Clinical Imaging of Microwave 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.