Ultra-Wideband (UWB) Systems in Biomedical Sensing.

The extremely low power transmission levels of ultra-wideband (UWB) technology, alongside its advantageously large bandwidth, make it a prime candidate for being used in numerous healthcare scenarios, which require short-range high-data-rate communications and safe radar-based applications [...].

Developing Artefact Removal Algorithms to Process Data from a Microwave Imaging Device for Haemorrhagic Stroke Detection.

In this paper, we present an investigation of different artefact removal methods for ultra-wideband Microwave Imaging (MWI) to evaluate and quantify current methods in a real environment through measurements using an MWI device. The MWI device measures the scattered signals in a multi-bistatic fashion and employs an imaging procedure based on Huygens principle. A simple two-layered phantom mimicking human head tissue is realised, applying a cylindrically shaped inclusion to emulate brain haemorrhage. Detection has been successfully achieved using the superimposition of five transmitter triplet positions, after applying different artefact removal methods, with the inclusion positioned at 0°, 90°, 180°, and 270°. The different artifact removal methods have been proposed for comparison to improve the stroke detection process. To provide a valid comparison between these methods, image quantification metrics are presented. An "ideal/reference" image is used to compare the artefact removal methods. Moreover, the quantification of artefact removal procedures through measurements using MWI device is performed.

SAR prediction in adults and children by combining measured B1+ maps and simulations at 7.0 Tesla.

PURPOSE: To predict local and global specific absorption rate (SAR) in individual subjects. MATERIALS AND METHODS: SAR was simulated for a head volume coil for two imaging sequences: axial T1-weighted "zero" time-of-echo (ZTE) sequence, sagittal T2-weighted fluid attenuated inversion recovery (FLAIR). Two head models (one adult, one child) were simulated inside the coil. For 19 adults and 27 children, measured B1 (+) maps were acquired, and global (head) SAR estimated by the system was recorded. We performed t-test between the B1 (+) in models and human subjects. The B1 (+) maps of individual subjects were used to scale the SAR simulated on the models, to predict local and global (head) SAR. A phantom experiment was performed to validate SAR prediction, using a fiberoptic temperature probe to measure the temperature rise due to ZTE scanning. RESULTS: The normalized B1 (+) standard deviation in subjects was not significantly different from that of the models (P > 0.68 and P > 0.54). The rise in temperature generated in the phantom by ZTE was 0.3°C; from the heat equation it followed that the temperature-based measured SAR was 2.74 W/kg, while the predicted value was 3.1 W/kg. CONCLUSION: For ZTE and FLAIR, limits on maximum local and global SAR were met in all subjects, both adults and children. To enhance safety in adults and children with 7.0 Tesla MR systems, we suggest the possibility of using SAR prediction. J. MAGN. RESON. IMAGING 2016;44:1048-1055.

Assessment of Silent T1-weighted head imaging at 7 T.

OBJECTIVES: This study aimed to assess the performance of a "Silent" zero time of echo (ZTE) sequence for T1-weighted brain imaging using a 7 T MRI system. METHODS: The Silent sequence was evaluated qualitatively by two neuroradiologists, as well as quantitatively in terms of tissue contrast, homogeneity, signal-to-noise ratio (SNR) and acoustic noise. It was compared to conventional T1-weighted imaging (FSPGR). Adequacy for automated segmentation was evaluated in comparison with FSPGR acquired at 7 T and 1.5 T. Specific absorption rate (SAR) was also measured. RESULTS: Tissue contrast and homogeneity in Silent were remarkable in deep brain structures and in the occipital and temporal lobes. Mean tissue contrast was significantly (p < 0.002) higher in Silent (0.25) than in FSPGR (0.11), which favoured automated tissue segmentation. On the other hand, Silent images had lower SNR with respect to conventional imaging: average SNR of FSPGR was 2.66 times that of Silent. Silent images were affected by artefacts related to projection reconstruction, which nevertheless did not compromise the depiction of brain tissues. Silent acquisition was 35 dB(A) quieter than FSPGR and less than 2.5 dB(A) louder than ambient noise. Six-minute average SAR was <2 W/kg. CONCLUSIONS: The ZTE Silent sequence provides high-contrast T1-weighted imaging with low acoustic noise at 7 T. KEY POINTS: • "Silent" is an MRI technique allowing zero time of echo acquisition • Its feasibility and performance were assessed on a 7 T MRI system • Image quality in several regions was higher than in conventional techniques • Imaging acoustic noise was dramatically reduced compared with conventional imaging • "Silent" is suitable for T1-weighted head imaging at 7 T.

STEAM-MiTiS: An MR spectroscopy method for the detection of scalar-coupled metabolites and its application to glutamate at 7 T.

PURPOSE: We herein present a spectroscopic technique for the detection of scalar-coupled metabolites based on stimulated echo acquisition mode (STEAM). The method is based on the time evolution of scalar-coupled metabolites at different mixing times and a constant echo time. The technique is optimized for targeting the metabolite glutamate at 7T. METHODS: Numerical simulations were used to optimize the parameters to maximize the chosen metabolite signal. The maximum detection efficiency and metabolite signal as a function of echo time were used to identify the optimal parameters. In vitro and in vivo validations of the method were also performed. RESULTS: This method canceled all the strong singlet lines and signals from macromolecules and preserved signals originating from the scalar-coupled metabolites. The subtracted spectrum was strongly simplified, but the complete spectral information of the traditional STEAM acquisition was retained in the sum spectrum. CONCLUSIONS: The simulations performed in this study were in agreement with the experimental results, and a clear detection of the metabolite of interest was obtained. The applicability in vivo was also demonstrated, with the selective detection of glutamate in human brain. This technique is simple, suitable for standard MR systems without sequence programming and could be used to detect other metabolites.

Investigation of maximum local specific absorption rate in 7 T magnetic resonance with respect to load size by use of electromagnetic simulations.

Local specific absorption rate (SAR) evaluation in ultra high field (UHF) magnetic resonance (MR) systems is a major concern. In fact, at UHF, radiofrequency (RF) field inhomogeneity generates hot-spots that could cause localized tissue heating. Unfortunately, local SAR measurements are not available in present MR systems; thus, electromagnetic simulations must be performed for RF fields and SAR analysis. In this study, we used three-dimensional full-wave numerical electromagnetic simulations to investigate the dependence of local SAR at 7.0 T with respect to subject size in two different scenarios: surface coil loaded by adult and child calves and quadrature volume coil loaded by adult and child heads. In the surface coil scenario, maximum local SAR decreased with decreasing load size, provided that the RF magnetic fields for the different load sizes were scaled to achieve the same slice average value. On the contrary, in the volume coil scenario, maximum local SAR was up to 15% higher in children than in adults.

Tissue Border Enhancement by inversion recovery MRI at 7.0 Tesla.

INTRODUCTION: This contribution presents a magnetic resonance imaging (MRI) acquisition technique named Tissue Border Enhancement (TBE), whose purpose is to produce images with enhanced visualization of borders between two tissues of interest without any post-processing. METHODS: The technique is based on an inversion recovery sequence that employs an appropriate inversion time to produce images where the interface between two tissues of interest is hypo-intense; therefore, tissue borders are clearly represented by dark lines. This effect is achieved by setting imaging parameters such that two neighboring tissues of interest have magnetization with equal magnitude but opposite sign; therefore, the voxels containing a mixture of each tissue (that is, the tissue interface) possess minimal net signal. The technique was implemented on a 7.0 T MRI system. RESULTS: This approach can assist the definition of tissue borders, such as that between cortical gray matter and white matter; therefore, it could facilitate segmentation procedures, which are often challenging on ultra-high-field systems due to inhomogeneous radiofrequency distribution. TBE allows delineating the contours of structural abnormalities, and its capabilities were demonstrated with patients with focal cortical dysplasia, gray matter heterotopia, and polymicrogyria. CONCLUSION: This technique provides a new type of image contrast and has several possible applications in basic neuroscience, neurogenetic research, and clinical practice, as it could improve the detection power of MRI in the characterization of cortical malformations, enhance the contour of small anatomical structures of interest, and facilitate cortical segmentation.

Microwave imaging for breast cancer screening: protocol for an open, multicentric, interventional, prospective, non-randomised clinical investigation to evaluate cancer detection capabilities of MammoWave system on an asymptomatic population across multiple European countries.

INTRODUCTION: Microwave imaging presents several potential advantages including its non-ionising and harmless nature. This open, multicentric, interventional, prospective, non-randomised trial aims to validate MammoWave's artificial intelligence (AI)-based classification algorithm, leveraging microwave imaging, to achieve a sensitivity exceeding 75% and a specificity exceeding 90% in breast screening. METHODS AND ANALYSIS: 10 000 volunteers undergoing regular mammographic breast cancer screening will be recruited across 9 European centres and invited to participate in the clinical study, involving MammoWave testing on both breasts. MammoWave results will be checked against the reference standard, to be intended as the output of conventional breast examination path (with histological confirmation of cancer cases) with 2 years follow-up. Anonymised clinical and MammoWave's results, including microwave images, associated features and a label provided by the AI-based classification algorithm, will be collected and stored in a dedicated electronic case report form. The prospective study will involve a comparative analysis between the output of the conventional breast examination path (control intervention) and the labels provided by MammoWave's AI system (experimental intervention). These labels will categorise breasts into two groups: breast With Suspicious Finding, indicating the presence of a suspicious lesion or No Suspicious Finding, indicating the absence of a lesion or the presence of a low-suspicion lesion. This trial aims to provide evidence regarding the novel MammoWave's AI system for detecting breast cancer in asymptomatic populations during screening. ETHICS AND DISSEMINATION: This study was approved by the Research Ethics Committee of the Liguria Region (CET), Italy (CET-Liguria: 524/2023-DB id 13399), the Research Ethics Committee of Complejo Hospitalario de Toledo (CEIC), Spain (CEIC-1094), the National Ethics Committee for Clinical Research (CEIC), Portugal (CEIC-2311KC814), the Bioethical Committee of Pomeranian Medical University in Szczecin, Poland (KB-006/23/2024) and the Zurich Cantonal Ethics Commission, Switzerland (BASEC 2023-D0101). The findings of this study will be disseminated through academic and scientific conferences as well as peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT06291896.

Radiation-Free Microwave Technology for Breast Lesion Detection Using Supervised Machine Learning Model.

Mammography is the gold standard technology for breast screening, which has been demonstrated through different randomized controlled trials to reduce breast cancer mortality. However, mammography has limitations and potential harms, such as the use of ionizing radiation. To overcome the ionizing radiation exposure issues, a novel device (i.e. MammoWave) based on low-power radio-frequency signals has been developed for breast lesion detection. The MammoWave is a microwave device and is under clinical validation phase in several hospitals across Europe. The device transmits non-invasive microwave signals through the breast and accumulates the backscattered (returned) signatures, commonly denoted as the S21 signals in engineering terminology. Backscattered (complex) S21 signals exploit the contrast in dielectric properties of breasts with and without lesions. The proposed research is aimed to automatically segregate these two types of signal responses by applying appropriate supervised machine learning (ML) algorithm for the data emerging from this research. The support vector machine with radial basis function has been employed here. The proposed algorithm has been trained and tested using microwave breast response data collected at one of the clinical validation centres. Statistical evaluation indicates that the proposed ML model can recognise the MammoWave breasts signal with no radiological finding (NF) and with radiological findings (WF), i.e., may be the presence of benign or malignant lesions. A sensitivity of 84.40% and a specificity of 95.50% have been achieved in NF/WF recognition using the proposed ML model.

A multicentric, single arm, prospective, stratified clinical investigation to evaluate MammoWave's ability in breast lesions detection.

Microwave imaging is a safe and promising new technology in breast radiology, avoiding discomfort of breast compression and usage of ionizing radiation. This paper presents the first prospective microwave breast imaging study during which both symptomatic and asymptomatic subjects were recruited. Specifically, a prospective multicentre international clinical trial was performed in 2020-2021, to investigate the capability of a microwave imaging device (MammoWave) in allowing distinction between breasts with no radiological finding (NF) and breasts with radiological findings (WF), i.e., with benign or malignant lesions. Each breast scan was performed with the volunteers lying on a dedicated examination table in a comfortable prone position. MammoWave output was compared to reference standard (i.e., radiologic study obtained within the last month and integrated with histological one if available and deemed necessary by responsible investigator) to classify breasts into NF/WF categories. MammoWave output consists of a selection of microwave images' features (determined prior to trials' start), which allow distinction between NF and WF breasts (using statistical significance p<0.05). 353 women were enrolled in the study (mean age 51 years ± 12 [SD], minimum age 19, maximum age 78); MammoWave data from the first 15 women of each site, all with NF breasts, were used for calibration. Following central assessor evaluation, 111 NF (48 dense) and 272 WF (136 dense) breasts were used for comparison with MammoWave output. 272 WF comprised 182 benign findings and 90 malignant histology-confirmed cancer. A sensitivity of 82.3% was achieved (95%CI: 0.78-0.87); sensitivity is maintained when limiting the investigation to histology-confirmed breasts cancer only (90 histology-confirmed breasts cancer have been included in this analysis, having sizes ranging from 3 mm to 60 mm). Specificity value of approximately 50% was achieved as expected, since thresholds were calculated (for each feature) using median value obtained after recruiting the first 15 women (of each site), all NF. This prospective trial may represent another step for introducing microwave imaging into clinical practice, for helping in breast lesion identification in asymptomatic women.

Dielectric Characterization of Breast Biopsied Tissues as Pre-Pathological Aid in Early Cancer Detection: A Blinded Feasibility Study.

Dielectric characterization has significant potential in several medical applications, providing valuable insights into the electromagnetic properties of biological tissues for disease diagnosis, treatment planning, and monitoring of therapeutic interventions. This work presents the use of a custom-designed electromagnetic characterization system, based on an open-ended coaxial probe, for discriminating between benign and malignant breast tissues in a clinical setting. The probe's development involved a well-balanced compromise between physical feasibility and its combined use with a reconstruction algorithm known as the virtual transmission line model (VTLM). Immediately following the biopsy procedure, the dielectric properties of the breast tissues were reconstructed, enabling tissue discrimination based on a rule-of-thumb using the obtained dielectric parameters. A comparative analysis was then performed by analyzing the outcomes of the dielectric investigation with respect to conventional histological results. The experimental procedure took place at Complejo Hospitalario Universitario de Toledo-Hospital Virgen de la Salud, Spain, where excised breast tissues were collected and subsequently analyzed using the dielectric characterization system. A comprehensive statistical evaluation of the probe's performance was carried out, obtaining a sensitivity, specificity, and accuracy of 81.6%, 61.5%, and 73.4%, respectively, compared to conventional histological assessment, considered as the gold standard in this investigation.

Integrating clinical research in an operative screening and diagnostic breast imaging department: First experience, results and perspectives using microwave imaging.

Rationale and objectives: Clinical research is crucial for evaluating new medical procedures and devices. It is important for healthcare units and hospitals to minimize the disruptions caused by conducting clinical studies; however, complex clinical pathways require dedicated recruitment and study designs.This work presents the effective introduction of novel microwave breast imaging (MBI), via MammoWave apparatus, into the clinical routine of an operative screening and diagnostic breast imaging department for conducting a multicentric clinical study. Materials and methods: Microwave breast imaging, using MammoWave apparatus, was performed on volunteers coming from different clinical pathways. Clinical data, comprising demographics and conventional radiologic reports (used as reference standard), was collected; a satisfaction questionnaire was filled by every volunteer. Microwave images were analyzed by an automatic clinical decision support system, which quantified their corresponding features to discriminate between breasts with no relevant radiological findings (NF) and breasts with described findings (WF). Results: Conventional breast imaging (DBT, US, MRI) and MBI were performed and adapted to assure best clinical practices and optimum pathways. 180 volunteers, both symptomatic and asymptomatic, were enrolled in the study. After microwave images' quality assessment, 48 NF (15 dense) and 169 WF (88 dense) breasts were used for the prospective study; 48 (18 dense) breasts suffered from a histology-confirmed carcinoma. An overall sensitivity of 85.8 % in breasts lesions' detection was achieved by the microwave imaging apparatus. Conclusion: An optimum recruitment strategy was implemented to assess MBI. Future trials may show the clinical usefulness of microwave imaging, which may play an important role in breast screening.

A Multicentric, Single Arm, Prospective, Stratified Clinical Investigation to Confirm MammoWave's Ability in Breast Lesions Detection.

Novel techniques, such as microwave imaging, have been implemented in different prototypes and are under clinical validation, especially for breast cancer detection, due to their harmless technology and possible clinical advantages over conventional imaging techniques. In the prospective study presented in this work, we aim to investigate through a multicentric European clinical trial (ClinicalTrials.gov Identifier NCT05300464) the effectiveness of the MammoWave microwave imaging device, which uses a Huygens-principle-based radar algorithm for image reconstruction and comprises dedicated image analysis software. A detailed clinical protocol has been prepared outlining all aspects of this study, which will involve adult females having a radiologist study output obtained using conventional exams (mammography and/or ultrasound and/or magnetic resonance imaging) within the previous month. A maximum number of 600 volunteers will be recruited at three centres in Italy and Spain, where they will be asked to sign an informed consent form prior to the MammoWave scan. Conductivity weighted microwave images, representing the homogeneity of the tissues' dielectric properties, will be created for each breast, using a conductivity = 0.3 S/m. Subsequently, several microwave image parameters (features) will be used to quantify the images' non-homogenous behaviour. A selection of these features is expected to allow for distinction between breasts with lesions (either benign or malignant) and those without radiological findings. For all the selected features, we will use Welch's t-test to verify the statistical significance, using the gold standard output of the radiological study review.

Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network.

MammoWave is a microwave imaging device for breast lesion detection, employing two antennas which rotate azimuthally (horizontally) around the breast. The antennas operate in the 1-9 GHz band and are set in free space, i.e., pivotally, no matching liquid is required. Microwave images, subsequently obtained through the application of Huygens Principle, are intensity maps, representing the homogeneity of the dielectric properties of the breast tissues under test. In this paper, MammoWave is used to realise tissues dielectric differences and localise lesions by segmenting microwave images adaptively employing pulse coupled neural network (PCNN). Subsequently, a non-parametric thresholding technique is modelled to differentiate between breasts having no radiological finding (NF) or benign (BF) and breasts with malignant finding (MF). Resultant findings verify that automated breast lesion localization with microwave imaging matches the gold standard achieving 81.82% sensitivity in MF detection. The proposed method is tested on microwave images acquired from a feasibility study performed in Foligno Hospital, Italy. This study is based on 61 breasts from 35 patients; performance may vary with larger number of datasets and will be subsequently investigated.

Radial Basis Function for Breast Lesion Detection from MammoWave Clinical Data.

Recently, a novel microwave apparatus for breast lesion detection (MammoWave), uniquely able to function in air with 2 antennas rotating in the azimuth plane and operating within the band 1-9 GHz has been developed. Machine learning (ML) has been implemented to understand information from the frequency spectrum collected through MammoWave in response to the stimulus, segregating breasts with and without lesions. The study comprises 61 breasts (from 35 patients), each one with the correspondent output of the radiologist's conclusion (i.e., gold standard) obtained from echography and/or mammography and/or MRI, plus pathology or 1-year clinical follow-up when required. The MammoWave examinations are performed, recording the frequency spectrum, where the magnitudes show substantial discrepancy and reveals dissimilar behaviours when reflected from tissues with/without lesions. Principal component analysis is implemented to extract the unique quantitative response from the frequency response for automated breast lesion identification, engaging the support vector machine (SVM) with a radial basis function kernel. In-vivo feasibility validation (now ended) of MammoWave was approved in 2015 by the Ethical Committee of Umbria, Italy (N. 6845/15/AV/DM of 14 October 2015, N. 10352/17/NCAV of 16 March 2017, N 13203/18/NCAV of 17 April 2018). Here, we used a set of 35 patients. According to the radiologists conclusions, 25 breasts without lesions and 36 breasts with lesions underwent a MammoWave examination. The proposed SVM model achieved the accuracy, sensitivity, and specificity of 91%, 84.40%, and 97.20%. The proposed ML augmented MammoWave can identify breast lesions with high accuracy.

Breast lesion detection through MammoWave device: Empirical detection capability assessment of microwave images' parameters.

MammoWave is a microwave imaging device for breast lesions detection, which operates using two (azimuthally rotating) antennas without any matching liquid. Images, subsequently obtained by resorting to Huygens Principle, are intensity maps, representing the homogeneity of tissues' dielectric properties. In this paper, we propose to generate, for each breast, a set of conductivity weighted microwave images by using different values of conductivity in the Huygens Principle imaging algorithm. Next, microwave images' parameters, i.e. features, are introduced to quantify the non-homogenous behaviour of the image. We empirically verify on 103 breasts that a selection of these features may allow distinction between breasts with no radiological finding (NF) and breasts with radiological findings (WF), i.e. with lesions which may be benign or malignant. Statistical significance was set at p<0.05. We obtained single features Area Under the receiver operating characteristic Curves (AUCs) spanning from 0.65 to 0.69. In addition, an empirical rule-of-thumb allowing breast assessment is introduced using a binary score S operating on an appropriate combination of features. Performances of such rule-of-thumb are evaluated empirically, obtaining a sensitivity of 74%, which increases to 82% when considering dense breasts only.

Design of Distributed Spiral Resonators for the Decoupling of MRI Double-Tuned RF Coils.

OBJECTIVE: A systematic analytical approach to design Spiral Resonators (SRs), acting as distributed magnetic traps (DMTs), for the decoupling of concentric Double-Tuned (DT) RF coils suitable for Ultra-High Field (7 T) MRI is presented. METHODS: The design is based on small planar SRs placed in between the two RF loops (used for signal detection of the two nuclei of interest). We developed a general framework based on a fully analytical approach to estimate the mutual coupling between the RF coils and to provide design guidelines for the geometry and number of SRs to be employed. Starting from the full-analytical estimations of the SRs geometry, electromagnetic simulations for improving and validating the performance can be carried out. RESULTS AND CONCLUSION: We applied the method to a test case of a DT RF coil consisting of two concentric and coplanar loops used for 7 T MRI, tuned at the Larmor frequencies of the proton (1H, 298 MHz) and sodium (23Na, 79 MHz) nuclei, respectively. We performed numerical simulations and experimental measurements on fabricated prototypes, which both demonstrated the effectiveness of the proposed design procedure. SIGNIFICANCE: The decoupling is achieved by printing the SRs on the same dielectric substrate of the RF coils thus allowing a drastic simplification of the fabrication procedure. It is worth noting that there are no physical connections between the decoupling SRs and the 1H/23Na RF coils, thus providing a mechanically robust experimental set-up, and improving the transceiver design with respect to other traditional decoupling techniques.

Machine Learning Approaches for Automated Lesion Detection in Microwave Breast Imaging Clinical Data.

Breast lesion detection employing state of the art microwave systems provide a safe, non-ionizing technique that can differentiate healthy and non-healthy tissues by exploiting their dielectric properties. In this paper, a microwave apparatus for breast lesion detection is used to accumulate clinical data from subjects undergoing breast examinations at the Department of Diagnostic Imaging, Perugia Hospital, Perugia, Italy. This paper presents the first ever clinical demonstration and comparison of a microwave ultra-wideband (UWB) device augmented by machine learning with subjects who are simultaneously undergoing conventional breast examinations. Non-ionizing microwave signals are transmitted through the breast tissue and the scattering parameters (S-parameter) are received via a dedicated moving transmitting and receiving antenna set-up. The output of a parallel radiologist study for the same subjects, performed using conventional techniques, is taken to pre-process microwave data and create suitable data for the machine intelligence system. These data are used to train and investigate several suitable supervised machine learning algorithms nearest neighbour (NN), multi-layer perceptron (MLP) neural network, and support vector machine (SVM) to create an intelligent classification system towards supporting clinicians to recognise breasts with lesions. The results are rigorously analysed, validated through statistical measurements, and found the quadratic kernel of SVM can classify the breast data with 98% accuracy.

Radiofrequency magnetic resonance coils and communication antennas: Simulation and design strategies.

Coils simulation and design is a fundamental task to maximize Signal-to-Noise Ratio in Magnetic Resonance applications. In the meantime, in the last years the issue of accurate communication antennas analysis has grown. Coil design techniques take advantage of computer simulations in dependence on the magnetic field wavelength and coil sizes. In particular, since at high frequencies coils start to behave as antennas, modern Magnetic Resonance coil development exploits numerical methods typically employed for antennas simulation. This paper reviews coil and antenna performance parameters and focuses on the different simulation approaches in dependence on the near/far field zones and operating frequency.

Occupational exposure in MR facilities due to movements in the static magnetic field.

PURPOSE: The exposure of operators moving in the static field of magnetic resonance (MR) facilities was assessed through measurements of the magnetic flux density, which is experienced as variable in time because of the movement. Collected data were processed to allow the comparison with most recent and authoritative safety standards. METHODS: Measurements of the experienced magnetic flux density B were performed using a probe worn by volunteers moving in MR environments. A total of 55 datasets were acquired nearby a 1.5 T, 3 T, and 7 T whole body scanners. Three different metrics were applied: the maximum intensity of B, to be compared with 2013/35/EU Directive exposure limit values for static fields; the maximum variation of the vector B on every 3s-interval, for comparison with the ICNIRP-2014 basic restriction aimed at preventing vertigo effects; two weighted-peak indices (for "sensory" and "health" effects: SENS-WP, HLTH-WP), assessing compliance with ICNIRP-2014 and EU Directive recommendations intended to prevent stimulation effects. RESULTS: Peak values of |B| were greater than 2 T in nine of the 55 datasets. All the datasets at 1.5 T and 3 T were compliant with the limit for vertigo effects, whereas six datasets at 7 T turned out to be noncompliant. At 7 T, all 36 datasets were noncompliant for the SENS-WP index and 26 datasets even for the HLTH-WP one. CONCLUSIONS: Results demonstrate that compliance with EU Directive limits for static fields does not guarantee compliance with ICNIRP-2014 reference levels and clearly show that movements in the static field could be the key component of the occupational exposure to EMF in MR facilities.