Synthetic-aperture based photoacoustic re-beamforming ( SPARE ) approach using beamformed ultrasound data

Photoacoustic (PA) imaging has been developed for various clinical and preclinical applications, and acquiring pre-beamformed channel data is necessary to reconstruct these images. However, accessing these pre-beamformed channel data requires custom hardware to enable parallel beamforming, and is available for a limited number of research ultrasound platforms. To broaden the impact of clinical PA imaging, our goal is to devise a new PA reconstruction approach that uses ultrasound post-beamformed radio frequency (RF) data rather than raw channel data, because this type of data is readily available in both clinical and research ultrasound systems. In our proposed Synthetic-aperture based photoacoustic rebeamforming (SPARE) approach, post-beamformed RF data from a clinical ultrasound scanner are considered as input data for an adaptive synthetic aperture beamforming algorithm. When receive focusing is applied prior to obtaining these data, the focal point is considered as a virtual element, and synthetic aperture beamforming is implemented assuming that the photoacoustic signals are received at the virtual element. The resolution and SNR obtained with the proposed method were compared to that obtained with conventional delayand-sum beamforming with 99.87% and 91.56% agreement, respectively. In addition, we experimentally demonstrated feasibility with a pulsed laser diode setup. Results indicate that the post-beamformed RF data from any commercially available ultrasound platform can potentially be used to create PA images. ©2016 Optical Society of America OCIS codes: (170.5120) Photoacoustic imaging; (100.0100) Image processing; (100.3010) Image reconstruction techniques. References and links 1. M. Xu and L. V. Wang, “Photoacoustic imaging in biomedicine,” Rev. Sci. Instrum. 77(4), 041101 (2006). 2. S. Park, S. R. Aglyamov, and S. Emelianov, “Beamforming for photoacoustic imaging using linear array transducer,” Proc. in IEEE Int. Ultrasonics Symp., 856–859 (2007). 3. B. Yin, D. Xing, Y. Wang, Y. Zeng, Y. Tan, and Q. Chen, “Fast photoacoustic imaging system based on 320element linear transducer array,” Phys. Med. Biol. 49(7), 1339–1346 (2004). 4. C. K. Liao, M. L. Li, and P. C. Li, “Optoacoustic imaging with synthetic aperture focusing and coherence weighting,” Opt. Lett. 29(21), 2506–2508 (2004). 5. R. G. M. Kolkman, P. J. Brands, W. Steenbergen, and T. G. van Leeuwen, “Real-time in vivo photoacoustic and ultrasound imaging,” J. Biomed. Opt. 13(5), 050510 (2008). 6. J. J. Niederhauser, M. Jaeger, and M. Frenz, “Comparision of laser-induced and classical ultrasound,” Proc. SPIE 4960, 118–123 (2003). 7. N. Kuo, H. J. Kang, D. Y. Song, J. U. Kang, and E. M. Boctor, “Real-time photoacoustic imaging of prostate brachytherapy seeds using a clinical ultrasound system,” J. Biomed. Opt. 17(6), 066005 (2012). 8. H. J. Kang, N. Kuo, X. Guo, D. Song, J. U. Kang, and E. M. Boctor, “Software framework of a real-time prebeamformed RF data acquisition of an ultrasound research scanner,” Proc. SPIE 8320, 83201F (2012). 9. T. Harrison and R. J. Zemp, “The applicability of ultrasound dynamic receive beamformers to photoacoustic imaging,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 58(10), 2259–2263 (2011). Vol. 7, No. 8 | 1 Aug 2016 | BIOMEDICAL OPTICS EXPRESS 3056 #264984 Journal © 2016 http://dx.doi.org/10.1364/BOE.7.003056 Received 11 May 2016; revised 17 Jun 2016; accepted 29 Jun 2016; published 19 Jul 2016 10. C. H. Frazier and W. D. O’Brien, “Synthetic aperture techniques with a virtual source element,” IEEE Trans. Ultrason., Ferroelec, Freq. Contr. 45(1), 196–207 (1998). 11. S. I. Nikolov and J. A. Jensen, “Virtual ultrasound sources in high resolution ultrasound imaging,” Proc. SPIE, Progress in biomedical optics and imaging, 3, 395–405 (2002). 12. J. Kortbek, J. A. Jensen, and K. L. Gammelmark, “Synthetic Aperture Sequential Beamforming,” Proc. in IEEE Int. Ultrasonics Symp., 966–969 (2008). 13. K. E. Thomenius, “Evolution of Ultrasound Beamformers,” Proc. IEEE Ultrason. Symp. 2, 1615–1622 (1996). 14. J. A. Jensen and N. B. Svendsen, ““Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers,” IEEE Trans. Ultrason., Ferroelec, Freq. Contr. 39(2), 262–267 (1992). 15. M. A. Lediju Bell, N. P. Kuo, D. Y. Song, J. U. Kang, and E. M. Boctor, “In vivo visualization of prostate brachytherapy seeds with photoacoustic imaging,” J. Biomed. Opt. 19(12), 126011 (2014). 16. M. A. Lediju Bell, X. Guo, D. Y. Song, and E. M. Boctor, “Transurethral light delivery for prostate photoacoustic imaging,” J. Biomed. Opt. 20(3), 036002 (2015). 17. L. Xi, G. Zhou, N. Gao, L. Yang, D. A. Gonzalo, S. J. Hughes, and H. Jiang, “Photoacoustic and fluorescence image-guided surgery using a multifunctional targeted nanoprobe,” Ann. Surg. Oncol. 21(5), 1602–1609 (2014). 18. M. A. Lediju Bell, A. K. Ostrowski, K. Li, P. Kazanzides, and E. M. Boctor, “Localization of Transcranial Targets for Photoacoustic-Guided Endonasal Surgeries,” Photoacoustics 3(2), 78–87 (2015). 19. A. Cheng, H. J. Kang, H. K. Zhang, R. H. Taylor, and E. M. Boctor, “Ultrasound to video registration using a biplane transrectal probe with photoacoustic markers,” Proc. SPIE 9786, 97860J (2016). 20. W. Xia, D. I. Nikitichev, J. M. Mari, S. J. West, R. Pratt, A. L. David, S. Ourselin, P. C. Beard, and A. E. Desjardins, “Performance characteristics of an interventional multispectral photoacoustic imaging system for guiding minimally invasive procedures,” J. Biomed. Opt. 20(8), 086005 (2015). 21. N. Dana, L. Di Biase, A. Natale, S. Emelianov, and R. Bouchard, “In vitro photoacoustic visualization of myocardial ablation lesions,” Heart Rhythm 11(1), 150–157 (2014). 22. M. L. Li, H. E. Zhang, K. Maslov, G. Stoica, and L. V. Wang, “Improved in vivo photoacoustic microscopy based on a virtual-detector concept,” Opt. Lett. 31(4), 474–476 (2006). 23. Y. Tsunoi, S. Sato, R. Watanabe, S. Kawauchi, H. Ashida, and M. Terakawa, “Compact acoustic-resolution photoacoustic imaging system with fiber-based illumination,” Jpn. J. Appl. Phys. 53(12), 126701 (2014). 24. H. K. Zhang, K. Kondo, M. Yamakawa, and T. Shiina, “Coded excitation using periodic and unipolar Msequences for photoacoustic imaging and flow measurement,” Opt. Express 24(1), 17–29 (2016).


Reason for Paper Selection
As stated in the proposal, photoacoustic (PA) imaging has the advantage of deep penetration, high resolution, and safety. However, hardware of PA imaging hinders a universal application of this technology. Implementation either relies on low-efficiency ultrasound (US) beamformers or vender-dependent PA platforms. If conventional US scanner implementing PA imaging is viable, the cost of PA imaging will be lower. In addition, PA imaging will be more practicable for either clinical diagnoses or research.
There are several challenges encountered to achieve the goal. First, because of a different time-of-flight (TOF) of ultrasound, US systems beamform PA-derived signals incorrectly. Therefore, a new beamforming method is needed. Second, signals received from ultrasound probe are not synchronized with laser pulse. Specifically, pulse repetition frequency (PRF) and line frequency, as well as phase of laser and acquired US data, are not synchronized.
The paper selected solved the first problem, i.e., the beamforming method, which is a huge step to realize PA imaging on US systems. Additionally, it is the stepping-stone for solving synchronization problem and it provides guidance on theory of PA imaging, US data acquisition (DAQ) and beamforming.

Overview
• Problem There are two critical problems to solve to develop new PA beamforming methods. The first problem is data acquisition. Since PA imaging is an innovative modality, it is no surprise that most clinical US systems do not offer PA beamforming. In conventional beamforming methods, channel data are applied, delayed, and summed. However, conventional US probes only provide beamformed data. US systems that provide prebeamformed channel data are expensive. Thus, we need to process the post-beamformed RF data to reconstruct the image. The second problem is implementation. On one hand, fixed data transfer rate of US systems prohibits high frame rate real-time imaging. On the other, we must take different TOF of US imaging and PA imaging into account when implementing beamforming.
• Goal In conclusion, the goal of this paper is to develop a new PA image reconstruction approach based on ultrasound RF data that has already been beamformed by the system.
• Significance Any commercially available US platform can potentially be used to create PA images.

Theory
• TOF Wave propagation process reveals the fundamental difference between US imaging and PA imaging. Namely, their TOF are different. Wave propagation in US imaging includes transmitting and receiving, so it is double-trip. While PA imaging doesn't contain a process of transmitting, so it is single-trip.
Acoustic TOF can be easily derived as , for US and PA imaging respectively. If fixed focusing is applied, then TR rr  . It can be easily derived . In another word, TOF of US imaging is two times of TOF of PA imaging.
It is an essential relationship for synthetic-aperture based re-beamforming (SPARE) method.

Figure 1
• Synthetic-aperture based re-beamforming (SPARE) Conventional PA imaging needs channel data for beamforming. Since US probes only provide beamformed data, re-beamform the beamformed data becomes the potential method.
The re-beamforming process for SPARE approach is demonstrated below. First, the US probe receives US waves and generate channel data. Then, it beamforms the channel data into prebeamformed data, which can be achieved from the platform. Finally, SPARE approach is applied to re-beamform the data. More specifically, the shapes of two point sources are illustrated in the figure to show the process of SPARE beamforming. The wave front of received RF signal is shown as the green curve in (a). After applying the fixed focusing delay as red dashed curves in (a), it becomes somewhat more but not totally focused in (b). Regard the focal point in (b) as a virtual point source and apply inverse and forward delay and sum, the signal is fully focused as shown in (c). (d) illustrates the situation for dynamic focusing, in which the virtual element depth zF is the half distance of re-beamforming focal depth zR. Figure 3

Simulation:
• Software: field II, which is a ultrasound Matlab program system that uses the concept of spatial impulse responses.

I. Simulation
Different photoacoustic waveforms, resolution and SNR of corresponding images are tested. In addition, dynamic focusing method is also tested. All results indicate good image quality comparing to ground truth. It proves a universality of SPARE approach.
(1) Results of different waveforms in the simulation are shown in figure 5. SPARE algorithm does not depend on the impulse responses determined by the absorber size and the ultrasound probe, which means in can be applied on all kinds of ultrasound probes.
(2) Result of resolution test is shown in figure 6. The resolution of the proposed method agrees well with the ground truth values with a correlation coefficient of 99.87%.
(3) Result of SNR test is shown in figure 7. The correlation coefficient of conventional ultrasound beamforming and proposed method is 91.56%.
(4) Result of dynamically focused beamformed ultrasound RF data is shown in figure 8. Grating lobe artifacts are visible in the near field. It is drastically reduced when a small aperture size is used.

Figure 5
Simulated photoacoustic waveforms, PA images from channel data, and PA images using SPARE beamformer are shown. Fixed focusing at 20 mm depth was used for SPARE beamforming. (a) 1 mm and (b) 0.5 mm diameter objects with N-shape impulse responses were simulated. For a point source, (c) 2 MHz, (d) 5 MHz center frequency waves were simulated assuming that a band-limited ultrasound transducer was used to receive these signals.

Figure 6
The FWHM of the proposed re-beamforming for the designated focusing depth The reconstructed images through ultrasound and SPARE beamforming.

II. Experimental evaluation
Ultrasound beamforming with fixed focusing and dynamic focusing was applied to experimental channel data to produce two types of ultrasound post-beamformed data.
(1) Experimental SPARE beamforming results are indicated in figure 9. The FWHM was similar to that of the ground truth when the fixed focusing was applied from 9 mm to 21 mm. However, the reconstructed point was degraded in the lateral direction when the fixed focal depth was far from the target.
(2) Experimental SPARE beamforming results from dynamically focused ultrasound beamforming are shown in figure 10. The single source point is well focused.

Conclusion
A synthetic-aperture based PA beamforming method utilizing ultrasound post-beamformed RF data is proposed. It is validated through simulation, and experiments with different parameter definitions.
SPARE method is a big step to realize PA imaging on US systems. The next step is to overcome the synchronization between laser and signal received by US probe.

Assessment
(1) About This paper This paper demonstrates convincing and significant results of the feasibility of SPARE approach under practical data acquisition. It is an important step to realize PA imaging on conventional US probes. Future work includes implementing the algorithm in real time, which has been done by Howard Xu, Kai etc. in CIS II 2016 project.
Some improvement may include get higher SNR of the image. In addition, this paper focused on beamforming, while there are problems in synchronization of acquired US prebeamformed data and the PA laser pulse, which would be solved in my project.
(2) About my project This paper provides theory guidance on PA imaging on US platform. This paper demonstrates simulation methods and experimental approaches for testing.
The difference is I will focus on synchronization, where the sampling process will be considered and new algorithms will be developed.