Accuracy of Patient-Specific Organ Dose Estimates Obtained Using an Automated Image Segmentation Algorithm

The overall goal of this work is to develop a rapid, accurate, and automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using simulations to generate dose maps combined with automated segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. We hypothesized that the autosegmentation algorithm is sufficiently accurate to provide organ dose estimates, since small errors delineating organ boundaries will have minimal effect when computing mean organ dose. A leave-one-out validation study of the automated algorithm was performed with 20 head-neck CT scans expertly segmented into nine regions. Mean organ doses of the automatically and expertly segmented regions were computed from Monte Carlo-generated dose maps and compared. The automated segmentation algorithm estimated the mean organ dose to be within 10% of the expert segmentation for regions other than the spinal canal, with the median error for each organ region below 2%. In the spinal canal region, the median error was -7%, with a maximum absolute error of 28% for the single-atlas approach and 11% for the multiatlas approach. The results demonstrate that the automated segmentation algorithm can provide accurate organ dose estimates despite some segmentation errors

GPU-based Iterative Cone Beam CT Reconstruction Using Tight Frame Regularization

X-ray imaging dose from serial cone-beam CT (CBCT) scans raises a clinical concern in most image guided radiation therapy procedures. It is the goal of this paper 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. For this purpose, we have developed an iterative tight frame (TF) based CBCT reconstruction algorithm. A condition that a real CBCT image has a sparse representation under a TF basis is imposed in the iteration process as regularization to the solution. To speed up the computation, a multi-grid method is employed. Our GPU implementation has achieved high computational efficiency and a CBCT image of resolution 512\times512\times70 can be reconstructed in ~5 min. We have tested our algorithm on a digital NCAT phantom and a physical Catphan phantom. It is found that our TF-based algorithm is able to reconstrct CBCT in the context of undersampling and low mAs levels. We have also quantitatively analyzed the reconstructed CBCT image quality in terms of modulation-transfer-function and contrast-to-noise ratio under various scanning conditions. The results confirm the high CBCT image quality obtained from our TF algorithm. Moreover, our algorithm has also been validated in a real clinical context using a head-and-neck patient case. Comparisons of the developed TF algorithm and the current state-of-the-art TV algorithm have also been made in various cases studied in terms of reconstructed image quality and computation efficiency.Comment: 24 pages, 8 figures, accepted by Phys. Med. Bio

Deterministic simulation of thermal neutron radiography and tomography

In recent years, thermal neutron radiography and tomography have gained much attention as one of the nondestructive testing methods. However, the application of thermal neutron radiography and tomography hindered by their technical complexity, radiation shielding, and time-consuming data collection processes. Monte Carlo simulations have been developed in the past to improve the neutron imaging facility\u27s ability. In this present work, a new deterministic simulation approach has been proposed and demonstrated to simulate neutron radiographs numerically using a ray tracing algorithm. This approach has made the simulation of neutron radiographs much faster than by previously used stochastic methods (i.e Monte Carlo methods). The major problem with neutron radiography and tomography simulation is finding a suitable scatter model. In this paper, an analytic scatter model has been proposed that is validated by a Monte Carlo simulation. Two simulation geometries have been analyzed in this work. One is a highly scattering medium, another is medium scattering media. It has been shown that this algorithm works well in the medium scattering media. The scatter model has been verified using Monte Carlo method (MCNP5).There are some empirical parameters that have been determined using curve fitting methods using MATLAB. There are some disadvantages of using the scatter correction algorithm proposed in this work, but the advantages are far more rewarding. This method of simulation reduces the time of simulation in 5-10 seconds compared to several hours using Monte Carlo methods and can be used for rapid prototyping for neutron imaging facilities. Filtered back-projection with a ramp filter has been used for reconstruction --Abstract, page iii

A fast, linear Boltzmann transport equation solver for computed tomography dose calculation (Acuros CTD)

Purpose To improve dose reporting of CT scans, patient‐specific organ doses are highly desired. However, estimating the dose distribution in a fast and accurate manner remains challenging, despite advances in Monte Carlo methods. In this work, we present an alternative method that deterministically solves the linear Boltzmann transport equation (LBTE), which governs the behavior of x‐ray photon transport through an object. Methods Our deterministic solver for CT dose (Acuros CTD) is based on the same approach used to estimate scatter in projection images of a CT scan (Acuros CTS). A deterministic method is used to compute photon fluence within the object, which is then converted to deposited energy by multiplying by known, material‐specific conversion factors. To benchmark Acuros CTD, we used the AAPM Task Group 195 test for CT dose, which models an axial, fan beam scan (10 mm thick beam) and calculates energy deposited in each organ of an anthropomorphic phantom. We also validated our own Monte Carlo implementation of Geant4 to use as a reference to compare Acuros against for other common geometries like an axial, cone beam scan (160 mm thick beam) and a helical scan (40 mm thick beam with table motion for a pitch of 1). Results For the fan beam scan, Acuros CTD accurately estimated organ dose, with a maximum error of 2.7% and RMSE of 1.4% when excluding organs with3provided marginal improvement to the accuracy for the cone beam scan but came at the expense of increased run time. Across the different scan geometries, run time of Acuros CTD ranged from 8 to 23 s. Conclusions In this digital phantom study, a deterministic LBTE solver was capable of fast and accurate organ dose estimates

Direct 3D Tomographic Reconstruction and Phase-Retrieval of Far-Field Coherent Diffraction Patterns

We present an alternative numerical reconstruction algorithm for direct tomographic reconstruction of a sample refractive indices from the measured intensities of its far-field coherent diffraction patterns. We formulate the well-known phase-retrieval problem in ptychography in a tomographic framework which allows for simultaneous reconstruction of the illumination function and the sample refractive indices in three dimensions. Our iterative reconstruction algorithm is based on the Levenberg-Marquardt algorithm. We demonstrate the performance of our proposed method with simulation studies

Cube-Cut: Vertebral Body Segmentation in MRI-Data through Cubic-Shaped Divergences

In this article, we present a graph-based method using a cubic template for volumetric segmentation of vertebrae in magnetic resonance imaging (MRI) acquisitions. The user can define the degree of deviation from a regular cube via a smoothness value Delta. The Cube-Cut algorithm generates a directed graph with two terminal nodes (s-t-network), where the nodes of the graph correspond to a cubic-shaped subset of the image's voxels. The weightings of the graph's terminal edges, which connect every node with a virtual source s or a virtual sink t, represent the affinity of a voxel to the vertebra (source) and to the background (sink). Furthermore, a set of infinite weighted and non-terminal edges implements the smoothness term. After graph construction, a minimal s-t-cut is calculated within polynomial computation time, which splits the nodes into two disjoint units. Subsequently, the segmentation result is determined out of the source-set. A quantitative evaluation of a C++ implementation of the algorithm resulted in an average Dice Similarity Coefficient (DSC) of 81.33% and a running time of less than a minute.Comment: 23 figures, 2 tables, 43 references, PLoS ONE 9(4): e9338

Digital predictions of complex cylinder packed columns

A digital computational approach has been developed to simulate realistic structures of packed beds. The underlying principle of the method is digitisation of the particles and packing space, enabling the generation of realistic structures. Previous publications [Caulkin, R., Fairweather, M., Jia, X., Gopinathan, N., & Williams, R.A. (2006). An investigation of packed columns using a digital packing algorithm. Computers & Chemical Engineering, 30, 1178–1188; Caulkin, R., Ahmad, A., Fairweather, M., Jia, X., & Williams, R. A. (2007). An investigation of sphere packed shell-side columns using a digital packing algorithm. Computers & Chemical Engineering, 31, 1715–1724] have demonstrated the ability of the code in predicting the packing of spheres. For cylindrical particles, however, the original, random walk-based code proved less effective at predicting bed structure. In response to this, the algorithm has been modified to make use of collisions to guide particle movement in a way which does not sacrifice the advantage of simulation speed. Results of both the original and modified code are presented, with bulk and local voidage values compared with data derived by experimental methods. The results demonstrate that collisions and their impact on packing structure cannot be disregarded if realistic packing structures are to be obtained

Parallelized X-Ray Tracing with GPU Ray-Tracing Engine

X-ray diffraction tomography (XDT) is used to probe material composition of objects, providing improved contrast between materials compared to conventional transmission based computed tomography (CT). In this work, a small angle approximation to Bragg\u27s Equation of diffraction is coupled with parallelized computing using Graphics Processing Units (GPUs) to accelerate XDT simulations. The approximation gives rise to a simple yet useful proportionality between momentum transfer, radial distance of diffracted signal with respect to incoming beam\u27s location, and depth of material, so that ray tracing may be parallelized. NVIDIA\u27s OptiX ray-tracing engine, a parallelized pipeline for GPUs, is employed to perform XDT by tracing rays in a virtual space, (x,y,zv), where zv is a virtual distance proportional to momentum transfer. The advantage gained in this approach is that ray tracing in this domain requires only 3D surface meshes, yielding calculations without the need of voxels. The simulated XDT projections demonstrate high consistency with voxel models, with a normalized mean square difference less than 0.66%, and ray-tracing times two orders of magnitude less than previously reported voxel-based GPU ray tracing results. Due to an accelerated simulation time, XDT projections of objects with three spatial dimensions (4D tensor) have also been reported, demonstrating the feasibility for largescale high-dimensional tensor tomography simulations