Pulmonary Nodule Detection in Chest Ct Using a Deep Learning-Based Reconstruction Algorithm

Franck, Caro; Snoeckx, Annemie; Spinhoven, Maarten; Addouli, Haroun El; Nicolay, Simon; Hoyweghen, Astrid Van; Deak, Paul; Zanca, Federica

doi:10.1093/rpd/ncab025

Cited by 14 publications

(10 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Channelized Hotelling Observer (CHO) model observer correlate well to human performances, especially for simple tasks such as a detection of a signal in a small region of interest for different anatomical area. Many studies have shown that model observer can correlate well with human performance include the detection of discs in white noise and clustered lumpy backgrounds [20], microcalcification in mammography [21], breast tomosynthesis [22] nodules in computed tomography (CT) ramp-noise spectrum [23], nodules in breast cone beam CT [24], lung region [25,11] and an abdominal CT acquisition [26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compared to partial model-based iterative reconstruction

Racine

Brat²,

Dufour³

et al. 2021

European Journal of Radiology

View full text Add to dashboard Cite

To compare deep learning (True Fidelity, TF) and partial model based Iterative Reconstruction (ASiR-V) algorithm for image texture, low contrast lesion detectability and potential dose reduction. Methods: Anthropomorphic phantoms (mimicking non-overweight and overweight patient), containing lesions of 6 mm in diameter with 20HU contrast, were scanned at five different dose levels (2,6,10,15,20 mGy) on a CT system, using clinical routine protocols for liver lesion detection. Images were reconstructed using ASiR-V 0% (surrogate for FBP), 60 % and TF at low, medium and high strength. Noise texture was characterized by computing a normalized Noise Power Spectrum filtered by an eye filter. The similarity against FBP texture was evaluated using peak frequency difference (PFD) and root mean square deviation (RMSD). Low contrast detectability was assessed using a channelized Hotelling observer and the area under the ROC curve (AUC) was used as figure of merit. Potential dose reduction was calculated to obtain the same AUC for TF and ASiR-V. Results: FBP-like noise texture was more preserved with TF (PFD from -0.043mm-1 to -0.09mm-1, RMSD from 0.12mm-1 to 0.21mm-1) than with ASiR-V (PFD equal to 0.12 mm-1, RMSD equal to 0.53mm-1), resulting in a sharper image. AUC was always higher with TF than ASIR-V. In average, TF compared to ASiR-V, enabled a radiation dose reduction potential of 7%, 25 % and 33 % for low, medium and high strength respectively. Conclusion: Compared to ASIR-V, TF at high strength does not impact noise texture and maintains low contrast liver lesions detectability at significant lower dose.

show abstract

Section: Introductionmentioning

confidence: 99%

Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compared to partial model-based iterative reconstruction

Racine

Brat²,

Dufour³

et al. 2021

European Journal of Radiology

View full text Add to dashboard Cite

show abstract

“…How to accurately locate and detect lung nodules so as to detect, diagnose, and treat lung nodules as early as possible is worthy of our attention [ 7 , 8 ]. Lung nodule detection algorithms generally mainly consist of image analysis and pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Computed Tomography Imaging to Diagnose the Lung Nodule and Treatment Effect of Radiofrequency Ablation

Guo

Li²,

Yang³

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

This study aimed to detect and diagnose the lung nodules as early as possible to effectively treat them, thereby reducing the burden on the medical system and patients. A lung computed tomography (CT) image segmentation algorithm was constructed based on the deep learning convolutional neural network (CNN). The clinical data of 69 patients with lung nodules diagnosed by needle biopsy and pathological comprehensive diagnosis at hospital were collected for specific analysis. The CT image segmentation algorithm was used to distinguish the nature and volume of lung nodules and compared with other computer aided design (CAD) software (Philips ISP). 69 patients with lung nodules were treated by radiofrequency ablation (RFA). The results showed that the diagnostic sensitivity of the CT image segmentation algorithm based on the CNN was obviously higher than that of the Philips ISP for solid nodules <5 mm (63 cases vs. 33 cases) ( P < 0.05 ); it was the same result for the subsolid nodule <5 mm (33 case vs. 5 cases) ( P < 0.05 ) that was slightly higher for solid and subsolid nodules with a diameter of 5–10 mm (37 cases vs. 28 cases) ( P < 0.05 ). In addition, the CNN algorithm can reach all detection for calcified nodules and pleural nodules (7 cases; 5 cases), and the diagnostic sensitivities were much better than those of Philips ISP (2 cases; 3 cases) ( P < 0.05 ). Patients with pulmonary nodules treated by RFA were in good postoperative condition, with a half-year survival rate of 100% and a one-year survival rate of 72.4%. Therefore, it could be concluded that the CT image segmentation algorithm based on the CNN could effectively detect and diagnose the lung nodules early, and the RFA could effectively treat the lung nodules.

show abstract

“…The SCPM-Net was tested on the LUNA16 dataset and achieved an average sensitivity of 89.2% at 7 FPs per image for lung nodule detection. Franck et al [ 121 ] investigated the effects on the performance of deep learning image reconstruction (DLIR) techniques on lung nodule detection in chest CT images. In this study, up to 6 artificial nodules were located within the lung phantom.…”

Section: Lung Cancer Prediction Using Deep Learningmentioning

confidence: 99%

Deep Learning Techniques to Diagnose Lung Cancer

Wang

2022

Cancers

View full text Add to dashboard Cite

Medical imaging tools are essential in early-stage lung cancer diagnostics and the monitoring of lung cancer during treatment. Various medical imaging modalities, such as chest X-ray, magnetic resonance imaging, positron emission tomography, computed tomography, and molecular imaging techniques, have been extensively studied for lung cancer detection. These techniques have some limitations, including not classifying cancer images automatically, which is unsuitable for patients with other pathologies. It is urgently necessary to develop a sensitive and accurate approach to the early diagnosis of lung cancer. Deep learning is one of the fastest-growing topics in medical imaging, with rapidly emerging applications spanning medical image-based and textural data modalities. With the help of deep learning-based medical imaging tools, clinicians can detect and classify lung nodules more accurately and quickly. This paper presents the recent development of deep learning-based imaging techniques for early lung cancer detection.

show abstract

Pulmonary Nodule Detection in Chest Ct Using a Deep Learning-Based Reconstruction Algorithm

Cited by 14 publications

References 27 publications

Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compared to partial model-based iterative reconstruction

Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compared to partial model-based iterative reconstruction

Deep Learning-Based Computed Tomography Imaging to Diagnose the Lung Nodule and Treatment Effect of Radiofrequency Ablation

Deep Learning Techniques to Diagnose Lung Cancer

Contact Info

Product

Resources

About