Calibrating Imaging Techniques to Study Blood Flow
This study will explore the uses of three noninvasive imaging techniques-thermography, laser Doppler imaging, and multispectral imaging-to test the blood flow of healthy volunteers. By comparing these three techniques, researchers will be able to train these imaging systems to better evaluate skin types and blood flow. The study will also test computer programs that have been developed to correct for the effects of curvature and body hair on the images.
Healthy volunteers must be older than 18 years of age and may not have had a history of malignant tumors, skin disease, or vascular disease.
Participants will undergo the following procedures on an outpatient basis:
Three different types of images taken of the participant's forearm
- Thermography - infrared thermal imaging to map skin temperature
- Laser Doppler imaging - low-powered laser imaging to map blood flow in the skin
- Multispectral imaging - near-infrared light imaging to measure total blood flow and oxygen levels in the skin
A reactive hyperemia experiment, in which multispectral images will be taken of the participant's forearm during and after the use of a blood pressure cuff
A hair removal experiment, in which images will be collected of the participant's forearm in separate scans done before and after the hair is removed with a topical hair removal solution
The entire series of exams will take approximately one hour to perform.
Healthy Volunteers Only
|Study Design:||Time Perspective: Prospective|
|Official Title:||Calibration of Non-Invasive Non-Ionizing Imaging Techniques to Study Vasculature of Healthy Volunteers|
|Study Start Date:||October 2007|
|Estimated Study Completion Date:||October 2012|
This study is designed to calibrate three non-invasive and non-ionizing imaging techniques on 12 healthy volunteers. The three imaging techniques-- thermography, laser Doppler imaging and multi-spectral imaging-- have been approved since 2001 for four clinical protocols already approved by the NIH/NCI IRB for use on patients with Kaposi's sarcoma (KS). However, as our laboratory continues to study and analyze the images collected on these protocols, we have found that our analysis algorithms require some additional data from healthy volunteers.
We aim to use the information we gather from healthy volunteers on this protocol to train our imaging systems and calibrate our analysis methods to validate the results of the KS data already collected. We aim to study different skin types, such as Caucasian, Asian and African American, so that we can calibrate the melanin input value in our multi-spectral imaging reconstruction algorithm. We also need to study the vasculature networks in the forearms of the volunteers to compare to 'normal' values in the literature for the parameters we are exploring, including temperature, vasculature, blood volume and blood oxygenation to validate our reconstruction algorithm. We will also perform experiments of reactive hyperemia, where the arm of the volunteer is occluded with an arm pressure cuff for five minutes, to study how blood volume and blood oxygenation change during the experiment. Trends of increasing/decreasing blood volume and blood oxygenation can also be compared with published literature to validate our reconstruction algorithm.
Since we started collecting data from the KS patients, we have noticed that hair and curvature of the surface of the skin interfere with our analysis techniques as well. Therefore, we aim to assess algorithms developed to remove the effects of curvature and hair on the images as part of our image analysis training. The intensity in the images is affected by the curvature and must be corrected. We have developed algorithms to correct for this curvature, but need to study normal disease-free skin to make sure that the values for blood volume and blood oxygenation remain the same after the curvature correction is performed. We also plan to collect images from a healthy volunteer's arm, remove the hair from the arm using a topical hair removal solution, and then image the arm again. With this information, we can optimize our hair removal algorithm. Combining all of the aforementioned information will allow us to develop a non-invasive strategy for repeated serial assessments of tissue vasculature.
When following KS lesions over time, the vascular / metabolic changes in the lesion are important. An additional parameter is of interest when following the treatment over time, which is the structure of the lesion. Optical Coherence Tomography (OCT) is a non-invasive, non-contact optical imaging technology, which provides this desired structural information with high resolution and in three dimensions (3D) over the area of interest. Therefore we aim to combine OCT with multi-spectral data, relating the metabolic state of the tissue with structure. By doing this, we hypothesize that we can not only get deeper understanding of tissue vasculature, but that we can also improve the multi-spectral imaging modality, by using the structure as prior information for the reconstruction.
|United States, Maryland|
|National Institutes of Health Clinical Center, 9000 Rockville Pike|
|Bethesda, Maryland, United States, 20892|
|Principal Investigator:||Margaret F Bevans, Ph.D.||National Institutes of Health Clinical Center (CC)|