3D Alternating Direction TV-Based Cone-Beam CT Reconstruction with Efficient GPU Implementation

Iterative image reconstruction (IIR) with sparsity-exploiting methods, such as total variation (TV) minimization, claims potentially large reductions in sampling requirements. However, the computation complexity becomes a heavy burden, especially in 3D reconstruction situations. In order to improve the performance for iterative reconstruction, an efficient IIR algorithm for cone-beam computed tomography (CBCT) with GPU implementation has been proposed in this paper. In the first place, an algorithm based on alternating direction total variation using local linearization and proximity technique is proposed for CBCT reconstruction. The applied proximal technique avoids the horrible pseudoinverse computation of big matrix which makes the proposed algorithm applicable and efficient for CBCT imaging. The iteration for this algorithm is simple but convergent. The simulation and real CT data reconstruction results indicate that the proposed algorithm is both fast and accurate. The GPU implementation shows an excellent acceleration ratio of more than 100 compared with CPU computation without losing numerical accuracy. The runtime for the new 3D algorithm is about 6.8 seconds per loop with the image size of 256×256×256 and 36 projections of the size of 512×512

GPU-based Fast Low-dose Cone Beam CT Reconstruction via Total Variation

Cone-beam CT (CBCT) has been widely used in image guided radiation therapy (IGRT) to acquire updated volumetric anatomical information before treatment fractions for accurate patient alignment purpose. However, the excessive x-ray imaging dose from serial CBCT scans raises a clinical concern in most IGRT procedures. The excessive imaging dose can be effectively reduced by reducing the number of x-ray projections and/or lowering mAs levels in a CBCT scan. The goal of this work is to develop a fast GPU-based algorithm to reconstruct high quality CBCT images from undersampled and noisy projection data so as to lower the imaging dose. The CBCT is reconstructed by minimizing an energy functional consisting of a data fidelity term and a total variation regularization term. We developed a GPU-friendly version of the forward-backward splitting algorithm to solve this model. A multi-grid technique is also employed. We test our CBCT reconstruction algorithm on a digital NCAT phantom and a head-and-neck patient case. The performance under low mAs is also validated using a physical Catphan phantom and a head-and-neck Rando phantom. It is found that 40 x-ray projections are sufficient to reconstruct CBCT images with satisfactory quality for IGRT patient alignment purpose. Phantom experiments indicated that CBCT images can be successfully reconstructed with our algorithm under as low as 0.1 mAs/projection level. Comparing with currently widely used full-fan head-and-neck scanning protocol of about 360 projections with 0.4 mAs/projection, it is estimated that an overall 36 times dose reduction has been achieved with our algorithm. Moreover, the reconstruction time is about 130 sec on an NVIDIA Tesla C1060 GPU card, which is estimated ~100 times faster than similar iterative reconstruction approaches.Comment: 20 pages, 10 figures, Paper was revised and more testing cases were added

Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models

Diffusion models have emerged as the new state-of-the-art generative model with high quality samples, with intriguing properties such as mode coverage and high flexibility. They have also been shown to be effective inverse problem solvers, acting as the prior of the distribution, while the information of the forward model can be granted at the sampling stage. Nonetheless, as the generative process remains in the same high dimensional (i.e. identical to data dimension) space, the models have not been extended to 3D inverse problems due to the extremely high memory and computational cost. In this paper, we combine the ideas from the conventional model-based iterative reconstruction with the modern diffusion models, which leads to a highly effective method for solving 3D medical image reconstruction tasks such as sparse-view tomography, limited angle tomography, compressed sensing MRI from pre-trained 2D diffusion models. In essence, we propose to augment the 2D diffusion prior with a model-based prior in the remaining direction at test time, such that one can achieve coherent reconstructions across all dimensions. Our method can be run in a single commodity GPU, and establishes the new state-of-the-art, showing that the proposed method can perform reconstructions of high fidelity and accuracy even in the most extreme cases (e.g. 2-view 3D tomography). We further reveal that the generalization capacity of the proposed method is surprisingly high, and can be used to reconstruct volumes that are entirely different from the training dataset.Comment: 14 pages, 10 figure

X-ray CT Image Reconstruction on Highly-Parallel Architectures.

Model-based image reconstruction (MBIR) methods for X-ray CT use accurate models of the CT acquisition process, the statistics of the noisy measurements, and noise-reducing regularization to produce potentially higher quality images than conventional methods even at reduced X-ray doses. They do this by minimizing a statistically motivated high-dimensional cost function; the high computational cost of numerically minimizing this function has prevented MBIR methods from reaching ubiquity in the clinic. Modern highly-parallel hardware like graphics processing units (GPUs) may offer the computational resources to solve these reconstruction problems quickly, but simply "translating" existing algorithms designed for conventional processors to the GPU may not fully exploit the hardware's capabilities. This thesis proposes GPU-specialized image denoising and image reconstruction algorithms. The proposed image denoising algorithm uses group coordinate descent with carefully structured groups. The algorithm converges very rapidly: in one experiment, it denoises a 65 megapixel image in about 1.5 seconds, while the popular Chambolle-Pock primal-dual algorithm running on the same hardware takes over a minute to reach the same level of accuracy. For X-ray CT reconstruction, this thesis uses duality and group coordinate ascent to propose an alternative to the popular ordered subsets (OS) method. Similar to OS, the proposed method can use a subset of the data to update the image. Unlike OS, the proposed method is convergent. In one helical CT reconstruction experiment, an implementation of the proposed algorithm using one GPU converges more quickly than a state-of-the-art algorithm converges using four GPUs. Using four GPUs, the proposed algorithm reaches near convergence of a wide-cone axial reconstruction problem with over 220 million voxels in only 11 minutes.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113551/1/mcgaffin_1.pd

Modeling and Development of Iterative Reconstruction Algorithms in Emerging X-ray Imaging Technologies

Many new promising X-ray-based biomedical imaging technologies have emerged over the last two decades. Five different novel X-ray based imaging technologies are discussed in this dissertation: differential phase-contrast tomography (DPCT), grating-based phase-contrast tomography (GB-PCT), spectral-CT (K-edge imaging), cone-beam computed tomography (CBCT), and in-line X-ray phase contrast (XPC) tomosynthesis. For each imaging modality, one or more specific problems prevent them being effectively or efficiently employed in clinical applications have been discussed. Firstly, to mitigate the long data-acquisition times and large radiation doses associated with use of analytic reconstruction methods in DPCT, we analyze the numerical and statistical properties of two classes of discrete imaging models that form the basis for iterative image reconstruction. Secondly, to improve image quality in grating-based phase-contrast tomography, we incorporate 2nd order statistical properties of the object property sinograms, including correlations between them, into the formulation of an advanced multi-channel (MC) image reconstruction algorithm, which reconstructs three object properties simultaneously. We developed an advanced algorithm based on the proximal point algorithm and the augmented Lagrangian method to rapidly solve the MC reconstruction problem. Thirdly, to mitigate image artifacts that arise from reduced-view and/or noisy decomposed sinogram data in K-edge imaging, we exploited the inherent sparseness of typical K-edge objects and incorporated the statistical properties of the decomposed sinograms to formulate two penalized weighted least square problems with a total variation (TV) penalty and a weighted sum of a TV penalty and an l1-norm penalty with a wavelet sparsifying transform. We employed a fast iterative shrinkage/thresholding algorithm (FISTA) and splitting-based FISTA algorithm to solve these two PWLS problems. Fourthly, to enable advanced iterative algorithms to obtain better diagnostic images and accurate patient positioning information in image-guided radiation therapy for CBCT in a few minutes, two accelerated variants of the FISTA for PLS-based image reconstruction are proposed. The algorithm acceleration is obtained by replacing the original gradient-descent step by a sub-problem that is solved by use of the ordered subset concept (OS-SART). In addition, we also present efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units (GPUs). Finally, we employed our developed accelerated version of FISTA for dealing with the incomplete (and often noisy) data inherent to in-line XPC tomosynthesis which combines the concepts of tomosynthesis and in-line XPC imaging to utilize the advantages of both for biological imaging applications. We also investigate the depth resolution properties of XPC tomosynthesis and demonstrate that the z-resolution properties of XPC tomosynthesis is superior to that of conventional absorption-based tomosynthesis. To investigate all these proposed novel strategies and new algorithms in these different imaging modalities, we conducted computer simulation studies and real experimental data studies. The proposed reconstruction methods will facilitate the clinical or preclinical translation of these emerging imaging methods

Doctor of Philosophy

dissertationX-ray computed tomography (CT) is a widely popular medical imaging technique that allows for viewing of in vivo anatomy and physiology. In order to produce high-quality images and provide reliable treatment, CT imaging requires the precise knowledge of t