Millimeter-Wave Systems for Real-Time Intraoperative Brain Tumor

Resection Assistance

H. Lopes

1,2 a

, P. M. Mendes

1,2 b

and H. Dinis

1,2 c

Center for MicroElectromechanical Systems (CMEMS-Uminho), University of Minho, 4800-058 Guimarães, Portugal

LABBELS–Associate Laboratory, 4710-057 Braga, Portugal

Keywords: Brain Cancer, Brain Tumor Detection, Microwave Imaging, mmWave, Dielectric Properties, Real-Time

Surgical Assistance.

Abstract: Brain cancer is one of the deadliest forms of cancer due to limited treatment options and challenges in tumor

differentiation during surgery. Current surgical assistance tools, such as intraoperative imaging systems and

advanced visualization techniques, often face limitations in cost, accessibility, and precision. Microwave and

millimeter-wave (mmWave) technologies have emerged as promising alternatives for real-time, non-invasive

differentiation of healthy and cancerous brain tissues, leveraging their sensitivity to dielectric property

variations. This paper reviews the state-of-the-art microwave and mmWave systems developed for medical

diagnostics, focusing on brain tumor detection. It highlights their underlying principles, performance, and

limitations while discussing their potential to address the drawbacks of existing tools. By analyzing recent

advancements, the review identifies key areas for future development, proposing characteristics of an ideal

system to support real-time surgical decision-making. Additionally, the paper proposes a system designed to

measure the dielectric properties of the brain tissue, aiming to enhance real-time surgical decision-making

and improve patient outcomes.

1 INTRODUCTION

The last GLOBOCAN report showed that in 2022 there

were nearly 20 million new cancer cases and that

almost 10 million deaths were caused by it, meaning

that one in five individuals can potentially develop

cancer throughout their lives. Despite ranking

nineteenth in new cases, brain cancer has limited

treatment options available, making it one of the most

devastating prognoses, often fatal (Bray et al., 2024).

According to the available data, brain cancer has

a 5-year relative survival rate of 33.4%, which drops

to less than 10% for the most aggressive brain tumors,

such as glioblastomas (Brain and Other Nervous

System Cancer — Cancer Stat Facts, 2020). To try

and help cure the brain tumor, as of today, the main

treatments used are radiotherapy and surgery,

craniotomy being the most common surgery in this

case. In this surgery, a neurosurgeon cuts out an area

of bone from the skull exposing the dura mater. After

https://orcid.org/0009-0002-0019-1591

https://orcid.org/0000-0003-2177-7321

https://orcid.org/0000-0002-2394-2119

the cut and removal of this tissue, a part of the brain

is exposed, and the resection of the tumor is possible

(Surgery for Brain Tumours - Cancer Research UK,

2023).

Despite being an area in constant evolution,

surgery is often insufficient to provide a permanent

cure for some of the most aggressive tumors, like

high-grade gliomas and medulloblastomas (Delaidelli

& Moiraghi, 2024). Many times it is not possible to

remove the full mass of the cancer leaving behind

some parts of it, given that distinguishing the tumor

from the surrounding healthy tissue is not an easy task

(Delaidelli & Moiraghi, 2024; Surgery for Brain

Tumours - Cancer Research UK, 2023). Usually, the

surgeon has to use expertise and previous knowledge

to decide if the tissue is healthy or malignant. Studies

show that 80% to 90% of tumor recurrence has origin

in an incomplete resection (Petrecca et al., 2013).

Even though the diagnostic techniques for brain

tumor detection have an extended characterization,

Lopes, H., Mendes, P. M. and Dinis, H.

Millimeter-Wave Systems for Real-Time Intraoperative Brain Tumor Resection Assistance.

DOI: 10.5220/0013399300003911

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2025) - Volume 1, pages 1085-1091

ISBN: 978-989-758-731-3; ISSN: 2184-4305

1085

real-time intraoperative information still needs more

development, especially in tumors where the main

treatment is the maximal removal of the mass. There

are several techniques and tools to help

neurosurgeons remove tumors, however, they come

with some drawbacks and limitations.

Tools like the exoscope, which is based on

positioning a camera alongside the surgeon, offering

two to three-dimensional high-resolution imaging on

a display monitor placed in front of the surgeon,

presents a learning curve for surgeons accustomed to

traditional microscopes, leading, in some cases, to the

change to the conventional operating microscope

during the surgery and has some substantial costs

(acquisition and maintenance), which may not be

feasible for all healthcare settings (Ariffin et al.,

2019; Montemurro et al., 2021).

The Fluorescence-guided surgery technique

provides real-time intraoperative tumor visualization

by using selective fluorescence compounds in tumor

cells to delineate cancer tissue during surgery

(Hadjipanayis et al., 2015). Nevertheless, it can still

cause some interpretation errors, is not available for all

types of brain cancer, and also necessitates the use of

specialized surgical visualization systems, resulting in

more costs for the hospital (Su et al., 2014).

Raman spectroscopy works by directing a single-

wavelength light beam onto a sample and observing

the scattered light as it interacts with the molecules

within the sample. However, this technique has a

weak intensity signal and long data acquisition and

processing times (Rivera et al., 2024).

The use of confocal microscopy, a technique that

creates a point source of light and eliminates out-of-

focus light, enabling high-resolution imaging deep

into tissues and optical sectioning for 3D

reconstructions, may blur and overlap regions of

hypercellularity, reducing confidence in the

classification (Elliott, 2020).

The more conventional intraoperative imaging

approaches, such as intraoperative magnetic

resonance imaging (iMRI) and intraoperative

computed tomography (iCT), have lengthy image

acquisition times and require the interruption of the

surgery, which translates into longer surgeries and

more time under anesthesia for the patient.

Additionally, these techniques are quite expensive

and are not available in most healthcare facilities.

The intraoperative ultrasound (iUS) normally

does not have enough spatial resolution for the

detection of microstructures or cellular elements.

With this, it is clear that there is a need for a way

to differentiate tissue during brain tumor resection

surgery that works in real-time, in situ and is low-

cost. Radiofrequency (RF) technology has shown an

increasing potential in the medical and healthcare

field because tumors and normal tissues have

different dielectric properties due to their different

tissue structure and vascularization, and generally,

cancer cells have a higher water content (Wang et al.,

2024). The RF short wavelengths may allow for the

achievement of higher spatial resolution, making

them very effective for sensing pathological changes.

Over the years, significant progress has been

made in characterizing the dielectric properties of

biological tissues, particularly up to 20 GHz.

However, studies on the dielectric properties of

human tumor tissues remain limited. This scarcity is

largely due to challenges in conducting

measurements, including the complex logistics of

systematic sample collection, proper handling, and

timely testing within hospital environments. These

practical constraints have hindered the

comprehensive study of tumor tissue dielectric

characterization. Additionally, the dielectric

properties of intracranial tumors appear to be

depending on histological class and malignancy

grade, showing significant intratumoral heterogeneity

(Kordić & Šarolić, 2023).

Nevertheless, Table 1 presents some of the results

reported in the literature, with the values calculated

separately for the real (𝛆

𝒓

′

) and imaginary (𝛆

𝒓

′′

) parts of

the measured permittivity as a percentage difference,

offering a concise overview of the dielectric

properties of tumor tissues.

Table 1: The average discrepancy in permittivity between

tumor tissues and their surrounding tissues is provided,

along with the temperature and frequency ranges reported

in the studies.

Authors

Tumor Tissue

∆𝛆

𝒓

′

(%)

∆𝛆

𝒓

′′

(%)

(Lu et al., 1992)

Glioma in comparison to white

matter

(0.005–0.5 GHz)

(Kordić &

Šarolić, 2023)

Meningioma in comparison to

white matter tissue

(0.5–18 GHz)

76.7

157.6

Meningioma in comparison to

gray matter tissue

(0.5–18 GHz)

11.6

16.7

To obtain values for dielectric properties above 20

GHz, empirical models can be employed, such as the

one proposed by Schepps and K. Foster (Schepps &

Foster, 1980). Figure 1 shows the dielectric properties

for both healthy brain tissue and brain cancer tissue,

providing a comparative analysis at higher

frequencies.

EM4Health 2025 - Special Session on Electromagnetic waves for healthcare

1086

Figure 1: Representation of the variation in dielectric

properties, including conductivity and permittivity,

calculated for the various constituents of the brain and brain

tumor as a function of frequency (Cardoso, 2019).

2 RF-BASED DETECTION OF

TISSUES: POTENTIAL AND

CHALLENGES

Microwaves represent a form of electromagnetic

radiation characterized by frequencies that vary from

3 GHz to 30 GHz. Various electromagnetic

measurement methods and imaging techniques have

been utilized on biological tissues since the different

dielectric properties between healthy tissues and

cancerous tumor tissues have raised optimism about

employing microwaves for medical diagnostic

purposes.

With this, several studies and works have been

conducted on diagnostic tools, both with and without

contact, as well as imaging techniques using

microwaves (Çalışkan et al., 2015; Raihan et al.,

2017; Zhang et al., 2013). Most of these tools were

developed to detect specific healthy or cancerous

tissues, such as tumors. The main concept behind

using microwaves for diagnostics is the use of

transmitting antennas to emit electromagnetic waves

and receiving antennas to capture the scattered waves,

enabling the detection or distinction of certain tissues

(Çalışkan et al., 2015). Since different tissues absorb

varying amounts of energy due to their differing

dielectric properties, such as electrical conductivity

(σ) and relative permittivity (ε

𝑟

), these properties play

a crucial role in identifying different tissue types.

The primary advantages of using microwaves in

diagnostic tools include the harmless nature of this

type of radiation when used at low levels and its

relatively low cost, even for more complex systems,

when compared to iCT or iMRI. Additionally, the

availability of a relatively wide frequency range is a

significant benefit of using these techniques (Rosen

et al., 2002). However, despite the fact that RF waves

do not possess the resolution necessary to visualize

microstructures or surpass the resolution of iUS, the

latter uses systems of 4 to 10 MHz approximately,

which translates into larger probes, difficult to insert

in the small craniotomy hole. This can be overturned

by the use of higher frequencies, since it is possible

to develop antennas with small physical dimensions

allowing for smaller devices.

In 2019, (Alqadami et al., 2019) proposed a

“Wearable Electromagnetic Head Imaging System

utilizing a Flexible Wideband Antenna Array Based

on Polymer Technology for Brain Stroke Diagnosis”.

The system features eight high-efficiency, flexible,

lightweight, and robust antennas, designed

specifically for stroke applications. Scanning and

image reconstruction are completed in just 8 seconds.

The antennas offer a 54% fractional bandwidth (1.16-

1.94 GHz) and over 80% radiation efficiency,

satisfactory wave penetration in head tissues.

(Chowdhury et al., 2017) designed a wearable

pentagon-shaped antenna for brain tumor detection in

a compact form to be placed on the patient's head. It

was specifically developed to detect tumors at an

early stage. The antenna achieved satisfactory results,

demonstrating a frequency shift of 18 MHz in the

resonance frequency between a normal head and a

head with a tumor. The bandwidth of the proposed

antenna was 2.4-2.4835 GHz.

(Mohammed et al., 2014) suggested a microwave

imaging system capable of producing brain images to

identify the position and extent of brain injuries, such

as strokes, with a bandwidth of 3 GHz (1-4 GHz). The

system features a semi-elliptical array of 16/32

antenna elements mounted on an adjustable platform,

a data acquisition unit, a Vector Network Analyzer

(VNA), and a computer. It offers portability and

executes scans in 20 seconds. Specifically designed

for stroke detection, the system accurately identifies

the presence of a stroke and predicts its location

within a margin of a few millimeters.

A common problem of the systems previously

described is their size. None of the systems are

adequate for use during surgery, especially

considering the small opening created by the surgeons

to access the brain tumor for resection.

A way to address this issue is the use of higher

frequencies, such as millimeter waves (mmWave).

Because of the small wavelength, mmWave devices

facilitate large antenna arrays packed in miniature

physical dimensions, allowing for packing more

antenna elements at mmWave frequencies than at

microwave frequencies, resulting in a narrower beam

Millimeter-Wave Systems for Real-Time Intraoperative Brain Tumor Resection Assistance

1087

and increased resolution (Chittimoju & Yalavarthi,

2021).

The short wavelengths of mmWave allow for the

achievement of higher spatial resolutions at the cost

of reduced penetration depths (600 μm to 1.2mm into

the body), making them very effective for sensing

pathological changes in different skin layers or the

outer tissue layers of excised organs (Mirbeik-

Sabzevari et al., 2018). Furthermore, existing

mmWave technologies have demonstrated a

significant decrease in unnecessary biopsies,

indicating a potential pathway for continued research

on non-invasive early cancer screening in the future.

Such technologies and systems are going to be shown

in the next section.

3 APPLICATIONS OF mmWAVE

TECHNOLOGY IN MEDICAL

DIAGNOSIS AND TREATMENT

Over the years, there have been developed some

prototypes of systems and tools that use mmWave for

medical diagnostics and has been studied the potential

therapy effects of these waves. In this section, some

of these prototypes will be shown, as well as the setup

used and the potential limitations of each.

(Töpfer et al., 2015) proposed a miniaturized

broadband mmWave near-field probe with a conical

probe tip, designed to operate at frequencies between

90 and 104 GHz, for skin cancer identification. The

probe utilizes a dielectric waveguide with a high-

resistivity silicon core, with the sensing end tapered

into a conical tip. This tapering focuses the electric

field into a small area, enhancing the probe’s lateral

resolution. Since the dielectric properties of the skin

vary between individuals and across different body

locations, the probe performs differential

measurements, i.e., the signal from a suspicious area

is compared with the surrounding tissue, enabling the

probe to detect subtle differences that may indicate

the presence of cancer. For the s-parameter measure,

the waveguide was connected to a VNA with

mmWave extension heads to 110 GHz through a

coaxial adapter. The network analyzer was calibrated

by thru-reflect-line (TRL) or one-port calibration

using WR-10 waveguide standards at the coax-to-

waveguide adapter output plane.

The probe demonstrated high responsivity at 96

GHz, with an S11 change of 1.83 dB for a tip size of

0.6 mm × 0.5 mm, and a sensing depth of 0.3–0.4 mm.

The long-term measurement stability was 0.66% over

8 hours, ensuring consistent and reliable

performance.

Still in skin cancer detection, a novel multi-tone

mmWave radar sensor was presented (Arab et al.,

2020). It is based on a low-cost Miniature Hybrid

Microwave Integrated Circuit design (MHMIC) at 77

GHz. The proposed radar system uses a six-port

interferometer with I/Q demodulation to recover

information. It employs a linear passive mmWave

circuit with four 90° hybrid couplers and a phase

shifter, fabricated on a ceramic substrate using

MHMIC technology. An 8×2 microstrip patch

antenna array is utilized for enhanced performance

and coverage. In the measurement setup, the source

frequency is set to 12.83 GHz with a 10 dBm power

output and the multiplier will generate a 77 GHz

signal with 0 dBm power at the input of the parallel

line coupler.

This sensor had promising results, showing that it

was able to distinguish between dry hand skin, wet

hand skin and water. However, these results are with

a limited number of samples, needing more to

represent the diversity of the human skin better.

Additionally, the calibration steps are not mentioned,

which are crucial for an accurate measure.

(Mansutti et al., 2020) proposed and designed a

probe for early skin cancer detection. The

measurement setup consists of connecting the probe

to the VNA via a high-frequency cable and mounted

on a Computer Numerical Control machine, used to

determine the correct height of the probe for

measurements, ensuring direct contact with the

surface material under test. The measurement

procedure involves scanning the probe over a 2D grid

with a 1 mm step size in both directions, acquiring

data from multiple points on the skin model since an

imaging algorithm is intended to be applied, to

generate tissue structure maps. The resonance

frequency obtained was nearly 40 GHz and a lateral

sensitivity and detection depth of 0.2 mm and 0.55

mm respectively.

The mmWave technology is also used for the

development of imaging systems (Chao et al., 2012).

The article proposes a method for breast cancer

imaging using quasi-optical free space mmWave

spectroscopy, generating 2D and 3D tissue structure

maps to differentiate normal tissue from cancerous

tissue. The measurement setup employs a quasi-

optical free space mmWave spectrometer with

tunable backward wave oscillators operating in the

30-120 GHz range. Key components include an

isolator, modulator, horn antennas, focusing lenses,

and a Schottky diode detector. Despite the proposed

system being capable of the referred 2D and 3D tissue

EM4Health 2025 - Special Session on Electromagnetic waves for healthcare

1088

structure maps, the relatively large diameter of the

energy beam used in the measurement limits the

spatial resolution of the image and the low power of

the mmWave used limits the depth penetration.

Still in the use of mmWave for imaging systems,

(Mirbeik et al., 2022) developed a high-resolution

mmWave imaging system for skin cancer detection.

The system comprises a set of antennas designed to

transmit and receive mmWave within a specific

frequency band optimized for skin imaging. These

antennas, using an ultra-wide synthetic bandwidth of

98 GHz (12-110 GHz), achieved by integrating two

sub-bands, scan the target skin area by emitting

mmWave and capture the reflected signals. The

antipodal Vivaldi antennas ensure perfect impedance

matching and stable gain across the frequency range,

enhancing the system's performance during scanning.

To further improve precision, the system incorporates

a motorized XYZ linear arm, enabling 3D scanning

of the target area. The data collected is processed

using a reconstruction algorithm that leverages the

dielectric properties of various tissue types to

generate detailed images, highlighting cancerous

tissue areas with high accuracy. Additionally, the

system operates in real-time, completing each

measurement in approximately 20 seconds, which

minimizes patient discomfort and mitigates the

effects of movement during the procedure. However,

despite the good results obtained, the system's

performance was not consistent across all skin lesion

classes.

4 PROPOSED mmWAVE

SYSTEM

Based on prior knowledge and advancements in

mmWave technologies, a system is proposed (Figure

2) to differentiate biological tissues based on the

dielectric properties. The system operates with

mmWave technology and is structured into several

key components, such as a signal generator (Agilent

83623B), a frequency multiplier (HAFMV4-187), a

mixer (QMC-MXB15-NBMCA), and a custom probe

developed in-house (Cardoso, 2019). The signal

generator produces an initial signal that is

upconverted to higher frequencies through a

multiplier. The signal generator produces a 13 GHz

continuous wave that is converted to 52 GHz through

the 4x multiplier, which is used as the local oscillator

(LO) of the mixer. The signal from the VNA

(Keysight E5071C), with a proposed frequency of 2

GHz, is the intermediate frequency (IF) and it will be

upconverted in the mixer, resulting in a 54 GHz

signal. After filtering (SWF-50346340-15-H1), this

signal is transmitted from the probe to interact with

the material under test (MUT).

Figure 2: Proposed mmWave system setup. Red lines

represent microwave signals, while green lines represent

mmWave signals.

The reflected signals are measured and analyzed

using the VNA, which calculates the S-parameters.

These parameters provide insights into the dielectric

characteristics of the MUT, enabling real-time

detection of changes in the tissue.

The proposed system's architecture allows for

modular testing and optimization of the components,

ensuring flexibility for adjustments in the frequency

range, signal power, or filtering capabilities.

Additionally, the combination of high-frequency

operation with precision measurement tools should

ensure reliable and repeatable results, crucial for

intraoperative applications.

The primary objective of this system is to test if it

is possible to adapt the existing equipment and a VNA

to detect subtle variations in tissue properties, such as

those between healthy and abnormal brain tissue. To

do this, human body phantoms with different

dielectric properties will be made, and it will be

attempted to distinguish them with the proposed setup

by analyzing the signal reflected by the phantom, in

order to validate and obtain the system sensibility, i.e,

the minimum detectable change in dielectric

properties that allows the differentiation between

healthy and tumor tissues. The human body phantoms

will be created with mixtures of deionized water,

Triton X-100 and diethylene glycol butyl ether

(DGBE), an alcohol, to test the system, as these are

standard ingredients for human body phantom

development (FCC, 1997).

It is expected that this system will pave the way

for the development of a stand-alone mmWave brain

tumor detector, as it will serve as an adaptable

platform to test different components and system

architectures.

Millimeter-Wave Systems for Real-Time Intraoperative Brain Tumor Resection Assistance

1089

5 CONCLUSIONS

This study highlights the potential of mmWave

technology for intraoperative applications,

particularly in distinguishing brain tissues during

oncological procedures. By leveraging the different

dielectric properties of tissues, mmWave systems

have demonstrated a capacity to provide accurate and

real-time feedback that can significantly assist

surgeons in differentiating between healthy and

tumor-affected regions. This approach addresses the

limitations of current imaging technologies, such as

low resolution or time delays, and offers a precise tool

for surgical guidance.

The use of mmWave frequencies is particularly

advantageous because the small wavelength enables

compact component design and high spatial

resolution, which are critical for detecting subtle

variations in tissue properties in a confined surgical

environment.

Based on the findings of the literature review, we

propose a system that, employing a VNA, aims to

study the possibility of detecting subtle variations in

tissue properties, in order to obtain the minimum

detectable change that allows the differentiation of

tissues. The next steps will focus on developing a

fully functional mmWave system for intraoperative

applications. This system will then be tested in

experimental settings to validate its performance,

accuracy, and reliability in differentiating between

phantoms with different dielectric properties.

Future work will include upgrades to improve the

probe's sensitivity and resolution, ensuring its

reliability for real-world intraoperative applications.

These advancements aim to further optimize the

system, making it a valuable tool for enhancing

surgical precision and improving patient outcomes.

Additionally, while the proposed system relies on a

VNA for signal measurement and analysis, its size,

complexity, and cost can pose challenges for practical

intraoperative applications. To address this,

alternative detection circuits, such as compact

spectrum analyzers or custom-designed integrated

circuits, could be explored. These options offer

potential for miniaturization and cost reduction while

maintaining adequate performance for detecting

variations in dielectric properties. Moreover, a stand-

alone system specifically designed to meet the

systems requirements is planned for development,

ensuring a more practical and efficient solution for

intraoperative use.

This initial study will open the way for precise

real-time intraoperative measurements and

potentially enhance surgical outcomes through real-

time differentiation of tissue types resorting to

mmWave technology.

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