Three-dimensional 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 are important for a wide range of applications including nonstandard geometries arising from irregular sampling, limited angular range, missing data, and low-dose CT. For iterative image reconstruction algorithms to be deployed in clinical settings, the images must be quantitatively accurate and computed in clinically useful times. We describe an acceleration method that is based on adaptively varying an update factor of the additive step of the alternating minimization (AM) algorithm. Our implementation combines this method with other acceleration techniques like ordered subsets (OS) which was originally proposed for transmission tomography by Ahn, Fessler et. al [1]. Results on both an NCAT phantom and real clinical data from a Siemens Sensation 16 scanner demonstrate an improved convergence rate compared to the straightforward implementations of the alternating minimization (AM) algorithm of O'Sullivan and Benac [2] with a Huber-type edge-preserving penalty, originally proposed by Lange [3]. Our proposed acceleration method on average yields 2X acceleration of the convergence rate for both baseline and ordered subset implementations of the AM algorithm.
Model-Based Image Reconstruction (MBIR) methods significantly enhance the quality of tomographic reconstruction in contrast to analytical techniques. However, the intensive computational time and memory required by MBIR limit its use for many practical real-time applications. But, with increasing availability of parallel computing resources, distributed MBIR algorithms can overcome this limitation on computational performance. In this paper, we propose a novel distributed and iterative approach to Computed Tomography (CT) reconstruction based on the Multi-Agent Consensus Equilibrium (MACE) framework. We formulate CT reconstruction as a consensus optimization problem wherein the objective function, and consequently the system matrix, is split across multiple disjoint view-subsets. This produces multiple regularized sparse-view reconstruction problems that are tied together by a consensus constraint, and these problems can be solved in parallel within the MACE framework. Further, we solve each sub-problem inexactly, using only 1 full pass of the Iterative Coordinate Descent (ICD) optimization technique. Yet, our distributed approach is convergent. Finally, we validate our approach with experiments on real 2D CT data.
Cone-beam computed tomography (CT) is an attractive tool for many kinds of non-destructive evaluation (NDE). Model-based iterative reconstruction (MBIR) has been shown to improve reconstruction quality and reduce scan time. However, the computational burden and storage of the system matrix is challenging. In this paper we present a separable representation of the system matrix that can be completely stored in memory and accessed cache-efficiently. This is done by quantizing the voxel position for one of the separable subproblems. A parallelized algorithm, which we refer to as zipline update, is presented that speeds up the computation of the solution by about 50 to 100 times on 20 cores by updating groups of voxels together. The quality of the reconstruction and algorithmic scalability are demonstrated on real cone-beam CT data from an NDE application. We show that the reconstruction can be done from a sparse set of projection views while reducing artifacts visible in the conventional filtered back projection (FBP) reconstruction. We present qualitative results using a Markov Random Field (MRF) prior and a Plug-and-Play denoiser.
Many important physical processes in fields such as materials science, ecology, structural biology, and clinical pathology involve the study of microscopic structures – from formation and propagation to steady-state behavior. The study of these phenomena is often very slow, creating an enormous need for accurate computer simulation of the underlying processes. In this paper, we provide a robust algorithm for simulation of images of such processes modeled by a Gibbs distribution. As part of our rare-event simulation solution, we adapt an importance sampling technique specifically for Markov random fields. We conclude by showing results of simulation of images of abnormal grain growth in poly-crystalline materials and NiCrAl super-alloy precipitates that find applications in several important real-life fields such as aircraft material design.
In this paper we incorporate an active contours energy into the Marked Point Process (MPP) framework. The addition of this energy allows the MPP model to detect objects with irregular shapes. This energy accounts for the elasticity and curvature properties of the detected objects. We employ the balloon method to prevent the contour from stagnating at local minima. We use calculus of variations to evolve each individual contour and we use stochastic multiple birth and death dynamics to optimize the MPP energy function. We demonstrate that our method successfully models components with irregular shape in material images, but the model can be extended to other applications.
Model-based image reconstruction (MBIR) techniques have the potential to generate high quality images from noisy measurements and a small number of projections which can reduce the x-ray dose in patients. These MBIR techniques rely on projection and backprojection to refine an image estimate. One of the widely used projectors for these modern MBIR based technique is called branchless distance driven (DD) projection and backprojection. While this method produces superior quality images, the computational cost of iterative updates keeps it from being ubiquitous in clinical applications. In this paper, we provide several new parallelization ideas for concurrent execution of the DD projectors in multi-GPU systems using CUDA programming tools. We have introduced some novel schemes for dividing the projection data and image voxels over multiple GPUs to avoid runtime overhead and inter-device synchronization issues. We have also reduced the complexity of overlap calculation of the algorithm by eliminating the common projection plane and directly projecting the detector boundaries onto image voxel boundaries. To reduce the time required for calculating the overlap between the detector edges and image voxel boundaries, we have proposed a pre-accumulation technique to accumulate image intensities in perpendicular 2D image slabs (from a 3D image) before projection and after backprojection to ensure our DD kernels run faster in parallel GPU threads. For the implementation of our iterative MBIR technique we use a parallel multi-GPU version of the alternating minimization (AM) algorithm with penalized likelihood update. The time performance using our proposed reconstruction method with Siemens Sensation 16 patient scan data shows an average of 24 times speedup using a single TITAN X GPU and 74 times speedup using 3 TITAN X GPUs in parallel for combined projection and backprojection. © 2017 Society for Imaging Science and Technology.
The determination of local components in human skin from in vivo spectral reflectance measurements is crucial for medical applications, especially for aiding the diagnostic of skin diseases. Hyperspectral imaging is a convenient technique since one spectrum is acquired in each pixel of the image, and by inverting a light scattering model, we can retrieve the concentrations of skin components in each pixel. The good performance of the method presented in this article comes from both the imaging system and the model. The hyperspectral camera that we conceived uses polarizing filters in order to remove gloss effects generated by the stratum corneum; it provides a high-resolution image (1120 × 900 pixels), with a thin spectral sampling of 10 nm over the visible spectrum. The acquisition time of 2 seconds is short enough to prevent movement effects of the imaged area, which is usually the main issue in hyperspectral imaging. The model relies on a two-layer model for the skin, and the Kubelka–Munk theory with Saunderson correction for the light reflection. An optimization method enables computing, in less than one hour, several skin parameters in each of the million of pixels. These parameters (blood, melanin and bilirubin volume fractions, oxygen saturation…) are then displayed under the form of density images. Different skin structures, such as veins, blood capillaries, hematoma or pigmented spots, can be highlighted. The deviation between the measured spectrum and the one computed from the fitted parameters is evaluated in each pixel. © 2016 Society for Imaging Science and Technology.