Xiaochuan Pan

Publications of Xiaochuan Pan

• Investigation of discrete imaging models and iterative image reconstruction in differential X-ray phase-contrast tomography.

  Authors: Qiaofeng Xu, Emil Y Sidky, Xiaochuan Pan, Marco Stampanoni, Peter Modregger, Mark A Anastasio

  Optics express. 05/2012; 20(10):10724-49.

  Differential X-ray phase-contrast tomography (DPCT) refers to a class of promising methods for reconstructing the X-ray refractive index distribution of materials that present weak X-ray absorptionDifferential X-ray phase-contrast tomography (DPCT) refers to a class of promising methods for reconstructing the X-ray refractive index distribution of materials that present weak X-ray absorption contrast. The tomographic projection data in DPCT, from which an estimate of the refractive index distribution is reconstructed, correspond to one-dimensional (1D) derivatives of the two-dimensional (2D) Radon transform of the refractive index distribution. There is an important need for the development of iterative image reconstruction methods for DPCT that can yield useful images from few-view projection data, thereby mitigating the long data-acquisition times and large radiation doses associated with use of analytic reconstruction methods. In this work, we analyze the numerical and statistical properties of two classes of discrete imaging models that form the basis for iterative image reconstruction in DPCT. We also investigate the use of one of the models with a modern image reconstruction algorithm for performing few-view image reconstruction of a tissue specimen.

• Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle-Pock algorithm.

  Authors: Emil Y Sidky, Jakob H Jørgensen, Xiaochuan Pan

  Physics in medicine and biology. 04/2012; 57(10):3065-3091.

  The primal-dual optimization algorithm developed in Chambolle and Pock (CP) (2011 J. Math. Imag. Vis. 40 1-26) is applied to various convex optimization problems of interest in computed tomographyThe primal-dual optimization algorithm developed in Chambolle and Pock (CP) (2011 J. Math. Imag. Vis. 40 1-26) is applied to various convex optimization problems of interest in computed tomography (CT) image reconstruction. This algorithm allows for rapid prototyping of optimization problems for the purpose of designing iterative image reconstruction algorithms for CT. The primal-dual algorithm is briefly summarized in this paper, and its potential for prototyping is demonstrated by explicitly deriving CP algorithm instances for many optimization problems relevant to CT. An example application modeling breast CT with low-intensity x-ray illumination is presented.

• Ensuring convergence in total-variation-based reconstruction for accurate microcalcification imaging in breast X-ray CT

  Authors: Jakob H. Jørgensen, Emil Y Sidky, Xiaochuan Pan

  11/2011;

  Breast X-ray CT imaging is being considered in screening as an extension to mammography. As a large fraction of the population will be exposed to radiation, low-dose imaging is essential. IterativeBreast X-ray CT imaging is being considered in screening as an extension to mammography. As a large fraction of the population will be exposed to radiation, low-dose imaging is essential. Iterative image reconstruction based on solving an optimization problem, such as Total-Variation minimization, shows potential for reconstruction from sparse-view data. For iterative methods it is important to ensure convergence to an accurate solution, since important image features, such as presence of microcalcifications indicating breast cancer, may not be visible in a non-converged reconstruction, and this can have clinical significance. To prevent excessively long computational times, which is a practical concern for the large image arrays in CT, it is desirable to keep the number of iterations low, while still ensuring a sufficiently accurate reconstruction for the specific imaging task. This motivates the study of accurate convergence criteria for iterative image reconstruction. In simulation studies with a realistic breast phantom with microcalcifications we compare different convergence criteria for reliable reconstruction. Our results show that it can be challenging to ensure a sufficiently accurate microcalcification reconstruction, when using standard convergence criteria. In particular, the gray level of the small microcalcifications may not have converged long after the background tissue is reconstructed uniformly. We propose the use of the individual objective function gradient components to better monitor possible regions of non-converged variables. For microcalcifications we find empirically a large correlation between nonzero gradient components and non-converged variables, which occur precisely within the microcalcifications. This supports our claim that gradient components can be used to ensure convergence to a sufficiently accurate reconstruction.

• Quantification of admissible undersampling for sparsity-exploiting iterative image reconstruction in X-ray CT

  Authors: Jakob H. Jørgensen, Emil Y. Sidky, Xiaochuan Pan

  09/2011;

  Iterative image reconstruction (IIR) with sparsity-exploiting methods proposed in compressive sensing (CS) claim potentially large reductions in sampling requirements. Quantifying this claim forIterative image reconstruction (IIR) with sparsity-exploiting methods proposed in compressive sensing (CS) claim potentially large reductions in sampling requirements. Quantifying this claim for computed tomography (CT) is difficult, because both full sampling and the reduction in sampling admitted by sparsity-exploiting methods are ill-defined. CS does not provide answers, aside from designing the optimization problem, because the CT imaging model does not belong to any class of measurement matrices for which CS provides theoretical guarantees. This article addresses quantification of admissible undersampling through sparsity-exploiting methods. Four sufficient-sampling conditions (SSCs) are introduced and their use as a reference of full sampling is discussed. The derived SSCs are based only on the condition number of the system matrix. In numerical simulation studies in the setting of breast CT the SSCs are evaluated for quantifying undersampling admitted by total variation minimization. A direct connection between sparsity and admitted undersampling is found.

• Accelerated gradient methods for total-variation-based CT image reconstruction

  Authors: Jakob Heide Jørgensen, Tobias Lindstrøm Jensen, Per Christian Hansen, Søren Holdt Jensen, Emil Y Sidky, Xiaochuan Pan

  05/2011;

  Total-variation (TV)-based Computed Tomography (CT) image reconstruction has shown experimentally to be capable of producing accurate reconstructions from sparse-view data. In particular TV-basedTotal-variation (TV)-based Computed Tomography (CT) image reconstruction has shown experimentally to be capable of producing accurate reconstructions from sparse-view data. In particular TV-based reconstruction is very well suited for images with piecewise nearly constant regions. Computationally, however, TV-based reconstruction is much more demanding, especially for 3D imaging, and the reconstruction from clinical data sets is far from being close to real-time. This is undesirable from a clinical perspective, and thus there is an incentive to accelerate the solution of the underlying optimization problem. The TV reconstruction can in principle be found by any optimization method, but in practice the large-scale systems arising in CT image reconstruction preclude the use of memory-demanding methods such as Newton's method. The simple gradient method has much lower memory requirements, but exhibits slow convergence. In the present work we consider the use of two accelerated gradient-based methods, GPBB and UPN, for reducing the number of gradient method iterations needed to achieve a high-accuracy TV solution in CT image reconstruction. The former incorporates several heuristics from the optimization literature such as Barzilai-Borwein (BB) step size selection and nonmonotone line search. The latter uses a cleverly chosen sequence of auxiliary points to achieve a better convergence rate. The methods are memory efficient and equipped with a stopping criterion to ensure that the TV reconstruction has indeed been found. An implementation of the methods (in C with interface to Matlab) is available for download from http://www2.imm.dtu.dk/~pch/TVReg/. We compare the proposed methods with the standard gradient method, applied to a 3D test problem with synthetic few-view data. We find experimentally that for realistic parameters the proposed methods significantly outperform the gradient method.

• Frequency extrapolation by nonconvex compressive sensing

  Authors: R. Chartrand, E.Y. Sidky, Xiaochuan Pan

  Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on; 05/2011

  Tomographic imaging modalities sample subjects with a discrete, finite set of measurements, while the underlying object function is continuous. Because of this, inversion of the imaging model, evenTomographic imaging modalities sample subjects with a discrete, finite set of measurements, while the underlying object function is continuous. Because of this, inversion of the imaging model, even under ideal conditions, necessarily entails approximation. The error incurred by this approximation can be important when there is rapid variation in the object function or when the objects of interest are small. In this work, we investigate this issue with the Fourier transform (FT), which can be taken as the imaging model for magnetic resonance imaging (MRI) or some forms of wave imaging. Compressive sensing has been successful for inverting this data model when only a sparse set of samples are available. We apply the compressive sensing principle to a somewhat related problem of frequency extrapolation, where the object function is represented by a super-resolution grid with many more pixels than FT measurements. The image on the super-resolution grid is obtained through nonconvex minimization. The method fully utilizes the available FT samples, while controlling aliasing and ringing. The algorithm is demonstrated with continuous FT samples of the Shepp-Logan phantom with additional small, high-contrast objects.

• Toward optimal X-ray flux utilization in breast CT

  Authors: Jakob H. Joergensen, Per Christian Hansen, Emil Y Sidky, Ingrid S. Reiser, Xiaochuan Pan

  04/2011;

  A realistic computer-simulation of a breast computed tomography (CT) system and subject is constructed. The model is used to investigate the optimal number of views for the scan given a fixed totalA realistic computer-simulation of a breast computed tomography (CT) system and subject is constructed. The model is used to investigate the optimal number of views for the scan given a fixed total X-ray fluence. The reconstruction algorithm is based on accurate solution to a constrained, TV-minimization problem, which has received much interest recently for sparse-view CT data.

• High resolution image reconstruction with constrained, total-variation minimization

  Authors: Emil Y Sidky, Rick Chartrand, Yuval Duchin, Christer Ullberg, Xiaochuan Pan

  04/2011;

  This work is concerned with applying iterative image reconstruction, based on constrained total-variation minimization, to low-intensity X-ray CT systems that have a high sampling rate. Such systemsThis work is concerned with applying iterative image reconstruction, based on constrained total-variation minimization, to low-intensity X-ray CT systems that have a high sampling rate. Such systems pose a challenge for iterative image reconstruction, because a very fine image grid is needed to realize the resolution inherent in such scanners. These image arrays lead to under-determined imaging models whose inversion is unstable and can result in undesirable artifacts and noise patterns. There are many possibilities to stabilize the imaging model, and this work proposes a method which may have an advantage in terms of algorithm efficiency. The proposed method introduces additional constraints in the optimization problem; these constraints set to zero high spatial frequency components which are beyond the sensing capability of the detector. The method is demonstrated with an actual CT data set and compared with another method based on projection up-sampling.

• Algorithm-Enabled Low-Dose Micro-CT Imaging

  Authors: Xiao Han, Junguo Bian, D.R. Eaker, T.L. Kline, E.Y. Sidky, E.L. Ritman, Xiaochuan Pan

  Medical Imaging, IEEE Transactions on. 04/2011;

  Micro-computed tomography (micro-CT) is an important tool in biomedical research and preclinical applications that can provide visual inspection of and quantitative information about imaged smallMicro-computed tomography (micro-CT) is an important tool in biomedical research and preclinical applications that can provide visual inspection of and quantitative information about imaged small animals and biological samples such as vasculature specimens. Currently, micro-CT imaging uses projection data acquired at a large number (300-1000) of views, which can limit system throughput and potentially degrade image quality due to radiation-induced deformation or damage to the small animal or specimen. In this work, we have investigated low-dose micro-CT and its application to specimen imaging from substantially reduced projection data by using a recently developed algorithm, referred to as the adaptive-steepest-descent-projection-onto-convex-sets (ASD-POCS) algorithm, which reconstructs an image through minimizing the image total-variation and enforcing data constraints. To validate and evaluate the performance of the ASD-POCS algorithm, we carried out quantitative evaluation studies in a number of tasks of practical interest in imaging of specimens of real animal organs. The results show that the ASD-POCS algorithm can yield images with quality comparable to that obtained with existing algorithms, while using one-sixth to one quarter of the 361-view data currently used in typical micro-CT specimen imaging.

• Algorithm-Enabled Low-Dose Micro-CT Imaging.

  Authors: Xiao Han, Junguo Bian, Diane R. Eaker, Timothy Kline, Emil Y. Sidky, Erik L. Ritman, Xiaochuan Pan

  IEEE Trans. Med. Imaging. 01/2011; 30:606-620.

• Frequency extrapolation by nonconvex compressive sensing.

  Authors: Rick Chartrand, Emil Y. Sidky, Xiaochuan Pan

  Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011, March 30 - April 2, 2011, Chicago, Illinois, USA; 01/2011

• Preliminary investigation of dose allocation in low-dose cone-beam CT

  Authors: Xiao Han, E. Pearson, Junguo Bian, Seungryong Cho, E.Y. Sidky, C.A. Pelizzari, Xiaochuan Pan

  Nuclear Science Symposium Conference Record (NSS/MIC), 2010 IEEE; 12/2010

  Current cone-beam CT imaging requires dense angular sampling that is imposed by the FDK-type image-reconstruction algorithm. In an attempt to investigate the possibility of low-dose CBCT by reducingCurrent cone-beam CT imaging requires dense angular sampling that is imposed by the FDK-type image-reconstruction algorithm. In an attempt to investigate the possibility of low-dose CBCT by reducing the number of projection views, we applied the adaptive-steepest-descent-projection-onto-convex-sets (ASD-POCS) algorithm for CBCT image reconstruction. Given a reduced amount of total radiation-dose, we applied both ASD-POCS and FDK algorithms for reconstruction of CBCT images from real measurement data acquired at different mass settings, and investigated how the quality of images reconstructed by these algorithms changes when the amount of total dose is allocated to angular views in different ways. Preliminary results of the study suggest that the ASD-POCS algorithm can yield images of quality better than that achieved by the FDK algorithm with each low-dose data set, and comparable to images obtained with the standard dose level. While FDK image quality degrades with decreased number of views, the ASD-POCS image quality appears to be less affected by different dose-allocation parameters under investigation.

• A constrained, total-variation minimization algorithm for low-intensity X-ray CT

  Authors: Emil Y Sidky, Yuval Duchin, Christer Ullberg, Xiaochuan Pan

  11/2010;

  Purpose: We develop an iterative image-reconstruction algorithm for application to low-intensity computed tomography (CT) projection data, which is based on constrained, total-variation (TV)Purpose: We develop an iterative image-reconstruction algorithm for application to low-intensity computed tomography (CT) projection data, which is based on constrained, total-variation (TV) minimization. The algorithm design focuses on recovering structure on length scales comparable to a detector-bin width. Method: Recovering the resolution on the scale of a detector bin, requires that pixel size be much smaller than the bin width. The resulting image array contains many more pixels than data, and this undersampling is overcome with a combination of Fourier upsampling of each projection and the use of constrained, TV-minimization, as suggested by compressive sensing. The presented pseudo-code for solving constrained, TV-minimization is designed to yield an accurate solution to this optimization problem within 100 iterations. Results: The proposed image-reconstruction algorithm is applied to a low-intensity scan of a rabbit with a thin wire, to test resolution. The proposed algorithm is compared with filtered back-projection (FBP). Conclusion: The algorithm may have some advantage over FBP in that the resulting noise-level is lowered at equivalent contrast levels of the wire. Comment: This article has been submitted to "Medical Physics" on 9/13/2010

• Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT.

  Authors: Junguo Bian, Jeffrey H Siewerdsen, Xiao Han, Emil Y Sidky, Jerry L Prince, Charles A Pelizzari, Xiaochuan Pan

  Physics in medicine and biology. 10/2010; 55(22):6575-99.

  Flat-panel-detector x-ray cone-beam computed tomography (CBCT) is used in a rapidly increasing host of imaging applications, including image-guided surgery and radiotherapy. The purpose of the workFlat-panel-detector x-ray cone-beam computed tomography (CBCT) is used in a rapidly increasing host of imaging applications, including image-guided surgery and radiotherapy. The purpose of the work is to investigate and evaluate image reconstruction from data collected at projection views significantly fewer than what is used in current CBCT imaging. Specifically, we carried out imaging experiments using a bench-top CBCT system that was designed to mimic imaging conditions in image-guided surgery and radiotherapy; we applied an image reconstruction algorithm based on constrained total-variation (TV)-minimization to data acquired with sparsely sampled view-angles and conducted extensive evaluation of algorithm performance. Results of the evaluation studies demonstrate that, depending upon scanning conditions and imaging tasks, algorithms based on constrained TV-minimization can reconstruct images of potential utility from a small fraction of the data used in typical, current CBCT applications. A practical implication of the study is that the optimization of algorithm design and implementation can be exploited for considerably reducing imaging effort and radiation dose in CBCT.

• TGFbeta/BMP type I receptors ALK1 and ALK2 are essential for BMP9-induced osteogenic signaling in mesenchymal stem cells.

  Authors: Jinyong Luo, Min Tang, Jiayi Huang, Bai-Cheng He, Jian-Li Gao, Liang Chen, Guo-Wei Zuo, Wenli Zhang, Qing Luo, Qiong Shi [......] Stephanie H Kim, Enyi Huang, Yanhong Gao, Jian-Zhong Zhou, Ke Yang, Hue H Luu, Xiaochuan Pan, Rex C Haydon, Zhong-Liang Deng, Tong-Chuan He

  The Journal of biological chemistry. 09/2010; 285(38):29588-98.

  Mesenchymal stem cells (MSCs) are bone marrow stromal cells that can differentiate into multiple lineages. We previously demonstrated that BMP9 is one of the most potent BMPs to induce osteogenicMesenchymal stem cells (MSCs) are bone marrow stromal cells that can differentiate into multiple lineages. We previously demonstrated that BMP9 is one of the most potent BMPs to induce osteogenic differentiation of MSCs. BMP9 is one of the least studied BMPs. Whereas ALK1, ALK5, and/or endoglin have recently been reported as potential BMP9 type I receptors in endothelial cells, little is known about type I receptor involvement in BMP9-induced osteogenic differentiation in MSCs. Here, we conduct a comprehensive analysis of the functional role of seven type I receptors in BMP9-induced osteogenic signaling in MSCs. We have found that most of the seven type I receptors are expressed in MSCs. However, using dominant-negative mutants for the seven type I receptors, we demonstrate that only ALK1 and ALK2 mutants effectively inhibit BMP9-induced osteogenic differentiation in vitro and ectopic ossification in MSC implantation assays. Protein fragment complementation assays demonstrate that ALK1 and ALK2 directly interact with BMP9. Likewise, RNAi silencing of ALK1 and ALK2 expression inhibits BMP9-induced BMPR-Smad activity and osteogenic differentiation in MSCs both in vitro and in vivo. Therefore, our results strongly suggest that ALK1 and ALK2 may play an important role in mediating BMP9-induced osteogenic differentiation. These findings should further aid us in understanding the molecular mechanism through which BMP9 regulates osteogenic differentiation of MSCs.

• Image reconstruction exploiting object sparsity in boundary-enhanced X-ray phase-contrast tomography.

  Authors: Emil Y Sidky, Mark A Anastasio, Xiaochuan Pan

  Optics express. 05/2010; 18(10):10404-22.

  Propagation-based X-ray phase-contrast tomography (PCT) seeks to reconstruct information regarding the complex-valued refractive index distribution of an object. In many applications, aPropagation-based X-ray phase-contrast tomography (PCT) seeks to reconstruct information regarding the complex-valued refractive index distribution of an object. In many applications, a boundary-enhanced image is sought that reveals the locations of discontinuities in the real-valued component of the refractive index distribution. We investigate two iterative algorithms for few-view image reconstruction in boundary-enhanced PCT that exploit the fact that a boundary-enhanced PCT image, or its gradient, is often sparse. In order to exploit object sparseness, the reconstruction algorithms seek to minimize the l(1)-norm or TV-norm of the image, subject to data consistency constraints. We demonstrate that the algorithms can reconstruct accurate boundary-enhanced images from highly incomplete few-view projection data.

• Backprojection-filtration reconstruction without invoking a spatially varying weighting factor.

  Authors: Dan Xia, Seungryong Cho, Xiaochuan Pan

  Medical physics. 03/2010; 37(3):1201-9.

  To develop a backprojection-filtration (BPF) algorithm with improved noise properties over the existing BPF algorithm through utilizing (approximate) redundant information in circular cone-beam orTo develop a backprojection-filtration (BPF) algorithm with improved noise properties over the existing BPF algorithm through utilizing (approximate) redundant information in circular cone-beam or fan-beam scans. The backprojection steps in the existing filtered-backprojection (FBP) and BPF algorithms for fan-beam and cone-beam projections invoke spatially varying weighting factors, which may not only increase the computational load in image reconstruction but also, more importantly, result in reconstruction artifacts. Redundant information in fan-beam projections has been exploited for eliminating the weighting factor in the existing FBP algorithm. However, the new FBP algorithm cannot be applied to image reconstruction in a region of interest from transversely truncated data. In this work, the authors identify approximate data redundancy in circular cone-beam projections and propose a new BPF algorithm in which the approximate data redundancy is exploited for eliminating the spatially varying weighting factor in the existing BPF algorithm. The authors have implemented and evaluated the proposed BPF algorithm in numerical studies of reconstructing 3D images from both the nontruncated and truncated projection data in a circular cone-beam scan. The results of numerical studies demonstrate that the proposed BPF algorithm retains the resolution property of the existing BPF algorithm, and that it can also reconstruct accurately ROI images from truncated data. More importantly, the results also indicate that the proposed BPF algorithm not only is computationally more efficient but also yields generally lower image variances than the existing BPF algorithm. A BPF algorithm was proposed that not only retains the desirable properties of the existing BPF algorithm but also possesses improved computational and noise properties over the latter.

• Consistency Conditions for Cone-Beam CT Data Acquired with a Straight-Line Source Trajectory.

  Authors: Margo S Levine, Emil Y Sidky, Xiaochuan Pan

  Tsinghua science and technology. 02/2010; 15(1):56-61.

  A consistency condition is developed for computed tomography (CT) projection data acquired from a straight-line X-ray source trajectory. The condition states that integrals of normalized projectionA consistency condition is developed for computed tomography (CT) projection data acquired from a straight-line X-ray source trajectory. The condition states that integrals of normalized projection data along detector lines parallel to the X-ray path must be equal. The projection data is required to be untruncated only along the detector lines parallel to the X-ray path, a less restrictive requirement compared to Fourier conditions that necessitate completely untruncated data. The condition is implemented numerically on simple image functions, a discretization error bound is estimated, and detection of motion inconsistencies is demonstrated. The results show that the consistency condition may be used to quantitatively compare the quality of projection data sets obtained from different scans of the same image object.

• Region of Interest Imaging for a General Trajectory with the Rebinned BPF Algorithm.

  Authors: Junguo Bian, Dan Xia, Emil Y Sidky, Xiaochuan Pan

  Tsinghua science and technology. 02/2010; 15(1):68-73.

  The back-projection-filtration (BPF) algorithm has been applied to image reconstruction for cone-beam configurations with general source trajectories. The BPF algorithm can reconstruct 3-DThe back-projection-filtration (BPF) algorithm has been applied to image reconstruction for cone-beam configurations with general source trajectories. The BPF algorithm can reconstruct 3-D region-of-interest (ROI) images from data containing truncations. However, like many other existing algorithms for cone-beam configurations, the BPF algorithm involves a back-projection with a spatially varying weighting factor, which can result in the non-uniform noise levels in reconstructed images and increased computation time. In this work, we propose a BPF algorithm to eliminate the spatially varying weighting factor by using a rebinned geometry for a general scanning trajectory. This proposed BPF algorithm has an improved noise property, while retaining the advantages of the original BPF algorithm such as minimum data requirement.

• Investigation of Sparse Data Mouse Imaging Using Micro-CT with a Carbon-Nanotube-Based X-ray Source.

  Authors: Junguo Bian, Xiao Han, Emil Y Sidky, Guohua Cao, Jianping Lu, Otto Zhou, Xiaochuan Pan

  Tsinghua science and technology. 02/2010; 15(1):74-78.

  There has been a renewed interest in algorithm development for image reconstruction from highly incomplete data in computed tomography (CT). Such algorithms may lead to reduced imaging dose and time,There has been a renewed interest in algorithm development for image reconstruction from highly incomplete data in computed tomography (CT). Such algorithms may lead to reduced imaging dose and time, and to the design of innovative configurations tailored to specific imaging tasks. In recent years, a carbon-nanotube (CNT)-based field-emission x-ray source has been developed, which offers easy electronic control of radiation and thus can be an ideal candidate for gated imaging. We have recently proposed algorithms for image reconstruction from fan- and cone-beam data collected at highly sparse angular views through minimization of the total-variation (TV) of the image subject to the condition that the estimated data are consistent with the measured data. In this work, we investigate and demonstrate the application of the TV-minimization algorithm to reconstructing images from mouse data acquired with a CNT-based CT scanner at a number of views much lower than what is used in conventional CT imaging. The results demonstrate that the TV-minimization algorithm can yield images with quality comparable to those obtained from a large number of views by use of the conventional algorithms. The significance of the work may lie in that the substantial reduction of projection views promised by the TV-minimization algorithm can be exploited for reducing imaging dose and time or for improving temporal resolution in tasks such as dynamic imaging.