A Novel Singular Value Decomposition-Based Denoising Method in 4-Dimensional Computed Tomography of the Brain in Stroke Patients with Statistical Evaluation

• Main Authors: Wonseok Yang, Jun-Yong Hong, Jeong-Youn Kim, Seung-ho Paik, Seung Hyun Lee, Ji-Su Park, Gihyoun Lee, Beop Min Kim, Young-Jin Jung
• Format: Article
• Language: English
• Published: MDPI AG 2020-05-01
• Series: Sensors
• Subjects: acute stroke; computed tomography; image quality; singular value decomposition; Gaussian noise; contrast-to-noise
• Online Access: https://www.mdpi.com/1424-8220/20/11/3063

Computed tomography (CT) is a widely used medical imaging modality for diagnosing various diseases. Among CT techniques, 4-dimensional CT perfusion (4D-CTP) of the brain is established in most centers for diagnosing strokes and is considered the gold standard for hyperacute stroke diagnosis. However, because the detrimental effects of high radiation doses from 4D-CTP may cause serious health risks in stroke survivors, our research team aimed to introduce a novel image-processing technique. Our singular value decomposition (SVD)-based image-processing technique can improve image quality, first, by separating several image components using SVD and, second, by reconstructing signal component images to remove noise, thereby improving image quality. For the demonstration in this study, 20 4D-CTP dynamic images of suspected acute stroke patients were collected. Both the images that were and were not processed via the proposed method were compared. Each acquired image was objectively evaluated using contrast-to-noise and signal-to-noise ratios. The scores of the parameters assessed for the qualitative evaluation of image quality improved to an excellent rating (<i>p</i> < 0.05). Therefore, our SVD-based image-denoising technique improved the diagnostic value of images by improving their quality. The denoising technique and statistical evaluation can be utilized in various clinical applications to provide advanced medical services.