OBJECTIVE: To investigate the correlation between glenohumeral joint congruence and stability in recurrent shoulder dislocations. METHODS: Eighty-nine patients (89 sides) with recurrent shoulder dislocation admitted between June 2022 and June 2023 and met the selection criteria were included as study subjects. There were 36 males and 53 females with an average age of 44 years (range, 20-79 years). There were 40 cases of left shoulder and 49 cases of right shoulder. The shoulder joints dislocated 2-6 times, with an average of 3 times. The three-dimensional models of the humeral head and scapular glenoid were reconstructed using Mimics 20.0 software based on CT scanning images. The glenoid track (GT), inclusion index, chimerism index, fit index, and Hill-Sachs interval (HSI) were measured, and the degree of on/off track was judged (K value, the difference between HSI and GT). Multiple linear regression was used to analyze the correlation between the degree of on/off track (K value) and inclusion index, chimerism index, and fit index. RESULTS: Multiple linear regression analysis showed that the K value had no correlation with the inclusion index ( P>0.05), and was positively correlated with the chimerism index and the fit index ( P<0.05). Regression equation was K=-24.898+35.982×inclusion index+8.280×fit index, R ²=0.084. CONCLUSION Humeral head and scapular glenoid bony area and curvature are associated with shoulder joint stability in recurrent shoulder dislocations. Increased humeral head bony area, decreased scapular glenoid bony area, increased humeral head curvature, and decreased scapular glenoid curvature are risk factors for glenohumeral joint stability.
Female ; Male ; Humans ; Adult ; Shoulder Joint/diagnostic imaging* ; Shoulder Dislocation/diagnostic imaging* ; Joint Dislocations ; Scapula/diagnostic imaging* ; Thorax

Precise segmentation of lung field is a crucial step in chest radiographic computer-aided diagnosis system. With the development of deep learning, fully convolutional network based models for lung field segmentation have achieved great effect but are poor at accurate identification of the boundary and preserving lung field consistency. To solve this problem, this paper proposed a lung segmentation algorithm based on non-local attention and multi-task learning. Firstly, an encoder-decoder convolutional network based on residual connection was used to extract multi-scale context and predict the boundary of lung. Secondly, a non-local attention mechanism to capture the long-range dependencies between pixels in the boundary regions and global context was proposed to enrich feature of inconsistent region. Thirdly, a multi-task learning to predict lung field based on the enriched feature was conducted. Finally, experiments to evaluate this algorithm were performed on JSRT and Montgomery dataset. The maximum improvement of Dice coefficient and accuracy were 1.99% and 2.27%, respectively, comparing with other representative algorithms. Results show that by enhancing the attention of boundary, this algorithm can improve the accuracy and reduce false segmentation.
X-Rays ; Algorithms ; Diagnosis, Computer-Assisted ; Thorax/diagnostic imaging* ; Lung/diagnostic imaging* ; Image Processing, Computer-Assisted

Percutaneous pulmonary puncture guided by computed tomography (CT) is one of the most effective tools for obtaining lung tissue and diagnosing lung cancer. Path planning is an important procedure to avoid puncture complications and reduce patient pain and puncture mortality. In this work, a path planning method for lung puncture is proposed based on multi-level constraints. A digital model of the chest is firstly established using patient's CT image. A Fibonacci lattice sampling is secondly conducted on an ideal sphere centered on the tumor lesion in order to obtain a set of candidate paths. Finally, by considering clinical puncture guidelines, an optimal path can be obtained by a proposed multi-level constraint strategy, which is combined with oriented bounding box tree (OBBTree) algorithm and Pareto optimization algorithm. Results of simulation experiments demonstrated the effectiveness of the proposed method, which has good performance for avoiding physical and physiological barriers. Hence, the method could be used as an aid for physicians to select the puncture path.
Humans ; Lung/diagnostic imaging* ; Lung Neoplasms/diagnostic imaging* ; Punctures ; Thorax ; Tomography, X-Ray Computed

In this article, the modern concepts of computer-aided diagnosis (CAD), the methods of pulmonary nodule detection, and facts derived from the literature on the pulmonary nodule differential CAD are compiled in one source and described in some detail. Several issues in lung cancer (LC) epidemiology and early diagnosis are discussed. Analysis of research done so far shows evidence that various CAD systems can be successfully applied to chest radiographs, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). These modalities can serve as useful potential alternative tools available to practicing medical professionals performing routine diagnostics.
Diagnosis, Differential ; Diagnostic Imaging ; Early Diagnosis ; Lung Neoplasms ; Magnetic Resonance Imaging ; Positron-Emission Tomography ; Thorax

To determine the relevance between MDCT features and anatomic-pathological basis of lymphoid neoplasm in cervico-thoracic junctional region, we performed a retrospective analysis of 69 patients with lymphoid neoplasm (lymphoma: 41 patients; metastatic tumor: 28 patients) involving the cervico-thoracic junctional region for MDCT features and distribution of lesions. The relevance between MDCT features and the anatomic-pathological basis in this region were evaluated. Among all the 41 patients with lymphoma, 29 with NHL (70.7%), 12 with HD (29.3%). The lymphomatous lymphadenopathy mainly located in superficial lateral cervix (51.2%, 21/41) ,deep jugular chain (65.9%, 27/41), supraclavicular fossa (75.6%, 31/41), paratrachea space in anterior mediastinum (46.3%, 19/41), around aortic arch (56.1%, 23/41), aortopulmonary window (53.7%, 22/41), upper anterior mediastinum (41.5%, 17/41), subcarinal space (26.8%, 11/41) and paraesophageal space (17.1%, 7/41). 28 patients had metastatic lymphoid tumor. The primary tumor were nasopharynx tumor (5 patients), thyroid cancer (7 patients), lung cancer (10 patients), and esophageal cancer (6 patients). Most metastasis took stage by stage in the way of lymphatic return, but a minority of cases migrated jumpily. The main metastatic sites were: beside jugular chain (82.1%), supraclavicular fossa (75%), paratracheal in anterior mediastinum (60.7%), upper anterior mediastinum (64.3%), beside aortic arch (35.7%), aortopulmonary window (39.2%), and paraesophageal space (28.6%). So lymphoid neoplasms in cervico-thoracic junctional region were involving both lower cervix and upper thorax simultaneously. The MDCT features and main distribution of lesions correlated with the anatomic-pathological characteristics in this region.
Adolescent ; Adult ; Aged ; Child ; Female ; Humans ; Lung Neoplasms ; diagnostic imaging ; pathology ; Lymphatic Metastasis ; diagnostic imaging ; Lymphoma ; diagnostic imaging ; Male ; Middle Aged ; Multidetector Computed Tomography ; Neck ; Thorax ; Young Adult

A 2-year-old, female Pomeranian dog was referred for dyspnea. Thoracic radiographs revealed left-sided mediastinal shift, increased soft tissue opacity in the caudal aspect of left thorax with loss of the left diaphragmatic silhouette, and dorsal elevation of mediastinal structures and heart from the sternum by lung tissue. The left main bronchus was visualized as an air-bronchogram and observed to abruptly discontinue at the level of the 10th rib. Thoracic computed tomography (CT) revealed absence of the left lung parenchyma and left pulmonary vessels with a rudimentary left main bronchus. The case was congenital pulmonary aplasia diagnosed via radiography and CT.
Animals ; Bronchi ; Child, Preschool ; Diagnostic Imaging* ; Dogs* ; Dyspnea ; Female ; Heart ; Humans ; Lung ; Radiography ; Ribs ; Sternum ; Thorax

Diaphragm excursion,diaphragm thickness,and diaphragmatic thickening fraction are three indicators for evaluating the two hemidiaphragms by ultrasound.Diaphragm ultrasound has been widely applied in clinical practice including anesthesia management.It can help to diagnose postoperative residual curarisation and identify patients at a high risk of suffering from postoperative pulmonary complications.It can serve to recognize patients with diaphragm paralysis due to surgical or anesthetic factors as early as possible.Moreover,diaphragm ultrasound plays a role in preoperative pulmonary function assessment for special sufferers with chronic obstructive pulmonary disease,adolescent idiopathic scoliosis,or neuromuscular disease.Apart from these,diaphragm ultrasound can give anesthesiologists and colleagues in intensive care unit an important clue for extubation and weaning from mechanical ventilation of patients.
Adolescent ; Humans ; Diaphragm/diagnostic imaging* ; Prospective Studies ; Ultrasonography ; Thorax ; Postoperative Complications ; Anesthesia

Objective To prospectively investigate the radiation dose and image quality of pediatric chest CT using Sn100 kV on a third-generation dual-source CT (DSCT)in comparison to standard 100 kV chest CT. Methods From December 12,2015 to June 30,2016,45 consecutive pediatric patients referred for non-contrast chest CT scan in Peking Union Medical College Hospital were prospectively enrolled as study group. They were examined at 100 kV with a dedicated tin filter on a third-generation DSCT. These patients were retrospectively matched with 45 patients who were examined on a second-generation DSCT at 100 kV without tin filter. The radiation dose as well as the lung and mediastinal window image quality(IQ)of the two groups were compared and analyzed statistically. IQ was evaluated using a five-point scale (1=unevaluable,5=excellent). Differences of radiation dose and noise between the two groups were determined with variance analysis and t test,IQ with Mann-Whitney U test,and the consistency of diagnosis with Kappa test. Results The average CT dose index volume of the study group was (0.24±0.11)mGy,which was decreased by 92% compared with the control group [(3.10+1.18)mGy] (t=16.287,P=0.000). Mean dose-length product and mean effective dose for study group were significantly lower than those of control group [(7.13±4.72)mGy·cm vs. (84.78±46.78)mGy·cm,t=11.077,P=0.000;(0.11±0.06)mSv vs.(1.23±0.61)mSv,t=12.334,P=0.000]. There was no significant difference between the two groups in terms of image noise (t=-0.003,P=0.397)and contrast to noise ratio (t=0.545,P=0.488). There was no significant difference between the two groups in lung window IQ (doctor 1:U=796.000,P=0.055;doctor 2:U=889.500,P=0.277),while significant difference was seen concerning of the mediastinal window IQ (doctor 1:U=305.000,P=0.000;doctor 2:U=276.500,P=0.000). Referring to the lung window,the median IQ for the study group and control group was 4 (3-5)and 4 (3-5),respectively. All imaging findings had acceptable IQ. The breath motion artifacts (χ=13.846,P=0.001)and heart beat artifacts (χ=53.519,P=0.000)of the study group were significantly lower than those of the control group. Conclusion Compared with standard 100 kV chest CT,the use of tin-filtered Sn100 kV on a third-generation DSCT provided 92% dose reduction in pediatric chest CT examinations while maintaining diagnostically acceptable lung window images.
Artifacts ; Child ; Feasibility Studies ; Humans ; Lung ; diagnostic imaging ; Radiation Dosage ; Radiographic Image Interpretation, Computer-Assisted ; Retrospective Studies ; Thorax ; diagnostic imaging ; Tomography, X-Ray Computed ; methods

Objective To evaluate the image quality and radiation dose of third-generation dual-source CT with tin filtration for spectral shaping and iterative reconstructions.Methods Thirty-five patients underwent low-dose CT (LDCT) for lung cancer screening on second-generation dual-source CT and follow-ups on third-generation dual-source CT. Image quality and radiation dose were compared between the two examinations.ResultsThe radiation dose of third-generation dual-source CT [dose-length product (DLP)(49.7±18.2)mGy·cm, effective dose (ED)(0.73±0.26)mSv] was lower than second-generation dual-source CT [DLP (86.37±13.44) mGy·cm, ED(1.20±0.42)mSv](t=6.01, P=0.000;t=6.57, P=0.000). The objective image noise of second-generation dual-source CT [(25.7±2.9)HU] was higher than that of third-generation dual-soure CT[(18.6±4.2)HU](t=5.24,P=0.000).The subjective image noise of second-generation dual-source CT [(4.60±0.49)scores] was significantly lower than that of third-generation dual-source CT [(4.80±0.40)scores] (t=4.15, P=0.000). Conclusion Chest CT for the detection of pulmonary nodules can be performed with third-generation dual-source CT that produces high image quality and low radiation dose when using a stellar infinity detector with spectral shaping.
Early Detection of Cancer ; methods ; Humans ; Lung Neoplasms ; diagnostic imaging ; Radiation Dosage ; Radiographic Image Interpretation, Computer-Assisted ; Thorax ; diagnostic imaging ; Tomography, X-Ray Computed ; methods

In recent years, deep learning has been used in many researches in cancer screening based on medical imaging. Among cancer screening using optical imaging, melanoma detection is the biggest concern. Stanford University researchers used CNNs (convolutional neural networks) to classify skin lesions comparing with 21 dermatologists for 2 tasks. CNN performed better than all the dermatologists' tasks. Finding pulmonary nodules on chest X-ray has the longest history in cancer screening using medical imaging and neural network technology began to be applied before the deep learning technology matured as it is now. But, the applications were mainly focused on screening in CT images. There is relatively few research on pulmonary nodule detection using deep learning in chest X-rays. For breast cancer screening in mammography, adoption of neural network technologies has already begun early. Many studies have shown that tumor detection using CNNs is useful in breast cancer screening. Most of the results are from mammography, but studies using tomosynthesis, ultrasound, and MRI have also been published. Although imaging modality and target cancer are different, we can see that there are similar kinds of future challenges. First, it is not easy to acquire a large amount of medical image data required for deep learning. Second, it is difficult to learn if there are many medical image data but they are not properly labeled. Finally, there is a need for technologies that can use different imaging modalities at the same time, link with electronic health records, and use genetic information for more comprehensive screening.
Breast Neoplasms ; Diagnostic Imaging* ; Early Detection of Cancer* ; Electronic Health Records ; Learning* ; Magnetic Resonance Imaging ; Mammography ; Mass Screening ; Melanoma ; Optical Imaging ; Skin ; Skin Neoplasms ; Thorax ; Ultrasonography