We have developed VSHARP®, a suite of scatter correction solutions that have been incorporated into the commercially available cone-beam software development toolkit, CST (Varex Imaging, Salt Lake City, UT) enabling scatter correction to be applied as part of an entire CBCT reconstruction pipeline. The suite includes 2D VSHARP®, a deconvolution correction using asymmetric Gaussian kernels, 2D VSHARP-ML, a U-NET machine-learning correction, and 3D VSHARP®, a correction using a rapid finite-element Linear Boltzmann Transport Equation (LBTE) solver to estimate scatter in a manner similar to traditional stochastic Monte Carlo (MC) simulations. Of the three corrections, 3D VSHARP is the most accurate and flexible since it can be readily applied to arbitrary scanner geometries, protocols, and scan parts while the 2D VSHARP models may need to be regenerated for each configuration. On the other hand, 3D VSHARP is inherently slower since a minimum of two reconstruction passes are needed and the LBTE solver, while much faster than traditional MC, is still computationally intensive. The goal of this work was to minimize LBTE run times for (typically large) industrial datasets by optimizing parameter settings, particularly the choice of the sampling grid dimensions. This was achieved by applying a multi-objective genetic algorithm to find the Pareto front characterizing the tradeoff between speed and accuracy and identifying key operating points on the curve. Testing with 720 frames of 3720x3720 projection data to make a reconstruction volume of size 500x500x600, we found that excellent image quality can be obtained by using a coarse scatter grid size of 27x27x32 volume and 44x44 detector and a primary grid size of 246x246 x295 volume and 295x295 detector, both over 42 frames for a grand total of 21 seconds LBTE computation time. We show the Pareto characterization, as well as demonstrations of 3D VSHARP image quality with significantly reduced scatter-induced artifacts such as streaking and shading.
High quality cone-beam tomography (CBCT) reconstruction requires accurately estimating and subtracting the (often) large amount of scatter from the raw projection data. Although considerable attention has been paid to scatter correction algorithm development over the past several years, there still exists the need for a practical, general-purpose tool that is accurate, fast, and requires minimal calibration. Here, we introduce 3D VSHARP® which utilizes a finite element solver of the Linear Boltzmann Transport Equation (LBTE) to accurately and rapidly simulate photon transport through a model of the object being scanned and then scale and subtract the estimated scatter from raw projections. 3D VSHARP has been incorporated into the commercially available reconstruction software development toolkit, CST (Varex Imaging, Salt Lake City, UT) enabling scatter correction to be applied to arbitrary scanner configurations and geometries as part of an entire reconstruction pipeline. To set parameters for 3D VSHARP, the user chooses from a library of files that describe key physical aspects of the CT system, including its x-ray spectrum, detector response, and, if they exist, bowtie filter, and anti-scatter grid. The object model, which characterizes the spatial distribution of the atomic number and density of the scanned object, is automatically generated from the first-pass reconstruction which may, if desired, include CST’s existing kernel-based scatter correction 2D VSHARP®. We describe the new correction tool and show example reconstructions. High accuracy of scatter correction and excellent image quality were achieved with total reconstruction times on the order of 1 minute.
For accurate CT reconstruction, it is important to know the geometric position of every detector channel relative to the X-ray source and the rotation axis. Often, such as for truly equally spaced detectors, it may suffice just to accurately know the gross geometry. However, for some detector designs, a detailed description of the fine-scale channel locations may also be necessary. While there are numerous methods to perform fine-scale calibration, such methods generally
assume a continuous distortion (typically for image intensifiers) and are thus unsuitable for detectors with discrete distortions such as irregularly placed discrete sensors, tiled flat panels, or multiple flat segments arranged to form a polygonal approximation to an arc. In this paper, a method is proposed to measure both gross and fine geometry from a single simple calibration scan in a way that properly characterizes discrete irregularities. Experimental results show the
proposed method to be rather effective on polygonal arrays. While the method is derived and demonstrated for fan beam, a discussion is given on extending it to cone beam CT.
To achieve good image quality for computed tomography, it is important to accurately know the geometrical relationship between the X-ray source, the axis of rotation, and all of the detector channels. This usually involves knowing gross parameters such as iso-ray coordinate, detector pixel pitch, and source-to-detector distance, but for some detector types such as distorted arrays, polygonal or tiled arrays, or arrays of irregularly placed sparse detectors, it is beneficial to measure a more detailed description of the individual channel locations. Typically, geometric calibration and distortion
calibration are performed using specialized phantoms, such as a pin, an array of pellets, or a wire grid, but these can have
their practical downsides for certain applications. A promising recent alternative is to calibrate geometry in a way that
requires no particular phantom or a priori knowledge of the scanned object -- these approaches are particularly helpful for high magnifications, large heavy objects, frequent calibration, and retrospective calibration. However, until now these approaches have only addressed gross geometry. In this paper, a framework is given which allows one to calibrate both gross and fine geometry from unknown objects. Example images demonstrate the success of the proposed methods on both real and simulated data.
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