Dr. Hao Dang Profile

Dr. Hao Dang

Research Scientist at Philips

SPIE Involvement:

Author | Student Chapter Vice President

Publications (9)

PROCEEDINGS ARTICLE | March 9, 2018

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

Proc. SPIE. 10573, Medical Imaging 2018: Physics of Medical Imaging

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

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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 | March 9, 2018

Deep residual learning enabled metal artifact reduction in CT

Proc. SPIE. 10573, Medical Imaging 2018: Physics of Medical Imaging

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

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PROCEEDINGS ARTICLE | March 9, 2017

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

Proc. SPIE. 10132, Medical Imaging 2017: Physics of Medical Imaging

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

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PROCEEDINGS ARTICLE | March 9, 2017

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

Proc. SPIE. 10132, Medical Imaging 2017: Physics of Medical Imaging

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

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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 | March 31, 2016

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

Proc. SPIE. 9783, Medical Imaging 2016: Physics of Medical Imaging

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

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PROCEEDINGS ARTICLE | March 22, 2016

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

Proc. SPIE. 9783, Medical Imaging 2016: Physics of Medical Imaging

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

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PROCEEDINGS ARTICLE | March 18, 2015

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

Proc. SPIE. 9412, Medical Imaging 2015: Physics of Medical Imaging

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

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PROCEEDINGS ARTICLE | March 19, 2014

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

Proc. SPIE. 9033, Medical Imaging 2014: Physics of Medical Imaging

KEYWORDS: Statistical analysis, Chest, Tumors, Signal attenuation, Image resolution, Medical imaging, Surveillance, Tomography, Image quality, 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 | March 19, 2013

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

Proc. SPIE. 8668, Medical Imaging 2013: Physics of Medical Imaging

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

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Showing 5 of 9 publications

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