Image quality comparison between model‐based iterative reconstruction and adaptive statistical iterative reconstruction chest computed tomography in cystic fibrosis patients

Lin, Sandra; Lin, Monica; Lau, Kenneth

doi:10.1111/1754-9485.12895

Cited by 9 publications

(8 citation statements)

References 26 publications

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“…One radiologist with 2 years of experience in radiology performed an objective image analysis of the axial images. Standardized 20-mm-diameter circular regions of interest (ROIs) were used to record signal and noise, which represented mean attenuation value and standard deviation (SD) in Hounsfield units (HU) for the lungs, mediastinum, https://doi.org/10.3348/kjr.2020.0116 kjronline.org liver, and background air for chest LDCT scan in ASiR-V 30%, DLIR-M, and DLIR-H image sets (21). Lung measurements were obtained from the lower lobes toward the periphery, mediastinal measurements from the left ventricle at the level of the coronary sinus, and liver measurements from the liver avoiding the blood vessels and biliary tree.…”

Section: Objective Image Analysismentioning

confidence: 99%

Validation of Deep-Learning Image Reconstruction for Low-Dose Chest Computed Tomography Scan: Emphasis on Image Quality and Noise

et al. 2021

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Objective: Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%). Materials and Methods: This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated. Results: Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001). Conclusion: DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.

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Section: Objective Image Analysismentioning

confidence: 99%

Validation of Deep-Learning Image Reconstruction for Low-Dose Chest Computed Tomography Scan: Emphasis on Image Quality and Noise

et al. 2021

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“…Similar results were also observed in IR, with four cases being diagnosed as indeterminate for UIP but probable for UIP on HRCT. Although IR has shown favorable results in the detection and assessment of ILD 8,9 it is known to have limitations in that the noise texture often differs from that of traditional FBP images and can negatively affect subjective acceptance and diagnostic con dence, altering noise texture 20,25 . In our study, there were cases in which both IR and DLM were suggestive of such results (Figs.…”

Section: Discussionmentioning

confidence: 99%

“…Signal, noise, and signal-to noise ratio. Standardized 20-mm-diameter circular regions of interest were used to record signal and noise, which represented the mean pixel intensity value and standard deviation of pixels for the lung parenchyma, and background air for LDCT scans in FBP, ASiR-V, and DLM image sets 25 . Lung measurements were obtained from the lower lobes towards the periphery to avoid parenchymal lesions.…”

Section: Methodsmentioning

confidence: 99%

The impact of deep learning reconstruction in low dose computed tomography on the evaluation of interstitial lung disease

Kim

Chung

et al. 2022

Preprint

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To evaluate the effect of the deep learning model reconstruction (DLM) method in terms of image quality and diagnostic efficacy of low-dose computed tomography (LDCT) for interstitial lung disease (ILD), 193 patients who underwent LDCT for suspected ILD were retrospectively reviewed. Datasets were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction Veo (ASiR-V), and DLM. For image quality analysis, the signal, noise, signal-to-noise ratio (SNR), blind/referenceless image spatial quality evaluator (BRISQUE), and visual scoring were evaluated. Also, CT patterns of usual interstitial pneumonia (UIP) were classified according to the 2022 idiopathic pulmonary fibrosis (IPF) diagnostic criteria. The differences between CT images subjected to FBP, ASiR-V 30%, and DLM were evaluated. The image noise and BRISQUE scores of DLM images was lower and SNR was higer than that of the ASiR-V and FBP images (ASiR-V vs. DLM, p < 0.001 and FBP vs. DLR-M, p < 0.001, respectively). The agreement of the diagnostic categorization of IPF between the three reconstruction methods was almost perfect (κ = 0.992, CI 0.990–0.994). Image quality was improved with DLM compared to ASiR-V and FBP. The diagnostic efficacy was maintained in the CT pattern diagnosis of UIP in DLM.

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“…Thus, CT allows a broader differential diagnosis. Due to the upcoming ultra-low-dose protocols [25], CT has the potential to be applied in routine care. DCE-CT also allows automated image post-processing and reading.…”

Section: Discussionmentioning

confidence: 99%

Perfusion in hand arthritis on dynamic contrast-enhanced computed tomography: a randomized prospective study using MRI as a standard of reference

et al. 2020

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Objective To evaluate the performance of dynamic contrast-enhanced CT (DCE-CT) in detecting and quantitatively assessing perfusion parameters in patients with arthritis of the hand compared with dynamic contrast-enhanced MRI (DCE-MRI) as a standard of reference. Materials and methods In this IRB-approved randomized prospective single-centre study, 36 consecutive patients with suspected rheumatoid arthritis underwent DCE-CT (320-row, tube voltage 80 kVp, tube current 8.25 mAs) and DCE-MRI (1.5 T) of the hand. Perfusion maps were calculated separately for mean transit time (MTT), time to peak (TTP), relative blood volume (rBV), and relative blood flow (rBF) using four different decomposition techniques. Region of interest (ROI) analysis was performed in metacarpophalangeal joints II–V and in the wrist. Pairs of perfusion parameters in DCE-CT and DCE-MRI were compared using a two-tailed t test for paired samples and interpreted for effect size (Cohen’s d). According to the Rheumatoid Arthritis Magnetic Resonance Imaging Score (RAMRIS) scoring results, differentiation of synovitis-positive and synovitis-negative joints with both modalities was assessed with the independent t test. Results The two modalities yielded similar perfusion parameters. Identified differences had small effects (d 0.01–0.4). DCE-CT additionally differentiates inflamed and noninflamed joints based on rBF and rBV but tends to underestimate these parameters in severe inflammation. The total dose-length product (DLP) was 48 mGy*cm with an estimated effective dose of 0.038 mSv. Conclusion DCE-CT is a promising imaging technique in arthritis. In patients with a contraindication to MRI or when MRI is not available, DCE-CT is a suitable alternative to detect and assess arthritis.

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Image quality comparison between model‐based iterative reconstruction and adaptive statistical iterative reconstruction chest computed tomography in cystic fibrosis patients

Cited by 9 publications

References 26 publications

Validation of Deep-Learning Image Reconstruction for Low-Dose Chest Computed Tomography Scan: Emphasis on Image Quality and Noise

Validation of Deep-Learning Image Reconstruction for Low-Dose Chest Computed Tomography Scan: Emphasis on Image Quality and Noise

The impact of deep learning reconstruction in low dose computed tomography on the evaluation of interstitial lung disease

Perfusion in hand arthritis on dynamic contrast-enhanced computed tomography: a randomized prospective study using MRI as a standard of reference

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