An Efficient Compensation Method for Limited-View Photoacoustic Imaging Reconstruction Based on Gerchberg–Papoulis Extrapolation

A New Deep Learning Network for Mitigating Limited-view and Under-sampling Artifacts in Ring-shaped Photoacoustic Tomography

Photoacoustic tomography (PAT) is a hybrid technique for high-resolution imaging of optical absorption in tissue. Among various transducer arrays proposed for PAT, the ring-shaped transducer array is widely used in cross-sectional imaging applications. However, due to the high fabrication cost, most ring-shaped transducer arrays have a sparse transducer arrangement, which leads to limited-view problems and under-sampling artifacts. To address these issues, we paired conventional PAT reconstruction with deep learning, which recently achieved a breakthrough in image processing and tomographic reconstruction. In this study, we designed a convolutional neural network (CNN) called a ring-array deep learning network (RADL-net), which can eliminate limited-view and under-sampling artifacts in PAT images. The method was validated on a three-quarter ring transducer array using numerical simulation, phantom imaging, and in vivo imaging. Our results indicate that the proposed RADL-net significantly improves the quality of reconstructed images on a three-quarter ring transducer array. The method is also superior to the conventional compressed sensing (CS) algorithm.

Image reconstruction based on compressed sensing for sparse-data endoscopic photoacoustic tomography

Endoscopic photoacoustic tomography (EPAT) is an interventional application of photoacoustic tomography (PAT) to visualize anatomical features and functional components of biological cavity structures such as nasal cavity, digestive tract or coronary arterial vessels. One of the main challenges in clinical applicability of EPAT is the incomplete acoustic measurements due to the limited detectors or the limited-view acoustic detection enclosed in the cavity. In this case, conventional image reconstruction methodologies suffer from significantly degraded image quality. This work introduces a compressed-sensing (CS)-based method to reconstruct a high-quality image that represents the initial pressure distribution on a luminal cross-section from incomplete discrete acoustic measurements. The method constructs and trains a complete dictionary for the sparse representation of the photoacoustically-induced acoustic measurements. The sparse representation of the complete acoustic signals is then optimally obtained based on the sparse measurements and a sensing matrix. The complete acoustic signals are recovered from the sparse representation by inverse sparse transformation. The image of the initial pressure distribution is finally reconstructed from the recovered complete signals by using the time reversal (TR) algorithm. It was shown with numerical experiments that high-quality images with reduced under-sampling artifacts can be reconstructed from sparse measurements. The comparison results suggest that the proposed method outperforms the standard TR reconstruction by 40% in terms of the structural similarity of the reconstructed images.

Enhancing Image Quality of Photoacoustic Tomography Using Sub-Pitch Array Translation Approach: Simulation and Experimental Validation

OBJECTIVE

The purpose of this paper is to present a more convenient and practical alternate way of increasing the lateral discrete array sampling while using a typical λ pitch linear array transducer at receive for photoacoustic tomography (PAT) application.

METHODS

We have employed a linear translation-based approach, in which the array transducer is translated by sub-pitch amount to create an augmented RF frame data having denser lateral spatial sampling. The denser λ/2 and λ/4 pitch data were reconstructed and compared against conventional λ pitch reconstructed PAT image using simulation and tissue mimicking phantom experiments in terms of improvements in resolution and contrast.

RESULTS

The results from experiments demonstrate a 34.48% improvement in lateral resolution (LR), measured in terms of full-width at half-maximum of the lateral profile of point spread function, and a maximum of 7-dB improvement in contrast is achieved while using a λ/2-pitch configuration when compared to the conventional λ-pitch configuration.

CONCLUSION

It was demonstrated that λ/2- and λ/4-pitch configurations result in better LR and contrast than λ-pitch configuration.

SIGNIFICANCE

Based on the results obtained, the proposed method has the potential to serve as an easy-to-integrate and simple way of achieving better image quality without requiring to increase the system complexity with existing transducer array probe technology in regular clinical scanners.

A Novel Dictionary-Based Image Reconstruction for Photoacoustic Computed Tomography

One of the major concerns in photoacoustic computed tomography (PACT) is obtaining a high-quality image using the minimum number of ultrasound transducers/view angles. This issue is of importance when a cost-effective PACT system is needed. On the other hand, analytical reconstruction algorithms such as back projection (BP) and time reversal, when a limited number of view angles is used, cause artifacts in the reconstructed image. Iterative algorithms provide a higher image quality, compared to BP, due to a model used for image reconstruction. The performance of the model can be further improved using the sparsity concept. In this paper, we propose using a novel sparse dictionary to capture important features of the photoacoustic signal and eliminate the artifacts while few transducers is used. Our dictionary is an optimum combination of Wavelet Transform (WT), Discrete Cosine Transform (DCT), and Total Variation (TV). We utilize two quality assessment metrics including peak signal-to-noise ratio and edge preservation index to quantitatively evaluate the reconstructed images. The results show that the proposed method can generate high-quality images having fewer artifacts and preserved edges, when fewer view angles are used for reconstruction in PACT.