scholarly journals Large-Deformation Image Registration of CT-TEE for Surgical Navigation of Congenital Heart Disease

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Shuiping Gou ◽  
Linlin Chen ◽  
Yu Gu ◽  
Liyu Huang ◽  
Meiping Huang ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Congenital Heart ◽  
Surgical Navigation ◽  
Probability Model ◽  
Target Registration Error ◽  
Coarse Registration ◽  
Preoperative Ct ◽  
Novel Method ◽  
Anatomical Information

The surgical treatment of congenital heart disease requires navigational assistance with transesophageal echocardiography (TEE); however, TEE images are often difficult to interpret and provide very limited anatomical information. Registering preoperative CT images to intraoperative TEE images provides surgeons with richer and more useful anatomical information. Yet, CT and TEE images differ substantially in terms of scale and geometry. In the present research, we propose a novel method for the registration of CT and TEE images for navigation during surgical repair of large defects in patients with congenital heart disease. Valve data was used for the coarse registration to determine the basic location. This was followed by the use of an enhanced probability model map to overcome gray-level differences between the two imaging modalities. Finally, the rapid optimization of mutual information was achieved by migrating parameters. This method was tested on a dataset of 240 images from 12 infant, children (≤ 3 years old), and adult patients with congenital heart disease. Compared to the “bronze standard” registration, the proposed method was more accurate with an average Dice coefficient of 0.91 and an average root mean square of target registration error of 1.2655 mm.

Artificial Intelligence–based Computed Tomography Processing Framework for Surgical Telementoring of Congenital Heart Disease

2021 ◽  
Vol 17 (4) ◽  
pp. 1-24
Author(s):  
Wen Xie ◽  
Zeyang Yao ◽  
Erchao Ji ◽  
Hailong Qiu ◽  
Zewen Chen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Developing Countries ◽  
Congenital Heart Disease ◽  
Heart Disease ◽  
Video Transmission ◽  
Congenital Heart ◽  
Developed Countries ◽  
High Resource ◽  
Surgical Teams ◽  
Anatomical Information

Congenital heart disease (CHD) is the most common birth defect, accounting for one-third of all congenital birth defects. As with complicated intracardiac structural abnormalities, CHD is usually treated with surgical repair, and computed tomography (CT) is the main examination method for diagnosis of CHD and also provides anatomical information to surgeons. Currently, there exists a serious shortage of professional surgeons in developing countries. Compared with developed countries where large hospitals and cardiovascular disease centers have professional surgical teams with rich treatment experience, surgeons in developing countries and remote areas suffer from lack of professional surgical skills resulting with low surgical quality and high mortality. Recently, surgical telementoring has been popular to tackle the above problems, in which less-skilled surgeons can get real-time guidance from skilled surgeons remotely through audio and video transmission. However, there still exists difficulties in applying telementoring to CHD surgeries including high resource consumption on medical data transmission and storage, large image noise, and inconvenient and inefficient discussion between surgeons on CT. In this article, we proposed a framework with an image compression module, an image denoising module, and an image segmentation module based on CT images in CHD. We evaluated the above three modules and compared them with existing works, respectively, and the results show that our methods achieve much better performance. Furthermore, with 3D printing, VR technology, and 5G communications, our framework was successfully used in a real case study to treat a patient who needed surgical treatment.

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