pith. sign in

arxiv: 2605.21350 · v1 · pith:IBYTQMXTnew · submitted 2026-05-20 · 📡 eess.SP · physics.med-ph

Advancements in Non-Invasive Neuroimaging: Exploring the Potential of Radar Technology for Brain Imaging and Tumour Detection

Keniel Peart , Indu Bodala , Shelly Vishwakarma This is my paper

Pith reviewed 2026-05-21 03:32 UTC · model grok-4.3

classification 📡 eess.SP physics.med-ph
keywords radar technologybrain imagingtumor detectionnon-invasive neuroimagingpatch antennavivaldi antennaelectromagnetic simulation
0
0 comments X

The pith

Simulations show Patch antennas excel at localizing brain tumors with radar while Vivaldi antennas suit wider scans.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper investigates radar technology as a non-invasive way to image the brain and detect tumors, using computer models to test how radio waves interact with brain tissue. The authors compare two antenna designs to measure signal penetration, strength, and ability to highlight tumors versus healthy areas. They conclude that antenna choice determines whether the system works best for precise tumor spotting or general brain scanning. A sympathetic reader would care if this approach can deliver safer and more portable brain imaging than current MRI or CT methods, particularly in settings without access to large medical equipment.

Core claim

Using Ansys HFSS electromagnetic simulations of brain tissues, the study establishes that Patch antennas deliver focused signals optimal for tumor localization while Vivaldi antennas provide the penetration and coverage needed for broader scanning applications.

What carries the argument

Comparison of Patch and Vivaldi antennas through simulated electromagnetic wave interactions with brain tissue models containing tumors.

If this is right

  • Radar imaging could serve as a safer alternative to MRI and CT by avoiding strong magnetic fields and ionizing radiation.
  • The technology may prove more accessible for brain tumor detection in resource-limited environments.
  • Different antenna types can be selected based on whether the goal is precise tumor location or general area coverage.
  • Additional validation with real patient data remains necessary before clinical use.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Portable radar devices based on these antenna designs could enable bedside or field monitoring of brain conditions.
  • The simulation framework might extend to testing radar imaging of other soft tissues beyond the brain.
  • Combining antenna outputs with signal processing algorithms could further improve tumor contrast without new hardware.

Load-bearing premise

The electromagnetic properties and tumor contrasts assigned to the brain tissue models accurately represent real human brain responses at the simulated frequencies.

What would settle it

Comparing actual radar return signals collected from a physical brain phantom or patient with known tumor locations against the simulation predictions.

Figures

Figures reproduced from arXiv: 2605.21350 by Indu Bodala, Keniel Peart, Shelly Vishwakarma.

Figure 2
Figure 2. Figure 2: Transmitting and Receiving Antennas To analyze the distribution and strength of transmitted electric fields and their interaction with the head model, we employ the Electric Field Pattern visualization technique. To assess safety, we calculate the Specific Absorption Rate (SAR) to measure tissue energy absorption. Additionally, we consider two key antenna design metrics: Return Loss, which quantifies power… view at source ↗
Figure 1
Figure 1. Figure 1: Head Model Schematic B. Electromagnetic Signal Model To simulate the propagation of electromagnetic waves through our head model, we employ Ansys HFSS, which allows us to analyze the behavior of these waves in detail. Our methodology incorporates adaptive meshing, dynamically adjusting mesh density based on field gradients to balance computational efficiency and simulation accuracy. The iterative solver is… view at source ↗
Figure 3
Figure 3. Figure 3: Schematic and Antenna Radiation Pattern: (a) patch [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Electric Field vs Distance Plot (a) Patch Antenna (b) Vivaldi Antenna) [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: SAR vs Distance Plot (a) Patch Antenna (b) Vivaldi Antenna [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Simulation setup for tumour Detection Experiment [PITH_FULL_IMAGE:figures/full_fig_p004_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Results (a) Patch Antenna Electric Field Strength plot (b) SAR plot with and without tumour (c) Patch Antenna [PITH_FULL_IMAGE:figures/full_fig_p005_7.png] view at source ↗
read the original abstract

This study investigates radar technology for non-invasive brain imaging and tumour detection, offering an alternative to MRI and CT scans. Using Ansys HFSS to simulate electromagnetic interactions in brain tissues, we evaluate the penetration, signal strength, and safety of Patch and Vivaldi antennas. Results show Patch antennas are optimal for tumour localization, while Vivaldi antennas suit broader scanning applications. Although promising for safer, more accessible imaging, especially in resource-limited environments, further research with diverse models and actual patient data is essential to advance this technology in non-invasive medical diagnostics.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript presents a simulation-based study using Ansys HFSS to model electromagnetic interactions of Patch and Vivaldi antennas with layered brain tissue models for non-invasive radar imaging and tumor detection. It evaluates penetration depth, signal strength, and safety, concluding that Patch antennas are optimal for tumor localization while Vivaldi antennas suit broader scanning applications, and positions radar technology as a potential safer and more accessible alternative to MRI and CT scans in resource-limited settings, while noting the need for further validation with diverse models and patient data.

Significance. If the simulated antenna rankings and tissue responses prove robust, the work could inform antenna selection in emerging microwave-based neuroimaging systems and support development of low-cost diagnostic tools. The simulation framework itself follows standard electromagnetic modeling practices, but the absence of experimental grounding limits immediate translational impact.

major comments (2)
  1. [Simulation setup and results sections] Simulation setup and results sections: The central claim that Patch antennas are optimal for tumour localization (and Vivaldi for broader scanning) rests on S-parameter and penetration results from the Ansys HFSS brain-tissue models without any reported sensitivity analysis or sweep on the assigned permittivity, conductivity, and tumor contrast values. Literature indicates these dielectric properties can vary 10-30% across subjects and frequencies in the 1-10 GHz range; a modest change could invert the performance ranking, making this a load-bearing assumption for the optimality conclusions.
  2. [Results and discussion] Results and discussion: Post-simulation conclusions about antenna optimality are presented without quantitative detection metrics (e.g., localization error, contrast-to-noise ratio, or ROC curves), error bars on the simulated outputs, or direct comparison to established microwave imaging baselines or measured phantoms, undermining the strength of the cross-antenna claims.
minor comments (2)
  1. [Abstract] Abstract and title: Inconsistent spelling of 'tumour' (British) and 'tumor' (American) appears across the abstract; standardize to one convention throughout the manuscript.
  2. [Methods/Simulation setup] The manuscript would benefit from explicit citation of the specific dielectric property values and references used for white/gray matter, skull, and tumor tissues in the HFSS model.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our simulation study. We address each major comment below and indicate the revisions planned for the manuscript.

read point-by-point responses

  1. Referee: [Simulation setup and results sections] Simulation setup and results sections: The central claim that Patch antennas are optimal for tumour localization (and Vivaldi for broader scanning) rests on S-parameter and penetration results from the Ansys HFSS brain-tissue models without any reported sensitivity analysis or sweep on the assigned permittivity, conductivity, and tumor contrast values. Literature indicates these dielectric properties can vary 10-30% across subjects and frequencies in the 1-10 GHz range; a modest change could invert the performance ranking, making this a load-bearing assumption for the optimality conclusions.

    Authors: We agree that variability in dielectric properties represents an important consideration for the robustness of our conclusions. The permittivity and conductivity values employed were taken from widely cited literature sources appropriate to the 1-10 GHz band. In the revised manuscript we will add a sensitivity analysis in which these parameters are varied by ±30 % around the nominal values; the resulting changes in S-parameters and penetration depth will be reported for both antennas. This addition will directly test whether the observed ranking remains stable under realistic tissue-property uncertainty. revision: yes

  2. Referee: [Results and discussion] Results and discussion: Post-simulation conclusions about antenna optimality are presented without quantitative detection metrics (e.g., localization error, contrast-to-noise ratio, or ROC curves), error bars on the simulated outputs, or direct comparison to established microwave imaging baselines or measured phantoms, undermining the strength of the cross-antenna claims.

    Authors: The present work is deliberately scoped to antenna-level electromagnetic performance (penetration and return loss) as a prerequisite for subsequent imaging-system design. We will include error bars on all plotted simulation outputs in the revision and will expand the discussion to compare our antenna rankings with published microwave neuroimaging studies. However, quantitative image-quality metrics such as localization error or ROC curves presuppose a full reconstruction algorithm and image-formation pipeline that lie outside the current manuscript’s focus on antenna selection; we will explicitly state this scope limitation and identify the missing metrics as a target for follow-on research. revision: partial

Circularity Check

0 steps flagged

No circularity: antenna rankings derive directly from HFSS simulation outputs

full rationale

The paper performs electromagnetic simulations in Ansys HFSS using standard layered head models to compute penetration, S-parameters, and tumor reflections for Patch and Vivaldi antennas. The reported optimality (Patch for localization, Vivaldi for scanning) follows from direct comparison of these simulation outputs without any fitted parameters, self-definitional equations, or load-bearing self-citations that reduce the result to its inputs. The derivation chain remains independent of the target claims and relies on external EM modeling conventions.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The simulation relies on standard electromagnetic assumptions for tissue dielectric properties and antenna radiation patterns drawn from prior literature. No new free parameters are introduced in the abstract; the work does not postulate new physical entities.

axioms (1)
  • domain assumption Standard dielectric properties of brain tissue and tumors at microwave frequencies are known and can be used directly in simulation.
    Invoked when modeling electromagnetic interactions in brain tissues.

pith-pipeline@v0.9.0 · 5627 in / 1246 out tokens · 37647 ms · 2026-05-21T03:32:32.236661+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

19 extracted references · 19 canonical work pages

  1. [1]

    Stem cells from the adult human brain develop into functional neurons in culture,

    U. Westerlund, M. C. Moe, M. Varghese, J. Berg-Johnsen, M. Ohlsson, I. A. Langmoen, and M. Svensson, “Stem cells from the adult human brain develop into functional neurons in culture,”Experimental Cell Research, vol. 289, pp. 378–383, Oct. 2003

    work page 2003

  2. [2]

    A Lightweight Deep Learning Based Microwave Brain Image Network Model for Brain Tumor Classification Using Reconstructed Microwave Brain (RMB) Images,

    A. Hossain, M. T. Islam, S. K. Abdul Rahim, M. A. Rahman, T. Rahman, H. Arshad, A. Khandakar, M. A. Ayari, and M. E. H. Chowdhury, “A Lightweight Deep Learning Based Microwave Brain Image Network Model for Brain Tumor Classification Using Reconstructed Microwave Brain (RMB) Images,”Biosensors, vol. 13, p. 238, Feb. 2023

    work page 2023

  3. [3]

    Compact ellipse shaped patch with ground slotted broadband monopole patch antenna for head imaging applications,

    M. S. Talukder, M. Samsuzzaman, M. T. Islam, R. Azim, M. Z. Mahmud, and M. T. Islam, “Compact ellipse shaped patch with ground slotted broadband monopole patch antenna for head imaging applications,” Chinese Journal of Physics, vol. 72, pp. 310–326, Aug. 2021

    work page 2021

  4. [4]

    Brick-Shaped Antenna Module for Microwave Brain Imaging Systems,

    D. O. Rodriguez-Duarte, J. A. T. Vasquez, R. Scapaticci, L. Crocco, and F. Vipiana, “Brick-Shaped Antenna Module for Microwave Brain Imaging Systems,”IEEE Antennas and Wireless Propagation Letters, vol. 19, pp. 2057–2061, Dec. 2020

    work page 2057

  5. [5]

    Experimental radar data for monitor- ing brain atrophy progression,

    R. Ullah, I. Saied, and T. Arslan, “Experimental radar data for monitor- ing brain atrophy progression,”Data in Brief, vol. 43, p. 108379, Aug. 2022

    work page 2022

  6. [6]

    Brain Tumor Segmentation and Classification from Sensor-Based Portable Microwave Brain Imaging System Using Lightweight Deep Learning Models,

    A. Hossain, M. T. Islam, T. Rahman, M. E. H. Chowdhury, A. Tahir, S. Kiranyaz, K. Mat, G. K. Beng, and M. S. Soliman, “Brain Tumor Segmentation and Classification from Sensor-Based Portable Microwave Brain Imaging System Using Lightweight Deep Learning Models,” Biosensors, vol. 13, p. 302, Feb. 2023

    work page 2023

  7. [7]

    Pattern-Reconfigurable Metasurface-Antenna Array for Functional Brain Imaging Applications,

    M. Ojaroudi and S. Bila, “Pattern-Reconfigurable Metasurface-Antenna Array for Functional Brain Imaging Applications,” in2021 15th Euro- pean Conference on Antennas and Propagation (EuCAP), (Dusseldorf, Germany), pp. 1–5, IEEE, Mar. 2021

    work page 2021

  8. [8]

    Multi-Element UWB Probe Optimization for Medical Microwave Imaging,

    Y . Akazzim, O. El Mrabet, J. Romeu, and L. Jofre-Roca, “Multi-Element UWB Probe Optimization for Medical Microwave Imaging,”Sensors, vol. 23, p. 271, Dec. 2022

    work page 2022

  9. [9]

    The Use of Metasurfaces to Enhance Microwave Imaging: Experimental Validation for Tomographic and Radar-Based Algorithms,

    N. Ghavami, E. Razzicchia, O. Karadima, P. Lu, W. Guo, I. Sotiriou, E. Kallos, G. Palikaras, and P. Kosmas, “The Use of Metasurfaces to Enhance Microwave Imaging: Experimental Validation for Tomographic and Radar-Based Algorithms,”IEEE Open Journal of Antennas and Propagation, vol. 3, pp. 89–100, 2022

    work page 2022

  10. [10]

    Analysis of SAR induced in Human Head due to the exposure of Non-ionizing Radiation,

    Margish S. Joshi, Gaurav R. Joshi, and B H Gardi College of Engineer- ing and Technology, “Analysis of SAR induced in Human Head due to the exposure of Non-ionizing Radiation,”International Journal of Engineering Research and, vol. V5, p. IJERTV5IS020466, Feb. 2016

    work page 2016

  11. [11]

    The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum of tissues,

    S. Gabriel, R. W. Lau, and C. Gabriel, “The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum of tissues,”Physics in Medicine and Biology, vol. 41, pp. 2271–2293, Nov. 1996

    work page 1996

  12. [12]

    Dielec- tric properties of the normal and malignant breast tissues in xenograft mice at low frequencies (100 Hz–1 MHz),

    S. M. Hesabgar, A. Sadeghi-Naini, G. Czarnota, and A. Samani, “Dielec- tric properties of the normal and malignant breast tissues in xenograft mice at low frequencies (100 Hz–1 MHz),”Measurement, vol. 105, pp. 56–65, July 2017

    work page 2017

  13. [13]

    A comprehensive examination of topographic thick- ness of skin in the human face,

    K. Chopra, D. Calva, M. Sosin, K. K. Tadisina, A. Banda, C. De La Cruz, M. R. Chaudhry, T. Legesse, C. B. Drachenberg, P. N. Manson, and M. R. Christy, “A comprehensive examination of topographic thick- ness of skin in the human face,”Aesthetic Surgery Journal, vol. 35, p. 1007–1013, Oct. 2015

    work page 2015

  14. [14]

    Calvarial thickness and its relation to cranial bone harvest,

    A. Moreira-Gonzalez, F. E. Papay, and J. E. Zins, “Calvarial thickness and its relation to cranial bone harvest,”Plastic and Reconstructive Surgery, vol. 117, p. 1964–1971, May 2006

    work page 1964

  15. [15]

    Skull base dural thickness and relationship to demo- graphic features: A postmortem study and literature review,

    M. Fam, A. Potash, M. Potash, R. Robinson, L. Karnell, E. O’Brien, and J. Greenlee, “Skull base dural thickness and relationship to demo- graphic features: A postmortem study and literature review,”Journal of Neurological Surgery Part B: Skull Base, vol. 79, p. 614–620, June 2018

    work page 2018

  16. [16]

    Simulation of near-infrared light absorption con- sidering individual head and prefrontal cortex anatomy: Implications for optical neuroimaging,

    F. B. Haeussinger, S. Heinzel, T. Hahn, M. Schecklmann, A.-C. Ehlis, and A. J. Fallgatter, “Simulation of near-infrared light absorption con- sidering individual head and prefrontal cortex anatomy: Implications for optical neuroimaging,”PLoS ONE, vol. 6, p. e26377, Oct. 2011

    work page 2011

  17. [17]

    Cortical thickness or grey matter volume? the importance of selecting the phenotype for imaging genetics studies,

    A. M. Winkler, P. Kochunov, J. Blangero, L. Almasy, K. Zilles, P. T. Fox, R. Duggirala, and D. C. Glahn, “Cortical thickness or grey matter volume? the importance of selecting the phenotype for imaging genetics studies,”NeuroImage, vol. 53, p. 1135–1146, Nov. 2010

    work page 2010

  18. [18]

    Changes in thickness and surface area of the human cortex and their relationship with intelligence,

    H. G. Schnack, N. E. M. van Haren, R. M. Brouwer, A. Evans, S. Durston, D. I. Boomsma, R. S. Kahn, and H. E. Hulshoff Pol, “Changes in thickness and surface area of the human cortex and their relationship with intelligence,”Cerebral Cortex, vol. 25, p. 1608–1617, Jan. 2014

    work page 2014

  19. [19]

    Microwave imaging for brain tumour detection using an UWB Vivaldi Antenna array,

    H. Zhang, B. Flynn, A. T. Erdogan, and T. Arslan, “Microwave imaging for brain tumour detection using an UWB Vivaldi Antenna array,” in2012 Loughborough Antennas & Propagation Conference (LAPC), (Loughborough, Leicestershire, United Kingdom), pp. 1–4, IEEE, Nov. 2012

    work page 2012