97 research outputs found
Noise Simulations For an Inverse-Geometry Volumetric CT System
This paper examines the noise performance of an inverse-geometry volumetric CT (IGCT) scanner through simulations. The IGCT system uses a large area scanned source and a smaller array of detectors to rapidly acquire volumetric data with negligible cone-beam artifacts. The first investigation compares the photon efficiency of the IGCT geometry to a 2D parallel ray system. The second investigation models the photon output of the IGCT source and calculates the expected noise. For the photon efficiency investigation. the same total number of photons was modeled in an IGCT acquisition and a comparable multi-slice 2D parallel ray acquisition. For both cases noise projections were simulated and the central axial slice reconstructed. In the second study. to investigate the noise in an IGCT system, the expected x-ray photon flux was modeled and projections simulated through ellipsoid phantoms. All simulations were compared to theoretical predictions. The results of the photon efficiency simulations verify that the IGCT geometry is as efficient in photon utilization as a 2D parallel ray geometry. For a 10 cm diameter 4 cm thick ellipsoid water phantom and for reasonable system parameters, the calculated standard deviation was approximately 15 HU at the center of the ellipsoid. For the same size phantom with maximum attenuation equivalent to 30 cm of water, the calculated noise was approximately 131 HU. The theoretical noise predictions for these objects were 15 HU and 112 HU respectively. These results predict acceptable noise levels for a system with a 0.16 second scan time and 12 lp/cm isotropic resolution
Three-Dimensional Reconstruction Algorithm for a Reverse-Geometry Volumetric CT System With a Large-Array Scanned Source
We have proposed a CT system design to rapidly produce volumetric images with negligible cone beam artifacts. The investigated system uses a large array scanned source with a smaller array of fast detectors. The x-ray source is electronically steered across a 2D target every few milliseconds as the system rotates. The proposed reconstruction algorithm for this system is a modified 3D filtered backprojection method. The data are rebinned into 2D parallel ray projections, most of which are tilted with respect to the axis of rotation. Each projection is filtered with a 2D kernel and backprojected onto the desired image matrix. To ensure adequate spatial resolution and low artifact level, we rebin the data onto an array that has sufficiently fine spatial and angular sampling. Due to finite sampling in the real system, some of the rebinned projections will be sparse, but we hypothesize that the large number of views will compensate for the data missing in a particular view. Preliminary results using simulated data with the expected discrete sampling of the source and detector arrays suggest that high resolution
Tenfold your photons -- a physically-sound approach to filtering-based variance reduction of Monte-Carlo-simulated dose distributions
X-ray dose constantly gains interest in the interventional suite. With dose
being generally difficult to monitor reliably, fast computational methods are
desirable. A major drawback of the gold standard based on Monte Carlo (MC)
methods is its computational complexity. Besides common variance reduction
techniques, filter approaches are often applied to achieve conclusive results
within a fraction of time. Inspired by these methods, we propose a novel
approach. We down-sample the target volume based on the fraction of mass,
simulate the imaging situation, and then revert the down-sampling. To this end,
the dose is weighted by the mass energy absorption, up-sampled, and distributed
using a guided filter. Eventually, the weighting is inverted resulting in
accurate high resolution dose distributions. The approach has the potential to
considerably speed-up MC simulations since less photons and boundary checks are
necessary. First experiments substantiate these assumptions. We achieve a
median accuracy of 96.7 % to 97.4 % of the dose estimation with the proposed
method and a down-sampling factor of 8 and 4, respectively. While maintaining a
high accuracy, the proposed method provides for a tenfold speed-up. The overall
findings suggest the conclusion that the proposed method has the potential to
allow for further efficiency.Comment: 6 pages, 3 figures, Bildverarbeitung f\"ur die Medizin 202
An Efficient Estimation Method for Reducing the Axial Intensity Drop in Circular Cone-Beam CT
Reconstruction algorithms for circular cone-beam (CB) scans have been extensively
studied in the literature. Since insufficient data are measured, an exact reconstruction
is impossible for such a geometry. If the reconstruction algorithm assumes zeros for
the missing data, such as the standard FDK algorithm, a major type of resulting CB
artifacts is the intensity drop along the axial direction. Many algorithms have been
proposed to improve image quality when faced with this problem of data missing; however,
development of an effective and computationally efficient algorithm remains a
major challenge. In this work, we propose a novel method for estimating the unmeasured
data and reducing the intensity drop artifacts. Each CB projection is analyzed in
the Radon space via Grangeat's first derivative. Assuming the CB projection is taken
from a parallel beam geometry, we extract those data that reside in the unmeasured region of the Radon space. These data are then used as in a parallel beam geometry
to calculate a correction term, which is added together with Hu's correction term to
the FDK result to form a final reconstruction. More approximations are then made
on the calculation of the additional term, and the final formula is implemented very
efficiently. The algorithm performance is evaluated using computer simulations on analytical
phantoms. The reconstruction comparison with results using other existing
algorithms shows that the proposed algorithm achieves a superior performance on the
reduction of axial intensity drop artifacts with a high computation efficiency
Precision Learning: Towards Use of Known Operators in Neural Networks
In this paper, we consider the use of prior knowledge within neural networks.
In particular, we investigate the effect of a known transform within the
mapping from input data space to the output domain. We demonstrate that use of
known transforms is able to change maximal error bounds.
In order to explore the effect further, we consider the problem of X-ray
material decomposition as an example to incorporate additional prior knowledge.
We demonstrate that inclusion of a non-linear function known from the physical
properties of the system is able to reduce prediction errors therewith
improving prediction quality from SSIM values of 0.54 to 0.88.
This approach is applicable to a wide set of applications in physics and
signal processing that provide prior knowledge on such transforms. Also maximal
error estimation and network understanding could be facilitated within the
context of precision learning.Comment: accepted on ICPR 201
Geometry Analysis of an Inverse-Geometry Volumetric CT System With Multiple Detector Arrays
An inverse-geometry volumetric CT (IGCT) system for imaging in a single fast rotation without cone-beam artifacts is being developed. It employs a large scanned source array and a smaller detector array. For a single-source/single-detector implementation, the FOV is limited to a fraction of the source size. Here we explore options to increase the FOV without increasing the source size by using multiple detectors spaced apart laterally to increase the range of radial distances sampled. We also look at multiple source array systems for faster scans. To properly reconstruct the FOV, Radon space must be sufficiently covered and sampled in a uniform manner. Optimal placement of the detectors relative to the source was determined analytically given system constraints (5cm detector width, 25cm source width, 45cm source-to-isocenter distance). For a 1x3 system (three detectors per source) detector spacing (DS) was 18deg and source-to-detector distances (SDD) were 113, 100 and 113cm to provide optimum Radon sampling and a FOV of 44cm. For multiple-source systems, maximum angular spacing between sources cannot exceed 125deg since detectors corresponding to one source cannot be occluded by a second source. Therefore, for 2x3 and 3x3 systems using the above DS and SDD, optimum spacing between sources is 115deg and 61deg respectively, requiring minimum scan rotations of 115deg and 107deg. Also, a 3x3 system can be much faster for full 360deg dataset scans than a 2x3 system (120deg vs. 245deg). We found that a significantly increased FOV can be achieved while maintaining uniform radial sampling as well as a substantial reduction in scan time using several different geometries. Further multi-parameter optimization is underway
Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details
Deconvolution-based analysis of CT and MR brain perfusion data is
widely used in clinical practice and it is still a topic of ongoing research activities. In this paper, we present a comprehensive derivation and explanation of the underlying physiological model for intravascular tracer systems. We also discuss practical details that are needed to properly implement algorithms for perfusion analysis. Our description of the practical computer implementation is focused on the most frequently employed algebraic deconvolution methods based on the singular value decomposition. In particular, we further discuss the need for regularization in order to obtain physiologically reasonable results. We include an overview of relevant preprocessing steps and provide numerous references to the literature. We cover both CT and MR brain perfusion imaging in this paper because they share many common aspects. The combination of both the theoretical as well as the practical aspects of perfusion analysis explicitly emphasizes the simplifications to the underlying physiological model that are necessary in order to apply it to measured data acquired with current CT and MR
scanners
Effects of Tissue Material Properties on X-Ray Image, Scatter and Patient Dose Determined using Monte Carlo Simulations
With increasing patient and staff X-ray radiation awareness, many efforts
have been made to develop accurate patient dose estimation methods. To date,
Monte Carlo (MC) simulations are considered golden standard to simulate the
interaction of X-ray radiation with matter. However, sensitivity of MC
simulation results to variations in the experimental or clinical setup of image
guided interventional procedures are only limited studied. In particular, the
impact of patient material compositions is poorly investigated. This is mainly
due to the fact, that these methods are commonly validated in phantom studies
utilizing a single anthropomorphic phantom. In this study, we therefore
investigate the impact of patient material parameters mapping on the outcome of
MC X-ray dose simulations. A computation phantom geometry is constructed and
three different commonly used material composition mappings are applied. We
used the MC toolkit Geant4 to simulate X-ray radiation in an interventional
setup and compared the differences in dose deposition, scatter distributions
and resulting X-ray images. The evaluation shows a discrepancy between
different material composition mapping up to 20 % concerning directly
irradiated organs. These results highlight the need for standardization of
material composition mapping for MC simulations in a clinical setup.Comment: 6 pages, 4 figures, Bildverarbeitung f\"ur die Medizin 201
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