Prof. Scott S. Hsieh Profile

Prof. Scott S. Hsieh

at Mayo Clinic

SPIE Involvement:

Author

Publications (51)

Proceedings Article | 3 April 2024 Poster

Automated web-based software for CT quality control testing of low-contrast detectability using model observers

Zhongxing Zhou, Jarod Wellinghoff, Mingdong Fan, Scott Hsieh, David Holmes, Cynthia McCollough, Lifeng Yu

Proceedings Volume 12925, 129252J (2024) https://doi.org/10.1117/12.3008777

KEYWORDS: Quality control, Image quality, Computed tomography, Software validation, Modulation transfer functions, Medical image reconstruction, Clinical research, Biomedical applications

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Proceedings Article | 1 April 2024 Presentation + Paper

Activating the interpixel septa of scintillator-photodiode detectors using an x-ray fluorescent reflector material

Scott Hsieh

Proceedings Volume 12925, 129250C (2024) https://doi.org/10.1117/12.3006477

KEYWORDS: Sensors, X-rays, Reflection, Gadolinium, Photons, X-ray detectors, Spatial resolution, Scintillators

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Proceedings Article | 1 April 2024 Presentation + Paper

Reconstruction with an alternating aperture, photon counting detector

Scott Hsieh, Katsuyuki Taguchi, Shuai Leng, Cynthia McCollough

Proceedings Volume 12925, 129250F (2024) https://doi.org/10.1117/12.3006478

KEYWORDS: Modulation transfer functions, Photon counting, Quantum detection, Spatial resolution, Spatial frequencies, Quantum noise, Point spread functions, Interpolation

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Proceedings Article | 1 April 2024 Poster + Paper

Synthesizing high-resolution dual-energy radiographs from coronary artery calcium CT images

Kian Shaker, Linxi Shi, Scott Hsieh, Akyl Swaby, Shiva Abbaszadeh, Adam Wang

Proceedings Volume 12925, 129253T (2024) https://doi.org/10.1117/12.3006250

KEYWORDS: Dual energy imaging, Numerical simulations, Image resolution, Super resolution, Pixel resolution, Radiography, Tissues, Calcium, Computed tomography, Arteries, Visualization, Spatial resolution, Sensors, Image segmentation, Anatomy

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Proceedings Article | 1 April 2024 Presentation + Paper

Evaluation of data uncertainty for deep-learning-based CT noise reduction using ensemble patient data and a virtual imaging trial framework

Zhongxing Zhou, Scott S. Hsieh, Hao Gong, Cynthia McCollough, Lifeng Yu

Proceedings Volume 12925, 1292508 (2024) https://doi.org/10.1117/12.3008581

KEYWORDS: Deep convolutional neural networks, Denoising, Computed tomography, Reconstruction algorithms, Image quality, Medical image reconstruction

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Proceedings Article | 7 April 2023 Presentation + Paper

Patient-specific uncertainty and bias quantification of non-transparent convolutional neural network model through knowledge distillation and Bayesian deep learning

Hao Gong, Lifeng Yu, Shuai Leng, Scott Hsieh, Joel Fletcher, Cynthia McCollough

Proceedings Volume 12463, 124631K (2023) https://doi.org/10.1117/12.2654318

KEYWORDS: Data modeling, Monte Carlo methods, Denoising, Deep learning, Calibration, Reliability, Convolutional neural networks, Correlation coefficients

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Proceedings Article | 7 April 2023 Poster + Paper

Improvements in dose efficiency with high resolution scan modes in photon counting CT

Picha Shunhavanich, Kishore Rajendran, Mingdong Fan, Cynthia McCollough, Shuai Leng, Lifeng Yu, Scott Hsieh

Proceedings Volume 12463, 124633O (2023) https://doi.org/10.1117/12.2654334

KEYWORDS: Computed tomography, Scanners, Spatial resolution, Photon counting, Image quality standards, Image sharpness

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Proceedings Article | 7 April 2023 Presentation + Paper

A dense search challenge phantom fabricated with pixel-based 3D printing for precise detectability assessment

Scott Hsieh, Kai Mei, Nadav Shapira, Picha Shunhavanich, J. Webster Stayman, Cynthia McCollough, Grace Gang, Shuai Leng, Michael Geagen, Lifeng Yu, Peter Noël

Proceedings Volume 12463, 124631D (2023) https://doi.org/10.1117/12.2654336

KEYWORDS: 3D printing, Target detection, Scanners, Reconstruction algorithms, Computed tomography

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Proceedings Article | 7 April 2023 Presentation + Paper

Charge sharing correction for photon counting detectors with coincidence counters

Katsuyuki Taguchi, Scott Hsieh

Proceedings Volume 12463, 124630F (2023) https://doi.org/10.1117/12.2654389

KEYWORDS: Photon counting, Distortion, X-rays, Monte Carlo methods, Imaging spectroscopy, Cardiovascular magnetic resonance imaging, Computed tomography, Algorithm development, Windows, Detection and tracking algorithms

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Proceedings Article | 7 April 2023 Presentation + Paper

Real-time single frame tomosynthesis: prototype and radiotherapy applications

Scott Hsieh, Lydia Ng, Minsong Cao, Percy Lee

Proceedings Volume 12463, 124630M (2023) https://doi.org/10.1117/12.2653996

KEYWORDS: Tomosynthesis, Lung, X-ray sources, Radiotherapy, Prototyping, Fluoroscopy, Anatomy, Collimators

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Proceedings Article | 7 April 2023 Paper

Contrast- and noise-dependent spatial resolution measurement for deep convolutional neural network-based noise reduction in CT using patient data

Zhongxing Zhou, Hao Gong, Scott Hsieh, Cynthia McCollough, Lifeng Yu

Proceedings Volume 12463, 124631J (2023) https://doi.org/10.1117/12.2654972

KEYWORDS: Deep convolutional neural networks, Modulation transfer functions, Denoising, Spatial resolution, Computed tomography

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Proceedings Article | 7 April 2023 Poster + Paper

Classification of high-risk coronary plaques using radiomic analysis of multi-energy photon-counting-detector computed tomography (PCD-CT) images

Chelsea A. Dunning, Prabhakar Shantha Rajiah, Scott Hsieh, Andrea Esquivel, Mariana Yalon, Nikkole Weber, Hao Gong, Joel Fletcher, Cynthia McCollough, Shuai Leng

Proceedings Volume 12465, 124652T (2023) https://doi.org/10.1117/12.2654412

KEYWORDS: Radiomics, Image segmentation, Iodine, Computed tomography, Cooccurrence matrices, Feature extraction, Arteries, Photon counting, Cardiac imaging

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Proceedings Article | 3 April 2023 Presentation + Paper

A training program to reduce reader search errors for liver metastasis detection in CT

Scott Hsieh, Akitoshi Inoue, Mariana Yalon, David Cook, Jeff Fidler, Hao Gong, Parvathy Sudhir Pillai, Andrew Vercnocke, Matthew Johnson, Shuai Leng, Lifeng Yu, David Holmes, Rickey Carter, Cynthia McCollough, Joel Fletcher

Proceedings Volume 12467, 1246704 (2023) https://doi.org/10.1117/12.2654007

KEYWORDS: Education and training, Liver, Cancer detection, Eye tracking, Eye, Error analysis, Calibration

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SPIE Journal Paper | 13 September 2022

Individualized and generalized models for predicting observer performance on liver metastasis detection using CT

Parvathy Sudhir Pillai, David Holmes, Rickey Carter, Akitoshi Inoue, David Cook, Ron Karwoski, Jeff Fidler, Joel Fletcher, Shuai Leng, Lifeng Yu, Cynthia McCollough, Scott Hsieh

JMI, Vol. 9, Issue 05, 055501, (September 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.5.055501

KEYWORDS: Performance modeling, Liver, Computed tomography, Feature extraction, Data modeling, 3D modeling, Radiology, RGB color model, Reconstruction algorithms, Convolutional neural networks

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Purpose: Radiologists exhibit wide inter-reader variability in diagnostic performance. This work aimed to compare different feature sets to predict if a radiologist could detect a specific liver metastasis in contrast-enhanced computed tomography (CT) images and to evaluate possible improvements in individualizing models to specific radiologists.Approach: Abdominal CT images from 102 patients, including 124 liver metastases in 51 patients were reconstructed at five different kernels/doses using projection domain noise insertion to yield 510 image sets. Ten abdominal radiologists marked suspected metastases in all image sets. Potentially salient features predicting metastasis detection were identified in three ways: (i) logistic regression based on human annotations (semantic), (ii) random forests based on radiologic features (radiomic), and (iii) inductive derivation using convolutional neural networks (CNN). For all three approaches, generalized models were trained using metastases that were detected by at least two radiologists. Conversely, individualized models were trained using each radiologist’s markings to predict reader-specific metastases detection.Results: In fivefold cross-validation, both individualized and generalized CNN models achieved higher area under the receiver operating characteristic curves (AUCs) than semantic and radiomic models in predicting reader-specific metastases detection ability (p < 0.001). The individualized CNN with an AUC of mean (SD) 0.85(0.04) outperformed the generalized one [AUC = 0.78 ( 0.06 ) , p = 0.004]. The individualized semantic [AUC = 0.70 ( 0.05 ) ] and radiomic models [AUC = 0.68 ( 0.06 ) ] outperformed the respective generalized versions [semantic AUC = 0.66 ( 0.03 ) , p = 0.009; radiomic AUC = 0.64 ( 0.06 ) , p = 0.03].Conclusions: Individualized models slightly outperformed generalized models for all three feature sets. Inductive CNNs were better at predicting metastases detection than semantic or radiomic features. Generalized models have implementation advantages when individualized data are unavailable.

Proceedings Article | 4 April 2022 Presentation + Paper

Pixel-wise bias approximation and correction for convolutional neural network noise reduction in CT

Nathan Huber, Hao Gong, Thomas Huber, David Campeau, Scott Hsieh, Shuai Leng, Lifeng Yu, Cynthia McCollough

Proceedings Volume 12031, 120311C (2022) https://doi.org/10.1117/12.2612703

KEYWORDS: Denoising, Computed tomography, Convolutional neural networks, Artificial intelligence

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Proceedings Article | 4 April 2022 Presentation + Paper

Implementation and initial experience with an interactive eye-tracking system for measuring radiologists' visual search in diagnostic tasks using volumetric CT images

Hao Gong, Scott Hsieh, David Holmes, David Cook, Akitoshi Inoue, David Bartlett, Francis Baffour, Hiroaki Takahashi, Shuai Leng, Lifeng Yu, Joel Fletcher, Cynthia McCollough

Proceedings Volume 12031, 120310Q (2022) https://doi.org/10.1117/12.2611808

KEYWORDS: Visualization, Diagnostics, Liver, Computed tomography, Cameras, X-ray computed tomography, Head, Target detection, Radiology, Manufacturing

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Proceedings Article | 4 April 2022 Presentation + Paper

Estimating the minimum SNR necessary for object detection in the projection domain

Scott Hsieh, Lifeng Yu, Nathan Huber, Shuai Leng, Cynthia McCollough

Proceedings Volume 12031, 120310T (2022) https://doi.org/10.1117/12.2611564

KEYWORDS: Signal to noise ratio, Reconstruction algorithms, Signal detection, Denoising, Scanners, Mathematical modeling, Convolutional neural networks, Computed tomography

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Proceedings Article | 4 April 2022 Paper

Design of a digital, motion-free mechanism for fluence field modulation

Scott Hsieh, Shuai Leng, Lifeng Yu, Cynthia McCollough, Adam Wang

Proceedings Volume 12031, 120310O (2022) https://doi.org/10.1117/12.2611559

KEYWORDS: Attenuators, Metals, Modulation, X-rays, Calibration, Fluctuations and noise, Scanners, Photons

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Proceedings Article | 4 April 2022 Presentation + Paper

A 25-reader performance study for hepatic metastasis detection: lessons from unsupervised learning

Scott Hsieh, Akitoshi Inoue, Parvathy Sudhir Pillai, Hao Gong, David Holmes, David Cook, Shuai Leng, Lifeng Yu, Rickey Carter, Joel Fletcher, Cynthia McCollough

Proceedings Volume 12031, 1203116 (2022) https://doi.org/10.1117/12.2611543

KEYWORDS: Machine learning, Liver, Visualization, MATLAB, Abdomen, Temporal resolution, Spatial resolution, Scanners, Radiology

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Proceedings Article | 4 April 2022 Presentation + Paper

Individualized and generalized learner models for predicting missed hepatic metastases

Parvathy Sudhir Pillai, Scott Hsieh, David Holmes, Rickey Carter, Joel Fletcher, Cynthia McCollough

Proceedings Volume 12035, 120350C (2022) https://doi.org/10.1117/12.2612745

KEYWORDS: Computed tomography, 3D image processing, Feature extraction, Diagnostics, Liver, Convolutional neural networks

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Proceedings Article | 15 February 2021 Presentation + Paper

Random search as a neural network optimization strategy for Convolutional-Neural-Network (CNN)-based noise reduction in CT

Nathan Huber, Andrew Missert, Hao Gong, Scott Hsieh, Shuai Leng, Lifeng Yu, Cynthia McCollough

Proceedings Volume 11596, 115961U (2021) https://doi.org/10.1117/12.2582143

KEYWORDS: Denoising, Neural networks, Network architectures, Visualization, Computed tomography, Bone

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Proceedings Article | 15 February 2021 Presentation + Paper

A web-based software platform for efficient and quantitative CT image quality assessment and protocol optimization

Mingdong Fan, Theodore Thayib, Liqiang Ren, Scott Hsieh, Cynthia McCollough, David Holmes, Lifeng Yu

Proceedings Volume 11595, 1159507 (2021) https://doi.org/10.1117/12.2582123

KEYWORDS: Image quality, Computed tomography, Denoising

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Proceedings Article | 10 March 2020 Presentation + Paper

An adversarial machine-learning-based approach and biomechanically guided validation for improving deformable image registration accuracy between a planning CT and cone-beam CT for adaptive prostate radiotherapy applications

Anand Santhanam, Michael Lauria, Brad Stiehl, Daniel Elliott, Saty Seshan, Scott Hsieh, Minsong Cao, Daniel Low

Proceedings Volume 11313, 113130P (2020) https://doi.org/10.1117/12.2550493

KEYWORDS: Computed tomography, Image registration, Image processing, Prostate, Medical imaging, Machine learning, Neural networks, Radiotherapy, Image quality, Data modeling, Finite element methods

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SPIE Journal Paper | 25 June 2019

Implementation of a piecewise-linear dynamic attenuator

Picha Shunhavanich, Nathaniel Robert Bennett, Scott Hsieh, Norbert Pelc

JMI, Vol. 6, Issue 02, 023502, (June 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.2.023502

KEYWORDS: Attenuators, Modulation, Sensors, Signal attenuation, Chest, Scanners, Computed tomography, Actuators, Fluctuations and noise, Abdomen

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Proceedings Article | 15 March 2019 Presentation + Paper

Open-source Gauss-Newton-based methods for refraction-corrected ultrasound computed tomography

Rehman Ali, Scott Hsieh, Jeremy Dahl

Proceedings Volume 10955, 1095508 (2019) https://doi.org/10.1117/12.2511319

KEYWORDS: Computed tomography, Reconstruction algorithms, Ultrasonography, Breast, Ray tracing, Refraction, Diffraction

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Proceedings Article | 1 March 2019 Presentation + Paper

Accelerating coordinate descent in iterative reconstruction

Scott Hsieh, John Hoffman, Frederic Noo

Proceedings Volume 10948, 1094811 (2019) https://doi.org/10.1117/12.2512615

KEYWORDS: Reconstruction algorithms, Computed tomography, Computer architecture, Diagnostics, Data modeling, Radiology, Graphics processing units

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Proceedings Article | 1 March 2019 Presentation + Paper

Adaptively-weighted total-variation (AwTV) in a prototype 4D digital tomosynthesis system for fast and low-dose target localization

Sunghoon Choi, Sooyeul Lee, Young-Nam Kang, Scott Hsieh, Hee-Joung Kim

Proceedings Volume 10948, 1094808 (2019) https://doi.org/10.1117/12.2512323

KEYWORDS: Prototyping, Reconstruction algorithms, Image restoration, Image quality, Imaging systems, Sensors, Detection and tracking algorithms, Medical research, Electronics

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On-board 4D cone-beam CT (CBCT) imaging using a linear accelerator (LINAC) is recently favored scanning protocol in image guided radiotherapy, but it raises the problems of excessive radiation dose. Alternatively, the 4D digital tomosynthesis (DTS) has been introduced for small-sized target localization, such as pancreas, prostate, or partial breast scan, which does not require a full 3D information. However, conventional filtered back-projection (FBP) reconstruction method produces severe aliasing artifacts due to sparsely sampled projections measured in each respiration phase within a limited angle range. This effect is even more severe when the LINAC gantry sweep speed is too fast to sufficiently cover the respiratory gating phase. Previous studies on total-variation (TV) minimization-based reconstruction framework would be an alternative approach to solve this problem, however, it presents the loss of sharpness during the iterations. In this study, we adopted an adaptively-weighted TV (AwTV) scheme which penalizes the images after the TV optimization. We introduced a look-up table which contains all possible weighting parameters during each iteration step. As a result, the proposed AwTV method provided better image quality compared to the conventional FBP and adaptive steepest descent-projection onto convex set (ASD-POCS) frameworks, showing higher structural similarities (SSIM) by factor of 1.12 compared to FBP and less root-mean-square error (RMSEs) by factor of 1.06 compared to ASD-POCS. The horizontal line profile of the spherical target inserted in the moving phantom showed that the images from FBP and ASD-POCS provided severe aliasing artifact and smoothed pixel intensities, but proposed AwTV scheme reduces the aliasing artifact while maintaining the object’s sharpness. In conclusion, the proposed AwTV method has a potential for low-dose and faster 4D-DTS imaging, which indicates an alternative option to 4D-CBCT for small region target localization.

Proceedings Article | 9 March 2018 Presentation + Paper

Fast low-dose compressed-sensing (CS) image reconstruction in four-dimensional digital tomosynthesis using on-board imager (OBI)

Sunghoon Choi, Scott Hsieh, Chang-Woo Seo, Hee-Joung Kim

Proceedings Volume 10573, 1057325 (2018) https://doi.org/10.1117/12.2293557

KEYWORDS: Image restoration, Reconstruction algorithms, Radiotherapy, Imaging systems, Image quality, Computer programming, Image compression, Optical spheres, Motion measurement, Head

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Patient respiration induces motion artifacts during cone-beam CT (CBCT) imaging in LINAC-based radiotherapy guidance. This could be relieved by retrospectively sorting the projection images, which could be called as a respiratorycorrelated CBCT or a four-dimensional (4D) CBCT imaging. However, the slowness of the LINAC head gantry rotation limits a rapid scan time so that 4D-CBCT usually involves large amounts of radiation dose. Digital tomosynthesis (DTS) which utilizes limited angular range would present a faster 4D imaging with much lower radiation dose. One drawback of 4D-DTS is strong streak artifacts represented in the tomosynthesis reconstructed images due to sparsely sampled projections in each phase bin. The authors suggest a fast low-dose 4D-DTS image reconstruction method in order to effectively reduce the artifacts in a sparse imaging tomosynthesis condition. We used a flat-panel detector to acquire tomosynthesis projections of respiratory moving phantom in anterior-posterior (AP) and lateral views. We entered a sinusoidal periodic respiratory motion for an input signal to the phantom. An external monitor of Varian real-time position management (RPM) system was used to estimate the input respiratory motion, thereby four respiratory gating phases were determined to retrospectively arrange the projections. For streak line reduction, we introduced a simple iterative scheme suggested by McKinnon and Bates (MKB) and regarded it as a prior input image of the proposed lowdose compressed sensing (CS) method. Three different 4D-DTS image reconstruction schemes of conventional Feldkamp (FDK), MKB, and MKB-CS were implemented to phase-wise projections of both AP and lateral views. All reconstructions were accelerated by using a GPU card to reduce the computation times. For assessment of algorithmic performance, we compared a streak reduction ratio (SRR) and a contrast-to-noise-ratio (CNR) among the outcome images. The results showed that SRRs for MKB and MKB-CS schemes were 0.24 and 0.69, respectively, which indicates that the proposed MKB-CS method provided smaller streaking artifacts than conventional one by factor of 2.88. The resulted CNRs of coronal tomosynthesis images at peak-inhale phase were 3.24, 6.36, and 10.56 for FDK, MKB, and MKB-CS, respectively. This shows that the proposed method provides better image quality compared to others. The reconstruction time for MKB-CS was 196.07 sec, showing that our GPU-accelerated programming would enhance the algorithmic performance to match clinically feasible times (~3 min). In conclusion, the proposed low-dose 4D-DTS reconstruction scheme may provide better outcomes than the conventional methods with fast speed, and could thus it could be applied in practical 4D imaging for radiotherapy.

Proceedings Article | 9 March 2018 Paper

Implementation of a piecewise-linear dynamic attenuator

Picha Shunhavanich, N. Robert Bennett, Scott Hsieh, Norbert Pelc

Proceedings Volume 10573, 105730T (2018) https://doi.org/10.1117/12.2293525

KEYWORDS: Photon counting, Modulation, Signal attenuation, Chest, Sensors, Abdomen, Attenuators, Fluctuations and noise, Scanners

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Proceedings Article | 9 March 2018 Presentation + Paper

Can image-domain filtering of FBP CT reconstructions match low-contrast performance of iterative reconstructions?

Sarah Divel, Scott Hsieh, Jia Wang, Norbert Pelc

Proceedings Volume 10573, 1057314 (2018) https://doi.org/10.1117/12.2292599

KEYWORDS: Image filtering, Gaussian filters, CT reconstruction, 3D image reconstruction, Image quality, Linear filtering, Scanners, 3D image processing

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Proceedings Article | 9 March 2018 Paper

Quantitative lung nodule detectability and dose reduction in low-dose chest tomosynthesis

Sunghoon Choi, Seungyeon Choi, Scott Hsieh, Donghoon Lee, Junyoung Son, Haenghwa Lee, Chang-Woo Seo, Hee-Joung Kim

Proceedings Volume 10573, 105735O (2018) https://doi.org/10.1117/12.2293546

KEYWORDS: Lung, Modulation transfer functions, Chest, Quantum electronics, 3D modeling, Image quality, Photovoltaics, Image restoration, Binary data, Spatial resolution

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Quantitative imaging analysis has become a focus of medical imaging fields in recent days. In this study, Fourier-based imaging metrics for task-based quantitative assessment of lung nodules were applied in low-dose chest tomosynthesis. Compared to the conventional filtered back-projection (FBP), a compressed-sensing (CS) image reconstruction has been proposed for dose and artifact reduction. We implemented the CS-based low-dose reconstruction scheme to a sparsely sampled projection dataset and compared the lung nodule detectability index (d’) between the FBP and CS methods. We used the non-prewhitening (NPW) model observer to estimate the in-plane slice detectability in tomosynthesis and theoretically calculated d’ using the weighted amounts of local noise, spatial resolution, and task function in Fourier domain. We considered spatially varying noise and spatial resolution properties because the iterative reconstruction showed non-stationary characteristics. For analysis of task function, we adopted a simple binary hypothesis-testing model which discriminates outer and inner region of the encapsulated shape of lung nodule. The results indicated that the local noise power spectrum showed smaller intensities with increasing the number of projections, whereas the local transfer function provided similar appearances between the FBP and CS schemes. The resulted task functions for the same size of lung nodules showed the same pattern with different intensity, whereas the task function for different size of lung nodules presented different shapes due to different object functions. The theoretically calculated d’ values showed that the CS schemes provided higher values than the FBP method by factors of 2.64-3.47 and 2.50-3.10 for two different lung nodules among all projection views. This could demonstrate that the low-dose CS algorithm provide a comparable lung nodule images in comparison to FBP from 37.9% up to 28.8% reduced dose in the same projection views. Moreover, we observed that the CS method implemented with small number of projections provided similar or somewhat higher d’ values compared to the FBP method with large number of projections. In conclusion, the CS scheme may present a potential dose reduction for lung nodule detection in the chest tomosynthesis by showing higher d’ in comparison to the conventional FBP method.

Proceedings Article | 9 March 2018 Paper

Focal spot rotation for improving CT resolution homogeneity

Scott Hsieh

Proceedings Volume 10573, 105733X (2018) https://doi.org/10.1117/12.2292472

KEYWORDS: Modulation transfer functions, Spatial resolution, Image resolution, X-ray sources, Photon counting, Image quality

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Proceedings Article | 27 February 2018 Paper

Expert identification of visual primitives used by CNNs during mammogram classification

Jimmy Wu, Diondra Peck, Scott Hsieh, Vandana Dialani, Constance Lehman, Bolei Zhou, Vasilis Syrgkanis, Lester Mackey, Genevieve Patterson

Proceedings Volume 10575, 105752T (2018) https://doi.org/10.1117/12.2293890

KEYWORDS: Visualization, Mammography, Breast cancer, Cancer, Neural networks, Digital mammography, Image segmentation, Tissues, Breast, Image processing

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SPIE Journal Paper | 18 May 2017

Segmented targeted least squares estimator for material decomposition in multibin photon-counting detectors

Paurakh Rajbhandary, Scott Hsieh, Norbert Pelc

JMI, Vol. 4, Issue 02, 023503, (May 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.2.023503

KEYWORDS: Photodetectors, Target detection, Sensors, Monte Carlo methods, Image segmentation, X-ray detectors, Gold, Aluminum

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Proceedings Article | 9 March 2017 Presentation + Paper

Effect of spatio-energy correlation in PCD due to charge sharing, scatter, and secondary photons

Paurakh Rajbhandary, Scott Hsieh, Norbert Pelc

Proceedings Volume 10132, 101320V (2017) https://doi.org/10.1117/12.2254999

KEYWORDS: Sensors, Photons, Photon counting, Monte Carlo methods, X-ray computed tomography, X-ray detectors, X-ray fluorescence spectroscopy, X-ray imaging, X-rays, Distortion, Imaging spectroscopy, Electrons, Matrices

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Proceedings Article | 9 March 2017 Paper

Improvements in low contrast detectability with iterative reconstruction and the effect of slice thickness

Scott Hsieh, Norbert Pelc

Proceedings Volume 10132, 1013253 (2017) https://doi.org/10.1117/12.2253937

KEYWORDS: Reconstruction algorithms, Computed tomography, Image filtering, Modulation transfer functions, Image quality, Quality measurement, Image restoration, Radiology, Medical imaging, Image processing, Physics, Current controlled current source

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SPIE Journal Paper | 1 June 2016

Improving pulse detection in multibin photon-counting detectors

Scott Hsieh, Norbert Pelc

JMI, Vol. 3, Issue 02, 023505, (June 2016) https://doi.org/10.1117/12.10.1117/1.JMI.3.2.023505

KEYWORDS: Sensors, Signal detection, Photons, Photodetectors, Bone, Logic, Monte Carlo methods, Computed tomography, Imaging spectroscopy, Signal analyzers

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Proceedings Article | 25 March 2016 Paper

Limits to dose reduction from iterative reconstruction and the effect of through-slice blurring

Scott Hsieh, Norbert Pelc

Proceedings Volume 9783, 97831C (2016) https://doi.org/10.1117/12.2216889

KEYWORDS: Reconstruction algorithms, Scanners, Image restoration, Computed tomography, Algorithm development, Computer simulations, Calibration, Image filtering, Data analysis, Iterative methods, Medical imaging, Statistical analysis, Matrices, Radiology, Modulation transfer functions

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Proceedings Article | 22 March 2016 Paper

Over-exposure correction in knee cone-beam CT imaging with automatic exposure control using a partial low dose scan

Jang-Hwan Choi, Kerstin Muller, Scott Hsieh, Andreas Maier, Garry Gold, Marc Levenston, Rebecca Fahrig

Proceedings Volume 9783, 97830L (2016) https://doi.org/10.1117/12.2217347

KEYWORDS: Sensors, Computed tomography, Imaging systems, Automatic exposure, Automatic control, Image filtering, Cones, Diagnostics, Standards development, In vivo imaging, Tissues, X-ray computed tomography, Human subjects, Visibility

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Proceedings Article | 22 March 2016 Paper

Striped ratio grids for scatter estimation

Scott Hsieh, Adam Wang, Josh Star-Lack

Proceedings Volume 9783, 97830J (2016) https://doi.org/10.1117/12.2216896

KEYWORDS: Modulation, Cones, Beam analyzers, Nonlinear image processing, Image processing, X-ray computed tomography, X-ray imaging, X-ray sources, Sensors, Bone, Monte Carlo methods, Modulators, Manufacturing, Medical imaging

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Proceedings Article | 18 March 2015 Paper

First results from a prototype dynamic attenuator system

Scott Hsieh, Mark Peng, Christopher May, Picha Shunhavanich, Norbert Pelc

Proceedings Volume 9412, 94121N (2015) https://doi.org/10.1117/12.2081482

KEYWORDS: Attenuators, Prototyping, Image quality, Calibration, X-rays, Dynamical systems, Sensors, Rapid manufacturing, Collimation, Fluctuations and noise

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Proceedings Article | 18 March 2015 Paper

Pulse detection logic for multibin photon counting detectors: beyond the simple comparator

Scott Hsieh, Norbert Pelc

Proceedings Volume 9412, 941210 (2015) https://doi.org/10.1117/12.2081479

KEYWORDS: Sensors, Logic, Photon counting, Optical character recognition, Imaging spectroscopy, Bone, Spatial resolution, Electroluminescence, Imaging systems, Computer simulations

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Proceedings Article | 18 March 2015 Paper

Fluid-filled dynamic bowtie filter: a feasibility study

Picha Shunhavanich, Scott Hsieh, Norbert Pelc

Proceedings Volume 9412, 94121L (2015) https://doi.org/10.1117/12.2081673

KEYWORDS: Signal attenuation, Attenuators, Fluid dynamics, Fluctuations and noise, Sensors, Detection and tracking algorithms, Image quality, Modulation, Liquids, X-rays

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Proceedings Article | 19 March 2014 Paper

Segmented targeted least squares estimator for material decomposition in multi bin PCXDs

Paurakh Rajbhandary, Scott Hsieh, Norbert Pelc

Proceedings Volume 9033, 903319 (2014) https://doi.org/10.1117/12.2043198

KEYWORDS: Image segmentation, Aluminum, Signal attenuation, X-ray detectors, Monte Carlo methods, Photodetectors, X-rays, Calibration, Error analysis, Medicine

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Proceedings Article | 19 March 2014 Paper

Use of depth information from in-depth photon counting detectors for x-ray spectral imaging: a preliminary simulation study

Yuan Yao, Hans Bornefalk, Scott Hsieh, Mats Danielsson, Norbert Pelc

Proceedings Volume 9033, 90333E (2014) https://doi.org/10.1117/12.2042839

KEYWORDS: Sensors, Gallium arsenide, Calcium, Silicon, X-ray detectors, X-rays, Imaging spectroscopy, Signal detection, Photon counting, Compton scattering

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Proceedings Article | 19 March 2014 Paper

Algorithms for optimizing CT fluence control

Scott Hsieh, Norbert Pelc

Proceedings Volume 9033, 90330M (2014) https://doi.org/10.1117/12.2042542

KEYWORDS: Modulation, Image quality, X-rays, Abdomen, Attenuators, Optical filters, Reconstruction algorithms, Image filtering, Signal attenuation, X-ray computed tomography

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Proceedings Article | 19 March 2014 Paper

Enabling photon counting detectors with dynamic attenuators

Scott Hsieh, Norbert Pelc

Proceedings Volume 9033, 90333J (2014) https://doi.org/10.1117/12.2042533

KEYWORDS: Sensors, Attenuators, Optical character recognition, Signal attenuation, Fluctuations and noise, Photodetectors, Photon counting, X-ray detectors, Imaging spectroscopy, X-rays

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Proceedings Article | 19 March 2013 Paper

Optimized control of a dynamic, prepatient attenuator

Scott Hsieh, Norbert Pelc

Proceedings Volume 8668, 86681Q (2013) https://doi.org/10.1117/12.2008229

KEYWORDS: Attenuators, Modulation, Signal attenuation, Monte Carlo methods, Image quality standards, Convex optimization, Abdomen, Optical filters, Fluctuations and noise, Photons

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Proceedings Article | 19 March 2013 Paper

Truncation artifact correction by support recovery

Scott Hsieh, Guangzhi Cao, Brian Nett, Norbert Pelc

Proceedings Volume 8668, 86683N (2013) https://doi.org/10.1117/12.2008224

KEYWORDS: Reconstruction algorithms, Tissues, Image restoration, Algorithm development, Radiotherapy, Data modeling, Detection and tracking algorithms, Data analysis, Image filtering, Medicine

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Proceedings Article | 3 March 2012 Paper

A volumetric reconstruction algorithm for stationary source inverse-geometry CT

Scott Hsieh, Norbert Pelc

Proceedings Volume 8313, 83133N (2012) https://doi.org/10.1117/12.912490

KEYWORDS: Reconstruction algorithms, Modulation transfer functions, Sensors, Computed tomography, Computer simulations, Optical spheres, Temporal resolution, Scanners, Data acquisition, Medical imaging

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Proceedings Article | 17 March 2011 Paper

An inverse geometry CT system with stationary source arrays

Scott Hsieh, Joseph Heanue, Tobias Funk, Waldo Hinshaw, Norbert Pelc

Proceedings Volume 7961, 79611W (2011) https://doi.org/10.1117/12.878801

KEYWORDS: Sensors, Imaging systems, X-ray computed tomography, Collimators, Collimation, Modulation transfer functions, Temporal resolution, Photons, X-rays, Computed tomography

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Showing 5 of 51 publications

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