Yiheng Li

All Publications

• A 3D lung lesion variational autoencoder. Cell reports methods Li, Y., Sadée, C. Y., Carrillo-Perez, F., Selby, H. M., Thieme, A. H., Gevaert, O. 2024: 100695

  Abstract

  In this study, we develop a 3D beta variational autoencoder (beta-VAE) to advance lung cancer imaging analysis, countering the constraints of conventional radiomics methods. The autoencoder extracts information from public lung computed tomography (CT) datasets without additional labels. It reconstructs 3D lung nodule images with high quality (structural similarity: 0.774, peak signal-to-noise ratio: 26.1, and mean-squared error: 0.0008). The model effectively encodes lesion sizes in its latent embeddings, with a significant correlation with lesion size found after applying uniform manifold approximation and projection (UMAP) for dimensionality reduction. Additionally, the beta-VAE can synthesize new lesions of varying sizes by manipulating the latent features. The model can predict multiple clinical endpoints, including pathological N stage or KRAS mutation status, on the Stanford radiogenomics lung cancer dataset. Comparisons with other methods show that the beta-VAE performs equally well in these tasks, suggesting its potential as a pretrained model for predicting patient outcomes in medical imaging.

  View details for DOI 10.1016/j.crmeth.2024.100695

  View details for PubMedID 38278157

• Piecewise Multivariate Linearity Between Kinematic Features and Cumulative Strain Damage Measure (CSDM) Across Different Types of Head Impacts. Annals of biomedical engineering Zhan, X., Li, Y., Liu, Y., Cecchi, N. J., Gevaert, O., Zeineh, M. M., Grant, G. A., Camarillo, D. B. 2022

  Abstract

  In a previous study, we found that the relationship between brain strain and kinematic features cannot be described by a generalized linear model across different types of head impacts. In this study, we investigate if such a linear relationship exists when partitioning head impacts using a data-driven approach. We applied the K-means clustering method to partition 3161 impacts from various sources including simulation, college football, mixed martial arts, and car crashes. We found piecewise multivariate linearity between the cumulative strain damage (CSDM; assessed at the threshold of 0.15) and head kinematic features. Compared with the linear regression models without partition and the partition according to the types of head impacts, K-means-based data-driven partition showed significantly higher CSDM regression accuracy, which suggested the presence of piecewise multivariate linearity across types of head impacts. Additionally, we compared the piecewise linearity with the partitions based on individual features used in clustering. We found that the partition with maximum angular acceleration magnitude at 4706 rad/s2 led to the highest piecewise linearity. This study may contribute to an improved method for the rapid prediction of CSDM in the future.

  View details for DOI 10.1007/s10439-022-03020-0

  View details for PubMedID 35922726

• Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation. Annals of biomedical engineering Zhan, X., Li, Y., Liu, Y., Domel, A. G., Alizadeh, H. V., Zhou, Z., Cecchi, N. J., Raymond, S. J., Tiernan, S., Ruan, J., Barbat, S., Gevaert, O., Zeineh, M. M., Grant, G. A., Camarillo, D. B. 2021

  Abstract

  Brain tissue deformation resulting from head impacts is primarily caused by rotation and can lead to traumatic brain injury. To quantify brain injury risk based on measurements of kinematics on the head, finite element (FE) models and various brain injury criteria based on different factors of these kinematics have been developed, but the contribution of different kinematic factors has not been comprehensively analyzed across different types of head impacts in a data-driven manner. To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in (1) the derivative order (angular velocity, angular acceleration, angular jerk), (2) the direction and (3) the power (e.g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events. Ordinary least squares regressions were built from kinematics factors to the 95% maximum principal strain (MPS95), and we compared zero-order correlation coefficients, structure coefficients, commonality analysis, and dominance analysis. The angular acceleration, the magnitude and the first power factors showed the highest predictive power for the majority of impacts including laboratory impacts, American football impacts, with few exceptions (angular velocity for MMA and NASCAR impacts). The predictive power of rotational kinematics about three directions (x: posterior-to-anterior, y: left-to-right, z: superior-to-inferior) of kinematics varied with different sports and types of head impacts.

  View details for DOI 10.1007/s10439-021-02813-z

  View details for PubMedID 34244908

• Time Window of Head Impact Kinematics Measurement for Calculation of Brain Strain and Strain Rate in American Football. Annals of biomedical engineering Liu, Y., Domel, A. G., Cecchi, N. J., Rice, E., Callan, A. A., Raymond, S. J., Zhou, Z., Zhan, X., Li, Y., Zeineh, M. M., Grant, G. A., Camarillo, D. B. 2021

  Abstract

  Wearable devices have been shown to effectively measure the head's movement during impacts in sports like American football. When a head impact occurs, the device is triggered to collect and save the kinematic measurements during a predefined time window. Then, based on the collected kinematics, finite element (FE) head models can calculate brain strain and strain rate, which are used to evaluate the risk of mild traumatic brain injury. To find a time window that can provide a sufficient duration of kinematics for FE analysis, we investigated 118 on-field video-confirmed football head impacts collected by the Stanford Instrumented Mouthguard. The simulation results based on the kinematics truncated to a shorter time window were compared with the original to determine the minimum time window needed for football. Because the individual differences in brain geometry influence these calculations, we included six representative brain geometries and found that larger brains need a longer time window of kinematics for accurate calculation. Among the different sizes of brains, a pre-trigger time of 40ms and a post-trigger time of 70ms were found to yield calculations of brain strain and strain rate that were not significantly different from calculations using the original 200ms time window recorded by the mouthguard. Therefore, approximately 110ms is recommended for complete modeling of impacts for football.

  View details for DOI 10.1007/s10439-021-02821-z

  View details for PubMedID 34231091

• AI-based analysis of CT images for rapid triage of COVID-19 patients. NPJ digital medicine Xu, Q., Zhan, X., Zhou, Z., Li, Y., Xie, P., Zhang, S., Li, X., Yu, Y., Zhou, C., Zhang, L., Gevaert, O., Lu, G. 2021; 4 (1): 75

  Abstract

  The COVID-19 pandemic overwhelms the medical resources in the stressed intensive care unit (ICU) capacity and the shortage of mechanical ventilation (MV). We performed CT-based analysis combined with electronic health records and clinical laboratory results on Cohort 1 (n=1662 from 17 hospitals) with prognostic estimation for the rapid stratification of PCR confirmed COVID-19 patients. These models, validated on Cohort 2 (n=700) and Cohort 3 (n=662) constructed from nine external hospitals, achieved satisfying performance for predicting ICU, MV, and death of COVID-19 patients (AUROC 0.916, 0.919, and 0.853), even on events happened two days later after admission (AUROC 0.919, 0.943, and 0.856). Both clinical and image features showed complementary roles in prediction and provided accurate estimates to the time of progression (p<0.001). Our findings are valuable for optimizing the use of medical resources in the COVID-19 pandemic. The models are available here: https://github.com/terryli710/COVID_19_Rapid_Triage_Risk_Predictor .

  View details for DOI 10.1038/s41746-021-00446-z

  View details for PubMedID 33888856

• Photosynthesis and Related Physiological Parameters Differences Affected the Isoprene Emission Rate among 10 Typical Tree Species in Subtropical Metropolises. International journal of environmental research and public health Lyu, J., Xiong, F., Sun, N., Li, Y., Liu, C., Yin, S. 2021; 18 (3)

  Abstract

  Volatile organic compound (VOCs) emission is an important cause of photochemical smog and particulate pollution in urban areas, and urban vegetation has been presented as an important source. Different tree species have different emission levels, so adjusting greening species collocation is an effective way to control biogenic VOC pollution. However, there is a lack of measurements of tree species emission in subtropical metropolises, and the factors influencing the species-specific differences need to be further clarified. This study applied an in situ method to investigate the isoprene emission rates of 10 typical tree species in subtropical metropolises. Photosynthesis and related parameters including photosynthetic rate, intercellular CO2 concentration, stomatal conductance, and transpiration rate, which can influence the emission rate of a single species, were also measured. Results showed Salix babylonica always exhibited a high emission level, whereas Elaeocarpus decipiens and Ligustrum lucidum maintained a low level throughout the year. Differences in photosynthetic rate and stomatal CO2 conductance are the key parameters related to isoprene emission among different plants. Through the establishment of emission inventory and determination of key photosynthetic parameters, the results provide a reference for the selection of urban greening species, as well as seasonal pollution control, and help to alleviate VOC pollution caused by urban forests.

  View details for DOI 10.3390/ijerph18030954

  View details for PubMedID 33499177

• The relationship between brain injury criteria and brain strain across different types of head impacts can be different Journal of Royal Society Interface Zhan, X., Li, Y., Liu, Y., Domel, A. G., Vahid Alidazeh, H., Raymond, S. J., Ruan, J., Barbat, S., Tienan, S., Gevaert, O., Zeineh, M., Grant, G., Camarillo, D. 2021; 18 (20210260)

  View details for DOI 10.1098/rsif.2021.0260