Dr. Hao Dang Profile

Dr. Hao Dang

Research Scientist at Philips Healthcare

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Publications (10)

Proceedings Article | 7 April 2023 Poster + Paper

Support-based extended field-of-view CT reconstruction for radiation treatment planning

Hao Dang, Xiyun Song, Bernhard Brendel, Claas Bontus, Thomas Koehler, Kevin Brown, Johannes Hammel, Daniela Pfeiffer

Proceedings Volume 12463, 1246331 (2023) https://doi.org/10.1117/12.2654102

KEYWORDS: Image restoration, CT reconstruction, Tissues, Radiotherapy, Computed tomography, Reconstruction algorithms, Medical image reconstruction, Image quality, Bone, Abdomen

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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

Deep residual learning enabled metal artifact reduction in CT

Shiyu Xu, Hao Dang

Proceedings Volume 10573, 105733O (2018) https://doi.org/10.1117/12.2293945

KEYWORDS: Metals, Computed tomography, Convolutional neural networks, X-rays, Reconstruction algorithms

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Proceedings Article | 9 March 2017 Presentation + Paper

Brain perfusion imaging using a Reconstruction-of-Difference (RoD) approach for cone-beam computed tomography

M. Mow, W. Zbijewski, A. Sisniega, J. Xu, H. Dang, J. Stayman, X. Wang, D. Foos, V. Koliatsos, N. Aygun, J. Siewerdsen

Proceedings Volume 10132, 1013212 (2017) https://doi.org/10.1117/12.2255690

KEYWORDS: Neuroimaging, Brain, Imaging systems, Computed tomography, Scanners, Image enhancement, Cones, Head, Brain imaging, Ischemic stroke, Tissues, Signal attenuation

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Proceedings Article | 9 March 2017 Presentation + Paper

Development and clinical translation of a cone-beam CT scanner for high-quality imaging of intracranial hemorrhage

A. Sisniega, J. Xu, H. Dang, W. Zbijewski, J. Stayman, M. Mow, V. Koliatsos, N. Aygun, X. Wang, D. Foos, J. Siewerdsen

Proceedings Volume 10132, 101320K (2017) https://doi.org/10.1117/12.2255670

KEYWORDS: Reconstruction algorithms, Computed tomography, Point spread functions, Scanners, Point-of-care devices, Prototyping, Imaging systems, Model-based design, Image quality, Head, Image resolution, Bone

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Purpose: Prompt, reliable detection of intracranial hemorrhage (ICH) is essential for treatment of stroke and traumatic brain injury, and would benefit from availability of imaging directly at the point-of-care. This work reports the performance evaluation of a clinical prototype of a cone-beam CT (CBCT) system for ICH imaging and introduces novel algorithms for model-based reconstruction with compensation for data truncation and patient motion.

Methods: The tradeoffs in dose and image quality were investigated as a function of analytical (FBP) and model-based iterative reconstruction (PWLS) algorithm parameters using phantoms with ICH-mimicking inserts. Image quality in clinical applications was evaluated in a human cadaver imaged with simulated ICH. Objects outside of the field of view (FOV), such as the head-holder, were found to introduce challenging truncation artifacts in PWLS that were mitigated with a novel multi-resolution reconstruction strategy. Following phantom and cadaver studies, the scanner was translated to a clinical pilot study. Initial clinical experience indicates the presence of motion in some patient scans, and an image-based motion estimation method that does not require fiducial tracking or prior patient information was implemented and evaluated.

Results: The weighted CTDI for a nominal scan technique was 22.8 mGy. The high-resolution FBP reconstruction protocol achieved < 0.9 mm full width at half maximum (FWHM) of the point spread function (PSF). The PWLS soft-tissue reconstruction showed <1.2 mm PSF FWHM and lower noise than FBP at the same resolution. Effects of truncation in PWLS were mitigated with the multi-resolution approach, resulting in 60% reduction in root mean squared error compared to conventional PWLS. Cadaver images showed clear visualization of anatomical landmarks (ventricles and sulci), and ICH was conspicuous. The motion compensation method was shown in clinical studies to restore visibility of fine bone structures, such as the subtle fracture, cranial sutures, and the cochlea as well as subtle low-contrast structures in the brain parenchyma.

Conclusion: The imaging performance of the prototype suggests sufficient quality for ICH imaging and motivates continued clinical studies to assess the diagnosis utility of the CBCT system in realistic clinical scenarios at the point of care.

Proceedings Article | 31 March 2016 Paper

Regularization design for high-quality cone-beam CT of intracranial hemorrhage using statistical reconstruction

H. Dang, J. W. Stayman, J. Xu, A. Sisniega, W. Zbijewski, X. Wang, D. Foos, N. Aygun, V. Koliatsos, J. Siewerdsen

Proceedings Volume 9783, 97832Y (2016) https://doi.org/10.1117/12.2216937

KEYWORDS: Spatial resolution, Computed tomography, Image quality, Statistical modeling, Traumatic brain injury, Blood, Head, Optical spheres, Brain, Modulation transfer functions, Point spread functions

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Proceedings Article | 22 March 2016 Paper

Design and characterization of a dedicated cone-beam CT scanner for detection of acute intracranial hemorrhage

J. Xu, A. Sisniega, W. Zbijewski, H. Dang, J. W. Stayman, X. Wang, D. Foos, N. Aygun, V. Koliatsos, J. Siewerdsen

Proceedings Volume 9783, 97830T (2016) https://doi.org/10.1117/12.2216544

KEYWORDS: Imaging systems, Traumatic brain injury, Prototyping, X-ray computed tomography, Scanners, Computing systems, Image quality, Model-based design, Sensors, Performance modeling

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Proceedings Article | 18 March 2015 Paper

Cone-beam CT of traumatic brain injury using statistical reconstruction with a post-artifact-correction noise model

H. Dang, J. Stayman, A. Sisniega, J. Xu, W. Zbijewski, J. Yorkston, N. Aygun, V. Koliatsos, J. Siewerdsen

Proceedings Volume 9412, 941207 (2015) https://doi.org/10.1117/12.2082075

KEYWORDS: Traumatic brain injury, Spatial resolution, Image quality, Head, Imaging systems, Model-based design, Monte Carlo methods, Statistical modeling, Point-of-care devices, Optical spheres

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Proceedings Article | 19 March 2014 Paper

Regularization design and control of change admission in prior-image-based reconstruction

Hao Dang, Jeffrey Siewerdsen, J. Webster Stayman

Proceedings Volume 9033, 90330O (2014) https://doi.org/10.1117/12.2043781

KEYWORDS: Image quality, Chest, Medical imaging, Statistical analysis, Signal attenuation, Image resolution, Tomography, Surveillance, Tumors, Model-based design

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Nearly all reconstruction methods are controlled through various parameter selections. Traditionally, such parameters are used to specify a particular noise and resolution trade-off in the reconstructed image volumes. The introduction of reconstruction methods that incorporate prior image information has demonstrated dramatic improvements in dose utilization and image quality, but has complicated the selection of reconstruction parameters including those associated with balancing information used from prior images with that of the measurement data. While a noise-resolution tradeoff still exists, other potentially detrimental effects are possible with poor prior image parameter values including the possible introduction of false features and the failure to incorporate sufficient prior information to gain any improvements. Traditional parameter selection methods such as heuristics based on similar imaging scenarios are subject to error and suboptimal solutions while exhaustive searches can involve a large number of time-consuming iterative reconstructions. We propose a novel approach that prospectively determines optimal prior image regularization strength to accurately admit specific anatomical changes without performing full iterative reconstructions. This approach leverages analytical approximations to the implicitly defined prior image-based reconstruction solution and predictive metrics used to estimate imaging performance. The proposed method is investigated in phantom experiments and the shift-variance and data-dependence of optimal prior strength is explored. Optimal regularization based on the predictive approach is shown to agree well with traditional exhaustive reconstruction searches, while yielding substantial reductions in computation time. This suggests great potential of the proposed methodology in allowing for prospective patient-, data-, and change-specific customization of prior-image penalty strength to ensure accurate reconstruction of specific anatomical changes.

Proceedings Article | 19 March 2013 Paper

Overcoming nonlinear partial volume effects in known-component reconstruction of Cochlear implants

J. Stayman, H. Dang, Y. Otake, Wojciech Zbijewski, J. Noble, B. Dawant, R. Labadie, J. Carey, J. Siewerdsen

Proceedings Volume 8668, 86681L (2013) https://doi.org/10.1117/12.2007945

KEYWORDS: Sensors, Electrodes, Data modeling, Image registration, Spatial resolution, Model-based design, Signal attenuation, Visualization, Metals, Image resolution

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