AIMC Topic: Photoacoustic Techniques

Clear Filters Showing 51 to 56 of 56 articles

Hybrid Neural Network for Photoacoustic Imaging Reconstruction.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Photoacoustic imaging (PAI) is an emerging noninvasive imaging modality combining the advantages of ultrasound imaging and optical imaging. Image reconstruction is an essential topic in photoacoustic imaging, which is unfortunately an ill-posed probl...

Photoacoustic Source Detection and Reflection Artifact Removal Enabled by Deep Learning.

IEEE transactions on medical imaging
Interventional applications of photoacoustic imaging typically require visualization of point-like targets, such as the small, circular, cross-sectional tips of needles, catheters, or brachytherapy seeds. When these point-like targets are imaged in t...

Model-Based Learning for Accelerated, Limited-View 3-D Photoacoustic Tomography.

IEEE transactions on medical imaging
Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed up. In this paper, we present a deep neural network that is specifically designed to prov...

Context encoding enables machine learning-based quantitative photoacoustics.

Journal of biomedical optics
Real-time monitoring of functional tissue parameters, such as local blood oxygenation, based on optical imaging could provide groundbreaking advances in the diagnosis and interventional therapy of various diseases. Although photoacoustic (PA) imaging...

Deep neural network-based bandwidth enhancement of photoacoustic data.

Journal of biomedical optics
Photoacoustic (PA) signals collected at the boundary of tissue are always band-limited. A deep neural network was proposed to enhance the bandwidth (BW) of the detected PA signal, thereby improving the quantitative accuracy of the reconstructed PA im...

Tuning to optimize SVM approach for assisting ovarian cancer diagnosis with photoacoustic imaging.

Bio-medical materials and engineering
Support vector machine (SVM) is one of the most effective classification methods for cancer detection. The efficiency and quality of a SVM classifier depends strongly on several important features and a set of proper parameters. Here, a series of cla...