Photostable, hydrophilic, and near infrared quaterrylene-based dyes for photoacoustic imaging

Reduction of photobleaching effects in photoacoustic imaging using noise agnostic, platform-flexible deep-learning methods

Significance

Molecular photoacoustic (PA) imaging with exogenous dyes faces a significant challenge due to the photobleaching of the dye that can compromise tissue visualization, particularly in 3D imaging. Addressing this limitation can revolutionize the field by enabling safer, more reliable imaging and improve real-time visualization, quantitative analysis, and clinical decision-making in various molecular PA imaging applications such as image-guided surgeries.

Aim

We tackle photobleaching in molecular PA imaging by introducing a platform-flexible deep learning framework that enhances SNR from single-laser pulse data, preserving contrast and signal integrity without requiring averaging of signals from multiple laser pulses.

Approach

The generative deep learning network was trained with an LED-illuminated PA image dataset and tested on acoustic resolution PA microscopy images obtained with single-laser pulse illumination. In vitro and ex vivo samples were first tested for demonstrating SNR improvement, and then, a 3D-scanning experiment with an ICG-filled tube was conducted to depict the usability of the technique in reducing the impact of photobleaching during PA imaging.

Results

Our generative deep learning model outperformed traditional nonlearning, filter-based algorithms and the U-Net deep learning network when tested with in vitro and ex vivo single pulse-illuminated images, showing superior performance in terms of signal-to-noise ratio ( 93.54 ± 6.07 , and 92.77 ± 10.74 compared with 86.35 ± 3.97 , and 84.52 ± 11.82 with U-Net for kidney, and tumor, respectively) and contrast-to-noise ratio ( 11.82 ± 4.42 , and 9.9 ± 4.41 compared with 7.59 ± 0.82 , and 6.82 ± 2.12 with U-Net for kidney, and tumor respectively). The use of cGAN with single-pulse rapid imaging has the potential to prevent photobleaching ( 9.51 ± 3.69 % with cGAN, and 35.14 ± 5.38 % with long-time laser exposure by averaging 30 pulses), enabling accurate, quantitative imaging suitable for real-time implementation, and improved clinical decision support.

Conclusions

We demonstrate the potential of a platform-flexible generative deep learning-based approach to mitigate the effects of photobleaching in PA imaging by enhancing signal-to-noise ratio from single pulse-illuminated data, thereby improving image quality and preserving contrast in real time.

Displacement Patterns in Magnetomotive Ultrasound Explored by Finite Element Analysis

Magnetomotive ultrasound is an emerging technique that enables detection of magnetic nanoparticles. This has implications for ultrasound molecular imaging, and potentially addresses clinical needs regarding determination of metastatic infiltration of the lymphatic system. Contrast is achieved by a time-varying magnetic field that sets nanoparticle-laden regions in motion. This motion is governed by vector-valued mechanical and magnetic forces. Understanding how these forces contribute to observed displacement patterns is important for the interpretation of magnetomotive ultrasound images. Previous studies have captured motion adjacent to nanoparticle-laden regions that was attributed to diamagnetism. While diamagnetism could give rise to a force, it cannot fully account for the observed displacements in magnetomotive ultrasound. To isolate explanatory variables of the observed displacements, a finite element model is set up. Using this model, we explore potential causes of the unexplained motion by comparing numerical models with earlier experimental findings. The simulations reveal motion outside particle-laden regions that could be attributed to mechanical coupling and the principle of mass conservation. These factors produced a motion that counterbalanced the time-varying magnetic excitation, and whose extent and distribution was affected by boundary conditions as well as compressibility and stiffness of the surroundings. Our findings emphasize the importance of accounting for the vector-valued magnetic force in magnetomotive ultrasound imaging. In an axisymmetric geometry, that force can be represented by a simple scalar expression, an oversimplification that rapidly becomes inaccurate with distance from the symmetry axis. Additionally, it results in an underestimation of the vertical force component by up to 30%. We therefore recommend using the full vector-valued force to capture the magnetic interaction. This study enhances our understanding of how forces govern magnetic nanoparticle displacement in tissue, contributing to accurate analysis and interpretation of magnetomotive ultrasound imaging.