1
|
Zheng S, Lu L, Yingsa H, Meichen S. Deep learning framework for three-dimensional surface reconstruction of object of interest in photoacoustic tomography. OPTICS EXPRESS 2024; 32:6037-6061. [PMID: 38439316 DOI: 10.1364/oe.507476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 01/23/2024] [Indexed: 03/06/2024]
Abstract
Photoacoustic tomography (PAT) is a non-ionizing hybrid imaging technology of clinical importance that combines the high contrast of optical imaging with the high penetration of ultrasonic imaging. Two-dimensional (2D) tomographic images can only provide the cross-sectional structure of the imaging target rather than its overall spatial morphology. This work proposes a deep learning framework for reconstructing three-dimensional (3D) surface of an object of interest from a series of 2D images. It achieves end-to-end mapping from a series of 2D images to a 3D image, visually displaying the overall morphology of the object. The framework consists of four modules: segmentation module, point cloud generation module, point cloud completion module, and mesh conversion module, which respectively implement the tasks of segmenting a region of interest, generating a sparse point cloud, completing sparse point cloud and reconstructing 3D surface. The network model is trained on simulation data sets and verified on simulation, phantom, and in vivo data sets. The results showed superior 3D reconstruction performance both visually and on the basis of quantitative evaluation metrics compared to the state-of-the-art non-learning and learning approaches. This method potentially enables high-precision 3D surface reconstruction from the tomographic images output by the preclinical PAT system without changing the imaging system. It provides a general deep learning scheme for 3D reconstruction from tomographic scanning data.
Collapse
|
2
|
Xu W, Leskinen J, Sahlström T, Happonen E, Tarvainen T, Lehto VP. Assembly of fluorophore J-aggregates with nanospacer onto mesoporous nanoparticles for enhanced photoacoustic imaging. PHOTOACOUSTICS 2023; 33:100552. [PMID: 38021288 PMCID: PMC10658600 DOI: 10.1016/j.pacs.2023.100552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 05/23/2023] [Accepted: 08/26/2023] [Indexed: 12/01/2023]
Abstract
Many fluorophores, such as indocyanine green (ICG), have poor photostability and low photothermal efficiency hindering their wide application in photoacoustic (PA) tomography. In the present study, a supramolecular assembly approach was used to develop the hybrid nanoparticles (Hy NPs) of ICG and porous silicon (PSi) as a novel contrast agent for PA tomography. ICG was assembled on the PSi NPs to form J-aggregates within 30 min. The Hy NPs presented a red-shifted absorption, improved photothermal stability, and enhanced PA performance. Furthermore, 1-dodecene (DOC) was assembled into the NPs as a 'nanospacer', which enhanced non-radiative decay for increased thermal release. Compared to the Hy NPs, adding DOC into the Hy NPs (DOC-Hy) increased the PA signal by 83%. Finally, the DOC-Hy was detectable in PA tomography at 1.5 cm depth in tissue phantom even though its concentration was as low as 6.25 µg/mL, indicating the potential for deep tissue PA imaging.
Collapse
Affiliation(s)
- Wujun Xu
- Department of Technical Physics, University of Eastern Finland, 70210 Kuopio, Finland
| | - Jarkko Leskinen
- Department of Technical Physics, University of Eastern Finland, 70210 Kuopio, Finland
| | - Teemu Sahlström
- Department of Technical Physics, University of Eastern Finland, 70210 Kuopio, Finland
| | - Emilia Happonen
- Department of Technical Physics, University of Eastern Finland, 70210 Kuopio, Finland
| | - Tanja Tarvainen
- Department of Technical Physics, University of Eastern Finland, 70210 Kuopio, Finland
| | - Vesa-Pekka Lehto
- Department of Technical Physics, University of Eastern Finland, 70210 Kuopio, Finland
| |
Collapse
|
3
|
Na S, Wang LV. Photoacoustic computed tomography for functional human brain imaging [Invited]. BIOMEDICAL OPTICS EXPRESS 2021; 12:4056-4083. [PMID: 34457399 PMCID: PMC8367226 DOI: 10.1364/boe.423707] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 06/05/2021] [Accepted: 06/08/2021] [Indexed: 05/02/2023]
Abstract
The successes of magnetic resonance imaging and modern optical imaging of human brain function have stimulated the development of complementary modalities that offer molecular specificity, fine spatiotemporal resolution, and sufficient penetration simultaneously. By virtue of its rich optical contrast, acoustic resolution, and imaging depth far beyond the optical transport mean free path (∼1 mm in biological tissues), photoacoustic computed tomography (PACT) offers a promising complementary modality. In this article, PACT for functional human brain imaging is reviewed in its hardware, reconstruction algorithms, in vivo demonstration, and potential roadmap.
Collapse
Affiliation(s)
- Shuai Na
- Caltech Optical Imaging Laboratory, Andrew
and Peggy Cherng Department of Medical Engineering,
California Institute of Technology, 1200
East California Boulevard, Pasadena, CA 91125, USA
| | - Lihong V. Wang
- Caltech Optical Imaging Laboratory, Andrew
and Peggy Cherng Department of Medical Engineering,
California Institute of Technology, 1200
East California Boulevard, Pasadena, CA 91125, USA
- Caltech Optical Imaging Laboratory,
Department of Electrical Engineering, California
Institute of Technology, 1200 East California Boulevard,
Pasadena, CA 91125, USA
| |
Collapse
|
4
|
Sahlstrom T, Pulkkinen A, Leskinen J, Tarvainen T. Computationally Efficient Forward Operator for Photoacoustic Tomography Based on Coordinate Transformations. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2021; 68:2172-2182. [PMID: 33600313 DOI: 10.1109/tuffc.2021.3060175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
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 3-D photoacoustic images with a significantly reduced computational cost both in memory requirements and time. In the studied cases, depending on the computational factors, such as discretization, over the 30-fold reduction in memory consumption was achieved without a reduction in image quality compared to a conventional approach.
Collapse
|
5
|
Yalavarthy PK, Kalva SK, Pramanik M, Prakash J. Non-local means improves total-variation constrained photoacoustic image reconstruction. JOURNAL OF BIOPHOTONICS 2021; 14:e202000191. [PMID: 33025761 DOI: 10.1002/jbio.202000191] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 09/30/2020] [Accepted: 10/01/2020] [Indexed: 05/20/2023]
Abstract
Photoacoustic/Optoacoustic tomography aims to reconstruct maps of the initial pressure rise induced by the absorption of light pulses in tissue. This reconstruction is an ill-conditioned and under-determined problem, when the data acquisition protocol involves limited detection positions. The aim of the work is to develop an inversion method which integrates denoising procedure within the iterative model-based reconstruction to improve quantitative performance of optoacoustic imaging. Among the model-based schemes, total-variation (TV) constrained reconstruction scheme is a popular approach. In this work, a two-step approach was proposed for improving the TV constrained optoacoustic inversion by adding a non-local means based filtering step within each TV iteration. Compared to TV-based reconstruction, inclusion of this non-local means step resulted in signal-to-noise ratio improvement of 2.5 dB in the reconstructed optoacoustic images.
Collapse
Affiliation(s)
- Phaneendra K Yalavarthy
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India
| | - Sandeep Kumar Kalva
- School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore
| | - Manojit Pramanik
- School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore
| | - Jaya Prakash
- Department of Instrumentation and Applied Physics, Indian Institute of Science, Bangalore, India
| |
Collapse
|
6
|
Xiang N. Model-based Bayesian analysis in acoustics-A tutorial. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2020; 148:1101. [PMID: 32873013 DOI: 10.1121/10.0001731] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 07/24/2020] [Indexed: 06/11/2023]
Abstract
Bayesian analysis has been increasingly applied in many acoustical applications. In these applications, prediction models are often involved to better understand the process under investigation by purposely learning from the experimental observations. When involving the model-based data analysis within a Bayesian framework, issues related to incorporating the experimental data and assigning probabilities into the inferential learning procedure need fundamental consideration. This paper introduces Bayesian probability theory on a tutorial level, including fundamental rules for manipulating the probabilities, and the principle of maximum entropy for assignment of necessary probabilities prior to the data analysis. This paper also employs a number of examples recently published in this journal to explain detailed steps on how to apply the model-based Bayesian inference to solving acoustical problems.
Collapse
Affiliation(s)
- Ning Xiang
- Graduate Program in Arcvhitectural Acoustics, Rensselaer Polytechnic Institute, Troy, New York 12180, USA
| |
Collapse
|
7
|
Sahlstrom T, Pulkkinen A, Tick J, Leskinen J, Tarvainen T. Modeling of Errors Due to Uncertainties in Ultrasound Sensor Locations in Photoacoustic Tomography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2140-2150. [PMID: 31940525 DOI: 10.1109/tmi.2020.2966297] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Photoacoustic tomography is an imaging modality based on the photoacoustic effect caused by the absorption of an externally introduced light pulse. In the inverse problem of photoacoustic tomography, the initial pressure generated through the photoacoustic effect is estimated from a measured photoacoustic time-series utilizing a forward model for ultrasound propagation. Due to the ill-posedness of the inverse problem, errors in the forward model or measurements can result in significant errors in the solution of the inverse problem. In this work, we study modeling of errors caused by uncertainties in ultrasound sensor locations in photoacoustic tomography using a Bayesian framework. The approach is evaluated with simulated and experimental data. The results indicate that the inverse problem of photoacoustic tomography is sensitive even to small uncertainties in sensor locations. Furthermore, these uncertainties can lead to significant errors in the estimates and reduction of the quality of the photoacoustic images. In this work, we show that the errors due to uncertainties in ultrasound sensor locations can be modeled and compensated using Bayesian approximation error modeling.
Collapse
|
8
|
Tick J, Pulkkinen A, Tarvainen T. Modelling of errors due to speed of sound variations in photoacoustic tomography using a Bayesian framework. Biomed Phys Eng Express 2019; 6:015003. [PMID: 33438591 DOI: 10.1088/2057-1976/ab57d1] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Inverse problem of estimating initial pressure in photoacoustic tomography is ill-posed and thus sensitive to errors in modelling and measurements. In practical experiments, accurate knowledge of the speed of sound of the imaged target is commonly not available, and therefore an approximate speed of sound is used in the computational model. This can result in errors in the solution of the inverse problem that can appear as artefacts in the reconstructed images. In this paper, the inverse problem of photoacoustic tomography is approached in a Bayesian framework. Errors due to uncertainties in the speed of sound are modelled using Bayesian approximation error modelling. Estimation of the initial pressure distribution together with information on the reliability of these estimates are considered. The approach was studied using numerical simulations. The results show that uncertainties in the speed of sound can cause significant errors in the solution of the inverse problem. However, modelling of these uncertainties improves the accuracy of the solution.
Collapse
Affiliation(s)
- Jenni Tick
- Department of Applied Physics, University of Eastern Finland, PO Box 1627, 70211 Kuopio, Finland
| | | | | |
Collapse
|
9
|
Abstract
Quantitative photoacoustic tomography is a novel imaging method which aims to reconstruct optical parameters of an imaged target based on initial pressure distribution, which can be obtained from ultrasound measurements. In this paper, a method for reconstructing the optical parameters in a Bayesian framework is presented. In addition, evaluating the credibility of the estimates is studied. Furthermore, a Bayesian approximation error method is utilized to compensate the modeling errors caused by coarse discretization of the forward model. The reconstruction method and the reliability of the credibility estimates are investigated with two-dimensional numerical simulations. The results suggest that the Bayesian approach can be used to obtain accurate estimates of the optical parameters and the credibility estimates of these parameters. Furthermore, the Bayesian approximation error method can be used to compensate for the modeling errors caused by a coarse discretization, which can be used to reduce the computational costs of the reconstruction procedure. In addition, taking the modeling errors into account can increase the reliability of the credibility estimates.
Collapse
|