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

• Breast Cancer:

– 32% of all cancers in women, ~40,460 deaths

annually (ACS)

– Most commonly diagnosed cancer

– Second cause of cancer death in women, after

lung cancer

• Patients’ long term survival: tumor detection at its

early stage

1. Introduction

• Prominent clinical technique: X-ray mammography– Low sensitivity, radiographically

higher density breasts– High false-positive rate (1% to

29% → mean 10%)– Unnecessary and costly surgical

interventions– Uncomfortable compression – Ionizing radiation

1. Introduction

• Other clinical standards: Ultrasound and MRI

– High spatial resolution but lack of functional

information

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1. Introduction

1. Introduction

Alternative/complementary medical imaging modalities Improve both sensitivity and specificity

Supply more functional information

Thayer School of Engineering: NIH Program Project directed by Dr. Keith Paulsen

Alternative Breast Cancer Imaging

Modalities

NIR MRE EIT MIS

• Active microwaves (ranging from high MHz to low GHz) to detect abnormalities in the breast

– Mammary glands: largest difference in dielectric properties of various normal and malignant tissues

• Advantages: – Substantial information about the malignancy or

healthiness of breast tissue– Fat → Easy penetration– Easy accessibility– Non-ionizing and non-compressive

Dielectric Properties of tissues:

Permittivity

Conductivity

2. MIS (microwave Imaging Spectroscopy)

Measuring the effects of the intervention of an electromagnetic field at specific frequency with tissue

2. MIS (microwave Imaging Spectroscopy)

2. MIS (microwave Imaging Spectroscopy)

Microwave Imaging at Dartmouth:

2. MIS (microwave Imaging Spectroscopy)

Clinical MIS system at Dartmouth Hitchcock Medical Center (DHMC)

3. Image Reconstruction• Microwave Imaging: 1) Forward problem, 2)

Inverse problem– Non-linear → Iterative solvers– Ill-posed → Gauss-Newton + regularization

• Reconstruction algorithm: Distribution of constitutive parameters– Squared complex wave number:

• Gauss-Newton iterative approach: Objective function

• Iterative property update:

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3. Image Reconstruction• Dual mesh

approach:– Forward solution:

2D and 3D rectangular uniform FDTD grid

– Parameter reconstruction: triangular/tetrahedron element mesh

3-1. Image Reconstruction (2D)

• Assumption: 3D scattering problem can be reasonably represented as a simplified 2D model.

• Advantages:– Simpler forward model– Computation speed

3-1. Image Reconstruction (2D)

• Limitations:– 2D model to characterize a 3D phenomenon: excessive

simplification that can introduce image artifacts– Small region of interest may fall between two

consecutive imaging slices– Accuracy

• Key points:– Antenna location– Lossy background medium– Log-transform algorithm

3-2. Image Reconstruction (3D)

• Extension of the 2D reconstruction algorithm:– Iterative regularized Gauss-Newton algorithm using the

dual-mesh approach…• What is different?

– 3D volumetric regions– Size of forward field and parameter modeling problems– More measurement data → more reconstruction parameters– Significant increase in computational time and effort

• How to approach?– MATLAB interface– MEX-Files– Parallel Computing

4-1. Reconstructed Images (2D Simulation)

• Blob-shape target • Target: εr,Inc = 40.0 and σInc = 1.3 S/m

• Background medium εr,bk = 15.6 and σbk = 0.9 S/m• -100 dBm added noise• 1300 MHz:

Permittivity

Conductivity

4-2. Reconstructed Images (3D Phantom Exp.)

• Square cylinder: 1cm side length• Target: εr,Inc = 50.6 and σInc = 1.28 S/m

• Background medium εr,bk = 22.4 and σbk = 1.26 S/m

• 1300 MHz• Iso-surface values: εr = 27.5 and σ = 1.35 S/m

4-3. Reconstructed Images (Patient Data)

Patients at Dartmouth Hitchcock Medical Center:3000 Series, Therapy Monitoring: patients diagnosed with

cancer → Chemotherapy Baseline: before chemo

2 day after 1st infusion of chemo

1 week post chemo

More treatment

4000 Series, Screening Patients with suspected tumor

600 Series: Probe patients: Imaged before and after surgery → ex-vivo

500 Series: Normal patient

4-3. Reconstructed Images (Patient Data)

Neo-adjuvant therapy monitoring patient (from 3000 series)

• 1300 MHz

εr

σ

5. Conclusion

• Using microwave imaging for breast cancer detection and therapy monitoring• 2D and 3D simulation and phantom experiments• Non-uniform complex geometry• Small-size inclusion

• Clinical patient data: size of the tumor decreased significantly during the course of treatment

• Microwave imaging has the potential to become an alternative and/or supplementary technique for breast cancer detection and therapy monitoring.

6. Future direction

• Improving the reconstruction algorithms • Recover more accurate dielectric property

distributions • Detect even smaller tumors

• Using 3D reconstruction algorithm for patient data • more detailed and accurate images

• Combining 3D microwave imaging and MR

Acknowledgement

• MIS group at Thayer School of Engineering

Matt Pallone, Tian Zhou, Neil Epstein

This work was sponsored by NIH/NCI grant # P01-CA080139

Prof. Paul Meaney Prof. Keith PaulsenShireen Geimer

Thank you!