Assembly of fluorophore J-aggregates with nanospacer onto mesoporous nanoparticles for enhanced photoacoustic imaging.

Many fluorophores, such as indocyanine green (ICG), have poor photostability and low photothermal efficiency hindering their wide application in photoacoustic (PA) tomography. In the present study, a supramolecular assembly approach was used to develop the hybrid nanoparticles (Hy NPs) of ICG and porous silicon (PSi) as a novel contrast agent for PA tomography. ICG was assembled on the PSi NPs to form J-aggregates within 30 min. The Hy NPs presented a red-shifted absorption, improved photothermal stability, and enhanced PA performance. Furthermore, 1-dodecene (DOC) was assembled into the NPs as a 'nanospacer', which enhanced non-radiative decay for increased thermal release. Compared to the Hy NPs, adding DOC into the Hy NPs (DOC-Hy) increased the PA signal by 83%. Finally, the DOC-Hy was detectable in PA tomography at 1.5 cm depth in tissue phantom even though its concentration was as low as 6.25 µg/mL, indicating the potential for deep tissue PA imaging.

Computationally Efficient Forward Operator for Photoacoustic Tomography Based on Coordinate Transformations.

Photoacoustic tomography (PAT) is an imaging modality that utilizes the photoacoustic effect. In PAT, a photoacoustic image is computed from measured data by modeling ultrasound propagation in the imaged domain and solving an inverse problem utilizing a discrete forward operator. However, in realistic measurement geometries with several ultrasound transducers and relatively large imaging volume, an explicit formation and use of the forward operator can be computationally prohibitively expensive. In this work, we propose a transformation-based approach for efficient modeling of photoacoustic signals and reconstruction of photoacoustic images. In the approach, the forward operator is constructed for a reference ultrasound transducer and expanded into a general measurement geometry using transformations that map the formulated forward operator in local coordinates to the global coordinates of the measurement geometry. The inverse problem is solved using a Bayesian framework. The approach is evaluated with numerical simulations and experimental data. The results show that the proposed approach produces accurate 3-D photoacoustic images with a significantly reduced computational cost both in memory requirements and time. In the studied cases, depending on the computational factors, such as discretization, over the 30-fold reduction in memory consumption was achieved without a reduction in image quality compared to a conventional approach.

Modeling of Errors Due to Uncertainties in Ultrasound Sensor Locations in Photoacoustic Tomography.

Photoacoustic tomography is an imaging modality based on the photoacoustic effect caused by the absorption of an externally introduced light pulse. In the inverse problem of photoacoustic tomography, the initial pressure generated through the photoacoustic effect is estimated from a measured photoacoustic time-series utilizing a forward model for ultrasound propagation. Due to the ill-posedness of the inverse problem, errors in the forward model or measurements can result in significant errors in the solution of the inverse problem. In this work, we study modeling of errors caused by uncertainties in ultrasound sensor locations in photoacoustic tomography using a Bayesian framework. The approach is evaluated with simulated and experimental data. The results indicate that the inverse problem of photoacoustic tomography is sensitive even to small uncertainties in sensor locations. Furthermore, these uncertainties can lead to significant errors in the estimates and reduction of the quality of the photoacoustic images. In this work, we show that the errors due to uncertainties in ultrasound sensor locations can be modeled and compensated using Bayesian approximation error modeling.