulse energy.

to:

http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png

After a target absorbs the laser pulse energy incident on it, the target will generate an ultrasound wave pulse due to thermoelastic expansion. By detecting the ultrasound pulses, we can reconstruct an image of the target. This technique can get both structural and functional images based on the absorption of the tissue to the laser pulse energy.

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http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png->After a target absorbs the laser pulse energy incident on it, the target will generate an ultrasound wave pulse due to thermoelastic expansion. By detecting the ultrasound pulses, we can reconstruct an image of the target. This technique can get both structural and functional images based on the absorption of the tissue to the laser pulse energy.

to:

http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png ->After a target absorbs the laser pulse energy incident on it, the target will generate an ultrasound wave pulse due to thermoelastic expansion. By detecting the ultrasound pulses, we can reconstruct an image of the target. This technique can get both structural and functional images based on the absorption of the tissue to the laser pulse energy.

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Changed line 3 from:

After a target absorbs the laser pulse energy incident on it, the target will generate an ultrasound wave pulse due to thermoelastic expansion. By detecting the ultrasound pulses, we can reconstruct an image of the target. This technique can get both structural and functional images based on the absorption of the tissue to the laser pulse energy.

to:

http://lh5.ggpht.com/_dHk4QtJVXYY/Sj5lGnMEF5I/AAAAAAAAF1k/Bjev-TtDNjQ/s400/setup.png->After a target absorbs the laser pulse energy incident on it, the target will generate an ultrasound wave pulse due to thermoelastic expansion. By detecting the ultrasound pulses, we can reconstruct an image of the target. This technique can get both structural and functional images based on the absorption of the tissue to the laser pulse energy.

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2. OCT: Optical Coherence Tomography

to:

2. OCT: Optical Coherence Tomography

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1. PAM: Photoacoustic Microscopy

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1. PAM: Photoacoustic Microscopy

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1. PAM: Photoacoustic Microscopy

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1. PAM: Photoacoustic Microscopy

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2. OCT: Optical Coherence Tomography

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2. OCT: Optical Coherence Tomography

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PAM: Photoacoustic Microscopy

After a target absorbs the laser pulse energy incident on it, the target will generate an ultrasound wave pulse due to thermoelastic expansion. By detecting the ultrasound pulses, we can reconstruct an image of the target. This technique can get both structural and functional images based on the absorption of the tissue to the laser pulse energy.

OCT: Optical Coherence Tomography

to:

1. PAM: Photoacoustic Microscopy

After a target absorbs the laser pulse energy incident on it, the target will generate an ultrasound wave pulse due to thermoelastic expansion. By detecting the ultrasound pulses, we can reconstruct an image of the target. This technique can get both structural and functional images based on the absorption of the tissue to the laser pulse energy.

2. OCT: Optical Coherence Tomography

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PAM & OCT

to:

PAM: Photoacoustic Microscopy

After a target absorbs the laser pulse energy incident on it, the target will generate an ultrasound wave pulse due to thermoelastic expansion. By detecting the ultrasound pulses, we can reconstruct an image of the target. This technique can get both structural and functional images based on the absorption of the tissue to the laser pulse energy.

OCT: Optical Coherence Tomography

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PAM & OCT

Mengyang Liu's Homepage Biomedical Imaging Group

Research Interests

Research.ResearchInterests History

1. PAM: Photoacoustic Microscopy

Mechanism

System

Results

Problems

Noise Caused by Quantization and Laser Source

Memory Length Limitation

2. OCT: Optical Coherence Tomography

Background Subtraction.

Spectrometer Calibration

Depth and Lateral Axis's Scaling

Lateral Scanning Range Control

A Line Number Compression

Depth and Lateral Axes Scaling

Depth and Lateral Axis's Scaling

A Line Number Compression

Lateral Scanning Range Control

Spectrometer Calibration

Depth and Lateral Axes Scaling

Background Subtraction.

2. OCT: Optical Coherence Tomography

2. OCT: Optical Coherence Tomography

Noise Caused by Quantization and Laser Source

Memory Length Limitation

Problems

Problems

Problems

Results

System

Mechanism

2. OCT: Optical Coherence Tomography

2. OCT: Optical Coherence Tomography

1. PAM: Photoacoustic Microscopy

1. PAM: Photoacoustic Microscopy

1. PAM: Photoacoustic Microscopy

2. OCT: Optical Coherence Tomography