Radiation Exposure and Image Quality of Normal Computed Tomography Brain Images Acquired With Automated and Organ-Based Tube Current Modulation Multiband Filtering and Iterative Reconstruction

2012 ◽  
Vol 47 (3) ◽  
pp. 202-207 ◽  
Author(s):  
Hans-Christoph Becker ◽  
Dominik Augart ◽  
Martina Karpitschka ◽  
Stefan Ulzheimer ◽  
Fabian Bamberg ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Radiation Exposure ◽  
Iterative Reconstruction ◽  
Tube Current Modulation ◽  
Brain Images ◽  
Current Modulation ◽  
Tube Current

scholarly journals Effect on Radiation Dose and Image Quality of the Computed Tomography Tube Current Modulation

2020 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Yasser Khedr ◽  
Magdi Ali ◽  
Bothaina Kandil ◽  
Mohamed El Safwany
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Radiation Dose ◽  
Tube Current Modulation ◽  
Current Modulation ◽  
Tube Current

Reduced Patient Dose and Improved Image Quality in Computed Tomography Examinations with On-Line Anatomically Adapted Tube Current Modulation

1998 ◽  
Vol 80 (1) ◽  
pp. 287-289
Author(s):  
D. Hentschel ◽  
K. Klingenbeck-Regn ◽  
S. Popescu ◽  
C. Suess ◽  
H. Wolf ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Patient Dose ◽  
Tube Current Modulation ◽  
Current Modulation ◽  
Tube Current ◽  
On Line

scholarly journals Image quality assessment of deep learning image reconstruction in computed tomography using tube current modulation: a phantom study

2021 ◽  
Author(s):  
kazuhiro takeuchi ◽  
Yasuhiro Ide ◽  
Yuichiro Mori ◽  
Yusuke Uehara ◽  
Hiroshi Sukeishi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Quality Characteristics ◽  
Image Noise ◽  
Tube Current Modulation ◽  
Current Modulation ◽  
Tube Current ◽  
The Impact

Abstract The novel deep learning image reconstruction (DLIR) is known to change its image quality characteristics according to object contrast and image noise. In clinical practice, computed tomography (CT) image noise is usually controlled by tube current modulation (TCM) to accommodate changes in object size. This study aimed to evaluate the image quality characteristics of DLIR for different object sizes when in-plane noise is controlled by TCM. We used Mercury 4.0 phantoms with different object sizes. Phantom image acquisition was performed on a GE Revolution CT system to investigate the impact of the DLIR algorithm compared to standard reconstructions: filtered back projection (FBP) and hybrid iterative reconstruction (hybrid-IR). For image quality evaluation, the noise power spectrum (NPS), task-based transfer function (TTF), and detectability index (d') were determined. The NPS of DLIR was very similar to that of FBP, and the information in the high-frequency region was maintained. In terms of TTF, DLIR showed higher resolution than hybrid-IR at low- to medium-contrast (Δ50, Δ90HU), but not necessarily higher than FBP. At the simulated contrast and lesion size, DLIR showed higher detectability than hybrid-IR, regardless of the phantom size. In this study, we evaluated a novel DLIR algorithm by reproducing clinical behaviors. The findings indicate that DLIR produces higher image quality than hybrid-IR regardless of the phantom size, although it depends on the reconstruction strength.

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