Microwave breast imaging: A review of clinical potential and technological advances

Table 1 Comparison of different imaging modalities by smallest visible tumor size

Modality	Smallest visible tumor	Best use case
Mammography	2-5 mm (ideal), about 5-10 mm avg	General screening, calcifications
Ultrasound	3-5 mm	Dense breasts, targeted follow-up
Magnetic resonance imaging	1-2 mm	High-risk screening, dense breasts
Microwave imaging	4-10 mm (experimental)	Radiation-free, low-cost adjunct (early phase)

Table 2 Summary of clinical and perfromance metrics of different tomography-based microwave radiometric imaging systems

System	Scan time	Breast size	Accuracy (true positive rate)	Sensitivity (true negative rate)	Specificity	Patients tested
Dartmouth college[20,21]	2.2 minutes	14.2 cm diameter	Detected a 4-cm tumor in a trial test with one patient	Not reported	Not reported	1
McMaster university[22]	5 hours	4.8 cm diameter	Reported a 247 mm resolution in simulation	No clinical trials	No clinical trials	0
Istanbul Technical University (SAFE)[23,24]	20 minutes	Small and large	Detected lesions as small as 4 mm	79%	77%	113
University of Bristol (MARIA)[25]	5 minutes	32A and 32DD	Smallest recorded lesion was 5 mm	76%	Not reported	8
University of Calgary (TSAR)[26]	< 30 minutes	B and C size	10 mm lesion detected with SCR ~5.0 dB	No clinical trials	No clinical trials	1
McGill University[27]	6 minutes	Amid A to D size	10 mm spherical phantom tumor detected	No clinical trials	No clinical trials	4
Galway University (Wavelia)[28]	6 minutes	Greater than 32B	Did not detect tumors < 10 mm in trials	75%	Not reported	5
Chalmers University[29,30]	Not reported	55.0-75.0 cm diameter	Used oil cups mimicking tumors (55-75 mm)	No clinical trials	No clinical trials	0
University of Perugia (Mammowave)[31]	8 minutes	Various cup sizes	73%	82%	353
Hiroshima University[32]	15 minutes	Small (Japanese patients)	Minimum size 40 mm in trials (Japanese only)	49%	55%	9
University of São Paulo[33]	28 minutes	34B (imperial system)	1 mm tumor in 150 mm breast phantom with SCR > 7.0 dB	No clinical trials	No clinical trials	3
University of Queensland[34]	45 minutes	12.4 cm	20 mm × 30 mm water target in phantom	No clinical trials	No clinical trials	1

SAFE: Scanning aperture focused examination; MARIA: Multistatic array processing for radio wave image acquisition; TSAR: Tissue sensing adaptive radar; SCR: Signal-to-clutter ratio.

Table 3 Comparison of radar-based microwave breast imaging techniques

Technique	Detection range (mm)	Resolution	Image type	No. of antennas	Ref.
CMI	2–4 mm	High	2D/3D	Varies	[35,36]
TSAR	> 4 mm	Moderate	2D	Multiple	[37-39]
MIST	About 4 mm	High	3D	16	[40]
HMI (near-field)	About 5 mm	Moderate	2D	Varies	[41]
HMI (far-field)	4–6 mm	Moderate	2D	Varies	[42,43]

CMI: Confocal microwave imaging; TSAR: Tissue sensing adaptive radar; MIST: Microwave imaging via space-time; HMI: Holographic microwave imaging.

Table 4 Summary of the technical specifications in different radar-based microwave imaging systems

Origin	Antennas and arrangement	Frequency range (GHz)	Patient position	Coupling	Hardware	Imaging algorithm	Development stage
Dartmouth College[20,21]	16-monopole array	0.5-2.3	Prone	Glycerin/water	Stationary array PC controlled function generator and Rx circuitry	Log-phase formulation	Concept and feasibility
	16-monopole array	0.5-2.5	Prone	Glycerin/water	Stationary array Ettus B210 SDR	Log-phase formulation	Concept and feasibility
McMaster University[22]	1-horn (Tx), 9-bowtie array (Rx)	3.0-8.0	Not specified	Not specified	Stationary array planar scanning	Scattered power mapping, quantitative microwave holography	Concept and feasibility
Istanbul Technical University (SAFE)[23]	1 (Tx), 36-antenna array (Rx)	1.4-8.0	Prone	Liquid coupling medium	Stationary array VNA usage	Linear sampling method, factorization method	Verification and validation

Tx: Transmitter; Rx: Receiver; VNA: Vector network analyzer; SDR: Software defined radio; SAFE: Scanning aperture focused examination.

Table 5 System specifications and clinical readiness of institution-based radar-based microwave breast imaging systems

Institution	Antenna setup	Frequency range (GHz)	Patient orientation	Coupling medium	System configuration	Imaging technique	Development phase
Bristol (United Kingdom)-MARIA[25]	60 wide-slot antennas	3.0-8.0	Prone	Paraffin oil, wax, aqueous media	Stationary, 16-port Keysight VNA	Modified delay-and-sum	Near product launch
Calgary (Canada)-TSAR[26]	Monostatic antipodal Vivaldi	0.05-15.0	Prone	Canola oil	PC-controlled synthetic array, laser	Confocal DAS, skin artifact correction	Verification and validation
McGill (Canada)[44]	16 resistively-loaded traveling wave antennas	2.0-4.0	Prone	Ultrasound gel	Oscilloscope and pulse generator array	Delay-multiply-and-sum	Early feasibility
Galway (Ireland)-Wavelia[45]	18-element Vivaldi array	1.0-4.0	Prone	Liquid	Stationary array with VNA	TR-MUSIC (time-reversal method)	Verification and validation
Chalmers (Sweden)[46]	20 monopole antennas	0.2-3.0/0.5-6.0	Prone	Air or oil-filled phantom	Off-the-shelf SDR components	Confocal DAS or no imaging	Feasibility
Perugia (Italy)–Mammowave[47]	1 horn + 1 microstrip monopole	1.0-9.0	Prone	Air	Cobalt C1209 and Copper M. VNA	Huygens-based approach	Verification and validation
Hiroshima (Japan)[48]	4 × 4 dome-shaped array	3.1-10.6	Supine	Glycerine	UWB CMOS synthetic array	Confocal DAS	Verification and validation
São Paulo (Brazil)[49]	Dual-patch bistatic array	5.0-7.0	Fowler	Silicone rubber interface	UWB transceiver setup	Enhanced confocal DAS	Feasibility

VNA: Vector network analyzer; DAS: Delay-and-sum; TR-MUSIC: Time-reversal multiple signal classification; SDR: Software defined radio; UWB: Ultra-wideband; CMOS: Complementary metal-oxide-semiconductor; MBI: Microwave breast imaging.

Table 6 Summary of clinical evaluation of microwave breast imaging systems

Application	Description	Advantages	Challenges/limitations	Ref.
Primary breast cancer detection	Detects malignant tumors based on dielectric contrast	Non-ionizing, safe, repeatable; good for dense breasts	Low spatial resolution; noise sensitivity	[54,55]
Differentiating benign vs malignant lesions	Uses dielectric differences for classification	Good contrast between tissues; non-invasive	Needs clinical validation	[56]
Post-treatment monitoring	Tracks tissue changes after surgery or radiation	Can detect permittivity changes over time	Limited scans at longer follow-ups	[57]
Lesion size and localization	Assesses size/location in real-time	Complements mammography where sensitivity is limited	Dense breasts can obscure signals	[58]
Lymph node metastasis detection	Detects axillary lymph nodes using radar MWI	Helps with TNM staging; reduces unnecessary surgeries	Difficult with overlapping tissues	[59]
Breast tissue classification with ML	Uses AI/ML to classify lesion-containing vs healthy scans	Improves sensitivity; useful for screening	Signal variability; system training needs	[60,61]
Early detection in dense breasts	MBI outperforms mammography in dense tissues	No compression, non-ionizing, suitable for frequent use	Still requires more data from large trials	[62]
Adjunct to conventional imaging	Used in combination with ultrasound/MRI	Improves diagnostic confidence	Requires data integration methods	[56,63]
ML-based diagnostic models	CNNs and U-Nets used for tumor classification & segmentation	Enhances accuracy and image reconstruction	Needs diverse, well-annotated datasets	[64,65]
Detection of treatment-related changes	Tracks dielectric changes post-radiotherapy	Shows significant permittivity differences	Limited follow-up data	[57]
Clinical feasibility of portable systems	Portable devices tested for in-clinic use	Cost-effective, accessible, repeatable	Small patient numbers so far	[66]
Synthetic breast phantoms and simulations	Used to validate algorithms and device configurations	Allows modeling of dielectric variability	Phantom data may not generalize to real breasts	[67,68]
Contrast-agent enhanced MWI	Use of nanoparticles (e.g., ZnO) for dielectric contrast	Enhances visibility of tumors	Needs further safety studies	[69]

MBI: Microwave breast imaging; MWI: Microwave imaging; TNM: Tumor-node-metastasis staging system; ML: Machine learning; AI: Artificial Intelligence; CNN: Convolutional neural network; UWB: Ultra-wideband; ZnO: Zinc oxide.