Improving image quality with model-based iterative reconstruction algorithm for chest CT in children with reduced contrast concentration

2019 ◽  
Vol 124 (7) ◽  
pp. 595-601 ◽  
Author(s):  
Jihang Sun ◽  
Di Hu ◽  
Yun Shen ◽  
Haiming Yang ◽  
Chenghao Chen ◽  
...  
Keyword(s):  
Image Quality ◽  
Iterative Reconstruction ◽  
Reconstruction Algorithm ◽  
Chest Ct ◽  
Iterative Reconstruction Algorithm ◽  
Model Based

scholarly journals Influence of model based iterative reconstruction algorithm on image quality of multiplanar reformations in reduced dose chest CT

2016 ◽  
Vol 5 (8) ◽  
pp. 205846011666229 ◽  
Author(s):  
Heloise Barras ◽  
Vincent Dunet ◽  
Anne-Lise Hachulla ◽  
Jochen Grimm ◽  
Catherine Beigelman-Aubry
Keyword(s):  
Image Quality ◽  
Iterative Reconstruction ◽  
Reconstruction Algorithm ◽  
Chest Ct ◽  
Iterative Reconstruction Algorithm ◽  
Reduced Dose ◽  
Model Based ◽  
Multiplanar Reformations

scholarly journals Inter-Observer Agreement and Image Quality of Model-Based Algorithm Applied to the Coronary Artery Disease Reporting and Data System Score

2021 ◽  
Author(s):  
Davide Ippolito ◽  
Cammillo Talei Franzesi ◽  
Cecilia Cangiotti ◽  
Luca Riva ◽  
Andrea De Vito ◽  
...  
Keyword(s):  
Image Quality ◽  
Effective Dose ◽  
Iterative Reconstruction ◽  
Low Dose ◽  
Reconstruction Algorithm ◽  
Observer Agreement ◽  
Iterative Reconstruction Algorithm ◽  
Model Based ◽  
The Mean ◽  
Lower Noise

Abstract Purpose: To evaluate the inter-observer agreement of the CAD-RADS reporting system and compare image quality between model-based iterative reconstruction algorithm (MBIR) and standard iterative reconstruction algorithm (IR) of low dose cardiac computed tomography angiography (CCTA). Methods: One-hundred-sixty patients undergone a 256-slice MDCT scanner using low-dose CCTA combined with prospective ECG-gated techniques were prospectively enrolled. CCTA protocols were reconstructed with both MBIR and IR. Each study was evaluated by two readers using the CAD-RADS lexicon. Vessels enhancement, image noise, SNR, and CNR were computed in the axial native images and inter-observer agreement was assessed. Radiation dose exposure as dose–length product (DLP) and effective dose (ED) were finally reported. Results: The overall agreement was very good (k = 0.90). Moreover, a significantly higher value of subjective qualitative analysis, SNR, and CNR in MBIR images compared to IR were found, due to a lower noise level (p<0.05). The mean DLP measured was 63.9 mGy*cm and the mean effective dose was 0.9 mSv.Conclusion: Inter-observer agreement of CAD-RADS was excellent confirming the importance, the feasibility, and the reproducibility of the CAD-RADS scoring system for CCTA. Moreover, lower noise and higher image quality with MBIR compared to IR were found.

Model-based reconstruction algorithm in the detection of acute trauma-related lesions in brain CT examinations

2021 ◽  
pp. 197140092110087
Author(s):  
Andrea De Vito ◽  
Cesare Maino ◽  
Sophie Lombardi ◽  
Maria Ragusi ◽  
Cammillo Talei Franzesi ◽  
...  
Keyword(s):  
Image Quality ◽  
Iterative Reconstruction ◽  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Signal To Noise ◽  
Subjective Image Quality ◽  
Model Based ◽  
Hybrid Iterative Reconstruction ◽  
Noise Ratio ◽  
Enhanced Computed Tomography

Background and purpose To evaluate the added value of a model-based reconstruction algorithm in the assessment of acute traumatic brain lesions in emergency non-enhanced computed tomography, in comparison with a standard hybrid iterative reconstruction approach. Materials and methods We retrospectively evaluated a total of 350 patients who underwent a 256-row non-enhanced computed tomography scan at the emergency department for brain trauma. Images were reconstructed both with hybrid and model-based iterative algorithm. Two radiologists, blinded to clinical data, recorded the presence, nature, number, and location of acute findings. Subjective image quality was performed using a 4-point scale. Objective image quality was determined by computing the signal-to-noise ratio and contrast-to-noise ratio. The agreement between the two readers was evaluated using k-statistics. Results A subjective image quality analysis using model-based iterative reconstruction gave a higher detection rate of acute trauma-related lesions in comparison to hybrid iterative reconstruction (extradural haematomas 116 vs. 68, subdural haemorrhages 162 vs. 98, subarachnoid haemorrhages 118 vs. 78, parenchymal haemorrhages 94 vs. 64, contusive lesions 36 vs. 28, diffuse axonal injuries 75 vs. 31; all P<0.001). Inter-observer agreement was moderate to excellent in evaluating all injuries (extradural haematomas k=0.79, subdural haemorrhages k=0.82, subarachnoid haemorrhages k=0.91, parenchymal haemorrhages k=0.98, contusive lesions k=0.88, diffuse axonal injuries k=0.70). Quantitatively, the mean standard deviation of the thalamus on model-based iterative reconstruction images was lower in comparison to hybrid iterative one (2.12 ± 0.92 vsa 3.52 ± 1.10; P=0.030) while the contrast-to-noise ratio and signal-to-noise ratio were significantly higher (contrast-to-noise ratio 3.06 ± 0.55 vs. 1.55 ± 0.68, signal-to-noise ratio 14.51 ± 1.78 vs. 8.62 ± 1.88; P<0.0001). Median subjective image quality values for model-based iterative reconstruction were significantly higher ( P=0.003). Conclusion Model-based iterative reconstruction, offering a higher image quality at a thinner slice, allowed the identification of a higher number of acute traumatic lesions than hybrid iterative reconstruction, with a significant reduction of noise.

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