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Showing posts with label Tanny Chavez. Show all posts
Showing posts with label Tanny Chavez. Show all posts
Friday, August 10, 2018
Abstract-Assessment of Terahertz Imaging for Excised Breast Cancer Tumors with Image Morphing
Tanny Chavez, Tyler Bowman, Jingxian Wu, Keith Bailey, Magda El-Shenawee
https://link.springer.com/article/10.1007/s10762-018-0529-8
This paper presents an image morphing algorithm for quantitative evaluation methodology of terahertz (THz) images of excised breast cancer tumors. Most current studies on the assessment of THz imaging rely on qualitative evaluation, and there is no established benchmark or procedure to quantify the THz imaging performance. The proposed morphing algorithm provides a tool to quantitatively align the THz image with the histopathology image. Freshly excised xenograft murine breast cancer tumors are imaged using the pulsed THz imaging and spectroscopy system in the reflection mode. Upon fixing the tumor tissue in formalin and embedding in paraffin, a formalin-fixed paraffin-embedded (FFPE) tissue block is produced. A thin slice of the block is prepared for the pathology image while another THz reflection image is produced directly from the block. We developed an algorithm of mesh morphing using homography mapping of the histopathology image to adjust the alignment, shape, and resolution to match the external contour of the tissue in the THz image. Unlike conventional image morphing algorithms that rely on internal features of the source and target images, only the external contour of the tissue is used to avoid bias. Unsupervised Bayesian learning algorithm is applied to THz images to classify the tissue regions of cancer, fat, and muscles present in xenograft breast tumors. The results demonstrate that the proposed mesh morphing algorithm can provide more effective and accurate evaluation of THz imaging compared with existing algorithms. The results also showed that while THz images of FFPE tissue are highly in agreement with pathology images, challenges remain in assessing THz imaging of fresh tissue.
Thursday, March 1, 2018
Researchers Move Closer to Improved Method of Detecting Breast Cancer
https://news.uark.edu/articles/41165/researchers-move-closer-to-improved-method-of-detecting-breast-cancer
FAYETTEVILLE, Ark. – Engineering researchers at the University of Arkansas have moved closer to developing an alternative method of detecting and possibly treating breast cancer.
The researchers, led by Magda El-Shenawee, professor of electrical engineering, work with pulsed, terahertz imaging, a type of electromagnetic radiation technology previously used to find land mines. They adapted the technology to detect tumors and provide highly specific images of them.
Standard breast cancer imaging techniques do not always provide clear assessment of breast tissue on the margins of a tumor. Without an accurate picture of the margins between the tumor and healthy tissue, surgeons cannot be sure they have removed the entire tumor during a surgery.
This shortcoming contributes to high rates – 20 to 40 percent – of secondary surgery, either lumpectomy or mastectomy. Terahertz imaging could lead to fast, noninvasive, and highly specific tumor margin assessment, which in turn could reduce the occurrence of second surgeries, cancer reoccurrence and metastasis.
FINDINGS
In their most recent study, funded by a $424,081 grant from the National Institutes of Health, the researchers created terahertz images of breast adenocarcinoma cells, a type of malignancy, excised from mice. These images were taken from 13 tumor samples. To test the accuracy of the terahertz method, the images were then statistically compared to high-resolution histopathology images of the same excised tumor samples. Histopathology is the microscopic examination of tissue changes caused by disease.
In all tissue samples containing only cancer and fat – a combination similar to tissue in the human breast – the terahertz images, when compared to the histopathology images, accurately detected cancerous tissue with a high level of specificity.
On the other hand, samples containing cancer, fat and muscle showed only a reasonable correlation, El-Shenawee said. In these samples, the terahertz images detected cancer cells at the margins of tumors and muscle tissue, but not at high enough levels of specificity.
“Overlap between muscle and cancer tissue in the terahertz image creates some challenge in correctly classifying these regions,” El Shenawee said. “While muscle is unlikely to be present in surgical sections of human breast cancer, other kinds of fibrous tissue may be, so this requires further investigation with more advanced models.”
Future work will focus on spontaneously generated breast cancer tumors from genetically modified mice, which have tumor and tissue structures closer to that of humans. El-Shenawee said these models will provide a more accurate assessment of terahertz imaging. The researchers will also compare the terahertz images to other standard imaging techniques, such as radiography and computed tomography, or CT scan.
TERAHERTZ IMAGING
![]() Pathology image, left, and corresponding terahertz image, right, of excised tissue from mouse breast tumor. Photo submitted by the researcher. |
Pulsed, terahertz spectroscopy produces high-quality images of the tissue, down to 80 micrometers. It scatters fewer waves than radiography, which enables deeper imaging into an object. Also, because terahertz radiation can transmit through most non-metallic materials, the systems can “see” through concealing barriers. For many years, El-Shenawee has focused on developing this detection system for health-care applications, while also investigating the unique electromagnetic signals emitted by breast cancer cells.
THE TEAM
The research team included Tyler Bowman, Tanny Chavez, graduate students in electrical engineering; Jingxian Wu, associate professor of electrical engineering; Kamrul Khan, graduate student in mathematical sciences; Avishek Chakraborty, assistant professor of statistics; Narasimhan Rajaram, assistant professor of biomedical engineering; and Keith Bailey, animal pathologist at Oklahoma State University.
Their findings were published in the February issue of Journal of Biomedical Optics.
This research was conducted in the Terahertz Imaging and Spectroscopy research facilities at the University of Arkansas.
About the College of Engineering: The University of Arkansas College of Engineering is the largest engineering program in the state of Arkansas. Over the past decade, the college has experienced unprecedented growth. Undergraduate enrollment has doubled since 2007, and total enrollment in the college is now over 4,000 students. The College of Engineering offers graduate and undergraduate degrees in nine engineering fields, as well as incorporating distance learning and interdisciplinary programs. Faculty in the college conduct research in many key areas, including electronics, energy, biomedical and healthcare engineering, materials science, transportation and logistics.
About the University of Arkansas: The University of Arkansas provides an internationally competitive education for undergraduate and graduate students in more than 200 academic programs. The university contributes new knowledge, economic development, basic and applied research, and creative activity while also providing service to academic and professional disciplines. The Carnegie Foundation classifies the University of Arkansas among only 2 percent of universities in America that have the highest level of research activity. U.S. News & World Report ranks the University of Arkansas among its top American public research universities. Founded in 1871, the University of Arkansas comprises 10 colleges and schools and maintains a low student-to-faculty ratio that promotes personal attention and close mentoring.
TOPICS
CONTACTS
Magda El-Shenawee, professor, electrical engineering
College of Engineering
479-575-6582, magda@uark.edu
College of Engineering
479-575-6582, magda@uark.edu
Matt McGowan, science and research communications officer
University Relations
479-575-4246, dmcgowa@uark.edu
University Relations
479-575-4246, dmcgowa@uark.edu
Friday, February 23, 2018
Abstract-Pulsed terahertz imaging of breast cancer in freshly excised murine tumors
Tyler Bowman, Tanny Chavez, Kamrul Khan, Jingxian Wu, Avishek Chakraborty, Narasimhan Rajaram, Keith Bailey, Magda El-Shenawee,
https://www.spiedigitallibrary.org/journals/Journal-of-Biomedical-Optics/volume-23/issue-2/026004/Pulsed-terahertz-imaging-of-breast-cancer-in-freshly-excised-murine/10.1117/1.JBO.23.2.026004.short
This paper investigates terahertz (THz) imaging
and classification of freshly excised murine xenograft breast cancer tumors.
These tumors are grown via injection of E0771 breast adenocarcinoma cells into
the flank of mice maintained on high-fat diet. Within 1 h of excision, the
tumor and adjacent tissues are imaged using a pulsed THz system in the
reflection mode. The THz images are classified using a statistical Bayesian
mixture model with unsupervised and supervised approaches. Correlation with digitized
pathology images is conducted using classification images assigned by a modal
class decision rule. The corresponding receiver operating characteristic curves
are obtained based on the classification results. A total of 13 tumor samples
obtained from 9 tumors are investigated. The results show good correlation of
THz images with pathology results in all samples of cancer and fat tissues. For
tumor samples of cancer, fat, and muscle tissues, THz images show reasonable
correlation with pathology where the primary challenge lies in the overlapping
dielectric properties of cancer and muscle tissues. The use of a supervised
regression approach shows improvement in the classification images although not
consistently in all tissue regions. Advancing THz imaging of breast tumors from
mice and the development of accurate statistical models will ultimately
progress the technique for the assessment of human breast tumor margins.
Saturday, November 25, 2017
Abstract-Statistical signal processing for quantitative assessment of pulsed terahertz imaging of human breast tumors
Tyler Bowman, Tanny Chavez, Kamrul Khan, Avishek Chakraborty, Jingxian Wu, Keith Bailey, Magda El-Shenawee,
http://ieeexplore.ieee.org/document/8067265/
This paper presents utilization of pulsed terahertz imaging to detect and assess the margins of excised human breast tumors. The freshly excised bulk tissue and the block of the same tissue fixed in formalin and embedded in paraffin (FFPE) are scanned using the reflection imaging module. The results show that while the THz images of block tissue demonstrate strong contrast between cancerous and fibroglandular (normal) regions, the contrast is less strong in the images of freshly excised bulk tissue. This could be due to remains of blood and presence of air bubbles in the specimen. To validate the THz images, we develop signal processing and statistical data analysis techniques based on image morphing and Bayesian modeling. The results are quantified in the form of receiver operating characteristic (ROC) curves.
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