Compensation for acoustic heterogeneities in photoacoustic computed tomography using a variable temporal data truncation reconstruction method

Photoacoustic computed tomography (PACT) is an emerging computed imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the absorbed optical energy density within tissue. If the object possesses spatially variant acoustic properties that are unaccounted for by the reconstruction method, the estimated image can contain distortions. While reconstruction methods have recently been developed to compensate for this effect, they generally require the object’s acoustic properties to be known a priori. To circumvent the need for detailed information regarding an object’s acoustic properties, we previously proposed a half-time reconstruction method for PACT. A half-time reconstruction method estimates the PACT image from a data set that has been temporally truncated to exclude the data components that have been strongly aberrated. However, this method can be improved upon when the approximate sizes and locations of isolated heterogeneous structures, such as bones or gas pockets, are known. To address this, we investigate PACT reconstruction methods that are based on a variable data truncation (VDT) approach. The VDT approach represents a generalization of the half-time approach, in which the degree of temporal truncation for each measurement is determined by the distance between the corresponding ultrasonic transducer location and the nearest known bone or gas void location. Computer-simulated and experimental data are employed to demonstrate the effectiveness of the approach in mitigating artifacts due to acoustic heterogeneities.

Conventional photoacoustic computed tomography (PACT) image reconstruction methods assume that the object and surrounding medium are described by a constant speed-of-sound (SOS) value. In order to accurately recover fine structures, SOS heterogeneities should be quantified and compensated for during PACT reconstruction. To address this problem, several groups have proposed hybrid systems that combine PACT with ultrasound computed tomography (USCT). In such systems, a SOS map is reconstructed first via USCT. Consequently, this SOS map is employed to inform the PACT reconstruction method. Additionally, the SOS map can provide structural information regarding tissue, which is complementary to the functional information from the PACT image. We propose a paradigm shift in the way that images are reconstructed in hybrid PACT-USCT imaging. Inspired by our observation that information about the SOS distribution is encoded in PACT measurements, we propose to jointly reconstruct the absorbed optical energy density and SOS distributions from a combined set of USCT and PACT measurements, thereby reducing the two reconstruction problems into one. This innovative approach has several advantages over conventional approaches in which PACT and USCT images are reconstructed independently: (1) Variations in the SOS will automatically be accounted for, optimizing PACT image quality; (2) The reconstructed PACT and USCT images will possess minimal systematic artifacts because errors in the imaging models will be optimally balanced during the joint reconstruction; (3) Due to the exploitation of information regarding the SOS distribution in the full-view PACT data, our approach will permit high-resolution reconstruction of the SOS distribution from sparse array data.

Photoacoustic computed tomography (PACT) has emerged as a unique and promising technology for multiscale biomedical imaging. To fully realize its potential for various preclinical and clinical applications, development of systems with high imaging speed, reasonable cost, and manageable data flow are needed. Sparse-sampling PACT with advanced reconstruction algorithms, such as compressed-sensing reconstruction, has shown potential as a solution to this challenge. However, most such algorithms require iterative reconstruction and thus intense computation, which may lead to excessively long image reconstruction times. Here, we developed a principal component analysis (PCA)-based PACT (PCA-PACT) that can rapidly reconstruct high-quality, three-dimensional (3-D) PACT images with sparsely sampled data without requiring an iterative process. In vivo images of the vasculature of a human hand were obtained, thus validating the PCA-PACT method. The results showed that, compared with the back-projection (BP) method, PCA-PACT required ∼50% fewer measurements and ∼40% less time for image reconstruction, and the imaging quality was almost the same as that for BP with full sampling. In addition, compared with compressed sensing-based PACT, PCA-PACT had approximately sevenfold faster imaging speed with higher imaging accuracy. This work suggests a promising approach for low-cost, 3-D, rapid PACT for various biomedical applications.

Photoacoustic computed tomography (PACT) is an emerging computed imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the absorbed optical energy density within tissue. The PACT reconstruction problem corresponds to a time-domain inverse source problem, where the initial pressure distribution is recovered from the wavefield recorded on a measurement aperture located outside the support of the source. A major challenge in transcranial PACT brain imaging is to compensate for aberrations in the measured photoacoustic data due to their propagation through the skull. The transmission of ultrasonic waves through the skull induces strong changes to the photoacoustic wavefield through the processes of absorption, scattering, and longitudinal-to-shear wave mode conversion. To properly account for these effects, a wave equation-based inversion method should be employed that can model the heterogeneous elastic properties of the medium.

Monitoring critical physiological processes in murine models like tumor vascular perfusion and its response to prospective anti-cancer treatments is a significant potential use case of dynamic photoacoustic computed tomography (PACT). Previously reported studies of dynamic PACT are based on a frame-by-frame image reconstruction (FBFIR) procedure in which full-view measurement data are assumed to be rapidly acquired. However, many commercial three-dimensional PACT imagers acquire measurements at each tomographic view rotating the object in discrete steps. The time to collect the full-view data is limited by the rotation speed of the object holder and the laser repetition rate. Therefore, FBFIR techniques are not applicable, and there is a critical need for accurate and efficient spatiotemporal image reconstruction (STIR) techniques that can account for spatiotemporal redundancies in the object’s features. To address this, we propose a low-rank matrix estimation-based STIR technique in which the sought-after dynamic image is approximated using a semiseparable approximation in space and time. To validate the proposed method, we also develop a virtual imaging framework for PACT employing dynamic numerical mouse phantoms with a physiologically realistic respiratory motion and perfusion model. The studies demonstrated that the proposed method accurately recovers the tumor vascular perfusion and object motion.

Purpose:We propose to verify quantifiable perfusion information generated by a Photoacoustic Computed Tomography (PCT) scanner using Dynamic Contrast‐Enhanced CT (DCE‐CT), and to investigate physicsbased models of acoustic properties of tissue and photon transport to improve quantification. These corrections first necessitate a skin identifying algorithms to reduce speed‐of‐sound blurring and empirical photon correction methods.Methods:Xenograft mice (n=5) of breast cancer was imaged using DCE‐CT which was followed by DCE‐PCT. To obtain CT perfusion data, each mouse was i.v. injected (0.2mL Isovue @0.5mL/min) and subsequent radio‐opaque time curves fit to a 2‐compartmental model on a voxel‐wise basis. For DCE‐PCT, different concentrations of ICG (250, 125, and 62.5 micro‐Molar) were injected at the same rate, but also acquired at different sampling rates (3, 6, and 12 seconds). The time intensity curves from PCT were fit to a 1‐compartmental model on a voxel by voxel basis. The images were coregistered (Oncentra) based on the structural similarities of the tumor vasculature after which we compared both the contrastenhanced dynamics and the vascular physiology.Results:Moderate to high doses of ICG impact the washin phase of the PCT contrast due to photon losses as a function of depth. A semi‐automatic algorithm has been developed to identify the skin margin, and subsequent MC and empirical models of photon transport and variations in speed‐of‐sound are being evaluated.Conclusion:From our results we find that there is a need to apply photon and speed‐of‐sound corrections to our PCT data to improve the quantifiable image data at depth in the tumor for PCT. The dose and injection rate may help in reducing large systematic effects.Our project is partially funded by a NIH SBIR grant.

Transcranial photoacoustic computed tomography (PACT) faces challenges due to skull-induced aberrations in measured pressure data. Although optimization-based image reconstruction methods have been developed to mitigate these aberrations, they require precise knowledge of skull properties that are generally unknown. Parameterized joint reconstruction (JR) methods improve computational tractability by estimating simplified skull models but only partially capture skull heterogeneity, potentially compromising modeling accuracy. Therefore, there remains a need for a reconstruction method that accounts for uncertainties in a fully heterogeneous skull model to enable accurate PACT image reconstruction in humans. In this study, a JR method for 3D transcranial PACT is developed to concurrently estimate the initial pressure distribution and CT-informed heterogeneous viscoelastic properties of the skull. The proposed approach leverages adjunct CT data and a linear relationship between CT voxel values and acoustic properties to parameterize a fully heterogeneous skull model. A gradientfree joint reconstruction method is used to estimate subject-dependent bone property parameters concurrently with the 3D initial pressure distribution. The method was systematically evaluated using realistic, full-scale 3D numerical head phantoms through in-silico studies. Results demonstrate that incorporating CT-informed heterogeneous skull modeling significantly improved image quality compared to model-based methods that assume a homogeneous skull model or a heterogeneous skull model with nominal parameter values. These findings highlight the importance of accurate skull modeling for robust transcranial PACT imaging.

Iterative image reconstruction algorithms for photoacoustic computed tomography (PACT) have the ability to improve image quality over analytic algorithms due to their ability to mitigate artifacts from incomplete data, incorporate the relevant imaging physics, and model the instrument response. In this work, Kaiser-Bessel functions are employed as the basis functions in an iterative reconstruction algorithm for PACT. Kaiser-Bessel functions, or blobs, can be made arbitrarily smooth (differentiable to arbitrary order), have finite spatial support, and can be made quasi-bandlimited in the spatial Fourier domain. Closed-form solutions exist in the time-domain and the temporal-frequency domain for the pressure signal generated by blobs.

As an emerging optical imaging modality, photoacoustic imaging provides optical absorption contrasts and ultrasonic high resolution. Artifacts appearing in photoacoustic computed tomography (PACT) always deteriorate image quality and resolution, and result in confusion of biological information. On the basis of different causing reasons, they are roughly classified as split artifacts and streak artifacts. Here we present an innovative Feature-Coupling (FC) method to weaken split artifacts with joint reconstruction of speed of sound and a new reconstruction algorithm, termed Contamination-Tracing Back-Projection (CTBP), is proposed for the mitigation of streak artifacts. The utility, effectiveness and robustness of our methods were demonstrated using numerical, phantom, and in vivo experiments.

Photoacoustic computed tomography (PACT) combines the advantages of optical and ultrasound imaging, offering significant potential for both preclinical and clinical applications. The development and refinement of PACT imaging systems and reconstruction methods should ideally be guided by performance measures that reflect the clinical utility of the images produced. However, widely employed conventional image quality (IQ) measures, including mean squared error (MSE) and structural similarity index (SSIM), do not always correlate with the utility of images with consideration of a specified diagnostic task, such as lesion detection. To address this, task-based IQ measures can be employed, which directly quantify the utility of an image for a specified clinical purpose. However, currently, such task-based IQ measures are not widely utilized in the photoacoustics research community. In this study, a task-based IQ measure is introduced and employed to quantify the performance of PACT reconstruction methods with respect to a clinically significant task, namely binary signal detection. Through systematic virtual imaging studies that involve realistic numerical breast phantoms and both physicsand learning-based image reconstruction methods, the advantages of task-based over conventional IQ measures are demonstrated. Examples are provided in which conventional IQ measures suggest comparable performance across reconstruction methods, while the task-based IQ measure demonstrates substantial differences, particularly when clinically significant lesions are missed. These results highlight the limitations of conventional IQ metrics in characterizing the diagnostic performance of PACT methods and underscore the necessity of integrating task-based IQ assessment into the development and evaluation of PACT reconstruction methods for clinical translation.

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.

Numerical phantoms are a common tool for the evaluation of registration and reconstruction algorithms. For applications concerning motion, dense deformation fields are of particular interest. Phantoms, however, are often described as surfaces and thus motion vectors can only be generated at these surfaces. In order to create dense motion fields, interpolation is required. A frequently used method for this purpose is the Parzen interpolator. However, with a high number of surface motion vectors and a high voxel count, its run time increases dramatically. In this paper, we investigate different methods to accelerate the creation of these motion fields using hierarchical sampling and the random ball cover. In the results, we show that a 64 3 volume can be sampled in less than one second with an error below 0.1 mm. Furthermore, we accelerate the interpolation of a 256 3 dense deformation field to only ˜. 5m inutes using the proposed methods from days with previous methods.

In silico studies for ultrasound computed tomography (USCT) can allow to explore imaging system parameters and reconstruction methods, without the economic burden and ethical concerns of clinical trials. A framework is proposed for virtual imaging trials of USCT. First, an ensemble of three-dimensional numerical breast phantoms consisting of anatomically realistic tissue structures and lesions is created. Next, acoustic properties are assigned to each tissue-type within physiological ranges. Finally, USCT measurement data are computed by simulating acoustic wave propagation. The proposed framework will establish a standard pipeline for USCT virtual imaging trials and provide publicly available large-scale datasets

This paper describes a fast-forward electromagnetic solver (FFS) for the image reconstruction algorithm of our microwave tomography system. Our apparatus is a preclinical prototype of a biomedical imaging system, designed for the purpose of early breast cancer detection. It operates in the 3-6-GHz frequency band using a circular array of probe antennas immersed in a matching liquid; it produces image reconstructions of the permittivity and conductivity profiles of the breast under examination. Our reconstruction algorithm solves the electromagnetic (EM) inverse problem and takes into account the real EM properties of the probe antenna array as well as the influence of the patient's body and that of the upper metal screen sheet. This FFS algorithm is much faster than conventional EM simulation solvers. In comparison, in the same PC, the CST solver takes ~45 min, while the FFS takes ~1 s of effective simulation time for the same EM model of a numerical breast phantom.

With the advances of x-ray excitable nanophosphors, x-ray luminescence computed tomography (XLCT) has become a promising hybrid imaging modality. In particular, a cone-beam XLCT (CB-XLCT) system has demonstrated its potential in in vivo imaging with the advantage of fast imaging speed over other XLCT systems. Currently, the imaging models of most XLCT systems assume that nanophosphors emit light based on the intensity distribution of x-ray within the object, not completely reflecting the nature of the x-ray excitation process. To improve the imaging quality of CB-XLCT, an imaging model that adopts an excitation model of nanophosphors based on x-ray absorption dosage is proposed in this study. To solve the ill-posed inverse problem, a reconstruction algorithm that combines the adaptive Tikhonov regularization method with the imaging model is implemented for CB-XLCT reconstruction. Numerical simulations and phantom experiments indicate that compared with the traditional forward model based on x-ray intensity, the proposed dose-based model could improve the image quality of CB-XLCT significantly in terms of target shape, localization accuracy, and image contrast. In addition, the proposed model behaves better in distinguishing closer targets, demonstrating its advantage in improving spatial resolution.