Publications by authors named "Won June Choi"

Article Synopsis
  • Differences in iron accumulation patterns in atypical parkinsonian syndromes can be identified using susceptibility-weighted images, and deep learning methods are effective for this task.
  • Traditional deep learning models need large labeled datasets, which are expensive and can compromise patient privacy.
  • A new few-shot learning framework is proposed to distinguish between multiple system atrophy parkinsonian (MSA-P) and progressive supranuclear palsy (PSP) using fewer data samples, achieving better performance through enhanced feature identification and a novel hyperbolic space technique.
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Mechanical property characterization of micro-scale material systems, such as free-standing films or small diameter wires (<20 µm), often requires expensive, specialized test systems. Conventional tensile test systems are usually designed for millimeter scale specimens with the force sensing capability of >1N while microdevice-based testers are intended for micro-/nano-scale specimens operating within a much smaller force range of <10 mN. This disparity leaves a technology gap in reliable and cost-effective characterization methods for specimens at the intermediate scale.

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Cavitation bubbles in the human body, when subjected to impact, are being increasingly considered as a possible brain injury mechanism. However, the onset of cavitation and its complex dynamics in biological materials remain unclear. Our experimental results using soft gels as a tissue simulant show that the critical acceleration (a) at cavitation nucleation monotonically increases with increasing stiffness of gelatin A/B, while a for agarose and agar initially increases but is followed by a plateau or even decrease after stiffness reach to ∼100 kPa.

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Cavitation bubbles form in soft biological systems when subjected to a negative pressure above a critical threshold, and dynamically change their size and shape in a violent manner. The critical threshold and dynamic response of these bubbles are known to be sensitive to the mechanical characteristics of highly compliant biological systems. Several recent studies have demonstrated different biological implications of cavitation events in biological systems, from therapeutic drug delivery and microsurgery to blunt injury mechanisms.

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Objectives: Successful implantation depends on interaction between a blastocyst and a receptive endometrium. Endometrial vasculature is important in the early endometrial response to blastocyst implantation, and vascular changes can affect uterine receptivity. This study aims to investigate whether vascular parameters measured using three-dimensional power Doppler ultrasound (3D PD-US) could predict pregnancy following fresh in vitro fertilization and embryo transfer (IVF-ET) using a gonadotropin releasing hormone (GnRH) agonist long protocol.

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