Prof. Chen Ting Lin Profile

Prof. Chen Ting Lin

at Johns Hopkins Univ

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Publications (3)

SPIE Journal Paper | 9 April 2021

Direct reconstruction of anatomical change in low-dose lung nodule surveillance

Jessica Flores, Grace Gang, Hao Zhang, Chen Ting Lin, Shui K. Fung, J. Webster Stayman

JMI, Vol. 8, Issue 02, 023503, (April 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.2.023503

KEYWORDS: Image registration, Lung, Surveillance, Image analysis, X-ray computed tomography, Tissues, Rigid registration, Data acquisition, Visualization, Reconstruction algorithms

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Proceedings Article | 15 February 2021 Presentation + Paper

Generative adversarial networks and radiomics supervision for lung lesion synthesis

Shaoyan Pan, Jessica Flores, Chen Ting Lin, J. Webster Stayman, Grace Gang

Proceedings Volume 11595, 115950O (2021) https://doi.org/10.1117/12.2582151

KEYWORDS: Lung, Solids, Databases, Optical inspection

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Realistic lesion generation is a useful tool for system evaluation and optimization. Generated lesions can serve as realistic imaging tasks for task-base image quality assessment, as well as targets in virtual clinical trials. In this work, we investigate a data-driven approach for categorical lung lesion synthesis using public lung CT databases. We propose a generative adversarial network with a Wasserstein discriminator and gradient penalty to stabilize training. We further included conditional inputs such that the network can generate user-specified lesion categories. Novel to our network, we directly incorporated radiomic features in an intermediate supervision step to encourage similar textures between generated and real lesions. We calculated the network using lung lesions from the Lung Image Database Consortium (LIDC) database. Lesions are divided into two categories: solid vs. non-solid. We performed quantitative evaluation of network performance based on four criteria: 1) overfitting, in terms of structural and morphological similarity to the training data, 2) diversity of generated lesions, in terms of similarity to other generated data, 3) similarity to real lesions, in terms of distribution of example radiomics features, and 4) conditional consistency, in terms of classification accuracy using a classifier trained on the training lesions. We imposed a quantitative threshold for similarity based on visual inspection. The percentage of non-solid and solid lesions that satisfy low overfitting and high diversity is 87.1% and 70.2% of non-solid and solid lesions respectively. The distribution of example radiomics features are similar in the generated and real lesions indicated by low Kullback–Leibler divergence scores: 1.62 for non-solid lesions and 1.13 for solid lesions. Classification accuracy for the generated lesions are comparable with that for the real lesions. The proposed network presents a promising approach for generating realistic lesions with clinically relevant features crucial for the comprehensive assessment of novel medical imaging systems.

Proceedings Article | 9 March 2018 Presentation + Paper

Prospective image quality analysis and control for prior-image-based reconstruction of low-dose CT

Hao Zhang, Grace Gang, Hao Dang, Marc Sussman, Cheng Ting Lin, Jeffrey Siewerdsen, J. Webster Stayman

Proceedings Volume 10573, 1057329 (2018) https://doi.org/10.1117/12.2293135

KEYWORDS: Image quality, Lung, Computed tomography, Image analysis, Image registration, Systems modeling, Surveillance, Analytical research, Data acquisition, Gold

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Purpose: Prior-image-based reconstruction (PIBR) is a powerful tool for low-dose CT, however, the nonlinear behavior of such approaches are generally difficult to predict and control. Similarly, traditional image quality metrics do not capture potential biases exhibited in PIBR images. In this work, we identify a new bias metric and construct an analytical framework for prospectively predicting and controlling the relationship between prior image regularization strength and this bias in a reliable and quantitative fashion. Methods: Bias associated with prior image regularization in PIBR can be described as the fraction of actual contrast change (between the prior image and current anatomy) that appears in the reconstruction. Using local approximation of the nonlinear PIBR objective, we develop an analytical relationship between local regularization, fractional contrast reconstructed, and true contrast change. This analytic tool allows prediction bias properties in a reconstructed PIBR image and includes the dependencies on the data acquisition, patient anatomy and change, and reconstruction parameters. Predictions are leveraged to provide reliable and repeatable image properties for varying data fidelity in simulation and physical cadaver experiments. Results: The proposed analytical approach permits accurate prediction of reconstructed contrast relative to a gold standard based on exhaustive search based on numerous iterative reconstructions. The framework is used to control regularization parameters to enforce consistent change reconstructions over varying fluence levels and varying numbers of projection angles – enabling bias properties that are less location- and acquisition-dependent. Conclusions: While PIBR methods have demonstrated a substantial ability for dose reduction, image properties associated with those images have been difficult to express and quantify using traditional metrics. The novel framework presented in this work not only quantifies this bias in an intuitive fashion, but it gives a way to predict and control the bias. Reliable and predictable reconstruction methods are a requirement for clinical imaging systems and the proposed framework is an important step translating PIBR methods to clinical application.

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