Evaluation of 10 Current Image Reconstruction Algorithms for Linear Array Photoacoustic Imaging.
Ravi PrakashRayyan ManwarKamran Mohammad AvanakiPublished in: Journal of biophotonics (2023)
Various reconstruction algorithms have been implemented for linear array photoacoustic imaging systems with the goal of accurately reconstructing the strength absorbers within the tissue being imaged. Since the existing algorithms have been introduced by different research groups and the context of performance evaluation was not consistent, it is difficult to make a fair comparison between them. In this study, we systematically compared the performance of ten published image reconstruction algorithms (DAS, UBP, pDAS, DMAS, MV, EIGMV, SLSC, GSC, TR, and FD) using in-vitro phantom data. Evaluations were conducted based on lateral resolution of the reconstructed images, computational time, target detectability, and noise sensitivity. We anticipate the outcome of this study will assist researchers in selecting appropriate algorithms for their linear array PA imaging applications. This article is protected by copyright. All rights reserved.
Keyphrases
- deep learning
- machine learning
- high resolution
- artificial intelligence
- convolutional neural network
- fluorescence imaging
- high throughput
- big data
- rheumatoid arthritis
- magnetic resonance imaging
- high density
- mass spectrometry
- systematic review
- air pollution
- single molecule
- photodynamic therapy
- electronic health record
- monte carlo