Computed Tomography Reconstruction from Undersampled Data: An Application to Biomedical Imaging

被引：0
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作者：
Purisha, Zenith [1 ]
机构：
[1] Department of Mathematics, Universitas Gadjah Mada, Indonesia
来源：
IAENG International Journal of Applied Mathematics | 2024年 / 54卷 / 01期
关键词：
In computed tomography; there is often a need to reduce the amount of radiation used due to its potential to alter living tissue properties; especially in the patient or in vivo samples. To achieve this reduction; a method of measuring objects through sparse sampling can be employed. However; in mathematics; this problem leads to an ill-posed inverse problem due to limited measurement data. To address this issue; a regularization method is proposed; where the constraint for a regularized solution is enforced by utilizing Daubechies wavelet expansion coefficients. In this work; the algorithm is iteratively computed; employing a soft-thresholding operation for the coefficients; with the thresholding parameter automatically selected. For the purpose of biomedical imaging; we propose incorporating prior knowledge of the thresholding parameter value based on a biological object. The method is tested on simulated data using the chest phantom and real data obtained from the ladybug X-ray measurements. © 2024; International Association of Engineers. All rights reserved;
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页码：25 / 32
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