A time-domain backprojection approach for medical image reconstruction: Advancements in tumor detection and identification

This paper presents a novel Backprojection Algorithm (BPA) aimed at improving tumor detection efficiency in medical microwave imaging. Operating in the time domain, the BPA enables rapid image reconstruction and real-time processing, ideal for dynamic medical applications. The algorithm is tested using an antenna array and phantom model, incorporating Hamming, Gaussian, and Median filters to reduce noise and distortion. Among them, the Hamming filter offers the best improvement in edge definition and tumor detection. A complexity analysis evaluates the algorithm’s efficiency and scalability, focusing on computational time and resource use. The results suggest that the algorithm has the potential to enhance healthcare diagnostics and improve patient outcomes, enabling clinical implementation.

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Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-12-19
DOI
https://doi.org/10.1051/e3sconf/202568000007/pdf
Primary Topic
Microwave Imaging and Scattering Analysis
Type
article
Field-Weighted Citation Impact
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article

A time-domain backprojection approach for medical image reconstruction: Advancements in tumor detection and identification

Ibtisam Amdaouch, Otman Aghzout, Juan Bautista Ruiz-Alzola, Jaouad El Gueri et al.
Springer Link (Chiba Institute of Technology)
Microwave Imaging and Scattering Analysis
article

A time-domain backprojection approach for medical image reconstruction: Advancements in tumor detection and identification

Ibtisam Amdaouch, Otman Aghzout, Juan Bautista Ruiz-Alzola, Jaouad El Gueri, Badiaa Ait Ahmed
article en

Abstract

This paper presents a novel Backprojection Algorithm (BPA) aimed at improving tumor detection efficiency in medical microwave imaging. Operating in the time domain, the BPA enables rapid image reconstruction and real-time processing, ideal for dynamic medical applications. The algorithm is tested using an antenna array and phantom model, incorporating Hamming, Gaussian, and Median filters to reduce noise and distortion. Among them, the Hamming filter offers the best improvement in edge definition and tumor detection. A complexity analysis evaluates the algorithm’s efficiency and scalability, focusing on computational time and resource use. The results suggest that the algorithm has the potential to enhance healthcare diagnostics and improve patient outcomes, enabling clinical implementation.

Springer Link (Chiba Institute of Technology)
Openalex Percentile: Top 43%
Microwave Imaging and Scattering Analysis
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