Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: A phantom study. (August 2020)

• Record Type: Journal Article
• Authors: Racine, Damien; Becce, Fabio; Viry, Anais; Monnin, Pascal; Thomsen, Brian; Verdun, Francis R.; Rotzinger, David C.
• Is Part Of: Physica medica. Volume 76(2020)
• Page Start: 28
• Page End: 37
• Publication Date: 2020-08
• Subjects: Computed tomography -- Deep learning -- Image quality -- Iterative reconstruction -- Model observer -- Radiation dose; 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.2020.06.004
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Deep learning image reconstruction enhances CT image quality and saves radiation dose. Deep learning image reconstruction reduces noise while maintaining noise texture. Deep learning image reconstruction maintains low- and high-contrast spatial resolution. Deep learning image reconstruction improves low- and high-contrast detectability. Deep learning image reconstruction outperforms partial model-based iterative reconstruction. Abstract: Purpose: We aimed to thoroughly characterize image quality of a novel deep learning image reconstruction (DLIR), and investigate its potential for dose reduction in abdominal CT in comparison with filtered back-projection (FBP) and a partial model-based iterative reconstruction (ASiR-V). Methods: We scanned a phantom at three dose levels: regular (7 mGy), low (3 mGy) and ultra-low (1 mGy). Images were reconstructed using DLIR (low, medium and high levels) and ASiR-V (0% = FBP, 50% and 100%). Noise and contrast-dependent spatial resolution were characterized by computing noise power spectra and target transfer functions, respectively. Detectability indexes of simulated acute appendicitis or colonic diverticulitis (low contrast), and calcium-containing urinary stones (high contrast) (|ΔHU| = 50 and 500, respectively) were calculated using the nonprewhitening with eye filter model observer. Results: At all dose levels, increasing DLIR and ASiR-V levels both markedly decreased noise magnitude compared with FBP, with DLIR low and medium maintaining noise texture overall. For both low- and high-contrast spatial resolution, DLIR not only maintained, but even slightly enhanced spatial resolution in comparison with FBP across all dose levels. Conversely, increasing ASiR-V impaired low-contrast spatial resolution compared with FBP. Overall, DLIR outperformed ASiR-V in all simulated clinical scenarios. For both low- and high-contrast diagnostic tasks, increasing DLIR substantially enhanced detectability at any dose and contrast levels for any simulated lesion size. Conclusions: Unlike ASiR-V, DLIR substantially reduces noise while maintaining noise texture and slightly enhancing spatial resolution overall. DLIR outperforms ASiR-V by enabling higher detectability of both low- and high-contrast simulated abdominal lesions across all investigated dose levels.