The potential of dynamic area telethermometry in assessing breast cancer

Detecting Vasodilation as Potential Diagnostic Biomarker in Breast Cancer Using Deep Learning-Driven Thermomics

Breast cancer is the most common cancer in women. Early diagnosis improves outcome and survival, which is the cornerstone of breast cancer treatment. Thermography has been utilized as a complementary diagnostic technique in breast cancer detection. Artificial intelligence (AI) has the capacity to capture and analyze the entire concealed information in thermography. In this study, we propose a method to potentially detect the immunohistochemical response to breast cancer by finding thermal heterogeneous patterns in the targeted area. In this study for breast cancer screening 208 subjects participated and normal and abnormal (diagnosed by mammography or clinical diagnosis) conditions were analyzed. High-dimensional deep thermomic features were extracted from the ResNet-50 pre-trained model from low-rank thermal matrix approximation using sparse principal component analysis. Then, a sparse deep autoencoder designed and trained for such data decreases the dimensionality to 16 latent space thermomic features. A random forest model was used to classify the participants. The proposed method preserves thermal heterogeneity, which leads to successful classification between normal and abnormal subjects with an accuracy of 78.16% (73.3-81.07%). By non-invasively capturing a thermal map of the entire tumor, the proposed method can assist in screening and diagnosing this malignancy. These thermal signatures may preoperatively stratify the patients for personalized treatment planning and potentially monitor the patients during treatment.

[Use of infrared thermography in ophthalmology]

Thermoregulation disorders are associated with Body temperature fluctuation. Both hyper- and hypothermia are evidence of an ongoing pathological process. Contralateral symmetry in the Body heat spread is considered normal, while asymmetry, if above a certain level, implies an underlying pathology. Infrared thermography (IRT) is employed in many medical fields including ophthalmology. The earliest attempts of eye surface temperature evaluation were made in the 19^th century. Over the last 50 years, different authors have been using this method to assess ocular adnexa, however, the technique remains insufficiently studied. The reported IRT data is often contradictory, which may be due to heterogeneity (in terms of severity) of patient groups and disparities between research parameters.

Hybrid analysis for indicating patients with breast cancer using temperature time series

Breast cancer is the most common cancer among women worldwide. Diagnosis and treatment in early stages increase cure chances. The temperature of cancerous tissue is generally higher than that of healthy surrounding tissues, making thermography an option to be considered in screening strategies of this cancer type. This paper proposes a hybrid methodology for analyzing dynamic infrared thermography in order to indicate patients with risk of breast cancer, using unsupervised and supervised machine learning techniques, which characterizes the methodology as hybrid. The dynamic infrared thermography monitors or quantitatively measures temperature changes on the examined surface, after a thermal stress. In the dynamic infrared thermography execution, a sequence of breast thermograms is generated. In the proposed methodology, this sequence is processed and analyzed by several techniques. First, the region of the breasts is segmented and the thermograms of the sequence are registered. Then, temperature time series are built and the k-means algorithm is applied on these series using various values of k. Clustering formed by k-means algorithm, for each k value, is evaluated using clustering validation indices, generating values treated as features in the classification model construction step. A data mining tool was used to solve the combined algorithm selection and hyperparameter optimization (CASH) problem in classification tasks. Besides the classification algorithm recommended by the data mining tool, classifiers based on Bayesian networks, neural networks, decision rules and decision tree were executed on the data set used for evaluation. Test results support that the proposed analysis methodology is able to indicate patients with breast cancer. Among 39 tested classification algorithms, K-Star and Bayes Net presented 100% classification accuracy. Furthermore, among the Bayes Net, multi-layer perceptron, decision table and random forest classification algorithms, an average accuracy of 95.38% was obtained.

Motion tracking in infrared imaging for quantitative medical diagnostic applications

In medical applications, infrared (IR) thermography is used to detect and examine the thermal signature of skin abnormalities by quantitatively analyzing skin temperature in steady state conditions or its evolution over time, captured in an image sequence. However, during the image acquisition period, the involuntary movements of the patient are unavoidable, and such movements will undermine the accuracy of temperature measurement for any particular location on the skin. In this study, a tracking approach using a template-based algorithm is proposed, to follow the involuntary motion of the subject in the IR image sequence. The motion tacking will allow to associate a temperature evolution to each spatial location on the body while the body moves relative to the image frame. The affine transformation model is adopted to estimate the motion parameters of the template image. The Lucas-Kanade algorithm is applied to search for the optimized parameters of the affine transformation. A weighting mask is incorporated into the algorithm to ensure its tracking robustness. To evaluate the feasibility of the tracking approach, two sets of IR image sequences with random in-plane motion were tested in our experiments. A steady-state (no heating or cooling) IR image sequence in which the skin temperature is in equilibrium with the environment was considered first. The thermal recovery IR image sequence, acquired when the skin is recovering from 60-s cooling, was the second case analyzed. By proper selection of the template image along with template update, satisfactory tracking results were obtained for both IR image sequences. The achieved tracking accuracies are promising in terms of satisfying the demands imposed by clinical applications of IR thermography.

Involuntary motion tracking for medical dynamic infrared thermography using a template-based algorithm

In medical applications, Dynamic Infrared (IR) Thermography is used to detect the temporal variation of the skin temperature. Dynamic Infrared Imaging first introduces a thermal challenge such as cooling on the human skin, and then a sequence of hundreds of consecutive frames is acquired after the removal of the thermal challenge. As a result, by analyzing the temporal variation of the skin temperature over the image sequence, the thermal signature of skin abnormality can be examined. However, during the acquisition of dynamic IR imaging, the involuntary movements of patients are unavoidable, and such movements will undermine the accuracy of diagnosis. In this study, based on the template-based algorithm, a tracking approach is proposed to compensate the motion artifact. The affine warping model is adopted to estimate the motion parameter of the image template, and then the Lucas-Kanade algorithm is applied to search for the optimized parameters of the warping function. In addition, the weighting mask is also incorporated in the computation to ensure the robustness of the algorithm. To evaluate the performance of the approach, two sets of IR image sequences of a subject's hand are analyzed: the steady-state image sequence, in which the skin temperature is in equilibrium with the environment, and the thermal recovery image sequence, which is acquired after cooling is applied on the skin for 60 seconds. By selecting the target region in the first frame as the template, satisfactory tracking results were obtained in both experimental trials, and the robustness of the approach can be effectively ensured in the recovery trial.

A perspective on medical infrared imaging

Since the early days of thermography in the 1950s, image processing techniques, sensitivity of thermal sensors and spatial resolution have progressed greatly, holding out fresh promise for infrared (IR) imaging techniques. Applications in civil, industrial and healthcare fields are thus reaching a high level of technical performance. The relationship between body temperature and disease was documented since 400 bc. In many diseases there are variations in blood flow, and these in turn affect the skin temperature. IR imaging offers a useful and non-invasive approach to the diagnosis and treatment (as therapeutic aids) of many disorders, in particular in the areas of rheumatology, dermatology, orthopaedics and circulatory abnormalities. This paper reviews many usages (and hence the limitations) of thermography in biomedical fields.

A Comparative Review of Thermography as a Breast Cancer Screening Technique

Breast cancer is the most frequently diagnosed cancer of women in North America. Despite advances in treatment that have reduced mortality, breast cancer remains the second leading cause of cancer induced death. Several well established tools are used to screen for breast cancer including clinical breast exams, mammograms, and ultrasound. Thermography was first introduced as a screening tool in 1956 and was initially well accepted. However, after a 1977 study found thermography to lag behind other screening tools, the medical community lost interest in this diagnostic approach. This review discusses each screening tool with a focus brought to thermography. No single tool provides excellent predictability; however, a combination that incorporates thermography may boost both sensitivity and specificity. In light of technological advances and maturation of the thermographic industry, additional research is required to confirm the potential of this technology to provide an effective non-invasive, low risk adjunctive tool for the early detection of breast cancer.

Evaluation of different marker sets for motion artifact reduction in breast dynamic infrared imaging

Dynamic infrared imaging is a promising technique to be applied to early breast cancer diagnosis. It is based on the acquisition of hundreds of consecutive thermal images with a frame rate ranging from 50 to 200 frames/s, followed by the spectral analysis of temperature time series at each image pixel. To improve the time series signal-to-noise ratio, it is useful to realign the thermal images of the acquisition sequence. Our previous studies demonstrated that a registration algorithm based on fiducial points is suitable to both clinical applications and research, when associated with a proper set of skin markers. In this paper, we evaluate the performance of different marker sets by means of a model that allows estimating the signal-to-noise ratio increment due to registration, and we conclude that a 9-marker set is a good compromise between motion artifact reduction and the time required to prepare the patient.

Intraoperative infrared imaging of brain tumors

OBJECT

Although clinical imaging defines the anatomical relationship between a brain tumor and the surrounding brain and neurological deficits indicate the neurophysiological consequences of the tumor, the effect of a brain tumor on vascular physiology is less clear.

METHODS

An infrared camera was used to measure the temperature of the cortical surface before, during, and after removal of a mass in 34 patients (primary brain tumor in 21 patients, brain metastases in 10 and falx meningioma, cavernous angioma, and radiation necrosis-astrocytosis in one patient each). To establish the magnitude of the effect on blood flow induced by the tumor, the images were compared with those from a group of six patients who underwent temporal lobectomy for epilepsy. In four cases a cerebral artery was temporarily occluded during the course of the surgery and infrared emissions from the cortex before and after occlusion were compared to establish the relationship of local temperature to regional blood flow. Discrete temperature gradients were associated with surgically verified lesions in all cases. Depending on the type of tumor, the cortex overlying the tumor was either colder or warmer than the surrounding cortex. Spatial reorganization of thermal gradients was observed after tumor resection. Temperature gradients of the cortex in patients with tumors exceeded those measured in the cortex of patients who underwent epilepsy surgery.

CONCLUSIONS

Brain tumors induce changes in cerebral blood flow (CBF) in the cortex, which can be made visible by performing infrared imaging during cranial surgery. A reduction in CBF beyond the tumor margin improves after removal of the lesion.

Vision of the future: initial experience with intraoperative real-time high-resolution dynamic infrared imaging. Technical note

High-resolution dynamic infrared (DIR) imaging provides intraoperative real-time physiological, anatomical, and pathological information; however, DIR imaging has rarely been used in neurosurgical patients. The authors report on their initial experience with intraoperative DIR imaging in 30 such patients. A novel, long-wave (8-10 microm), narrow-band, focal-plane-array infrared photodetector was incorporated into a camera system with a temperature resolution of 0.006 degrees C, providing 65,000 pixels/frame at a data acquisition rate of 200 frames/second. Intraoperative imaging of patients was performed before and after surgery. Infrared data were subsequently analyzed by examining absolute differences in cortical temperatures, changes in temperature over time, and infrared intensities at varying physiological frequencies. Dynamic infrared imaging was applied in a variety of neurosurgical cases. After resection of an arteriovenous malformation, there was postoperative hyperperfusion of the surrounding brain parenchyma, which was consistent with a loss of autoregulation. Bypass patency and increased perfusion of adjacent brain were documented during two of three extracranial-intracranial bypasses. In seven of nine patients with epilepsy the results of DIR imaging corresponded to seizure foci that had been electrocorticographically mapped preoperatively. Dynamic infrared imaging demonstrated the functional cortex in four of nine patients undergoing awake resection and cortical stimulation. Finally, DIR imaging exhibited the distinct thermal footprints of 14 of 16 brain tumors. Dynamic infrared imaging may prove to be a powerful adjunctive intraoperative diagnostic tool in the neurosurgical imaging armamentarium. Real-time assessment of cerebral vessel patency and cerebral perfusion are the most direct applications of this technology. Uses of this imaging modality in the localization of epileptic foci, identification of functional cortex during awake craniotomy, and determination of tumor border and intraoperative brain shift are avenues of inquiry that require further investigation.

Assessment of physiologic and pathologic radiative heat dissipation using dynamic infrared imaging

This paper reviews the mechanism and assessment of regulated radiative heat dissipation, involving the circulatory system and the skin. It describes the quantitative assessment of skin temperature modulation. The main regulating process, which can be quantitatively monitored by fast and sensitive dynamic infrared imaging, involves autonomic nervous control of cutaneous and subcutaneous perfusion. This control is significantly affected by a variety of local or systemic pathologic conditions, including cancer and certain neuropathies. A potential clinical application that objectively assesses local attenuation of temperature modulation in the presence of breast cancer is described in some detail. Systemic aberrations in skin temperature modulation can be clinically useful also in neurology. It can be used also in psychology and psychiatry to evaluate transient effects of mental stress on the autonomic nervous system.