scholarly journals Applying Compressed Sensing Volumetric Interpolated Breath-Hold Examination and Spiral Ultrashort Echo Time Sequences for Lung Nodule Detection in MRI

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 93
Author(s):  
Yu-Sen Huang ◽  
Emi Niisato ◽  
Mao-Yuan Marine Su ◽  
Thomas Benkert ◽  
Ning Chien ◽  
...  
Keyword(s):  
Detection Rate ◽  
Lung Nodule ◽  
Chest Ct ◽  
Breath Hold ◽  
Limits Of Agreement ◽  
Ultrashort Echo Time ◽  
Nodule Size ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Echo Time

This prospective study aimed to investigate the ability of spiral ultrashort echo time (UTE) and compressed sensing volumetric interpolated breath-hold examination (CS-VIBE) sequences in magnetic resonance imaging (MRI) compared to conventional VIBE and chest computed tomography (CT) in terms of image quality and small nodule detection. Patients with small lung nodules scheduled for video-assisted thoracoscopic surgery (VATS) for lung wedge resection were prospectively enrolled. Each patient underwent non-contrast chest CT and non-contrast MRI on the same day prior to thoracic surgery. The chest CT was performed to obtain a standard reference for nodule size, location, and morphology. The chest MRI included breath-hold conventional VIBE and CS-VIBE with scanning durations of 11 and 13 s, respectively, and free-breathing spiral UTE for 3.5–5 min. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and normal structure visualizations were measured to evaluate MRI quality. Nodule detection sensitivity was evaluated on a lobe-by-lobe basis. Inter-reader and inter-modality reliability analyses were performed using the Cohen κ statistic and the nodule size comparison was performed using Bland–Altman plots. Among 96 pulmonary nodules requiring surgery, the average nodule diameter was 7.7 ± 3.9 mm (range: 4–20 mm); of the 73 resected nodules, most were invasive cancer (74%) or pre-invasive carcinoma in situ (15%). Both spiral UTE and CS-VIBE images achieved significantly higher overall image quality scores, SNRs, and CNRs than conventional VIBE. Spiral UTE (81%) and CS-VIBE (83%) achieved a higher lung nodule detection rate than conventional VIBE (53%). Specifically, the nodule detection rate for spiral UTE and CS-VIBE reached 95% and 100% for nodules >8 and >10 mm, respectively. A 90% detection rate was achieved for nodules of all sizes with a part-solid or solid morphology. Spiral UTE and CS-VIBE under-estimated the nodule size by 0.2 ± 1.4 mm with 95% limits of agreement from −2.6 to 2.9 mm and by 0.2 ± 1.7 mm with 95% limits of agreement from −3.3 to 3.5 mm, respectively, compared to the reference CT. In conclusion, chest CT remains the gold standard for lung nodule detection due to its high image resolutions. Both spiral UTE and CS-VIBE MRI could detect small lung nodules requiring surgery and could be considered a potential alternative to chest CT; however, their clinical application requires further investigation.

Lung Nodule Detectability of Artificial Intelligence-assisted CT Image Reading in Lung Cancer Screening

2021 ◽  
Vol 17 ◽  
Author(s):  
Yaping Zhang ◽  
Beibei Jiang ◽  
Lu Zhang ◽  
Marcel J.W. Greuter ◽  
Geertruida H. de Bock ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Detection Rate ◽  
Lung Nodule ◽  
Chest Ct ◽  
Lung Nodules ◽  
Lung Nodule Detection ◽  
Radiology Reports ◽  
Nodule Detection ◽  
Assisted Reading

Background: Artificial intelligence (AI)-based automatic lung nodule detection system improves the detection rate of nodules. It is important to evaluate the clinical value of AI system by comparing AI-assisted nodule detection with actu-al radiology reports. Objective: To compare the detection rate of lung nodules between the actual radiology reports and AI-assisted reading in lung cancer CT screening. Methods: Participants in chest CT screening from November to December 2019 were retrospectively included. In the real-world radiologist observation, 14 residents and 15 radiologists participated to finalize radiology reports. In AI-assisted reading, one resident and one radiologist reevaluated all subjects with the assistance of an AI system to lo-cate and measure the detected lung nodules. A reading panel determined the type and number of detected lung nodules between these two methods. Results: In 860 participants (57±7 years), the reading panel confirmed 250 patients with >1 solid nodule, while radiolo-gists observed 131, lower than 247 by AI-assisted reading (p<0.001). The panel confirmed 111 patients with >1 non-solid nodule, whereas radiologist observation identified 28, lower than 110 by AI-assisted reading (p<0.001). The accuracy and sensitivity of radiologist observation for solid nodules were 86.2% and 52.4%, lower than 99.1% and 98.8% by AI-assisted reading, respectively. These metrics were 90.4% and 25.2% for non-solid nodules, lower than 98.8% and 99.1% by AI-assisted reading, respectively. Conclusion: Comparing with the actual radiology reports, AI-assisted reading greatly improves the accuracy and sensi-tivity of nodule detection in chest CT, which benefits lung nodule detection, especially for non-solid nodules.

Efficacy of Ultrashort Echo Time Pulmonary MRI for Lung Nodule Detection and Lung-RADS Classification

Radiology ◽  
2021 ◽  
Author(s):  
Yoshiharu Ohno ◽  
Daisuke Takenaka ◽  
Takeshi Yoshikawa ◽  
Masao Yui ◽  
Hisanobu Koyama ◽  
...  
Keyword(s):  
Lung Nodule ◽  
Ultrashort Echo Time ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Echo Time

Deep Neural Networks Ensemble for Lung Nodule Detection on Chest CT Scans

2021 ◽  
Author(s):  
Pasquale Ardimento ◽  
Lerina Aversano ◽  
Mario Luca Bernardi ◽  
Marta Cimitile
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Lung Nodule ◽  
Chest Ct ◽  
Ct Scans ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Neural Networks Ensemble

scholarly journals Evaluation of the Effectiveness of Artificial Intelligence Chest CT Lung Nodule Detection Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fukui Liang ◽  
Caiqin Li ◽  
Xiaoqin Fu
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Deep Learning ◽  
Medical Imaging ◽  
Lung Nodule ◽  
Pulmonary Nodules ◽  
Chest Ct ◽  
Detection Sensitivity ◽  
Lung Nodule Detection ◽  
Nodule Detection

Lung cancer is one of the most malignant tumors. If it can be detected early and treated actively, it can effectively improve a patient’s survival rate. Therefore, early diagnosis of lung cancer is very important. Early-stage lung cancer usually appears as a solitary lung nodule on medical imaging. It usually appears as a round or nearly round dense shadow in the chest radiograph. It is difficult to distinguish lung nodules and lung soft tissues with the naked eye. Therefore, this article proposes a deep learning-based artificial intelligence chest CT lung nodule detection performance evaluation study, aiming to evaluate the value of chest CT imaging technology in the detection of noncalcified nodules and provide help for the detection and treatment of lung cancer. In this article, the Lung Medical Imaging Database Consortium (LIDC) was selected to obtain 536 usable cases based on inclusion criteria; 80 cases were selected for examination, artificial intelligence software, radiologists, and thoracic imaging specialists. Using 80 pulmonary nodules detection in each case, the pathological type of pulmonary nodules, nonlime tuberculous test results, detection sensitivity, false negative rate, false positive rate, and CT findings were individually analyzed, and the detection efficiency software of artificial intelligence was evaluated. Experiments have proved that the sensitivity of artificial intelligence software to detect noncalcified nodules in the pleural, peripheral, central, and hilar areas is higher than that of radiologists, indicating that the method proposed in this article has achieved good detection results. It has a better nodule detection sensitivity than a radiologist, reducing the complexity of the detection process.

Lung nodule detection in pediatric chest CT: Quantitative relationship between image quality and radiologist performance

2011 ◽  
Vol 38 (5) ◽  
pp. 2609-2618 ◽  
Author(s):  
Xiang Li ◽  
Ehsan Samei ◽  
Huiman X. Barnhart ◽  
Ana Maria Gaca ◽  
Caroline L. Hollingsworth ◽  
...  
Keyword(s):  
Image Quality ◽  
Lung Nodule ◽  
Quantitative Relationship ◽  
Chest Ct ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Pediatric Chest

Wavelet Compression of Low-Dose Chest CT Data: Effect on Lung Nodule Detection

Radiology ◽  
2003 ◽  
Vol 228 (1) ◽  
pp. 70-75 ◽  
Author(s):  
Jane P. Ko ◽  
Henry Rusinek ◽  
David P. Naidich ◽  
Georgeann McGuinness ◽  
Ami N. Rubinowitz ◽  
...  
Keyword(s):  
Low Dose ◽  
Lung Nodule ◽  
Chest Ct ◽  
Wavelet Compression ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Ct Data

scholarly journals Lung Nodule Detection on Chest CT: Evaluation of a Computer-Aided Detection (CAD) System

2005 ◽  
Vol 6 (2) ◽  
pp. 89 ◽  
Author(s):  
In Jae Lee ◽  
Gordon Gamsu ◽  
Julianna Czum ◽  
Ning Wu ◽  
Rebecca Johnson ◽  
...  
Keyword(s):  
Lung Nodule ◽  
Chest Ct ◽  
Computer Aided Detection ◽  
Cad System ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Computer Aided ◽  
Ct Evaluation

A comparison of the fusion model of deep learning neural networks with human observation for lung nodule detection and classification

2021 ◽  
pp. 20210222
Author(s):  
Ayşegül Gürsoy Çoruh ◽  
Bülent Yenigün ◽  
Çağlar Uzun ◽  
Yusuf Kahya ◽  
Emre Utkan Büyükceran ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Intraclass Correlation ◽  
Lung Nodule ◽  
Fusion Model ◽  
Roc Curve Analysis ◽  
Perfect Agreement ◽  
Nodule Size ◽  
Lung Nodule Detection ◽  
Nodule Detection

Objectives: To compare the diagnostic performance of a newly developed artificial intelligence (AI) algorithm derived from the fusion of convolution neural networks (CNN) versus human observers in the estimation of malignancy risk in pulmonary nodules. Methods: The study population consists of 158 nodules from 158 patients. All nodules (81 benign and 77 malignant) were determined to be malignant or benign by a radiologist based on pathologic assessment and/or follow-up imaging. Two radiologists and an AI platform analyzed the nodules based on the Lung-RADS classification. The two observers also noted the size, location, and morphologic features of the nodules. An intraclass correlation coefficient was calculated for both observers and the AI; ROC curve analysis was performed to determine diagnostic performances. Results: Nodule size, presence of spiculation, and presence of fat were significantly different between the malignant and benign nodules (p < 0.001, for all three). Eighteen (11.3%) nodules were not detected and analyzed by the AI. Observer 1, observer 2, and the AI had an AUC of 0.917 ± 0.023, 0.870 ± 0.033, and 0.790 ± 0.037 in the ROC analysis of malignity probability, respectively. The observers were in almost perfect agreement for localization, nodule size, and lung-RADS classification [κ (95% CI)=0.984 (0.961–1.000), 0.978 (0.970–0.984), and 0.924 (0.878–0.970), respectively]. Conclusion: The performance of the fusion AI algorithm in estimating the risk of malignancy was slightly lower than the performance of the observers. Fusion AI algorithms might be applied in an assisting role, especially for inexperienced radiologists. Advances in knowledge: In this study we proposed a fusion model using four state-of-art object detectors for lung nodule detection and discrimination. The use of fusion of deep learning neural networks might be used in a supportive role for radiologists when interpreting lung nodule discrimination

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