Mr. Sijia Li Profile

Sijia Li

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Proceedings Article | 15 March 2019 Presentation + Paper

Ultrasound backscattered tensor imaging of the brain: an ex vivo feasibility study

Si Jia Li, Parvin Mousavi, Phillip Jason White

Proceedings Volume 10955, 1095512 (2019) https://doi.org/10.1117/12.2512806

KEYWORDS: Brain, Ultrasonography, Neuroimaging, Anisotropy, Transducers, Brain mapping, Coherence (optics), Diffusion tensor imaging, Backscatter, Tissues

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In neurosurgeries, brain shift and tumor removal may render the preoperative MRI diffusion tensor scan irrelevant. There is a need for real time and accurate mapping of the brain white matter fibers. Towards solving this problem, for the first time, we demonstrate the feasibility of ultrasound backscatter tensor imaging (BTI) in locating and measuring the corpus callosum (CC), the largest white matter fibers, in an ex vivo formalin-fixed rat brain. BTI analyzes the coherence in the backscatter signal at different transducer-to-fiber orientations to estimate regions of high anisotropy and thus the presence of fibers. We collected ultrasound radio frequency signals for 180° with a step-size of 10° . At each step, we used focused ultrasound beams to scan the central axis of the rat transverse plane. We then calculated and mapped the coherence factors (CF) to infer the size and location of the CC at two locations of high and low anisotropy, respectively. Lastly, we compared our results in the high anisotropy plane to a rat brain MRI Diffusion Tensor Imaging (DTI) atlas. The CC thickness in the measured plane was 0.87 mm (atlas) vs. 1.0±0.3 mm (CF map), while the distance to the rat brain medial dorsal surface was at 1.53 mm (atlas) vs. 1.7±0.3 mm (CF map). This is an ongoing study with limitations in the axial and lateral resolution, speed of acquisition, and signal to noise ratio. To our best knowledge, this is a first study in demonstrating the potential of BTI in detecting the corpus callosum with promising results to warrant further efforts towards clinical translation.

Proceedings Article | 8 March 2019 Presentation + Paper

Temporal enhanced ultrasound and shear wave elastography for tissue classification in cancer interventions: an experimental evaluation

Si Jia Li, Jack Barnes, Purang Abolmaesumi, Hans-Peter Loock, Parvin Mousavi

Proceedings Volume 10951, 109510V (2019) https://doi.org/10.1117/12.2514103

KEYWORDS: Tissues, Ultrasonography, Elastography, Cancer, Scattering, Wave propagation, Fourier transforms, Acoustics

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Conventional B-mode ultrasound imaging lacks soft tissue contrast to differentiate various tissue types. Emerging ultrasound imaging technologies have therefore focused on extracting tissue parameters important for tissue differentiation such as scatterer size, tissue elasticity, and micro-vasculatures. Among these technologies, shear wave elastography (SWE) is an approach that measures tissue viscoelastic parameters. Our group has proposed Temporal Enhanced Ultrasound (TeUS) that differentiates tissue types without requiring any external stimuli. Through analytical derivations and simulations, we previously showed that the source of tissue typing information in TeUS is physiological micro-vibrations resulting mainly from perfusion. We further demonstrated that TeUS is sensitive to the size and distributions of scatterers in the tissue, as well as its visco-elasticity. In this paper, we designed ultrasound phantoms to mimic tissue with two different elasticities and two scatterer sizes. A exible microtube was embedded in the phantoms to generate local micro-vibrations. We experimentally demonstrate the relationship between TeUS and SWE and their sensitivity to tissue elasticity and scatterer size. This work indicated that while shear wave measurements are sensitive to the phantoms viscoelasticity, they are not sensitive to ultrasound scatterer size. On the contrary, the TeUS amplitude depends on both scatterer size and tissue viscoelasticity. This work could potentially inform clinicians of choosing imaging modalities and interventions based on each cancer's unique traits and properties.

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