Dr. Lifeng Yu Profile

Dr. Lifeng Yu

Professor at Mayo Clinic

SPIE Involvement:

Conference Program Committee | Author | Editor

Publications (78)

SPIE Journal Paper | 24 May 2024

Low-contrast detectability of photon-counting-detector CT at different scan modes and image types in comparison with energy-integrating-detector CT

Mingdong Fan, Zhongxing Zhou, Jarod Wellinghoff, Cynthia H. McCollough, Lifeng Yu

JMI, Vol. 11, Issue S1, S12803, (May 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.S1.S12803

KEYWORDS: Computed tomography, Infrared imaging, Image restoration, Photodetectors, Sensors, Scanners, Quality control, Image quality, Interference (communication), Image sharpness

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PurposeWe aim to compare the low-contrast detectability of a clinical whole-body photon-counting-detector (PCD)-CT at different scan modes and image types with an energy-integrating-detector (EID)-CT.ApproachWe used a channelized Hotelling observer (CHO) previously optimized for quality control purposes. An American College of Radiology CT accreditation phantom was scanned on both PCD-CT and EID-CT with 10 phantom positionings. For PCD-CT, images were generated using two scan modes, standard resolution (SR) and ultra-high-resolution (UHR); two image types, virtual monochromatic images at 70 keV and low-energy threshold (T3D); both filtered-back-projection (FBP) and iterative reconstruction (IR) reconstruction methods; and three reconstruction kernels. For each positioning, three repeated scans were acquired for each scan mode, image type, and CTDIvol of 6, 12, and 24 mGy. For EID-CT, images acquired from scans (10 positionings × 3 repeats × 3 doses) were reconstructed using the closest counterpart FBP and IR kernels. CHO was applied to calculate the index of detectability (d′) on both scanners.ResultsWith the smooth Br44 kernel, the d′ of UHR was mostly comparable with that of the SR mode (difference: −11.4% to 8.3%, p=0.020 to 0.956), and the T3D images had a higher d′ (difference: 0.7% to 25.6%) than 70 keV images on PCD-CT. Compared with the EID-CT, UHR-T3D of PCD-CT had non-inferior d′ (difference: −2.7% to 12.9%) with IR and non-superior d′ (difference: 0.8% to 11.2%) with FBP using the Br44 kernel. PCD-CT produced higher d′ than EID-CT by 61.8% to 247.1% with the sharper reconstruction kernels.ConclusionsThe comparison between PCD-CT and EID-CT was significantly influenced by the reconstruction method and kernel. With a smooth kernel that is typically used in low-contrast detection tasks, the PCD-CT demonstrated low-contrast detectability that was comparable to EID-CT with IR and showed no superiority when using FBP. With the use of sharper kernels, the PCD-CT significantly outperformed EID-CT in low-contrast detectability.

Proceedings Article | 3 April 2024 Presentation

High-temporal-resolution dynamic imaging of airways in photon-counting-detector CT

Jelena Mihailovic, Michael Bruesewitz, Nathan Hull, Nadir Demirel, Cynthia McCollough, Lifeng Yu

Proceedings Volume 12925, 129251G (2024) https://doi.org/10.1117/12.3008732

KEYWORDS: Computed tomography, Temporal resolution, Visualization, Scanners, Pulmonary disorders, Optical spheres, Lung imaging, Lung, Image resolution, Gold

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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

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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SPIE Journal Paper | 24 August 2023

Self-trained deep convolutional neural network for noise reduction in CT

Zhongxing Zhou, Akitoshi Inoue, Cynthia McCollough, Lifeng Yu

JMI, Vol. 10, Issue 04, 044008, (August 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.4.044008

KEYWORDS: Education and training, Data modeling, Denoising, Computed tomography, Infrared imaging, Image restoration, Performance modeling, Image quality, Deep convolutional neural networks, Quantum noise

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PurposeSupervised deep convolutional neural network (CNN)-based methods have been actively used in clinical CT to reduce image noise. The networks of these methods are typically trained using paired high- and low-quality data from a massive number of patients and/or phantom images. This training process is tedious, and the network trained under a given condition may not be generalizable to patient images acquired and reconstructed under different conditions. We propose a self-trained deep CNN (ST_CNN) method for noise reduction in CT that does not rely on pre-existing training datasets.ApproachThe ST_CNN training was accomplished using extensive data augmentation in the projection domain, and the inference was applied to the data itself. Specifically, multiple independent noise insertions were applied to the original patient projection data to generate multiple realizations of low-quality projection data. Then, rotation augmentation was adopted for both the original and low-quality projection data by applying the rotation angle directly on the projection data so that images were rotated at arbitrary angles without introducing additional bias. A large number of paired low- and high-quality images from the same patient were reconstructed and paired for training the ST_CNN model.ResultsNo significant difference was found between the ST_CNN and conventional CNN models in terms of the peak signal-to-noise ratio and structural similarity index measure. The ST_CNN model outperformed the conventional CNN model in terms of noise texture and homogeneity in liver parenchyma as well as better subjective visualization of liver lesions. The ST_CNN may sacrifice the sharpness of vessels slightly compared to the conventional CNN model but without affecting the visibility of peripheral vessels or diagnosis of vascular pathology.ConclusionsThe proposed ST_CNN method trained from the data itself may achieve similar image quality in comparison with conventional deep CNN denoising methods pre-trained on external datasets.

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 Paper

Multi-energy CT material decomposition using Bayesian deep convolutional neural network with explicit penalty of uncertainty and bias

Hao Gong, Shuai Leng, Francis Baffour, Lifeng Yu, Joel Fletcher, Cynthia McCollough

Proceedings Volume 12463, 124633M (2023) https://doi.org/10.1117/12.2654317

KEYWORDS: Data modeling, Computed tomography, Monte Carlo methods, Image quality, Data acquisition, Reliability, Deep convolutional neural networks, Algorithm development

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Proceedings Article | 7 April 2023 Poster + Paper

Evaluation of low-contrast detectability of photon-counting-detector CT using channelized Hotelling observer and an ACR accreditation phantom

Mingdong Fan, Zhongxing Zhou, Michael Bruesewitz, Cynthia McCollough, Lifeng Yu

Proceedings Volume 12463, 1246348 (2023) https://doi.org/10.1117/12.2655619

KEYWORDS: Computed tomography, Quality control, Image quality, Photon counting

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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 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

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 | 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 | 31 January 2023 Open Access

Multislice input for 2D and 3D residual convolutional neural network noise reduction in CT

Zhongxing Zhou, Nathan R. Huber, Akitoshi Inoue, Cynthia McCollough, Lifeng Yu

JMI, Vol. 10, Issue 01, 014003, (January 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.1.014003

KEYWORDS: 3D modeling, Performance modeling, Denoising, Computed tomography, Education and training, 3D image processing, Veins, Infrared imaging, Image quality, Convolutional neural networks

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Proceedings Article | 18 October 2022 Open Access

Self-trained deep convolutional neural network for noise reduction in CT

Zhongxing Zhou, Akitoshi Inoue, Cynthia McCollough, Lifeng Yu

Proceedings Volume 12304, 123042B (2022) https://doi.org/10.1117/12.2646717

KEYWORDS: Denoising, Computed tomography, Convolutional neural networks, Image quality, Performance modeling, Network architectures

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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

Efficient evaluation of low-contrast detectability of deep-CNN-based CT reconstruction using channelized Hotelling observer on the ACR accreditation phantom

Mingdong Fan, Zhongxing Zhou, Thomas Vrieze, Jia Wang, Cynthia McCollough, Lifeng Yu

Proceedings Volume 12031, 1203118 (2022) https://doi.org/10.1117/12.2612414

KEYWORDS: Image quality, Computed tomography, CT reconstruction

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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 Poster + Paper

Residual-based convolutional-neural-network (CNN) for low-dose CT denoising: impact of multi-slice input

Zhongxing Zhou, Nathan Huber, Akitoshi Inoue, McCollough Cynthia, Lifeng Yu

Proceedings Volume 12031, 120312B (2022) https://doi.org/10.1117/12.2612872

KEYWORDS: Denoising, Computed tomography, Network architectures

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Proceedings Article | 4 April 2022 Presentation + Paper

Improved assessment of coronary artery luminal stenosis with heavy calcifications using high-resolution photon-counting detector CT

Emily Koons, Patrick VanMeter, Kishore Rajendran, Lifeng Yu, Cynthia McCollough, Shuai Leng

Proceedings Volume 12031, 120311A (2022) https://doi.org/10.1117/12.2613019

KEYWORDS: Arteries, Visualization, Spatial resolution, Calcium, Scanners, Iodine, Computer aided diagnosis and therapy

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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

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 | 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 | 15 February 2021 Presentation + Paper

Deep-learning lesion and noise insertion for virtual clinical trial in chest CT

Hao Gong, Jeffrey Marsh, Jamison Thorne, Shuai Leng, Cynthia McCollough, Joel Fletcher, Lifeng Yu

Proceedings Volume 11595, 115950S (2021) https://doi.org/10.1117/12.2582106

KEYWORDS: Computed tomography, Clinical trials, Chest, Image quality standards, Image quality, Systems modeling, Lung, Diagnostics, 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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SPIE Journal Paper | 1 October 2020

Overcoming calcium blooming and improving the quantification accuracy of percent area luminal stenosis by material decomposition of multi-energy computed tomography datasets

Zhoubo Li, Shuai Leng, Ahmed Halaweish, Zhicong Yu, Lifeng Yu, Erik Ritman, Cynthia McCollough

JMI, Vol. 7, Issue 05, 053501, (October 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.5.053501

KEYWORDS: Iodine, Calcium, Computed tomography, Image segmentation, Error analysis, Spatial resolution, Sensors, Signal attenuation, Computer simulations, Arteries

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Purpose: Conventional stenosis quantification from single-energy computed tomography (SECT) images relies on segmentation of lumen boundaries, which suffers from partial volume averaging and calcium blooming effects. We present and evaluate a method for quantifying percent area stenosis using multienergy CT (MECT) images. Approach: We utilize material decomposition of MECT images to measure stenosis based on the ratio of iodine mass between vessel locations with and without a stenosis, thereby eliminating the requirement for segmentation of iodinated lumen. The method was first assessed using simulated MECT images created with different spatial resolutions. To experimentally assess this method, four phantoms with different stenosis severity (30% to 51%), vessel diameters (5.5 to 14 mm), and calcification densities (700 to 1100 mgHA / cc) were fabricated. Conventional SECT images were acquired using a commercial CT system and were analyzed with commercial software. MECT images were acquired using a commercial dual-energy CT (DECT) system and also from a research photon-counting detector CT (PCD-CT) system. Three-material-decomposition was performed on MECT data, and iodine density maps were used to quantify stenosis. Clinical radiation doses were used for all data acquisitions. Results: Computer simulation verified that this method reduced partial volume and blooming effects, resulting in consistent stenosis measurements. Phantom experiments showed accurate and reproducible stenosis measurements from MECT images. For DECT and two-threshold PCD-CT images, the estimation errors were 4.0% to 7.0%, 2.0% to 9.0%, 10.0% to 18.0%, and −1.0 % to −5.0 % (ground truth: 51%, 51%, 51%, and 30%). For four-threshold PCD-CT images, the errors were 1.0% to 3.0%, 4.0% to 6.0%, −1.0 % to 9.0%, and 0.0% to 6.0%. Errors using SECT were much larger, ranging from 4.4% to 46%, and were especially worse in the presence of dense calcifications. Conclusions: The proposed approach was shown to be insensitive to acquisition parameters, demonstrating the potential to improve the accuracy and precision of stenosis measurements in clinical practice.

SPIE Journal Paper | 30 June 2020 Open Access

Deep-learning-based model observer for a lung nodule detection task in computed tomography

Hao Gong, Qiyuan Hu, Andrew Walther, Chi Wan Koo, Edwin A. Takahashi, David Levin, Tucker Johnson, Megan Hora, Shuai Leng, Joel Fletcher, Cynthia McCollough, Lifeng Yu

JMI, Vol. 7, Issue 04, 042807, (June 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.4.042807

KEYWORDS: Lung, Computed tomography, Molybdenum, Calibration, Image quality, X-ray computed tomography, Solids, Chest, Glasses

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Proceedings Article | 16 March 2020 Paper

Multi-energy CT with triple x-ray beams: a feasibility animal study

Liqiang Ren, Kishore Rajendran, Joel Fletcher, Thomas Allmendinger, Ahmed Halaweish, Bernhard Schmidt, Thomas Flohr, Cynthia McCollough, Lifeng Yu

Proceedings Volume 11312, 113121R (2020) https://doi.org/10.1117/12.2549780

KEYWORDS: X-ray computed tomography, Iodine, Bismuth, X-rays, Gold, Tin, Scanners, Beam splitters, Radiology, Sensors

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Proceedings Article | 16 March 2020 Paper

Ultra-fast-pitch acquisition and reconstruction in helical CT

Hao Gong, Liqiang Ren, Cynthia McCollough, Lifeng Yu

Proceedings Volume 11312, 1131209 (2020) https://doi.org/10.1117/12.2549315

KEYWORDS: Computed tomography, Data acquisition, Image restoration, Image quality, CT reconstruction, Chest, Neural networks

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SPIE Journal Paper | 14 March 2019

Impact of prior information on material decomposition in dual- and multienergy computed tomography

Liqiang Ren, Shengzhen Tao, Kishore Rajendran, Cynthia McCollough, Lifeng Yu

JMI, Vol. 6, Issue 01, 013503, (March 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.1.013503

KEYWORDS: Iodine, Gadolinium, Computed tomography, Calcium, Materials processing, Computer simulations, Denoising, X-rays, X-ray computed tomography, Signal attenuation

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Proceedings Article | 4 March 2019 Presentation + Paper

Correlation between a deep-learning-based model observer and human observer for a realistic lung nodule localization task in chest CT

Hao Gong, Andrew Walther, Qiyuan Hu, Chi Wan Koo, Edwin Takahashi, David Levin, Tucker Johnson, Megan Hora, Shuai Leng, J. Fletcher, Cynthia McCollough, Lifeng Yu

Proceedings Volume 10952, 109520K (2019) https://doi.org/10.1117/12.2513451

KEYWORDS: Lung, Chest, Computed tomography, X-ray computed tomography, Image quality, Performance modeling, Reconstruction algorithms, Statistical modeling

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Proceedings Article | 1 March 2019 Paper

Material decomposition in photon-counting-detector CT: threshold or bin images?

Liqiang Ren, Shengzhen Tao, Cynthia McCollough, Lifeng Yu

Proceedings Volume 10948, 109482S (2019) https://doi.org/10.1117/12.2513463

KEYWORDS: Iodine, X-ray computed tomography, Gadolinium, Computed tomography, X-rays, Medical physics, Radiology, Numerical analysis, Neodymium, Image processing

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Proceedings Article | 1 March 2019 Presentation + Paper

Multi-energy CT with triple x-ray beams and photon-counting-detector CT for simultaneous imaging of two contrast agents: an experimental comparison

Liqiang Ren, Cynthia McCollough, Lifeng Yu

Proceedings Volume 10948, 109481B (2019) https://doi.org/10.1117/12.2513460

KEYWORDS: X-ray computed tomography, X-rays, Iodine, Scanners, X-ray imaging, Bismuth, Computed tomography, Medical physics, Liver, Gadolinium

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Proceedings Article | 1 March 2019 Paper

Simulation of CT images reconstructed with different kernels using a convolutional neural network and its implications for efficient CT workflow

Andrew Missert, Shuai Leng, Cynthia McCollough, Joel Fletcher, Lifeng Yu

Proceedings Volume 10948, 109482Y (2019) https://doi.org/10.1117/12.2513240

KEYWORDS: Computed tomography, CT reconstruction, Convolutional neural networks, Machine learning, Image processing

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SPIE Journal Paper | 24 July 2018

Dual-source multienergy CT with triple or quadruple x-ray beams

Lifeng Yu, Liqiang Ren, Zhoubo Li, Shuai Leng, Cynthia H. McCollough

JMI, Vol. 5, Issue 03, 033502, (July 2018) https://doi.org/10.1117/12.10.1117/1.JMI.5.3.033502

KEYWORDS: X-ray computed tomography, X-rays, Scanners, Iodine, Bismuth, Tin, Gold, Image filtering, Optical filters, Sensors

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Proceedings Article | 9 March 2018 Paper

Simulating low-dose cone-beam CT: a phantom study

Andrea Ferrero, Ken Fetterly, Lifeng Yu, Beth Schueler

Proceedings Volume 10573, 1057347 (2018) https://doi.org/10.1117/12.2294939

KEYWORDS: Computed tomography, Image quality, Data modeling

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Proceedings Article | 9 March 2018 Paper

Motion-compensated reconstruction for limited-angle multiphase cardiac CT

Mingwu Jin, Cong Zhao, Xun Jia, Lifeng Yu

Proceedings Volume 10573, 1057333 (2018) https://doi.org/10.1117/12.2294922

KEYWORDS: Data acquisition, Heart, Motion estimation, Electrocardiography, Reconstruction algorithms, Arteries, Computed tomography, Computer aided diagnosis and therapy, Image quality, X-rays

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Proceedings Article | 9 March 2018 Presentation + Paper

Three-material decomposition in multi-energy CT: impact of prior information on noise and bias

Liqiang Ren, Cynthia McCollough, Lifeng Yu

Proceedings Volume 10573, 105731G (2018) https://doi.org/10.1117/12.2294953

KEYWORDS: Gadolinium, X-ray computed tomography, Condition numbers, X-rays, Computer simulations, Medicine, Tin, Beam splitters, Denoising, Matrices

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Proceedings Article | 7 March 2018 Presentation + Paper

Correlation between model observers in uniform background and human observers in patient liver background for a low-contrast detection task in CT

Hao Gong, Lifeng Yu, Shuai Leng, Samantha Dilger, Wei Zhou, Liqiang Ren, Cynthia McCollough

Proceedings Volume 10577, 105770M (2018) https://doi.org/10.1117/12.2294955

KEYWORDS: Computed tomography, Liver, Image quality, X-ray computed tomography, Signal detection, Liver cancer

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SPIE Journal Paper | 3 October 2017

Use of a channelized Hotelling observer to assess CT image quality and optimize dose reduction for iteratively reconstructed images

Christopher Favazza, Andrea Ferrero, Lifeng Yu, Shuai Leng, Kyle McMillan, Cynthia McCollough

JMI, Vol. 4, Issue 03, 031213, (October 2017) https://doi.org/10.1117/12.10.1117/1.JMI.4.3.031213

KEYWORDS: Image quality, Scanners, 3D acquisition, Infrared imaging, 3D image processing, Computed tomography, Reconstruction algorithms, 3D image reconstruction, Spatial resolution, Diagnostics

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Proceedings Article | 10 March 2017 Presentation + Paper

Task-based image quality assessment in radiation therapy: initial characterization and demonstration with CT simulation images

Steven Dolly, Mark Anastasio, Lifeng Yu, Hua Li

Proceedings Volume 10136, 101360Y (2017) https://doi.org/10.1117/12.2254063

KEYWORDS: Radiotherapy, Image quality, Process modeling, Image segmentation, Computed tomography, Computer simulations, Stochastic processes, X-ray computed tomography, Imaging systems, Image processing

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Proceedings Article | 9 March 2017 Presentation + Paper

Learning-based stochastic object models for use in optimizing imaging systems

Steven Dolly, Mark Anastasio, Lifeng Yu, Hua Li

Proceedings Volume 10132, 101321T (2017) https://doi.org/10.1117/12.2254055

KEYWORDS: Data modeling, Systems modeling, Stochastic processes, Imaging systems, Optimization (mathematics), Image quality, Quality measurement, Statistical modeling, X-ray imaging, X-rays, X-ray computed tomography, Natural surfaces, Prostate, Image segmentation

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Proceedings Article | 9 March 2017 Paper

A virtual clinical trial using projection-based nodule insertion to determine radiologist reader performance in lung cancer screening CT

Lifeng Yu, Qiyuan Hu, Chi Wan Koo, Edwin Takahashi, David Levin, Tucker Johnson, Megan Hora, Shane Dirks, Baiyu Chen, Kyle McMillan, Shuai Leng, J. Fletcher, Cynthia McCollough

Proceedings Volume 10132, 101321R (2017) https://doi.org/10.1117/12.2255593

KEYWORDS: Lung imaging, Computed tomography, Lung cancer, Image quality, Performance modeling, Lung

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Proceedings Article | 9 March 2017 Presentation + Paper

Practical implementation of channelized hotelling observers: effect of ROI size

Andrea Ferrero, Christopher Favazza, Lifeng Yu, Shuai Leng, Cynthia McCollough

Proceedings Volume 10132, 101320G (2017) https://doi.org/10.1117/12.2255530

KEYWORDS: Systems modeling, Computed tomography, Medical imaging, Imaging systems, Performance modeling, Image analysis, Data modeling, Image quality, Neodymium

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SPIE Journal Paper | 7 July 2016

Construction of realistic phantoms from patient images and a commercial three-dimensional printer

Shuai Leng, Baiyu Chen, Thomas Vrieze, Joel Kuhlmann, Lifeng Yu, Amy Alexander, Jane Matsumoto, Jonathan Morris, Cynthia McCollough

JMI, Vol. 3, Issue 03, 033501, (July 2016) https://doi.org/10.1117/12.10.1117/1.JMI.3.3.033501

KEYWORDS: Scattering, Optical tomography, Light sources, Sensors, Light scattering, Light, Mass attenuation coefficient, Monte Carlo methods, Inverse problems, Computed tomography

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SPIE Journal Paper | 26 May 2016

Impact of number of repeated scans on model observer performance for a low-contrast detection task in computed tomography

Chi Ma, Lifeng Yu, Baiyu Chen, Christopher Favazza, Shuai Leng, Cynthia McCollough

JMI, Vol. 3, Issue 02, 023504, (May 2016) https://doi.org/10.1117/12.10.1117/1.JMI.3.2.023504

KEYWORDS: Computed tomography, Performance modeling, Image quality, Infrared imaging, Optical filters, Infrared radiation, Statistical modeling, Diagnostics, Image filtering, Modulation transfer functions

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Proceedings Article | 4 April 2016 Paper

Evaluation of a projection-domain lung nodule insertion technique in thoracic CT

Chi Ma, Baiyu Chen, Chi Wan Koo, Edwin Takahashi, Joel Fletcher, Cynthia McCollough, David Levin, Ronald Kuzo, Lyndsay Viers, Stephanie Vincent Sheldon, Shuai Leng, Lifeng Yu

Proceedings Volume 9783, 97835Y (2016) https://doi.org/10.1117/12.2217009

KEYWORDS: Computed tomography, Lung, Image segmentation, Cognitive informatics, Image quality, Signal attenuation, Receivers, CT reconstruction, Radiology, Image processing, Data acquisition, Medicine

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Proceedings Article | 30 March 2016 Paper

Predicting detection performance with model observers: Fourier domain or spatial domain?

Baiyu Chen, Lifeng Yu, Shuai Leng, James Kofler, Christopher Favazza, Thomas Vrieze, Cynthia McCollough

Proceedings Volume 9783, 978326 (2016) https://doi.org/10.1117/12.2216962

KEYWORDS: Infrared imaging, Performance modeling, Computed tomography, Reconstruction algorithms, Medical physics, Eye models, Mathematical modeling, Visual process modeling, X-ray computed tomography, Optical filters

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Proceedings Article | 25 March 2016 Paper

An open library of CT patient projection data

Baiyu Chen, Shuai Leng, Lifeng Yu, David Holmes, Joel Fletcher, Cynthia McCollough

Proceedings Volume 9783, 97831B (2016) https://doi.org/10.1117/12.2216823

KEYWORDS: Computed tomography, Algorithm development, Head, Chest, Abdomen, Reconstruction algorithms, Data acquisition, Scanners

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Proceedings Article | 22 March 2016 Paper

Dual-source multi-energy CT with triple or quadruple x-ray beams

Lifeng Yu, Zhoubo Li, Shuai Leng, Cynthia McCollough

Proceedings Volume 9783, 978312 (2016) https://doi.org/10.1117/12.2217446

KEYWORDS: Gold, Iodine, Tin, Image filtering, Ions

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Proceedings Article | 18 March 2015 Paper

Construction of realistic liver phantoms from patient images using 3D printer and its application in CT image quality assessment

Shuai Leng, Lifeng Yu, Thomas Vrieze, Joel Kuhlmann, Baiyu Chen, Cynthia McCollough

Proceedings Volume 9412, 94124E (2015) https://doi.org/10.1117/12.2082121

KEYWORDS: Liver, Computed tomography, 3D printing, Image segmentation, Image quality, Reconstruction algorithms, Printing, Tissues, 3D modeling, Image filtering

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Proceedings Article | 18 March 2015 Paper

Initial results from a prototype whole-body photon-counting computed tomography system

Z. Yu, S. Leng, S. Jorgensen, Z. Li, R. Gutjahr, B. Chen, X. Duan, A. Halaweish, L. Yu, E. Ritman, C. McCollough

Proceedings Volume 9412, 94120W (2015) https://doi.org/10.1117/12.2082739

KEYWORDS: Prototyping, Iodine, Head, Modulation transfer functions, Spatial resolution, X-ray computed tomography, Sensors, Image quality, X-rays, Computed tomography

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Proceedings Article | 18 March 2015 Paper

Lesion insertion in projection domain for computed tomography image quality assessment

Baiyu Chen, Chi Ma, Zhicong Yu, Shuai Leng, Lifeng Yu, Cynthia McCollough

Proceedings Volume 9412, 94121R (2015) https://doi.org/10.1117/12.2082049

KEYWORDS: Computed tomography, Image quality, CT reconstruction, Liver, Scanners, Digital imaging, Image segmentation, Image resolution, Radiology, Medical imaging

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Proceedings Article | 18 March 2015 Paper

Image-based material decomposition with a general volume constraint for photon-counting CT

Zhoubo Li, Shuai Leng, Lifeng Yu, Zhicong Yu, Cynthia McCollough

Proceedings Volume 9412, 94120T (2015) https://doi.org/10.1117/12.2082069

KEYWORDS: Sensors, Iron, X-ray computed tomography, X-rays, Calibration, Calcium, X-ray detectors, X-ray imaging, Prototyping, Scanners

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Proceedings Article | 17 March 2015 Paper

Impact of number of repeated scans on model observer performance for a low-contrast detection task in CT

Chi Ma, Lifeng Yu, Baiyu Chen, Thomas Vrieze, Christopher Favazza, Shuai Leng, Cynthia McCollough

Proceedings Volume 9416, 94160K (2015) https://doi.org/10.1117/12.2082836

KEYWORDS: Computed tomography, Signal detection, Image quality, Performance modeling, Reconstruction algorithms, Matrices, CT reconstruction, Optical filters, Infrared imaging, Image analysis

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Proceedings Article | 21 March 2014 Paper

CT image noise reduction using rotational-invariant feature in Stockwell transform

Jian Su, Zhoubo Li, Lifeng Yu, Joshua Warner, Daniel Blezek, Bradley Erickson

Proceedings Volume 9034, 903433 (2014) https://doi.org/10.1117/12.2044360

KEYWORDS: Denoising, Computed tomography, Image filtering, Image processing, Image quality, Optical filters, Image analysis, Computer simulations, Medical imaging, Sensors

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Proceedings Article | 9 March 2012 Paper

Adaptive non-local means filtering based on local noise level for CT denoising

Zhoubo Li, Lifeng Yu, Joshua Trzasko, Joel Fletcher, Cynthia McCollough, Armando Manduca

Proceedings Volume 8313, 83131H (2012) https://doi.org/10.1117/12.913353

KEYWORDS: Computed tomography, Denoising, Photons, Signal attenuation, Sensors, Optical filters, Image filtering, Reconstruction algorithms, Scanners, Digital filtering

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Proceedings Article | 24 February 2012 Paper

Correlation between model observer and human observer performance in CT imaging when lesion location is uncertain

Shuai Leng, Lifeng Yu, Lingyun Chen, Juan C. Ramirez Giraldo, Cynthia McCollough

Proceedings Volume 8313, 83131M (2012) https://doi.org/10.1117/12.912126

KEYWORDS: Computed tomography, Performance modeling, Signal detection, Image quality, Data modeling, Diagnostics, Mathematical modeling, X-ray computed tomography, Abdomen, Scanners

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Proceedings Article | 23 March 2010 Paper

Evaluation of dual-front active contour segmentation and metal shadow filling methods on metal artifact reduction in multi-slice helical CT

Hua Li, Lifeng Yu, Luis Guimaraes, Joel Fletcher, Cynthia McCollough

Proceedings Volume 7622, 76222N (2010) https://doi.org/10.1117/12.844277

KEYWORDS: Metals, Image segmentation, Image quality, Image restoration, Computed tomography, Data corrections, Machine vision, Computer vision technology, Image processing, Scanners

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Proceedings Article | 23 March 2010 Paper

Adaptive modulation of bilateral filtering based on a practical noise model for streaking and noise reduction in multi-slice CT

Lifeng Yu, Armando Manduca, Megan Jacobsen, Joshua Trzasko, Joel Fletcher, David DeLone, Cynthia McCollough

Proceedings Volume 7622, 76222O (2010) https://doi.org/10.1117/12.845403

KEYWORDS: Digital filtering, Image filtering, Denoising, Computed tomography, Modulation, Data modeling, Signal attenuation, Spatial resolution, X-rays, Angiography

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Proceedings Article | 22 March 2010 Paper

A super resolution technique for clinical multislice CT

Xin Liu, Lifeng Yu, Armando Manduca, Erik Ritman, Cynthia McCollough

Proceedings Volume 7622, 76221Q (2010) https://doi.org/10.1117/12.844831

KEYWORDS: Image resolution, Super resolution, Spatial resolution, Sensors, Image quality, Data processing, X-rays, Computed tomography, X-ray computed tomography, Detection and tracking algorithms

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Proceedings Article | 14 March 2009 Paper

Dual source CT (DSCT) imaging of obese patients: evaluation of CT number accuracy, uniformity, and noise

A. Walz-Flannigan, B, Schmidt, A. Apel, C. Eusemann, L. Yu, C. McCollough

Proceedings Volume 7258, 72585N (2009) https://doi.org/10.1117/12.813737

KEYWORDS: X-ray computed tomography, Polymethylmethacrylate, X-rays, Sensors, X-ray imaging, Image quality, Imaging systems, Collimation, X-ray detectors, Tissues

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Proceedings Article | 14 March 2009 Paper

Pre-reconstruction three-material decomposition in dual-energy CT

Lifeng Yu, Xin Liu, Cynthia McCollough

Proceedings Volume 7258, 72583V (2009) https://doi.org/10.1117/12.813686

KEYWORDS: Iron, Bone, Error analysis, Computed tomography, Chemical analysis, Calibration, X-rays, Calcium, Signal attenuation, Statistical analysis

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Proceedings Article | 13 March 2009 Paper

Quantitative evaluation of noise reduction algorithms for very low dose renal CT perfusion imaging

Xin Liu, Andrew Primak, Lifeng Yu, Hua Li, James Krier, Lilach Lerman, Cynthia McCollough

Proceedings Volume 7258, 72581T (2009) https://doi.org/10.1117/12.813777

KEYWORDS: Signal to noise ratio, Denoising, Image processing, Modulation transfer functions, Linear filtering, Spatial resolution, Computed tomography, Image filtering, Anisotropic diffusion, Anisotropic filtering

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Proceedings Article | 25 March 2008 Paper

Quantitative imaging of chemical composition using dual-energy, dual-source CT

Xin Liu, Andrew Primak, Lifeng Yu, Cynthia McCollough, Richard Morin

Proceedings Volume 6913, 69134Z (2008) https://doi.org/10.1117/12.773042

KEYWORDS: Iron, X-ray computed tomography, Chemical elements, Liver, X-rays, Signal attenuation, Mass attenuation coefficient, Tissues, Magnesium, In vivo imaging

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Proceedings Article | 18 March 2008 Paper

Sinogram smoothing with bilateral filtering for low-dose CT

Lifeng Yu, Armando Manduca, Joshua Trzasko, Natalia Khaylova, James Kofler, Cynthia McCollough, Joel Fletcher

Proceedings Volume 6913, 691329 (2008) https://doi.org/10.1117/12.772084

KEYWORDS: Image filtering, Denoising, X-rays, Image quality, Optical filters, Sensors, Scanners, Digital filtering, Spatial resolution, Data modeling

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Proceedings Article | 18 March 2008 Paper

A preliminary investigation of ROI-image reconstruction with the rebinned BPF algorithm

Junguo Bian, Dan Xia, Lifeng Yu, Emil Sidky, Xiaochuan Pan

Proceedings Volume 6913, 69132I (2008) https://doi.org/10.1117/12.772096

KEYWORDS: Reconstruction algorithms, Detection and tracking algorithms, Data acquisition, Medical imaging, Sensors, Physics, Image restoration

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Proceedings Article | 10 May 2007 Paper

Automatic segmentation and co-registration of gated CT angiography datasets: measuring abdominal aortic pulsatility

Robert Wentz, Armando Manduca, J. Fletcher, Hassan Siddiki, Raymond Shields, Terri Vrtiska, Garrett Spencer, Andrew Primak, Jie Zhang, Theresa Nielson, Cynthia McCollough, Lifeng Yu

Proceedings Volume 6511, 65111I (2007) https://doi.org/10.1117/12.713759

KEYWORDS: Angiography, Image segmentation, Computed tomography, Visualization, Heart, Temporal resolution, Optical spheres, Arteries, Resistance, Beam propagation method

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Proceedings Article | 19 March 2007 Paper

Image reconstruction from rebinned helical cone-beam projection data

Dan Xia, Lifeng Yu, Junguo Bian, Xiaochuan Pan

Proceedings Volume 6510, 651057 (2007) https://doi.org/10.1117/12.713720

KEYWORDS: Reconstruction algorithms, Image segmentation, Algorithm development, Image processing algorithms and systems, Image restoration, Data acquisition, Sensors, 3D image reconstruction, Computed tomography, Image filtering

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Proceedings Article | 2 March 2006 Paper

Suppression of motion-induced streak artifacts along chords in fan-beam BPF-reconstructions of motion-contaminated projection data

Martin King, Dan Xia, Lifeng Yu, Xiaochuan Pan, Maryellen Giger

Proceedings Volume 6142, 614221 (2006) https://doi.org/10.1117/12.653835

KEYWORDS: Reconstruction algorithms, Image segmentation, Fluctuations and noise, Image processing algorithms and systems, Computed tomography, Detector arrays, Detection and tracking algorithms, Heart, Medical imaging, Data acquisition

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Proceedings Article | 29 April 2005 Paper

Image reconstruction on virtual PI-lines using filtered-backprojection in circular cone-beam CT

Lifeng Yu, Dan Xia, Yu Zou, Xiaochuan Pan

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.595857

KEYWORDS: Reconstruction algorithms, Image processing algorithms and systems, Algorithm development, Image segmentation, Image restoration, 3D image processing, Computed tomography, Image filtering, 3D image reconstruction, Sensors

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Proceedings Article | 29 April 2005 Paper

Region of interest reconstruction from truncated data in circular cone-beam CT

Lifeng Yu, Dan Xia, Yu Zou, Xiaochuan Pan, Charles Pelizzari, Peter Munro

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.595893

KEYWORDS: Reconstruction algorithms, Image segmentation, Image processing algorithms and systems, Computed tomography, Image restoration, Algorithm development, 3D image reconstruction, Sensors, Image filtering, Detection and tracking algorithms

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SPIE Journal Paper | 1 October 2004

Asymmetric fan-beam configurations with spatially varying focal lengths and shift-variant filtering reconstruction

Lifeng Yu, Xiaochuan Pan, Charles Pelizzari

OE, Vol. 43, Issue 10, (October 2004) https://doi.org/10.1117/12.10.1117/1.1786295

KEYWORDS: Reconstruction algorithms, Data acquisition, Sensors, Detection and tracking algorithms, Algorithms, Algorithm development, Image restoration, Image filtering, Computed tomography, Optical engineering

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Proceedings Article | 12 May 2004 Paper

Few-view and limited-angle cone-beam megavoltage CT for breast localization in radiation therapy

Lifeng Yu, Xiaochuan Pan, Charles Pelizzari, Mary Martel

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.535772

KEYWORDS: Reconstruction algorithms, Breast, Radiotherapy, Lung, Computed tomography, Tomography, Image quality, Detection and tracking algorithms, Breast cancer, Skin

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Proceedings Article | 12 May 2004 Paper

Image reconstruction in asymmetric fan-beam scans with spatially varying focal lengths

Lifeng Yu, Xiaochuan Pan, Charles Pelizzari

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.535326

KEYWORDS: Reconstruction algorithms, Data acquisition, Image restoration, Sensors, Computed tomography, Detection and tracking algorithms, Algorithm development, Spatial resolution, Image filtering, Radiology

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Proceedings Article | 6 May 2004 Paper

Application of expectation maximization to x-ray spectrum estimation for medical accelerators from transmission data

Emil Sidky, Lifeng Yu, Xiaochuan Pan

Proceedings Volume 5368, (2004) https://doi.org/10.1117/12.535989

KEYWORDS: X-rays, Signal attenuation, Data modeling, Expectation maximization algorithms, Mathematical modeling, X-ray sources, Tungsten, Aluminum, Lead, Reconstruction algorithms

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Proceedings Article | 15 May 2003 Paper

A new approach for CT image reconstruction with asymmetric configuration

Lifeng Yu, Xiaochuan Pan, Charles Pelizzari, Patrick La Riviere, Tin-Su Pan

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.483537

KEYWORDS: Reconstruction algorithms, Detection and tracking algorithms, Sensors, Algorithm development, Data acquisition, Image restoration, Computed tomography, Image quality, CT reconstruction, Data centers

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