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 cancer detection using infrared thermal imaging and a deep learning model

Women’s breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. Over the past 20 years several techniques have been proposed for this purpose, such as mammography, which is frequently used for breast cancer diagnosis. However, false positives of mammography can occur in which the patient is diagnosed positive by another technique. Additionally, the potential side effects of using mammography may encourage patients and physicians to look for other diagnostic techniques. Our review of the literature first explored infrared digital imaging, which assumes that a basic thermal comparison between a healthy breast and a breast with cancer always shows an increase in thermal activity in the precancerous tissues and the areas surrounding developing breast cancer. Furthermore, through our research, we realized that a Computer-Aided Diagnostic (CAD) undertaken through infrared image processing could not be achieved without a model such as the well-known hemispheric model. The novel contribution of this paper is the production of a comparative study of several breast cancer detection techniques using powerful computer vision techniques and deep learning models

Investigation of Heat Therapies using Multi-Scale Models and Statistical Methods

Ph.DDOCTOR OF PHILOSOPH

Prédiction par transfert inverse de l'évolution temporelle du front de solidification : applications aux réacteurs métallurgiques et à la cryochirurgie

Ce projet de recherche porte sur deux problématiques différentes, cependant, elles partagent les mêmes phénomènes physiques. Il s’agit de la prédiction inverse de l’évolution temporelle du front de solidification : (1) dans les réacteurs métallurgiques à haute température et (2) dans les tissus vivants pendant la cryochirurgie. Problématique #1 : Afin de réduire l’érosion et l’agression chimique des parois internes de briques réfractaires par la matière en fusion au sein des réacteurs métallurgiques, on laisse croître par changement de phase solide/liquide une couche protectrice sur leur surface interne. Cette couche joue un rôle extrêmement important, car elle assure l’intégrité de l’installation et prolonge sa durée de vie. Toutefois, une couche protectrice trop épaisse réduit le volume utile de réacteur et diminue ainsi la production industrielle. Le défi, pour l’industriel, consiste alors à exploiter ces réacteurs tout en maintenant une couche dont l’épaisseur est optimale. L’environnement hostile qui règne au cœur du réacteur interdit toutefois les mesures directes. Les sondes qu’on y plonge sont détruites. Pour remédier à ce problème, l’industriel recourt à la simulation numérique et, plus récemment, à une approche par transfert inverse. Cette thèse présente une procédure inverse de transfert de chaleur qui permet, à partir des mesures de température non invasives provenant d’un thermocouple situé dans les parois extérieures de briques, de prédire simultanément les paramètres thermiques inconnus ainsi que l’épaisseur de la couche protectrice au sein des réacteurs métallurgiques. La technique inverse repose sur la méthode de Levenberg-Marquardt (LMM) combinée avec la méthode de Broyden (BM). La Problématique #2 aborde quant à elle la cryochirurgie. C’est une technique récente, peu invasive, qui utilise le froid extrême pour détruire les tissus indésirables tels que les tumeurs. Elle s’adresse donc à des tumeurs internes et externes. L’objectif de la cryochirurgie est de détruire les tumeurs tout en minimisant les dommages des tissus sains adjacents. La fiabilité de cette technique dépend d'un certain nombre de paramètres thermiques tels que la température de la cryosonde, les propriétés thermiques des tissus, la durée de congélation, etc. Pour y parvenir, des méthodes expérimentales et numériques ont été développées. Cependant, chaque méthode a ses propres limites. En effet, le problème majeur est associé à la méconnaissance de certains paramètres thermiques, ce qui rend l’analyse de la transmission dans les tissus biologiques difficile. Pour pallier ces limites et améliorer la technique de cryochirurgie, une approche novatrice est retenue : il s’agit du transfert de chaleur inverse. À partir de mesures thermiques de températures provenant d’un thermocouple implanté dans la tumeur, cette approche permet de prédire les paramètres inconnus tels que la perfusion sanguine et, ensuite, de déterminer l’évolution temporelle de l’interface de congélation et la distribution de la température dans le tissu

A Patient-Specific Infrared Imaging Technique for Adjunctive Breast Cancer Screening: A Clinical and Simulation - Based Approach

Breast cancer is currently the most prevalent form of cancer in women with over 266,000 new diagnoses every year. The various methods used for breast cancer screening range in accuracy and cost, however there is no easily reproducible, reliable, low-cost screening method currently available for detecting cancer in breasts, especially with dense tissue. Steady-state Infrared Imaging (IRI) is unaffected by tissue density and has the potential to detect tumors in the breast by measuring and capturing the thermal profile on the breast surface induced by increased blood perfusion and metabolic activity in a rapidly growing malignant tumor. The current work presents a better understanding of IRI as an accurate breast cancer detection modality. A detailed study utilizing IRI-MRI approach with clinical design and validation of an elaborate IRI-Mammo study are presented by considering patient population, clinical study design, image interpretation, and recommended future path. Clinical IRI images are obtained in this study and an ANSYS-based modeling process developed earlier at RIT is used to localize and detect tumor in seven patients without subjective human interpretation. Further, the unique thermal characteristics of tumors that make their signatures distinct from benign conditions are identified. This work is part of an ongoing multidisciplinary collaboration between a team of thermal engineers and numerical modelers at the Rochester Institute of Technology and a team of clinicians at the Rochester General Hospital. The following components were developed to ensure valid experimentation while considering ethical considerations: IRB documentation, patient protocols, an image acquisition system (camera setup and screening table), and the necessary tools needed for image analysis without human interpretation. IRI images in the prone position were obtained and were used in accurately detecting the presence of a cancerous tumor in seven subjects. The size and location of tumor was also confirmed within 7 mm as compared to biopsy-proven pathology information. The study indicates that the IRI-Mammo approach has potential to be a highly effective adjunctive screening tool that can improve the breast cancer detection rates especially for subjects with dense breast tissue. This method is low cost, no-touch, radiation-free and highly portable, making it an attractive candidate as a breast cancer detection modality. Further, the developed method provided insight into infrared features corresponding to other biological images, pathology reports and patient history

APPLICATIONS OF HEAT AND MASS TRANSFER ANALYSIS IN BIO-MEDICINE AND MATERIALS

Heat and mass transfer analysis has its application in various fields including automobile, steam-electric power generation, energy systems, HVAC, electronic device cooling and in characterizing and diagnosing diseases. Here we have focused on applying the principles of heat and mass transfer to biological tissue and materials. In the first part we introduce a computational method to simultaneously estimate size, location and blood perfusion of model cancerous breast lesions from surface temperature data. A 2-dimensional computational phantom of axisymmetric tumorous breast with six tissue layers, epidermis, papillary dermis, reticular dermis, fat, gland, muscle layer and spherical tumor was used to generate surface temperature distributions and estimate tumor characteristics iteratively using an inverse algorithm based on the Levenberg-Marquardt method. However, similar steady state temperature profiles for different tumors are insufficient to simultaneously estimate blood perfusion, size and location of tumor. This becomes possible when transient temperature data are used along with steady state data. Thus, in addition to the steady state temperature data, we modified and expanded the inverse algorithm to include transient data that can be captured by dynamic infrared imaging. Blood perfusion is an indicator of the growth rate of the tumor and therefore its evaluation can lead to assessment of tumor malignancy. In the second part we treat X-ray computed tomography (CT) perfusion. The goal was to reduce the total radiation exposure by reducing the number of scans without compromising information integrity. CT scan images obtained from a rabbit model of liver and tumors were processed using the maximum slope (MS) method to estimate blood perfusion in the liver. Limitations of MS method are also discussed. The MS method makes use of key time points, forming the basis of the rationale to explore optimization strategies that utilize variable time intervals, rather than the more common approach of fixed time intervals. Results show that this leads to significant improvement, without compromising diagnostic information. In the last section we explore the magnetic shielding efficacy of superconducting materials and methods to mitigate the effect of necessary discontinuities in superconducting shield

Meshfree and Particle Methods in Biomechanics: Prospects and Challenges

The use of meshfree and particle methods in the field of bioengineering and biomechanics has significantly increased. This may be attributed to their unique abilities to overcome most of the inherent limitations of mesh-based methods in dealing with problems involving large deformation and complex geometry that are common in bioengineering and computational biomechanics in particular. This review article is intended to identify, highlight and summarize research works on topics that are of substantial interest in the field of computational biomechanics in which meshfree or particle methods have been employed for analysis, simulation or/and modeling of biological systems such as soft matters, cells, biological soft and hard tissues and organs. We also anticipate that this review will serve as a useful resource and guide to researchers who intend to extend their work into these research areas. This review article includes 333 references

Simulation methods and tissue property models for non-invasive transcranial focused ultrasound surgery

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2005.Includes bibliographical references.Many brain tumors are localized deeply and are currently surgically inaccessible without causing severe damage to the overlying structures of the brain. The current spectrum of non-invasive methods for treating such tumors includes radiotherapy, which requires exposure to ionizing radiation, and chemotherapy, which is systemically toxic. However, these tumors may also potentially be attacked by focusing highly intense ultrasound onto them. Focused ultrasound surgery is without the side effects of radiotherapy and chemotherapy, and the therapeutic effect of ultrasound therapy can be monitored in real- time using the proton chemical shift MRI technique. However, in order for brain tumors to be treated non-invasively, the ultrasound must be focused onto the targeted brain tissue through the intact cranium. Transcranial focusing of ultrasound is a longstanding and difficult problem as skull is a highly heterogeneous material. As the ultrasound field propagates through the bones of the skull, it undergoes substantatial distortion due to the variations in density and speed of sound therein. There is substantial individual variation in skull size, thickness and composition. Furthermore, the acoustic attenuation coefficient in bone is high, so the skull may also be heated by the ultrasound propagating through it. This thesis contains novel simulation techniques for analyzing transcranial acoustic propagation and for analyzing the temperature changes so produced in the brain, skull and scalp. These techniques have also been applied to modeling non-invasive treatment of the liver, and to producing therapeutic ultrasound fields that harness non-linear acoustic effects advantageously.(cont.) The thesis also contains unified models for the speed of sound and the acoustic attenuation coeffiecient in human skull. These models were generated by combining genetic optimization algorithms, acoustic propagation modeling and empirical measurement of intracranial ultrasound fields; they are valid across the full range of trabecular and cortical cranial bone.by Christopher W. Connor.Ph.D

Simulation Based Strategies for Clinical Translation of Magnetic Nanoparticle Hyperthermia

Magnetic nanoparticles have gained significant importance in the recent past for their use in biomedical applications such as drug delivery, imaging, diagnosis, and therapy. Magnetic nanoparticle hyperthermia is the selective heating of tumor tissue using magnetic nanoparticles which generate heat when exposed to an alternating magnetic field. It is a minimally invasive method which can cause effective and localized tumor thermal damage. The challenge to achieve consistent heating with this modality is the variable distribution upon delivery, which results in variable heat distribution in the tumor and surrounding normal tissue. In this thesis, using computational methods we explore optimization strategies to modulate magnetic field amplitude using limited temperature feedback to achieve clinically effective thermal dose in tumor and minimize healthy tissue damage. The magnetic field amplitude is modulated by using a Proportional-Integral-Derivative (PID) controller based on temperature feedback from tumor-healthy tissue boundary. We consider nanoparticle distributions obtained from animal studies and idealized mathematical constructs. Two and three dimensional (2D & 3D) models of tumor and healthy tissue were considered. Temperature effects on perfusion were considered. Results of thermal damage, temperature distributions and thermal dose obtained from modulated power heating were then compared to constant power heating. It is shown that controlling the tumor-healthy tissue boundary temperature by modulating the heating power of the nanoparticles can compensate for variable nanoparticle distributions to deliver effective treatment. The strategy was then implemented in mouse models of liver cancer. Two nanoparticle distributions were generated by using two injection methods. It was shown that the temperature at the tumor-healthy tissue boundary can be consistently controlled for the two nanoparticle distributions. The challenges associated with implementation of our proposed strategy have been identified and future steps for further accurate testing have been presented. Another challenge for magnetic nanoparticle hyperthermia is the onset of eddy current heating when the treatment modality is applied to tumors in large organs. Monitoring of eddy current heating in in vivo studies is challenging. Hence, we developed a computational tool which couples thermal and electromagnetic modeling to predict the temperatures achieved due to eddy current heating. The model was verified with the analytical solution and validated with gel phantom experiments. We then implemented it to generate 3D liver model from computed tomography (CT) images of rabbit liver. The temperatures attained due to eddy current heating from exposure to alternating magnetic fields were calculated to demonstrate the utility of the model in estimating temperature during magnetic nanoparticle hyperthermia of large organs. In the last chapter, we characterized the thermal and magnetic properties of dual contrast nanoparticle formulations used in image guided thermal therapy of liver cancer. Dual contrast nanoparticle formulations are magnetic iron oxide nanoparticles combined with lipiodol. The heating potential of these lipiodol nanoparticle formulations was extensively characterized by measuring their thermal properties at fixed frequency with different magnetic field amplitudes. These were then compared to original aqueous formulations for assessing the differences between both the formulations. Bulk magnetic properties of both the formulations was measured and compared. It is observed that when nanoparticles are mixed with lipiodol, the specific loss power of these particles is reduced. These results highlight the importance of evaluating the heating performance of new nanoparticle formulations