Bio


Dr. Wang is a research scientist in the Wang group. He previously completed his postdoctoral fellowship in the Wang group and his BS and PhD in the Department of Engineering Physics at Tsinghua University. Sen's research interests focus on technologies and methods for image processing, reconstruction, and recognition, especially in the medical field. His PhD work investigated reconstruction algorithms and applications of x-ray spectral imaging, including photon counting detector modeling and correction, as well as quantitative imaging and computer vision with deep learning on x-ray images and other medical images.

At Stanford, Dr. Wang works on advanced CT detector designs and imaging algorithms.

Supervisors


Education & Certifications


  • PhD, Tsinghua University (2019)
  • Bachelor, Tsinghua University (2014)

Work Experience


  • Postdoctoral Scholar, Stanford University (2/1/2020 - 6/26/2023)

    Location

    1201 Welch Rd, Stanford, CA 94305

All Publications


  • Empirical optimization of energy bin weights for compressing measurements with realistic photon counting x-ray detectors. Medical physics Yang, Y., Wang, S., Pal, D., Yin, Z., Pelc, N. J., Wang, A. S. 2023

    Abstract

    BACKGROUND: Photon counting detectors (PCDs) provide higher spatial resolution, improved contrast-to-noise ratio (CNR), and energy discriminating capabilities. However, the greatly increased amount of projection data in photon counting computed tomography (PCCT) systems becomes challenging to transmit through the slip ring, process, and store.PURPOSE: This study proposes and evaluates an empirical optimization algorithm to obtain optimal energy weights for energy bin data compression. This algorithm is universally applicable to spectral imaging tasks including 2 and 3 material decomposition (MD) tasks and virtual monoenergetic images (VMIs). This method is simple to implement while preserving spectral information for the full range of object thicknesses and is applicable to different PCDs, for example, silicon detectors and CdTe detectors.METHODS: We used realistic detector energy response models to simulate the spectral response of different PCDs and an empirical calibration method to fit a semi-empirical forward model for each PCD. We numerically optimized the optimal energy weights by minimizing the average relative Cramer-Rao lower bound (CRLB) due to the energy-weighted bin compression, for MD and VMI tasks over a range of material area density rho A , m ${\rho }_{A,m}$ (0-40g/cm2 water, 0-2.16g/cm2 calcium). We used Monte Carlo simulation of a step wedge phantom and an anthropomorphic head phantom to evaluate the performance of this energy bin compression method in the projection domain and image domain, respectively.RESULTS: The results show that for 2 MD, the energy bin compression method can reduce PCCT data size by 75% and 60%, with an average variance penalty of less than 17% and 3% for silicon and CdTe detectors, respectively. For 3 MD tasks with a K-edge material (iodine), this method can reduce the data size by 62.5% and 40% with an average variance penalty of less than 12% and 13% for silicon and CdTe detectors, respectively.CONCLUSIONS: We proposed an energy bin compression method that is broadly applicable to different PCCT systems and object sizes, with high data compression ratio and little loss of spectral information.

    View details for DOI 10.1002/mp.16590

    View details for PubMedID 37401203

  • Fast kV Switching for Improved Material Decomposition with Photon Counting X-ray Detectors Wang, S., Yang, Y., Pal, D., Pelc, N. J., Wang, A. S., Zhao, W., Yu, L. SPIE-INT SOC OPTICAL ENGINEERING. 2022

    View details for DOI 10.1117/12.2611601

    View details for Web of Science ID 000836294000014

  • Empirical Optimization of Energy Bin Weights for Compressing Measurements with Photon Counting X-ray Detectors Yang, Y., Wang, S., Pal, D., Pelc, N. J., Wang, A. S., Zhao, W., Yu, L. SPIE-INT SOC OPTICAL ENGINEERING. 2022

    View details for DOI 10.1117/12.2611555

    View details for Web of Science ID 000836294000013

  • Multimodal Contrastive Learning for Prospective Personalized Estimation of CT Organ Dose Imran, A., Wang, S., Pal, D., Dutta, S., Zucker, E., Wang, A., Wang, L., Dou, Q., Fletcher, P. T., Speidel, S., Li, S. SPRINGER INTERNATIONAL PUBLISHING AG. 2022: 634-643
  • Personalized CT organ noise estimation from scout images SPIE Medical Imaging: Physics of Medical Imaging Imran, A., Pal, D., Wang, S., Dutta, S., Zucker, E., Wang, A. 2022

    View details for DOI 10.1117/12.2610986

  • Personalized CT Organ Dose Estimation from Scout Images International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Imran, A., Wang, S., Pal, D., Dutta, S., Patel, B., Zucker, E., Wang, A. 2021
  • A systematic evaluation and optimization of automatic detection of ulcers in wireless capsule endoscopy on a large dataset using deep convolutional neural networks. Physics in medicine and biology Wang, S., Xing, Y., Zhang, L., Gao, H., Zhang, H. 2019; 64 (23): 235014

    Abstract

    Compared with conventional gastroscopy which is invasive and painful, wireless capsule endoscopy (WCE) can provide noninvasive examination of gastrointestinal (GI) tract. The WCE video can effectively support physicians to reach a diagnostic decision while a huge number of images need to be analyzed (more than 50 000 frames per patient). In this paper, we propose a computer-aided diagnosis method called second glance (secG) detection framework for automatic detection of ulcers based on deep convolutional neural networks that provides both classification confidence and bounding box of lesion area. We evaluated its performance on a large dataset that consists of 1504 patient cases (the largest WCE ulcer dataset to our best knowledge, 1076 cases with ulcers, 428 normal cases). We use 15 781 ulcer frames from 753 ulcer cases and 17 138 normal frames from 300 normal cases for training. Validation dataset consists of 2040 ulcer frames from 108 cases and 2319 frames from 43 normal cases. For test, we use 4917 ulcer frames from 215 ulcer cases and 5007 frames from 85 normal cases. Test results demonstrate the 0.9469 ROC-AUC of the proposed secG detection framework outperforms state-of-the-art detection frameworks including Faster-RCNN (0.9014) and SSD-300 (0.8355), which implies the effectiveness of our method. From the ulcer size analysis, we find the detection of ulcers is highly related to the size. For ulcers with size larger than 1% of the full image size, the sensitivity exceeds 92.00%. For ulcers that are smaller than 1% of the full image size, the sensitivity is around 85.00%. The overall sensitivity, specificity and accuracy are 89.71%, 90.48% and 90.10%, at a threshold value of 0.6706, which implies the potential of the proposed method to suppress oversights and to reduce the burden of physicians.

    View details for DOI 10.1088/1361-6560/ab5086

    View details for PubMedID 31645019

  • Deep Convolutional Neural Network for Ulcer Recognition in Wireless Capsule Endoscopy: Experimental Feasibility and Optimization COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE Wang, S., Xing, Y., Zhang, L., Gao, H., Zhang, H. 2019; 2019: 7546215

    Abstract

    Wireless capsule endoscopy (WCE) has developed rapidly over the last several years and now enables physicians to examine the gastrointestinal tract without surgical operation. However, a large number of images must be analyzed to obtain a diagnosis. Deep convolutional neural networks (CNNs) have demonstrated impressive performance in different computer vision tasks. Thus, in this work, we aim to explore the feasibility of deep learning for ulcer recognition and optimize a CNN-based ulcer recognition architecture for WCE images. By analyzing the ulcer recognition task and characteristics of classic deep learning networks, we propose a HAnet architecture that uses ResNet-34 as the base network and fuses hyper features from the shallow layer with deep features in deeper layers to provide final diagnostic decisions. 1,416 independent WCE videos are collected for this study. The overall test accuracy of our HAnet is 92.05%, and its sensitivity and specificity are 91.64% and 92.42%, respectively. According to our comparisons of F1, F2, and ROC-AUC, the proposed method performs better than several off-the-shelf CNN models, including VGG, DenseNet, and Inception-ResNet-v2, and classical machine learning methods with handcrafted features for WCE image classification. Overall, this study demonstrates that recognizing ulcers in WCE images via the deep CNN method is feasible and could help reduce the tedious image reading work of physicians. Moreover, our HAnet architecture tailored for this problem gives a fine choice for the design of network structure.

    View details for DOI 10.1155/2019/7546215

    View details for Web of Science ID 000488776000001

    View details for PubMedID 31641370

    View details for PubMedCentralID PMC6766681

  • Second Glance framework (secG): Enhanced Ulcer Detection with Deep Learning on a Large Wireless Capsule Endoscopy Dataset Wang, S., Xing, Y., Zhang, L., Gao, H., Zhang, H., Jiang, Chen, Z., Chen, G. SPIE-INT SOC OPTICAL ENGINEERING. 2019

    View details for DOI 10.1117/12.2540456

    View details for Web of Science ID 000502121300030

  • Systematic implementation of spectral CT with a photon counting detector for liquid security inspection NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT Xu, X., Xing, Y., Wang, S., Zhang, L. 2018; 893: 99–108
  • Enhanced material separation with a quasi-monochromatic CT imaging method using a photon counting detector NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT Wang, S., Xing, Y., Zhang, L., Xu, X. 2018; 881: 9–15
  • Quasi-monochromatic imaging in x-ray CT via spectral deconvolution using photon-counting detectors PHYSICS IN MEDICINE AND BIOLOGY Wang, S., Gao, H., Zhang, L., Wu, D., Xu, X. 2017; 62 (6): 2208–23

    Abstract

    Photon-counting detectors can obtain the spectral information from an incident x-ray spectrum, although the detected counts may differ from the incident counts due to the detector response. If uncorrected or uncompensated, the response will lead to distortion in CT reconstruction. With the intention of reducing the distortion and exploring the potential of photon-counting detectors, a novel reconstruction strategy with spectral deconvolution, which attempts to set itself apart from traditional material decomposition frameworks, is proposed in this paper. It applies deconvolution to the energy window counts using a calibrated detector response and then uses the post-deconvolution photon counts to reconstruct images in multi-energy windows. The output has a quantitative meaning as a quasi-monochromatic attenuation coefficient, because a relatively narrow energy window width is selected. The deconvolution settings and results are carefully discussed in the numerical simulation. An experimental study is then carried out to verify the effectiveness and robustness. The results show that the reconstructed attenuation coefficients after deconvolution fit the standard reference data very well in most of the energy windows, which implies the feasibility of this quasi-monochromatic imaging method.

    View details for DOI 10.1088/1361-6560/aa5a47

    View details for Web of Science ID 000395801000004

    View details for PubMedID 28099156

  • More Accurate and Less Noisy Spectral Deconvolution Strategy using Photon Counting Detectors Wang, S., Zhang, L., Xu, X., IEEE IEEE. 2017
  • Preliminary Study of Quantitative X-ray Spectral Imaging with Spectral Deconvolution Wang, S., Zhang, L., Xu, X., Wu, D., IEEE IEEE. 2016