Super-resolution ultrasound imaging and genetically encoded gas vesicle for the early detection of cancer
Research Description
Super-resolution (SR) ultrasound imaging system by utilizing microbubbles has been demonstrated as a useful tool for vascular imaging beyond the diffraction limit. This system particularly has shown the great potential to visualize the microvasculature deep inside tissue. Besides, the gas vesicles (GVs) have been introduced as nanoscale contrast agents for ultrasound imaging. GVs have been used to target specific biomarkers by chemical modification and genetic engineering. Thus, we developed a singular value decomposition (SVD) and compressed sensing (CS) based super-resolution ultrasound localization of GVs to visualize the micro-vasculature. We used an ultrasound imaging system (Vantage 256, Verasonics) with a high-frequency linear array transducer (L35-16vX). A five angle (−7° to 7°) plane-wave compounding with an effective pulse repetition frequency of 100 Hz and the transmit frequency of 25 MHz was used. A total of 500 compounded frames were acquired. Then, we developed the SVD and CS techniques which were evaluated using the system point spread function and randomly distributed point targets with the density from 0 to 175 mm-2 in simulation. Furthermore, we developed the GVs and confirmed the production using the transmission electron microscopy (TEM). After that, the SR ultrasound imaging of microvessel-mimicking phantom with a diameter of 500 μm was performed. The Mega GVs with an optical density at 500 nm of 20 were injected as nanoscale contrast agents at a flow velocity of 40 mm/sec through a microvessel. For the reconstruction of the SR image, we first applied the SVD technique for IQ signals to detect the GVs signals by rejecting the clutter signals. Furthermore, the center of each GVs at each frame was localized using the CS technique. Then, each localization result was superposed for the reconstruction of the SR image. To evaluate the performance of the CS algorithm, by comparing it with 2D cross-correlation, the average intensity and the non-zero areas of the region of interest (indicated at the white box) were evaluated by increasing the number of frames. Fig. 1(a) shows the TEM images of Ana and Mega GVs. The micro-vasculature in the phantom was visualized with Mega GVs by applying the SVD and 2D CC techniques [Fig. 1(b)]. Also, the compressed sensing technique shows better efficiency than the 2D CC technique as shown in Fig. 1(c). Altogether, these results demonstrate that the SVD and CS-based localization approach can be used for super-resolution ultrasound imaging to visualize microvasculature.
Super-resolution (SR) ultrasound imaging system by utilizing microbubbles has been demonstrated as a useful tool for vascular imaging beyond the diffraction limit. This system particularly has shown the great potential to visualize the microvasculature deep inside tissue. Besides, the gas vesicles (GVs) have been introduced as nanoscale contrast agents for ultrasound imaging. GVs have been used to target specific biomarkers by chemical modification and genetic engineering. Thus, we developed a singular value decomposition (SVD) and compressed sensing (CS) based super-resolution ultrasound localization of GVs to visualize the micro-vasculature. We used an ultrasound imaging system (Vantage 256, Verasonics) with a high-frequency linear array transducer (L35-16vX). A five angle (−7° to 7°) plane-wave compounding with an effective pulse repetition frequency of 100 Hz and the transmit frequency of 25 MHz was used. A total of 500 compounded frames were acquired. Then, we developed the SVD and CS techniques which were evaluated using the system point spread function and randomly distributed point targets with the density from 0 to 175 mm-2 in simulation. Furthermore, we developed the GVs and confirmed the production using the transmission electron microscopy (TEM). After that, the SR ultrasound imaging of microvessel-mimicking phantom with a diameter of 500 μm was performed. The Mega GVs with an optical density at 500 nm of 20 were injected as nanoscale contrast agents at a flow velocity of 40 mm/sec through a microvessel. For the reconstruction of the SR image, we first applied the SVD technique for IQ signals to detect the GVs signals by rejecting the clutter signals. Furthermore, the center of each GVs at each frame was localized using the CS technique. Then, each localization result was superposed for the reconstruction of the SR image. To evaluate the performance of the CS algorithm, by comparing it with 2D cross-correlation, the average intensity and the non-zero areas of the region of interest (indicated at the white box) were evaluated by increasing the number of frames. Fig. 1(a) shows the TEM images of Ana and Mega GVs. The micro-vasculature in the phantom was visualized with Mega GVs by applying the SVD and 2D CC techniques [Fig. 1(b)]. Also, the compressed sensing technique shows better efficiency than the 2D CC technique as shown in Fig. 1(c). Altogether, these results demonstrate that the SVD and CS-based localization approach can be used for super-resolution ultrasound imaging to visualize microvasculature.
Figure 1. Super-resolution ultrasound imaging: (a) TEM images of Ana (top) and Mega GVs (bottom), (b) Reconstruction of the super-resolution image of a microvessel-mimicking phantom with the Mega GVs, (c) Evaluation of localization techniques based on simulation (1st row) and phantom experiment (2nd row)