Computationally Efficient Forward Operator for Photoacoustic Tomography Based on Coordinate Transformations

IEEE Photoacoustic tomography (PAT) is an imaging modality that utilizes the photoacoustic effect. In PAT, a photoacoustic image is computed from measured data by modeling ultrasound propagation in the imaged domain and solving an inverse problem utilizing a discrete forward operator. However, in realistic measurement geometries with several ultrasound transducers and relatively large imaging volume, an explicit formation and use of the forward operator can be computationally prohibitively expensive. In this work, we propose a transformation based approach for efficient modeling of photoacoustic signals and reconstruction of photoacoustic images. In the approach, the forward operator is constructed for a reference ultrasound transducer and expanded into a general measurement geometry using transformations that map the formulated forward operator in local coordinates to the global coordinates of the measurement geometry. The inverse problem is solved using a Bayesian framework. The approach is evaluated with numerical simulations and experimental data. The results show that the proposed approach produces accurate three-dimensional photoacoustic images with a significantly reduced computational cost both in memory requirements and in time. In the studied cases, depending on the computational factors such as discretization, over 30-fold reduction in memory consumption and was achieved without a reduction in image quality compared to a conventional approach

A Spatial-Domain Factor for Sparse-Sampling Circular-View Photoacoustic Tomography

Circular full-view configuration of photoacoustic imaging systems (C-PAI) has many applications in biomedicine (e.g., breast and brain imaging). To obtain a high-quality reconstructed image, dense spatial sampling (a large number of acoustic detectors) is needed, which makes the system expensive and challenging. Unfortunately, by reducing the number of spatial samples, streak artifacts appear, which degrade the quality of the reconstructed image. In this article, we propose a spatial-domain factor to suppress the streak artifacts and enhance the reconstructed image quality in a sparse sampling C-PAI system. Numerical and experimental studies are conducted to evaluate the proposed method. The results show that by reducing the number of spatial samples by one-fifth of the minimum required value to meet the Nyquist criteria, the proposed method provides a higher quality reconstructed image in terms of artifacts suppression and resolution improvement compared to the conventional method with dense spatial sampling. The proposed method improves the structural similarity index measure (SSIM), generalized contrast-to-noise ratio (gCNR), CNR, and tangential resolution values up to 100%, 9%, 38.6 dB, and ∼45%, respectively. Based on the advantages of the proposed method, a low-cost version of a C-PAI system for clinical applications can be developed.This is a manuscript of the article published as Hakakzadeh, Soheil, Praveenbalaji Rajendran, Vahid Amin Nili, Zahra Kavehvash, and Manojit Pramanik. "A spatial-domain factor for sparse-sampling circular-view photoacoustic tomography." IEEE Journal of Selected Topics in Quantum Electronics 29, no. 4: Biophotonics (2022): 1-9. doi: https://doi.org/10.1109/JSTQE.2022.3229622. Copyright 2022 Institute of Electrical and Electronics Engineers. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Posted with permission

Efficient 3-D Model-Based Reconstruction Scheme for Arbitrary Optoacoustic Acquisition Geometries

Optimal optoacoustic tomographic sampling is often hindered by the frequency-dependent directivity of ultrasound sensors, which can only be accounted for with an accurate three-dimensional (3D) model. Herein, we introduce a 3D modelbased reconstruction method applicable to optoacoustic imaging systems employing detection elements with arbitrary size and shape. The computational complexity and memory requirements are mitigated by introducing an efficient graphics processing unit (GPU)-based implementation of the iterative inversion. On-the-fly calculation of the entries of the model-matrix via a small look-up table avoids otherwise unfeasible storage of matrices typically occupying more than 300GB of memory. Superior imaging performance of the suggested method with respect to standard optoacoustic image reconstruction methods is first validated quantitatively using tissue-mimicking phantoms. Significant improvements in the spatial resolution, contrast to noise ratio and overall 3D image quality are also reported in real tissues by imaging the finger of a healthy volunteer with a hand-held volumetric optoacoustic imaging system