Dr. Hao Zhang Profile

Dr. Hao Zhang

Assistant Attending at Memorial Sloan Kettering Cancer Ctr

SPIE Involvement:

Author

Area of Expertise:

Medical Physics , Computed Tomography , Cone-beam CT , Image-guided radiotherapy , Image reconstruction

Publications (21)

Proceedings Article | 18 October 2022 Open Access

Using tissue-energy response to generate virtual monoenergetic images from conventional CT for computer-aided diagnosis of lesions

Shaojie Chang, Yongfeng Gao, Marc Pomeroy, Ti Bai, Hao Zhang, Zhengrong Liang

Proceedings Volume 12304, 123041L (2022) https://doi.org/10.1117/12.2646551

KEYWORDS: Tissues, Computer aided diagnosis and therapy, X-ray computed tomography, Lung, Data modeling, 3D modeling, Tumor growth modeling, Computed tomography, Signal attenuation, X-rays

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Proceedings Article | 18 October 2022 Open Access

Fully utilizing contrast enhancement on lung tissue as a novel basis material for lung nodule characterization by multi-energy CT

Shaojie Chang, Yongfeng Gao, Marc Pomeroy, Ti Bai, Hao Zhang, Zhengrong Liang

Proceedings Volume 12304, 1230429 (2022) https://doi.org/10.1117/12.2646550

KEYWORDS: Tissues, Lung, Computer aided diagnosis and therapy, Bone, X-ray computed tomography, Signal attenuation, Computed tomography, Data modeling, 3D modeling, X-rays

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Proceedings Article | 4 April 2022 Presentation + Paper

Markov random field texture generation with an internalized database using a conditional encoder-decoder structure

Yongfeng Gao, Ti Bai, Siming Lu, Shaojie Chang, Hao Zhang, Mahsa Hoshmand-Kochi, Zhengrong Liang

Proceedings Volume 12031, 120311G (2022) https://doi.org/10.1117/12.2613033

KEYWORDS: Tissues, Databases, Computed tomography, Neural networks, Magnetorheological finishing, CT reconstruction, Computer programming, Brain-machine interfaces, Lung, Image filtering

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SPIE Journal Paper | 9 April 2021

Direct reconstruction of anatomical change in low-dose lung nodule surveillance

Jessica Flores, Grace Gang, Hao Zhang, Chen Ting Lin, Shui K. Fung, J. Webster Stayman

JMI, Vol. 8, Issue 02, 023503, (April 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.2.023503

KEYWORDS: Image registration, Lung, Surveillance, Image analysis, X-ray computed tomography, Tissues, Rigid registration, Data acquisition, Visualization, Reconstruction algorithms

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Proceedings Article | 1 March 2019 Paper

Bayesian reconstruction of ultralow-dose CT images with texture prior from existing diagnostic full-dose CT database

Yongfeng Gao, Jiaxing Tan, Hao Zhang, William Moore, Priya Bhattacharji, Amit Gupta, Haifang Li, Hongbing Lu, Jianhua Ma, Zhengrong Liang

Proceedings Volume 10948, 1094855 (2019) https://doi.org/10.1117/12.2512787

KEYWORDS: Databases, Tissues, Computed tomography, Principal component analysis, Lung, Machine learning

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Proceedings Article | 9 March 2018 Presentation + Paper

Prospective image quality analysis and control for prior-image-based reconstruction of low-dose CT

Hao Zhang, Grace Gang, Hao Dang, Marc Sussman, Cheng Ting Lin, Jeffrey Siewerdsen, J. Webster Stayman

Proceedings Volume 10573, 1057329 (2018) https://doi.org/10.1117/12.2293135

KEYWORDS: Image quality, Lung, Computed tomography, Image analysis, Image registration, Systems modeling, Surveillance, Analytical research, Data acquisition, Gold

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Purpose: Prior-image-based reconstruction (PIBR) is a powerful tool for low-dose CT, however, the nonlinear behavior of such approaches are generally difficult to predict and control. Similarly, traditional image quality metrics do not capture potential biases exhibited in PIBR images. In this work, we identify a new bias metric and construct an analytical framework for prospectively predicting and controlling the relationship between prior image regularization strength and this bias in a reliable and quantitative fashion. Methods: Bias associated with prior image regularization in PIBR can be described as the fraction of actual contrast change (between the prior image and current anatomy) that appears in the reconstruction. Using local approximation of the nonlinear PIBR objective, we develop an analytical relationship between local regularization, fractional contrast reconstructed, and true contrast change. This analytic tool allows prediction bias properties in a reconstructed PIBR image and includes the dependencies on the data acquisition, patient anatomy and change, and reconstruction parameters. Predictions are leveraged to provide reliable and repeatable image properties for varying data fidelity in simulation and physical cadaver experiments. Results: The proposed analytical approach permits accurate prediction of reconstructed contrast relative to a gold standard based on exhaustive search based on numerous iterative reconstructions. The framework is used to control regularization parameters to enforce consistent change reconstructions over varying fluence levels and varying numbers of projection angles – enabling bias properties that are less location- and acquisition-dependent. Conclusions: While PIBR methods have demonstrated a substantial ability for dose reduction, image properties associated with those images have been difficult to express and quantify using traditional metrics. The novel framework presented in this work not only quantifies this bias in an intuitive fashion, but it gives a way to predict and control the bias. Reliable and predictable reconstruction methods are a requirement for clinical imaging systems and the proposed framework is an important step translating PIBR methods to clinical application.

Proceedings Article | 9 March 2018 Paper

A feasibility study of extracting tissue textures from a previous normal-dose CT database as prior for Bayesian reconstruction of current ultra-low-dose CT images

Yongfeng Gao, Zhengrong Liang, Yuxiang Xing, Hao Zhang, Jianhua Ma, Hongbing Lu, Lihong Li, Bo Chen, Marc Pomeroy, William Moore

Proceedings Volume 10573, 105733C (2018) https://doi.org/10.1117/12.2293854

KEYWORDS: Magnetorheological finishing, Tissues, X-ray computed tomography, Lung, Image segmentation, Databases, X-rays, Reconstruction algorithms, Computed tomography, CT reconstruction

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Tremendous research efforts have been devoted to lower the X-ray radiation exposure to the patient in order to expand the utility of computed tomography (CT), particularly to pediatric imaging and population-based screening. When the exposure dosage goes down, both the X-ray quanta fluctuation and the system electronic background noise become significant factors affecting the image quality. Conventional edge-preserving noise smoothing would sacrifice tissue textures and compromise the clinical tasks. To relieve these challenges, this work models the noise problem by pre-log shifted Poisson statistics and extracts tissue textures from previous normal-dose CT scans as prior knowledge for texturepreserving Bayesian reconstruction of current ultralow-dose CT images. The pre-log shift Poisson model considers accurately both the X-ray quanta fluctuation and the system electronic noise while the prior knowledge of tissue textures removes the limitation of the conventional edge-preserving noise smoothing. The Bayesian reconstruction was tested by experimental studies. One patient chest scan was selected from a database of 133 patients’ scans at 100mAs/120kVp normal-dose level. From the selected patient scan, ultralow-dose data was simulated at 5mAs/120kVp level. The other 132 normal-dose scans were grouped according to how close their lung tissue texture patterns are from that of the selected patient scan. The tissue textures of each group were used to reconstruct the ultralow-dose scan by the Bayesian algorithm. The closest group to the selected patient produced almost identical results to the reconstruction when the tissue textures of the selected patient’s normal-dose scan were used, indicating the feasibility of extracting tissue textures from a previous normal-dose database to reconstruct any current ultralow-dose CT image. Since the Bayesian reconstruction can be time consuming, this work further investigates a strategy to efficiently store the projection matrix rather than computing the line integrals on-flight. This strategy accelerated the computing speed by more than 18 times.

Proceedings Article | 9 March 2017 Presentation + Paper

Integration of prior CT into CBCT reconstruction for improved image quality via reconstruction of difference: first patient studies

Hao Zhang, Grace Gang, Junghoon Lee, John Wong, J. Webster Stayman

Proceedings Volume 10132, 1013211 (2017) https://doi.org/10.1117/12.2255513

KEYWORDS: Computed tomography, Image quality, Radiotherapy, Diagnostics, Image-guided intervention, CT reconstruction, Data processing, Signal attenuation, Image registration, Cones, Abdomen, Data acquisition

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Proceedings Article | 30 March 2016 Paper

Texture-preserving Bayesian image reconstruction for low-dose CT

Hao Zhang, Hao Han, Yifan Hu, Yan Liu, Jianhua Ma, Lihong Li, William Moore, Zhengrong Liang

Proceedings Volume 9783, 97834I (2016) https://doi.org/10.1117/12.2217297

KEYWORDS: CT reconstruction, X-ray computed tomography, Lung, Colon, X-ray imaging, X-rays, Tissues, Reconstruction algorithms, Bone, Computed tomography

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Proceedings Article | 30 March 2016 Paper

A new look at signal sparsity paradigm for low-dose computed tomography image reconstruction

Yan Liu, Hao Zhang, William Moore, Zhengrong Liang

Proceedings Volume 9783, 97834H (2016) https://doi.org/10.1117/12.2216536

KEYWORDS: Computed tomography, Image restoration, CT reconstruction, Televisions, Tissues, Electronic filtering, Lung, Single photon emission computed tomography, Positron emission tomography, Optical coherence tomography, Image quality, Reconstruction algorithms, Diagnostics, Medical imaging

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Proceedings Article | 22 March 2016 Paper

Texture-preserved penalized weighted least-squares reconstruction of low-dose CT image via image segmentation and high-order MRF modeling

Hao Han, Hao Zhang, Xinzhou Wei, William Moore, Zhengrong Liang

Proceedings Volume 9783, 97834F (2016) https://doi.org/10.1117/12.2216428

KEYWORDS: Image restoration, Computed tomography, Image segmentation, Reconstruction algorithms, CT reconstruction, Image processing algorithms and systems, Quantization, Statistical analysis, Lung, Binary data, X-ray computed tomography

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Proceedings Article | 18 March 2015 Paper

A clinical evaluation of total variation-Stokes image reconstruction strategy for low-dose CT imaging of the chest

Yan Liu, Hao Zhang, William Moore, Priya Bhattacharji, Zhengrong Liang

Proceedings Volume 9412, 94123S (2015) https://doi.org/10.1117/12.2081614

KEYWORDS: Televisions, Lung, Image quality, Computed tomography, Tissues, Denoising, Chest, Diagnostics, Image restoration, Data acquisition

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Proceedings Article | 18 March 2015 Paper

Adaptive nonlocal means-based regularization for statistical image reconstruction of low-dose X-ray CT

Hao Zhang, Jianhua Ma, Jing Wang, Yan Liu, Hao Han, Lihong Li, William Moore, Zhengrong Liang

Proceedings Volume 9412, 94123K (2015) https://doi.org/10.1117/12.2082244

KEYWORDS: Image filtering, X-ray computed tomography, Image restoration, X-rays, X-ray imaging, Data acquisition, CT reconstruction, Computed tomography, Scanners, Denoising

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Proceedings Article | 19 March 2014 Paper

LBP-based penalized weighted least-squares approach to low-dose cone-beam computed tomography reconstruction

Ming Ma, Huafeng Wang, Yan Liu, Hao Zhang, Xianfeng Gu, Zhengrong Liang

Proceedings Volume 9033, 903336 (2014) https://doi.org/10.1117/12.2043289

KEYWORDS: Reconstruction algorithms, Image restoration, Diffusion, Binary data, X-rays, Computed tomography, Image quality, CT reconstruction, X-ray computed tomography, Algorithm development

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Proceedings Article | 19 March 2014 Paper

Nonlocal means-based regularizations for statistical CT reconstruction

Hao Zhang, Jianhua Ma, Yan Liu, Hao Han, Lihong Li, Jing Wang, Zhengrong Liang

Proceedings Volume 9033, 903337 (2014) https://doi.org/10.1117/12.2043949

KEYWORDS: CT reconstruction, Reconstruction algorithms, Data modeling, Image quality, Denoising, Image filtering, Computed tomography, Image resolution, X-rays, Control systems

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Proceedings Article | 19 March 2014 Paper

Noise study on cone-beam CT FDK image reconstruction by improved area-simulating-volume technique

Yan Liu, Jin Wang, Hao Zhang, Yi Fan, Zhengrong Liang

Proceedings Volume 9033, 903339 (2014) https://doi.org/10.1117/12.2043721

KEYWORDS: Sensors, Detection and tracking algorithms, Reconstruction algorithms, Image restoration, X-rays, Algorithms, X-ray computed tomography, Digital imaging, 3D image reconstruction, Lithium

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Proceedings Article | 18 March 2014 Paper

A novel computer-aided detection system for pulmonary nodule identification in CT images

Hao Han, Lihong Li, Huafeng Wang, Hao Zhang, William Moore, Zhengrong Liang

Proceedings Volume 9035, 90352D (2014) https://doi.org/10.1117/12.2043964

KEYWORDS: Lung, Computer aided diagnosis and therapy, 3D image processing, Computed tomography, Image segmentation, Chest, Databases, Feature extraction, Lung cancer

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Proceedings Article | 6 March 2013 Paper

A comparison study of sinogram- and image-domain penalized re-weighted least-squares approaches to noise reduction for low-dose cone-beam CT

Hao Zhang, Yan Liu, Hao Han, Jing Wang, Jianhua Ma, Lihong Li, Zhengrong Liang

Proceedings Volume 8668, 86683E (2013) https://doi.org/10.1117/12.2007111

KEYWORDS: Image restoration, Reconstruction algorithms, X-ray computed tomography, Denoising, X-rays, 3D image reconstruction, Image resolution, Image quality, CT reconstruction, Head

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Reducing X-ray exposure to the patients is one of the major research efforts in the computed tomography (CT) field, and one of the common strategies to achieve it is to lower the mAs setting (by lowering the X-ray tube current and/or shortening the exposure time) in currently available CT scanners. However, the image quality from low mAs acquisition is severely degraded due to excessive quantum noise, if no adequate noise control is applied during image reconstruction. Different from filter-based algorithms, statistical reconstruction algorithms model the statistical property of the noise using a cost function and minimize the cost function for an optimal solution in statistical sense. The algorithms have shown to be feasible and effective in both sinogram and image domain. In our previous researches, we proposed penalized reweighted least-squares (PRWLS) approaches to sinogram noise reduction and image reconstruction for low-dose CT imaging, which are in this statistical category. This work is a continuation of the research along this direction and aims to compare the reconstruction quality of two different PRWLS implementations for low-dose cone-beam CT reconstruction: (1) PRWLS sinogram restoration followed by analytical Feldkamp-Davis- Kress reconstruction, (2) fully iterative PRWLS image reconstruction. Inspired by our recent study on the variance of low-mAs projection data in presence of electric noise background, a more accurate weight was adopted in the weighted least-squares term. An anisotropic quadratic form penalty was utilized in both PRWLS implementations to preserve edges during noise reduction. Experiments using the CatPhan® 600 phantom and anthropomorphic head phantom were carried to study the relevant performance of these two implementations on image reconstruction. The results revealed that the implementation (2) can outperform implementation (1) in terms of noise-resolution tradeoff measurement and analysis of the reconstructed small objects due to its matched image edge-preserved penalty in the image domain. However, those gains are offset by the cost of increased computational time. Thus, further examination of real patient data is necessary to show the clinical significance of the iterative PRWLS image reconstruction over the PRWLS sinogram restoration.

Proceedings Article | 6 March 2013 Paper

TV-Stokes strategy for sparse-view CT image reconstruction

Yan Liu, Lin Chen, Hao Zhang, Ke Wang, Jianhua Ma, Zhengrong Liang

Proceedings Volume 8668, 86683D (2013) https://doi.org/10.1117/12.2006957

KEYWORDS: Televisions, CT reconstruction, Computed tomography, Neuroimaging, Brain, Image restoration, Image resolution, Data acquisition, Scanners, Image analysis

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Proceedings Article | 6 March 2013 Paper

Noise reduction for cone-beam SPECT by penalized reweighted least-squares projection restoration

Hao Zhang, Junhai Wen, Donghao Shi, Rui Yang, Jing Wang, Zhengrong Liang

Proceedings Volume 8668, 86685C (2013) https://doi.org/10.1117/12.2007745

KEYWORDS: Single photon emission computed tomography, Signal to noise ratio, Reconstruction algorithms, Signal attenuation, Denoising, Photon counting, CT reconstruction, Tomography, Spatial resolution, Biomedical optics

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Proceedings Article | 23 February 2012 Paper

An integrated electronic colon cleansing for CT colonoscopy via MAP-EM segmentation and scale-based scatter correction

Hao Zhang, Lihong Li, Hongbin Zhu, Qin Lin, Donald Harrington, Zhengrong Liang

Proceedings Volume 8315, 83151L (2012) https://doi.org/10.1117/12.910922

KEYWORDS: Tissues, Image segmentation, Colon, Thulium, Image processing algorithms and systems, Photovoltaics, Virtual colonoscopy, Computed tomography, Statistical analysis, Signal attenuation

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Showing 5 of 21 publications

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