This is the first special issue dedicated to the theme of "Machine Learning for Image Reconstruction". Computer vision and image analysis are great examples of machine learning, especially deep learning. While computer vision and image analysis deal with existing images and produce features of these images (images to features), tomographic image reconstruction produces images of internal structures from measurement data, which are various features (attenuated/non-attenuated line integrals, Fourier/harmonic components, echoed/scattered/transmitted ultrasound signatures, diffused/excited/interfered light signals, and so on) of the underlying images (features to images). Recently, machine learning, especially deep learning, techniques are being actively developed worldwide for tomographic image reconstruction, which has become a new area of research as evidenced by the 20 high-quality papers included in this special issue, as well as similar publications in other journals and conferences. In addition to well-known analytic and iterative methods for tomographic image reconstruction, machine learning is an emerging approach for image reconstruction, and likewise image reconstruction is a new frontier of machine learning. There are exciting research and application opportunities ahead for smart imaging and precision medicine.
TMI Special Issue on Machine Learning for Image Reconstruction
The Functional and Molecular Optical Imaging Lab is currently accepting applications for graduate students through RPI’s BME PhD program. We are looking for candidates who are interested in developing new biomedical optics methods and applying them to clinical and/or preclinical model studies. We have open positions for highly-motivated individuals on our current NIH-funded ongoing projects.
Congratulations to Arun, he successfully defended his thesis on 11/17/2017. Well done!!!
Congratulations to Denzel. He won the Young Scholar’s Award, at "Road from Nanomedicine to Precision Medicine" conference, Albany (Sept. 2017). Well done! Click here for more details.
Dr. Intes is joining the Editorial Board for Scientific Reports, a Nature Research journal.
Dr. Pingkun Yan, who just joined the department of Biomdeical Engineering as Assistant Professor, will serve as Co-director of BIC. He brings tremendous expertise in machine learning and image analysis, and will collaborate closely with Dr. Wang's and Dr. Intes's research groups. Welcome aboard! Check out this link to find out more about Dr. Yan's experience and research goals.
Dr. Intes's group is awarded a GPU grant from NVIDIA and will receive a GeForce Titan Xp card to pursue its cutting edge development in computational optics, including GPU-enhanced stochastic light propagation models and deep learning.
Congratulations to Dr. Pingkun Yan, who just joined BIC as Co-director. One of his papers has won the best paper award for the MICCAI special issue published in the International Journal of Computer Assisted Radiology and Surgery. The title of the paper is "Detection and Grading of Prostrate Cancer using Temporal Enhanced Ultrasound: Combining Deep Neural Networks and Tissue Mimicking Simulations".
Our work published in Nature Photonics has been highlighted in multiple news outlet including: EurekAlert!, Newswise, Phys.org, Health Medicinet and The Medical News.
New Bioimaging technique is fast and economical EurekAlert! Posted on: 18 August 2017
New Bioimaging technique is fast and economical Newswise Posted on: 18 August 2017
Congratulation to Wenxiang, his paper “Optical Tomographic Imaging for Breast Cancer Detection" has been accepted for publication in the Journal of Biomedical Optics.