TY - GEN
T1 - COMPUTATIONAL IMAGING IN 3D X-RAY MICROSCOPY
T2 - 2021 IEEE International Conference on Image Processing, ICIP 2021
AU - Niverty, Sridhar
AU - Torbatissaraf, Hamid
AU - Nikitin, V.
AU - de Andrade, Vincent
AU - Niauzorau, S.
AU - Kublik, N.
AU - Azeredo, B.
AU - Tekawade, A.
AU - de Carlo, Francesco
AU - Chawla, Nikhilesh
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Time-dependent X-ray Microscopy (XRM) is an excellent technique to develop a fundamental mechanistic understanding of material behavior. Computational imaging plays a critical role in XRM, in a variety of ways. 2D projections are acquired and the resulting datasets are reconstructed using a filtered back projection algorithm. Several imaging artifacts are typically present, such as beam hardening, misalignment of the data, drift during time-evolved experiments (particularly at high temperatures and/or nanometer resolution scans), etc. Minimizing and removing these artifacts is, thus, very important. This is all the more important, because image segmentation is then done to quantify the statistics of the microstructure (often, as a function of time). The efficiency and accuracy of image segmentation is directly proportional to the quality of the initial reconstructed data. Thus, there is a need to develop efficient, robust algorithms that can handle large datasets obtained by 4D, time-dependent x-ray microscopy. In this paper, we will describe the challenges associated with computation imaging during x-ray microscopy. The use of Convolutional Neural Network (CNN) architectures based on a deep learning approach, as means of automating and handling x-ray microscopy data sets, in both lab-scale and synchrotron, will be discussed. The use of CNN techniques to robustly process ultra-large volumes of data in relatively small-time frames can exponentially accelerate tomographic data analysis, opening up novel avenues for performing 4D characterization experiments.
AB - Time-dependent X-ray Microscopy (XRM) is an excellent technique to develop a fundamental mechanistic understanding of material behavior. Computational imaging plays a critical role in XRM, in a variety of ways. 2D projections are acquired and the resulting datasets are reconstructed using a filtered back projection algorithm. Several imaging artifacts are typically present, such as beam hardening, misalignment of the data, drift during time-evolved experiments (particularly at high temperatures and/or nanometer resolution scans), etc. Minimizing and removing these artifacts is, thus, very important. This is all the more important, because image segmentation is then done to quantify the statistics of the microstructure (often, as a function of time). The efficiency and accuracy of image segmentation is directly proportional to the quality of the initial reconstructed data. Thus, there is a need to develop efficient, robust algorithms that can handle large datasets obtained by 4D, time-dependent x-ray microscopy. In this paper, we will describe the challenges associated with computation imaging during x-ray microscopy. The use of Convolutional Neural Network (CNN) architectures based on a deep learning approach, as means of automating and handling x-ray microscopy data sets, in both lab-scale and synchrotron, will be discussed. The use of CNN techniques to robustly process ultra-large volumes of data in relatively small-time frames can exponentially accelerate tomographic data analysis, opening up novel avenues for performing 4D characterization experiments.
UR - http://www.scopus.com/inward/record.url?scp=85125583690&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125583690&partnerID=8YFLogxK
U2 - 10.1109/ICIP42928.2021.9506268
DO - 10.1109/ICIP42928.2021.9506268
M3 - Conference contribution
AN - SCOPUS:85125583690
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 3502
EP - 3506
BT - 2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
PB - IEEE Computer Society
Y2 - 19 September 2021 through 22 September 2021
ER -