Sparsity-based photoacoustic image reconstruction with a linear array transducer and direct measurement of the forward model.

Photoacoustic (PA) imaging is an emerging imaging technique for many clinical applications. One of the challenges posed by clinical translation is that imaging systems often rely on a finite-aperture transducer rather than a full tomography system. This results in imaging artifacts arising from an underdetermined reconstruction of the initial pressure distribution (IPD). Furthermore, clinical applications often require deep imaging, resulting in a low-signal-to-noise ratio for the acquired signal because of strong light attenuation in tissue. Conventional approaches to reconstruct the IPD, such as back projection and time-reversal, do not adequately suppress the artifacts and noise. We propose a sparsity-based optimization approach that improves the reconstruction of IPD in PA imaging with a linear array ultrasound transducer. In simulation studies, the forward model matrix was measured from k-Wave simulations, and the approach was applied to reconstruct simulated point objects and the Shepp-Logan phantom. The results were compared with the conventional back projection, time-reversal approach, frequency-domain reconstruction, and the iterative least-squares approaches. In experimental studies, the forward model of our imaging system is directly measured by scanning a graphite point source through the imaging field of view. Experimental images of graphite inclusions in tissue-mimicking phantoms are reconstructed and compared with the back projection and iterative least-squares approaches. Overall these results show that our proposed optimization approach can leverage the sparsity of the PA images to improve the reconstruction of the IPD and outperform the existing popular reconstruction approaches.

Complex optimization for big computational and experimental neutron datasets.

We present a framework to use high performance computing to determine accurate solutions to the inverse optimization problem of big experimental data against computational models. We demonstrate how image processing, mathematical regularization, and hierarchical modeling can be used to solve complex optimization problems on big data. We also demonstrate how both model and data information can be used to further increase solution accuracy of optimization by providing confidence regions for the processing and regularization algorithms. We use the framework in conjunction with the software package SIMPHONIES to analyze results from neutron scattering experiments on silicon single crystals, and refine first principles calculations to better describe the experimental data.

Big, Deep, and Smart Data in Scanning Probe Microscopy.

Scanning probe microscopy (SPM) techniques have opened the door to nanoscience and nanotechnology by enabling imaging and manipulation of the structure and functionality of matter at nanometer and atomic scales. Here, we analyze the scientific discovery process in SPM by following the information flow from the tip-surface junction, to knowledge adoption by the wider scientific community. We further discuss the challenges and opportunities offered by merging SPM with advanced data mining, visual analytics, and knowledge discovery technologies.

Big-deep-smart data in imaging for guiding materials design.

Harnessing big data, deep data, and smart data from state-of-the-art imaging might accelerate the design and realization of advanced functional materials. Here we discuss new opportunities in materials design enabled by the availability of big data in imaging and data analytics approaches, including their limitations, in material systems of practical interest. We specifically focus on how these tools might help realize new discoveries in a timely manner. Such methodologies are particularly appropriate to explore in light of continued improvements in atomistic imaging, modelling and data analytics methods.

Differentially ligand-functionalized microcantilever arrays for metal ion identification and sensing.

A microcantilever array sensor with cantilevers differentially functionalized with self-assembled monolayers (SAMs) of thiolated ligands is prepared by simultaneous capillary coating. This array is described for the detection of metal ions including Li+, Cs+, Cu2+, Co2+, Fe3+, and Al3+. Binding of the charged metal cations to the surface of the microcantilever sensors produces surface stress that causes bending of the cantilevers that is detected as tip deflection using an array of vertical cavity surface emitting lasers and a position-sensitive detector. Optimization studies of the nanostructured dealloyed surface were performed for SAMs based on their response to Cu2+ cations. Sensor performance experiments demonstrate good sensitivity toward metal ions, with limits of detection as low as 10(-8) molar. A multiplex capillary coating method for cantilever array creation is demonstrated and validated based on surface-enhanced Raman spectra obtained from adjacent cantilevers that were functionalized with different thiolated SAMs. The cantilever array coated with a range of thiolated ligands was exposed to the group of metal ions. The response characteristics of each metal ion show substantial diversity, varying not only in response magnitude, but response kinetics. A pattern recognition algorithm based on a combination of independent component analysis and support vector machines was able to validate that the sensor array response profiles produced enough information content that metal ions could be reliably classified with probabilities as high as 89%.