Image Quality Assessment of Abdominal CT by Use of New Deep Learning Image Reconstruction: Initial Experience

Jensen, Corey T.; Liu, Xinming; Tamm, Eric P.; Chandler, Adrian; Sun, Jia; Morani, Ajaykumar C.; Javadi, Sanaz; Wagner‐Bartak, Nicolaus A.

doi:10.2214/ajr.19.22332

Cited by 164 publications

(139 citation statements)

References 28 publications

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“…The subjective assessment of overall image quality and noise were also better on DLR images both on medium and high strength DLR compared to 50% ASIR-V. Our study showed no signi cant difference in attenuation values of the organs in pediatric chest and abdomen. This result is comparable with a previous report with an adult population [12]. Therefore, we can use CT images with DLR for attenuation analyses such as emphysema index measurements.…”

Section: Discussionsupporting

confidence: 90%

“…A phantom study demonstrated that DLR had superior noise, magnitude, noise texture, and spatial resolution [11]. Another study also showed that DLR improves the image quality through noise reduction and increased CNR without altering the image texture on abdomen CT [12]. They demonstrated that subjective diagnostic con dence was increased in all DLR images when compared with ASIR-V with a 30% blending factor, and the higher strength in DLR lowers the noise with increased sharpness [13].…”

Section: Discussionmentioning

confidence: 97%

“…Hybrid IR images that blend IR with FBP can be used to decrease this texture problem, although a trade-off between image noise and image texture occurs [9]. 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].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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

Yoon

Kim

Lim

et al. 2021

Preprint

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“…A study by Jensen et al. 10 investigated image quality in abdominal CT where images were reconstructed with TrueFidelity (GE Healthcare) of various strength and compared to IR (ASIR-V 30%). Demonstrated by reader scores for two radiologists, scores for overall image quality and overall lesion diagnostic confidence were progressively higher for higher strength of DLIR, and significantly higher than scores for IR.…”

Section: Discussionmentioning

confidence: 99%

“…Noteworthy, although novel noise reduction methods (such as DLIR) may seem promising, it is imperative that they are comprehensively evaluated in phantom and clinical studies to ensure that established clinical benefits are not compromised. 8 Preliminary investigations have shown that DLIR improves perceived overall image quality in abdominal CT. 10 However, specific anatomical criteria have not been applied, such as the European guidelines on quality criteria for CT. 11 Furthermore, there may be benefit of enhanced spatial resolution in images reconstructed in thinner slices than conventional thicker slices. 12…”

Section: Introductionmentioning

confidence: 99%

Improved image quality in abdominal computed tomography reconstructed with a novel Deep Learning Image Reconstruction technique – initial clinical experience

Njølstad

Schulz

Godt

et al. 2021

Acta Radiologica Open

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Background A novel Deep Learning Image Reconstruction (DLIR) technique for computed tomography has recently received clinical approval. Purpose To assess image quality in abdominal computed tomography reconstructed with DLIR, and compare with standardly applied iterative reconstruction. Material and methods Ten abdominal computed tomography scans were reconstructed with iterative reconstruction and DLIR of medium and high strength, with 0.625 mm and 2.5 mm slice thickness. Image quality was assessed using eight visual grading criteria in a side-by-side comparative setting. All series were presented twice to evaluate intraobserver agreement. Reader scores were compared using univariate logistic regression. Image noise and contrast-to-noise ratio were calculated for quantitative analyses. Results For 2.5 mm slice thickness, DLIR images were more frequently perceived as equal or better than iterative reconstruction across all visual grading criteria (for both DLIR of medium and high strength, p < 0.001). Correspondingly, DLIR images were more frequently perceived as better (as opposed to equal or in favor of iterative reconstruction) for visual reproduction of liver parenchyma, intrahepatic vascular structures as well as overall impression of image noise and texture (p < 0.001). This improved image quality was also observed for 0.625 mm slice images reconstructed with DLIR of high strength when directly comparing to traditional iterative reconstruction in 2.5 mm slices. Image noise was significantly lower and contrast-to-noise ratio measurements significantly higher for images reconstructed with DLIR compared to iterative reconstruction (p < 0.01). Conclusions Abdominal computed tomography images reconstructed using a DLIR technique shows improved image quality when compared to standardly applied iterative reconstruction across a variety of clinical image quality criteria.

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Applying a CT texture analysis model trained with deep‐learning reconstruction images to iterative reconstruction images in pulmonary nodule diagnosis

Wang

Zhang

et al. 2022

J Applied Clin Med Phys

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Objective To investigate the feasibility and accuracy of applying a computed tomography (CT) texture analysis model trained with deep‐learning reconstruction images to iterative reconstruction images for classifying pulmonary nodules. Materials and methods CT images of 102 patients, with a total of 118 pulmonary nodules (52 benign, 66 malignant) were retrospectively reconstructed with a deep‐learning reconstruction (artificial intelligence iterative reconstruction [AIIR]) and a hybrid iterative reconstruction (HIR) technique. The AIIR data were divided into a training (n = 96) and a validation set (n = 22), and the HIR data were set as the test set (n = 118). Extracted texture features were compared using the Mann‐Whitney U test and t‐test. The diagnostic performance of the classification model was analyzed with the receiver operating characteristic curve (ROC), the area under ROC (AUC), sensitivity, specificity, and accuracy. Results Among the obtained 68 texture features, 51 (75.0%) were not influenced by the change of reconstruction algorithm (p > 0.05). Forty‐four features were significantly different between benign and malignant nodules (p < 0.05) for the AIIR dataset, which were selected to build the classification model. The accuracy and AUC of the classification model were 92.3% and 0.91 (95% confidence interval [CI], 0.74−0.90) with the validation set, which were 80.0% and 0.80 (95% CI, 0.68−0.86) with the test set. Conclusion With the CT texture analysis model trained with deep‐learning reconstruction (AIIR) images showing favorable diagnostic accuracy in discriminating benign and malignant pulmonary nodules, it also has certain applicability to the iterative reconstruction (HIR) images.

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Image Quality Assessment of Abdominal CT by Use of New Deep Learning Image Reconstruction: Initial Experience

Cited by 164 publications

References 28 publications

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

Improved image quality in abdominal computed tomography reconstructed with a novel Deep Learning Image Reconstruction technique – initial clinical experience

Applying a CT texture analysis model trained with deep‐learning reconstruction images to iterative reconstruction images in pulmonary nodule diagnosis

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