Iterative Reconstruction: State-of-the-Art and Future Perspectives. Issue 2 (13th March 2023)

• Record Type: Journal Article
• Authors: Guido, Gisella; Polici, Michela; Nacci, Ilaria; Bozzi, Fernando; De Santis, Domenico; Ubaldi, Nicolò; Polidori, Tiziano; Zerunian, Marta; Bracci, Benedetta; Laghi, Andrea; Caruso, Damiano
• Is Part Of: Journal of computer assisted tomography. Volume 47:Issue 2(2023)
• Page Start: 244
• Page End: 254
• Publication Date: 2023-03-13
• Subjects: computed tomography -- filtered back-projection -- iterative reconstruction -- deep learning-based image reconstruction; Tomography -- Periodicals; Tomography -- Periodicals; Tomography; Periodicals; 616.0757
• DOI: 10.1097/RCT.0000000000001401
• Languages: English
• ISSNs: 0363-8715

Abstract

Abstract: Image reconstruction processing in computed tomography (CT) has evolved tremendously since its creation, succeeding at optimizing radiation dose while maintaining adequate image quality. Computed tomography vendors have developed and implemented various technical advances, such as automatic noise reduction filters, automatic exposure control, and refined imaging reconstruction algorithms. Focusing on imaging reconstruction, filtered back-projection has represented the standard reconstruction algorithm for over 3 decades, obtaining adequate image quality at standard radiation dose exposures. To overcome filtered back-projection reconstruction flaws in low-dose CT data sets, advanced iterative reconstruction algorithms consisting of either backward projection or both backward and forward projections have been developed, with the goal to enable low-dose CT acquisitions with high image quality. Iterative reconstruction techniques play a key role in routine workflow implementation (eg, screening protocols, vascular and pediatric applications), in quantitative CT imaging applications, and in dose exposure limitation in oncologic patients. Therefore, this review aims to provide an overview of the technical principles and the main clinical application of iterative reconstruction algorithms, focusing on the strengths and weaknesses, in addition to integrating future perspectives in the new era of artificial intelligence.