Multi-segmented feature coupling for jointly reconstructing initial pressure and speed of sound in photoacoustic computed tomography

Abstract

Significance: Photoacoustic computed tomography (PACT) is a fast-growing imaging modality. In PACT, the image quality is degraded due to the unknown distribution of the speed of sound (SoS). Emerging initial pressure (IP) and SoS joint-reconstruction methods promise reduced artifacts in PACT. However, previous joint-reconstruction methods have some deficiencies. A more effective method has promising prospects in preclinical applications.Aim: We propose a multi-segmented feature coupling (MSFC) method for SoS-IP joint reconstruction in PACT.Approach: In the proposed method, the ultrasound detectors were divided into multiple subarrays with each sub-array and its opposite counterpart considered to be a pair. The delay and sum algorithm was then used to reconstruct two images based on a subarray pair and estimated a direction-specific SoS, based on image correlation and the orientation of the subarrays. Once the data generated by all pairs of subarrays were processed, an image that was optimized in terms of minimal feature splitting in all directions was generated. Further, based on the direction-specific SoS, a model-based method was used to directly reconstruct the SoS distribution.Results: Both phantom and animal experiments demonstrated feasibility and showed promising results compared with conventional methods, with less splitting and blurring and fewer distortions. Conclusions:The developed MSFC method shows promising results for both IP and SoS reconstruction. The MSFC method will help to optimize the image quality of PACT in clinical applications.

Challenges and advances in two-dimensional photoacoustic computed tomography: a review

“…Panel (b) is reproduced with permission from Ref. 113. (c)(i) Visualization of the raw transducer data.…”

“…Building upon the concept of feature coupling, multi-segmented feature coupling (MSFC) divides the ring array into eight subgroups. Two subgroups located at opposite sides reconstruct a region with different SOS 113 . MSFC measures the correlation coefficients between the two reconstructed images from two opposite subgroup transducers [Fig.…”

Compensating unknown speed of sound in learned fast 3D limited-view photoacoustic tomography

“…Because of its central importance to PA imaging, a variety of approaches have been proposed to tackle the problem of the unknown SoS. A spatially-varying SoS could, in principle, be recovered jointly with the PAT reconstruction, although this problem is inherently unstable and requires additional data or prior knowledge [11] , [12] , [13] , [14] , [15] , [16] , [17] . A SoS image could be found using a separate modality, such as ultrasound tomography [18] , [19] , [20] , [21] , but this requires additional time-consuming measurements.…”

Spatiotemporal SVD for denoising in LED-based photoacoustic imaging

“…Various methods were proposed to mitigate the SoS induced aberration artefacts in PA tomography [6]- [19]. In 2011, Treeby et al reported an autofocus algorithm to arrive at an optimal single SoS for the medium by iterative optimistion [6].…”

“…To address this issue, a multi-segment feature coupling (MSFC) method was developed. The full ring array was partitioned into finer groups, and the FC method was applied to each group individually to estimate a directionspecific SoS [19].…”