Ms. Wanyi Fu Profile

Wanyi Fu

Doctoral Candidate

SPIE Involvement:

Author

Publications (15)

Proceedings Article | 4 April 2022 Poster + Paper

Quality or quantity: toward a unified approach for multi-organ segmentation in body CT

Fakrul Islam Tushar, Husam Nujaim, Wanyi Fu, Ehsan Abadi, Maciej Mazurowski, William Segars, Ehsan Samei, Joseph Y. Lo

Proceedings Volume 12031, 120312P (2022) https://doi.org/10.1117/12.2613101

KEYWORDS: Data modeling, Image segmentation, 3D modeling, Computed tomography, Performance modeling, Bladder, Stomach, Pancreas, Medical imaging, Liver

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SPIE Journal Paper | 25 October 2021

Development and validation of an automated methodology to assess perceptual in vivo noise texture in liver CT

Taylor Brunton Smith, Ehsan Abadi, Thomas Sauer, Wanyi Fu, Justin Solomon, Ehsan Samei

JMI, Vol. 8, Issue 05, 052113, (October 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.5.052113

KEYWORDS: Liver, Gold, Computed tomography, In vivo imaging, Reconstruction algorithms, Image quality, Target detection, Image quality standards, Image processing, Data modeling

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Purpose: Developing, validating, and evaluating a method for measuring noise texture directly from patient liver CT images (i.e., in vivo). Approach: The method identifies target regions within patient scans that are least likely to have major contribution of patient anatomy, detrends them locally, and measures noise power spectrum (NPS) there using a previously phantom-validated technique targeting perceptual noise–non-anatomical fluctuations in the image that may interfere with the detection of focal lesions. Method development and validation used scanner-specific CT simulations of computational, anthropomorphic phantom (XCAT phantom, three phases of contrast-enhancement) with known ground truth of the NPS. Simulations were based on a clinical scanner (Definition Flash, Siemens) and clinically relevant settings (tube voltage of 120 kV at three dose levels). Images were reconstructed with filtered backprojection (kernel: B31, B41, and B50) and Sinogram Affirmed Iterative Reconstruction (kernel: I31, I41, and I50) using a manufacturer-specific reconstruction software (ReconCT, Siemens). All NPS measurements were made in the liver. Ground-truth NPS were taken as the sum of (1) a measurement in parenchymal regions of anatomy-subtracted (i.e., noise only) scans, and (2) a measurement in the same region of noise-free (pre-noise-insertion) images. To assess in vivo NPS performance, correlation of NPS average frequency (favg), was reported. Sensitivity of accuracy [root-mean-square-error (RMSE)] to number of pixels included in measurement was conducted via bootstrapped pixel-dropout. Sensitivity of NPS to dose and reconstruction kernel was assessed to confirm that ground truth NPS similarities were maintained in patient-specific measurements. Results: Pearson and Spearman correlation coefficients 0.97 and 0.96 for favg indicated good correlation. Results suggested accurate NPS measurements (within 5% total RMSE) could be acquired with ∼106 pixels. Conclusions: Relationships of similar NPS due to reconstruction kernel and dose were preserved between gold standard and observed in vivo estimations. The NPS estimation method was further deployed on clinical cases to demonstrate the feasibility of clinical analysis.

Proceedings Article | 15 February 2021 Presentation + Paper

iPhantom: an automated framework in generating personalized computational phantoms for organ-based radiation dosimetry

Wanyi Fu, Paul W. Segars, Shobhit Sharma, Joseph Lo, Ehsan Samei

Proceedings Volume 11595, 1159521 (2021) https://doi.org/10.1117/12.2582238

KEYWORDS: Radiation dosimetry, Medical imaging, Image segmentation, Monte Carlo methods, Computed tomography, 3D image processing, Error analysis, Computer simulations

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Automated patient-specific and organ-based image quality metrics on dual-energy CT datasets for large scale studies

Wanyi Fu, Juan Carlos Ramirez-Giraldo, Daniele Marin, Taylor Brunton Smith, Thomas Sauer, Yuqin Ding, Ehsan Samei

Proceedings Volume 11595, 1159530 (2021) https://doi.org/10.1117/12.2582231

KEYWORDS: Image quality, Computed tomography, Image segmentation, 3D modeling, Liver, Stomach, Spleen, Kidney, Image quality standards, Dual energy imaging

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Proceedings Article | 16 March 2020 Presentation + Paper

In vivo noise texture estimation: development and validation of an automated methodology

Taylor Brunton Smith, Ehsan Abadi, T. Sauer, Wanyi Fu, Justin Solomon, Ehsan Samei

Proceedings Volume 11312, 113121U (2020) https://doi.org/10.1117/12.2549268

KEYWORDS: Computed tomography, Image quality, Computer simulations

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Proceedings Article | 16 March 2020 Presentation + Paper

Patient-informed modelling of hepatic contrast dynamics in contrast-enhanced CT imaging

Hananiel Setiawan, Ehsan Abadi, Francesco Ria, Wanyi Fu, Taylor Smith, Ehsan Samei

Proceedings Volume 11312, 1131214 (2020) https://doi.org/10.1117/12.2548879

KEYWORDS: Data modeling, Liver, Computed tomography, Mathematical modeling, Diagnostics, Image segmentation, Brain-machine interfaces, Modeling, Image quality standards, Safety

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Proceedings Article | 15 March 2019 Paper

Deep learning of 3D computed tomography (CT) images for organ segmentation using 2D multi-channel SegNet model

Yingzhou Liu, Wanyi Fu, Vignesh Selvakumaran, Matthew Phelan, W. Paul Segars, Ehsan Samei, Maciej Mazurowski, Joseph Lo, Geoffrey Rubin, Ricardo Henao

Proceedings Volume 10954, 109541D (2019) https://doi.org/10.1117/12.2512887

KEYWORDS: Image segmentation, Data modeling, 3D modeling, Computed tomography, Kidney, 3D image processing, Medical imaging, Machine learning

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Proceedings Article | 1 March 2019 Paper

Patient-informed and physiology-based modelling of contrast dynamics in cross-sectional imaging

Hananiel Setiawan, Ehsan Abadi, Wanyi Fu, Taylor Smith, Ehsan Samei

Proceedings Volume 10948, 109485Y (2019) https://doi.org/10.1117/12.2513431

KEYWORDS: Image segmentation, Neural networks, Computed tomography, Modeling, Liver

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Proceedings Article | 1 March 2019 Paper

Multi-organ segmentation in clinical-computed tomography for patient-specific image quality and dose metrology

Wanyi Fu, Shobhit Sharma, Taylor Smith, Rui Hou, Ehsan Abadi, Vignesh Selvakumaran, Ruixiang Tang, Joseph Lo, W. Paul Segars, Anuj Kapadia, Justin Solomon, Geoffrey Rubin, Ehsan Samei

Proceedings Volume 10948, 1094829 (2019) https://doi.org/10.1117/12.2512883

KEYWORDS: Image segmentation, Image quality, Computed tomography, 3D modeling, Machine learning, Monte Carlo methods, Imaging systems, Safety

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The purpose of this study was to develop a robust, automated multi-organ segmentation model for clinical adult and pediatric CT and implement the model as part of a patient-specific safety and quality monitoring system. 3D convolutional neural network (Unet) models were setup to segment 30 different organs and structures at the diagnostic image resolution. For each organ, 200 manually-labeled cases were used to train the network, fitting it to different clinical imaging resolutions and contrast enhancement stages. The dataset was randomly shuffled, and divided with 6/2/2 train/validation/test set split. The model was deployed to automatically segment 1200 clinical CT images as a demonstration of the utility of the method. Each case was made into a patient-specific phantom based on the segmentation masks, with unsegmented organs and structures filled in by deforming a template XCAT phantom of similar anatomy. The organ doses were then estimated using a validated scanner-specific MC-GPU package using the actual scan information. The segmented organ information was likewise used to assess contrast, noise, and detectability index within each organ. The neural network segmentation model showed dice similarity coefficients (DSC) above 0.85 for the majority of organs. Notably, the lungs and liver showed a DSC of 0.95 and 0.94, respectively. The segmentation results produced patient-specific dose and quality values across the tested 1200 patients with representative the histogram distributions. The measurements were compared in global-to-organ (e.g. CTDvol vs. liver dose) and organ-to-organ (e.g. liver dose vs. spleen dose) manner. The global-to-organ measurements (liver dose vs. CTDIvol: 𝑅 = 0.62; liver vs. global d’: 𝑅 = 0.78; liver vs. global noise: 𝑅 = 0.55) were less correlated compared to the organ-to-organ measurements (liver vs. spleen dose: 𝑅 = 0.93; liver vs. spleen d’: 𝑅 = 0.82; liver vs. spleen noise: 𝑅 = 0.78). This variation of measurement is more prominent for iterative reconstruction kernel compared to the filtered back projection kernel (liver vs. global noise: 𝑅𝐼𝑅 = 0.47 vs. 𝑅𝐹𝐵𝑃 = 0.75; liver vs. global d’: 𝑅𝐼𝑅 = 0.74 vs. 𝑅𝐹𝐵𝑃 = 0.83). The results can help derive meaningful relationships between image quality, organ doses, and patient attributes.

Proceedings Article | 9 March 2018 Paper

A rapid GPU-based Monte Carlo simulation tool for individualized dose estimations in CT

Shobhit Sharma, Anuj Kapadia, Ehsan Abadi, Wanyi Fu, W. Paul Segars, Ehsan Samei

Proceedings Volume 10573, 105733V (2018) https://doi.org/10.1117/12.2294965

KEYWORDS: Monte Carlo methods, X-ray computed tomography, Computed tomography, Scanners, Clinical trials

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Proceedings Article | 9 March 2018 Paper

From patient-informed to patient-specific organ dose estimation in clinical computed tomography

Wanyi Fu, William Segars, Ehsan Abadi, Shobhit Sharma, Anuj Kapadia, Ehsan Samei

Proceedings Volume 10573, 1057315 (2018) https://doi.org/10.1117/12.2294954

KEYWORDS: Image segmentation, Computed tomography, Monte Carlo methods, Medical physics, Convolutional neural networks, Data modeling, Physics, Algorithm development, Image processing algorithms and systems

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SPIE Journal Paper | 4 August 2017

Breast dose reduction with organ-based, wide-angle tube current modulated CT

Wanyi Fu, Gregory Sturgeon, Greeshma Agasthya, Wiliam Paul Segars, Anuj Kapadia, Ehsan Samei

JMI, Vol. 4, Issue 03, 031208, (August 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.3.031208

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Proceedings Article | 9 March 2017 Paper

Estimation of breast dose reduction potential for organ-based tube current modulated CT with wide dose reduction arc

Wanyi Fu, Gregory Sturgeon, Greeshma Agasthya, W. Paul Segars, Anuj Kapadia, Ehsan Samei

Proceedings Volume 10132, 1013246 (2017) https://doi.org/10.1117/12.2255797

KEYWORDS: Breast, Medicine, Monte Carlo methods, Computed tomography, Scanners, Image quality standards, Ray tracing, Image quality, X-ray computed tomography, Tissues, Manufacturing, Biological research

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Proceedings Article | 3 May 2016 Paper

Estimation of breast dose saving potential using a breast positioning technique for organ-based tube current modulated CT

Wanyi Fu, Xiaoyu Tian, Gregory Sturgeon, Greeshma Agasthya, William Paul Segars, Mitchell Goodsitt, Ella Kazerooni, Ehsan Samei

Proceedings Volume 9783, 97833C (2016) https://doi.org/10.1117/12.2217239

KEYWORDS: Breast, Monte Carlo methods, Modulation, Medicine, Tissues, Computed tomography, Signal attenuation, Image quality, Scanners, Image quality standards

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Proceedings Article | 31 March 2016 Paper

Organ dose conversion coefficients for tube current modulated CT protocols for an adult population

Wanyi Fu, Xiaoyu Tian, Pooyan Sahbaee, Yakun Zhang, William Paul Segars, Ehsan Samei

Proceedings Volume 9783, 97833E (2016) https://doi.org/10.1117/12.2217271

KEYWORDS: Modulation, Monte Carlo methods, Computed tomography, Liver, Kidney, Bladder, Skin, Neuroimaging, Brain, Esophagus

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Showing 5 of 15 publications

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