Chinese Optics Letters, Volume. 13, Issue 6, 061001(2015)
Adaptive multi-sample-based photoacoustic tomography with imaging quality optimization
Figures & Tables(6)
Fig. 3. Human hair phantom reconstruction: (a) Model of the phantom. (b) PA image with single sample frame. (c) PA image with traditional multi-sample-based method. (d) PA image with our multi-sample-based method.
Fig. 4. Images of human index finger joint: (a) PA image using one sample frame. (b) PA image using simple multi-frames average method. (c) PA image using our proposed multi-frame-based method. (d) B-mode ultrasound image; BO, bone; IN, inner structure of tendon; JO, joint; PE, periosteum; TE, tendon.
Table 1. PSNR Improved with Multi-Sample-Based Approach
View tableView in ArticleTable 1. PSNR Improved with Multi-Sample-Based Approach
Hair No. 1 2 3 4 PSNR_S (dB) 58.6098 59.2684 56.7737 59.4863 PSNR_N (dB) 62.8133 63.1143 60.4926 63.0089 PSNR_M (dB) 64.4487 65.5755 62.9599 65.3259
Table 2. PSNR Comparison between Different Approaches
View tableView in ArticleTable 2. PSNR Comparison between Different Approaches
Region One-Sample- Based Method Simple Multi-Sample- Based Method Our Method A 36.5235 43.3422 46.4543 B 33.3890 41.9003 44.0908 C 35.7781 43.0783 45.6740
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Yuxin Wang, Jie Yuan, Sidan Du, Xiaojun Liu, Guan Xu, Xueding Wang, "Adaptive multi-sample-based photoacoustic tomography with imaging quality optimization," Chin. Opt. Lett. 13, 061001 (2015)
Paper Information
Category: Image processing
Received: Nov. 28, 2014
Accepted: Mar. 26, 2015
Published Online: Sep. 14, 2018
The Author Email: Jie Yuan (yuanjie@nju.edu.cn), Xueding Wang (xdwang@umich.edu)
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