Recently, image-based computational fluid dynamic simulations (CFD) have been proposed to investigate the local
hemodynamics inside human cerebral aneurysms. It is suggested that the knowledge of the computed three-dimensional
flow fields can be used to assist clinical risk assessment and treatment decision making.
However, the reliability of CFD for accurately representing the human cerebral blood flow is difficult to assess due to
the impossibility of ground truth measurements. A recently proposed virtual angiography method has been used to
indirectly validate CFD results by comparing virtually constructed and clinically acquired angiograms. However, the
validations are not yet comprehensive as they lack either from patient-specific boundary conditions (BCs) required for
CFD simulations or from quantitative comparison methods.
In this work, a simulation pipeline is built up including image-based geometry reconstruction, CFD simulations
solving the dynamics of blood flow and contrast agent (CA), and virtual angiogram generation. In contrast to previous
studies, the patient-specific blood flow rates obtained by transcranial color coded Doppler (TCCD) ultrasound are used to
impose CFD BCs. Quantitative measures are defined to thoroughly evaluate the correspondence between the clinically
acquired and virtually constructed angiograms, and thus, the reliability of CFD simulations. Exemplarily, two patient
cases are presented.
Close similarities are found in terms of spatial and temporal variations of CA distribution between acquired and
virtual angiograms. Besides, for both patient cases, discrepancies of less than 15% are found for the relative root mean
square errors (rRMSE) in time intensity curve (TIC) comparisons from selected characteristic positions.
Diagnosis and treatment decisions of cerebrovascular diseases are currently based on structural information like
the endovascular lumen. In future, clinical diagnosis will increasingly be based on functional information which
gives direct information about the physiological parameters and, hence, is a direct measure for the severity of
the pathology. In this context, an important functional quantity is the volumetric blood flow over time. The
proposed flow quantification method uses contrasted X-ray images from cerebrovascular interventions and a
model of contrast agent dispersion to estimate the flow parameters from the spatial and temporal development
of the contrast agent concentration through the vascular system.
To evaluate the model-based blood flow quantification under realistic circumstances, dedicated cerebrovascular
data has been acquired during clinical interventions. To this aim, a clinical protocol for this novel procedure
has been defined and optimized. For the verification of the measured flow results ultrasound Doppler measurements
have been performed acting as reference measurements.
The clinical data available so far indicates the ability of the proposed flow model to explain the in-vivo
transport of contrast agent in blood. The flow quantification results show good correspondence of flow waveform
and mean volumetric flow rate with the accomplished ultrasound measurements before or after angiography.
KEYWORDS: Sensors, Beam shaping, Monte Carlo methods, Signal detection, Optical simulations, Image filtering, Optical filters, X-rays, Signal to noise ratio, X-ray computed tomography
While cone-beam CT using flat x-ray detectors has gained increased popularity in the past years, the 3D imaging
quality is still limited by a large amount of scatter, low dynamic range, and small field of view of the detector.
Especially for large objects, the high dynamic range of the projections is a common source for detector specific
artifacts. In conventional CT, the application of beam shapers (or bowtie filters) to decrease the signal dynamic
in the projections is quite common. In this paper we investigate the use of a beam shaper for cone-beam CT
with an off-centered flat detector by means of Monte-Carlo (MC) simulations and test-bench experiments.
The shift of the detector out of the central axis increases the field of view and allows the imaging of larger
patients, but in turn leads to a very high dynamic signal range and poor scatter-to-primary ratios (SPR). The
impact of a half bowtie filter on key performance parameters of the imaging chain is investigated with MC
simulations. It is demonstrated that a beam shaper significantly improves the peak SPR especially for large
patients and that the reshaping of the SPR has a dominant impact on the homogeneity of the reconstructed image.
The use of beam shapers for CBCT requires a modified pre-processing chain that also accounts for secondary effects introduced by the beam modulation filter. Beside patient scatter correction, the inhomogeneous spectral hardening of the x-ray beam and scattered radiation from the beam shaper itself have to be corrected. A comparison of phantom scans with and without beam shaper after pre-processing demonstrates the potential of beam shapers for dose reduction and SNR improvement in flat detector cone-beam CT.
This paper describes the image quality improvements achieved by developing a new fully physical imaging chain.
The key enablers for this imaging chain are a new scatter correction technique and an analytic computation of
the beam hardening correction for each detector. The new scatter correction technique uses off-line Monte Carlo
simulations to compute a large database of scatter kernels representative of a large variety of patient shapes
and an on-line combination of those based on the attenuation profile of the patient in the measured projections.
In addition, profiles of scatter originating from the wedge are estimated and subtracted. The beam hardening
coefficients are computed using analytic simulations of the full beam path of each individual ray through the
scanner. Due to the new approach, scatter and beam hardening are computed from first principles with no
further tuning factors, and are thus straight forward to adapt to any patient and scan geometry. Using the new
fully physical imaging chain unprecedented image quality was achieved. This is demonstrated with a special
scatter phantom. With current image correction techniques this phantom typically shows position dependent
inhomogeneity and streak artifacts resulting from the impact of scattered radiation. With the new imaging
chain these artifacts are almost completely eliminated, independent of position and scanning mode (kV). Further
preliminary patient studies show that in addition to fully guaranteeing an absolute Hounsfield scale in arbitrary
imaging conditions, the new technique also strongly sharpens object boundaries such as the edges of the liver.
This paper presents a new iterative motion correction technique composed of motion estimation in projection space, motion segmentation in image space, and motion compensation within an analytical filtered-backprojection (FBP) image reconstruction algorithm. The motion is estimated by elastic registration of acquired projections on reference projections. Reference projections are sampled from the image, reconstructed in a previous iteration step. To apply the motion compensation locally, the image regions significantly affected by motion are segmented. First the perceived motion is identified in projection space by computing the absolute difference between acquired line integrals and reference line integrals. Then, differences are reconstructed in image space, and the image is regularized with a pipeline of standard image processing operators. The result of this procedure is a normalized motion map, associating each image element with a measure of the local motion detected there. The estimated displacement vectors in projection space and the reconstructed motion map in image space are then used by an adaptive motion-compensated FBP algorithm to reconstruct a sharper image. Results are shown qualitatively and quantitatively for reconstructions from realistic projections, simulated from clinical patient data. Since the method does not assume any periodicity of the motion model, it can correct reconstruction artifacts due to unstructured patient motion, such as breath-hold failure, abdominal contractions, and nervous movements.
KEYWORDS: Computed tomography, Medical imaging, CT reconstruction, Motion estimation, Physics, Current controlled current source, Iterative methods, Computing systems, Data acquisition, Imaging systems
This paper presents an iterative method for compensation of motion artifacts for slowly rotating computed tomography (CT) systems. The inconsistencies among projections introduce severe reconstruction artifacts for free breathing acquisitions. Streaks and false structures appear and the resolution is limited by strong blurring. The rationale of the motion compensation method is to iteratively correct the reconstructed image by first extracting the motion artifacts in projection space, then reconstructing the artifacts in image space, and finally subtracting the artifacts from the original reconstruction. The perceived motion is extracted in projection space from the difference between acquired and reference projections, sampled from the image reconstructed in a previous iteration step. The initial image is reconstructed from acquired data and is nevertheless considered as the reference, although it contains artifacts. This image is iteratively corrected by subtraction of the estimated motion artifacts. The originality of the technique stems from the fact that the patient motion is not estimated but the artifacts are reconstructed in image space. It can provide sharp static anatomical images on slowly rotating on-board imagers in radiotherapy or interventional C-arm systems. Qualitative and quantitative figures are shown for experiments based on simulated projections of a sequence of clinical images resulting from a respiratory-gated helical CT acquisition. The border of the diaphragm becomes sharper and the contrast improves for small structures in the lungs.
In this paper we propose a novel scatter correction methodology for X-ray based cone-beam CT that allows to
combine the advantages of projection-based and volume-based correction approaches. The basic idea is to use a
potentially non-optimal projection-based scatter correction method and to iteratively optimize its performance
by repeatedly assessing remaining scatter-induced artifacts in intermediately reconstructed volumes. The novel
approach exploits the fact that due to the flatness of the
scatter-background, compensation itself is most easily
performed in the projection-domain, while the scatter-induced artifacts can be better observed in the reconstructed
volume. The presented method foresees to evaluate the scatter correction efficiency after each iteration
by means of a quantitative measure characterizing the amount of residual cupping and to adjust the parameters
of the projection-based scatter correction for the next iteration accordingly. The potential of this iterative scatter
correction approach is demonstrated using voxelized Monte Carlo scatter simulations as ground truth. Using the
proposed iterative scatter correction method, remarkable scatter correction performance was achieved both using
simple parametric heuristic techniques as well as by optimizing previously published scatter estimation schemes.
For the human head, scatter induced artifacts were reduced from initially 148 HU to less than 8.1 HU to 9.1 HU
for different studied methods, corresponding to an artifact reduction exceeding 93%.
In flat detector cone-beam computed tomography (CBCT), scattered radiation is a major source of image degradation,
making accurate a posteriori scatter correction inevitable. A potential solution to this problem is provided by
computerized scatter correction based on Monte-Carlo simulations. Using this technique, the detected distributions of
X-ray scatter are estimated for various viewing directions using Monte-Carlo simulations of an intermediate
reconstruction. However, as a major drawback, for standard CBCT geometries and with standard size flat detectors such
as mounted on interventional C-arms, the scan field of view is too small to accommodate the human body without lateral
truncations, and thus this technique cannot be readily applied. In this work, we present a novel method for constructing a
model of the object in a laterally and possibly also axially extended field of view, which enables meaningful application
of Monte-Carlo based scatter correction even in case of heavy truncations. Evaluation is based on simulations of a
clinical CT data set of a human abdomen, which strongly exceeds the field of view of the simulated C-arm based CBCT
imaging geometry. By using the proposed methodology, almost complete removal of scatter-caused inhomogeneities is
demonstrated in reconstructed images.
It is well known that rotational C-arm systems are capable of providing 3D tomographic X-ray images with much higher spatial resolution than conventional CT systems. Using flat X-ray detectors, the pixel size of the detector typically is in the range of the size of the test objects. Therefore, the finite extent of the "point" source cannot be neglected for the determination of the MTF. A practical algorithm has been developed that includes bias estimation and subtraction, averaging in the spatial domain, and correction for the frequency content of the imaged bead or wire. Using this algorithm, the wire and the bead method are analyzed for flat detector based 3D X-ray systems with the use of standard CT performance phantoms. Results on both experimental and simulated data are presented. It is found that the approximation of applying the analysis of the wire method to a bead measurement is justified within 3% accuracy up to the first zero of the MTF.
This paper presents a novel framework for the systematic assessment of the impact of scattered radiation in
.at-detector based cone-beam CT. While it is well known that scattered radiation causes three di.erent types of
artifacts in reconstructed images (inhomogeneity artifacts such as cupping and streaks, degradation of contrast,
and enhancement of noise), investigations in the literature quantify the impact of scatter mostly only in terms
of inhomogeneity artifacts, giving little insight, e.g., into the visibility of low contrast lesions. Therefore, for
this study a novel framework has been developed that in addition to normal reconstruction of the CT (HU)
number allows for reconstruction of voxelized expectation values of three additional important characteristics
of image quality: signal degradation, contrast reduction, and noise variances. The new framework has been
applied to projection data obtained with voxelized Monte-Carlo simulations of clinical CT data sets of high
spatial resolution. Using these data, the impact of scattered radiation was thoroughly studied for realistic and
clinically relevant patient geometries of the head, thorax, and pelvis region. By means of spatially resolved
reconstructions of contrast and noise propagation, the image quality of a scenario with using standard antiscatter
grids could be evaluated with great detail. Results show the spatially resolved contrast degradation and
the spatially resolved expected standard deviation of the noise at any position in the reconstructed object. The
new framework represents a general tool for analyzing image quality in reconstructed images.
Scattered radiation is a major source of artifacts in flat detector based cone-beam computed tomography. In this paper, a novel software-based method for retrospective scatter correction is described and evaluated. The method is based on approximation of the imaged object by a simple geometric model (e.g., a homogeneous water-like ellipsoid) that is estimated from the set of acquired projections. This is achieved by utilizing a numerical optimization procedure to determine the model parameters for which there is maximum correspondence between the measured projections and the projections of the model. Monte-Carlo simulations of this model are used for calculation of scatter estimates for the acquired projections. Finally, using the scatter-corrected projections, tomographic reconstruction is conducted by means of cone-beam filtered back-projection. The correction method is evaluated using simulated and experimentally acquired projection data sets of geometric and physical head phantoms. It is found that the method is able to accurately estimate mean scatter levels in X-ray projections, allowing to significantly reduce scatter-caused artifacts in 3D reconstructed images.
This paper presents a systematic assessment of scattered radiation in flat-detector based cone-beam CT. The analysis is based on simulated scatter projections of voxelized CT images of different body regions allowing to accurately quantify scattered radiation of realistic and clinically relevant patient geometries. Using analytically
computed primary projection data of high spatial resolution in combination with Monte-Carlo simulated scattered radiation, practically noise-free reference data sets are computed with and without inclusion of scatter. The impact of scatter is studied both in the projection data and in the reconstructed volume for the head, thorax, and pelvis regions. Currently available anti-scatter grid geometries do not sufficiently compensate scatter induced cupping and streak artifacts, requiring additional software-based scatter correction. The required accuracy of scatter compensation approaches increases with increasing patient size.
KEYWORDS: Sensors, Monte Carlo methods, Head, Computer simulations, X-rays, Signal attenuation, X-ray computed tomography, Photons, Linear filtering, Neodymium
This study deals with a systematic assessment of the potential of different schemes for computerized scatter correction in flat detector based cone-beam X-ray computed tomography. The analysis is based on simulated scatter of a CT image of a human head. Using a Monte-Carlo cone-beam CT simulator, the spatial distribution of scattered radiation produced by this object has been calculated with high accuracy for the different projected views of a circular tomographic scan. Using this data and, as a reference, a scatter-free forward projection of the phantom, the potential of different schemes for scatter correction has been evaluated. In particular, the ideally achievable degree of accuracy of schemes based on estimating a constant scatter level in each projection was compared to approaches aiming at estimation of a more complex spatial shape of the scatter distribution. For each scheme, remaining cupping artifacts in the reconstructed volumetric image were quantified and analyzed. It was found that already accurate estimation of a constant scatter level for each projection allows for comparatively accurate compensation of scatter-caused artifacts.
KEYWORDS: Scattering, Sensors, Signal attenuation, Monte Carlo methods, Photons, X-rays, Computed tomography, X-ray computed tomography, Signal to noise ratio, Compton scattering
Scattered radiation is a major source of image degradation and nonlinearity in flat detector based cone-beam CT. Due to the bigger irradiated volume the amount of scattered radiation in true cone-beam geometry is considerably higher than for fan beam CT. This on the one hand reduces the signal to noise ratio, since the additional scattered photons contribute only to the noise and not to the measured signal, and on the other hand cupping and streak artifacts arise in the reconstructed volume. Anti-scatter grids composed of lead lamellae and interspacing material decrease the SNR for flat detector based CB-CT geometry, because the beneficial scatter attenuating effect is overcompensated by the absorption of primary radiation. Additionally, due to the high amount of scatter that still remains behind the grid, cupping and streak artifacts cannot be reduced sufficiently. Computerized scatter correction schemes are therefore essential for achieving artifact-free reconstructed images in cone-beam CT. In this work, a fast model based scatter correction algorithm is proposed, aiming at accurately estimating the level and spatial distribution of scattered radiation background in each projection. This will allow for effectively reducing streak and cupping artifacts due to scattering in cone-beam CT applications.
KEYWORDS: Sensors, Collimation, Monte Carlo methods, Signal to noise ratio, Photons, Fluoroscopy, Head, Fluctuations and noise, X-rays, Signal attenuation
In this paper, the performance of focused lamellar anti-scatter grids, which are currently used in fluoroscopy, is studied in order to determine guidelines of grid usage for flat detector based cone beam CT. The investigation aims at obtaining the signal to noise ratio improvement factor by the use of anti-scatter grids.
First, the results of detailed Monte Carlo simulations as well as measurements are presented. From these the general characteristics of the impinging field of scattered and primary photons are derived. Phantoms modeling the head, thorax and pelvis regions have been studied for various imaging geometries with varying phantom size, cone and fan angles and patient-detector distances.
Second, simulation results are shown for ideally focused and vacuum spaced grids as best case approach as well as for grids with realistic spacing materials. The grid performance is evaluated by means of the primary and scatter transmission and the signal to noise ratio improvement factor as function of imaging geometry and grid parameters.
For a typical flat detector cone beam CT setup, the grid selectivity and thus the performance of anti-scatter grids is much lower compared to setups where the grid is located directly behind the irradiated object. While for small object-to-grid distances a standard grid improves the SNR, the SNR for geometries as used in flat detector based cone beam CT is deteriorated by the use of an anti-scatter grid for many application scenarios. This holds even for the pelvic region.
Standard fluoroscopy anti-scatter grids were found to decrease the SNR in many application scenarios of cone beam CT due to the large patient-detector distance and have, therefore, only a limited benefit in flat detector based cone beam CT.
In this paper, soft tissue contrast visibility in neural applications is investigated for volume imaging based on flat X-ray detector cone-beam CT. Experiments have been performed on a high precision bench-top system with rotating object table and fixed X-ray tube-detector arrangement. Several scans of a post mortem human head specimen have been performed under various conditions. Hereby two different flat X-ray detectors with 366 x 298mm2 (Trixell Pixium 4700) and 176 x 176mm2 (Trixell Pixium 4800) active area have been employed. During a single rotation up to 720 projections have been acquired.
For reconstruction of the 3D images a Feldkamp algorithm has been employed. Reconstructed images of the head of human cadaver demonstrate that added soft tissue contrast down to 10 HU is detectable for X-ray dose comparable to CT. However, the limited size of the smaller detector led to truncation artifacts, which were partly compensated by extrapolation of the projections outside the field of view.
To reduce cupping artifacts resulting from scattered radiation and to improve visibility of low contrast details, a novel homogenization procedure based on segmentation and polynomial fitting has been developed and applied on the reconstructed voxel data. Even for narrow HU-Windows, limitations due to scatter induced cupping artifacts are no longer noticeable after applying the homogenization procedure.
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