Hsin-Hsiung Huang

Prof. Dr. Hsin-Hsiung Huang

University of Central Florida, Orlando, Florida, USA
orcid

Research Focus & Metrics

Academic Career Length

13Years (2014-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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17 June 2014 in BMC Proceedings
Verified
Journal Article
Open access
PDF file
HHHsin-Hsiung Huang
Hsin-Hsiung Huang
TXTu Xu
Tu Xu
Jie YangJie Yang
Jie Yang
In this paper, we compare logistic regression and 2 other classification methods in predicting hypertension given the genotype information. We use logistic regression analysis in the first step to detect significant single-nucleotide polymorphisms (SNPs). In the second step, we use the significant SNPs with logistic regression, support vector machines (SVMs), and a newly developed permanental classification method for prediction purposes. We also detect rare variants and investigate their impact on prediction. Our results show that SVMs and permanental classification both outperform logistic regression, and they are comparable in predicting hypertension status.

15 July 2024 in Frontiers in Physics
Verified
Journal Article
Open access
PDF file
ZCZhuo Chen
Zhuo Chen
CKChien-Min Kao
Chien-Min Kao
HHHsin-Hsiung Huang
Hsin-Hsiung Huang
LALingling An
Lingling An
Positronium lifetime imaging (PLI) is a newly demonstrated technique possible with time-of-flight (TOF) positron emission tomography (PET), capable of producing an image reflecting the lifetime of the positron, more precisely ortho-positronium (o-Ps), before annihilation, in addition to the traditional uptake image of the PET tracer. Due to the limited time resolution of TOF-PET systems and the added complexities in physics and statistics, lifetime image reconstruction presents a challenge. Recently, we described a maximum-likelihood approach for PLI by considering only o-Ps. In real-world scenarios, other populations of positrons that exhibit different lifetimes also exist. This paper introduces a novel two-component model aimed at enhancing the accuracy of o-Ps lifetime images. Through simulation studies, we compare this new model with the existing single-component model and demonstrate its superior performance in accurately capturing complex lifetime distributions.

17 January 2025 in IEEE Transactions on Radiation and Plasma Medical Sciences
Verified
Journal Article
Closed access
HHHsin-Hsiung Huang
Hsin-Hsiung Huang
ZZZheyuan Zhu
Zheyuan Zhu
SBSlun Booppasiri
Slun Booppasiri
ZCZhuo Chen
Zhuo Chen
SPShuo Pang
Shuo Pang
CKChien-Min Kao
Chien-Min Kao
Positron emission tomography (PET) is an important modality for diagnosing diseases, such as cancer and Alzheimer’s disease, capable of revealing the uptake of radiolabeled molecules that target specific pathological markers of the diseases. Recently, positronium lifetime imaging (PLI) that adds to traditional PET the ability to explore properties of the tissue microenvironment beyond tracer uptake has been demonstrated with time-of-flight (TOF) PET and the use of nonpure positron emitters. However, achieving accurate reconstruction of lifetime images from data acquired by systems having a finite TOF resolution still presents a challenge. This article focuses on the 2-D PLI, introducing a maximum-likelihood estimation (MLE) method that employs an exponentially modified Gaussian (EMG) probability distribution that describes the positronium lifetime data produced by TOF PET. We evaluate the performance of our EMG-based MLE method against approaches using exponential likelihood functions and penalized surrogate methods. Results from computer-simulated data reveal that the proposed EMG-MLE method can yield quantitatively accurate lifetime images. We also demonstrate that the proposed MLE formulation can be extended to handle PLI data containing multiple positron populations.

31 December 2023 in Bio-Algorithms and Med-Systems
Verified
Journal Article
Open access
ZCZhuo Chen
Zhuo Chen
LALingling An
Lingling An
CKChien-Min Kao
Chien-Min Kao
HHHsin-Hsiung Huang
Hsin-Hsiung Huang
The positronium lifetime imaging (PLI) reconstruction is a technique used in time-of-flight (TOF) positron emission tomography (PET) imaging that involves measuring the lifespan of positronium, which is a metastable electron-positron pair that arises when a PET molecule releases a positron, prior to its annihilation. We have previously developed a maximum likelihood (ML) algorithm for PLI reconstruction and demonstrated that it can generate quantitatively accurate lifetime images for a 570 ps (pico-seconds) TOF PET system. In this study, we conducted further investigations into the statistical properties of the algorithm, including the variability of the reconstruction results, the sensitivity of the algorithm to the number of acquired PLI events and its robustness to hyperparameter choices. Our findings indicate that the proposed ML method produces sufficiently stable lifetime images to enable reliable distinction of regions of interest. Moreover, the number of PLI events required to produce quantitatively accurate lifetime images is computationally plausible. These results demonstrate the potential of our ML algorithm for advancing the capabilities of TOF PET imaging.

1 October 2016 in Journal of Theoretical Biology
Verified
Journal Article
Closed access
HHHsin-Hsiung Huang
Hsin-Hsiung Huang
CYChenglong Yu
Chenglong Yu

22 August 2023 in Journal of Statistical Planning and Inference
Verified
Journal Article
Closed access
QHQing He
Qing He
HHHsin-Hsiung Huang
Hsin-Hsiung Huang