Dr. Qi Duan Profile

Dr. Qi Duan

Research Scientist

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Publications (5)

Proceedings Article | 11 March 2009 Paper

Regularization of parallel MRI reconstruction using in vivo coil sensitivities

Qi Duan, Ricardo Otazo, Jian Xu, Daniel Sodickson

Proceedings Volume 7258, 72580Z (2009) https://doi.org/10.1117/12.812440

KEYWORDS: In vivo imaging, Magnetic resonance imaging, Calibration, Computer programming, Signal to noise ratio, Interference (communication), Brain, Image resolution, 3D acquisition, Neuroimaging

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Proceedings Article | 22 March 2007 Paper

Augmenting CT cardiac roadmaps with segmented streaming ultrasound

Qi Duan, Guy Shechter, Luis Gutiérrez, Douglas Stanton, Lyubomir Zagorchev, Andrew Laine, Daniel Elgort

Proceedings Volume 6509, 65090V (2007) https://doi.org/10.1117/12.711431

KEYWORDS: Image segmentation, Ultrasonography, Image registration, X-ray computed tomography, 3D modeling, Finite element methods, 3D image processing, Computed tomography, Image resolution, Motion models

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Proceedings Article | 12 April 2005 Paper

Adaptive spatial-temporal filtering applied to x-ray fluoroscopy angiography

Gert Schoonenberg, Marc Schrijver, Qi Duan, Richard Kemkers, Andrew Laine

Proceedings Volume 5744, (2005) https://doi.org/10.1117/12.596535

KEYWORDS: Image filtering, Digital filtering, Spatial filters, X-rays, Fluoroscopy, X-ray imaging, Angiography, Image processing, Detection and tracking algorithms, Electronic filtering

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Proceedings Article | 12 April 2005 Paper

Evaluation of optical flow algorithms for tracking endocardial surfaces on three-dimensional ultrasound data

Qi Duan, Elsa Angelini, Susan Herz, Christopher Ingrassia, Olivier Gerard, Kevin Costa, Jeffrey Holmes, Andrew Laine

Proceedings Volume 5750, (2005) https://doi.org/10.1117/12.596890

KEYWORDS: Optical flow, Ultrasonography, Optical tracking, Detection and tracking algorithms, Data acquisition, 3D image processing, Transducers, Visualization, 3D acquisition, Automatic tracking

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With relatively high frame rates and the ability to acquire volume data sets with a stationary transducer, 3D ultrasound systems, based on matrix phased array transducers, provide valuable three-dimensional information, from which quantitative measures of cardiac function can be extracted. Such analyses require segmentation and visual tracking of the left ventricular endocardial border. Due to the large size of the volumetric data sets, manual tracing of the endocardial border is tedious and impractical for clinical applications. Therefore the development of automatic methods for tracking three-dimensional endocardial motion is essential. In this study, we evaluate a four-dimensional optical flow motion tracking algorithm to determine its capability to follow the endocardial border in three dimensional ultrasound data through time. The four-dimensional optical flow method was implemented using three-dimensional correlation. We tested the algorithm on an experimental open-chest dog data set and a clinical data set acquired with a Philips' iE33 three-dimensional ultrasound machine. Initialized with left ventricular endocardial data points obtained from manual tracing at end-diastole, the algorithm automatically tracked these points frame by frame through the whole cardiac cycle. A finite element surface was fitted through the data points obtained by both optical flow tracking and manual tracing by an experienced observer for quantitative comparison of the results. Parameterization of the finite element surfaces was performed and maps displaying relative differences between the manual and semi-automatic methods were compared. The results showed good consistency between manual tracing and optical flow estimation on 73% of the entire surface with fewer than 10% difference. In addition, the optical flow motion tracking algorithm greatly reduced processing time (about 94% reduction compared to human involvement per cardiac cycle) for analyzing cardiac function in three-dimensional ultrasound data sets.

Proceedings Article | 28 April 2004 Paper

Assessment of visual quality and spatial accuracy of fast anisotropic diffusion and scan conversion algorithms for real-time three-dimensional spherical ultrasound

Qi Duan, Elsa Angelini, Andrew Laine

Proceedings Volume 5373, (2004) https://doi.org/10.1117/12.535813

KEYWORDS: Anisotropic diffusion, Ultrasonography, Spherical lenses, Denoising, Data conversion, Image quality, Anisotropic filtering, Speckle, Diffusion, Visualization

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