SU-FF-I-09: Quantitative Image Quality Assessment of High-Dose CT Images Simulated by Averaging Multiple Low-Dose CT Images for Prospective CT Dose Reduction Studies

2009 ◽  
Vol 36 (6Part2) ◽  
pp. 2436-2436
Author(s):  
K Hulme ◽  
S Kappadath
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Dose Reduction ◽  
Low Dose ◽  
Image Quality Assessment ◽  
Ct Images ◽  
High Dose ◽  
Low Dose Ct ◽  
Ct Dose ◽  
Quantitative Image

Ultra-Low-Dose CT of the Thorax Using Iterative Reconstruction: Evaluation of Image Quality and Radiation Dose Reduction

2015 ◽  
Vol 204 (6) ◽  
pp. 1197-1202 ◽  
Author(s):  
Yookyung Kim ◽  
Yoon Kyung Kim ◽  
Bo Eun Lee ◽  
Seok Jeong Lee ◽  
Yon Ju Ryu ◽  
...  
Keyword(s):  
Image Quality ◽  
Radiation Dose ◽  
Dose Reduction ◽  
Iterative Reconstruction ◽  
Low Dose ◽  
Radiation Dose Reduction ◽  
Low Dose Ct

scholarly journals Comparison of low-contrast detectability between uniform and anatomically realistic phantoms—influences on CT image quality assessment

2021 ◽  
Author(s):  
Juliane Conzelmann ◽  
Ulrich Genske ◽  
Arthur Emig ◽  
Michael Scheel ◽  
Bernd Hamm ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Dose Reduction ◽  
Iterative Reconstruction ◽  
Image Quality Assessment ◽  
Lesion Detection ◽  
Detection Accuracy ◽  
Test Environment ◽  
Low Contrast ◽  
Dose Effects

Abstract Objectives To evaluate the effects of anatomical phantom structure on task-based image quality assessment compared with a uniform phantom background. Methods Two neck phantom types of identical shape were investigated: a uniform type containing 10-mm lesions with 4, 9, 18, 30, and 38 HU contrast to the surrounding area and an anatomically realistic type containing lesions of the same size and location with 10, 18, 30, and 38 HU contrast. Phantom images were acquired at two dose levels (CTDIvol of 1.4 and 5.6 mGy) and reconstructed using filtered back projection (FBP) and adaptive iterative dose reduction 3D (AIDR 3D). Detection accuracy was evaluated by seven radiologists in a 4-alternative forced choice experiment. Results Anatomical phantom structure impaired lesion detection at all lesion contrasts (p < 0.01). Detectability in the anatomical phantom at 30 HU contrast was similar to 9 HU contrast in uniform images (91.1% vs. 89.5%). Detection accuracy decreased from 83.6% at 5.6 mGy to 55.4% at 1.4 mGy in uniform FBP images (p < 0.001), whereas AIDR 3D preserved detectability at 1.4 mGy (80.7% vs. 85% at 5.6 mGy, p = 0.375) and was superior to FBP (p < 0.001). In the assessment of anatomical images, superiority of AIDR 3D was not confirmed and dose reduction moderately affected detectability (74.6% vs. 68.2%, p = 0.027 for FBP and 81.1% vs. 73%, p = 0.018 for AIDR 3D). Conclusions A lesion contrast increase from 9 to 30 HU is necessary for similar detectability in anatomical and uniform neck phantom images. Anatomical phantom structure influences task-based assessment of iterative reconstruction and dose effects. Key Points • A lesion contrast increase from 9 to 30 HU is necessary for similar low-contrast detectability in anatomical and uniform neck phantom images. • Phantom background structure influences task-based assessment of iterative reconstruction and dose effects. • Transferability of CT assessment to clinical imaging can be expected to improve as the realism of the test environment increases.

Image quality assessment of ultra low-dose chest CT using sinogram-affirmed iterative reconstruction

2014 ◽  
Vol 24 (4) ◽  
pp. 817-826 ◽  
Author(s):  
So Won Lee ◽  
Yookyung Kim ◽  
Sung Shine Shim ◽  
Jeong Kyong Lee ◽  
Seok Jeong Lee ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Iterative Reconstruction ◽  
Low Dose ◽  
Image Quality Assessment ◽  
Chest Ct

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 Design and implementation of convolutional neural network architectures for low-dose CT image noise reduction

2021 ◽  
Author(s):  
Seyyedomid Badretale
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Low Dose ◽  
Ct Images ◽  
Learning Approaches ◽  
Low Dose Ct ◽  
Image Noise Reduction ◽  
Low Dose Computed Tomography

An essential objective in low-dose Computed Tomography (CT) imaging is how best to preserve the image quality. While the image quality lowers with reducing the X-ray dosage, improving the quality is crucial. Therefore, a novel method to denoise low-dose CT images has been presented in this thesis. Different from the traditional algorithms which utilize similar shared features of CT images in the spatial domain, the deep learning approaches are suggested for low-dose CT denoising. The proposed algorithm learns an end-to-end mapping from the low-dose CT images for denoising the low-dose CT images. The first method is based on a fully convolutional neural network. The second approach is a deep convolutional neural network architecture consisting of five major sections. The results of two frameworks are compared with the state-of-the-art methods. Several metrics for assessing image quality are applied in this thesis in order to highlight the supremacy of the performed method.

Application of low-dose CT combined with model-based iterative reconstruction algorithm in oncologic patients during follow-up: dose reduction and image quality

2021 ◽  
pp. 20201223
Author(s):  
Davide Ippolito ◽  
Cesare Maino ◽  
Anna Pecorelli ◽  
Ilaria Salemi ◽  
Davide Gandola ◽  
...  
Keyword(s):  
Image Quality ◽  
Radiation Dose ◽  
Dose Reduction ◽  
Iterative Reconstruction ◽  
Low Dose ◽  
Ct Images ◽  
Ct Attenuation ◽  
Oncologic Patients ◽  
Attenuation Values

Objectives: To compare image quality and radiation dose of CT images reconstructed with model-based iterative reconstruction (MBIR) and hybrid-iterative (HIR) algorithm in oncologic patients. Methods: 125 oncologic patients underwent both contrast-enhanced low- (100 kV), and standard (120 kV) dose CT, were enrolled. Image quality was assessed by using a 4-point Likert scale. CT attenuation values, expressed in Hounsfield unit (HU), were recorded within a regions of interest (ROI) of liver, spleen, paraspinal muscle, aortic lumen, and subcutaneous fat tissue. Image noise, expressed as standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated. Radiation dose were analyzed. Paired Student’s t-test was used to compare all continuous variables. Results: The overall median score assessed as image quality for CT images with the MBIR algorithm was significantly higher in comparison with HIR [4 (range 3–4) vs 3 (3-4), p = 0.017]. CT attenuation values and SD were significantly higher and lower, respectively, in all anatomic districts in images reconstructed with MBIR in comparison with HIR ones (all p < 0.001). SNR and CNR values were higher in CT images reconstructed with MBIR, reaching a significant difference in all districts (all p < 0.001). Radiation dose were significantly lower in the MBIR group compared with the HIR group (p < 0.001). Conclusions: MBIR combined with low-kV setting allows an important dose reduction in whole-body CT imaging, reaching a better image quality both qualitatively and quantitatively. Advances in knowledge: MBIR with low-dose approach allows a reduction of dose exposure, maintaining high image quality, especially in patients which deserve a longlasting follow-up.

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