Evaluation of Low-dose Computed Tomography Images Reconstructed Using Artificial Intelligence-based Adaptive Filtering for Denoising: A Comparison with Computed Tomography Reconstructed with Iterative Reconstruction Algorithm

Purpose: Awareness of radiation-induced risk led to the development of various dose optimization techniques in iterative reconstruction (IR) algorithms and deep learning algorithms to improve low-dose image quality. PixelShine (PS) by AlgoMedica Inc., USA, is a vendor-neutral deep learning denoising...

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Main Authors: Suyash Kulkarni, Vasundhara Patil, Aniruddha Nene, Nitin Shetty, Amitkumar Choudhari, Akansha Joshi, CS Pramesh, Akshay Baheti, Kalpesh Mahadik
Format: Article
Language:English
Published: Wolters Kluwer Medknow Publications 2025-01-01
Series:Journal of Medical Physics
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Online Access:https://journals.lww.com/10.4103/jmp.jmp_115_24
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Summary:Purpose: Awareness of radiation-induced risk led to the development of various dose optimization techniques in iterative reconstruction (IR) algorithms and deep learning algorithms to improve low-dose image quality. PixelShine (PS) by AlgoMedica Inc., USA, is a vendor-neutral deep learning denoising tool for low-dose studies, and this study analyzed its images. Aim: The aim of this study was to assess the diagnostic value of PS-reconstructed images obtained at various low doses (LDs). Materials and Methods: A retrospective study qualitatively and quantitatively evaluated the low-dose PS-reconstructed images by comparing them with other reconstruction methods and standard dose (SD) images. A total of 85 cases were evaluated, of which 32 cases were scanned on a scanner with filtered back projection (FBP) reconstruction with LD scans performed at 70%–50% of SD. The remaining 53 cases were performed on the scanner with IR, 35 of them had LD scan at 50% of SD and 18 cases had LD scan at 33% of SD. Results: Qualitative image analysis – The quality of low-dose images with PS and IR was almost equivalent in terms of noise magnitude and texture at 50% dose, and PS images were slightly better at 33% dose reduction. Quantitative image analysis – Low-dose PS-reconstructed images and low-dose iterative reconstructed images had similar contrast-to-noise ratio at 50% dose reduction; however, at 33% of the SD, PS-reconstructed images outperformed. The SD FBP images were equivalent to LD PS-reconstructed images (50% dose reduction). Conclusions: Artificial intelligence-based denoising algorithms produce similar images as IR at 50% dose reduction and outperform it at 33% of the SD.
ISSN:0971-6203
1998-3913