This paper explores the relationship between the cardiac volume and time, which is applied to control dynamic heart phantom. We selected 50 patients to collect their cardiac computed tomography angiography (CTA) images, which have 20 points in time series CTA images using retrospective electrocardiograph gating, and measure the volume of four chamber in 20-time points with cardiac function analysis software. Then we grouped patients by gender, age, weight, height, heartbeat, and utilize repeated measurement design to conduct statistical analyses. We proposed structured sparse learning to estimate the mathematic expression of cardiac volume variation. The research indicates that all patients’ groups are statistically significant in time factor (P = 0.000); there are interactive effects between time and gender groups in left ventricle (F = 8.597, P = 0.006) while no interactive effects in other chambers with the remaining groups; and the different weight groups’ volume is statistically significant in right ventricle (F = 9.004, P = 0.005) while no statistical significance in other chambers with remaining groups. The accuracy of cardiac volume and time relationship utilizing structured sparse learning is close to the least square method, but the former’s expression is more concise and more robust. The number of nonzero basic function of the structured sparse model is just 2.2 percent of that of least square model. Hence, the work provides more the accurate and concise expression of the cardiac for cardiac motion simulation.
Pectus carinatum (PC) is one of the most common chest wall anomalies, which is characterized by the protrusion of the anterior chest wall including the sternum and adjacent costal cartilages. Mildly patients suffer from mental problems such as self-abasement, while severely suffering patients are disturbed by significant cardiopulmonary symptoms. The traditional Haller index, which is widely used clinically to evaluate the severity of PC, is deficient in diagnosis efficiency and classification. This paper presents an improved Haller index algorithm for PC: first, the contour of the patient chest in the axial computed tomography (CT) slice where the most convex thorax presents is extracted; and then a cubic B-spline curve is employed to fit the extracted contour followed by an eclipse fitting procedure; finally, the improved Haller index and the classification index are automatically calculated based on the analytic curves. The results of CT data analysis using 22 preoperative and postoperative patient CT datasets show that the proposed diagnostic index for PC can diagnose and classify PC patients correctly, which confirms the feasibility of the evaluation index. Furthermore, digital measurement techniques can be employed to improve the diagnostic efficiency of PC, achieving one small step towards the computer-aided intelligent diagnosis and treatment for pediatric chest wall malformations.
Lung nodules are the main manifestation of early lung cancer. So accurate detection of lung nodules is of great significance for early diagnosis and treatment of lung cancer. However, the rapid and accurate detection of pulmonary nodules is a challenging task due to the complex background, large detection range of pulmonary computed tomography (CT) images and the different sizes and shapes of pulmonary nodules. Therefore, this paper proposes a multi-scale feature fusion algorithm for the automatic detection of pulmonary nodules to achieve accurate detection of pulmonary nodules. Firstly, a three-layer modular lung nodule detection model was designed on the deep convolutional network (VGG16) for large-scale image recognition. The first-tier module of the network is used to extract the features of pulmonary nodules in CT images and roughly estimate the location of pulmonary nodules. Then the second-tier module of the network is used to fuse multi-scale image features to further enhance the details of pulmonary nodules. The third-tier module of the network was fused to analyze the features of the first-tier and the second-tier module of the network, and the candidate box of pulmonary nodules in multi-scale was obtained. Finally, the candidate box of pulmonary nodules under multi-scale was analyzed with the method of non-maximum suppression, and the final location of pulmonary nodules was obtained. The algorithm is validated by the data of pulmonary nodules on LIDC-IDRI common data set. The average detection accuracy is 90.9%.
Image registration is of great clinical importance in computer aided diagnosis and surgical planning of liver diseases. Deep learning-based registration methods endow liver computed tomography (CT) image registration with characteristics of real-time and high accuracy. However, existing methods in registering images with large displacement and deformation are faced with the challenge of the texture information variation of the registered image, resulting in subsequent erroneous image processing and clinical diagnosis. To this end, a novel unsupervised registration method based on the texture filtering is proposed in this paper to realize liver CT image registration. Firstly, the texture filtering algorithm based on L0 gradient minimization eliminates the texture information of liver surface in CT images, so that the registration process can only refer to the spatial structure information of two images for registration, thus solving the problem of texture variation. Then, we adopt the cascaded network to register images with large displacement and large deformation, and progressively align the fixed image with the moving one in the spatial structure. In addition, a new registration metric, the histogram correlation coefficient, is proposed to measure the degree of texture variation after registration. Experimental results show that our proposed method achieves high registration accuracy, effectively solves the problem of texture variation in the cascaded network, and improves the registration performance in terms of spatial structure correspondence and anti-folding capability. Therefore, our method helps to improve the performance of medical image registration, and make the registration safely and reliably applied in the computer-aided diagnosis and surgical planning of liver diseases.