Imaging technology

About: Imaging technology is a research topic. Over the lifetime, 1450 publications have been published within this topic receiving 26186 citations.

The Effect of a New Angiographic Imaging Technology on Radiation Dose in Visceral Embolization Procedures

Abstract: Purpose:To evaluate the impact of a new angiographic imaging technology on radiation dose during visceral embolization procedures involving both fluoroscopy and digital subtraction angiography.Mate...

Skin Characterizations by Using Contact Capacitive Imaging and High-Resolution Ultrasound Imaging with Machine Learning Algorithms

Abstract: We present our latest research on skin characterizations by using Contact Capacitive Imaging and High-Resolution Ultrasound Imaging with Machine Learning algorithms. Contact Capacitive Imaging is a novel imaging technology based on the dielectric constant measurement principle, with which we have studied the skin water content of different skin sites and performed image classification by using pre-trained Deep Learning Neural Networks through Transfer Learning. The results show lips and nose have the lowest water content, whilst cheek, eye corner and under-eye have the highest water content. The classification yields up to 83.8% accuracy. High-Resolution Ultrasound Imaging is a state-of-the-art ultrasound technology, and can produce high-resolution images of the skin and superficial soft tissue to a vertical resolution of about 40 microns, with which we have studied the thickness of different skin layers, such as stratum corneum, epidermis and dermis, around different locations on the face and around different body parts. The results show the chin has the highest stratum corneum thickness, and the arm has the lowest stratum corneum thickness. We have also developed two feature-based image classification methods which yield promising results. The outcomes of this study could provide valuable guidelines for cosmetic/medical research, and methods developed in this study can also be extended for studying damaged skin or skin diseases. The combination of Contact Capacitive Imaging and High-Resolution Ultrasound Imaging could be a powerful tool for skin studies.

Super-resolution photoacoustic microscopy based on deep learning

Abstract: Photoacoustic imaging is an emerging imaging technology based on the photoacoustic effect. As a hybrid imaging technology that combines pure optical imaging and ultrasound imaging, it also has the advantages of optical imaging with high resolution and rich contrast. And the advantage of high penetration depth of acoustic imaging. With its advantages, photoacoustic imaging has extremely broad applications in biomedical testing, such as brain imaging and tumor imaging. Due to the optical diffraction limit of the objective lens, the image resolution of the obtained image is hard to be further improved, therefore, finer structural information is difficult to obtain. In order to solve this problem, we use an end-to-end convolutional neural network from low resolution to high resolution to further process the obtained low-resolution images to obtain optimized high-resolution image and improve the quality of imaging. A convolutional neural network is built on the pycharm platform through the open source Tensorflow library. Bicubic interpolation is used to preprocess the original data. Then we perform network training on the processed sample data and finally a series of photoacoustic microscopy images of cerebral blood vessels[1,2] were tested. The test results show that the resolution of the image is significantly improved, and a clearer image is obtained. The experimental results verify that this end-to-end convolutional neural network from low resolution to high resolution can effectively improve the resolution of photoacoustic imaging. This has laid a good foundation for the follow-up biomedical research[3] of photoacoustic imaging technology.

RSNA-MICCAI Panel Discussion: Machine Learning for Radiology from Challenges to Clinical Applications.

Abstract: On October 5, 2020, the Medical Image Computing and Computer Assisted Intervention Society (MICCAI) 2020 conference hosted a virtual panel discussion with members of the Machine Learning Steering Subcommittee of the Radiological Society of North America. The MICCAI Society brings together scientists, engineers, physicians, educators, and students from around the world. Both societies share a vision to develop radiologic and medical imaging techniques through advanced quantitative imaging biomarkers and artificial intelligence. The panel elaborated on how collaborations between radiologists and machine learning scientists facilitate the creation and clinical success of imaging technology for radiology. This report presents structured highlights of the moderated dialogue at the panel. Keywords: Back-Propagation, Artificial Neural Network Algorithms, Machine Learning Algorithms © RSNA, 2021.

Foundations and Applications of 3D Imaging

Abstract: Two-dimensional imaging through digital photography has been a main application of mobile computing devices, such as smart phones, during the last decade. Expanding the dimensions of digital imaging, the recent advances in 3D imaging technology are about to be combined with such smart devices, resulting in broadened applications of 3D imaging. This chapter presents the foundations of 3D imaging, that is, the relationship between disparity and depth in a stereo camera system, and it surveys a general workflow to build a 3D model from sensor data. In addition, recent advanced 3D imaging applications are introduced: hyperspectral 3D imaging, multispectral photometric stereo and stereo fusion of refractive and binocular stereo.