Dr. Matthew Tivnan Profile

Dr. Matthew Tivnan

Graduate Student Researcher at Johns Hopkins Univ

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Publications (16)

Proceedings Article | 1 April 2024 Presentation + Paper

Diffusion posterior sampling for nonlinear CT reconstruction

Shudong Li, J. Webster Stayman, Matthew Tivnan

Proceedings Volume 12925, 1292519 (2024) https://doi.org/10.1117/12.3007693

KEYWORDS: Diffusion, Double positive medium, Education and training, CT reconstruction, Data modeling, Reverse modeling, Computed tomography, Statistical modeling, Systems modeling, Stochastic processes

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Proceedings Article | 1 April 2024 Presentation + Paper

Design optimization of a triple-layer flat-panel detector for three-material decomposition

Xiao Jiang, J. Webster Stayman, Grace Gang, Xiaoxuan Zhang, Matthew Tivnan

Proceedings Volume 12925, 129250U (2024) https://doi.org/10.1117/12.3006385

KEYWORDS: Design, Imaging systems, X-rays, Model based design, Radiography, Fluoroscopy, X ray detectors

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SPIE Journal Paper | 4 May 2023

Tunable neural networks for CT image formation

Matthew Tivnan, Grace J. Gang, Wenying Wang, Peter Noël, Jeremias Sulam, J. Webster Stayman

JMI, Vol. 10, Issue 03, 033501, (May 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.3.033501

KEYWORDS: Education and training, Image quality, Computed tomography, Neural networks, Image acquisition, Data modeling, Image restoration, Covariance, Anatomy, Spatial resolution

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Proceedings Article | 7 April 2023 Poster + Paper

Deep learning CT image restoration using system blur models

Grace Gang, J. Webster Stayman, Yijie Yuan, Matthew Tivnan

Proceedings Volume 12463, 124634J (2023) https://doi.org/10.1117/12.2655806

KEYWORDS: Image restoration, Deep learning, Computed tomography, Education and training, Network architectures, X-ray computed tomography, Data modeling, Performance modeling

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Proceedings Article | 7 April 2023 Presentation + Paper

Task-driven CT image quality optimization for low-contrast lesion detectability with tunable neural networks

Matthew Tivnan, Tzu-Cheng Lee, Ruoqiao Zhang, Kirsten Boedeker, Liang Cai, Jeremias Sulam, J. Webster Stayman

Proceedings Volume 12463, 124631M (2023) https://doi.org/10.1117/12.2653936

KEYWORDS: Computed tomography, Neural networks, Education and training, Image quality, Denoising, CT reconstruction, Deep learning, Performance modeling, 3D modeling, Medical image reconstruction

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Proceedings Article | 18 October 2022 Open Access

Local linearity analysis of deep learning CT denoising algorithms

Junyuan Li, Wenying Wang, Matthew Tivnan, Jeremias Sulam, Jerry Prince, Michael McNitt-Gray, J. Webster Stayman, Grace Gang

Proceedings Volume 12304, 123040T (2022) https://doi.org/10.1117/12.2646371

KEYWORDS: Neural networks, Denoising, Computed tomography, Complex systems, Algorithm development, Medical imaging, Visualization, Spherical lenses, Space operations, Signal to noise ratio

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The rapid development of deep-learning methods in medical imaging has called for an analysis method suitable for non-linear and data-dependent algorithms. In this work, we investigate a local linearity analysis where a complex neural network can be represented as piecewise linear systems. We recognize that a large number of neural networks consists of alternating linear layers and rectified linear unit (ReLU) activations, and are therefore strictly piecewise linear. We investigated the extent of these locally linear regions by gradually adding perturbations to an operating point. For this work, we explored perturbations based on image features of interest, including lesion contrast, background, and additive noise. We then developed strategies to extend these strictly locally linear regions to include neighboring linear regions with similar gradients. Using these approximately linear regions, we applied singular value decomposition (SVD) analysis to each local linear system to investigate and explain the overall nonlinear and data-dependent behaviors of neural networks. The analysis was applied to an example CT denoising algorithm trained on thorax CT scans. We observed that the strictly local linear regions are highly sensitive to small signal perturbations. Over a range of lesion contrast from 0.007 to 0.04 mm⁻¹, there is a total of 33992 linear regions. The Jacobians are also shift-variant. However, the Jacobians of neighboring linear regions are very similar. By combining linear regions with similar Jacobians, we narrowed down the number of approximately linear regions to four over lesion contrast from 0.001 to 0.08 mm⁻¹. The SVD analysis to different linear regions revealed denoising behavior that is highly dependent on the background intensity. Analysis further identified greater amount of noise reduction in uniform regions compared to lesion edges. In summary, the local linearity analysis framework we proposed has the potential for us to better characterize and interpret the non-linear and data-dependent behaviors of neural networks.

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Proceedings Article | 4 April 2022 Presentation + Paper

Control of variance and bias in CT image processing with variational training of deep neural networks

Matthew Tivnan, J. Webster Stayman, Wenying Wang, Grace Gang, Peter Noël

Proceedings Volume 12031, 120310P (2022) https://doi.org/10.1117/12.2612417

KEYWORDS: Data modeling, Image quality, Computed tomography, Neural networks, Image restoration, Spatial resolution, Image processing, Spatial frequencies, Image acquisition, Data acquisition

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Proceedings Article | 4 April 2022 Presentation + Paper

Performance assessment framework for neural network denoising

Grace Gang, Wenying Wang, Joseph Stayman, Matthew Tivnan, Junyuan Li

Proceedings Volume 12031, 1203114 (2022) https://doi.org/10.1117/12.2612732

KEYWORDS: Lung, Denoising, Neural networks, Image quality, Computed tomography

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The proliferation of deep learning image processing calls for a quantitative image quality assessment framework that is suitable for nonlinear, data-dependent algorithms. In this work, we propose a method to systematically evaluate the system and noise responses such that the nonlinear transfer properties can be mapped out. The method involves sampling of lesion perturbations as a function of size, contrast, as well as clinically relevant features such as shape and texture that may be important for diagnosis. We embed the perturbations in backgrounds of varying attenuation levels, noise magnitude and correlation that are associated with different patient anatomies and imaging protocols. The range of system and noise response are further used to evaluate performance for clinical tasks such as signal detection and classification. We performed the assessment for an example CNN-denoising algorithm for low does lung CT screening. The system response of the CNN-denoising algorithm exhibits highly nonlinear behavior where both contrast and higher order lesion features such as spiculated boundaries are not reliably represented for lesions perturbations with small size and low contrast. The noise properties are potentially highly nonstationary, and should be assumed to be the same between the signal-present and signal-absent images. Furthermore, we observer a high degree dependency of both system and noise response on the background attenuation levels. Inputs around zeros are effectively imposed a non-negativity constraint; transfer properties for higher background levels are highly variable. For a detection task, CNN-denoised images improved detectability index by 16-18% compared to low dose CT inputs. For classification task between spiculated and smooth lesions, CNN-denoised images result in a much larger improvement up to 50%. The performance assessment framework propose in this work can systematically map out the nonlinear transfer functions for deep learning algorithms and can potentially enable robust deployment of such algorithms in medical imaging applications.

Proceedings Article | 4 April 2022 Presentation + Paper

Data-dependent nonlinearity analysis in CT denoising CNNs

Wenying Wang, Matthew Tivnan, Junyuan Li, Joseph Stayman, Grace Gang

Proceedings Volume 12031, 1203117 (2022) https://doi.org/10.1117/12.2612569

KEYWORDS: Neural networks, Denoising, Computed tomography

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Proceedings Article | 15 February 2021 Presentation + Paper

Manifold reconstruction of differences: a model-based iterative statistical estimation algorithm with a data-driven prior

Matthew Tivnan, J. Webster Stayman

Proceedings Volume 11595, 115951R (2021) https://doi.org/10.1117/12.2582268

KEYWORDS: Reconstruction algorithms, Statistical modeling, Statistical analysis, Data modeling, Model-based design, Denoising, Pathology, Neural networks, Lung, Computer simulations

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Proceedings Article | 16 March 2020 Presentation + Paper

Combining spectral CT acquisition methods for high-sensitivity material decomposition

Matthew Tivnan, Grace Gang, Wenying Wang, Eleni Liapi, Peter Noël, J. Webster Stayman

Proceedings Volume 11312, 1131218 (2020) https://doi.org/10.1117/12.2550025

KEYWORDS: Sensors, Computed tomography, Iodine, Optical filters, Model-based design, Tissues, X-ray computed tomography, Data modeling, Imaging systems, Signal attenuation

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Proceedings Article | 16 March 2020 Presentation + Paper

Prospective prediction and control of image properties in model-based material decomposition for spectral CT

Matthew Tivnan, Grace Gang, J. Webster Stayman, Wenying Wang

Proceedings Volume 11312, 113121Z (2020) https://doi.org/10.1117/12.2549777

KEYWORDS: Dual energy imaging, Spectral computed tomography, Fluctuations and noise, Model-based design, Computed tomography, Statistical analysis, Statistical modeling, Spatial resolution

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Proceedings Article | 16 March 2020 Paper

Model-based material decomposition with system blur modeling

Wenying Wang, Matthew Tivnan, Grace Gang, Yiqun Ma, Qian Cao, Minghui Lu, Josh Star-Lack, Richard Colbeth, Wojciech Zbijewski, J. Webster Stayman

Proceedings Volume 11312, 113123Q (2020) https://doi.org/10.1117/12.2549549

KEYWORDS: Systems modeling, Iodine, Scintillators, Imaging systems, X-rays, Computed tomography, Dual energy imaging

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Proceedings Article | 28 May 2019 Paper

Local response prediction in model-based CT material decomposition

Wenying Wang, Steven Tilley, Matthew Tivnan, J. Webster Stayman

Proceedings Volume 11072, 110720Z (2019) https://doi.org/10.1117/12.2534437

KEYWORDS: Point spread functions, Model-based design, X-ray computed tomography, Image quality, Spectral computed tomography

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Proceedings Article | 28 May 2019 Paper

Optimized spatial-spectral CT for multi-material decomposition

Matthew Tivnan, Wenying Wang, Steven Tilley, Jeffrey Siewerdsen, J. Stayman

Proceedings Volume 11072, 1107211 (2019) https://doi.org/10.1117/12.2534333

KEYWORDS: Optical filters, Linear filtering, Image filtering, Sensors, Model-based design, X-ray computed tomography, Imaging systems, Gold, Nonlinear filtering, Data acquisition

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Proceedings Article | 1 March 2019 Presentation + Paper

Physical modeling and performance of spatial-spectral filters for CT material decomposition

Matthew Tivnan, Steven Tilley, J. Stayman

Proceedings Volume 10948, 109481A (2019) https://doi.org/10.1117/12.2513481

KEYWORDS: Sensors, Motion models, Performance modeling, Iodine, Data modeling, Gold, X-rays, Detection and tracking algorithms, Reconstruction algorithms

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Showing 5 of 16 publications

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