Jihye Baek

All Publications

• Renal cancer early diagnosis using a novel B7-H3 targeted ultrasound contrast imaging Natarajan, A., Baek, J., Paulmurugan, R., Dahl, J. AMER ASSOC CANCER RESEARCH. 2026

  View details for DOI 10.1158/1538-7445.AM2026-7901

  View details for Web of Science ID 001734420600027

• Enhancing Ultrasound Molecular Imaging: Toward Real-Time RPCA-Based Filtering to Differentiate Bound and Free Microbubbles. IEEE transactions on ultrasonics Hashemi, H. S., Hyun, D., Nguyen, N., Baek, J., Natarajan, A., Tabesh, F., Andrzejek, A., Paulmurugan, R., Dahl, J. J. 2025

  Abstract

  Ultrasound molecular imaging (UMI) is an advanced imaging modality that shows promise in detecting cancer at early stages. It uses microbubbles as contrast agents, which are functionalized to bind to cancer biomarkers overexpressed on endothelial cells. A major challenge in UMI is isolating bound microbubble signal, which represents the molecular imaging signal, from that of free-floating microbubbles, which is considered background noise. In this work, we propose a fast GPU-based method using robust principal component analysis (RPCA) to distinguish bound microbubbles from free-floating ones. We explore the method using simulations and measure the accuracy using the Dice coefficient and RMS error as functions of the number of frames used in RPCA reconstruction. Experiments using stationary and flowing microbubbles in tissue-mimicking phantoms were used to validate the method. Additionally, the method was applied to data from ten transgenic mouse models of breast cancer development, injected with B7-H3 targeted microbubbles, and two mice injected with non-targeted microbubbles. The results showed that RPCA using 20 frames achieved a Dice score of 0.95 and a computation time of 0.2 seconds, indicating that 20 frames is potentially suitable for real-time implementation. On in vivo data, RPCA using 20 frames achieved a Dice score of 0.82 with DTE, indicating good agreement between the two, given the limitations of each method.

  View details for DOI 10.1109/tuson.2025.3647590

  View details for PubMedID 42078652

  View details for PubMedCentralID PMC13132560

• Multiparametric quantification and visualization of liver fat using ultrasound. WFUMB ultrasound open Baek, J., Kaffas, A. E., Kamaya, A., Hoyt, K., Parker, K. J. 2024; 2 (1)

  Abstract

  Several ultrasound measures have shown promise for assessment of steatosis compared to traditional B-scan, however clinicians may be required to integrate information across the parameters. Here, we propose an integrated multiparametric approach, enabling simple clinical assessment of key information from combined ultrasound parameters.We have measured 13 parameters related to ultrasound and shear wave elastography. These were measured in 30 human subjects under a study of liver fat. The 13 individual measures are assessed for their predictive value using independent magnetic resonance imaging-derived proton density fat fraction (MRI-PDFF) measurements as a reference standard. In addition, a comprehensive and fine-grain analysis is made of all possible combinations of sub-sets of these parameters to determine if any subset can be efficiently combined to predict fat fraction.We found that as few as four key parameters related to ultrasound propagation are sufficient to generate a linear multiparametric parameter with a correlation against MRI-PDFF values of greater than 0.93. This optimal combination was found to have a classification area under the curve (AUC) approaching 1.0 when applying a threshold for separating steatosis grade zero from higher classes. Furthermore, a strategy is developed for applying local estimates of fat content as a color overlay to produce a visual impression of the extent and distribution of fat within the liver.In principle, this approach can be applied to most clinical ultrasound systems to provide the clinician and patient with a rapid and inexpensive estimate of liver fat content.

  View details for DOI 10.1016/j.wfumbo.2024.100045

  View details for PubMedID 41676455

  View details for PubMedCentralID PMC12889900

• Enhancing Ultrasound Molecular Imaging: RPCA-Based Filtering to Differentiate Tumor-Bound and Free Microbubbles Hashemi, H. S., Hyun, D., Baek, J., Natarajan, A., Tabesh, F., Paulmurugan, R., Dahl, J. J., IEEE IEEE. 2024

  View details for DOI 10.1109/UFFC-JS60046.2024.10793633

  View details for Web of Science ID 001428150100142

• A multi-parametric model for progression of metabolic dysfunction-associated steatohepatitis (MASH) in humans Baek, J., Sanabria, S., Oyarzabal, I., Echevarria-Uraga, J. J., Quesada, C., Dahl, J., Parker, K. J., IEEE IEEE. 2024

  View details for DOI 10.1109/UFFC-JS60046.2024.10793524

  View details for Web of Science ID 001428150100042

• Differentiable Beamforming for Distributed Attenuation Estimation and Spatial Gain Compensation (SGC) Frey, B. N., Hyun, D., Simson, W., Brevett, T., Zhuang, L., Baek, J., Sanabria, S. J., Dahl, J. J., IEEE IEEE. 2024

  View details for DOI 10.1109/UFFC-JS60046.2024.10794091

  View details for Web of Science ID 001428150100552

• Frequency estimator to improve H-scan tissue characterization Baek, J., Brevett, T., Hyun, D., El Kaffas, A., Parker, K. J., Dahl, J., IEEE IEEE. 2024

  View details for DOI 10.1109/UFFC-JS60046.2024.10793751

  View details for Web of Science ID 001428150100216

• Nondestructive ultrasound molecular imaging based on a neural network approach utilizing post-processed ultrasound images Baek, J., Hyun, D., Nataraj, A., Tabesh, F., Paulmurugan, R., Dahl, J. J., IEEE IEEE. 2024

  View details for DOI 10.1109/UFFC-JS60046.2024.10793498

  View details for Web of Science ID 001428150100019

• Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning MACHINE LEARNING-SCIENCE AND TECHNOLOGY Baek, J., O'Connell, A. M., Parker, K. J. 2022; 3 (4): 045013

  Abstract

  The improved diagnostic accuracy of ultrasound breast examinations remains an important goal. In this study, we propose a biophysical feature-based machine learning method for breast cancer detection to improve the performance beyond a benchmark deep learning algorithm and to furthermore provide a color overlay visual map of the probability of malignancy within a lesion. This overall framework is termed disease-specific imaging. Previously, 150 breast lesions were segmented and classified utilizing a modified fully convolutional network and a modified GoogLeNet, respectively. In this study multiparametric analysis was performed within the contoured lesions. Features were extracted from ultrasound radiofrequency, envelope, and log-compressed data based on biophysical and morphological models. The support vector machine with a Gaussian kernel constructed a nonlinear hyperplane, and we calculated the distance between the hyperplane and each feature's data point in multiparametric space. The distance can quantitatively assess a lesion and suggest the probability of malignancy that is color-coded and overlaid onto B-mode images. Training and evaluation were performed on in vivo patient data. The overall accuracy for the most common types and sizes of breast lesions in our study exceeded 98.0% for classification and 0.98 for an area under the receiver operating characteristic curve, which is more precise than the performance of radiologists and a deep learning system. Further, the correlation between the probability and Breast Imaging Reporting and Data System enables a quantitative guideline to predict breast cancer. Therefore, we anticipate that the proposed framework can help radiologists achieve more accurate and convenient breast cancer classification and detection.

  View details for DOI 10.1088/2632-2153/ac9bcc

  View details for Web of Science ID 000880819300001

  View details for PubMedID 36698865

  View details for PubMedCentralID PMC9855672