scholarly journals Superior objective and subjective image quality of deep learning reconstruction for low-dose abdominal CT imaging in comparison with model-based iterative reconstruction and filtered back projection

2021 ◽  
Vol 94 (1123) ◽  
pp. 20201357
Author(s):  
Akio Tamura ◽  
Eisuke Mukaida ◽  
Yoshitaka Ota ◽  
Masayoshi Kamata ◽  
Shun Abe ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Abdominal Aorta ◽  
Low Dose ◽  
Image Noise ◽  
Obese Patients ◽  
Back Projection ◽  
Hepatic Parenchyma ◽  
Abdominal Ct

Objective: This study aimed to conduct objective and subjective comparisons of image quality among abdominal computed tomography (CT) reconstructions with deep learning reconstruction (DLR) algorithms, model-based iterative reconstruction (MBIR), and filtered back projection (FBP). Methods: Datasets from consecutive patients who underwent low-dose liver CT were retrospectively identified. Images were reconstructed using DLR, MBIR, and FBP. Mean image noise and contrast-to-noise ratio (CNR) were calculated, and noise, artifacts, sharpness, and overall image quality were subjectively assessed. Dunnett’s test was used for statistical comparisons. Results: Ninety patients (67 ± 12.7 years; 63 males; mean body mass index [BMI], 25.5 kg/m2) were included. The mean noise in the abdominal aorta and hepatic parenchyma of DLR was lower than that in FBP and MBIR (p < .001). For FBP and MBIR, image noise was significantly higher for obese patients than for those with normal BMI. The CNR for the abdominal aorta and hepatic parenchyma was higher for DLR than for FBP and MBIR (p < .001). MBIR images were subjectively rated as superior to FBP images in terms of noise, artifacts, sharpness, and overall quality (p < .001). DLR images were rated as superior to MBIR images in terms of noise (p < .001) and overall quality (p = .03). Conclusions: Based on objective and subjective comparisons, the image quality of DLR was found to be superior to that of MBIR and FBP on low-dose abdominal CT. DLR was the only method for which image noise was not higher for obese patients than for those with a normal BMI. Advances in knowledge: This study provides previously unavailable information on the properties of DLR systems and their clinical utility.

A feasibility study of realizing low-dose abdominal CT using deep learning image reconstruction algorithm

2021 ◽  
pp. 1-12
Author(s):  
Lu-Lu Li ◽  
Huang Wang ◽  
Jian Song ◽  
Jin Shang ◽  
Xiao-Ying Zhao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Radiation Dose ◽  
Low Dose ◽  
Standard Dose ◽  
Image Noise ◽  
Arterial Phase ◽  
Abdominal Ct ◽  
Reconstruction Methods

OBJECTIVES: To explore the feasibility of achieving diagnostic images in low-dose abdominal CT using a Deep Learning Image Reconstruction (DLIR) algorithm. METHODS: Prospectively enrolled 47 patients requiring contrast-enhanced abdominal CT scans. The late-arterial phase scan was added and acquired using lower-dose mode (tube current range, 175–545 mA; 80 kVp for patients with BMI ≤24 kg/m2 and 100 kVp for patients with BMI >  24 kg/m2) and reconstructed with DLIR at medium setting (DLIR-M) and high setting (DLIR-H), ASIR-V at 0% (FBP), 40% and 80% strength. Both the quantitative measurement and qualitative analysis of the five types of reconstruction methods were compared. In addition, radiation dose and image quality between the early-arterial phase ASIR-V images using standard-dose and the late-arterial phase DLIR images using low-dose were compared. RESULTS: For the late-arterial phase, all five reconstructions had similar CT value (P >  0.05). DLIR-H, DLIR-M and ASIR-V80% images significantly reduced the image noise and improved the image contrast noise ratio, compared with the standard ASIR-V40% images (P <  0.05). ASIR-V80% images had undesirable image characteristics with obvious “waxy” artifacts, while DLIR-H images maintained high spatial resolution and had the highest subjective image quality. Compared with the early-arterial scans, the late-arterial phase scans significantly reduced the radiation dose (P <  0.05), while the DLIR-H images exhibited lower image noise and good display of the specific image details of lesions. CONCLUSIONS: DLIR algorithm improves image quality under low-dose scan condition and may be used to reduce the radiation dose without adversely affecting the image quality.

scholarly journals Influence of a novel deep-learning based reconstruction software on the objective and subjective image quality in low-dose abdominal computed tomography

2021 ◽  
Vol 94 (1117) ◽  
pp. 20200677
Author(s):  
Andrea Steuwe ◽  
Marie Weber ◽  
Oliver Thomas Bethge ◽  
Christin Rademacher ◽  
Matthias Boschheidgen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Dose Reduction ◽  
Low Dose ◽  
Ct Images ◽  
Image Noise ◽  
The Novel ◽  
Subjective Image Quality ◽  
Image Information ◽  
Noise Ratio

Objectives: Modern reconstruction and post-processing software aims at reducing image noise in CT images, potentially allowing for a reduction of the employed radiation exposure. This study aimed at assessing the influence of a novel deep-learning based software on the subjective and objective image quality compared to two traditional methods [filtered back-projection (FBP), iterative reconstruction (IR)]. Methods: In this institutional review board-approved retrospective study, abdominal low-dose CT images of 27 patients (mean age 38 ± 12 years, volumetric CT dose index 2.9 ± 1.8 mGy) were reconstructed with IR, FBP and, furthermore, post-processed using a novel software. For the three reconstructions, qualitative and quantitative image quality was evaluated by means of CT numbers, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in six different ROIs. Additionally, the reconstructions were compared using SNR, peak SNR, root mean square error and mean absolute error to assess structural differences. Results: On average, CT numbers varied within 1 Hounsfield unit (HU) for the three assessed methods in the assessed ROIs. In soft tissue, image noise was up to 42% lower compared to FBP and up to 27% lower to IR when applying the novel software. Consequently, SNR and CNR were highest with the novel software. For both IR and the novel software, subjective image quality was equal but higher than the image quality of FBP-images. Conclusion: The assessed software reduces image noise while maintaining image information, even in comparison to IR, allowing for a potential dose reduction of approximately 20% in abdominal CT imaging. Advances in knowledge: The assessed software reduces image noise by up to 27% compared to IR and 48% compared to FBP while maintaining the image information. The reduced image noise allows for a potential dose reduction of approximately 20% in abdominal imaging.

scholarly journals Abdominal CT radiation dose optimization at Siriraj Hospital

2020 ◽  
Vol 21 (2) ◽  
pp. 28-43
Author(s):  
Piyaporn Apisarnthanarak ◽  
Chosita Buranont ◽  
Chulaluck Boonma ◽  
Sureerat Janpanich ◽  
Tarntip Suwatananonthakij ◽  
...  
Keyword(s):  
Image Quality ◽  
Radiation Dose ◽  
Low Dose ◽  
Standard Dose ◽  
Image Noise ◽  
Abdominal Ct ◽  
Low Dose Ct ◽  
Excellent Image Quality ◽  
Ct Dose ◽  
The Mean

OBJECTIVE: To compare radiation dose and image quality between standard dose abdominal CT currently performed at our hospital and new low dose abdominal CT using various percentages (0%, 10%, 20%, and 30%) of Adaptive Statistical Iterative Reconstruction (ASiR). MATERIALS AND METHODS: We prospectively performed low dose abdominal CT (30% reduction of standard tube current) in 119 participants. The low dose CT images were post processed with four parameters (0%, 10%, 20% and 30%) of ASiR. The volume CT dose index (CTDIvol) of standard and low dose CT were compared. Four experienced abdominal radiologists independently assessed the quality of low dose CT with aforementioned ASiR parameters using a 5-point-scale satisfaction score (1 = unacceptable, 2 = poor, 3 = average, 4 = good, and 5 = excellent image quality) by using prior standard dose CT as a reference of excellent image quality (5). Each reader selected the preference ASiR parameter for each participant. The image noise of the liver and the aorta in all 5 (1 prior standard dose and 4 current low dose) image sets was measured.     RESULTS: The mean CTDIvol of low dose CT was significantly lower than of standard dose CT (7.17 ± 0.08 vs 12.02 ±1.61 mGy, p<0.001). The mean satisfaction scores for low dose CT with 0%, 10%, 20% and 30% ASiR were 3.95, 3.99, 3.91 and 3.87, respectively with the ranges of 3 to 5 in all techniques. The preferred ASiR parameters of each participant randomly selected by each reader were varied, depending on the readers’ opinions. The mean image noise of the aorta on standard dose CT and low dose CT with 0%, 10%, 20%, and 30% ASiR was 29.07, 36.97, 33.92, 31.49, and 29.11, respectively, while the mean image noise of the liver was 24.60, 30.21, 28.33, 26.25, and 24.32, respectively. CONCLUSION: Low dose CT with 30% reduction of standard mA had acceptable image quality with significantly reduced radiation dose. The increment of ASiR was helpful in reducing image noise.  

Sign in / Sign up

Export Citation Format

Share Document