PMID- 36764064
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230314
LR  - 20230314
IS  - 1874-9968 (Electronic)
IS  - 0041-624X (Linking)
VI  - 131
DP  - 2023 May
TI  - Detection of defects in highly attenuating materials using ultrasonic 
      least-squares reverse time migration with preconditioned stochastic gradient 
      descent.
PG  - 106930
LID - S0041-624X(23)00006-9 [pii]
LID - 10.1016/j.ultras.2023.106930 [doi]
AB  - Accurate detection and characterization of defects in high-density polyethylene 
      (HDPE) pipe materials are very important in assessing the structural integrity of 
      critical structures in the nuclear industry. One specific challenge here is the 
      presence of the viscoelastic attenuation of this material, which can lead to 
      resolution degradation and loss of detail in ultrasound imaging. In this work, an 
      effective ultrasonic imaging technique using the least-squares reverse time 
      migration with preconditioned stochastic gradient descent (LSRTM-PSGD) is 
      developed to improve image quality. Compared with standard ultrasonic imaging 
      methods which only consider the direct ray path of ultrasound, least-squares 
      reverse time migration (LSRTM) is a powerful wave-equation-based approach and it 
      has the ability to account for rapid spatial velocity variations and to utilize 
      all wavefield information. The LSRTM is an inversion method, which iteratively 
      updates the reflectivity model by minimizing the modeled data and measured data. 
      The proposed LSRTM-PSGD combines the advantages of stochastic gradient descent 
      (SGD) and adaptive learning rate. The SGD updates the parameter on each 
      transmitter and the fluctuation of SGD can enable it to reach a better minimum, 
      thus improving the imaging quality. Compared with the conventional LSRTM 
      algorithm using a fixed step size, the proposed LSRTM-PSGD algorithm can use the 
      adaptive moment estimation to calculate the adaptive learning rate for the 
      parameter, thereby updating the parameter appropriately. The performance of the 
      LSRTM-PSGD algorithm is tested with experimental data. The results show 
      high-quality reconstructed images with good resolution for defect identification 
      in HDPE pipe materials, especially for deep defects.
CI  - Copyright (c) 2023 Elsevier B.V. All rights reserved.
FAU - Rao, Jing
AU  - Rao J
AD  - Key Laboratory of Precision Opto-mechatronics Technology of Education Ministry, 
      School of Instrumentation and Opto-Electronic Engineering, Beihang University, 
      Beijing 100191, China; School of Engineering and Information Technology, The 
      University of New South Wales, Canberra, ACT 2600, Australia. Electronic address: 
      zdraojing@gmail.com.
FAU - Tao, Yangji
AU  - Tao Y
AD  - Zhejiang Academy of Special Equipment Science, Hangzhou 310020, China.
FAU - Sun, Yan
AU  - Sun Y
AD  - Institute of Laser & Optoelectronics, School of Precision Instruments and 
      Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China.
FAU - Miao, Cunjian
AU  - Miao C
AD  - Zhejiang Academy of Special Equipment Science, Hangzhou 310020, China.
FAU - Wang, Wenlong
AU  - Wang W
AD  - Harbin Institute of Technology, Center of Geophysics, Department of Mathematics 
      and Artificial Intelligence Laboratory, Harbin 150001, China. Electronic address: 
      wenlong.wang2015@gmail.com.
LA  - eng
PT  - Journal Article
DEP - 20230204
PL  - Netherlands
TA  - Ultrasonics
JT  - Ultrasonics
JID - 0050452
SB  - IM
OTO - NOTNLM
OT  - Defect characterization
OT  - Highly attenuating materials
OT  - Least-squares reverse time migration
OT  - Stochastic gradient descent
OT  - Ultrasonic imaging
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/02/11 06:00
MHDA- 2023/02/11 06:01
CRDT- 2023/02/10 18:07
PHST- 2022/08/05 00:00 [received]
PHST- 2022/12/29 00:00 [revised]
PHST- 2023/01/13 00:00 [accepted]
PHST- 2023/02/11 06:00 [pubmed]
PHST- 2023/02/11 06:01 [medline]
PHST- 2023/02/10 18:07 [entrez]
AID - S0041-624X(23)00006-9 [pii]
AID - 10.1016/j.ultras.2023.106930 [doi]
PST - ppublish
SO  - Ultrasonics. 2023 May;131:106930. doi: 10.1016/j.ultras.2023.106930. Epub 2023 
      Feb 4.