Lu Ding

Efficient non-negative constrained model-based inversion in optoacoustic tomography

The inversion accuracy in optoacoustic tomography depends on a number of parameters, including the number of detectors employed, discrete sampling issues or imperfectness of the forward model. These parameters result in ambiguities on the reconstructed image. A common ambiguity is the appearance of negative values, which have no physical meaning since optical absorption can only be higher or equal than zero. We investigate herein algorithms that impose non-negative constraints in model-based optoacoustic inversion. Several state-of-the-art non-negative constrained algorithms are analyzed. Furthermore, an algorithm based on the conjugate gradient method is introduced in this work. We are particularly interested in investigating whether positive restrictions lead to accurate solutions or drive the appearance of errors and artifacts. It is shown that the computational performance of non-negative constrained inversion is higher for the introduced algorithm than for the other algorithms, while yielding equivalent results. The experimental performance of this inversion procedure is then tested in phantoms and small animals, showing an improvement in image quality and quantitativeness with respect to the unconstrained approach. The study performed validates the use of non-negative constraints for improving image accuracy compared to unconstrained methods, while maintaining computational efficiency.

Real-Time Model-Based Inversion in Cross-Sectional Optoacoustic Tomography

Analytical (closed-form) inversion schemes have been the standard approach for image reconstruction in optoacoustic tomography due to their fast reconstruction abilities and low memory requirements. Yet, the need for quantitative imaging and artifact reduction has led to the development of more accurate inversion approaches, which rely on accurate forward modeling of the optoacoustic wave generation and propagation. In this way, multiple experimental factors can be incorporated, such as the exact detection geometry, spatio-temporal response of the transducers, and acoustic heterogeneities. The model-based inversion commonly results in very large sparse matrix formulations that require computationally extensive and memory demanding regularization schemes for image reconstruction, hindering their effective implementation in real-time imaging applications. Herein, we introduce a new discretization procedure for efficient model-based reconstructions in two-dimensional optoacoustic tomography that allows for parallel implementation on a graphics processing unit (GPU) with a relatively low numerical complexity. By on-the-fly calculation of the model matrix in each iteration of the inversion procedure, the new approach results in imaging frame rates exceeding 10 Hz, thus enabling real-time image rendering using the model-based approach.

Dynamic particle enhancement in limited-view optoacoustic tomography

Limited-view artifacts are commonly present in optoacoustic tomography images, mainly due to practical geometrical and physical constraints imposed by the imaging systems. Herein, a new approach called dynamic particle-enhanced optoacoustic tomography (DPOT) is proposed for improving image contrast and visibility of optoacoustic images under limited-view scenarios. The method is based on a nonlinear combination of a temporal sequence of tomographic reconstructions representing sparsely distributed moving particles. We demonstrate experimental performance by dynamically imaging the flow of suspended microspheres in three dimensions, which shows promise for DPOT applicability in angiographic imaging in living organisms.

Three-dimensional optoacoustic reconstruction using fast sparse representation

Optoacoustic tomography based on insufficient spatial sampling of ultrasound waves leads to loss of contrast and artifacts on the reconstructed images. Compared to reconstructions based on L2-norm regularization, sparsity-based reconstructions may improve contrast and reduce image artifacts but at a high computational cost, which has so far limited their use to 2D optoacoustic tomography. Here we propose a fast, sparsity-based reconstruction algorithm for 3D optoacoustic tomography, based on gradient descent with Barzilai-Borwein line search (L1-GDBB). Using simulations and experiments, we show that the L1-GDBB offers fourfold faster reconstruction than the previously reported L1-norm regularized reconstruction based on gradient descent with backtracking line search. Moreover, the new algorithm provides higher-quality images with fewer artifacts than the L2-norm regularized reconstruction and the back-projection reconstruction.

Image reconstruction in cross-sectional optoacoustic tomography based on non-negative constrained model-based inversion

In optoacoustic tomography, images representing the light absorption distribution are reconstructed from the measured acoustic pressure waves at several locations around the imaged sample. Most reconstruction algorithms typically yield negative absorption values due to modelling inaccuracies and imperfect measurement conditions. Those negative optical absorption values have no physical meaning and their presence hinders image quantification and interpretation of biological information. We investigate herein the performance of optimization methods that impose non-negative constraints in model-based optoacoustic inversion. Specifically, we analyze the effects of the non-negative restrictions on image quality and accuracy as compared to the unconstrained approach. An efficient algorithm based on the projected quasi-Newton scheme and the limitedmemory Broyden-Fletcher-Goldfarb-Shannon method is used for the non-negative constrained inversion. We showcase that imposing non-negative constraints in model-based reconstruction leads to a quality increase in cross-sectional tomographic optoacoustic images.

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 3-D model. Herein, we introduce a 3-D model-based 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 graphic 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 3-D image quality are also reported in real tissues by imaging the finger of a healthy volunteer with a hand-held volumetric optoacoustic imaging system.

Constrained Inversion and Spectral Unmixing in Multispectral Optoacoustic Tomography

Accurate extraction of physical and biochemical parameters from optoacoustic images is often impeded due to the use of unrigorous inversion schemes, incomplete tomographic detection coverage, or other experimental factors that cannot be readily accounted for during the image acquisition and reconstruction process. For instance, inaccurate assumptions in the physical forward model may lead to negative optical absorption values in the reconstructed images. Any artifacts present in the single wavelength optoacoustic images can be significantly aggravated when performing a two-step reconstruction consisting in acoustic inversion and spectral unmixing aimed at rendering the distributions of spectrally distinct absorbers. We investigate a number of algorithmic strategies with non-negativity constraints imposed at the different phases of the reconstruction process. Performance is evaluated in cross-sectional multispectral optoacoustic tomography recordings from tissue-mimicking phantoms and in vivo mice embedded with varying concentrations of contrast agents. Additional in vivo validation is subsequently performed with molecular imaging data involving subcutaneous tumors labeled with genetically expressed iRFP proteins and organ perfusion by optical contrast agents. It is shown that constrained reconstruction is essential for reducing the critical image artifacts associated with inaccurate modeling assumptions. Furthermore, imposing the non-negativity constraint directly on the unmixed distribution of the probe of interest was found to maintain the most robust and accurate reconstruction performance in all experiments.

Noninvasive Anatomical and Functional Imaging of Orthotopic Glioblastoma Development and Therapy using Multispectral Optoacoustic Tomography

PURPOSE: Here we demonstrate the potential of multispectral optoacoustic tomography (MSOT), a new non-invasive structural and functional imaging modality, to track the growth and changes in blood oxygen saturation (sO2) in orthotopic glioblastoma (GBMs) and the surrounding brain tissues upon administration of a vascular disruptive agent (VDA). METHODS: Nude mice injected with U87MG tumor cells were longitudinally monitored for the development of orthotopic GBMs up to 15 days and observed for changes in sO2 upon administration of combretastatin A4 phosphate (CA4P, 30 mg/kg), an FDA approved VDA for treating solid tumors. We employed a newly-developed non-negative constrained approach for combined MSOT image reconstruction and unmixing in order to quantitatively map sO2 in whole mouse brains. RESULTS: Upon longitudinal monitoring, tumors could be detected in mouse brains using single-wavelength data as early as 6 days post tumor cell inoculation. Fifteen days post-inoculation, tumors had higher sO2 of 63 ± 11% (n = 5, P < .05) against 48 ± 7% in the corresponding contralateral brain, indicating their hyperoxic status. In a different set of animals, 42 days post-inoculation, tumors had lower sO2 of 42 ± 5% against 49 ± 4% (n = 3, P < .05) in the contralateral side, indicating their hypoxic status. Upon CA4P administration, sO2 in 15 days post-inoculation tumors dropped from 61 ± 9% to 36 ± 1% (n = 4, P < .01) within one hour, then reverted to pre CA4P treatment values (63 ± 6%) and remained constant until the last observation time point of 6 hours. CONCLUSION: With the help of advanced post processing algorithms, MSOT was capable of monitoring the tumor growth and assessing hemodynamic changes upon administration of VDAs in orthotopic GBMs.

Improving visibility in limited-view scenarios with dynamic particle-enhanced optoacoustic tomography

Limited-view artefacts affect most optoacoustic (photoacoustic) imaging systems due to geometrical constraints that impede achieving full tomographic coverage as well as limited light penetration into scattering and absorbing objects. Indeed, it has been theoretically established and experimentally verified that accurate optoacoustic images can only be obtained if the imaged sample is fully enclosed (< π angular coverage) by the measuring locations. Since in many cases full angular coverage cannot be achieved, the visibility of structures along certain orientations is hampered. These effects are of particular relevance in the case of hand-held scanners with the imaged volume only accessible from one side. Herein, a new approach termed dynamic particle-enhanced optoacoustic tomography (DPOT) is described for accurate structural imaging in limited-view scenarios. The method is based on the non-linear combination of a sequence of tomographic reconstructions representing sparsely distributed moving particles. Good performance of the method is demonstrated in experiments consisting of dynamic visualization of flow of suspended microspheres in three-dimensions. The method is expected to be applicable for improving accuracy of angiographic optoacoustic imaging in living organisms.

20 frames per second model-based reconstruction in cross-sectional optoacoustic tomography

In order to achieve real-time image rendering, optoacoustic tomography reconstructions are commonly done with back-projection algorithms due to their simplicity and low computational complexity. However, model-based algorithms have been shown to attain more accurate reconstruction performance due to their ability to model arbitrary detection geometries, transducer shapes and other experimental factors. The high computational complexity of the model-based schemes makes it challenging to be implemented for real time inversion. Herein, we introduce a novel discretization method for model-based optoacoustic tomography that enables its efficient parallel implementation on graphics processing units with extremely low memory overhead. We demonstrate that, when employing a tomographic scanner with 256 detectors, the new method achieves model-based optoacoustic inversion at 20 frames per second for a 200 × 200 image grid.