"Image quality evaluation of the Precise image CT deep learning reconstruction algorithm compared to Filtered Back-projection and iDose4: a phantom study at different dose levels". (February 2023)

• Record Type: Journal Article
• Authors: Barca, Patrizio; Domenichelli, Sara; Golfieri, Rita; Pierotti, Luisa; Spagnoli, Lorenzo; Tomasi, Silvia; Strigari, Lidia
• Is Part Of: Physica medica. Volume 106(2023)
• Publication Date: 2023-02
• Subjects: Deep learning CT image reconstruction -- Philips Precise Image -- Image quality -- Abdominal protocol; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2022.102517
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: The Precise Image (PI) CT reconstruction algorithm can be adopted for low dose abdominal protocols. Noise/detectability index decreases/increases with the reconstruction level from Sharper to Smoother. TTF of PI-reconstructed images varies with the object contrast, dose and level of reconstruction. Image uniformity and CT numbers are preserved with respect to FBP reconstruction. The NPS is deeply altered by the PI algorithm only with Smooth and Smoother levels. Abstract: Purpose: To characterize the performance of the Precise Image (PI) deep learning reconstruction (DLR) algorithm for abdominal Computed Tomography (CT) imaging. Methods: CT images of the Catphan-600 phantom (equipped with an external annulus) were acquired using an abdominal protocol at four dose levels and reconstructed using FBP, iDose 4 (levels 2, 5) and PI ('Soft Tissue' definition, levels 'Sharper', 'Sharp', 'Standard', 'Smooth', 'Smoother'). Image noise, image non-uniformity, noise power spectrum (NPS), target transfer function (TTF), detectability index (d'), CT numbers accuracy and image histograms were analyzed. Results: The behavior of the PI algorithm depended strongly on the selected level of reconstruction. The phantom analysis suggested that the PI image noise decreased linearly by varying the level of reconstruction from Sharper to Smoother, expressing a noise reduction up to 80% with respect to FBP. Additionally, the non-uniformity decreased, the histograms became narrower, and d' values increased as PI reconstruction levels changed from Sharper to Smoother. PI had no significant impact on the average CT number of different contrast objects. The conventional FBP NPS was deeply altered only by Smooth and Smoother levels of reconstruction. Furthermore, spatial resolution was found to be dose- and contrast-dependent, but in each analyzed condition it was greater than or comparable to FBP and iDose 4 TTFs. Conclusions: The PI algorithm can reduce image noise with respect to FBP and iDose 4 ; spatial resolution, CT numbers and image uniformity are generally preserved by the algorithm but changes in NPS for the Smooth and Smoother levels need to be considered in protocols implementation.