Influence of CT effective dose and convolution kernel on the detection of pulmonary nodules in different artificial intelligence software systems: A phantom study

Fu, B.; Wang, Guo‐shu; Wu, Mingyue; Li, Wang-jia; Zheng, Yineng; Chu, Zhi‐gang; Lv, Fajin

doi:10.1016/j.ejrad.2020.108928

Cited by 20 publications

(29 citation statements)

References 32 publications

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“…A severe limitation to traditional CAD approaches is an inability to acquire knowledge from new information. Thus, various research groups investigate deep learning ML approaches (23)(24)(25). In a proof-of-concept study reported in (25), the potential of deep learning ML software system has been investigated with the conclusion that results strongly agree with expert radiologist determination of lung nodule detection.…”

Section: Discussionmentioning

confidence: 99%

“…The effective dose and convolution kernels effects on the detection of pulmonary nodules in IR were investigated in studies with anthropomorphic lung phantoms by quantifying the perception of expert radiologists' at a Likert scale, contrast-to-noise and signal-to-noise ratio (SNR) (19)(20)(21). Other studies evaluated machine learning (ML), computer-aided detection (CAD) software systems or commercially available deep learning ML based CAD systems by investigating receiver-operating-curve measures in artificial or ex-vivo lung phantoms (22)(23)(24)(25). In the future ML algorithms might enable risk assessment to determine the indication for lung scans in clinical routine and even define CT imaging parameters.…”

Section: Introductionmentioning

confidence: 99%

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Computed tomography lung-cancer screening: does iterative reconstruction matter?

König¹,

Kühl²,

Zylka³

2021

Shanghai Chest

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Background: By reviewing image quality and diagnostic perception, the suitability of a statistical modelbased iterative reconstruction algorithm in conjunction with low-dose computed tomography for lung cancer screening is investigated.Methods: Artificial lung nodules shaped as spheres and spiculated spheres made from material with calibrated Hounsfield units were attached on marked positions in the lung structure of anthropomorphic phantoms. The phantoms were scanned using standard high contrast, and two low-dose computed tomography protocols: low-dose and ultra-low-dose. For the reconstruction, the filtered back projection and the iterative reconstruction algorithm ADMIRE at different strength levels (S1-S5) and the kernels Bl57, Br32, Br69 were used. Expert radiologists assessed image quality by performing 4-field-ranking tests and reading all image series to examine the aptitude for the detectability of lung nodules. Signal-to-noise ratio was investigated as objective image quality parameter. Results:In ranking tests for lung foci detection expert radiologists prefer medium to high iterative reconstruction strength levels. For the standard clinical kernel Bl57 and varying phantom diameter, a noticeable preference for S4 was detected. Experienced radiologists graded filtered back projection reconstructed images with the highest perceptibility. Less experienced readers assessed filtered back projection and iterative reconstruction equally with the highest grades for the Bl57 kernel. Independently of the dose protocol, the signal-to-noise ratio increases with the iterative reconstruction strength level, specifically for Br69 and Bl57.Conclusions: Subjective image perception does not significantly correlate with the experience of the radiologist, which presumably mirrors reader's training and accustomed reading adjustments. Regarding signal-to-noise ratio, iterative reconstruction outperforms filtered back projection for spheres and spiculated spheres. Iterative reconstruction matters. It promises to be an alternative to filtered back projection allowing for lung-cancer screening at markedly decreased radiation exposure but comparable or even improved image quality.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computed tomography lung-cancer screening: does iterative reconstruction matter?

König¹,

Kühl²,

Zylka³

2021

Shanghai Chest

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“…One hurdle is the differences between characteristics of the target population relative to the population that the models were trained on. Fu et al investigated the impact of differences in dose and reconstruction kernel on AI-based detection of simulated pulmonary nodules in chest phantoms scanned using CT [ 23 ]. The authors found that while differences in dose did not affect the volume measurements using the four evaluated AI algorithms, the kernel had a significant effect.…”

Section: Emerging Trends and New Directionsmentioning

confidence: 99%

Notable Papers and New Directions in Sensors, Signals, and Imaging Informatics

Hsu¹,

Baumgartner

Deserno

2021

Yearb Med Inform

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Summary Objective: To identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2020. Method: A broad literature search was conducted on PubMed and Scopus databases. We combined Medical Subject Heading (MeSH) terms and keywords to construct particular queries for sensors, signals, and image informatics. We only considered papers that have been published in journals providing at least three articles in the query response. Section editors then independently reviewed the titles and abstracts of preselected papers assessed on a three-point Likert scale. Papers were rated from 1 (do not include) to 3 (should be included) for each topical area (sensors, signals, and imaging informatics) and those with an average score of 2 or above were subsequently read and assessed again by two of the three co-editors. Finally, the top 14 papers with the highest combined scores were considered based on consensus. Results: The search for papers was executed in January 2021. After removing duplicates and conference proceedings, the query returned a set of 101, 193, and 529 papers for sensors, signals, and imaging informatics, respectively. We filtered out journals that had less than three papers in the query results, reducing the number of papers to 41, 117, and 333, respectively. From these, the co-editors identified 22 candidate papers with more than 2 Likert points on average, from which 14 candidate best papers were nominated after intensive discussion. At least five external reviewers then rated the remaining papers. The four finalist papers were found using the composite rating of all external reviewers. These best papers were approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. Conclusions. Sensors, signals, and imaging informatics is a dynamic field of intense research. The four best papers represent advanced approaches for combining, processing, modeling, and analyzing heterogeneous sensor and imaging data. The selected papers demonstrate the combination and fusion of multiple sensors and sensor networks using electrocardiogram (ECG), electroencephalogram (EEG), or photoplethysmogram (PPG) with advanced data processing, deep and machine learning techniques, and present image processing modalities beyond state-of-the-art that significantly support and further improve medical decision making.

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“…Computer-aided diagnostic systems have been used for automatic detection and analysis of pulmonary nodules for many years (23). With the development of computer technology and the improvement of hardware, Artificial Intelligence (AI) is now widely used in imaging.…”

Section: Introductionmentioning

confidence: 99%

The influence of a deep learning image reconstruction algorithm on the image quality and auto-analysis of pulmonary nodules at ultra-low dose chest CT: a phantom study

Yao¹,

Guo²,

Li³

et al. 2022

Quant Imaging Med Surg

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Background: To investigate the effect of a new deep learning image reconstruction (DLIR) algorithm on the detection, characterization and image quality of pulmonary nodules (PNs) in ultra-low dose chest computed tomography (CT) in comparison with the adaptive statistical iterative reconstruction (ASIR-V) algorithm.Methods: Nine artificial pulmonary nodules [six ground glass nodules (GGNs) and three solid nodules (SNs); density: −800 HU, −630 HU, 100 HU; diameter: 12 mm, 10 mm, 8 mm] were randomly placed in a thorax anthropomorphic phantom (Lungman, Kyoto Kagaku Inc.) and scanned on a 256-row CT (Revolution CT, GE Healthcare). Eight scans were performed at 70 kVp with different tube currents (20, 30, 50, 70, 90, 100, 120, 150 mA). Raw data were reconstructed using the filtered back projection (FBP), ASIR-V (30%, 50%, 80%) and DLIR (Low, Medium, High; TrueFidelity™) at 0.625 mm thickness. The effective radiation dose was recorded. All images were automatically analyzed using a commercially available artificial intelligence software (Intelligent 4D Imaging System for Chest CT 5.5, YITU Healthcare) and CT value, standard deviation (SD), long and short diameters of each nodule and SD of air (background) were measured.The detection rate, deformation degree (long diameter/short diameter), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of pulmonary nodules were calculated.Results: Nodule CT values were the same in all mA settings for all three types of reconstruction algorithms (all P>0.05). DLIR groups had significantly lower SD and higher SNR and CNR values, with better overall image quality than ASIR-V and FBP groups at each mA, ranging from 65-85% reduction in SD, 67-83% increase in SNR with DLIR-H over 50%ASIR-V and 75-91% reduction in SD and 77-89% increase in SNR with DLIR-H over FBP (all P<0.05). At ultra-low dose conditions (30 mA), the DLIR-H images had the highest detection rate of PNs (100%). In addition, the DLIR-M had a minimal negative effect on the characterization of PNs.Conclusions: DLIR algorithm can be a potential reconstruction technique to optimize image quality and improve detection rate of PNs in ultra-low dose lung screening

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Influence of CT effective dose and convolution kernel on the detection of pulmonary nodules in different artificial intelligence software systems: A phantom study

Cited by 20 publications

References 32 publications

Computed tomography lung-cancer screening: does iterative reconstruction matter?

Computed tomography lung-cancer screening: does iterative reconstruction matter?

Notable Papers and New Directions in Sensors, Signals, and Imaging Informatics

The influence of a deep learning image reconstruction algorithm on the image quality and auto-analysis of pulmonary nodules at ultra-low dose chest CT: a phantom study

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