Current advances in pulmonary functional imaging

Nakamura, Hidetoshi; Hirai, Toyohiro; Kurosawa, Hajime; Hamada, Kazuki; Matsunaga, Kazuto; Shimizu, Kaoruko; Konno, Satoshi; Muro, Shigeo; Fukunaga, Koichi; Nakano, Yasutaka; Kuwahira, Ichiro; Hanaoka, Masayuki

doi:10.1016/j.resinv.2023.09.004

Cited by 2 publications

(2 citation statements)

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“…Lung diseases are common and significant health concerns, particularly due to factors such as air pollution, an aging population, and the impact of conditions like COVID-19 [1][2][3]. Therefore, accurate, safe, and convenient monitoring of lung conditions is of great significance for the diagnosis and control of pulmonary diseases [4]. As a visualization technique, electrical impedance tomography (EIT) has gradually been commercialized and introduced into medical applications.…”

Section: Introductionmentioning

confidence: 99%

“…EIT offers advantages such as non-invasiveness, portability, low cost, and continuous monitoring [5]. It has become an increasingly important imaging modality, providing significant advantages over traditional imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) in early screening, diagnosis, and bedside monitoring of lung diseases [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Research on Human Lung Impedance Tomography Based on Soft Thresholding Image Segmentation and Reduced-Order Tikhonov Regularization

Song,

Xiong,

Liu

et al. 2024

Applied Sciences

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The lung is one of the most vital organs in the human body, and its condition is closely correlated with overall health. Electrical impedance tomography (EIT), as a biomedical imaging technique, often produces low-quality reconstructed images due to its inherent ill-posedness in solving the inverse problem. To address this issue, this paper proposes a soft-threshold region segmentation algorithm with a relaxation factor. This algorithm segments the reconstructed lung images into internal regions, edge regions, and background regions, resulting in clearer boundaries in the reconstructed images. This facilitates the intuitive identification of regions of interest by healthcare professionals. Additionally, this segmentation algorithm is suitably combined with a dimension-reduced Tikhonov regularization algorithm. By utilizing the joint capabilities of these algorithms, the partition points belonging to the background region can be excluded from the sought grayscale vector, thereby improving the ill-posedness of the image reconstruction process and enhancing the quality of image reconstruction. Finally, a 16-electrode human lung EIT simulation model is established for the thoracic region and verified through simulation. Experimental validation is conducted using a human lung tank simulation platform to further demonstrate the effectiveness of the proposed method.

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