Performance Evaluation of Two Iterative Reconstruction Algorithms--MBIR and ASIR, in Low Radiation Dose and Low Contrast Dose Abdominal CT in Children

Sun, Jiawei; Yang, Lijun; Zhou, Zuofu; Zhang, Dan; Han, Wei; Zhang, Zuo‐Feng; Peng, Yun

doi:10.21203/rs.2.10744/v1

Cited by 3 publications

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“…Only when the radiation dose is very low, 100% IR weighted is recommended as a mean to salvage the scans (24). This is the major limitation of low-dose CTA applicated in TAK.…”

Section: Discussionmentioning

confidence: 99%

Application of deep learning image reconstruction algorithm to improve image quality in CT angiography of children with Takayasu arteritis

Sun

et al. 2022

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BACKGROUND: The inflammatory indexes of children with Takayasu arteritis (TAK) usually tend to be normal immediately after treatment, therefore, CT angiography (CTA) has become an important method to evaluate the status of TAK and sometime is even more sensitive than laboratory test results. OBJECTIVE: To evaluate image quality improvement in CTA of children diagnosed with TAK using a deep learning image reconstruction (DLIR) in comparison to other image reconstruction algorithms. METHODS: hirty-two TAK patients (9.14±4.51 years old) underwent neck, chest and abdominal CTA using 100 kVp were enrolled. Images were reconstructed at 0.625 mm slice thickness using Filtered Back-Projection (FBP), 50%adaptive statistical iterative reconstruction-V (ASIR-V), 100%ASIR-V and DLIR with high setting (DLIR-H). CT number and standard deviation (SD) of the descending aorta and back muscle were measured and contrast-to-noise ratio (CNR) for aorta was calculated. The vessel visualization, overall image noise and diagnostic confidence were evaluated using a 5-point scale (5, excellent; 3, acceptable) by 2 observers. RESULTS: There was no significant difference in CT number across images reconstructed using different algorithms. Image noise values (in HU) were 31.36±6.01, 24.96±4.69, 18.46±3.91 and 15.58±3.65, and CNR values for aorta were 11.93±2.12, 15.66±2.37, 22.54±3.34 and 24.02±4.55 using FBP, 50%ASIR-V, 100%ASIR-V and DLIR-H, respectively. The 100%ASIR-V and DLIR-H images had similar noise and CNR (all P > 0.05), and both had lower noise and higher CNR than FBP and 50%ASIR-V images (all P < 0.05). The subjective evaluation suggested that all images were diagnostic for large arteries, however, only 50%ASIR-V and DLIR-H met the diagnostic requirement for small arteries (3.03±0.18 and 3.53±0.51). CONCLUSION: DLIR-H improves CTA image quality and diagnostic confidence for TAK patients compared with 50%ASIR-V, and best balances image noise and spatial resolution compared with 100%ASIR-V.

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“…Only when the radiation dose is very low, 100% IR weighted is recommended as a mean to salvage the scans (24). This is the major limitation of low-dose CTA applicated in TAK.…”

Section: Discussionmentioning

confidence: 99%

Application of deep learning image reconstruction algorithm to improve image quality in CT angiography of children with Takayasu arteritis

Sun

et al. 2022

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Low-Dose Imaging: Prediction of Projections in Sinogram Space

Sunag

Desai

2021

Computational Vision and Bio-Inspired Computing

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Application of Deep Learning Image Reconstruction (DLIR) Algorithm to Improve Image Quality in CT Angiography of Children with Takayasu Arteritis

Sun

et al. 2021

Preprint

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purpose: To evaluate the image quality improvement in CTA of children with Takayasu arteritis (TAK) using a Deep learning image reconstruction (DLIR) in comparison to other reconstruction algorithms.Methods: 32 patients (9.14±4.51 years old) with TAK underwent neck, chest and abdominal CTA with 100kVp were enrolled. Images were reconstructed at 0.625mm slice thickness using Filtered Back-Projection (FBP), 50% adaptive statistical iterative reconstruction-V (ASIR-V), 100%ASIR-V and DLIR with high setting (DLIR-H). The CT number and standard deviation (SD) of the descending aorta and back muscle were measured and contrast-to-noise ratio (CNR) for aorta was calculated. The vessel visualization, overall image noise and diagnostic confidence were evaluated using a 5-point scale (5, excellent; 3, acceptable) by 2 observers.Results: There was no significant difference in CT number across all reconstructions. The image noise values (in HU) were 31.36±6.01, 24.96±4.69, 18.46±3.91 and 15.58±3.65, and CNR values for aorta were 11.93±2.12, 15.66±2.37, 22.54±3.34 and 24.02±4.55 with FBP, 50%ASIR-V, 100%ASIR-V and DLIR-H, respectively. The 100%ASIR-V and DLIR-H images had similar noise and CNR (all P>0.05), and both had lower noise and higher CNR than FBP and 50%ASIR-V images (all P<0.05). The subjective evaluation suggested that all images were diagnostic for large arteries, but only 50%ASIR-V and DLIR-H met the diagnostic requirement for small arteries (3.03±0.18 and 3.53±0.51). Conclusions: DLIR-H improves the CTA image quality and diagnostic confidence for TAK patients compared with 50%ASIR-V, and best balances image noise and spatial resolution compared with 100%ASIR-V.

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Performance Evaluation of Two Iterative Reconstruction Algorithms--MBIR and ASIR, in Low Radiation Dose and Low Contrast Dose Abdominal CT in Children

Cited by 3 publications

References 22 publications

Application of deep learning image reconstruction algorithm to improve image quality in CT angiography of children with Takayasu arteritis

Application of deep learning image reconstruction algorithm to improve image quality in CT angiography of children with Takayasu arteritis

Low-Dose Imaging: Prediction of Projections in Sinogram Space

Application of Deep Learning Image Reconstruction (DLIR) Algorithm to Improve Image Quality in CT Angiography of Children with Takayasu Arteritis

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