Regional admittivity spectra with Tomosynthesis images for breast cancer detection Tzu-Jen. Kao 1, G. Boverman 1, J.C. Newell 1, D. Isaacson 2, G.J. Saulnier.

Slides:



Advertisements
Similar presentations
Ernest Wollin, MD, PE, FACR Wollin Ventures Incorporated Sarasota, Florida John J. Heine, PhD Associate Professor Cancer Epidemiology Department H. Lee.
Advertisements

Patient information extraction in digitized X-ray imagery Hsien-Huang P. Wu Department of Electrical Engineering, National Yunlin University of Science.
Image Selection T1 and T1 phantom images based on colin27 are used Segmentation Segmentation was performed using BrainSuite Finite Element Mesh Generation.
Original Figures for "Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring"
GEO-ELECTRIC INVESTIGATION OF UNDERGROUND LEACHATE DISTRIBUTION AT A CLOSED LANDFILL IN SOUTHWESTERN ONTARIO, CANADA Joshi, Siddharth 1, Yang, Jianwen.
Advanced Biomedical Imaging Dr. Azza Helal A. Prof. of Medical Physics Faculty of Medicine Alexandria University Lecture 6 Basic physical principles of.
Design and Simulation of a Novel MEMS Dual Axis Accelerometer Zijun He, Advisor: Prof. Xingguo Xiong Department of Electrical and Computer Engineering,
THE COMPLETE ELECTRODE MODEL FOR IMAGING AND ELECTRODE CONTACT COMPENSATION IN ELECTRICAL IMPEDANCE TOMOGRAPHY G. Boverman 1, B.S. Kim 1, T.-J. Kao 3,
Qianqian Fang, Stefan Carp, Mark Martino, Richard Moore, Daniel Kopans, David Boas Optics Division, Martinos Center for Biomedical Imaging, Massachusetts.
Predicting the parameters of a prostate IMRT objective function based on dose statistics under fixed parameter settings Renzhi Lu, Richard J. Radke 1,
COMPUTATIONAL INTELLIGENCE FOR THE DETECTION AND CLASSIFICATION OF MALIGNANT LESIONS IN SCREENING MAMMOGRAPHY DATA E. Panourgias,
Computer Aided Diagnosis: CAD overview
Methods Methods ConclusionConclusion Improving Image Quality of Digital Mammographic Images Using an Undecimated Discrete Wavelet Transform Method: Performance.
La Parguera Hyperspectral Image size (250x239x118) using Hyperion sensor. INTEREST POINTS FOR HYPERSPECTRAL IMAGES Amit Mukherjee 1, Badrinath Roysam 1,
1 Electrical Impedance Tomography with Tomosynthesis for Breast Cancer Detection Jonathan Newell Rensselaer Polytechnic Institute With: David Isaacson.
A Computer Aided Detection System For Digital Mammograms Based on Radial Basis Functions and Feature Extraction Techniques By Mohammed Jirari Shanghai,
A Computer-Aided Diagnosis System For Digital Mammograms Based on Radial Basis Functions and Feature Extraction Techniques Dissertation written by Mohammed.
In The Nam of God.
Method for Determining Apparent Diffusion Coefficient Values for Cerebral Lesions from Diffusion Weighted Magnetic Resonance Imaging Examinations T.H.
Automatic Detection And Classification Of Microcalcifications In Digital Mammograms Institute for Brain and Neural Systems Brown University Providence.
Breast Imaging Made Brief and Simple
Background on: Breast Cancer, X-Ray and MRI Mammography
Digital Image Characteristic
High frequency ultrasound in monitoring organ viability for transplantation Roxana Vlad 1, Anoja Giles 1, 2, G.J Czarnota 1, 2, J.W. Hunt 1, 2, M.D. Sherar.
Theoretical Analysis of a Nanosensor based on WO 3 coated Carbon Nanotube for Ultra-high Sensitive Breath Acetone Sensing Ming Xia, Advisor: Prof. Xingguo.
Bayesian Network for Predicting Invasive and In-situ Breast Cancer using Mammographic Findings Jagpreet Chhatwal1 O. Alagoz1, E.S. Burnside1, H. Nassif1,
Articulating Confocal Endoscope for Imaging Cancers in vivo This work is supported in part by the Center for Subsurface Sensing and Imaging Systems, under.
Parameter selection in prostate IMRT Renzhi Lu, Richard J. Radke 1, Andrew Jackson 2 Rensselaer Polytechnic Institute 1,Memorial Sloan-Kettering Cancer.
Introduction Many clinicians routinely use multiple receive coils for magnetic resonance imaging (MRI) studies of the human brain. In conjunction with.
ACRIN Breast Committee Fall Meeting : Comparison of Full-Field Digital Mammography with Digital Breast Tomosynthesis Image Acquisition in Relation.
Measuring shape complexity of breast lesions on ultrasound images Wei Yang, Su Zhang, Yazhu Chen Dept. of Biomedical Engineering, Shanghai Jiao Tong Univ.,
Impedance Imaging for Breast Cancer Diagnosis Tzu-Jen Kao 1, Ning Liu 3, Hongjun Xia 1, Bong Seok Kim 1, David Isaacson 2, Gary J. Saulnier 3, Jonathan.
Improving the object depth localization in fluorescence diffuse optical tomography in an axial outward imaging geometry using a geometric sensitivity difference.
Surface Reconstruction of Blood Vessels from 3D Fluorescence Microscopy Images Abstract This project aims at doing a surface reconstruction of 3D fluorescence.
Accuracy of Interpretation of Computerized Multichannel Lung Sound Analyses R. Murphy, A. Wong-Tse, and A. Vyshedskiy, Brigham and Women’s / Faulkner Hospitals,
Tao Yuan, Jingzhou Xu, and Xicheng Zhang Rensselaer Polytechnic Institute, Troy, New York Scanning THz Emission Microscope Abstract A THz image system.
Powerpoint Templates Page 1 Depth Effects of DEP Chip with Microcavities Array on Impedance Measurement for Live and Dead Cells Cheng-Hsin Chuang - STUST.
EXTENSIBLE ELECTRICAL CAPACITANCE TOMOGRAPHY SYSTEM FOR GAS–LIQUID TWO-PHASE FLOW From S. Xin H. Wang, “Extensible electrical capacitance tomography system.
Suppression of the eyelash artifact in ultra-widefield retinal images Vanessa Ortiz-Rivera – Dr. Badrinath Roysam, Advisor –
AdvisorStudent Dr. Jia Li Shaojun Liu Dept. of Computer Science and Engineering, Oakland University Automatic 3D Image Segmentation of Internal Lung Structures.
STATISTICS AND OPTIMIZATION Dr. Asawer A. Alwasiti.
Visualization of Tumors in 4D Medical CT Datasets Visualization of Tumors in 4D Medical CT Datasets Burak Erem 1, David Kaeli 1, Dana Brooks 1, George.
ACT4: A High-Precision, Multi-frequency Electrical Impedance Tomograph. Chandana Tamma 1, Ning Liu 1, G.J. Saulnier 1 J.C. Newell 2 and D. Isaacson 3.
Visceral Adiposity at Diagnosis Correlates with Tumor Size and Metastatic Progression in Clear Cell Renal Carcinoma A Shuster, MD (1), M Patlas, MD (1),
BME 353 – BIOMEDICAL MEASUREMENTS AND INSTRUMENTATION MEASUREMENT PRINCIPLES.
Hemodynamically Constrained Dynamic Diffuse Optical Tomography Under Mammographic Compression Eleonora Vidolova 1, Dana Brooks 1, Eric Miller 2, Stefan.
The current density at each interfacial layer. The forward voltage is continuous at every point inside the body. A Layered Model for Breasts in Electrical.
Properties of the bakelite used for standard RPC chambers as a function of the operating temperature F. Bruni – G. Hull – S.M. Mari 4 digital thermometers.
A WIRELESS PASSIVE SENSOR FOR TEMPERATURE COMPENSATED REMOTE PH MONITORING IEEE SENSORS JOURNAL VOLUME 13, NO.6, JUNE 2013 WEN-TSAI SUNG, YAO-CHI HSU Ching-Hong.
Evaluation of an Automatic Algorithm Based on Kernel Principal Component Analysis for Segmentation of the Bladder and Prostate in CT Scans Siqi Chen and.
Date of download: 6/23/2016 Copyright © ASME. All rights reserved. From: Design of Bioimpedance Spectroscopy Instrument With Compensation Techniques for.
A Study of Electrical Impedance Property of an L 2 ePt Electrode 1 Interdisciplinary Program, Bioengineering Major, Graduate School, Seoul National University,
Date of download: 6/29/2016 Copyright © 2016 SPIE. All rights reserved. The schematic of the rotational probe in noncontact diffuse correlation tomography.
Figure 1: a 32-year-old woman presented with RT breast mass, MRI showed false positive diagnosis of cancer. Dynamic contrast enhanced MRI, axial subtraction.
Date of download: 9/20/2016 Copyright © 2016 SPIE. All rights reserved. (a) Photographs of the anthropomorphic phantom used in this study and (b) the inner.
Descriptive Statistics The means for all but the C 3 features exhibit a significant difference between both classes. On the other hand, the variances for.
J Cho, G Ibbott, M Kerr, R Amos, and O Mawlawi
Tomography for Intraoperative Evaluation of Breast Tumor Margins:
Date of download: 10/17/2017 Copyright © ASME. All rights reserved.
Quantification of tumor localization needle displacement prior to tumor excision in navigated lumpectomy Christina Yan1, Tamas Ungi1, Gabrielle Gauvin2,
Contrast-enhanced Dedicated Breast CT: Initial Clinical Experience
Excitation based cone-beam X-ray luminescence tomography of nanophosphors with different concentrations Peng Gao*, Huangsheng Pu*, Junyan Rong, Wenli Zhang,
From: Tipping the Balance of Benefits and Harms to Favor Screening Mammography Starting at Age 40 YearsA Comparative Modeling Study of Risk Ann Intern.
Dipole Antennas Driven at High Voltages in the Plasmasphere
CS 698 | Current Topics in Data Science
VALUES OF ELASTOGRAPHY IN DIAGNOSIS OF THYROID CANCER
Volume 148, Issue 1, Pages (January 2012)
Volume 103, Issue 9, Pages (November 2012)
H. Sadeghi, D.E.T. Shepherd, D.M. Espino  Osteoarthritis and Cartilage 
Submitted By : Pratish Singh Kuldeep Choudhary Chinmay Panchal
Presentation transcript:

Regional admittivity spectra with Tomosynthesis images for breast cancer detection Tzu-Jen. Kao 1, G. Boverman 1, J.C. Newell 1, D. Isaacson 2, G.J. Saulnier 3, R.H. Moore 4 and D.B. Kopans 4 Departments of 1 Biomedical Engineering, 2 Mathematical Sciences, and 3 Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 4 Department of Radiology, Massachusetts General Hospital, Boston, MA Introduction: Research on freshly-excised malignant breast tissues and surrounding normal tissues in an in vitro impedance cell has shown that breast tumors have significant differences in the frequency spectrum of the admittivity between normal or non- malignant tissues and tumors [4]. This contrast may provide a basis for breast cancer detection using frequency scanning in electrical impedance imaging. We present a method for analyzing electrical impedance spectroscopy (EIS) data from breast cancer patients with co-registered EIT image and Tomosynthesis image. We can find a region of interest by Tomosynthesis and analyze the admittivity spectra of the corresponding region by 3-D EIT reconstructions. An EIS plot is generated and displayed for each of the reconstructed voxels or mesh elements at 5 frequencies: 5, 10, 30, 100 and 300 kHz. The distribution of the admittivity spectra for normal breast tissue from patients are compared with those from patients with breast tumor as verified by the pathology report of a biopsy sample. The potential usefulness of this analysis is to distinguish breast cancer from normal tissue with the admittivity data. It is also possible that suspicious regions may be found by the EIS plots and then further analyzed by Tomosynthesis. Future Plans: The LCM parameter that we have defined has clearly identified the malignancies in our small patient sample. It is premature to assert that the LCM parameter is the best parameter for detecting malignancies. We will further investigate LCM and other parameters in a systematic and quantitative way in order to assess and compare their performance. Despite our success many of the data sets that we have collected from patients are not presently usable due to electrode contact problems. For this reason we will study effects of skin treatments and breast compression on electrical contact between the patient and the electrode arrays. We will improve our hardware and reconstruction algorithms with the goals of greatly reducing the fraction of unusable data sets and increasing the accuracy of the estimated electrical parameters within the breast. This may lead to the increased detection and localization of smaller malignancies. In conclusion the study of additional patients and the associated improvements in hardware and software is an important step in determining whether EIT/EIS can be used to improve the sensitivity and specificity of mammography for breast cancer screening. For this reason the proposed study may have a significant impact on the ability to detect and treat this cause of mortality. References: Publications Acknowledging NSF Support: 1. Ning Liu, Gary J. Saulnier, J.C. Newell, D. Isaacson and T-J Kao. “ACT4: A High-Precision, Multi-frequency Electrical Impedance Tomography” Conference on Biomedical Applications of Electrical Impedance Tomography, University College London, June 22-24th, Tzu-Jen Kao, G. J. Saulnier, Hongjun Xia, Chandana Tamma, J.C. Newell and D. Isaacson “A compensated radiolucent electrode array for combined EIT and mammography” Physiol. Meas (in Press). 3. Choi, M.H., T-J. Kao, D. Isaacson, G.J. Saulnier and J.C. Newell “A Reconstruction Algorithm for Breast Cancer Imaging with Electrical Impedance Tomography in Mammography Geometry” IEEE Trans. Biomed. Eng. 54(4): (In Press), Others: 4. Jossinet, J. and M. Schmitt. “A review of Parameters for Bioelectrical characterization of Breast Tissue” Ann. NY Acad. Sci. Vol. 873:30-41, 1999 EIT and Tomosynthesis co-registered The ACT 4 system [1] is the electrical impedance imaging system being developed at Rensselaer. It is a high-speed, high-precision, multi-frequency, multi-channel instrument which supports 64 channels and electrodes. Each electrode is driven by a high precision voltage source, and has a circuit for measuring the resulting electrode current. These circuits are digitally controlled to produce and measure signals at 5k, 10k, 30k, 100k, 300k and 1MHz. The magnitude and phase of each source are controlled independently. The system has been used to study breast cancer patients at Massachusetts General Hospital in conjunction with a tomosynthesis machine and verified with biopsy results. The EIT images are co-registered with tomosynthesis images since the EIT electrodes are placed on the mammograph plates as shown. Importance of the work and technology transfer: The EIT clinical data and analysis in mammogram geometry provide a foundation to assess the value of EIT as an adjunct to mammography for breast cancer screening and diagnosis. This work is supported in part by CenSSIS, the Center for Subsurface Sensing and Imaging Systems, under the Engineering Research Centers Program of the National Science Foundation (Award Number EEC ) and by NIBIB, the National Institute of Biomedical Imaging and Bioengineering under Grant Number R01-EB Contact Info: Jonathan Newell, Ph. D. Research Professor of Biomedical Engineering Rensselaer Polytechnic Institute Web site: Eighth St. Troy, NY Phone : FAX : Model of the mammogram geometry: Patient #Pathology reportGradeEIS spectra / LCM value HS14_RScreening patient, normal breast No biopsy report All EIS Plots have good curvature. LCM < 137 for all regions. Maximum value of LCM: 137. HS21_RHyalinized Fibroadenoma No evidence of malignancy Most EIS Plots have good curvature. LCM < 328 for the tumor region. LCM < 200 for most other regions Maximum value of LCM: 328. HS25_LInvasive ductal carcinoma Ductal carcinoma in-situ A few cylindrical to irregular tan- yellow soft tissue cores ranging from 0.3 to 1.2 cm in length and averaging 0.1 cm in diameter. 3/3EIS plots on bottom right corner are abnormal. Others have good curvature. LCM > 400 for the tumor region. Maximum value of LCM: 709. HS10_LInvasive ductal carcinoma, (Proliferation is worrisome) Ductal carcinoma in-situ Atypical ductal hyperplasia Tumor size: 1..1 x 0.9 x 0.7 cm and two satellite nodules, 0.14 cm and < 0.1 cm. 2/3Most EIS plots are close to a straight line. LCM > 400 for most plots. Maximum value of LCM: Figure 6. The LCM distributions from 11 normal breasts. Figure 7. The distributions of the LCM for the regions of interest identified in Figures 4. Note the LCM values are much larger for voxels associated with the malignant lesions. ROI_1 refers to region associated with the EIS plots at the left of Figure 4 while ROI_2 refers to the region associated with those on the right. Figure 4. Tomosynthesis images for HS14_R, HS21_R, HS_25_L and HS10_L with EIS plots for reconstructed layer 3 for the indicated regions. Note that the cancer tissue produces more nearly linear EIS plots. We superimpose a grid over the tomosynthesis images to show where the reconstructed voxels are located in the breast. Figure 5. Tomosynthesis images and LCM images from layer 3. Note the more linear the EIS curve in Figure 4 the larger the LCM value and, hence, the brighter the corresponding voxel in the LCM image. Figure 2. Side view of volume and mesh elements between the arrays used in patient studies. Reconstructions [3] from layer 3 (labeled III above) are displayed in the figures below. Figure 3. Admittance loci of excised tissue samples by Jossinet EIS for Breast tissue and the LCM parameter: The studies of excised tissue in Fig. 3, and our reconstructed EIS curves in Fig. 4 suggested that EIS graphs of malignant tissue should be highly correlated with straight lines. We tested this hypothesis by making a gray scale image for each patient of how correlated the EIS curve in each voxel is with a straight line. The measure of correlation is given by fitting the EIS curve to a line. The line is then used to predict the values of the scaled permittivities (vertical coordinates) denoted by the vector Y that correspond to the conductivities (horizontal coordinates). The reconstructed permittivities are denoted by the vector Ym. This Linear Correlation Measure, hereafter called LCM, is defined to be: where and ||A|| denote the inner product and norm, respectively. Clinical results & analysis: Table 1. Summary of the pathology reports and the analysis of EIS plots Figure 1. ACT 4 with the mammography unit ( top left), radiolucent electrode array [2] attached to the lower compression plate (upper right), one slice of the tomosynthesis image made with the electrode arrays in place of the left breast from human subject HS14 (lower left) and tomosynthesis image with an overlaid grid showing the location of the active electrode surfaces (lower right). Note that the copper leads and ribbon cables are visible on the left and right of the tomosynthesis images but the radiolucent portion of the arrays is not visible.