Real-Time Passive Brain Monitoring System Using Near-Field Microwave Radiometry

Model-predicted brain temperature computational imaging by multimodal noninvasive functional neuromonitoring of cerebral oxygen metabolism and hemodynamics: MRI-derived and clinical validation

Brain temperature, a crucial yet under-researched neurophysiological parameter, is governed by the equilibrium between cerebral oxygen metabolism and hemodynamics. Therapeutic hypothermia has been demonstrated as an effective intervention for acute brain injuries, enhancing survival rates and prognosis. The success of this treatment hinges on the precise regulation of brain temperature. However, the absence of comprehensive brain temperature monitoring methods during therapy, combined with a limited understanding of human brain heat transmission mechanisms, significantly hampers the advancement of hypothermia-based neuroprotective therapies. Leveraging the principles of bioheat transfer and MRI technology, this study conducted quantitative analyses of brain heat transfer during mild hypothermia therapy. Utilizing MRI, we reconstructed brain structures, estimated cerebral blood flow and oxygen consumption parameters, and developed a brain temperature calculation model founded on bioheat transfer theory. Employing computational cerebral hemodynamic simulation analysis, we established an intracranial arterial fluid dynamics model to predict brain temperature variations across different therapeutic hypothermia modalities. We introduce a noninvasive, spatially resolved, and optimized mathematical bio-heat model that synergizes model-predicted and MRI-derived data for brain temperature prediction and imaging. Our findings reveal that the brain temperature images generated by our model reflect distinct spatial variations across individual participants, aligning with experimentally observed temperatures.

Review of the Capacity to Accurately Detect the Temperature of Human Skin Tissue Using the Microwave Radiation Method

Microwave radiometry (MWR) is instrumental in detecting thermal variations in skin tissue before anatomical changes occur, proving particularly beneficial in the early diagnosis of cancer and inflammation. This study concisely traces the evolution of microwave radiometers within the medical sector. By analyzing a plethora of pertinent studies and contrasting their strengths, weaknesses, and performance metrics, this research identifies the primary factors limiting temperature measurement accuracy. The review establishes the critical technologies necessary to overcome these limitations, examines the current state and prospective advancements of each technology, and proposes comprehensive implementation strategies. The discussion elucidates that the precise measurement of human surface and subcutaneous tissue temperatures using an MWR system is a complex challenge, necessitating an integration of antenna directionality for temperature measurement, radiometer error correction, hardware configuration, and the calibration and precision of a multilayer tissue forward and inversion method. This study delves into the pivotal technologies for non-invasive human tissue temperature monitoring in the microwave frequency range, offering an effective approach for the precise assessment of human epidermal and subcutaneous temperatures, and develops a non-contact microwave protocol for gauging subcutaneous tissue temperature distribution. It is anticipated that mass-produced measurement systems will deliver substantial economic and societal benefits.

Microwave Radiometry for the Diagnosis and Monitoring of Inflammatory Arthritis

The ability of microwave radiometry (MWR) to detect with high accuracy in-depth temperature changes in human tissues is under investigation in various medical fields. The need for non-invasive, easily accessible imaging biomarkers for the diagnosis and monitoring of inflammatory arthritis provides the background for this application in order to detect the local temperature increase due to the inflammatory process by placing the appropriate MWR sensor on the skin over the joint. Indeed, a number of studies reviewed herein have reported interesting results, suggesting that MWR is useful for the differential diagnosis of arthritis as well as for the assessment of clinical and subclinical inflammation at the individual large or small joint level and the patient level. MWR showed higher agreement with musculoskeletal ultrasound, used as a reference, than with clinical examination in rheumatoid arthritis (RA), while it also appeared useful for the assessment of back pain and sacroiliitis. Further studies with a larger number of patients are warranted to confirm these findings, taking into account the current limitations of the available MWR devices. This may lead to the production of easily accessible and inexpensive MWR devices that will provide a powerful impetus for personalized medicine.

A Core Body Temperature Retrieval Method for Microwave Radiometry when Tissue Permittivity is Unknown

This paper presents a novel method for core temperature retrieval using microwave radiometry when complex permittivity and heat transfer parameters of the tissue layers of the human subject are unknown. Previous works present methods for core temperature retrieval, but these methods do not account for population variation in the relevant electromagnetic and thermal parameters, which can increase measurement error beyond the clinically acceptable limit of 0.5°C. Pennes' bioheat model of a six-tissue-layer human head model combined with a coherent electromagnetic model simulate experimental data. To retrieve core temperature, nonlinear least squares optimization is then used to minimize the difference between the simulated experimental data and an exponential model for physical temperature and the coherent electromagnetic model. By using 20 frequencies spanning from 1-5 GHz, core temperature is retrieved while accounting for population variation in the permittivity and thermal parameters. A Monte Carlo simulation in which the thermal parameters and permittivity vary according to literature-derived, population-representative distributions and the core body temperature varies from 18-46°C is used to assess the utility of the retrieval method. Different antenna patterns are tested to explore the effect on retrieval accuracy. The retrieval method has a retrieval error of <0.1°C when only the thermal parameters are unknown and a retrieval error of <0.5°C when the thermal parameters and permittivity are unknown, which is within the clinically acceptable error range of 0.5°C. These results help progress the field of medical microwave radiometry toward being a clinically viable noninvasive measurement that is accurate when measuring all patients.

Microminiaturization of Multichannel Multifrequency Radiographs

Due to the COVID-19 epidemic, the challenge of introducing methods for investigating patients reducing or eliminating the probability of infection of medical staff is currently relevant. This article provides an analytical review of new technological approaches to organizing the work of medical personnel in carrying out auscultation of patients with COVID-19. The development and approval of such technologies is shown to have started around the world. The ubiquitous and large-scale introduction of these methods into medical practice therefore seems expedient.

Development of a Coherent Model for Radiometric Core Body Temperature Sensing

This paper examines the utility of a wideband, physics-based model to determine human core body or brain temperature via microwave radiometry. Pennes's bioheat equation is applied to a six-layer human head model to generate the expected layered temperature profile during the development of a fever. The resulting temperature profile is fed into the forward electromagnetic (EM) model to determine the emitted brightness temperature at various points in time. To accurately retrieve physical temperature via radiometry, the utilized model must incorporate population variation statistics and cover a wide frequency band. The effect of human population variation on emitted brightness temperature is studied by varying the relevant thermal and EM parameters, and brightness temperature emissions are simulated from 0.1 MHz to 10 GHz. A Monte Carlo simulation combined with literature-derived statistical distributions for the thermal and EM parameters is performed to analyze population-level variation in resulting brightness temperature. Variation in thermal parameters affects the offset of the resulting brightness temperature signature, while EM parameter variation shifts the key maxima and minima of the signature. The layering of high and low permittivity layers creates these key maxima and minima via wave interference. This study is one of the first to apply a coherent model to and the first to examine the effect of population-representative variable distributions on radiometry for core temperature measurement. These results better inform the development of an on-body radiometer useful for core body temperature measurement across the human population.

Improving the Efficiency of Oncological Diagnosis of the Breast Based on the Combined Use of Simulation Modeling and Artificial Intelligence Algorithms

This work includes a brief overview of the applications of the powerful and easy-to-perform method of microwave radiometry (MWR) for the diagnosis of various diseases. The main goal of this paper is to develop a method for diagnosing breast oncology based on machine learning algorithms using thermometric data, both real medical measurements and simulation results of MWR examinations. The dataset includes distributions of deep and skin temperatures calculated in numerical models of the dynamics of thermal and radiation fields inside biological tissue. The constructed combined dataset allows us to explore the limits of applicability of the MWR method for detecting weak tumors. We use convolutional neural networks and classic machine learning algorithms (k-nearest neighbors, naive Bayes classifier, support vector machine) to classify data. The construction of Kohonen self-organizing maps to explore the structure of our combined dataset demonstrated differences between the temperatures of patients with positive and negative diagnoses. Our analysis shows that the MWR can detect tumors with a radius of up to 0.5 cm if they are at the stage of rapid growth, when the tumor volume doubling occurs in approximately 100 days or less. The use of convolutional neural networks for MWR provides both high sensitivity (sens=0.86) and specificity (spec=0.82), which is an advantage over other methods for diagnosing breast cancer. A new modified scheme for medical measurements of IR temperature and brightness temperature is proposed for a larger number of points in the breast compared to the classical scheme. This approach can increase the effectiveness and sensitivity of diagnostics by several percent.

Non-invasive Brain Temperature Measurement in Acute Ischemic Stroke

Selective therapeutic hypothermia in the setting of mechanical thrombectomy (MT) is promising to further improve the outcomes of large vessel occlusion stroke. A significant limitation in applying hypothermia in this setting is the lack of real-time non-invasive brain temperature monitoring mechanism. Non-invasive brain temperature monitoring would provide important information regarding the brain temperature changes during cooling, and the factors that might influence any fluctuations. This review aims to provide appraisal of brain temperature changes during stroke, and the currently available non-invasive modalities of brain temperature measurement that have been developed and tested over the past 20 years. We cover modalities including magnetic resonance spectroscopy imaging (MRSI), radiometric thermometry, and microwave radiometry, and the evidence for their accuracy from human and animal studies. We also evaluate the feasibility of using these modalities in the acute stroke setting and potential ways for incorporating brain temperature monitoring in the stroke workflow.

Quantitative Interpretation of UWB Radar Images for Non-Invasive Tissue Temperature Estimation during Hyperthermia

The knowledge of temperature distribution inside the tissue to be treated is essential for patient safety, workflow and clinical outcomes of thermal therapies. Microwave imaging represents a promising approach for non-invasive tissue temperature monitoring during hyperthermia treatment. In the present paper, a methodology for quantitative non-invasive tissue temperature estimation based on ultra-wideband (UWB) radar imaging in the microwave frequency range is described. The capabilities of the proposed method are demonstrated by experiments with liquid phantoms and three-dimensional (3D) Delay-and-Sum beamforming algorithms. The results of our investigation show that the methodology can be applied for detection and estimation of the temperature induced dielectric properties change.

Passive microwave radiometry in biomedical studies

Passive microwave radiometry (MWR) measures natural emissions in the range 1-10GHz from proteins, cells, organs and the whole human body. The intensity of intrinsic emission is determined by biochemical and biophysical processes. The nature of this process is still not very well known. Infrared thermography (IRT) can detect emission several microns deep (skin temperature), whereas MWR allows detection of thermal abnormalities down to several centimeters (internal or deep temperature). MWR is noninvasive and inexpensive. It requires neither fluorescent nor radioactive labels, nor ionizing or other radiation. MWR can be used in early drug discovery as well as preclinical and clinical studies.