The role of malignant tissue on the thermal distribution of cancerous breast

The present work focuses on the integration of analytical and numerical strategies to investigate the thermal distribution of cancerous breasts. Coupled stationary bioheat transfer equations are considered for the glandular and heterogeneous tumor regions, which are characterized by different thermophysical properties. The cross-section of the cancerous breast is identified by a homogeneous glandular tissue that surrounds the heterogeneous tumor tissue, which is assumed to be a two-phase periodic composite with non-overlapping circular inclusions and a square lattice distribution, wherein the constituents exhibit isotropic thermal conductivity behavior. Asymptotic periodic homogenization method is used to find the effective properties in the heterogeneous region. The tissue effective thermal conductivities are computed analytically and then used in the homogenized model, which is solved numerically. Results are compared with appropriate experimental data reported in the literature. In particular, the tissue scale temperature profile agrees with experimental observations. Moreover, as a novelty result we find that the tumor volume fraction in the heterogeneous zone influences the breast surface temperature

A Patient-Specific Approach for Breast Cancer Detection and Tumor Localization Using Infrared Imaging

Breast cancer (BC) is the most common cancer among women in the United States; approximately one out of every 24 women die of related causes. BC screening is a critical factor for improving patient prognosis and survival rate. Infrared (IR) thermography is an accurate, inexpensive and operator independent modality that is not affected by tissue density as it captures surface temperature variations induced by the presence of tumors. A novel patient-specific approach for IR imaging and simulation is proposed. In this work, multi-view IR images of isolated breasts are obtained in the prone position (face down), which allows access to the entire breast surface because the breasts hang freely. The challenge of accurately determining size and location of tumors within the breasts is addressed through numerical simulations of a patient-specific digital breast model. The digital breast models for individual patients are created from clinical images of the breast, such as IR imaging, digital photographs or magnetic resonance images. The numerical simulations of the digital breast model are conducted using ANSYS Fluent, where computed temperature images are generated in the same corresponding views as clinical IRI images. The computed and clinical IRI images are aligned and compared to measure their match. The determination of tumor size and location was conducted through the Levenberg-Marquardt algorithm, which iteratively minimized the mean squared error. The methodology was tested on the breasts of seven patients with biopsy-proven breast cancer with tumor diameters ranging from 8 mm to 27 mm. The method successfully predicted the equivalent tumor diameter within 2 mm and the location was predicted within 6.3 mm in all cases. The time required for the estimation is 48 minutes using a 10-core, 3.41 GHz workstation. The method presented is accurate, fast and has potential to be used as an adjunct modality to mammography in BC screening, especially for dense breasts

Breast Tumor Simulation and Parameters Estimation Using Evolutionary Algorithms

An estimation methodology is presented to determine the breast tumor parameters using the surface temperature profile that may be obtained by infrared thermography. The estimation methodology involves evolutionary algorithms using artificial neural network (ANN) and genetic algorithm (GA). The ANN is used to map the relationship of tumor parameters (depth, size, and heat generation) to the temperature profile over the idealized breast model. The relationship obtained from ANN is compared to that obtained by finite element software. Results from ANN training/testing were in good agreement with those obtained from finite element model. After ANN validation, GA is used to estimate tumor parameters by minimizing a fitness function involving comparing the temperature profiles from simulated or clinical data to those obtained by ANN. Results show that it is possible to determine the depth, diameter, and heat generation rate from the surface temperature data (with 5% random noise) with good accuracy for the 2D model. With 10% noise, the accuracy of estimation deteriorates for deep-seated tumors with low heat generation. In order to further develop this methodology for use in a clinical scenario, several aspects such as 3D breast geometry and the effects of nonuniform cooling should be considered in future investigations

Dynamic Thermal Imaging for Intraoperative Monitoring of Neuronal Activity and Cortical Perfusion

Neurosurgery is a demanding medical discipline that requires a complex interplay of several neuroimaging techniques. This allows structural as well as functional information to be recovered and then visualized to the surgeon. In the case of tumor resections this approach allows more fine-grained differentiation of healthy and pathological tissue which positively influences the postoperative outcome as well as the patient's quality of life. In this work, we will discuss several approaches to establish thermal imaging as a novel neuroimaging technique to primarily visualize neural activity and perfusion state in case of ischaemic stroke. Both applications require novel methods for data-preprocessing, visualization, pattern recognition as well as regression analysis of intraoperative thermal imaging. Online multimodal integration of preoperative and intraoperative data is accomplished by a 2D-3D image registration and image fusion framework with an average accuracy of 2.46 mm. In navigated surgeries, the proposed framework generally provides all necessary tools to project intraoperative 2D imaging data onto preoperative 3D volumetric datasets like 3D MR or CT imaging. Additionally, a fast machine learning framework for the recognition of cortical NaCl rinsings will be discussed throughout this thesis. Hereby, the standardized quantification of tissue perfusion by means of an approximated heating model can be achieved. Classifying the parameters of these models yields a map of connected areas, for which we have shown that these areas correlate with the demarcation caused by an ischaemic stroke segmented in postoperative CT datasets. Finally, a semiparametric regression model has been developed for intraoperative neural activity monitoring of the somatosensory cortex by somatosensory evoked potentials. These results were correlated with neural activity of optical imaging. We found that thermal imaging yields comparable results, yet doesn't share the limitations of optical imaging. In this thesis we would like to emphasize that thermal imaging depicts a novel and valid tool for both intraoperative functional and structural neuroimaging

Parametric Study of Infrared Imaging Based Breast Cancer Detection Program

Breast cancer is one of the most common cancers among women and is responsible for over 41,000 lives every year in the US according to The American Cancer Society. Current screening and imaging methods such as mammography, breast magnetic resonance imaging, and breast ultrasound imaging have helped in improving survival rate when the cancer is detected at an early stage. The problems with these techniques include: low sensitivity, patient discomfort, invasiveness, and cost. Due to current advancements in infrared and computational technologies, infrared thermography has been utilized as a noninvasive adjunctive screening modality. A computerized approach using infrared imaging (IRI) has been recently developed at RIT in collaboration with Rochester General Hospital for breast cancer detection and image localization. The parameters used in this simulation have been selected based on limited information available in the literature. This study focuses on analyzing the effects of different tissue thermal parameters used in the simulation on the accuracy of prediction. Thermal conductivity and perfusion rate are systematically varied, and their effects are presented by comparing simulated images with the actual infrared images captured from a biopsy-proven breast cancer patient. The results indicate a strong influence of perfusion rate within the breast tissue surrounding the tumor on heat transfer within the breast. This study is expected to help in proper selection of thermal properties while conducting the simulations. Future directions for research are also presented

Advances in Breast Thermography

Thermography‐based breast cancer screening has several advantages as it is non-contact, non-invasive and safe. Many clinical trials have shown its effectiveness to detect cancer earlier than any other modality. Historically, thermography has only been used as an adjunct modality due to the high expertise required for manual interpretation of the thermal images and high false‐positive rates otherwise found in general use. Recent developments in thermal sensors, image capture protocols and computer‐aided software diagnostics are showing great promise in making this modality a mainstream cancer screening method. This chapter describes some of these advances in breast thermography and computer‐aided diagnostics that are poised to improve the quality of cancer care

Shape optimization for tumor location

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Challenges in the Design of Microwave Imaging Systems for Breast Cancer Detection

Among the various breast imaging modalities for breast cancer detection, microwave imaging is attractive due to the high contrast in dielectric properties between the cancerous and normal tissue. Due to this reason, this modality has received a significant interest and attention from the microwave community. This paper presents the survey of the ongoing research in the field of microwave imaging of biological tissues, with major focus on the breast tumor detection application. The existing microwave imaging systems are categorized on the basis of the employed measurement concepts. The advantages and disadvantages of the implemented imaging techniques are discussed. The fundamental tradeoffs between the various system requirements are indicated. Some strategies to overcome these limitations are outlined