Microwave Imaging for Breast Cancer Detection and Therapy Monitoring
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This work was presented at the first Annual IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems (BioWireleSS) held as part of the IEEE Radio and Wireless Symposium 2011, in Phoenix, AZ.
![1. Introduction ,[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Microwave Imaging for Breast Cancer Detection and Therapy Monitoring
- 1. Microwave Imaging for Breast Cancer Detection Amir H. Golnabi Thayer School of Engineering at Dartmouth College, NH 1st Annual IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems 16 – 20 January 2011, Phoenix, Az, USA
- 3. 32% of all cancers in women, ~40,460 deaths annually (ACS)
- 4. Most commonly diagnosed cancer
- 5. Second cause of cancer death in women, after lung cancer
- 7. Low sensitivity, radiographically higher density breasts
- 8. High false-positive rate (1% to 29% -> mean 10%)
- 9. Unnecessary and costly surgical interventions
- 14. Mammary glands: largest difference in dielectric properties of various normal and malignant tissues
- 15. Advantages:
- 16. Substantial information about the malignancy or healthiness of breast tissue
- 17. Fat -> Easy penetration
- 20. 2. MIS (microwave Imaging Spectroscopy) Microwave Imaging at Dartmouth:
- 21. 2. MIS (microwave Imaging Spectroscopy) Clinical MIS system at Dartmouth Hitchcock Medical Center (DHMC)
- 23. Non-linear -> Iterative solvers
- 24. Ill-posed -> Gauss-Newton + regularization
- 25. Reconstruction algorithm: Distribution of constitutive parameters
- 26. Squared complex wave number:
- 27. Gauss-Newton iterative approach: Objective function
- 29. Forward solution: 2D and 3D rectangular uniform FDTD grid
- 31. Advantages:
- 34. 2D model to characterize a 3D phenomenon: excessive simplification that can introduce image artifacts
- 35. Small region of interest may fall between two consecutive imaging slices
- 36. Accuracy
- 37. Key points:
- 38. Antenna location
- 41. Iterative regularized Gauss-Newton algorithm using the dual-mesh approach…
- 44. Size of forward field and parameter modeling problems
- 45. More measurement data -> more reconstruction parameters
- 46. Significant increase in computational time and effort
- 47. How to approach?
- 48. MATLAB interface
- 49. MEX-Files
- 51. Target: εr,Inc= 40.0 and σInc= 1.3 S/m
- 52. Background medium εr,bk= 15.6 and σbk= 0.9 S/m
- 53. -100 dBm added noise
- 54. 1300 MHz: Permittivity Conductivity
- 56. Target: εr,Inc= 50.6 and σInc= 1.28 S/m
- 57. Background medium εr,bk= 22.4and σbk= 1.26S/m
- 58. 1300 MHz
- 62. 2D and 3D simulation and phantom experiments
- 65. Clinical patient data: size of the tumor decreased significantly during the course of treatment
- 67. Recover more accurate dielectric property distributions
- 68. Detect even smaller tumors
- 69. Using 3D reconstruction algorithm for patient data
- 70. more detailed and accurate images
- 72. Thank you!
Editor's Notes
- Good morning,Today I’m going to talk about one of the research projects at Thayer School of Engineering at Dartmouth College, entitled “microwave imaging for breast cancer detection”.
- According to the American Cancer Society, breast cancer accounts for about 32% of all cancers in women causing over 40,000 deaths each year. Breast cancer is also the most commonly diagnosed cancer and the second cause of cancer death in women after lung cancer. Researchers have shown that the best way to improve patients’ long term survival is to detect tumor at its early stage.
- Today, the prominent clinical technique to detect breast cancer is X-ray mammography which is good for a wide range of breast densities, but has low sensitivity to radiographically denser breasts. In addition, mammography has a significantly high false-positive rate which can lead into unnecessary and expensive surgical interventions. From patients’ perspective, it’s uncomfortable and exposes to ionizing radiation.
- There are other clinical standards such as ultrasound and MRI, but while both of them provide high spatial resolution images, they lack functional information about molecular changes in tissue.
- Therefore, alternative and/or complementary imaging techniques are desired to improve both sensitivity and specificity and to supply more functional information. At Thayer School of Engineering at Dartmouth, there is an NIH funded project for alternative breast cancer imaging modalities, directed by Dr. Keith Paulsen. There are 4 research groups working on this project, namely, the Near Infrared, Magnetic Resonance Elastography, Electrical Impedance Tomography, and Microwave Imaging Spectroscopy.
- In particular, I’m presenting the microwave imaging spectroscopy, which is based on active microwaves to detect abnormalities in the breast. We are interested in dielectric properties of tissues which are permittivity and conductivity, and it has been shown that mammary glands have the largest difference in dielectric properties of various normal and malignant tissues. Some of the advantages of microwave imaging for breast cancer detection is the functional information it can provide about the malignancy or healthiness of the tissue. Since breast is mainly composed of fat, it’s easy for our signals to penetrate in the tissue and breast is relatively easy accessible comparing to other internal tissues. Finally, the non-ionizing and non-compressive nature of microwave imaging is very attractive to patients.
- As shown in this cartoon, our goal is to measure the effects of the interventional of an electromagnetic field at specific frequency with tissue
- Looking at our imaging system, there are 16 monopole antennas that can act as both transmitter and receivers. They operate in a frequency range from 500 MHz to 3 GHz and they’re mounted on two separate plates what can move independently. This configuration enables us to acquire 3D data.
- These are some pictures of our clinical imaging system at Dartmouth Hitchcock Medical Center….
- Just a brief overview of mathematics behind the image reconstruction procedure: Mainly there are 2 problems to solve. Forward problem, and the inverse problem. The forward model is based on Maxwell’s equations and we because of the ill-posedness and nonlinearity of the inverse problem, we use Gauss-Newton methods with some extra regularization. During the reconstruction, the distribution of constitutive parameters which are embedded in the squared complex wave number are calculated. Dielectric properties, permittivity and conductivity, appear here. The objective function is shown here which will be minimized for the iterative property update value.
- We also use the dual mesh approach where the forward solution is calculated on a 2D or 3D rectangular uniform grid whereas the parameter reconstruction is calculated on a triangular or tetrahedron element mesh.
- Using a 2D model is under the assumption that the 3D scattering problem can be reasonably represented as a simplified 2D model. This makes the forward model much simpler and reduces the computational time.
- However, there are some limitations, such as more image artifacts caused by excessive simplification of the model. In addition, a small region of interest may fall between two consecutive imaging slice. Nonetheless, we have been very successful reconstructing our images in 2D, because our antennas location, we use a lossy medium for reflection, and we used long-transform algorithm for the reconstruction.
- The 3D image reconstruction algorithm is a direct extension of the 2D version, but here we’re dealing with 3D volumetric regions, so the size of the forward and inverse problems are bigger and this leads to a significant computational time increase. The way we’re handling the large size of this problem is to use MEX files in MATLAB and run the whole problem is parallel, to save time.