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Library-Based X-Ray Scatter Correction for Dedicated Cone-Beam Breast CT: Clinical Validation

MEDICAL PHYSICS 2016年第6期43卷 3819-3819页

作者： Shi, L. Vedantham, S. Karellas, A. Zhu, L. Nuclear and Radiological Engineering and Medical Physics Programs The George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta Georgia 30332. Department of Radiology University of Massachusetts Medical School Worcester Massachusetts 01655.

OBJECTIVE:The image quality of dedicated cone beam breast CT (CBBCT) is limited by substantial scatter contamination, resulting in cupping artifacts and contrast-loss in reconstructed images. Such effects obscure the visibility of soft-tissue lesions and calcifications, which hinders breast cancer detection and diagnosis. In this work, we propose a library-based software approach to suppress scatter on CBBCT images with high efficiency, accuracy, and reliability.;METHODS:The authors precompute a scatter library on simplified breast models with different sizes using the geant4-based Monte Carlo (MC) toolkit. The breast is approximated as a semiellipsoid with homogeneous glandular/adipose tissue mixture. For scatter correction on real clinical data, the authors estimate the breast size from a first-pass breast CT reconstruction and then select the corresponding scatter distribution from the library. The selected scatter distribution from simplified breast models is spatially translated to match the projection data from the clinical scan and is subtracted from the measured projection for effective scatter correction. The method performance was evaluated using 15 sets of patient data, with a wide range of breast sizes representing about 95% of general population. Spatial nonuniformity (SNU) and contrast to signal deviation ratio (CDR) were used as metrics for evaluation.;RESULTS:Since the time-consuming MC simulation for library generation is precomputed, the authors' method efficiently corrects for scatter with minimal processing time. Furthermore, the authors find that a scatter library on a simple breast model with only one input parameter, i.e., the breast diameter, sufficiently guarantees improvements in SNU and CDR. For the 15 clinical datasets, the authors' method reduces the average SNU from 7.14% to 2.47% in coronal views and from 10.14% to 3.02% in sagittal views. On average, the CDR is improved by a factor of 1.49 in coronal views and 2.12 in sagittal views.;

关键词： biological organs biological tissues cancer computerised tomography image reconstruction image representation medical image processing Monte Carlo methods reliability Computed tomography Cancer Probability theory, stochastic processes, and statistics Computerised tomographs Biological material, e.g. blood, urine Haemocytometers Digital computing or data processing equipment or methods, specially adapted for specific applications image data processing or generation, in general breast computed tomography cone-beam breast CT scatter correction Monte Carlo simulation X-ray scattering Monte Carlo methods X-ray detectors Photons Computed tomography Medical image reconstruction Photon scattering Medical image artifacts Tissues

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A new look for Medical Physics and refocused editorial vision

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MEDICAL PHYSICS 2016年第1期43卷 I-III页

作者： Williamson, Jeffrey F. Das, Shiva K. Goodsitt, Mitchell M.

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Introduction: Advances and trends in image formation in X-ray computed tomography

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MEDICAL PHYSICS 2015年第5期42卷 2656-2656页

作者： Noo, Frederic Karellas, Andrew Univ Utah Utah Ctr Adv Imaging Res Salt Lake City UT 84108 USA Univ Massachusetts Sch Med Dept Radiol Worcester MA 01605 USA

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Imaging in Nuclear Medicine

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Medical physics 2016年第6期43卷 3207页

作者： Matthew Palmer

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Molecular Imaging of Small Animals

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Medical physics 2016年第6期43卷 3208页

作者： Jamal Zweit Sundaresan Gobalakrishnan

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image processing in Radiation Therapy

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Medical Physics 2015年第2期42卷 1138-1139页

作者： James M. Balter

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Tomosynthesis Imaging

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Medical Physics 2015年第11期42卷 6770-6771页

作者： John M. Sabol

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Accelerated Barrier Optimization Compressed Sensing (ABOCS) Reconstruction for Cone-Beam CT

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MEDICAL PHYSICS 2012年第6期39卷 3892-3893页

作者： Niu, T. Zhu, L. Georgia Inst Tech Atlanta GA USA

OBJECTIVE:Recent advances in compressed sensing (CS) enable accurate CT image reconstruction from highly undersampled and noisy projection measurements, due to the sparsifiable feature of most CT images using total variation (TV). These novel reconstruction methods have demonstrated advantages in clinical applications where radiation dose reduction is critical, such as onboard cone-beam CT (CBCT) imaging in radiation therapy. The image reconstruction using CS is formulated as either a constrained problem to minimize the TV objective within a small and fixed data fidelity error, or an unconstrained problem to minimize the data fidelity error with TV regularization. However, the conventional solutions to the above two formulations are either computationally inefficient or involved with inconsistent regularization parameter tuning, which significantly limit the clinical use of CS-based iterative reconstruction. In this paper, we propose an optimization algorithm for CS reconstruction which overcomes the above two drawbacks.;METHODS:The data fidelity tolerance of CS reconstruction can be well estimated based on the measured data, as most of the projection errors are from Poisson noise after effective data correction for scatter and beam-hardening effects. We therefore adopt the TV optimization framework with a data fidelity constraint. To accelerate the convergence, we first convert such a constrained optimization using a logarithmic barrier method into a form similar to that of the conventional TV regularization based reconstruction but with an automatically adjusted penalty weight. The problem is then solved efficiently by gradient projection with an adaptive Barzilai-Borwein step-size selection scheme. The proposed algorithm is referred to as accelerated barrier optimization for CS (ABOCS), and evaluated using both digital and physical phantom studies.;RESULTS:ABOCS directly estimates the data fidelity tolerance from the raw projection data. Therefore, as demonstrate

关键词： Computed tomography Numerical optimization computerised tomography convergence image reconstruction mathematics computing medical image processing noise optimisation phantoms radiation therapy iterative reconstruction compressed sensing total variation logarithmic barrier cone-beam CT Computerised tomographs Radiation therapy Complex mathematical operations Digital computing or data processing equipment or methods, specially adapted for specific applications image data processing or generation, in general Medical imaging Medical image reconstruction image reconstruction Medical image noise Cone beam computed tomography image sensors Computed tomography Poisson's equation Medical image quality Optimization

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Anapproach for elasticity properties reconstruction

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Medical Physics 2011年第1期39卷 475-481页

作者： Huafeng Liu Hongjie Hu Albert J. Sinusas Pengcheng Shi State Key Laboratory of Modern Optical Instrumentation Zhejiang University Hangzhou 310027 China and B. Thomas Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester New York 14623 Department of Radiology Sir Run Run Shaw Hospital Zhejiang University Hangzhou 310016 China Section of Cardiology Departments of Diagnostic Radiology and Internal Medicine Yale University New Haven Connecticut 06520 B. Thomas Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester New York 14623

Purpose: Quantification of object elasticity properties has significant technical implications as well as important practical applications, such as medical disease diagnosis. in general, given noisy measurements on the kinematic states of the objects from imaging data, the aim is to recover the elasticity parameters for assumed material constitutive models of the objects. The implementation is complicated caused by the large dimensionality of the parameters. Methods: Various versions of the least-square (LS) methods have been widely used, which, however, do not perform well under reasonably realistic levels of disturbances. Another popular strategy, based on the extended Kalman filter (EKF), is also far from optimal and subject to divergence if either the initializations are poor or the noises are not Gaussian. In this paper, the authors propose a robust system identification paradigm for the quantitative analysis of object elasticity. It is derived and extended from the filtering principles and is particularly powerful for real-world situations where the types and levels of the disturbances are unknown. Results: Using synthetic data, the authors investigate the sensitivity of the strategies toward different types (Gaussian and Poisson) and levels of noises, as well as various initializations. The experimental results show consistently superior performance of the proposed method over the LS and EKF algorithms in reliably identifying object elastic modulus distributions. Conclusions: Results from phase contrast imaging data of canine hearts and human MRI data are also presented, which demonstrate the power of the framework.

关键词： Magnetic resonance imaging Electrical, thermal, and mechanical properties of biological matter Reconstruction Numerical optimization biomechanics biomedical MRI cardiology elasticity image reconstruction medical image processing Young's modulus material characterization robust estimation state space representation least-square extended Kaman filter ℋ∞ filter Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging Digital computing or data processing equipment or methods, specially adapted for specific applications image data processing or generation, in general Elasticity Medical imaging Tissues Kinematics Elastic moduli Medical image noise Finite element methods Poisson's equation image analysis Medical image contrast

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