Project Description: NIST is developing a novel neutron interferometric phase imaging method using a grating-based, far-field interferometer with 1000× increase in time resolution and 10× improvement in spatial resolution over prototypes. Such an instrument enables to measure the structure of heterogeneous systems including ageing concrete, blood clots, highly-efficient batteries, multiphase flow in colloidal and geological systems, and the burgeoning field of metal 3D printing.
Job Description: As this far-field neutron interferometer will be generating terabytes of image data per day, there are several open research problems that would become the research focus of a postdoctoral candidate. The problems include (a) finding optimal imaging configurations that minimize acquisition time and maximize measurement quality, (b) estimating measurement uncertainty due to camera defects, (c) accelerating 3D tomographic reconstruction from multiple 2D images and multiple channels, (d) optimizing 2D projection viewpoints (dose reduction and time savings), (e) applying artificial intelligence and traditional machine learning models to noise removal and 3D volume segmentation, (f) designing tools for 3D segmentation annotation and verification, (g) registering 3D volumes, (h) evaluating accuracy and uncertainty of image-based measurements, (i) visualizing multiple terabyte-sized 3D volumes, (j) quantifying and statistically analyzing volume characteristics, and (k) disseminating petabyte-sized 3D image collections with web-based visual inspection and browsing capabilities. A small subset of project relevant publications is listed below.
Requirements: A candidate should have at least a master’s degree in computer science or related fields (PhD is preferred) and have a background in imaging, image analyses, machine learning, and software engineering.
(1) A. J. Brooks et al, “Neutron interferometry detection of early crack formation caused by bending fatigue in additively manufactured SS316 dogbones,” Materials and Design, 140 (2018) 420–430 https://doi.org/10.1016/j.matdes.2017.12.001
(2) D. A. Pushin et al, “Far-field interference of a neutron white beam and the applications to noninvasive phase-contrast imaging,” PHYSICAL REVIEW A 95, 043637 (2017)
(3) T. Pipatsrisawat et al, “Performance Analysis of The Filtered Backprojection Image Reconstruction Algorithms,” ICASSP 2015.
(4) D.M. Pelt and J.A. Sethian, “A mixed-scale dense convolutional neural network for image analysis,” PNAS, January 8, 2019.
If interested then, please, contact:
Peter Bajcsy, PhD
Information Technology Laboratory (ITL)
National Institute of Standards and Technologies (NIST)