Variational Bayesian inversion for microwave breast imaging

International audienceMicrowave imaging is considered as a nonlinear inverse scattering problem and tackled in a Bayesian estimation framework. The object under test (a breast affected by a tumor) is assumed to be composed of compact regions made of a restricted number of different homogeneous materials. This a priori knowledge is defined by a Gauss-Markov-Potts distribution. First, we express the joint posterior of all the unknowns; then, we present in detail the variational Bayesian approximation used to compute the estimators and reconstruct both permittivity and conductivity maps. This approximation consists of the best separable probability law that approximates the true posterior distribution in the Kullback-Leibler sense. This leads to an implicit parametric optimization scheme which is solved iteratively. Some preliminary results, obtained by applying the proposed method to synthetic data, are presented and compared with those obtained by means of the classical contrast source inversion method

Variational Bayesian inversion for microwave breast imaging

International audienceMicrowave imaging is considered as a nonlinear inverse scattering problem and tackled in a Bayesian estimation framework. The object under test (a breast affected by a tumor) is assumed to be composed of compact regions made of a restricted number of different homogeneous materials. This a priori knowledge is defined by a Gauss-Markov-Potts distribution. First, we express the joint posterior of all the unknowns; then, we present in detail the variational Bayesian approximation used to compute the estimators and reconstruct both permittivity and conductivity maps. This approximation consists of the best separable probability law that approximates the true posterior distribution in the Kullback-Leibler sense. This leads to an implicit parametric optimization scheme which is solved iteratively. Some preliminary results, obtained by applying the proposed method to synthetic data, are presented and compared with those obtained by means of the classical contrast source inversion method

Microwave breast imaging by the filtered forward-backward time-stepping method

In this paper, an inverse scattering technique referred to as the filtered forward-backward time-stepping method is applied to microwave imaging for breast cancer detection. A two-dimensional numerical breast phantom (derived from MR images) with high contrast between fat and fibroglandular tissues, and low contrast between fibroglandular and tumor tissues are used to assess the efficacy of the proposed method.2010 URSI International Symposium on Electromagnetic Theory (EMTS 2010) : Berlin, Germany, 2010.08.16-2010.08.1

Nanomagnetic Resonance Imaging (Nano-MRI) Gives Personalized Medicine a New Perspective

This chapter provides a brief overview of molecular imaging techniques and its present and future potential in personalized medicine, with special a focus on the magnetic resonance imaging (MRI) approach. It discusses the current techniques that allow for the in vivo visualization of molecular processes at the nanoscale resolution (nano-MRI). Nano-MRI is progressing rapidly thanks to the work of a very small but extremely brilliant community of experts. This paper is not intended to be a comprehensive review of nano-MRI written for these experts, but rather a concise description of the present achievements for a much broader audience of medical professionals. The goal is to bridge the gap between the nano- MRI community and those in the medical field that will ultimately benefit from the further development of nano-MRI targeting specific medical goals. The aim of this review is to highlight the potential of nano-MRI in the improvement of MRI sensitivity and consequently on the impact of this widely used technique for diagnosis and personalized treatment. Sensitivity improvements are based on the use of magnetic nanoprobes in conventional MRI as well as novel nanoscale imaging based on nitrogen-vacancy (NV) centers in diamonds

Brain Stroke Detection by Microwaves Using Prior Information from Clinical Databases

Microwave tomographic imaging is an inexpensive, noninvasive modality of media dielectric properties reconstruction which can be utilized as a screening method in clinical applications such as breast cancer and brain stroke detection. For breast cancer detection, the iterative algorithm of structural inversion with level sets provides well-defined boundaries and incorporates an intrinsic regularization, which permits to discover small lesions. However, in case of brain lesion, the inverse problem is much more difficult due to the skull, which causes low microwave penetration and highly noisy data. In addition, cerebral liquid has dielectric properties similar to those of blood, which makes the inversion more complicated. Nevertheless, the contrast in the conductivity and permittivity values in this situation is significant due to blood high dielectric values compared to those of surrounding grey and white matter tissues. We show that using brain MRI images as prior information about brain's configuration, along with known brain dielectric properties, and the intrinsic regularization by structural inversion, allows successful and rapid stroke detection even in difficult cases. The method has been applied to 2D slices created from a database of 3D real MRI phantom images to effectively detect lesions larger than 2.5 × 10−2 m diameter