Purpose To quantitatively evaluate lung burden changes in patients with COVID-19 using serial CT scan by an automated deep learning method. Materials and Methods Patients with COVID-19 who underwen...

CT is crucial for diagnosing chest diseases, with image quality affected by spatial resolution. Thick-slice CT remains prevalent in practice due to cost considerations, yet its coarse spatial resolution may hinder accurate diagnoses. Our multicenter study develops a deep learning synthetic model with Convolutional-Transformer hybrid encoder-decoder architecture for generating thin-slice CT from thick-slice CT on a single center (1576 participants) and access the synthetic CT on three cross-regional centers (1228 participants). The qualitative image quality of synthetic and real thin-slice CT is comparable (p = 0.16). Four radiologists’ accuracy in diagnosing community-acquired pneumonia using synthetic thin-slice CT surpasses thick-slice CT (p < 0.05), and matches real thin-slice CT (p > 0.99). For lung nodule detection, sensitivity with thin-slice CT outperforms thick-slice CT (p < 0.001) and comparable to real thin-slice CT (p > 0.05). These findings indicate the potential of our model to generate high-quality synthetic thin-slice CT as a practical alternative when real thin-slice CT is preferred but unavailable.

Background: Cerebral venous thrombosis (CVT) is a rare cerebrovascular disease. Routine brain magnetic resonance imaging is commonly used to diagnose CVT. This study aimed to develop and evaluate a novel deep learning (DL) algorithm for detecting CVT using routine brain magnetic resonance imaging. Methods: Routine brain magnetic resonance imaging, including T1-weighted, T2-weighted, and fluid-attenuated inversion recovery images of patients suspected of CVT from April 2014 through December 2019 who were enrolled from a CVT registry, were collected. The images were divided into 2 data sets: a development set and a test set. Different DL algorithms were constructed in the development set using 5-fold cross-validation. Four radiologists with various levels of expertise independently read the images and performed diagnosis within the test set. The diagnostic performance on per-patient and per-segment diagnosis levels of the DL algorithms and radiologist’s assessment were evaluated and compared. Results: A total of 392 patients, including 294 patients with CVT (37±14 years, 151 women) and 98 patients without CVT (42±15 years, 65 women), were enrolled. Of these, 100 patients (50 CVT and 50 non-CVT) were randomly assigned to the test set, and the other 292 patients comprised the development set. In the test set, the optimal DL algorithm (multisequence multitask deep learning algorithm) achieved an area under the curve of 0.96, with a sensitivity of 96% (48/50) and a specificity of 88% (44/50) on per-patient diagnosis level, as well as a sensitivity of 88% (129/146) and a specificity of 80% (521/654) on per-segment diagnosis level. Compared with 4 radiologists, multisequence multitask deep learning algorithm showed higher sensitivity both on per-patient (all P <0.05) and per-segment diagnosis levels (all P <0.001). Conclusions: The CVT-detected DL algorithm herein improved diagnostic performance of routine brain magnetic resonance imaging, with high sensitivity and specificity, which provides a promising approach for detecting CVT.

Purpose: To develop a bounding box (BBOX)-based radiomics model for the preoperative diagnosis of occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC) patients.Materials and Methods: 599 AGC patients from 3 centers were retrospectively enrolled and were divided into training, validation, and testing cohorts. The minimum circumscribed rectangle of the ROIs for the largest tumor area (R_BBOX), the nonoverlapping area between the tumor and R_BBOX (peritumoral area; PERI) and the smallest rectangle that could completely contain the tumor determined by a radiologist (M_BBOX) were used as inputs to extract radiomic features. Multivariate logistic regression was used to construct a radiomics model to estimate the preoperative probability of OPM in AGC patients.Results: The M_BBOX model was not significantly different from R_BBOX in the validation cohort [AUC: M_BBOX model 0.871 (95% CI, 0.814–0.940) vs. R_BBOX model 0.873 (95% CI, 0.820–0.940); p = 0.937]. M_BBOX was selected as the final radiomics model because of its extremely low annotation cost and superior OPM discrimination performance (sensitivity of 85.7% and specificity of 82.8%) over the clinical model, and this radiomics model showed comparable diagnostic efficacy in the testing cohort.Conclusions: The BBOX-based radiomics could serve as a simpler reliable and powerful tool for the preoperative diagnosis of OPM in AGC patients. And M_BBOX-based radiomics is simpler and less time consuming.

Objective: We aimed to quantitatively study the characteristic of diaphragm and chest wall motion using free-breathing dynamic magnetic resonance imaging (D-MRI) in Chinese people with normal lung function. Methods: 74 male subjects (mean age, 37 ± 11 years old) were prospectively enrolled, and they underwent high-resolution CT(HRCT), pulmonary functional tests (PFTs), and D-MRI in the same day. D-MRI was acquired with a gradient-echo sequence during the quiet and deep breathing. The motion of the diaphragm and chest wall were respectively assessed by measuring thoracic anteroposterior diameter (AP), left–right diameter (LR), cranial–caudal diameter (CC), and thoracic area ratios between end-inspiration and end-expiration. The effect of age, body mass index (BMI), and smoking on respiratory muscle function was also analyzed. Results: The mean ratio of right and left AP was greater than that of LR on three transversal planes during both quiet and deep breathing. The mean ratio at the anterior diaphragm (AND, Quiet: 1.04 ± 0.03; Deep: 1.15 ± 0.09) was weaker than that of the apex (vs. APD, Quiet: 1.08 ± 0.05, p < 0.001; Deep: 1.29 ± 0.12, p < 0.001) and posterior diaphragm (vs. POD, Quiet: 1.09 ± 0.04, p < 0.001; Deep: 1.30 ± 0.12, p < 0.001) both in quiet and deep breathing. Compared with non-smokers, the left AP and thoracic area ratios in smokers were significantly decreased (p < 0.05). However, the ratios of AP, LR, CC, and thoracic area on each plane were similar among groups in different age and BMI. Conclusions: During both quiet and deep breathing, the chest wall motion is prominent in the anteroposterior direction. The motions of diaphragm apex and posterior diaphragm were more prominent than that of the anterior diaphragm. Smoking may affect the respiratory muscle mobility. Dynamic MRI can quantitatively evaluate the motion of respiratory muscles.

Purpose: To quantitatively analyze lung elasticity in idiopathic pulmonary fibrosis (IPF) using elastic registration based on 3-dimensional pulmonary magnetic resonance imaging (3D-PMRI) and to assess its’ correlations with the severity of IPF patients. Material and Methods: Thirty male patients with IPF (mean age: 62±6 y) and 30 age-matched male healthy controls (mean age: 62±6 y) were prospectively enrolled. 3D-PMRI was acquired with a 3-dimensional ultrashort echo time sequence in end-inspiration and end-expiration. MR images were registered from end-inspiration to end-expiration with the elastic registration algorithm. Jacobian determinants were calculated from deformation fields on color maps. The log means of the Jacobian determinants (Jac-mean) and Dice similarity coefficient were used to describe lung elasticity between 2 groups. Then, the correlation of lung elasticity with dyspnea Medical Research Council (MRC) score, exercise tolerance, health-related quality of life, lung function, and the extent of pulmonary fibrosis on chest computed tomography were analyzed. Results: The Jac-mean of IPF patients (−0.19, [IQR: −0.22, −0.15]) decreased (absolute value), compared with healthy controls (−0.28, [IQR: −0.31, −0.24], P<0.001). The lung elasticity in IPF patients with dyspnea MRC≥3 (Jac-mean: −0.15; Dice: 0.06) was significantly lower than MRC 1 (Jac-mean: −0.22, P=0.001; Dice: 0.10, P=0.001) and MRC 2 (Jac-mean: −0.21, P=0.007; Dice: 0.09, P<0.001). In addition, the Jac-mean negatively correlated with forced vital capacity % (r=−0.487, P<0.001), forced expiratory volume 1% (r=−0.413, P=0.004), TLC% (r=−0.488, P<0.001), diffusing capacity of the lungs for carbon monoxide % predicted (r=−0.555, P<0.001), 6-minute walk distance (r=−0.441, P=0.030) and positively correlated with respiratory symptoms (r=0.430, P=0.042). Meanwhile, the Dice similarity coefficient positively correlated with forced vital capacity % (r=0.577, P=0.004), forced expiratory volume 1% (r=0.526, P=0.012), diffusing capacity of the lungs for carbon monoxide % predicted (r=0.435, P=0.048), 6-minute walk distance (r=0.473, P=0.016), final peripheral oxygen saturation (r=0.534, P=0.004), the extent of fibrosis on chest computed tomography (r=−0.421, P=0.021) and negatively correlated with activity (r=−0.431, P=0.048). Conclusion: Lung elasticity decreased in IPF patients and correlated with dyspnea, exercise tolerance, health-related quality of life, lung function, and the extent of pulmonary fibrosis. The lung elasticity based on elastic registration of 3D-PMRI may be a new nonradiation imaging biomarker for quantitative evaluation of the severity of IPF.