Performance Assessment of Low-Cost Thermal Cameras for Medical Applications

Enhanced thyroid nodule segmentation through U-Net and VGG16 fusion with feature engineering: A comprehensive study

BACKGROUND AND OBJECTIVE

The thyroid gland, a key component of the endocrine system, is pivotal in regulating bodily functions. Thermography, a non-invasive imaging technique utilizing infrared cameras, has emerged as a diagnostic tool for thyroid-related conditions, offering advantages such as early detection and risk stratification. Artificial intelligence (AI) has demonstrated success in medical diagnostics, and its integration into thermal imaging analysis holds promise for improving diagnostic capabilities. This study aims to explore the potential of AI, specifically convolutional neural networks (CNNs), in enhancing the analysis of thyroid thermograms for the detection of nodules and abnormalities.

METHODS

Artificial intelligence (AI) and machine learning techniques are integrated to enhance thyroid thermal image analysis. Specifically, a fusion of U-Net and VGG16, combined with feature engineering (FE), is proposed for accurate thyroid nodule segmentation. The novelty of this research lies in leveraging feature engineering in transfer learning for the segmentation of thyroid nodules, even in the presence of a limited dataset.

RESULTS

The study presents results from four conducted studies, demonstrating the efficacy of this approach even with a limited dataset. It's observed that in study 4, using FE has led to a significant improvement in the value of the dice coefficient. Even for the small size of the masked region, incorporating radiomics with FE resulted in significant improvements in the segmentation dice coefficient. It's promising that one can achieve higher dice coefficients by employing different models and refining them.

CONCLUSION

The findings here underscore the potential of AI for precise and efficient segmentation of thyroid nodules, paving the way for improved thyroid health assessment.

Precision Beekeeping Systems: State of the Art, Pros and Cons, and Their Application as Tools for Advancing the Beekeeping Sector

The present review aims to summarize the more recent scientific literature and updated state of the art on the research effort spent in adapting hardware-software tools to understand the true needs of honeybee colonies as a prerequisite for any sustainable management practice. A SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis was also performed with the aim of identifying the key factors that could support or impair the diffusion of precision beekeeping (PB) systems. Honeybee husbandry, or beekeeping, is starting to approach precision livestock farming (PLF), as has already happened in other animal husbandry sectors. A transition from the current paradigm of rational beekeeping to that of precision beekeeping (PB) is thus expected. However, due to the peculiarities of this species and the related farming practices, the PB technological systems (PB systems) are still undergoing a development process that, to some extent, limits their large-scale practical application. Several physical-chemical (weight, temperature, humidity, sound, gases) and behavioral traits (flight activity, swarming) of the hive are reviewed in light of the evolution of sensors, communication systems, and data management approaches. These advanced sensors are equipped with a microprocessor that records data and sends it to a remote server for processing. In this way, through a Wireless Sensor Network (WSN) system, the beekeeper, using specific applications on a personal computer, tablet, or smartphone, can have all the above-mentioned parameters under remote control. In general, weight, temperature, and humidity are the main hive traits monitored by commercial sensors. Surprisingly, flight activity sensors are rarely available as an option in modular PB systems marketed via the web. The SWOT analysis highlights that PB systems have promising strength points and represent great opportunities for the development of beekeeping; however, they have some weaknesses, represented especially by the high purchasing costs and the low preparedness of the addressed operators, and imply some possible threats for beekeeping in terms of unrealistic perception of the apiary status if they applied to some hives only and a possible adverse impact on the honeybees' colony itself. Even if more research is expected to take place in the next few years, indubitably, the success of commercial PB systems will be measured in terms of return on investment, conditioned especially by the benefits (higher yields, better colonies' health) that the beekeeper will appraise as a consequence of their use.

Region-wise severity analysis of diabetic plantar foot thermograms

OBJECTIVES

Diabetic foot ulcers (DFU) can be avoided if symptoms of diabetic foot complications are detected early and treated promptly. Early detection requires regular examination, which might be limited for many reasons. To identify affected or potentially affected regions in the diabetic plantar foot, the region-wise severity of the plantar foot must be known.

METHODS

A novel thermal diabetic foot dataset of 104 subjects was developed that is suitable for Indian healthcare conditions. The entire plantar foot thermogram is divided into three parts, i.e., forefoot, midfoot, and hindfoot. The division of plantar foot is based on the prevalence of foot ulcers and the load on the foot. To classify the severity levels, conventional machine learning (CML) techniques like logistic regression, decision tree, KNN, SVM, random forest, etc., and convolutional neural networks (CNN), such as EfficientNetB1, VGG-16, VGG-19, AlexNet, InceptionV3, etc., were applied and compared for robust outcomes.

RESULTS

The study successfully developed a thermal diabetic foot dataset, allowing for effective classification of diabetic foot ulcer severity using the CML and CNN techniques. The comparison of different methods revealed variations in performance, with certain approaches outperforming others.

CONCLUSIONS

The region-based severity analysis offers valuable insights for targeted interventions and preventive measures, contributing to a comprehensive assessment of diabetic foot ulcer severity. Further research and development in these techniques can enhance the detection and management of diabetic foot complications, ultimately improving patient outcomes.

State-of-the-Art Features for Early-Stage Detection of Diabetic Foot Ulcers Based on Thermograms

Diabetic foot ulcers represent the most frequently recognized and highest risk factor among patients affected by diabetes mellitus. The associated recurrent rate is high, and amputation of the foot or lower limb is often required due to infection. Analysis of infrared thermograms covering the entire plantar aspect of both feet is considered an emerging area of research focused on identifying at an early stage the underlying conditions that sustain skin and tissue damage prior to the onset of superficial wounds. The identification of foot disorders at an early stage using thermography requires establishing a subset of relevant features to reduce decision variability and data misinterpretation and provide a better overall cost-performance for classification. The lack of standardization among thermograms as well as the unbalanced datasets towards diabetic cases hinder the establishment of this suitable subset of features. To date, most studies published are mainly based on the exploitation of the publicly available INAOE dataset, which is composed of thermogram images of healthy and diabetic subjects. However, a recently released dataset, STANDUP, provided data for extending the current state of the art. In this work, an extended and more generalized dataset was employed. A comparison was performed between the more relevant and robust features, previously extracted from the INAOE dataset, with the features extracted from the extended dataset. These features were obtained through state-of-the-art methodologies, including two classical approaches, lasso and random forest, and two variational deep learning-based methods. The extracted features were used as an input to a support vector machine classifier to distinguish between diabetic and healthy subjects. The performance metrics employed confirmed the effectiveness of both the methodology and the state-of-the-art features subsequently extracted. Most importantly, their performance was also demonstrated when considering the generalization achieved through the integration of input datasets. Notably, features associated with the MCA and LPA angiosomes seemed the most relevant.

Fabrication of Temperature Sensors with High-Performance Uniformity through Thermal Annealing

It is considered to be of great significance to monitor human health and track the effect of drugs by measuring human temperature mapping through flexible temperature sensors. In this work, we found that the thermal annealing of flexible temperature sensors based on graphite-acrylate copolymer composites can not only improve the temperature coefficient of resistance (TCR) values of the devices, but also greatly improve the uniformity of the performance of the devices prepared in parallel. The best results were obtained when the devices were annealed at 100 °C, which is believed to be due to the rearrangement of graphite particles to generate more uniform and numerous conductive channels within the conductive composite. We believe this finding might promote the practical development of flexible temperature sensors in body temperature sensing for health maintenance and medical applications.

Feature Ranking by Variational Dropout for Classification Using Thermograms from Diabetic Foot Ulcers

Diabetes mellitus presents a high prevalence around the world. A common and long-term derived complication is diabetic foot ulcers (DFUs), which have a global prevalence of roughly 6.3%, and a lifetime incidence of up to 34%. Infrared thermograms, covering the entire plantar aspect of both feet, can be employed to monitor the risk of developing a foot ulcer, because diabetic patients exhibit an abnormal pattern that may indicate a foot disorder. In this study, the publicly available INAOE dataset composed of thermogram images of healthy and diabetic subjects was employed to extract relevant features aiming to establish a set of state-of-the-art features that efficiently classify DFU. This database was extended and balanced by fusing it with private local thermograms from healthy volunteers and generating synthetic data via synthetic minority oversampling technique (SMOTE). State-of-the-art features were extracted using two classical approaches, LASSO and random forest, as well as two variational deep learning (DL)-based ones: concrete and variational dropout. Then, the most relevant features were detected and ranked. Subsequently, the extracted features were employed to classify subjects at risk of developing an ulcer using as reference a support vector machine (SVM) classifier with a fixed hyperparameter configuration to evaluate the robustness of the selected features. The new set of features extracted considerably differed from those currently considered state-of-the-art but provided a fair performance. Among the implemented extraction approaches, the variational DL ones, particularly the concrete dropout, performed the best, reporting an F1 score of 90% using the aforementioned SVM classifier. In comparison with features previously considered as the state-of-the-art, approximately 15% better performance was achieved for classification.

Multifrequency Microwave Radiometry for Characterizing the Internal Temperature of Biological Tissues

The analysis of near-field radiometry is described for characterizing the internal temperature of biological tissues, for which a system based on multifrequency pseudo-correlation-type radiometers is proposed. The approach consists of a new topology with multiple output devices that enables real-time calibration and performance assessment, recalibrating the receiver through simultaneous measurable outputs. Experimental characterization of the prototypes includes a well-defined calibration procedure, which is described and demonstrated, as well as DC conversion from the microwave input power. Regarding performance, high sensitivity is provided in all the bands with noise temperatures around 100 K, reducing the impact of the receiver on the measurements and improving its sensitivity. Calibrated temperature retrievals exhibit outstanding results for several noise sources, for which temperature deviations are lower than 0.1% with regard to the expected temperature. Furthermore, a temperature recovery test for biological tissues, such as a human forearm, provides temperature values on the order of 310 K. In summary, the radiometers design, calibration method and temperature retrieval demonstrated significant results in all bands, validating their use for biomedical applications.

A Thermographic Disease Activity Index for remote assessment of rheumatoid arthritis

OBJECTIVES

Remote assessment of patients with rheumatoid arthritis (RA) has increased during recent years. However, telematic consultations preclude the possibility of carrying out a physical examination and obtaining objective inflammation. In this study, we developed and validated two novel composite disease activity indexes (Thermographic Disease Activity Index (ThermoDAI) and ThermoDAI-CRP) based on thermography of hands and machine learning, in order to assess disease activity easily, rapidly and without formal joint counts.

METHODS

ThermoDAI was developed as the sum of Thermographic Joint Inflammation Score (ThermoJIS), a novel joint inflammation score based on the analysis of thermal images of the hands by machine learning, the Patient Global Assessment (PGA) and, for ThermoDAI-CRP, the C reactive protein (CRP). Construct validity was tested in 146 patients with RA by using Spearman's correlation with ultrasound-determined grey-scale synovial hypertrophy (GS) and power Doppler (PD) scores, CDAI, SDAI and DAS28-CRP.

RESULTS

Correlations of ultrasound scores with ThermoDAI (GS=0.52; PD=0.56) and ThermoDAI-CRP (GS=0.58; PD=0.61) were moderate to strong, while the correlations of ultrasound scores with PGA (GS=0.35; PD=0.39) and PGA+CRP (GS=0.44; PD=0.46) were weak to moderate. ThermoDAI and ThermoDAI-CRP also showed strong correlations with Clinical Disease Activity Index (ρ>0.83), Simplified Disease Activity Index (ρ>0.85) and Disease Activity Score with 28-Joint Counts-CRP (ρ>0.81) and high sensitivity for detecting active synovitis using remission criteria.

CONCLUSIONS

ThermoDAI and ThermoDAI-CRP showed stronger correlations with ultrasound-determined synovitis than PGA and PGA + CRP, thus presenting an opportunity to improve remote consultations with patients with RA.

Assessment of inflammation in patients with rheumatoid arthritis using thermography and machine learning: a fast and automated technique

Objectives

Sensitive detection of joint inflammation in rheumatoid arthritis (RA) is crucial to the success of the treat-to-target strategy. In this study, we characterise a novel machine learning-based computational method to automatically assess joint inflammation in RA using thermography of the hands, a fast and non-invasive imaging technique.

Methods

We recruited 595 patients with arthritis and osteoarthritis, as well as healthy subjects at two hospitals over 4 years. Machine learning was used to assess joint inflammation from the thermal images of the hands using ultrasound as the reference standard, obtaining a Thermographic Joint Inflammation Score (ThermoJIS). The machine learning model was trained and tuned using data from 449 participants with different types of arthritis, osteoarthritis or without rheumatic disease (development set). The performance of the method was evaluated based on 146 patients with RA (validation set) using Spearman’s rank correlation coefficient, area under the receiver-operating curve (AUROC), average precision, sensitivity, specificity, positive and negative predictive value and F1-score.

Results

ThermoJIS correlated moderately with ultrasound scores (grey-scale synovial hypertrophy=0.49, p<0.001; and power Doppler=0.51, p<0.001). The AUROC for ThermoJIS for detecting active synovitis was 0.78 (95% CI, 0.71 to 0.86; p<0.001). In patients with RA in clinical remission, ThermoJIS values were significantly higher when active synovitis was detected by ultrasound.

Conclusions

ThermoJIS was able to detect joint inflammation in patients with RA, even in those in clinical remission. These results open an opportunity to develop new tools for routine detection of joint inflammation.

Implementation of Thermal Camera for Non-Contact Physiological Measurement: A Systematic Review

Non-contact physiological measurements based on image sensors have developed rapidly in recent years. Among them, thermal cameras have the advantage of measuring temperature in the environment without light and have potential to develop physiological measurement applications. Various studies have used thermal camera to measure the physiological signals such as respiratory rate, heart rate, and body temperature. In this paper, we provided a general overview of the existing studies by examining the physiological signals of measurement, the used platforms, the thermal camera models and specifications, the use of camera fusion, the image and signal processing step (including the algorithms and tools used), and the performance evaluation. The advantages and challenges of thermal camera-based physiological measurement were also discussed. Several suggestions and prospects such as healthcare applications, machine learning, multi-parameter, and image fusion, have been proposed to improve the physiological measurement of thermal camera in the future.

Noise Improvement of a-Si Microbolometers by the Post-Metal Annealing Process

To realize high-resolution thermal images with high quality, it is essential to improve the noise characteristics of the widely adopted uncooled microbolometers. In this work, we applied the post-metal annealing (PMA) process under the condition of deuterium forming gas, at 10 atm and 300 °C for 30 min, to reduce the noise level of amorphous-Si microbolometers. Here, the DC and temperature coefficient of resistance (TCR) measurements of the devices as well as 1/f noise analysis were performed before and after the PMA treatment, while changing the width of the resistance layer of the microbolometers with 35 μm or 12 μm pixel. As a result, the microbolometers treated by the PMA process show the decrease in resistance by about 60% and the increase in TCR value up to 48.2% at 10 Hz, as compared to the reference device. Moreover, it is observed that the noise characteristics are improved in inverse proportion to the width of the resistance layer. This improvement is attributed to the cured poly-silicon grain boundary through the hydrogen passivation by heat and deuterium atoms applied during the PMA, which leads to the uniform current path inside the pixel.

Comparison of cutaneous facial temperature using infrared thermography to standard temperature measurement in the critical care setting

To assess the accuracy and precision of infrared cameras compared to traditional measures of temperature measurement in a temperature, humidity, and distance controlled intensive care unit (ICU) population. This was a prospective, observational methods comparison study in a single centre ICU in Metropolitan Melbourne, Australia. A convenience sample of 39 patients admitted to a single room equipped with two ceiling mounted thermal imaging cameras was assessed, comparing measured cutaneous facial temperature via thermal camera to clinical temperature standards. Uncorrected correlation of camera measurement to clinical standard in all cases was poor, with the maximum reported correlation 0.24 (Wide-angle Lens to Bladder temperature). Using the wide-angle lens, mean differences were − 11.1 °C (LoA  − 14.68 to  − 7.51),  − 11.1 °C ( − 14.3 to  − 7.9), and  − 11.2 °C ( − 15.23 to  − 7.19) for axillary, bladder, and oral comparisons respectively (Fig. 1a). With respect to the narrow-angle lens compared to the axillary, bladder and oral temperatures, mean differences were  − 7.6 °C ( − 11.2 to  − 4.0),  − 7.5 °C ( − 12.1 to  − 2.9), and  − 7.9 °C ( − 11.6 to  − 4.2) respectively. AUCs for the wide-angle lens and narrow-angle lens ranged from 0.53 to 0.70 and 0.59 to 0.79 respectively, with axillary temperature demonstrating the greatest values. Infrared thermography is a poor predictor of patient temperature as measured by existing clinical standards. It has a moderate ability to discriminate fever. It is unclear if this would be sensitive enough for infection screening purposes.Fig. 1

Assessment of Registration Methods for Thermal Infrared and Visible Images for Diabetic Foot Monitoring

This work presents a revision of four different registration methods for thermal infrared and visible images captured by a camera-based prototype for the remote monitoring of diabetic foot. This prototype uses low cost and off-the-shelf available sensors in thermal infrared and visible spectra. Four different methods (Geometric Optical Translation, Homography, Iterative Closest Point, and Affine transform with Gradient Descent) have been implemented and analyzed for the registration of images obtained from both sensors. All four algorithms' performances were evaluated using the Simultaneous Truth and Performance Level Estimation (STAPLE) together with several overlap benchmarks as the Dice coefficient and the Jaccard index. The performance of the four methods has been analyzed with the subject at a fixed focal plane and also in the vicinity of this plane. The four registration algorithms provide suitable results both at the focal plane as well as outside of it within 50 mm margin. The obtained Dice coefficients are greater than 0.950 in all scenarios, well within the margins required for the application at hand. A discussion of the obtained results under different distances is presented along with an evaluation of its robustness under changing conditions.

Influence of infrared camera model and evaluator reproducibility in the assessment of skin temperature responses to physical exercise

Infrared thermography (IRT) has been gaining in popularity in clinical and scientific research due to the increasing availability of affordable infrared cameras. This study aims to determine the similarity of measurement performance between three models of IRT camera during assessment of skin temperature before and after physical exercise. Three models of FLIR thermographic cameras (E60bx, Flir-One Pro LT, and C2) were tested. Thermal images were taken of the foot sole, anterior leg, and anterior thigh from 12 well-trained men, before and after a 30-min run on a treadmill. Image files were blinded and processed by three evaluators to extract the mean, maximum, and standard deviation of skin temperature of the region of interest. Time for data processing and rate of perceived effort was also recorded. Data processing was slower on the E60bx (CI95% E60 vs C2 [0.2, 2.6 min], p = 0.02 and ES = 0.6); vs. Flir-One [0.0, 3.4 min], p = 0.03 and ES = 0.6) and was associated with lower effort perception (E60 3.0 ± 0.1 vs. Flir-One 5.6 ± 0.2 vs C2 7.0 ± 0.2 points; p < 0.001 and ES > 0.8). The C2 and Flir-One cameras underestimated the temperature compared with the E60. In general, measuring mean temperature provided higher camera and examiner intra-class correlations than maximum and standard deviation, especially before exercise. Moreover, post exercise mean skin temperatures provided the most consistent values across cameras and evaluators. We recommend the use of mean temperature and caution when using more than one camera model in a study.

Segmentation Approaches for Diabetic Foot Disorders

Thermography enables non-invasive, accessible, and easily repeated foot temperature measurements for diabetic patients, promoting early detection and regular monitoring protocols, that limit the incidence of disabling conditions associated with diabetic foot disorders. The establishment of this application into standard diabetic care protocols requires to overcome technical issues, particularly the foot sole segmentation. In this work we implemented and evaluated several segmentation approaches which include conventional and Deep Learning methods. Multimodal images, constituted by registered visual-light, infrared and depth images, were acquired for 37 healthy subjects. The segmentation methods explored were based on both visual-light as well as infrared images, and optimization was achieved using the spatial information provided by the depth images. Furthermore, a ground truth was established from the manual segmentation performed by two independent researchers. Overall, the performance level of all the implemented approaches was satisfactory. Although the best performance, in terms of spatial overlap, accuracy, and precision, was found for the Skin and U-Net approaches optimized by the spatial information. However, the robustness of the U-Net approach is preferred.

Design of a Didactical Activity for the Analysis of Uncertainties in Thermography through the Use of Robust Statistics as Teacher-Oriented Approach

The thermography as a methodology to quantitative data acquisition is not usually addressed in the degrees of university programs. The present manuscript proposes a novel approach for the acquisition of advanced competences in engineering courses associated with the use of thermographic images via free/open-source software solutions. This strategy is established from a research based on the statistical and three-dimensional visualization techniques over thermographic imagery to improve the interpretation and comprehension of the different sources of error affecting the measurements and, thereby, the conclusions and analysis arising from them. The novelty is focused on the detection of non-normalities in thermographic images, which is illustrates in the experimental section. Additionally, the specific workflow for the generation of learning material related with this aim is raised for asynchronous and e-learning programs. These virtual materials can be easily deployed in an institutional learning management system, allowing the students to work with the models by means of free/open-source solutions easily. Subsequently, the present approach will give new tools to improve the application of professional techniques, will improve the students’ critical sense to know how to interpret the uncertainties in thermography using a single thermographic image, therefore they will be better prepared to face future challenges with more critical thinking.

Noncontact Body Temperature Measurement: Uncertainty Evaluation and Screening Decision Rule to Prevent the Spread of COVID-19

The need to measure body temperature contactless and quickly during the COVID-19 pandemic emergency has led to the widespread use of infrared thermometers, thermal imaging cameras and thermal scanners as an alternative to the traditional contact clinical thermometers. However, limits and issues of noncontact temperature measurement devices are not well known and technical–scientific literature itself sometimes provides conflicting reference values on the body and skin temperature of healthy subjects. To limit the risk of contagion, national authorities have set the obligation to measure body temperature of workers at the entrance to the workplace. In this paper, the authors analyze noncontact body temperature measurement issues from both clinical and metrological points of view with the aim to (i) improve body temperature measurements accuracy; (ii) estimate the uncertainty of body temperature measurement on the field; (iii) propose a screening decision rule for the prevention of the spread of COVID-19. The approach adopted in this paper takes into account both the traditional instrumental uncertainty sources and clinical–medical ones related to the subjectivity of the measurand. A proper screening protocol for body temperature measurement considering the role of uncertainty is essential to correctly choose the threshold temperature value and measurement method to access critical places during COVID-19 pandemic emergency.

Review of the efficacy of infrared thermography for screening infectious diseases with applications to COVID-19

Purpose: The recent coronavirus disease 2019 (COVID-19) pandemic, which spread across the globe in a very short period of time, revealed that the transmission control of disease is a crucial step to prevent an outbreak and effective screening for viral infectious diseases is necessary. Since the severe acute respiratory syndrome (SARS) outbreak in 2003, infrared thermography (IRT) has been considered a gold standard method for screening febrile individuals at the time of pandemics. The objective of this review is to evaluate the efficacy of IRT for screening infectious diseases with specific applications to COVID-19. Approach: A literature review was performed in Google Scholar, PubMed, and ScienceDirect to search for studies evaluating IRT screening from 2002 to present using relevant keywords. Additional literature searches were done to evaluate IRT in comparison to traditional core body temperature measurements and assess the benefits of measuring additional vital signs for infectious disease screening. Results: Studies have reported on the unreliability of IRT due to poor sensitivity and specificity in detecting true core body temperature and its inability to identify asymptomatic carriers. Airport mass screening using IRT was conducted during occurrences of SARS, Dengue, Swine Flu, and Ebola with reported sensitivities as low as zero. Other studies reported that screening other vital signs such as heart and respiratory rates can lead to more robust methods for early infection detection. Conclusions: Studies evaluating IRT showed varied results in its efficacy for screening infectious diseases. This suggests the need to assess additional physiological parameters to increase the sensitivity and specificity of non-invasive biosensors.

Bimodal microwave and ultrasound phantoms for non-invasive clinical imaging

A precise and thorough methodology is presented for the design and fabrication of bimodal phantoms to be used in medical microwave and ultrasound applications. Dielectric and acoustic properties of human soft tissues were simultaneously mimicked. The phantoms were fabricated using polyvinyl alcohol cryogel (PVA-C) as gelling agent at a 10% concentration. Sucrose was employed to control the dielectric properties in the microwave spectrum, whereas cellulose was used as acoustic scatterer for ultrasound. For the dielectric properties at microwaves, a mathematical model was extracted to calculate the complex permittivity of the desired mimicked tissues in the frequency range from 500 MHz to 20 GHz. This model, dependent on frequency and sucrose concentration, was in good agreement with the reference Cole-Cole model. Regarding the acoustic properties, the speed of sound and attenuation coefficient were employed for validation. In both cases, the experimental data were consistent with the corresponding theoretical values for soft tissues. The characterization of these PVA-C phantoms demonstrated a significant performance for simultaneous microwave and ultrasound operation. In conclusion, PVA-C has been validated as gelling agent for the fabrication of complex multimodal phantoms that mimic soft tissues providing a unique tool to be used in a range of clinical applications.