Name: Clinical Imaging of Microwave Mammography (Video) | JoVE Journal
Uploaded: 2025-11-14T14:45:00.000+00:00
Duration: 5 min 28 s
Description: Discover microwave mammography for early breast cancer detection using non-invasive imaging technology. This clinical method enables safe, radiation-free screening with AI-enhanced image reconstruction.

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5 minutes

This study was conducted with the support of Science and Technology Research Grant of Japan, 24K10872. This research was supported by grants from JSPS KAKENHI and the Japan Agency for Medical Research and Development (AMED). The authors have filed patent applications related to the technology described in this article.

All clinical procedures were approved by the Institutional Review Board of Aichi Medical University (Certification Number of Specific Clinical Research: CRB4200004).
1. Device setup
<ol>
	<li>Prepare a room sufficiently large to accommodate the main unit, office automation (OA) rack, and patient bed. 
	NOTE: To ensure patient privacy, it is recommended to separate the main unit and bed using a curtain or partition (Figure 4A). A power supply of at least AC 100 V, 10 A is required.</li>
	<li>Place the VNA, control unit, and laptop on the OA rack (Figure 4B).</li>
	<li>Connect the electronic calibration module (Figure 4C) to the VNA via a USB cable.</li>
	<li>Connect the antenna switch to the control unit using five D-sub cables.</li>
	<li>Connect the cable linking the digital input/output (DIO) interface and the D-sub connector to the control unit (Figure 4D).</li>
	<li>Connect the DIO interface to the laptop using a USB cable.</li>
	<li>Connect the input/output ports of the VNA to the antenna switch using RF cables (Figure 4B,E).</li>
	<li>Connect the GPIB port of the VNA to a GPIB-USB conversion cable (Figure 4F), and attach the USB connector to the laptop.</li>
	<li>Connect the intake tube from the imaging sensor to the aspirator (Figure 4E).</li>
	<li>Plug in the power cables of the VNA, antenna switch, laptop, and aspirator into AC outlets and turn on the power.</li>
</ol>
2. Input of initial parameters
<ol>
	<li>Launch the measurement program Analyzer Control on the laptop. 
	NOTE: The start window appears (Figure 5A).</li>
	<li>Enter the following parameters: start frequency = 1000 MHz, stop frequency = 6000 MHz, number of data points = 101, and response waiting time = 200 ms. 
	NOTE: Default values should be used for all other parameters.</li>
	<li>Press the DIO button, followed by the GPIB Confirmation button. 
	NOTE: If all connections are correct, the CAL Wizard, CAL Load, Auto Measurement, and Manual Measurement buttons become active (Figure 5B).</li>
</ol>
3. Calibration of the Vector Network Analyzer (VNA)
<ol>
	<li>Disconnect the cables connected to antennas 4 and 6 of the imaging sensor, and connect them to the input/output terminals of the electronic calibration module (Figure 6A).</li>
	<li>Set both AUTO/MANUAL switches on the control unit to MANUAL, and configure the Tx toggle switch to binary 5 and the Rx toggle switch to binary 9 (Figure 6B).</li>
	<li>Press the CAL Wizard button. 
	NOTE: The CAL Wizard window appears (Figure 6C).</li>
	<li>Press the Set Wizard button (Figure 6C).</li>
	<li>Perform two-port calibration over the 1-6 GHz frequency band using the electronic calibration module19.</li>
	<li>Upon completion, press CAL Wizard Exit to close the window (Figure 6C).</li>
	<li>Press the CAL Save button. 
	NOTE: The data save window appears (Figure 6D).</li>
	<li>Enter a name for the calibrated data (e.g., 20250717.sta) and press Save. 
	NOTE Calibration data are saved in the VNA. If calibration has already been performed, repeating it is not necessary. In that case, press CAL Load, select the appropriate file (Figure 6E: e.g., 20240829.sta), and click Apply to load the calibration data into the VNA.</li>
	<li>Set the AUTO/MANUAL switch on the control unit back to AUTO.</li>
</ol>
4. Imaging procedure
<ol>
	<li>Position the patient prone on the bed (Figure 7A).</li>
	<li>Pull up the patient's top and place one breast into the imaging sensor, ensuring no clothing obstructs the sensor.</li>
	<li>Turn on the aspirator to initiate suction.</li>
	<li>Adjust the vacuum pressure to 0.04 - 0.06 MPa using the CTRL knob (Figure 7B). If the pressure cannot be reduced, reposition the patient until proper suction is achieved.</li>
	<li>Stop imaging if the aspirator pressure remains at 0 MPa. 
	NOTE: A gap between the sensor and the breast prevents accurate image acquisition.</li>
	<li>Press the Auto Measurement button (Figure 5B). 
	NOTE: The measurement status window appears (Figure 7C).</li>
	<li>Set the measurement range to 1-28 and enable Skip Reciprocity.</li>
	<li>Press Start Measurement to begin data acquisition. 
	NOTE: The transmission and reception combinations are displayed during measurement (Figure 7D). Upon completion, results are saved in the VNA_Results folder in CSV format (Figure 7E).</li>
</ol>
5. Image reconstruction
<ol>
	<li>Open the data_read program in MATLAB.</li>
	<li>Set the path (e.g., C:\Users\yoshihiko\Desktop\VNA_Results) and specify the measurement files (e.g., P31_L.csv and P31_R.csv) along with the output data name (Figure 8A).</li>
	<li>Click the <img alt="Micellization illustrated; thermodynamic formula. Graph shows surface tension vs. concentration curve." src="/files/ftp_upload/69288/69288eq1.jpg" style="margin: auto;" data-altupdatedat="1783396841000" data-alt="Equation 1"> button in the editor to generate a .mat file for equipment calibration (Figure 8B).</li>
	<li>Open the initialize_for_clinic program in MATLAB.</li>
	<li>Enter the following parameters (Figure 8C): Reconstruction frequency: f = 1.9 × 109 Hz; Path and names of the measurement files (e.g., C:\Users\kuwahara\OneDrive\patient_data\xxxxxxxx); Designation of the left and right breast (L(:,:,fn)); Path and name of the patient worksheet ('xxxxxxxx.xlsx', sheet='sheet1')</li>
	<li>Click <img alt="Micellization illustrated; thermodynamic formula. Graph shows surface tension vs. concentration curve." src="/files/ftp_upload/69288/69288eq1.jpg" style="margin: auto;" data-altupdatedat="1783396841000" data-alt="Equation 1"> to generate a patient worksheet for use in the main Excel VBA program. 
	NOTE: A directory for storing reconstruction results is also created.</li>
	<li>Open the image_reconstruction_# MATLAB file (# indicates processing number).</li>
	<li>Specify the patient worksheet path and name (e.g., G:\patient_sheet\xxxxxxxx_L.xlsx), the reconstruction frequency (1.9 × 109 Hz), and the calibration file (cal_2314_ver2), then save the settings (Figure 8D).</li>
	<li>Open the main image reconstruction program multi_person in Excel.</li>
	<li>Enter the patient worksheet names created in step 6 (e.g., xxxxxxxx_L to vvvvvvv_R) (Figure 9A). 
	NOTE: Multiple patients' images can be reconstructed simultaneously by entering the series of worksheet names.</li>
	<li>Open the VBA program from the Excel add-in.</li>
	<li>Specify the range of worksheets to process (np = 1 to 8).</li>
	<li>Confirm that the directories for worksheets are correctly set in the Data_read and Data_write standard modules.</li>
	<li>Select getPDTdata and click <img alt="Micellization illustrated; thermodynamic formula. Graph shows surface tension vs. concentration curve." src="/files/ftp_upload/69288/69288eq1.jpg" style="margin: auto;" data-altupdatedat="1783396841000" data-alt="Equation 1"> in the editor to start the image reconstruction process (Figure 9B).</li>
	<li>After processing, verify the reconstructed images (3D tomographic longitudinal and transverse sections), which are saved in the specified directory with filenames such as mwsdbim_20250617-14h36m04s, alongside the patient worksheets.</li>
</ol>

<ol><li>Foundation for Promotion of Cancer Research. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=%22Cancer+statistics+in+Japan+-+2024%22">Cancer statistics in Japan - 2024</a>. , Foundation for Promotion of Cancer Research. Tokyo. (2024).</li>
<li>Lehman, C. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Cancer+yield+of+mammography%2C+MR%2C+and+US+in+high-risk+women%5BTitle%5D)+AND+%22Radiology%22%5BJournal%5D)">Cancer yield of mammography, MR, and US in high-risk women.</a> Radiology. 224 (2), 381-388 (2007).</li>
<li>Nass, S. J., Henderson, I. C., Lashof, J. C. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aNass%2C+SJ+%22Mammography+and+beyond%3A+developing+technologies+for+the+early+detection+of+breast+cancer%22">Mammography and beyond: developing technologies for the early detection of breast cancer</a>. , National Academies Press. Washington, DC. (2001).</li>
<li>Pandya, H. N., Ghosh, D. K., Singh, J. S. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aPandya%2C+HN+%22Breast+image+reconstruction+and+cancer+detection+using+microwave+imaging%22">Breast image reconstruction and cancer detection using microwave imaging</a>. , Institute of Physics Publishing. Bristol. (2023).</li>
<li>Rachida, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Recent+advancements+in+breast+cancer+detection%3A+a+holistic+review+of+microwaves%2C+ultrasound%2C+and+photo-acoustic+imaging+techniques%5BTitle%5D)+AND+%22IEEE+J+Microw%22%5BJournal%5D)">Recent advancements in breast cancer detection: a holistic review of microwaves, ultrasound, and photo-acoustic imaging techniques.</a> IEEE J Microw. 5 (4), 776-792 (2025).</li>
<li>Pastrino, M. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aPastrino%2C+M+%22Microwave+imaging%22">Microwave imaging</a>. , John Wiley & Sons. Hoboken, NJ. (2010).</li>
<li>Kuwahara, Y. Microwave imaging for breast cancer detection. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aKuwahara%2C+Y+%22Breast+cancer%3A+evolving+challenges+and+next+frontiers%22">Breast cancer: evolving challenges and next frontiers</a>. , Intechopen. London. (2021).</li>
<li>Fischer, B. E., LaHale, I. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+University+of+Manitoba+microwave+imaging+repository%3A+a+two-dimensional+microwave+scattering+database+for+testing+inversion+and+calibration+algorithms%5BTitle%5D)+AND+%22IEEE+Antennas+Propag+Mag%22%5BJournal%5D)">The University of Manitoba microwave imaging repository: a two-dimensional microwave scattering database for testing inversion and calibration algorithms.</a> IEEE Antennas Propag Mag. 53 (5), 126-133 (2011).</li>
<li>Bond, E. J., Li, X., Hagness, S. C., Van Veen, B. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Microwave+imaging+via+space-time+beamforming+for+early+detection+of+breast+cancer%5BTitle%5D)+AND+%22IEEE+Trans+Antennas+Propag%22%5BJournal%5D)">Microwave imaging via space-time beamforming for early detection of breast cancer.</a> IEEE Trans Antennas Propag. 51 (8), 1690-1705 (2003).</li>
<li>Shea, J. D., Kosmas, P., Van Veen, B. D., Hagness, S. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Contrast-enhanced+microwave+imaging+of+breast+tumors%3A+a+computational+study+using+3D+realistic+numerical+phantoms%5BTitle%5D)+AND+%22Inverse+Probl%22%5BJournal%5D)">Contrast-enhanced microwave imaging of breast tumors: a computational study using 3D realistic numerical phantoms.</a> Inverse Probl. 26, 074009(2010).</li>
<li>A microwave imaging sensor composed of a dielectric-loaded double-polarized horn antenna. Kuwahara, Y., Fujii, K. Proc Eur Conf Antennas Propag (EuCAP), , (2023).</li>
<li>Clinical imaging by the microwave mammography. Kuwahara, Y., Fuji, K. 2025 IEEE MTT-S Int Microwave Biomed Conf, , (2025).</li>
<li>Conformal array antenna with the aspirator for the microwave mammography. Kuwahara, Y., Suzuki, K., Horie, H., Hatano, H. 2010 IEEE APS Int Symp, , (2010).</li>
<li>Mohamed, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Study+of+multi-polarization+in+microwave+tomography+for+breast+cancer+detection%5BTitle%5D)+AND+%22IEICE+Trans+J%22%5BJournal%5D)">Study of multi-polarization in microwave tomography for breast cancer detection.</a> IEICE Trans J. J99-C, 393-401 (2016).</li>
<li>Kuwahara, Y., Osaki, T., Nozaki, A., Fujii, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Utilization+of+preliminary+knowledge+in+microwave+tomography+for+breast+cancer+detection%5BTitle%5D)+AND+%22IEICE+Trans+J%22%5BJournal%5D)">Utilization of preliminary knowledge in microwave tomography for breast cancer detection.</a> IEICE Trans J. J102-C, 86-92 (2018).</li>
<li>Kuwahara, Y. Microwave imaging for early breast cancer detection. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aKuwahara%2C+Y+%22New+perspective+in+breast+imaging%22">New perspective in breast imaging</a>. , Croatia. (2017).</li>
<li>Microwave mammography with a small sensor and a commercial electromagnetic simulator. Proc Eur Microw Conf (EuMC). Kuwahara, Y. , 663-666 (2016).</li>
<li>Application of S-parameter to the inverse scattering problem. Kuwahara, Y. 11th Eur Conf Antennas Propag (EuCAP), , (2017).</li>
<li>Hammerschmidt, C., John, R. T., Tran, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Calibration+of+vector+network+analyzer+for+measurements+in+radio+frequency+propagation+channels%5BTitle%5D)+AND+%22J+Vis+Exp%22%5BJournal%5D)">Calibration of vector network analyzer for measurements in radio frequency propagation channels.</a> J Vis Exp. (160), e60874(2020).</li>
<li>Janjik, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((SAFE%3A+a+novel+microwave+imaging+system+design+for+breast+cancer+screening+and+early+detection-clinical+evaluation%5BTitle%5D)+AND+%22Diagnostics%22%5BJournal%5D)">SAFE: a novel microwave imaging system design for breast cancer screening and early detection-clinical evaluation.</a> Diagnostics. 11 (3), 533(2021).</li>
<li>Rana, S. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Radiation-free+microwave+technology+for+breast+lesion+detection+using+supervised+machine+learning+model%5BTitle%5D)+AND+%22Tomography%22%5BJournal%5D)">Radiation-free microwave technology for breast lesion detection using supervised machine learning model.</a> Tomography. 9 (1), 105-129 (2023).</li>
<li>Nguyen, P. T., Abbosh, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((3D+focused+microwave+hyperthermia+for+breast+cancer+treatment+with+experimental+validation%5BTitle%5D)+AND+%22IEEE+Trans+Antennas+Propag%22%5BJournal%5D)">3D focused microwave hyperthermia for breast cancer treatment with experimental validation.</a> IEEE Trans Antennas Propag. 65 (7), 3489-3500 (2017).</li>
<li>Li, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+preclinical+system+prototype+for+focused+microwave+breast+hyperthermia+guided+by+compressive+thermoacoustic+tomography%5BTitle%5D)+AND+%22IEEE+Trans+Biomed+Eng%22%5BJournal%5D)">A preclinical system prototype for focused microwave breast hyperthermia guided by compressive thermoacoustic tomography.</a> IEEE Trans Biomed Eng. 68 (7), 2289-2300 (2021).</li>
</ol>

The authors declare no commercial or financial relationships with any companies related to this work.

Calibration in this context has two distinct meanings. First, it refers to the correction of measured scattering data so that the results can be accurately incorporated into numerical models. Second, it refers to the calibration of the vector network analyzer (VNA) itself.
Prior to any calibration or measurement, the VNA should be powered on at least 15 min in advance, and the READY indicator on the electronic calibration module must be green. All device connections should be ...

Breast cancer is the most common malignancy among women, with approximately 95,000 new cases diagnosed annually in Japan1. Early detection and treatment are essential for improving patient outcomes.
X-ray mammography is currently the standard screening tool. However, its sensitivity is limited in women with dense breast tissue, making small tumor detection difficult2. To address this limitation, ultrasound is often used as an adjunctive diagnostic method. Ultrasound examinations require a high level of operator skill, and the diagnostic accuracy of mammography or ultrasound alone is approximately 60%. Contrast-enhanced magnetic resonance imaging (MRI) achieves accuracy exceeding 90%; nevertheless, due to high equipment and examination costs, MRI is not suitable for routine screening and is mainly reserved for surgical decision-making and treatment planning3.
Recently, microwave imaging has attracted increasing attention as a potential alternative capable of overcoming the limitations of mammography and ultrasound4. When microwave radiation interacts with biological tissue, strong reflections occur at dielectric boundaries. Reflectivity is more than an order of magnitude higher than that of X-rays or ultrasound, enabling the detection of small lesions. Research on breast cancer detection using microwave imaging has been ongoing for over 25 years, with several clinical studies reported5.
Microwave imaging can be broadly classified into two categories: confocal imaging, which reconstructs the distribution of scattered power, and scattering tomography, which reconstructs the distribution of complex permittivity (dielectric constant and electrical conductivity)6,7. Confocal imaging is conceptually similar to ultrasound diagnostics; however, due to multiple reflections within tissue, internal structures cannot be accurately reconstructed. In contrast, scattering tomography provides accurate reconstructions even in the presence of multiple reflections but requires precise numerical modeling of electromagnetic scattering. Current computational electromagnetic techniques still face challenges in fully replicating these phenomena. Therefore, calibration that aligns measured data with numerical models is critical for practical implementation8. Moreover, because lesion responses are less than 1/1000 of the skin reflection9, highly sensitive antennas and receivers with excellent signal-to-noise performance are indispensable.
Our system addresses these challenges by introducing innovative approaches to solving the inverse scattering problem using an iterative distorted Born approximation (IDBA)10. Several key technologies have been developed, including a dual-polarized dielectric-loaded horn antenna11, a calibration method using two homogeneous phantoms12, breast fixation to the sensor via suction13, multi-polarized wave excitation14, and exploitation of the linear relationship between tissue permittivity and conductivity15. A prototype device incorporating these technologies was completed in September 2023, and clinical trials commenced in November 2023.
Although microwave imaging has been extensively investigated, there is still no widely accepted clinical protocol that guarantees reproducible data acquisition, robust calibration, and stable image reconstruction across patients. Establishing such a protocol is essential for the translation of microwave imaging from experimental research to practical clinical application. The protocol presented in this study, incorporating suction-based breast fixation, dual-phantom calibration, and multi-polarized wave excitation, addresses these requirements and provides a standardized framework for reliable clinical evaluation of microwave tomography.
Figure 1A illustrates the principle of microwave imaging16. Multiple antennas are arranged around the breast, with one antenna transmitting while the others receive scattered waves. A vector network analyzer (VNA) functions as both transmitter and receiver. If only a single port is available, transmission and reception are alternated sequentially, and scattered signals are acquired for all possible antenna pairs. The collected data are then used to reconstruct tomographic images of the breast.
The hardware configuration of the prototype is summarized in Table 1 and shown in Figure 1B, while the external appearance of the prototype is presented in Figure 2A. The device consists of a ceramic container for the breast, 16 antennas attached to the container, an antenna switch for selecting transmitting and receiving antennas, a VNA for measuring transmission characteristics between antenna pairs, a laptop computer with measurement software, a control unit for coordinating the laptop and antenna switch, and an aspirator for suction-based breast fixation (Figure 2C).
The ceramic container with attached antennas (referred to as the imaging sensor, Figure 2B) and the antenna switch are housed within the main body of the device. Although there are 16 physical antennas, 12 of them are dual-polarized, resulting in an effective antenna count of 28. The device is positioned beside the bed, and the patient lies prone, placing her breast into the imaging sensor (Figure 1C). To minimize imaging artifacts caused by body movement, the breast is suction-fixed to the sensor using the aspirator.
During measurement, a transmitting antenna is selected, and microwaves at 1.9 GHz with an output power of -5 dBm are irradiated into the breast. Scattering parameters (S-parameters) are measured by the VNA while sequentially switching transmitting and receiving antennas, and the data are recorded on the laptop. This process is repeated for all antenna combinations. Acquisition of one breast takes approximately 14 min. The relatively long measurement time is due to the serial acquisition process and additional waiting intervals implemented to prevent communication errors. Measurement duration is consistent across patients.
The recorded measurement files are transferred to a dedicated workstation for image reconstruction. The core control program is implemented in Excel VBA, which coordinates data processing and interfaces with the electromagnetic field simulator FEMTET and the numerical analysis platform MATLAB, as illustrated in Figure 317,18. Reconstruction of a single breast image requires approximately 7 h on a workstation equipped with an Intel Core i7-14700K CPU and 96 GB of RAM. Since the reconstruction algorithm is based on IDBA10, large-scale three-dimensional full-wave electromagnetic simulations must be executed repeatedly. Each forward electromagnetic field analysis requires approximately 10 min, and retrieving the computed field distribution from FEMTET takes an additional 30 min. This cycle is repeated ten times per reconstruction, yielding a cumulative processing time of roughly 400 min.
The software environment is summarized in Table 2. The laptop is configured with the Analyzer Control&#160;program to manage communication with the VNA and control unit for the antenna switch. Hardware connectivity requires a digital input/output (DIO) interface and a general-purpose interface bus (GPIB)-USB conversion cable to ensure reliable command transfer. All other reconstruction programs are installed on the workstation, which must support Microsoft Excel, FEMTET, and MATLAB. FEMTET is used to solve Maxwell&#39;s equations in the imaging domain via the finite element method, while MATLAB executes numerical routines required for the IDBA inversion.
A rigorous calibration procedure is required to align measured scattering data with the numerical forward model. Calibration is performed using two homogeneous dielectric phantoms (Figure 2D) designed to approximate tissue-mimicking media. At 1.9 GHz, phantom 1 exhibits a relative permittivity of 6.65 and a dielectric loss tangent of 0.438, whereas phantom 2 has a relative permittivity of 4.66 and a loss tangent of 0.289. A calibration file, cal_2314_94_ver2.mat, is pre-generated, containing correction coefficients that compensate for systematic discrepancies between measured and simulated S-parameters. These coefficients are derived from FEMTET-based simulations of the phantoms&#39; transmission characteristics and cross-validated against experimental measurements obtained with the prototype12. This calibration ensures consistency between numerical modeling and experimental data, stabilizing the convergence of the iterative reconstruction process.

<table><tbody><tr><td>Aspirator</td><td>AS ONE</td><td>DAS-01</td><td></td></tr><tr><td>digital IO</td><td>National Instruments</td><td>USB6501</td><td></td></tr><tr><td>Elecreonic Calibration Module</td><td>Agilent</td><td>85093C</td><td>Discontinued. Replace with 85093D.</td></tr><tr><td>Electromagnetic simulator</td><td>Murata Software</td><td>FEMTET</td><td></td></tr><tr><td>Excel VBA</td><td>Microsoft</td><td>Office</td><td></td></tr><tr><td>GPIB USB Conversion Cable</td><td>National Instruments</td><td>GPIB-USB-HS</td><td></td></tr><tr><td>Numerical Software</td><td>Mathworks</td><td>Matlab</td><td></td></tr><tr><td>VNA</td><td>Keysight</td><td>E5071C</td><td></td></tr></tbody></table>

clinical imaging of microwave mammography

Clinical Imaging of Microwave Mammography

Breast cancer is the most common malignancy among women, and early detection and treatment are critical for improving clinical outcomes. X-ray mammography remains the standard screening modality; however, it has several limitations, including radiation exposure, patient discomfort, and reduced sensitivity in women with dense breast tissue. Microwave imaging, a non-ionizing technique, has emerged as a promising alternative. We have developed a device that reconstructs breast tissue structures by solving the inverse scattering problem and is currently undergoing clinical trials. This system generates three-dimensional tomographic images without the use of contrast agents and without causing pain during examination. To date, 24 breast cancer patients have been imaged, with an accuracy of 86% for tumors &#8805; 1 cm in diameter, and 58% when tumors &#60; 1 cm are included. In this article, we present a detailed protocol for device preparation, clinical imaging, and data processing, along with representative imaging results from selected patients.

The imaging results of three representative patients are presented below:
Patient 1 
A 78-year-old woman with a 34 mm × 21 mm cancer located at the 2 o'clock position of the left breast. The mammary glands are predominantly fatty. Figure 10A and Figure 10B show X-ray mammography images, whereas Figure 10C-F present imaging results obtained with the pro...

This protocol describes the imaging procedure of a microwave imaging device for breast imaging.

Breast cancer is the most common malignancy among women, and early detection and treatment are critical for improving clinical outcomes. X-ray mammography ...

We aim to develop a clinical imaging system for early breast cancer detection using microwave technology. The use of AI is being activity considered, as progress in image reconstruction algorithm and antennas has stalled. To begin, launch the measurement program analyzer control on the laptop.Enter the start frequency as 1, 000 megahertz, the stop frequency as 6, 000 megahertz, the number of data points as 101, and the response waiting time as 200 milliseconds. Use default values for all other parameters. Press the DIO button, then press the GPIB confirmation button.If all connections are correct, the CAL wizard, CAL load, auto measurement, and manual measurement buttons will become active. Press CAL Load, select the appropriate file, and click Apply to load the calibration data into the vector network analyzer. Set the auto or manual switch on the control unit to auto.Next, position the patient in a prone position on the bed. Ask the patient to insert one breast into the imaging sensor, ensuring that no clothing obstructs the sensor. Turn on the aspirator to initiate suction.Adjust the vacuum pressure to 0.04 to 0.06 megapascal using the control knob. If the pressure cannot be reduced, reposition the patient until proper suction is achieved. Press the auto measurement button.The measurement status window will appear. Set the measurement range to 1 through 28 and enable skip reciprocity. Now, press Start Measurement to begin data acquisition.The transmission and reception combinations will be displayed during measurement. After completion, results are saved in the VNA_Results folder in CSV format. To create a patient spreadsheet for use in the image reconstruction program, open the data_read program in MATLAB.Set the file path and specify the measurement files, then assign an output data name. Click the Run button in the MATLAB editor to generate a MAT file for a spreadsheet. Next, open the initialize_for_clinic program in MATLAB.Enter the reconstruction frequency, path, and names of the measurement files, breast designations, and patient worksheet path and name. Click the Run button to generate a patient worksheet for Excel VBA use. A directory is automatically created to store the reconstruction results.Open the image_reconstruction_#MATLAB file. Specify the patient worksheet and name, reconstruction frequency, and calibration file, then save the settings. Now, open the multi_person Excel image reconstruction program.Enter the patient worksheet names created earlier, then open the VBA program from the Excel add-in. Specify the worksheet processing range from one to eight. Confirm that worksheet directories are correctly set in the data-read and data-write standard modules.Select getPDTdata and click the play button to begin the image reconstruction process. After processing, verify that the reconstructed 3D tomographic images are saved in the correct directory alongside the patient worksheets. High-permittivity and high-conductivity regions were observed in the left breast containing cancer, whereas no such regions appeared in the right, cancer-free breast of patient one.In patient two, contrast-enhanced MRI clearly detected the cancer in the left breast, but it was difficult to identify on X-ray mammography. High-permittivity and high-conductivity areas were also observed in the left breast using the prototype, but no significant regions were detected in the cancer-free right breast. In patient three, the cancer was visible near the nipple on MRI images before chemotherapy, but was absent after treatment.Images obtained with the prototype also confirmed the disappearance of the cancer after chemotherapy. We demonstrated that micro-imaging can distinguish malignant from normal breast tissue in clinical tests. We addressed the lack of non-invasive, radiation-free breast cancer imaging techniques.Our method of a safe, low-cost imaging without radiation or contrast agents.

clinical-imaging-of-microwave-mammography

Research

JoVE Journal

Medicine

Bioimaging methods used to assess cell biodistribution of nanoparticles are applicable for therapeutic and diagnostic monitoring of nanoformulated compounds. The methods described herein are sensitive and specific when assessed by histological coregistration. The methodologies provide a translational pathway from rodent to human applications.

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring 

In the pathogenesis of bone metastasis, angiogenesis is a crucial process and therefore represents a target for imaging and therapy. Here, we present a rat model of site-specific breast cancer bone metastasis and describe strategies to non-invasively image angiogenesis in vivo using magnetic resonance imaging, volumetric computed tomography and ultrasound.

Multi-modal Imaging of Angiogenesis in a Nude Rat Model of Breast Cancer Bone Metastasis Using Magnetic Resonance Imaging, Volumetric Computed Tomography and Ultrasound

We demonstrate methods for the detection of architectural distortion in prior mammograms. Oriented structures are analyzed using Gabor filters and phase portraits to detect sites of radiating tissue patterns. Each site is characterized and classified using measures to represent spiculating patterns. The methods should assist in the detection of breast cancer.

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

We describe a non-invasive animal imaging platform that allows the detection, quantification, and monitoring of ovarian cancer growth and recurrence. This intra-peritoneal xenograft model mimics the clinical profile of patients with ovarian cancer.

Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence

We describe how to obtain parametric and vector maps of the diffusion tensor of the breast using magnetic resonance imaging. The protocol and final output following imaging processing are tailored for tracking breast architectural features and detecting breast malignancy.

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

We describe a method for the detection of tumor nodule development in the lungs of an adenocarcinoma mouse model using micro-computed tomography and its use for monitoring changes in nodule size over time and in response to treatment. The accuracy of the assessment was confirmed with end-point histological quantification.

Using Micro-computed Tomography for the Assessment of Tumor Development and Follow-up of Response to Treatment in a Mouse Model of Lung Cancer

In the past, small animal irradiation was usually performed without the ability to target a well-delineated tumor volume. The goal was to mimic the treatment of human glioblastoma in rats. Using a small animal irradiation platform, we performed MRI-guided 3D conformal irradiation with PET-based sub-volume boosting in a preclinical setting.

PET and MRI Guided Irradiation of a Glioblastoma Rat Model Using a Micro-irradiator

This protocol describes a novel mammary imaging window with a replaceable lid (R.MIW). Intravital microscopy after implantation of the R.MIW allows for longitudinal and multi-day imaging of the healthy and diseased mammary gland with a cellular resolution during the different developmental stages.

Longitudinal Intravital Microscopy Using a Mammary Imaging Window with Replaceable Lid

A procedure for the delivery of a chemical ablative solution to the rat mammary ductal tree for image-guided preventive treatment of breast cancer is described. Mammary epithelial cells can be targeted with minimal collateral tissue damage through cannulation directly into the nipple opening and intraductal infusion of a 70% ethanol-based ablative solution.

X-Ray Visualization of Intraductal Ethanol-Based Ablative Treatment for Prevention of Breast Cancer in Rat Models

Translation of Intravital microscopy findings is challenged by its shallow depth penetration into tissue. Here we describe a dorsal window chamber mouse model that enables co-registration of intravital microscopy and clinically applicable imaging modalities (e.g., CT, MRI) for direct spatial correlation, potentially streamlining clinical translation of intravital microscopy findings.

A Dorsal Skinfold Window Chamber Tumor Mouse Model for Combined Intravital Microscopy and Magnetic Resonance Imaging in Translational Cancer Research

This protocol uses a multichamber bed for parallel magnetic resonance imaging (MRI) of up to four animals to detect pancreatic adenocarcinoma tumors in genetically engineered mouse models. This multianimal MRI protocol is fast and cost-effective for detecting and measuring tumors, facilitating animal selection for preclinical studies and longitudinal monitoring of tumor growth.

Multianimal Magnetic Resonance Imaging for Tumor Measurements in Pancreatic Cancer Mouse Models

Discover microwave mammography for early breast cancer detection using non-invasive imaging technology. This clinical method enables safe, radiation-free screening with AI-enhanced image reconstruction.

Clinical Imaging of Microwave Mammography

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