Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Efficient Nonlinear Beamformer Based on P 'Th Root of Detected Signals for Linear-Array Photoacoustic Tomography: Application to Sentinel Lymph Node Imaging Publisher Pubmed



Mozaffarzadeh M1, 2 ; Periyasamy V3 ; Pramanik M3 ; Makkiabadi B1, 4
Authors
Show Affiliations
Authors Affiliations
  1. 1. Institute for Advanced Medical Technologies (IAMT), Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran, Iran
  2. 2. Tarbiat Modares University, Department of Biomedical Engineering, Tehran, Iran
  3. 3. Nanyang Technological University, School of Chemical and Biomedical Engineering, Singapore, Singapore
  4. 4. Tehran University of Medical Sciences, School of Medicine, Department of Medical Physics and Biomedical Engineering, Tehran, Iran

Source: Journal of Biomedical Optics Published:2018


Abstract

In linear-array transducer-based photoacoustic (PA) imaging, B-scan PA images are formed using the raw channel PA signals. Delay-and-sum (DAS) is the most prevalent algorithm due to its simple implementation, but it leads to low-quality images. Delay-multiply-and-sum (DMAS) provides a higher image quality in comparison with DAS while it imposes a computational burden of O(M 2). We introduce a nonlinear (NL) beamformer for linear-array PA imaging, which uses the p'th root of the detected signals and imposes the complexity of DAS [O(M)]. The proposed algorithm is evaluated numerically and experimentally [wire-target and in-vivo sentinel lymph node (SLN) imaging], and the effects of the parameter p are investigated. The results show that the NL algorithm, using a root of p (NL-p), leads to lower sidelobes and higher signal-to-noise ratio compared with DAS and DMAS, for (p>2). The sidelobes level (for the wire-target phantom), at the depth of 11.4 mm, are about -31, -52, -52, -67, -88, and -109dB, for DAS, DMAS, NL-2, NL-3, NL-4, and NL-5, respectively, indicating the superiority of the NL-p algorithm. In addition, the best value of p for SLN imaging is reported to be 12. © 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).