146 research outputs found

    Fast kVp-Switching Dual Energy CT for PET Attenuation Correction

    Full text link
    X-ray CT images are used routinely for attenuation correction in PET/CT systems. However, conventional CT-based attenuation correction (CTAC) can be inaccurate in regions containing iodine contrast agent. Dual-energy (DE) CT has the potential to improve the accuracy of attenuation correction in PET, but conventional DECT can suffer from motion artifacts. Recent X-ray CT systems can collect DE sinograms by rapidly switching the X-ray tube voltage between two levels for alternate projection views, reducing motion artifacts. The goal of this work is to study statistical methods for image reconstruction from both fast kVp-switching DE scans and from conventional dual-rotate DE scans in the context of CTAC for PET.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86003/1/Fessler244.pd

    Model-Based Image Reconstruction for Dual-Energy X-Ray CT with Fast KVP Switching

    Full text link
    The most recent generation of X-ray CT systems can collect dual energy (DE) sinograms by rapidly switching the X-ray tube voltage between two levels for alternate projection views. This reduces motion artifacts in DE imaging, but yields sinograms that may be angularly under-sampled. This paper describes an iterative algorithm for statistical image reconstruction of material component images (e.g., soft tissue and bone) directly from such under-sampled DE data, without resorting to the interpolation operations required by conventional DE reconstruction methods.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85826/1/Fessler241.pd

    Basis Vector Model Method for Proton Stopping Power Estimation using Dual-Energy Computed Tomography

    Get PDF
    Accurate estimation of the proton stopping power ratio (SPR) is important for treatment planning and dose prediction for proton beam therapy. The state-of-the-art clinical practice for estimating patient-specific SPR distributions is the stoichiometric calibration method using single-energy computed tomography (SECT) images, which in principle may introduce large intrinsic uncertainties into estimation results. One major factor that limits the performance of SECT-based methods is the Hounsfield unit (HU) degeneracy in the presence of tissue composition variations. Dual-energy computed tomography (DECT) has shown the potential of reducing uncertainties in proton SPR prediction via scanning the patient with two different source energy spectra. Numerous methods have been studied to estimate the SPR by dual-energy CT DECT techniques using either image-domain or sinogram-domain decomposition approaches. In this work, we implement and evaluate a novel DECT approach for proton SPR mapping, which integrates image reconstruction and material characterization using a joint statistical image reconstruction (JSIR) method based on a linear basis vector model (BVM). This method reconstructs two images of material parameters simultaneously from the DECT measurement data and then uses them to predict the electron densities and the mean excitation energies, which are required by the Bethe equation for computing proton SPR. The proposed JSIR-BVM method is first compared with image-domain and sinogram-domain decomposition approaches based on three available SPR models including the BVM in a well controlled simulation framework that is representative of major uncertainty sources existing in practice. The intrinsic SPR modeling accuracy of the three DECT-SPR models is validated via theoretical computed radiological quantities for various reference human tissues. The achievable performances of the investigated methods in the presence of image formation uncertainties are evaluated using synthetic DECT transmission sinograms of virtual cylindrical phantoms and virtual patients, which consist of reference human tissues with known densities and compositions. The JSIR-BVM method is then experimentally commissioned using the DECT measurement data acquired on a Philips Brilliance Big Bore CT scanner at 90 kVp and 140 kVp for two phantoms of different sizes, each of which contains 12 different soft and bony tissue surrogates. An image-domain decomposition method that utilizes the two HU images reconstructed via the scanner\u27s software is implemented for comparison The JSIR-BVM method outperforms the other investigated methods in both the simulation and experimental settings. Although all investigated DECT-SPR models support low intrinsic modeling errors (i.e., less than 0.2% RMS errors for reference human tissues), the achievable accuracy of the image- and sinogram-domain methods is limited by the image formation uncertainties introduced by the reconstruction and decomposition processes. In contrast, by taking advantage of an accurate polychromatic CT data model and a joint DECT statistical reconstruction algorithm, the JSIR-BVM method accounts for both systematic bias and random noise in the acquired DECT measurement data. Therefore, the JSIR-BVM method achieves much better accuracy and precision on proton SPR estimation compared to the image- and sinogram-domain methods for various materials and object sizes, with an overall RMS-of-mean error of 0.4% and a maximum absolute-mean error of 0.7% for test samples in the experimental setting. The JSIR-BVM method also reduces the pixel-wise random variation by 4-fold to 6-fold within homogeneous regions compared to the image- and sinogram-domain methods while exhibiting relatively higher spatial resolution. The results suggest that the JSIR-BVM method has the potential for better SPR prediction in clinical settings

    Multi-dimensional extension of the alternating minimization algorithm in x-ray computed tomography

    Get PDF
    X-ray computed tomography (CT) is an important and effective tool in medical and industrial imaging applications. The state-of-the-art methods to reconstruct CT images have had great development but also face challenges. This dissertation derives novel algorithms to reduce bias and metal artifacts in a wide variety of imaging modalities and increase performance in low-dose scenarios. The most widely available CT systems still use the single-energy CT (SECT), which is good at showing the anatomic structure of the patient body. However, in SECT image reconstruction, energy-related information is lost. In applications like radiation treatment planning and dose prediction, accurate energy-related information is needed. Spectral CT has shown the potential to extract energy-related information. Dual-energy CT (DECT) is the first successful implementation of spectral CT. By using two different spectra, the energy-related information can be exported by reconstructing basis-material images. A sinogram-based decomposition method has shown good performance in clinical applications. However, when the x-ray dose level is low, the sinogram-based decomposition methods generate biased estimates. The bias increases rapidly when the dose level decreases. The bias comes from the ill-posed statistical model in the sinogram-decomposition method. To eliminate the bias in low-dose cases, a joint statistical image reconstruction (JSIR) method using the dual-energy alternating minimization (DEAM) algorithm is proposed. By correcting the ill-posed statistical model, a relative error as high as 15% in the sinogram-based decomposition method can be reduced to less than 1% with DEAM, which is an approximately unbiased estimation. Photon counting CT (PCCT) is an emerging CT technique that also can resolve the energy information. By using photon-counting detectors (PCD), PCCT keeps track of the energy of every photon received. Though PCDs have an entirely different physical performance from the energy-integrating detectors used in DECT, the problem of biased estimation with the sinogram-decomposition method remains. Based on DEAM, a multi-energy alternating minimization (MEAM) algorithm for PCCT is proposed. In the simulation experiments, MEAM can effectively reduce bias by more than 90%. Metal artifacts have been a concern since x-ray CT came into medical imaging. When there exist dense or metal materials in the scanned object, the image quality may suffer severe artifacts. The auxiliary sinogram alternating minimization (ASAM) algorithm is proposed to take advantages of two major categories of methods to deal with metal artifacts: the pre-processing method and statistical image reconstruction. With a phantom experiment, it has been shown that ASAM has better metal-artifact reduction performance compared with the current methods. A significant challenge in security imaging is that due to the large geometry and power consumption, low photon statistics are detected. The detected photons suffer high noise and heavy artifacts. Image-domain regularized iterative reconstruction algorithms can reduce the noise but also result in biased reconstruction. A wavelet-domain penalty is introduced which does not bring in bias and can effectively eliminate steaking artifacts. By combining the image-domain and wavelet-domain penalty, the image quality can be further improved. When the wavelet penalty is used, a concern is that no empirical way, like in the image-domain penalty, is available to determine the penalty weight. Laplace variational automatic relevance determination (Lap-VARD) method is proposed to reconstruct the image and optimal penalty weight choice at the same time

    Algorithms for enhanced artifact reduction and material recognition in computed tomography

    Full text link
    Computed tomography (CT) imaging provides a non-destructive means to examine the interior of an object which is a valuable tool in medical and security applications. The variety of materials seen in the security applications is higher than in the medical applications. Factors such as clutter, presence of dense objects, and closely placed items in a bag or a parcel add to the difficulty of the material recognition in security applications. Metal and dense objects create image artifacts which degrade the image quality and deteriorate the recognition accuracy. Conventional CT machines scan the object using single source or dual source spectra and reconstruct the effective linear attenuation coefficient of voxels in the image which may not provide the sufficient information to identify the occupying materials. In this dissertation, we provide algorithmic solutions to enhance CT material recognition. We provide a set of algorithms to accommodate different classes of CT machines. First, we provide a metal artifact reduction algorithm for conventional CT machines which perform the measurements using single X-ray source spectrum. Compared to previous methods, our algorithm is robust to severe metal artifacts and accurately reconstructs the regions that are in proximity to metal. Second, we propose a novel joint segmentation and classification algorithm for dual-energy CT machines which extends prior work to capture spatial correlation in material X-ray attenuation properties. We show that the classification performance of our method surpasses the prior work's result. Third, we propose a new framework for reconstruction and classification using a new class of CT machines known as spectral CT which has been recently developed. Spectral CT uses multiple energy windows to scan the object, thus it captures data across higher energy dimensions per detector. Our reconstruction algorithm extracts essential features from the measured data by using spectral decomposition. We explore the effect of using different transforms in performing the measurement decomposition and we develop a new basis transform which encapsulates the sufficient information of the data and provides high classification accuracy. Furthermore, we extend our framework to perform the task of explosive detection. We show that our framework achieves high detection accuracy and it is robust to noise and variations. Lastly, we propose a combined algorithm for spectral CT, which jointly reconstructs images and labels each region in the image. We offer a tractable optimization method to solve the proposed discrete tomography problem. We show that our method outperforms the prior work in terms of both reconstruction quality and classification accuracy

    Segmentation-Driven Tomographic Reconstruction.

    Get PDF

    Advanced Statistical Modeling for Model-Based Iterative Reconstruction for Single-Energy and Dual-Energy X-Ray CT

    Get PDF
    Model-based iterative reconstruction (MBIR) has been increasingly broadly applied as an improvement over traditional, analytical image reconstruction methods in X-ray CT, primarily due to its significant advantage in drastic dose reduction without diagnostic loss. Early success of the method in conventional CT has encouraged the extension to a wide range of applications that includes more advanced imaging modalities, such as dual-energy X-ray CT, and more challenging imaging conditions, such as low-dose and sparse-sampling scans, each requiring refined statistical models including the data model and the prior model. In this dissertation, we developed an MBIR algorithm for dual-energy CT that included a joint data-likelihood model to account for correlated data noise. Moreover, we developed a Gaussian-Mixture Markov random filed (GM-MRF) image model that can be used as a very expressive prior model in MBIR for X-ray CT reconstruction. The GM-MRF model is formed by merging individual patch-based Gaussian-mixture models and therefore leads to an expressive MRF model with easily estimated parameters. Experimental results with phantom and clinical datasets have demonstrated the improvement in image quality due to the advanced statistical modeling

    A Convex Reconstruction Model for X-ray Tomographic Imaging with Uncertain Flat-fields

    Get PDF
    Classical methods for X-ray computed tomography are based on the assumption that the X-ray source intensity is known, but in practice, the intensity is measured and hence uncertain. Under normal operating conditions, when the exposure time is sufficiently high, this kind of uncertainty typically has a negligible effect on the reconstruction quality. However, in time- or dose-limited applications such as dynamic CT, this uncertainty may cause severe and systematic artifacts known as ring artifacts. By carefully modeling the measurement process and by taking uncertainties into account, we derive a new convex model that leads to improved reconstructions despite poor quality measurements. We demonstrate the effectiveness of the methodology based on simulated and real data sets.Comment: Accepted at IEEE Transactions on Computational Imagin

    Multi-GPU Acceleration of Iterative X-ray CT Image Reconstruction

    Get PDF
    X-ray computed tomography is a widely used medical imaging modality for screening and diagnosing diseases and for image-guided radiation therapy treatment planning. Statistical iterative reconstruction (SIR) algorithms have the potential to significantly reduce image artifacts by minimizing a cost function that models the physics and statistics of the data acquisition process in X-ray CT. SIR algorithms have superior performance compared to traditional analytical reconstructions for a wide range of applications including nonstandard geometries arising from irregular sampling, limited angular range, missing data, and low-dose CT. The main hurdle for the widespread adoption of SIR algorithms in multislice X-ray CT reconstruction problems is their slow convergence rate and associated computational time. We seek to design and develop fast parallel SIR algorithms for clinical X-ray CT scanners. Each of the following approaches is implemented on real clinical helical CT data acquired from a Siemens Sensation 16 scanner and compared to the straightforward implementation of the Alternating Minimization (AM) algorithm of O’Sullivan and Benac [1]. We parallelize the computationally expensive projection and backprojection operations by exploiting the massively parallel hardware architecture of 3 NVIDIA TITAN X Graphical Processing Unit (GPU) devices with CUDA programming tools and achieve an average speedup of 72X over a straightforward CPU implementation. We implement a multi-GPU based voxel-driven multislice analytical reconstruction algorithm called Feldkamp-Davis-Kress (FDK) [2] and achieve an average overall speedup of 1382X over the baseline CPU implementation by using 3 TITAN X GPUs. Moreover, we propose a novel adaptive surrogate-function based optimization scheme for the AM algorithm, resulting in more aggressive update steps in every iteration. On average, we double the convergence rate of our baseline AM algorithm and also improve image quality by using the adaptive surrogate function. We extend the multi-GPU and adaptive surrogate-function based acceleration techniques to dual-energy reconstruction problems as well. Furthermore, we design and develop a GPU-based deep Convolutional Neural Network (CNN) to denoise simulated low-dose X-ray CT images. Our experiments show significant improvements in the image quality with our proposed deep CNN-based algorithm against some widely used denoising techniques including Block Matching 3-D (BM3D) and Weighted Nuclear Norm Minimization (WNNM). Overall, we have developed novel fast, parallel, computationally efficient methods to perform multislice statistical reconstruction and image-based denoising on clinically-sized datasets
    • …
    corecore