Improved photoacoustic images via wavefront shaping modulation based on the scattering structure: retraction.

The referenced article [Opt. Express30, 36489 (2022)10.1364/OE.470330] has been retracted by the authors.

Improved photoacoustic images via wavefront shaping modulation based on the scattering structure.

Multispectral optoacoustic tomography (MSOT) has become the dominant technical solution for photoacoustic imaging (PAI). However, the laser source of fiber output in the current MSOT method is typically a TEM00 Gaussian beam, which is prone to artifacts and incomplete due to the uneven distribution of the irradiated light intensity. Here, we propose a novel method to improve the quality of photoacoustic image reconstruction by modulating the wavefront shaping of the incident laser beam based on the designed scattering structure. In the experiment, we add the designed scattering structure to the current hemispherical photoacoustic transducer array device. Through experiments and simulations, we investigate and compare the effects of different scattering structures on laser intensity modulation. The results show that an ED1-C20 diffusion structure with a scattering angle of 20 degrees has the most effective modulation of the beam intensity distribution. And we choose gold nanoparticles of 50-100 nanometers (nm) diameters and index finger capillary vessels respectively as the medium of PAI. We obtain the highest ratio of PAI area increases of gold nanoparticles and index finger to devices compare without scattering structure is 29.69% and 634.94%, respectively. Experimental results demonstrate that our method is significantly higher quality than traditional methods, which has great potential for theoretical application in medical PAI.

Non-invasive imaging of tau-targeted probe uptake by whole brain multi-spectral optoacoustic tomography.

PURPOSE: Abnormal tau accumulation within the brain plays an important role in tauopathies such as Alzheimer's disease and frontotemporal dementia. High-resolution imaging of tau deposits at the whole-brain scale in animal disease models is highly desired. METHODS: We approached this challenge by non-invasively imaging the brains of P301L mice of 4-repeat tau with concurrent volumetric multi-spectral optoacoustic tomography (vMSOT) at ~ 115 µm spatial resolution using the tau-targeted pyridinyl-butadienyl-benzothiazole derivative PBB5 (i.v.). In vitro probe characterization, concurrent vMSOT and epi-fluorescence imaging of in vivo PBB5 targeting (i.v.) was performed in P301L and wild-type mice, followed by ex vivo validation using AT-8 antibody for phosphorylated tau. RESULTS: PBB5 showed specific binding to recombinant K18 tau fibrils by fluorescence assay, to post-mortem Alzheimer's disease brain tissue homogenate by competitive binding against [11C]PBB3 and to tau deposits (AT-8 positive) in post-mortem corticobasal degeneration and progressive supranuclear palsy brains. Dose-dependent optoacoustic and fluorescence signal intensities were observed in the mouse brains following i.v. administration of different concentrations of PBB5. In vivo vMSOT brain imaging of P301L mice showed higher retention of PBB5 in the tau-laden cortex and hippocampus compared to wild-type mice, as confirmed by ex vivo vMSOT, epi-fluorescence, multiphoton microscopy, and immunofluorescence staining. CONCLUSIONS: We demonstrated non-invasive whole-brain imaging of tau in P301L mice with vMSOT system using PBB5 at a previously unachieved ~ 115 µm spatial resolution. This platform provides a new tool to study tau spreading and clearance in a tauopathy mouse model, foreseeable in monitoring tau targeting putative therapeutics.

Deep learning facilitates fully automated brain image registration of optoacoustic tomography and magnetic resonance imaging.

Multispectral optoacoustic tomography (MSOT) is an emerging optical imaging method providing multiplex molecular and functional information from the rodent brain. It can be greatly augmented by magnetic resonance imaging (MRI) which offers excellent soft-tissue contrast and high-resolution brain anatomy. Nevertheless, registration of MSOT-MRI images remains challenging, chiefly due to the entirely different image contrast rendered by these two modalities. Previously reported registration algorithms mostly relied on manual user-dependent brain segmentation, which compromised data interpretation and quantification. Here we propose a fully automated registration method for MSOT-MRI multimodal imaging empowered by deep learning. The automated workflow includes neural network-based image segmentation to generate suitable masks, which are subsequently registered using an additional neural network. The performance of the algorithm is showcased with datasets acquired by cross-sectional MSOT and high-field MRI preclinical scanners. The automated registration method is further validated with manual and half-automated registration, demonstrating its robustness and accuracy.