A machine learning decision criterion for reducing scan time for hyperspectral neutron computed tomography systems

Tang, Shimin; Venkatakrishnan, Singanallur V.; Chowdhury, Mohammad S. N.; Yang, Diyu; Gober, Megan; Nelson, George J.; Cekanova, Maria; Biris, Alexandru S.; Buzzard, Gregery T.; Bouman, Charles A.; Skorpenske, Harley D.; Bilheux, Hassina Z.

doi:10.1038/s41598-024-63931-x

A machine learning decision criterion for reducing scan time for hyperspectral neutron computed tomography systems

Journal Article · Tue Jul 02 00:00:00 EDT 2024 · Scientific Reports

DOI:https://doi.org/10.1038/s41598-024-63931-x· OSTI ID:2406790

^[1]; Venkatakrishnan, Singanallur V. ^[1]; Chowdhury, Mohammad S. N. ^[2]; Yang, Diyu ^[2]; Gober, Megan ^[3]; Nelson, George J. ^[3]; Cekanova, Maria ^[4]; Biris, Alexandru S. ^[5]; Buzzard, Gregery T. ^[2]; Bouman, Charles A. ^[2]; Skorpenske, Harley D. ^[1]; ^[1]

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Purdue Univ., West Lafayette, IN (United States)
Univ. of Alabama, Huntsville, AL (United States)
Integrity Laboratories, Knoxville, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
Univ. of Arkansas, Little Rock, AR (United States)

We present the first machine learning-based autonomous hyperspectral neutron computed tomography experiment performed at the Spallation Neutron Source. Hyperspectral neutron computed tomography allows the characterization of samples by enabling the reconstruction of crystallographic information and elemental/isotopic composition of objects relevant to materials science. High quality reconstructions using traditional algorithms such as the filtered back projection require a high signal-to-noise ratio across a wide wavelength range combined with a large number of projections. This results in scan times of several days to acquire hundreds of hyperspectral projections, during which end users have minimal feedback. To address these challenges, a golden ratio scanning protocol combined with model-based image reconstruction algorithms have been proposed. This novel approach enables high quality real-time reconstructions from streaming experimental data, thus providing feedback to users, while requiring fewer yet a fixed number of projections compared to the filtered back projection method. In this paper, we propose a novel machine learning criterion that can terminate a streaming neutron tomography scan once sufficient information is obtained based on the current set of measurements. Our decision criterion uses a quality score which combines a reference-free image quality metric computed using a pre-trained deep neural network with a metric that measures differences between consecutive reconstructions. The results show that our method can reduce the measurement time by approximately a factor of five compared to a baseline method based on filtered back projection for the samples we studied while automatically terminating the scans.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2406790

Journal Information:: Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 14; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

References (42)

3D Mapping of Crystallographic Phase Distribution using Energy-Selective Neutron Tomography Woracek, Robin; Penumadu, Dayakar; Kardjilov, Nikolay Advanced Materials, Vol. 26, Issue 24 https://doi.org/10.1002/adma.201400192	journal	April 2014
CNN as model observer in a liver lesion detection task for x‐ray computed tomography: A phantom study Kopp, Felix K.; Catalano, Marco; Pfeiffer, Daniela Medical Physics, Vol. 45, Issue 10 https://doi.org/10.1002/mp.13151	journal	September 2018
An Introduction to Statistical Learning James, Gareth; Witten, Daniela; Hastie, Trevor Springer Texts in Statistics https://doi.org/10.1007/978-1-4614-7138-7	book	January 2013
Engineering applications of Bragg-edge neutron transmission Santisteban, J. R.; Edwards, L.; Fizpatrick, M. E. Applied Physics A: Materials Science & Processing, Vol. 74, Issue 0 https://doi.org/10.1007/s003390101241	journal	December 2002
Monitoring residual strain relaxation and preferred grain orientation of additively manufactured Inconel 625 by in-situ neutron imaging Tremsin, A. S.; Gao, Y.; Makinde, A. Additive Manufacturing, Vol. 46 https://doi.org/10.1016/j.addma.2021.102130	journal	October 2021
The design of the neutron radiography and tomography facility at the new research reactor FRM-II at Technical University Munich Schillinger, B.; Calzada, E.; Grünauer, F. Applied Radiation and Isotopes, Vol. 61, Issue 4 https://doi.org/10.1016/j.apradiso.2004.03.091	journal	October 2004
Image quality in CT: From physical measurements to model observers Verdun, F. R.; Racine, D.; Ott, J. G. Physica Medica, Vol. 31, Issue 8 https://doi.org/10.1016/j.ejmp.2015.08.007	journal	December 2015
CNR—the new beamline for cold neutron imaging at the Swiss spallation neutron source SINQ Kühne, Guido; Frei, Gabriel; Lehmann, Eberhard Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 542, Issue 1-3 https://doi.org/10.1016/j.nima.2005.01.146	journal	April 2005
Energy-selective neutron transmission imaging at a pulsed source Kockelmann, W.; Frei, G.; Lehmann, E. H. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 578, Issue 2 https://doi.org/10.1016/j.nima.2007.05.207	journal	August 2007
NeXT-Grenoble, the Neutron and X-ray tomograph in Grenoble Tengattini, Alessandro; Lenoir, Nicolas; Andò, Edward Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 968 https://doi.org/10.1016/j.nima.2020.163939	journal	July 2020
Overview of the Conceptual Design of the Future VENUS Neutron Imaging Beam Line at the Spallation Neutron Source Bilheux, Hassina; Herwig, Ken; Keener, Scott Physics Procedia, Vol. 69 https://doi.org/10.1016/j.phpro.2015.07.007	journal	January 2015
The CG-1D Neutron Imaging Beamline at the Oak Ridge National Laboratory High Flux Isotope Reactor Santodonato, Lou; Bilheux, Hassina; Bailey, Barton Physics Procedia, Vol. 69 https://doi.org/10.1016/j.phpro.2015.07.015	journal	January 2015
Detectors Requirements for the ODIN Beamline at ESS Morgano, Manuel; Lehmann, Eberhard; Strobl, Markus Physics Procedia, Vol. 69 https://doi.org/10.1016/j.phpro.2015.07.022	journal	January 2015
Unique capabilities and applications of Microchannel Plate (MCP) detectors with Medipix/Timepix readout Tremsin, A. S.; Vallerga, J. V. Radiation Measurements, Vol. 130 https://doi.org/10.1016/j.radmeas.2019.106228	journal	January 2020
A suite-level review of the neutron powder diffraction instruments at Oak Ridge National Laboratory Calder, S.; An, K.; Boehler, R. Review of Scientific Instruments, Vol. 89, Issue 9 https://doi.org/10.1063/1.5033906	journal	September 2018
Commissioning start of Energy-Resolved Neutron Imaging System, RADEN in J-PARC Shinohara, Takenao; Kai, Tetsuya Neutron News, Vol. 26, Issue 2 https://doi.org/10.1080/10448632.2015.1028271	journal	April 2015
DINGO – the neutron imaging station at ANSTO: embracing material science, palaeontology, and cultural heritage Salvemini, F.; Bevitt, J.; Liss, K. D. Neutron News, Vol. 27, Issue 2 https://doi.org/10.1080/10448632.2016.1163982	journal	April 2016
First neutron Bragg-edge imaging experimental results at CSNS* Chen, Jie; Tan, Zhijian; Liu, Weiqiang Chinese Physics B, Vol. 30, Issue 9 https://doi.org/10.1088/1674-1056/ac0da7	journal	September 2021
Final design of the Energy-Resolved Neutron Imaging System “RADEN” at J-PARC Shinohara, T.; Kai, T.; Oikawa, K. Journal of Physics: Conference Series, Vol. 746 https://doi.org/10.1088/1742-6596/746/1/012007	journal	September 2016
Regridding reconstruction algorithm for real-time tomographic imaging Marone, F.; Stampanoni, M. Journal of Synchrotron Radiation, Vol. 19, Issue 6 https://doi.org/10.1107/S0909049512032864	journal	September 2012
Characterization and application of Bragg-edge transmission imaging for strain measurement and crystallographic analysis on the IMAT beamline Ramadhan, Ranggi S.; Kockelmann, Winfried; Minniti, Triestino Journal of Applied Crystallography, Vol. 52, Issue 2 https://doi.org/10.1107/S1600576719001730	journal	March 2019
Analysis of the direct Fourier method for computer tomography Walden, J. IEEE Transactions on Medical Imaging, Vol. 19, Issue 3 https://doi.org/10.1109/42.845179	journal	March 2000
On the direct Fourier method for computer tomography Gottleib, D.; Gustafsson, B.; Forssen, P. IEEE Transactions on Medical Imaging, Vol. 19, Issue 3 https://doi.org/10.1109/42.845180	journal	March 2000
Convolutional Neural Networks for No-Reference Image Quality Assessment Kang, Le; Ye, Peng; Li, Yi 2014 IEEE Conference on Computer Vision and Pattern Recognition https://doi.org/10.1109/CVPR.2014.224	conference	June 2014
Deep Residual Learning for Image Recognition He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2016.90	conference	June 2016
C3DVQA: Full-Reference Video Quality Assessment with 3D Convolutional Neural Network Xu, Munan; Chen, Junming; Wang, Haiqiang ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) https://doi.org/10.1109/ICASSP40776.2020.9053031	conference	May 2020
An Edge Alignment-Based Orientation Selection Method for Neutron Tomography Yang, Diyu; Tang, Shimin; Venkatakrishnan, Singanallur V. ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) https://doi.org/10.1109/ICASSP49357.2023.10097185	conference	June 2023
Deep Neural Networks for No-Reference Video Quality Assessment You, Junyong; Korhonen, Jari 2019 IEEE International Conference on Image Processing (ICIP) https://doi.org/10.1109/ICIP.2019.8803395	conference	September 2019
Image Quality Assessment: From Error Visibility to Structural Similarity Wang, Z.; Bovik, A. C.; Sheikh, H. R. IEEE Transactions on Image Processing, Vol. 13, Issue 4 https://doi.org/10.1109/TIP.2003.819861	journal	April 2004
Deep CNN-Based Blind Image Quality Predictor Kim, Jongyoo; Nguyen, Anh-Duc; Lee, Sanghoon IEEE Transactions on Neural Networks and Learning Systems, Vol. 30, Issue 1 https://doi.org/10.1109/TNNLS.2018.2829819	journal	January 2019
High Resolution Neutron Resonance Absorption Imaging at a Pulsed Neutron Beamline Tremsin, A. S.; McPhate, J. B.; Vallerga, J. V. IEEE Transactions on Nuclear Science, Vol. 59, Issue 6 https://doi.org/10.1109/Tns.2012.2215627	journal	December 2012
High-Resolution Strain Mapping Through Time-of-Flight Neutron Transmission Diffraction with a Microchannel Plate Neutron Counting Detector: High-Resolution Strain Mapping Tremsin, A. S.; McPhate, J. B.; Steuwer, A. Strain, Vol. 48, Issue 4 https://doi.org/10.1111/j.1475-1305.2011.00823.x	journal	September 2011
Spatiotemporal computed tomography of dynamic processes Münch, Beat Optical Engineering, Vol. 50, Issue 12 https://doi.org/10.1117/1.3660298	journal	December 2011
Design of a practical model-observer-based image quality assessment method for x-ray computed tomography imaging systems Tseng, Hsin-Wu; Fan, Jiahua; Kupinski, Matthew A. Journal of Medical Imaging, Vol. 3, Issue 3 https://doi.org/10.1117/1.JMI.3.3.035503	journal	July 2016
Principles of Computerized Tomographic Imaging Kak, Avinash C.; Slaney, Malcolm Classics in Applied Mathematics https://doi.org/10.1137/1.9780898719277	book	January 2001
Image Quality Assessment by Comparing CNN Features between Images Amirshahi, Seyed Ali; Pedersen, Marius; Yu, Stella X. Journal of Imaging Science and Technology, Vol. 60, Issue 6 https://doi.org/10.2352/J.ImagingSci.Technol.2016.60.6.060410	journal	November 2016
The XTRA Option at the NEUTRA Facility—More Than 10 Years of Bi-Modal Neutron and X-ray Imaging at PSI Lehmann, Eberhard H.; Mannes, David; Kaestner, Anders P. Applied Sciences, Vol. 11, Issue 9 https://doi.org/10.3390/app11093825	journal	April 2021
Characterization of Crystallographic Structures Using Bragg-Edge Neutron Imaging at the Spallation Neutron Source Song, Gian; Lin, Jiao; Bilheux, Jean Journal of Imaging, Vol. 3, Issue 4 https://doi.org/10.3390/jimaging3040065	journal	December 2017
Neutron Imaging at LANSCE—From Cold to Ultrafast Nelson, Ronald; Vogel, Sven; Hunter, James Journal of Imaging, Vol. 4, Issue 2 https://doi.org/10.3390/jimaging4020045	journal	February 2018
Time-of-Flight Neutron Imaging on IMAT@ISIS: A New User Facility for Materials Science Kockelmann, Winfried; Minniti, Triestino; Pooley, Daniel Journal of Imaging, Vol. 4, Issue 3 https://doi.org/10.3390/jimaging4030047	journal	February 2018
Improved Acquisition and Reconstruction for Wavelength-Resolved Neutron Tomography Venkatakrishnan, Singanallur; Zhang, Yuxuan; Dessieux, Luc Journal of Imaging, Vol. 7, Issue 1 https://doi.org/10.3390/jimaging7010010	journal	January 2021
scikit-image: image processing in Python van der Walt, Stéfan; Schönberger, Johannes L.; Nunez-Iglesias, Juan PeerJ, Vol. 2 https://doi.org/10.7717/peerj.453	journal	January 2014

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