Lou, Yifei

Permanent URI for this collectionhttps://hdl.handle.net/10735.1/4794

Dr. Yifei Lou is an Associate Professor of Mathematical Sciences. Her research interests include:

  • Compressive sensing and its applications
  • Image analysis (medical imaging, hyperspectral, imaging through turbulence)
  • Numerical analysis and optimization algorithms

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Now showing 1 - 3 of 3
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    Multienergy Element-Resolved Cone Beam CT (MEER-CBCT) Realized on a Conventional CBCT Platform
    (John Wiley and Sons Ltd.) Shen, C.; Li, B.; Lou, Yifei; Yang, M.; Zhou, L.; Jia, X.; 0000-0003-1973-5704 (Lou, Y); Lou, Yifei
    Purpose: Cone beam CT (CBCT) has been widely used in radiation therapy. However, its main application is still to acquire anatomical information for patient positioning. This study proposes a multienergy element-resolved (MEER) CBCT framework that employs energy-resolved data acquisition on a conventional CBCT platform and then simultaneously reconstructs images of x-ray attenuation coefficients, electron density relative to water (rED), and elemental composition (EC) to support advanced applications. Methods: The MEER-CBCT framework is realized on a Varian TrueBeam CBCT platform using a kVp-switching scanning scheme. A simultaneous image reconstruction and elemental decomposition model is formulated as an optimization problem. The objective function uses a least square term to enforce fidelity between x-ray attenuation coefficients and projection measurements. Spatial regularization is introduced via sparsity under a tight wavelet-frame transform. Consistency is imposed among rED, EC, and attenuation coefficients and inherently serves as a regularization term along the energy direction. The EC is further constrained by a sparse combination of ECs in a dictionary containing tissues commonly existing in humans. The optimization problem is solved by a novel alternating-direction minimization scheme. The MEER-CBCT framework was tested in a simulation study using an NCAT phantom and an experimental study using a Gammex phantom. Results: MEER-CBCT framework was successfully realized on a clinical Varian TrueBeam onboard CBCT platform with three energy channels of 80, 100, and 120 kVp. In the simulation study, the attenuation coefficient image achieved a structural similarity index of 0.98, compared to 0.61 for the image reconstructed by the conventional conjugate gradient least square (CGLS) algorithm, primarily because of reduction in artifacts. In the experimental study, the attenuation image obtained a contrast-to-noise ratio ≥60, much higher than that of CGLS results (~16) because of noise reduction. The median errors in rED and EC were 0.5% and 1.4% in the simulation study and 1.4% and 2.3% in the experimental study. Conclusion: We proposed a novel MEER-CBCT framework realized on a clinical CBCT platform. Simulation and experimental studies demonstrated its capability to simultaneously reconstruct x-ray attenuation coefficient, rED, and EC images accurately. ©2018 American Association of Physicists in Medicine
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    Multienergy Cone-Beam Computed Tomography Reconstruction with a Spatial Spectral Nonlocal Means Algorithm
    (Society for Industrial and Applied Mathematics Publications) Li, B.; Shen, C.; Chi, Y.; Yang, M.; Lou, Yifei; Zhou, L.; Jia, X.; 0000-0003-1973-5704 (Lou, Y); Lou, Yifei
    Multienergy computed tomography (CT) is an emerging medical image modality with a number of potential applications in diagnosis and therapy. However, high system cost and technical barriers obstruct its step into routine clinical practice. In this study, we propose a framework to realize multienergy cone beam CT (ME-CBCT) on the CBCT system that is widely available and has been routinely used for radiotherapy image guidance. In our method, a kVp switching technique is realized, which acquires x-ray projections with kVp levels cycling through a number of values. For this kVp-switching based ME-CBCT acquisition, x-ray projections of each energy channel are only a subset of all the acquired projections. This leads to an undersampling issue, posing challenges to the reconstruction problem. We propose a spatial spectral nonlocal means (NLM) method to reconstruct ME-CBCT, which employs image correlations along both spatial and spectral directions to suppress noisy and streak artifacts. To address the intensity scale difference at different energy channels, a histogram matching method is incorporated. Our method is different from conventionally used NLM methods in that spectral dimension is included, which helps to effectively remove streak artifacts appearing at different directions in images with different energy channels. Convergence analysis of our algorithm is provided. A comprehensive set of simulation and real experimental studies demonstrate feasibility of our ME-CBCT scheme and the capability of achieving superior image quality compared to conventional filtered backprojection-type and NLM reconstruction methods. © 2018 Society for Industrial and Applied Mathematics.
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    A Weighted Difference of Anisotropic and Isotropic Total Variation Model for Image Processing
    (Society for Industrial and Applied Mathematics Publications, 2015-09-10) Lou, Yifei; Zeng, T.; Osher, S.; Xin, J.; 0000-0003-1973-5704 (Lou, Y)
    We propose a weighted difference of anisotropic and isotropic total variation (TV) as a regularization for image processing tasks, based on the well-known TV model and natural image statistics. Due to the form of our model, it is natural to compute via a difference of convex algorithm (DCA). We draw its connection to the Bregman iteration for convex problems and prove that the iteration generated from our algorithm converges to a stationary point with the objective function values decreasing monotonically. A stopping strategy based on the stable oscillatory pattern of the iteration error from the ground truth is introduced. In numerical experiments on image denoising, image deblurring, and magnetic resonance imaging (MRI) reconstruction, our method improves on the classical TV model consistently and is on par with representative state-of-the-art methods.

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