Pulmonary nodule volumetric accuracy of a deep learning-based reconstruction algorithm in low-dose computed tomography: A phantom study

Abstract

PurposeTo compare the image properties and pulmonary nodule volumetric accuracies among deep learning-based reconstruction (DLR), filtered back projection (FBP), and hybrid iterative reconstruction (hybrid IR) in low-dose computed tomography (LDCT). MethodsA multipurpose chest phantom containing artificial spherical pulmonary nodules with 5-, 8-, 10-, and 12-mm diameters and Hounsfield units (HUs) of −630 and +100 HU was scanned 20 times at a standard dose, based on a low-dose screening CT trial, and at 1/2, 1/6, and 1/12 of the standard dose. To assess noise reduction performance and volumetric accuracy, the standard deviations (SDs) of the pixel values and volumetric percentage errors (PEs) were compared among FBP, hybrid IR, and DLR. The noise non-stationarity index (NNSI) was calculated from 20 image replicates and compared among FBP, hybrid IR, and DLR to evaluate noise stationarity. ResultsThe SD reduction rates for FBP in hybrid IR and DLR were 62 %–85 % and 79 %–90 %, respectively. For the four nodules with +100 HU, the PE of all reconstruction methods was <±25 % (not clinically relevant). For the four nodules with −630 HU, the PEs were equivalent or lower for hybrid IR and DLR than for FBP, and the PE difference between hybrid IR and DLR ranged from 0 % to 7%. The NNSI was significantly higher for DLR than for FBP and hybrid IR (p < 0.01). ConclusionsGreater noise suppression was achieved with DLR than with hybrid IR without compromising nodule volumetric accuracy in LDCT despite the representative noise non-stationarity.