Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT

Singh, Ramandeep; Digumarthy, Subba R.; Muse, Victorine V.; Kambadakone, Avinash; Blake, Michael A.; Tabari, Azadeh; Hoi, Yiemeng; Akino, Naruomi; Angel, Erin; Madan, Rachna; Kalra, Mannudeep K.

doi:10.2214/ajr.19.21809

Cited by 175 publications

(126 citation statements)

References 28 publications

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“…Relative to the images presented in Figs. 3, 4, there was no significant change in HU values (<5 HU difference) for lowdose chest and abdomen CT images reconstructed with HI-RT (AIDR3D), MI-RT (FIRST), and DL-IR (AiCE) there was no significant change in the HU value (<5 HU difference between the three techniques) [78].…”

Section: Evidence For Dl-based Image Reconstructionmentioning

confidence: 88%

“…Primary limitation of some commercial DL methods is the slight loss of sharpness of small structures or blurring of fine details when compared with CT images reconstructed with FBP and iterative reconstruction techniques. Although this did not compromise lesion detection in some clinical studies published to date [76][77][78], it can potentially affect visualization or reader confidence when assessing subtle lung and bone abnormalities. Another limitation of DL-based methods is that currently, they cannot be applied with DECT scan mode.…”

Section: Limitations Of Dl-based Reconstructionmentioning

confidence: 98%

“…There are at least two commercially available and United States Food and Drug Administration (FDA)-approved DL-based image reconstruction algorithms -Advanced Intelligent Clear-IQ engine (AiCE, Canon Medical Systems) and TrueFidelity (GE Healthcare). The AiCE is based on a deep CNN trained on high-quality CT images acquired at high radiation dose and reconstructed with an MI-RT technique [78]. During training with projection data, DCNN learns the statistical method of differentiating signals and noise and then generates a high signal image as the output.…”

Section: Deep Learning-based Reconstructionmentioning

confidence: 99%

“…A comparison was done between the HI-RT and DL-based image reconstruction. Singh et al assessed the effect of DL-based image reconstruction in 59 adult patients who underwent chest (n = 22) and abdomen CT (n = 37) on a 320-slice multidetector-row CT scanner (Aquilion ONE, Canon Medical Systems) and compared the image quality and detection of the clinically significant lesions at submillisievert radiation dose levels [78]. In this study, AIDR3D, FBP, FIRST, FBP, and AiCE methods at submillisievert and AIDR3D at standard-dose levels were compared.…”

Section: Evidence For Dl-based Image Reconstructionmentioning

confidence: 99%

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Artificial intelligence in image reconstruction: The change is here

Singh

Wang

et al. 2020

Physica Medica

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Innovations in CT have been impressive among imaging and medical technologies in both the hardware and software domain. The range and speed of CT scanning improved from the introduction of multidetector-row CT scanners with wide-array detectors and faster gantry rotation speeds. To tackle concerns over rising radiation doses from its increasing use and to improve image quality, CT reconstruction techniques evolved from filtered back projection to commercial release of iterative reconstruction techniques, and recently, of deep learning (DL)based image reconstruction. These newer reconstruction techniques enable improved or retained image quality versus filtered back projection at lower radiation doses. DL can aid in image reconstruction with training data without total reliance on the physical model of the imaging process, unique artifacts of PCD-CT due to charge sharing, K-escape, fluorescence x-ray emission, and pulse pileups can be handled in the data-driven fashion. With sufficiently reconstructed images, a well-designed network can be trained to upgrade image quality over a practical/clinical threshold or define new/killer applications. Besides, the much smaller detector pixel for PCD-CT can lead to huge computational costs with traditional model-based iterative reconstruction methods whereas deep networks can be much faster with training and validation. In this review, we present techniques, applications, uses, and limitations of deep learning-based image reconstruction methods in CT.

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Section: Evidence For Dl-based Image Reconstructionmentioning

confidence: 88%

Section: Limitations Of Dl-based Reconstructionmentioning

confidence: 98%

Section: Deep Learning-based Reconstructionmentioning

confidence: 99%

Section: Evidence For Dl-based Image Reconstructionmentioning

confidence: 99%

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Artificial intelligence in image reconstruction: The change is here

Singh

Wang

et al. 2020

Physica Medica

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“…For example, FIRST (forward projected model-based iterative reconstruction solution), which is an iterative reconstruction technology, 14 and AiCE (advanced intelligent clear-IQ engine), which is an image reconstruction method using deep learning, and one of the AI technologies. 15,16 Further reductions by these technologies will therefore promote reduced exposure.…”

Section: Dovepressmentioning

confidence: 99%

<p>Difference in Local Lung Movement During Tidal Breathing Between COPD Patients and Asthma Patients Assessed by Four-dimensional Dynamic-ventilation CT Scan</p>

Mochizuki¹,

Kawai²,

Morikawa³

et al. 2020

COPD

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Background: The validity of four-dimensional dynamic-ventilation CT scan for distinguishing COPD from asthma has not been established. Purpose: To assess whether four-dimensional dynamic-ventilation CT scan can aid in the diagnosis of COPD by comparing local lung movement during tidal breathing between COPD and asthma. Patients and Methods: Thirty-three COPD patients (30 males and three females; median age 74; range 44-89 years) and 11 asthma patients (five males and six females; median age 55; range: 32-75 years) underwent whole-lung dynamic-ventilation CT scan. CT data were reconstructed, one respiratory cycle to 10 phases, and in addtion we reconstructed threefold new phase data sets. We then analyzed local lung movement during tidal breathing using unpaired t-tests and chi-squared tests. Results: The local lung movement in COPD patients was significantly smaller than in asthma patients, especially in the ventral part of the lung. This was so even in patients who had mild emphysema (Goddard score <8). Conclusion: Quantitative evaluation using four-dimensional dynamic-ventilation CT scan demonstrated that local lung movement during tidal breathing, particularly in the ventral lung, was smaller in COPD than in asthma patients, which may help distinguish COPD from asthma.

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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 and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT

Cited by 175 publications

References 28 publications

Artificial intelligence in image reconstruction: The change is here

Artificial intelligence in image reconstruction: The change is here

<p>Difference in Local Lung Movement During Tidal Breathing Between COPD Patients and Asthma Patients Assessed by Four-dimensional Dynamic-ventilation CT Scan</p>

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

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