Tag Archives: Image Processing

New Publication

Congratulations to Jimmy! His first manuscript was recently published in the Optics Express Journal.

James P. McLean, Yuye Ling, and Christine P. Hendon. Frequency-Constrained Robust Principle Component Analysis: A Sparse Representations approach to Segmentation of Dynamic Features in Optical Coherence imaging. Optics Express. 25(21). pp. 25819-25830. (2017)

New Grant

Dr. Christine Hendon and Dr. Richard Ha were recently selected as a recipient of a 2017 Irving Institute Collaborative and Multidisciplinary Pilot Research award for Basic Science and Clinical/Translational Investigators (CaMPR-BASIC) for our project “Deep Learning for Computer Aided Identification of Breast Cancer Margins within Optical Images.”  Award duration: August 1, 2017July 31, 2018.

Congratulations Dr. Hendon and Dr. Ha

New Publications

Two of our manuscripts have been recently published, both utilizing optical coherence tomography to further study collagen fiber organization within organ systems.

Qu D, Chuang PJ, Prateepchinda S, Spalazzi JP, Balasubramanian P, Yao X, Boskey AL, Doty SB, Hendon CP, and Lu HH. Micro- and Ultrastructural Characterization of Age-Related Changes at the Anterior Cruciate Ligament-to-Bone Insertion. ACS Biomaterials Science & Engineering. (2016) In Press

Wang Yao **, Yu Gan *,* Kristin Myers, Joy Vink, Ronald Wapner, and Christine P. HendonPregnant and Non-Pregnant Collagen Fiber Orientation and Dispersion of the Upper Cervix. PLOS One. 11(11): e0166709. (2016) . [dataset]

Electrical Engineering Seminar

Professor Ricardo Otazo
New York University School of Medicine

Imaging at the information rate: Sparse sampling & reconstruction

Wednesday, November 2, 2016 10:30 AM
Costa Commons, CESPR 750


Recent developments during the last decade in the field of applied mathematics have started to change the way we think about image acquisition and reconstruction in MRI. The conventional approach of acquiring one k-space sample per image pixel (Nyquist rate) is inefficient and rather wasteful, since the information content is usually much lower than the number of pixels. The introduction of compressed sensing, which attempts to acquire data at the information rate rather than at the Nyquist rate by exploiting image sparsity and incoherent sampling, started a new era in the development of rapid, efficient and information-rich MRI techniques. This talk will discuss recent developments that go beyond increasing imaging speed and provide new information of clinical interest, such as (1) continuous data acquisition with extra-dimensional reconstruction (e.g., 5D cardiac MRI and 5D contrast-enhanced abdominal MRI); (2) low-rank plus sparse (L+S) models for automatic background suppression and learning of motion fields.


Ricardo Otazo (ricardo.otazo@nyumc.org) is an Associate Professor of Radiology at New York University School of Medicine. He received his B.Sc. in Electrical Engineering from Universidad Catolica de Asuncion, Paraguay in 2001, and his M.Sc. and Ph.D. in Electrical Engineering from the University of New Mexico in 2005 and 2007 respectively. His research work aims at developing rapid MRI and low-dose CT techniques using advanced mathematical and physical models based on compressed sensing and low-rank matrix completion.

Hosted by Professor Christine Hendon