Dr. Hao Gong Profile

Dr. Hao Gong

Associate Consultant & Assistant Professor at Mayo Clinic

SPIE Involvement:

Author

Area of Expertise:

Computed Tomography , Artifact Correction , Machine Learning , Image Processing , Image Reconstruction

Profile Summary

Hao Gong is an assistant professor and associate consultant of Radiology, Mayo Clinic, Rochester, MN. His research interests include but not limited to CT physics, imaging science, diagnostic image quality assessment, dual-/multi-energy and photon-counting-detector CT, and machine-/deep-learning.

Publications (25)

SPIE Journal Paper | 24 May 2024

Improved noise reduction in photon-counting detector CT using prior knowledge-aware iterative denoising neural network

Shaojie Chang, Jeffrey F. Marsh, Emily K. Koons, Hao Gong, Cynthia H. McCollough, Shuai Leng

JMI, Vol. 11, Issue S1, S12804, (May 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.S1.S12804

KEYWORDS: Denoising, Education and training, Infrared imaging, Computed tomography, Sensors, Photodetectors, Neural networks, Image quality, Image sharpness, Image processing

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Proceedings Article | 1 April 2024 Presentation + Paper

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 | 1 April 2024 Poster + Paper

Poster + Paper

Learned high-resolution cardiac CT imaging from ultra-high-resolution PCD-CT

Emily Koons, Hao Gong, Andrew Missert, Shaojie Chang, Tim Winfree, Zhongxing Zhou, Cynthia McCollough, Shuai Leng

Proceedings Volume 12925, 129252Q (2024) https://doi.org/10.1117/12.3006463

KEYWORDS: Arteries, Computed tomography, Image resolution, Image restoration, Computer aided detection, Medical image reconstruction, Spatial resolution, Image sharpness

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Coronary computed tomography angiography (cCTA) is a widely used non-invasive diagnostic exam for patients with coronary artery disease (CAD). However, most clinical CT scanners are limited in spatial resolution from use of energy-integrating detectors (EIDs). Radiological evaluation of CAD is challenging, as coronary arteries are small (3-4 mm diameter) and calcifications within them are highly attenuating, leading to blooming artifacts. As such, this is a task well suited for high spatial resolution. Recently, photon-counting-detector (PCD) CT became commercially available, allowing for ultrahigh resolution (UHR) data acquisition. However, PCD-CTs are costly, restricting widespread accessibility. To address this problem, we propose a super resolution convolutional neural network (CNN): ILUMENATE (Improved LUMEN visualization through Artificial super-resoluTion imagEs), creating a high resolution (HR) image simulating UHR PCD-CT. The network was trained and validated using patches extracted from 8 patients with a modified U-Net architecture. Training input and labels consisted of UHR PCD-CT images reconstructed with a smooth kernel degrading resolution (LR input) and sharp kernel (HR label). The network learned the resolution difference and was tested on 5 unseen LR patients. We evaluated network performance quantitatively and qualitatively through visual inspection, line profiles to assess spatial resolution improvements, ROIs for CT number stability and noise assessment, structural similarity index (SSIM), and percent diameter luminal stenosis. Overall, ILUMENATE improved images quantitatively and qualitatively, creating sharper edges more closely resembling reconstructed HR reference images, maintained stable CT numbers with less than 4% difference, reduced noise by 28%, maintained structural similarity (average SSIM = 0.70), and reduced percent diameter stenosis with respect to input images. ILUMENATE demonstrates potential impact for CAD patient management, improving the quality of LR CT images bringing them closer to UHR PCD-CT images.

Proceedings Article | 1 April 2024 Poster + Paper

Poster + Paper

Improving stenosis assessment in energy integrating detector CT via learned monoenergetic imaging capability

Shaojie Chang, Emily Koons, Hao Gong, Jamison Thorne, Cynthia McCollough, Shuai Leng

Proceedings Volume 12925, 129252R (2024) https://doi.org/10.1117/12.3006468

KEYWORDS: Computed tomography, Sensors, Education and training, Visualization, Temporal resolution, Arteries, Visibility, Scanners, Photon counting, Computer aided detection

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Coronary CT angiography (cCTA) is a fast non-invasive imaging exam for coronary artery disease (CAD) but struggles with dense calcifications and stents due to blooming artifacts, potentially causing stenosis overestimation. Virtual monoenergetic images (VMIs) at higher keV (e.g., 100 keV) from photon counting detector (PCD) CT have shown promise in reducing blooming artifacts and improving lumen visibility through its simultaneous high-resolution and multi-energy imaging capability. However, most cCTA exams are performed with single-energy CT (SECT) using conventional energy-integrating detectors (EID). Generating VMIs through EID-CT requires advanced multi-energy CT (MECT) scanners and potentially sacrifices temporal resolution. Given these limitations, MECT cCTA exams are not commonly performed on EID-CT and VMIs are not routinely generated. To tackle this, we aim to enhance the multi-energy imaging capability of EIDCT through the utilization of a convolutional neural network to LEarn MONoenergetic imAging from VMIs at Different Energies (LEMONADE). The neural network was trained using ten patient cCTA exams acquired on a clinical PCD-CT (NAEOTOM Alpha, Siemens Healthineers), with 70 keV VMIs as input (which is nominally equivalent to the SECT from EID-CT scanned at 120 kV) and 100 keV VMIs as the target. Subsequently, we evaluated the performance of EID-CT equipped with LEMONADE on both phantom and patient cases (n=10) for stenosis assessment. Results indicated that LEMONADE accurately quantified stenosis in three phantoms, aligning closely wi th ground truth and demonstrating stenosis percentage area reductions of 13%, 8%, and 9%. In patient cases, it led to a 12.9% reduction in average diameter luminal stenosis when compared to the original SECT without LEMONADE. These outcomes highlight LEMONADE's capacity to enable multi-energy CT imaging, mitigate blooming artifacts, and improve stenosis assessment for the widely available EID-CT. This has a high potential impact as most cCTA exams are performed on EID-CT.

Proceedings Article | 7 April 2023 Paper

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 Presentation + Paper

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

Poster + Paper

PKAID-Net: prior knowledge aware iterative denoising neural network for photon counting detector CT

Shaojie Chang, Jeffrey Marsh, Emily Koons, Hao Gong, Cynthia McCollough, Shuai Leng

Proceedings Volume 12463, 124632Q (2023) https://doi.org/10.1117/12.2653904

KEYWORDS: Denoising, Education and training, Neural networks, Computed tomography, Infrared imaging, Prior knowledge, Photon counting, Interference (communication), Image processing, Image quality

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Proceedings Article | 7 April 2023 Paper

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

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

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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Proceedings Article | 4 April 2022 Presentation + Paper

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

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

Presentation + Paper

Improving coronary artery imaging in single source CT with cardiac motion correction using attention and spatial transformer based neural networks

Hao Gong, Zaki Ahmed, Jamison Thorne, Joel Fletcher, Cynthia McCollough, Shuai Leng

Proceedings Volume 12031, 120311E (2022) https://doi.org/10.1117/12.2611794

KEYWORDS: Computed tomography, Arteries, Temporal resolution, Transformers, Image quality, Heart, Neural networks, Systems modeling, Motion models, Medicine

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Proceedings Article | 4 April 2022 Presentation + Paper

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

Presentation + Paper

Quantitative assessment of motion effects in dual-source dual energy CT and dual-source photon-counting detector CT

Zaki Ahmed, Kishore Rajendran, Hao Gong, Cynthia McCollough, Shuai Leng

Proceedings Volume 12031, 120311P (2022) https://doi.org/10.1117/12.2611030

KEYWORDS: Computed tomography, Temporal resolution, Iodine, X-ray computed tomography, Sensors, Scanners, Image segmentation, Heart, Dual energy imaging, Photodetectors

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SPIE Journal Paper | 19 April 2021

Deep-learning-based direct synthesis of low-energy virtual monoenergetic images with multi-energy CT

Hao Gong, Jeffrey Marsh, Karen D’Souza, Nathan Huber, Kishore Rajendran, Joel Fletcher, Cynthia McCollough, Shuai Leng

JMI, Vol. 8, Issue 05, 052104, (April 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.5.052104

KEYWORDS: X-ray computed tomography, Computed tomography, Iodine, Denoising, Image quality, Data acquisition, X-rays, Image processing, Scanners, X-ray imaging

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Proceedings Article | 15 February 2021 Presentation + Paper

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

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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SPIE Journal Paper | 30 June 2020 Open Access

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

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 | 15 October 2019

Determination of iodine detectability in different types of multiple-energy images for a photon-counting detector computed tomography system

Wei Zhou, Gregory Michalak, Jayse Weaver, Andrea Ferrero, Hao Gong, Kenneth Fetterly, Cynthia McCollough, Shuai Leng

JMI, Vol. 6, Issue 04, 043501, (October 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.4.043501

KEYWORDS: Iodine, Computed tomography, Sensors, Photodetectors, Medicine, Optical computing, Computing systems, X-ray computed tomography, Calibration, Imaging systems

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Proceedings Article | 4 March 2019 Presentation + Paper

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 | 7 March 2018 Presentation + Paper

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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Proceedings Article | 18 March 2015 Paper

Paper

Implementation of interior micro-CT on a carbon nanotube dynamic micro-CT scanner for lower radiation dose

Hao Gong, Jianping Lu, Otto Zhou, Guohua Cao

Proceedings Volume 9412, 94124N (2015) https://doi.org/10.1117/12.2082146

KEYWORDS: X-rays, Sensors, X-ray sources, Tomography, Collimators, Reconstruction algorithms, Image quality, Carbon nanotubes, Imaging systems, Calibration

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SPIE Journal Paper | 3 November 2014

Fast and accurate computation of system matrix for area integral model-based algebraic reconstruction technique

Shunli Zhang, Dinghua Zhang, Hao Gong, Omid Ghasemalizadeh, Ge Wang, Guohua Cao

OE, Vol. 53, Issue 11, 113101, (November 2014) https://doi.org/10.1117/12.10.1117/1.OE.53.11.113101

KEYWORDS: Reconstruction algorithms, Computing systems, Sensors, Image quality, Projection systems, Optical engineering, Computed tomography, Model-based design, Image restoration, Iterative methods

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Showing 5 of 25 publications

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