An Efficient Nonlinear Beamformer Based on P^{th} Root of Detected Signals for Linear-Array Photoacoustic Tomography: Application to Sentinel Lymph Node Imaging

TL;DR

This paper introduces a nonlinear beamformer for linear-array photoacoustic imaging that improves image quality by reducing sidelobes and increasing SNR, while maintaining computational efficiency similar to DAS.

Contribution

A novel nonlinear beamformer based on the p-th root of signals is proposed, offering enhanced image quality over DAS and DMAS with similar computational complexity.

Findings

NL_p reduces sidelobes significantly compared to DAS and DMAS.

NL_p achieves higher SNR in phantom and in vivo imaging.

Optimal p value for sentinel lymph node imaging is 12.

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). In this work, 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 (SNR) compared to DAS and DMAS, for (p > 2). The sidelobes level (for the wire-target phantom), at the depth of 11.4 mm, are about -31 dB, -52 dB, -52 dB, -67 dB, -88 dB and -109 dB, 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.