Improving Image Quality and Reducing Radiation Dose for Pediatric CT                    by Using Deep Learning Reconstruction

Brady, Samuel L.; Trout, Andrew T.; Somasundaram, Elanchezhian; Anton, Christopher; Li, Yinan; Dillman, Jonathan R.

doi:10.1148/radiol.2020202317

Cited by 88 publications

(85 citation statements)

References 28 publications

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“…However, many studies have shown that the potential dose reduction obtained with IR algorithms was limited to low contrast liver lesions and the outcomes found on phantoms overestimated the real dose reduction in patients (44)(45)(46). Nevertheless, the first patient studies published on pulmonary or cardiac CT angiography and chest and abdominal CT have also confirmed that AiCE improved both image quality and lesion detection as compared with AIDR 3D for a given dose level (22,25,26,30) or with a dose reduction (26,29,34,35). Singh et al found that the dose could be reduced by −84% between a standard protocol (CTDI vol : 13.0±4.4 mGy) with AIDR 3D and a low-dose protocol (CTDI vol : 2.1±0.8 mGy) with AiCE for the detection of the same abdominal lesions and an overall image quality scored acceptable for more than 95% of patients (34).…”

Section: Discussionmentioning

confidence: 99%

“…In addition, in that same study, only the iterative level "Standard" was assessed and not the three levels available for each IR algorithms (Mild, Standard and Strong). On another note, many clinical studies have shown the impact of AiCE on dose reduction and image quality for different anatomical locations in both adult and pediatric patients (22,(25)(26)(27)29,34,35).…”

Section: Introductionmentioning

confidence: 99%

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Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study

Greffier¹,

Dabli²,

Hamard³

et al. 2022

Quant Imaging Med Surg

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Background: New reconstruction algorithms based on deep learning have been developed to correct the image texture changes related to the use of iterative reconstruction algorithms. The purpose of this study was to evaluate the impact of a new deep learning image reconstruction [Advanced intelligent Clear-IQ Engine (AiCE)] algorithm on image-quality and dose reduction compared to a hybrid iterative reconstruction (AIDR 3D) algorithm and a model-based iterative reconstruction (FIRST) algorithm.Methods: Acquisitions were carried out using the ACR 464 phantom (and its body ring) at six dose levels (volume computed tomography dose index 15/10/7.5/5/2.5/1 mGy). Raw data were reconstructed using three levels (Mild/Standard/Strong) of AIDR 3D, of FIRST and AiCE. Noise-power-spectrum (NPS) and task-based transfer function (TTF) were computed. Detectability index was computed to model the detection of a small calcification (1.5-mm diameter and 500 HU) and a large mass in the liver (25-mm diameter and 120 HU).Results: NPS peaks were lower with AiCE than with AIDR 3D (−41%±6% for all levels) or FIRST (−15%±6% for Strong level and −41%±11% for both other levels). The average NPS spatial frequency was lower with AICE than AIDR 3D (−9%±2% using Mild and −3%±2% using Strong) but higher than FIRST for Standard (6%±3%) and Strong (25%±3%) levels. For acrylic insert, values of TTF at 50 percent were higher with AICE than AIDR 3D and FIRST, except for Mild level (−6%±6% and −13%±3%, respectively).For bone insert, values of TTF at 50 percent were higher with AICE than AIDR 3D but lower than FIRST (−19%±14%). For both simulated lesions, detectability index values were higher with AICE than AIDR 3D and FIRST (except for Strong level and for the small feature; −21%±14%). Using the Standard level, dose could be reduced by −79% for the small calcification and −57% for the large mass using AICE compared to AIDR 3D. Conclusions:The new deep learning image reconstruction algorithm AiCE generates an image-quality with less noise and/or less smudged/smooth images and a higher detectability than the AIDR 3D or FIRST algorithms. The outcomes of our phantom study suggest a good potential of dose reduction using AiCE but it should be confirmed clinically in patients.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study

Greffier¹,

Dabli²,

Hamard³

et al. 2022

Quant Imaging Med Surg

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“…Lim et al [ 16 ] studied a 5-year-old patient’s phantom and pediatric abdomen CT exams using a vendor-neutral DLR technique and demonstrated similar image quality with a hybrid IR technique. Brady et al [ 17 ] used contrast-enhanced abdomen CT with DLR algorithm showing improved object detectability, reduced image noise, and high radiologist preference when compared to conventional IR images. About a 51% dose reduction using DLR was hypothesized based on mathematical extrapolation from this retrospective study.…”

Section: Discussionmentioning

confidence: 99%

Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction

et al. 2021

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Background Efforts to reduce the radiation dose have continued steadily, with new reconstruction techniques. Recently, image denoising algorithms using artificial neural networks, termed deep learning reconstruction (DLR), have been applied to CT image reconstruction to overcome the drawbacks of iterative reconstruction (IR). The purpose of our study was to compare the objective and subjective image quality of DLR and IR on pediatric abdomen and chest CT images. Methods This retrospective study included pediatric body CT images from February 2020 to October 2020, performed on 51 patients (34 boys and 17 girls; age 1–18 years). Non-contrast chest CT (n = 16), contrast-enhanced chest CT (n = 12), and contrast-enhanced abdomen CT (n = 23) images were included. Standard 50% adaptive statistical iterative reconstruction V (ASIR-V) images were compared to images with 100% ASIR-V and DLR at medium and high strengths. Attenuation, noise, contrast to noise ratio (CNR), and signal to noise (SNR) measurements were performed. Overall image quality, artifacts, and noise were subjectively assessed by two radiologists using a four-point scale (superior, average, suboptimal, and unacceptable). A phantom scan was performed including the dose range of the clinical images used in our study, and the noise power spectrum (NPS) was calculated. Quantitative and qualitative parameters were compared using repeated-measures analysis of variance (ANOVA) with Bonferroni correction and Wilcoxon signed-rank tests. Results DLR had better CNR and SNR than 50% ASIR-V in both pediatric chest and abdomen CT images. When compared with 50% ASIR-V, high strength DLR was associated with noise reduction in non-contrast chest CT (33.0%), contrast-enhanced chest CT (39.6%), and contrast-enhanced abdomen CT (38.7%) with increases in CNR at 149.1%, 105.8%, and 53.1% respectively. The subjective assessment of overall image quality and the noise was also better on DLR images (p < 0.001). However, there was no significant difference in artifacts between reconstruction methods. From NPS analysis, DLR methods showed a pattern of reducing the magnitude of noise while maintaining the texture. Conclusion Compared with 50% ASIR-V, DLR improved pediatric body CT images with significant noise reduction. However, artifacts were not improved by DLR, regardless of strength.

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“…Recently, image denoising algorithms using arti cial neural networks, termed deep learning reconstruction (DLR), have been applied to CT image reconstruction to overcome the drawbacks of IR while achieving good image quality [10][11][12][13][14][15]. However, there have been a limited number of studies evaluating this technique in a small number of children and the technique was only evaluated in abdomen CT images [16][17][18]. The purpose of our study was to compare objective and subjective image quality of DLR and IR on pediatric abdomen and chest CT images.…”

Section: Introductionmentioning

confidence: 99%

“…DLR has been introduced to pediatric patients in a few studies of abdomen.Lim et al [16] studied a 5-year-old patient's phantom and pediatric abdomen CT exams using a vendor-neutral DLR technique and demonstrated similar image quality with hybrid IR technique. Brady et al[17] used contrast-enhanced abdomen CT with DLR algorithm showing improved object detectability, reduced image noise, and high radiologist preference when compared to conventional IR images. About a 51% dose reduction using DLR was hypothesized on the basis of mathematical extrapolation from this retrospective study.Lee et al [18] used DLR with low iodine concentration abdominal dual energy CT and showed decreased noise in DLR images without difference in CNR, overall image quality, and diagnostic quality of lesions.…”

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confidence: 99%

Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction

Yoon

Kim

Lim

et al. 2021

Preprint

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Background Efforts to reduce the radiation dose have continued steadily, with new reconstruction techniques. Recently, image denoising algorithms using artificial neural networks, termed deep learning reconstruction (DLR), have been applied to CT image reconstruction to overcome the drawbacks of iterative reconstruction (IR). The purpose of our study was to compare objective and subjective image quality of DLR and IR on pediatric abdomen and chest CT images.Methods This retrospective study included pediatric body CT images from February 2020 to October 2020, performed on 51 patients (34 boys and 17 girls; age 1–18 years). Non-contrast chest CT (n = 16), contrast-enhanced chest CT (n = 12), and contrast-enhanced abdomen CT (n = 23) images were included. Standard 50% adaptive statistical iterative reconstruction V (ASIR-V) images were compared to images with 100% ASIR-V and DLR at medium and high strengths. Attenuation, noise, contrast to noise ratio (CNR), and signal to noise (SNR) measurements were performed. Overall image quality, artifacts, and noise were subjectively assessed by two radiologists using a four-point scale (superior, average, suboptimal, and unacceptable). Quantitative and qualitative parameters were compared using repeated measures analysis of variance (ANOVA) with Bonferroni correction and Wilcoxon signed-rank tests.Results DLR had better CNR and SNR than 50% ASIR-V in both pediatric chest and abdomen CT images. When compared with 50% ASIR-V, high strength DLR was associated with noise reduction in non-contrast chest CT (33.0%), contrast-enhanced chest CT (39.6%), and contrast-enhanced abdomen CT (38.7%) with increases in CNR at 149.1%, 105.8% and 53.1% respectively. The subjective assessment of overall image quality and noise was also better on DLR images (p < 0.001). However, there was no significant difference in artifacts between reconstruction methods.Conclusion Compared with 50% ASIR-V, DLR improved pediatric body CT images with significant noise reduction. However, artifacts were not improved by DLR, regardless of strength.

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Improving Image Quality and Reducing Radiation Dose for Pediatric CT by Using Deep Learning Reconstruction

Cited by 88 publications

References 28 publications

Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study

Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study

Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction

Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction

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