Super-resolution photoacoustic and ultrasound imaging with sparse arrays

Renal-clearable nanoprobes for optical imaging and early diagnosis of diseases

Optical imaging has played an indispensable role in clinical diagnostics and fundamental biomedical research due to its high sensitivity, high spatiotemporal resolution, cost-effectiveness, and easy accessibility. However, the issues of light scattering and low tissue penetration make them effective only for superficial imaging. To overcome these issues, renal-clearable optical nanoprobes have recently emerged, which are activated by abnormal disease-associated biomarkers and initiate a pharmacokinetic switch by undergoing degradation and eventually releasing signal reporters into urine, for simple imaging and sensitive optical in vitro urinalysis. In this review, we focus on the advancements of renal-clearable organic nanoprobes for optical imaging and remote urinalysis. The versatile design strategies of these nanoprobes are discussed along with their sensing mechanisms toward biomolecules of interest as well as their unique biological applications. Finally, challenges and perspectives are discussed to further advance the next-generation renal-clearable nanoprobes for in vivo imaging and in vitro urinalysis.

Convolutional sparse coding for compressed sensing photoacoustic CT reconstruction with partially known support

In photoacoustic tomography (PAT), imaging speed is an essential metric that is restricted by the pulse laser repetition rate and the number of channels on the data acquisition card (DAQ). Reconstructing the initial sound pressure distribution with fewer elements can significantly reduce hardware costs and back-end acquisition pressure. However, undersampling will result in artefacts in the photoacoustic image, degrading its quality. Dictionary learning (DL) has been utilised for various image reconstruction techniques, but they disregard the uniformity of pixels in overlapping blocks. Therefore, we propose a compressive sensing (CS) reconstruction algorithm for circular array PAT based on gradient domain convolutional sparse coding (CSCGR). A small number of non-zero signal positions in the sparsely encoded feature map are used as partially known support (PKS) in the reconstruction procedure. The CS-CSCGR-PKS-based reconstruction algorithm can use fewer ultrasound transducers for signal acquisition while maintaining image fidelity. We demonstrated the effectiveness of this algorithm in sparse imaging through imaging experiments on the mouse torso, brain, and human fingers. Reducing the number of array elements while ensuring imaging quality effectively reduces equipment hardware costs and improves imaging speed.

Adaptive machine learning method for photoacoustic computed tomography based on sparse array sensor data

BACKGROUND AND OBJECTIVE

Photoacoustic computed tomography (PACT) is a non-invasive biomedical imaging technology that has developed rapidly in recent decades, especially has shown potential for small animal studies and early diagnosis of human diseases. To obtain high-quality images, the photoacoustic imaging system needs a high-element-density detector array. However, in practical applications, due to the cost limitation, manufacturing technology, and the system requirement in miniaturization and robustness, it is challenging to achieve sufficient elements and high-quality reconstructed images, which may even suffer from artifacts. Different from the latest machine learning methods based on removing distortions and artifacts to recover high-quality images, this paper proposes an adaptive machine learning method to firstly predict and complement the photoacoustic sensor channel data from sparse array sampling and then reconstruct images through conventional reconstruction algorithms.

METHODS

We develop an adaptive machine learning method to predict and complement the photoacoustic sensor channel data. The model consists of XGBoost and a neural network named SS-net. To handle data sets of different sizes and improve the generalization, a tunable parameter is used to control the weights of XGBoost and SS-net outputs.

RESULTS

The proposed method achieved superior performance as demonstrated by simulation, phantom experiments, and in vivo experiment results. Compared with linear interpolation, XGBoost, CAE, and U-net, the simulation results show that the SSIM value is increased by 12.83%, 6.78%, 21.46%, and 12.33%. Moreover, the median R2 is increased by 34.4%, 8.1%, 28.6%, and 84.1% with the in vivo data.

CONCLUSIONS

This model provides a framework to predict the missed photoacoustic sensor data on a sparse ring-shaped array for PACT imaging and has achieved considerable improvements in reconstructing the objects. Compared with linear interpolation and other deep learning methods qualitatively and quantitatively, our proposed methods can effectively suppress artifacts and improve image quality. The advantage of our methods is that there is no need for preparing a large number of images as the training dataset, and the data for training is directly from the sensors. It has the potential to be applied to a wide range of photoacoustic imaging detector arrays for low-cost and user-friendly clinical applications.

Enabling the autofocus approach for parameter optimization in planar measurement geometry clinical optoacoustic imaging

In optoacoustic (photoacoustic) tomography, several parameters related to tissue and detector features are needed for image formation, but they may not be known a priori. An autofocus (AF) algorithm is generally used to estimate these parameters. However, the algorithm works iteratively and is therefore impractical for clinical imaging with planar geometry systems due to the long reconstruction times. We have developed a fast autofocus (FAF) algorithm for 3D optoacoustic systems with planar geometry. Such an algorithm exploits the symmetries of the planar geometry and a virtual source concept to reduce the dimensionality of the parameter estimation problem. The dimensionality reduction makes FAF much simpler computationally than the conventional AF algorithm. We show that the FAF algorithm required about 5 s to provide accurate estimates of the speed of sound in simulated data and experimental data obtained using an imaging system that is poised to enter the clinic. The applicability of FAF for estimating other image formation parameters is discussed. We expect the FAF algorithm to contribute decisively to the clinical use of optoacoustic tomography systems with planar geometry.

High-fidelity deconvolution for acoustic-resolution photoacoustic microscopy enabled by convolutional neural networks

Acoustic-resolution photoacoustic microscopy (AR-PAM) image resolution is determined by the point spread function (PSF) of the imaging system. Previous algorithms, including Richardson-Lucy (R-L) deconvolution and model-based (MB) deconvolution, improve spatial resolution by taking advantage of the PSF as prior knowledge. However, these methods encounter the problems of inaccurate deconvolution, meaning the deconvolved feature size and the original one are not consistent (e.g., the former can be smaller than the latter). We present a novel deep convolution neural network (CNN)-based algorithm featuring high-fidelity recovery of multiscale feature size to improve lateral resolution of AR-PAM. The CNN is trained with simulated image pairs of line patterns, which is to mimic blood vessels. To investigate the suitable CNN model structure and elaborate on the effectiveness of CNN methods compared with non-learning methods, we select five different CNN models, while R-L and directional MB methods are also applied for comparison. Besides simulated data, experimental data including tungsten wires, leaf veins, and in vivo blood vessels are also evaluated. A custom-defined metric of relative size error (RSE) is used to quantify the multiscale feature recovery ability of different methods. Compared to other methods, enhanced deep super resolution (EDSR) network and residual in residual dense block network (RRDBNet) model show better recovery in terms of RSE for tungsten wires with diameters ranging from 30 μ m to 120 μ m . Moreover, AR-PAM images of leaf veins are tested to demonstrate the effectiveness of the optimized CNN methods (by EDSR and RRDBNet) for complex patterns. Finally, in vivo images of mouse ear blood vessels and rat ear blood vessels are acquired and then deconvolved, and the results show that the proposed CNN method (notably RRDBNet) enables accurate deconvolution of multiscale feature size and thus good fidelity.

Dual-Mode Volumetric Optoacoustic and Contrast Enhanced Ultrasound Imaging With Spherical Matrix Arrays

Spherical matrix arrays represent an advantageous tomographic detection geometry for non-invasive deep tissue mapping of vascular networks and oxygenation with volumetric optoacoustic tomography (VOT). Hybridization of VOT with ultrasound (US) imaging remains difficult with this configuration due to the relatively large inter-element pitch of spherical arrays. We suggest a new approach for combining VOT and US contrast-enhanced 3D imaging employing injection of clinically-approved microbubbles. Power Doppler (PD) and US localization imaging were enabled with a sparse US acquisition sequence and model-based inversion based on infimal convolution of total variation (ICTV) regularization. In vitro experiments in tissue-mimicking phantoms and in living mouse brain demonstrate the powerful capabilities of the new dual-mode imaging approach attaining 80 μm spatial resolution and a more than 10 dB signal to noise improvement with respect to a classical delay and sum beamformer. Microbubble localization and tracking allowed for flow velocity mapping up to 40 mm/s.

Image enhancement in acoustic-resolution photoacoustic microscopy enabled by a novel directional algorithm

By considering the line pattern of acoustic-resolution photoacoustic microscopy (AR-PAM) vessel images, we develop modified algorithms for synthetic aperture focusing technique (SAFT) and deconvolution based on a directional approach to enhance images. The modified algorithms consist of Fourier accumulation SAFT (FA-SAFT) and directional model-based (D-MB) deconvolution. To evaluate the performance of our algorithms, we conduct a series of imaging experiments and apply our algorithms, and existing SAFT and deconvolution algorithms are also applied for side-by-side comparison. By imaging tungsten wire phantom, our algorithms enable full width at half maximum of 26 - 31 µm over depth of focus of 1.8 mm and minimum resolvable distance of 46 - 49 µm, besting existing SAFT and deconvolution algorithms. Imaging of leaf skeleton phantom and in vivo imaging of mouse blood vessels also prove that our algorithm is capable of providing high-resolution, high-signal-to-noise ratio, and good-fidelity results for complex structures and for in vivo applications, especially for the images with the line pattern. The proposed directional approach can not only be used in AR-PAM but also in other imaging modalities to deal with the line pattern, such as FA-SAFT for ultrasound imaging and D-MB deconvolution for optical coherence tomography angiography.

A scalable open-source MATLAB toolbox for reconstruction and analysis of multispectral optoacoustic tomography data

Multispectral photoacoustic tomography enables the resolution of spectral components of a tissue or sample at high spatiotemporal resolution. With the availability of commercial instruments, the acquisition of data using this modality has become consistent and standardized. However, the analysis of such data is often hampered by opaque processing algorithms, which are challenging to verify and validate from a user perspective. Furthermore, such tools are inflexible, often locking users into a restricted set of processing motifs, which may not be able to accommodate the demands of diverse experiments. To address these needs, we have developed a Reconstruction, Analysis, and Filtering Toolbox to support the analysis of photoacoustic imaging data. The toolbox includes several algorithms to improve the overall quantification of photoacoustic imaging, including non-negative constraints and multispectral filters. We demonstrate various use cases, including dynamic imaging challenges and quantification of drug effect, and describe the ability of the toolbox to be parallelized on a high performance computing cluster.

Single laser-shot super-resolution photoacoustic tomography with fast sparsity-based reconstruction

Recently, ℓ 1 -norm based reconstruction approaches have been used with linear array systems to improve photoacoustic resolution and demonstrate undersampled imaging when there is sufficient sparsity in some domain. However, such approaches have yet to beat the half-wavelength resolution limit. In this paper, the ability to beat the half-wavelength diffraction limit is demonstrated using a 5 MHz ring array photoacoustic tomography system and ℓ 1 -norm based reconstruction approaches. We used the array system to image wire targets at ≈ 2 - 3 cm depth in both intralipid scattering solution and water. The minimum observable separation was estimated as 70 ± 10 μ m , improving on the half-wavelength resolution limit of 145 μ m . This improvement was demonstrated even when using a random projection transform to reduce data by 99 % , enabling substantially faster reconstruction times. This is the first photoacoustic tomography approach capable of beating the half-wavelength resolution limit with a single laser shot.

Design and Challenges of Sonodynamic Therapy System for Cancer Theranostics: From Equipment to Sensitizers

As a novel noninvasive therapeutic modality combining low-intensity ultrasound and sonosensitizers, sonodynamic therapy (SDT) is promising for clinical translation due to its high tissue-penetrating capability to treat deeper lesions intractable by photodynamic therapy (PDT), which suffers from the major limitation of low tissue penetration depth of light. The effectiveness and feasibility of SDT are regarded to rely on not only the development of stable and flexible SDT apparatus, but also the screening of sonosensitizers with good specificity and safety. To give an outlook of the development of SDT equipment, the key technologies are discussed according to five aspects including ultrasonic dose settings, sonosensitizer screening, tumor positioning, temperature monitoring, and reactive oxygen species (ROS) detection. In addition, some state-of-the-art SDT multifunctional equipment integrating diagnosis and treatment for accurate SDT are introduced. Further, an overview of the development of sonosensitizers is provided from small molecular sensitizers to nano/microenhanced sensitizers. Several types of nanomaterial-augmented SDT are in discussion, including porphyrin-based nanomaterials, porphyrin-like nanomaterials, inorganic nanomaterials, and organic-inorganic hybrid nanomaterials with different strategies to improve SDT therapeutic efficacy. There is no doubt that the rapid development and clinical translation of sonodynamic therapy will be promoted by advanced equipment, smart nanomaterial-based sonosensitizer, and multidisciplinary collaboration.

Sparse channel sampling for ultrasound localization microscopy (SPARSE-ULM)

Ultrasound localization microscopy (ULM) has recently enabled the mapping of the cerebral vasculaturein vivowith a resolution ten times smaller than the wavelength used, down to ten microns. However, with frame rates up to 20000 frames per second, this method requires large amount of data to be acquired, transmitted, stored, and processed. The transfer rate is, as of today, one of the main limiting factors of this technology. Herein, we introduce a novel reconstruction framework to decrease this quantity of data to be acquired and the complexity of the required hardware by randomly subsampling the channels of a linear probe. Method performance evaluation as well as parameters optimization were conductedin silicousing the SIMUS simulation software in an anatomically realistic phantom and then compared toin vivoacquisitions in a rat brain after craniotomy. Results show that reducing the number of active elements deteriorates the signal-to-noise ratio and could lead to false microbubbles detections but has limited effect on localization accuracy. In simulation, the false positive rate on microbubble detection deteriorates from 3.7% for 128 channels in receive and 7 steered angles to 11% for 16 channels and 7 angles. The average localization accuracy ranges from 10.6μm and 9.93μm for 16 channels/3 angles and 128 channels/13 angles respectively. These results suggest that a compromise can be found between the number of channels and the quality of the reconstructed vascular network and demonstrate feasibility of performing ULM with a reduced number of channels in receive, paving the way for low-cost devices enabling high-resolution vascular mapping.

Compensating for visibility artefacts in photoacoustic imaging with a deep learning approach providing prediction uncertainties

Conventional photoacoustic imaging may suffer from the limited view and bandwidth of ultrasound transducers. A deep learning approach is proposed to handle these problems and is demonstrated both in simulations and in experiments on a multi-scale model of leaf skeleton. We employed an experimental approach to build the training and the test sets using photographs of the samples as ground truth images. Reconstructions produced by the neural network show a greatly improved image quality as compared to conventional approaches. In addition, this work aimed at quantifying the reliability of the neural network predictions. To achieve this, the dropout Monte-Carlo procedure is applied to estimate a pixel-wise degree of confidence on each predicted picture. Last, we address the possibility to use transfer learning with simulated data in order to drastically limit the size of the experimental dataset.