A unique deep learning network, Deep-E, is proposed, which utilizes 2D training data to solve a 3D problem. The novelty of this simulation method is to generate a 2D matrix in the axial-elevational plane using an arc-shaped transducer element, instead of generating a 3D matrix using the linear transducer arrays. Deep-E exhibited significant resolution improvement on the in vivo human breast data. In addition, we were able to restore deeper vascular structures and remove the noise artifact. We envision that Deep-E will have a significant impact in linear-array-based photoacoustic imaging studies by providing high-speed and high-resolution image enhancement.
This study demonstrated the performance of photoacoustic imaging at 1064 nm using phosphorus phthalocyanine (P-Pc), a contrast agent with strong absorption at 1064 nm. Due to high maximum permissible exposure of 1064 nm laser light and strong absorbance of P-Pc at 1064 nm, we demonstrated an imaging depth of 11.6 cm in chicken breast tissue. For animal imaging, we used P-Pc to target tumor and to track intestine dynamics. Thus, using a contrast medium with extreme absorption at 1064 nm readily enables high quality photoacoustic imaging at exceptional depths.
Slit-enabled photoacoustic tomography (PAT) is a newly developed technique that improves the elevation resolution and signal to noise ratio of a linear array. The slit, placed along the transducer array focus, forms an array of virtual detectors with high receiving angle, which subsequently allows for three dimensional (3D) imaging with near-isotropic spatial resolution. Our development addressed the long-standing issue of high quality 3D imaging with a linear array and will have broad applications in preclinical and clinical imaging. This study presented the principle of slit-PAT and demonstrated its efficiency in phantom, animal, and human experiments.
Here, we introduce a new image reconstruction algorithm that combines coherent weighting with focal-line-based three-dimensional image reconstruction. The new algorithm addresses the major limitation of a linear ultrasound transducer array, i.e., the poor elevation resolution, and does not require any modification to the imaging system or the scanning geometry. We first numerically validated our approach through simulation and then experimentally tested it in phantom and in vivo. Both simulation and experimental results proved that the method can significantly improve the elevation resolution (up to 3.4 times in our experiment) and enhance object contrast.
Photoacoustic-computed microscopy (PACM) differs from conventional photoacoustic microscopy (PAM) imaging techniques in a way that thousands of optical foci are generated simultaneously using a two-dimensional microlens array, and raster-scanning these optical foci provides wide-field images. A major limitation of PACM is the slow imaging speed caused by the high power pulsed lasers and large amount of acoustic detectors. Here, we addressed this problem through compressed sensing and image inpainting. Compressed sensing minimizes the number of transducer elements used to acquire each frame, while inpainting minimizes the scanning steps. Combining these two approaches, we improved the imaging speed by sixteen times.
Photoacoustic (PA) tomography (PAT) is a hybrid imaging modality that integrates rich optical contrasts with a high-ultrasonic spatial resolution in deep tissue. Among various imaging applications, PA neuroimaging is becoming increasingly important as it nicely complements the limitations of conventional neuroimaging modalities, such as the low-temporal resolution in magnetic resonance imaging and the low depth-to-resolution ratio in optical microscopy/tomography. In addition, the intrinsic hemoglobin contrast PA neuroimaging has also been greatly improved by recent developments in nanoparticles (NPs). For instance, near-infrared absorbing NPs greatly enhanced the vascular contrast in deep-brain PAT; tumor-targeting NPs allowed highly sensitive and highly specific delineation of brain tumors; and multifunctional NPs enabled comprehensive examination of the brain through multimodal imaging. We aim to give an overview of NPs used in PA neuroimaging. Classifications of various NPs used in PAT will be introduced at the beginning, followed by an overview of PA neuroimaging systems, and finally we will discuss major applications of NPs in PA neuroimaging and highlight representative studies.
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