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DC Field | Value | Language |
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dc.contributor.author | Chircop, Francesca | - |
dc.contributor.author | Debono, Carl James | - |
dc.contributor.author | Bezzina, Paul | - |
dc.contributor.author | Zarb, Francis | - |
dc.date.accessioned | 2023-02-17T12:47:49Z | - |
dc.date.available | 2023-02-17T12:47:49Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Chircop, F., Debono, C. J., Bezzina, P., Zarb, F. (2022). A model to improve the quality of low-dose CT scan images. IEEE 21st Mediterranean Electrotechnical Conference (MELECON), Palermo. 131-136 | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/106469 | - |
dc.description.abstract | Computed Tomography (CT) scans are used during medical imaging diagnosis as they provide detailed cross-sectional images of the human body by making use of X-rays. X-ray radiation as part of medical diagnosis poses health risks to patients leading experts to opt for low doses of radiation when possible. In accordance with European Directives, ionising radiation doses for medical purposes are to be kept as low as reasonably achievable (ALARA). While reduced radiation is beneficial from a health perspective, this impacts the quality of the images as the noise in the images increases, reducing the radiologist’s confidence in diagnosis. Various low-dose CT (LDCT) image denoising strategies available in the literature attempt to solve this conflict. However, current models face problems like over-smoothed results and lose detailed information. Consequently, the quality of LDCT images after denoising is still an important problem. The models presented in this work use deep learning techniques that are modified and trained for this problem. The results show that the best model in terms of image quality achieved a peak signal to noise ratio (PSNR) of 19.5 dB, a structural similarity index measure (SSIM) of 0.7153 and a root mean square error (RMSE) of 43.34. It performed the required operations in an average time of 4843.80s. Furthermore, tests at different dose levels were done to test the robustness of the best performing models. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Tomography | en_GB |
dc.subject | Deep learning (Machine learning) | en_GB |
dc.subject | Cardiovascular system -- Diseases -- Tomography | en_GB |
dc.subject | Heart -- Magnetic resonance imaging | en_GB |
dc.subject | Radiography, Medical -- Image quality | en_GB |
dc.title | A model to improve the quality of low-dose CT scan images | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder | en_GB |
dc.bibliographicCitation.conferencename | 21st Mediterranean Electrotechnical Conference (MELECON) | en_GB |
dc.bibliographicCitation.conferenceplace | Palermo, Italy. 14-16/06/2022. | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1109/MELECON53508.2022.9843000. | - |
Appears in Collections: | Scholarly Works - FacHScRad |
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File | Description | Size | Format | |
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A_model_to_improve_the_quality_of_low-dose_CT_scan_images.pdf Restricted Access | 16.86 MB | Adobe PDF | View/Open Request a copy |
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