Noise reduction performance of a deep learning-based reconstruction in brain computed tomography images acquired with organ-based tube current modulation.

We aimed to evaluate the image quality of brain computed tomography (CT) images reconstructed using deep learning-based reconstruction (DLR) in organ-based tube current modulation (OB-TCM) acquisition. An anthropomorphic head phantom and a cylindrical low-contrast phantom were scanned at the standard dose level for adult brain CT in axial volume acquisition without OB-TCM. Moreover, image acquisition with OB-TCM was performed. The radiation dose on the eye lens was measured using a scintillation fibre-optic dosimeter placed on the anthropomorphic phantom's eye surface. The task transfer function (TTF), contrast-to-noise ratio (CNR), and low-contrast object specific CNR obtained from low-contrast phantom images reconstructed with filtered back projection (FBP), hybrid iterative reconstruction (HIR), and two types of DLR (DLRCTA and DLRLCD) were compared. In result, OB-TCM achieved a 32.5% dose reduction in the eye lens. Although HIR, DLRCTA, and DLRLCD showed lower TTF than FBP, the difference in TTF at the highest contributing spatial frequency corresponding to the contrast rod diameter was < 10%. Despite the OB-TCM acquisition, DLRCTA and DLRLCD achieved significantly lower noise and a higher CNR than FBP without OB-TCM (p < 0.05). However, low-contrast object specific CNR was equivalent among all reconstruction methods for the objective diameter of 5 mm and slightly improved in DLRLCD for the objective diameter of 7 mm. DLR with OB-TCM acquisition enabled dose reduction for the eye lens and high CNR image appearance, whereas the low contrast detectability evaluated by low-contrast object specific CNR did not always improve.

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

PURPOSE: To 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). METHODS: A 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. RESULTS: The 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). CONCLUSIONS: Greater noise suppression was achieved with DLR than with hybrid IR without compromising nodule volumetric accuracy in LDCT despite the representative noise non-stationarity.

[Quantitative Evaluation of Metal Artifacts on CT Images on the Basis of Statistics of Extremes].

It is well-known that metal artifacts have a harmful effect on the image quality of computed tomography (CT) images. However, the physical property remains still unknown. In this study, we investigated the relationship between metal artifacts and tube currents using statistics of extremes. A commercially available phantom for measuring CT dose index 160 mm in diameter was prepared and a brass rod 13 mm in diameter was placed at the centerline of the phantom. This phantom was used as a target object to evaluate metal artifacts and was scanned using an area detector CT scanner with various tube currents under a constant tube voltage of 120 kV. Sixty parallel line segments with a length of 100 pixels were placed to cross metal artifacts on CT images and the largest difference between two adjacent CT values in each of 60 CT value profiles of these line segments was employed as a feature variable for measuring metal artifacts; these feature variables were analyzed on the basis of extreme value theory. The CT value variation induced by metal artifacts was statistically characterized by Gumbel distribution, which was one of the extreme value distributions; namely, metal artifacts have the same statistical characteristic as streak artifacts. Therefore, Gumbel evaluation method makes it possible to analyze not only streak artifacts but also metal artifacts. Furthermore, the location parameter in Gumbel distribution was shown to be in inverse proportion to the square root of a tube current. This result suggested that metal artifacts have the same dose dependence as image noises.