Abstract
PurposeWe 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). MethodsWe 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. ResultsAt 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. ConclusionsUnlike 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.
Highlights
Computed tomography (CT) has evolved into an unrivaled diagnostic tool in many diseases and clinical scenarios, owing in particular to its wide availability, speed and diagnostic performance
At all dose levels, increasing deep learning image reconstruction (DLIR) and a partial model-based iterative reconstruction (ASiR-V) levels both markedly decreased noise magnitude compared with filtered back-projection (FBP), with DLIR low and medium maintaining noise texture overall
ASiR-V = adaptive statistical iterative reconstruction, CTDIvol = volume CT dose index, DLIR = deep learning image reconstruction, FBP = filtered backprojection, TTF = target transfer function, TTF10 = target transfer function at 10% of its maximum value, TTF at 50% (TTF50) = target transfer function at 50% of its maximum value
Summary
Computed tomography (CT) has evolved into an unrivaled diagnostic tool in many diseases and clinical scenarios, owing in particular to its wide availability, speed and diagnostic performance. Statistical “hybrid” and partial model-based IR techniques reduce noise magnitude and change noise texture, especially when used at high levels [17,18,19]. This results in CT images with an artificial “plastic” appearance, which are less well accepted by the radiology community and may affect diagnostic confidence [20,21]. Full model-based IR techniques can provide even higher noise reduction [22,23], their computing power requirements have far prevented widespread use and sustainability in increasingly busy clinical workflows
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