Sparsity-based photoacoustic image reconstruction with a linear array transducer and direct measurement of the forward model

Abstract

Photoacoustic (PA) imaging is an emerging imaging technique for many clinical applications. One of the challenges posed by clinical translation is that imaging systems often rely on a finite-aperture transducer rather than a full tomography system. This results in imaging artifacts arising from an underdetermined reconstruction of the initial pressure distribution (IPD). Furthermore, clinical applications often require deep imaging, resulting in a low-signal-to-noise ratio for the acquired signal because of strong light attenuation in tissue. Conventional approaches to reconstruct the IPD, such as back projection and time-reversal, do not adequately suppress the artifacts and noise. We propose a sparsity-based optimization approach that improves the reconstruction of IPD in PA imaging with a linear array ultrasound transducer. In simulation studies, the forward model matrix was measured from k-Wave simulations, and the approach was applied to reconstruct simulated point objects and the Shepp-Logan phantom. The results were compared with the conventional back projection, time-reversal approach, frequency-domain reconstruction, and the iterative least-squares approaches. In experimental studies, the forward model of our imaging system is directly measured by scanning a graphite point source through the imaging field of view. Experimental images of graphite inclusions in tissue-mimicking phantoms are reconstructed and compared with the back projection and iterative least-squares approaches. Overall these results show that our proposed optimization approach can leverage the sparsity of the PA images to improve the reconstruction of the IPD and outperform the existing popular reconstruction approaches. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Investigation of photoacoustic tomography reconstruction with a limited view from linear array

“…The discrete model has been reported in literature and will only be explained briefly here. 12 , 13 , 15 For a linear array transducer, the PA data received by each transducer element can be expressed as where represents the initial pressure distribution, represents the acoustic velocity potential received by the ’th transducer element, is the submatrix that maps to , and is the total number of transducer elements. For matrix operation, and are vectorized.…”

“…Shang et al. 13 developed an iterative approach for PA imaging with a linear array transducer, where they built a forward model based on the actual measurement of impulse response at each point on the image plane. However, the measurement of impulse response was not feasible for clinical imaging dealing with different patients and their approach required long processing time.…”

Single laser-shot super-resolution photoacoustic tomography with fast sparsity-based reconstruction

“…Some recent work has used a SBR approach optimized for resolving point targets but did not surpass the resolution limit. This recent work [24] , [25] , [26] , [27] , [28] makes use of linear arrays and a dictionary designed to sparsify point targets and performs reconstruction directly in the channel-data domain. The approach in [24] was able to reduce artifacts and obtain higher signal-to-noise ratio.…”

“…This recent work [24] , [25] , [26] , [27] , [28] makes use of linear arrays and a dictionary designed to sparsify point targets and performs reconstruction directly in the channel-data domain. The approach in [24] was able to reduce artifacts and obtain higher signal-to-noise ratio. Prior work from our group [25] used a high-frequency linear array transducer and a SBR approach to beat the aperture-limited diffraction limit.…”

“…In addition to computational burden, the potential presence of non-sparse signal sources is an additional challenge for the practical implementation of SBR. However, to our knowledge, none of the recent work applying SBR optimized for point-target reconstruction has sought to characterize reconstruction performance in the presence of less-sparse signal sources [24] , [26] , [27] , [25] . Instead, testing of this SBR approach so far has been restricted to the imaging of point targets.…”