Multiple efforts have been made in x-ray angiography to transition from traditional image quality metrics to mathematical observer models. Recent works have successfully implemented the channelized Hotelling observer (CHO) model for x-ray angiography systems. However, in these works the channel selection process is ambiguous and limits to identifying a range of frequencies and other channel parameters that are believed to represent the most relevant features of the imaging tasks. This channel selection rationale can be sufficient for certain simple scenarios but it might not be enough for more complex ones. On the other hand, it has been shown that besides dealing with the well-known bias caused by a finite number of samples, there is also another source of bias in the estimation of the detectability index in x-ray angiography. Such source of bias has been attributed to nonrandom differences in noise between images acquired at different time points, also referred as temporally variable nonstationary noise. This work proposes a task-specific automated method for optimal channel selection and corrects for the influence of bias due to temporally variable nonstationary noise, particular from x-ray angiography systems. The proposed method is computationally inexpensive, provides time efficient selection of optimal channels, and contributes to minimize bias, all of these without significantly compromising the accuracy of the detectability index estimation. This method for channel optimization can be readily adapted to other imaging modalities.
Our institution routinely uses limited-angle cone-beam CT (CBCT) from a C-arm with 3D capabilities to diagnose and treat cardiovascular and orthopedic diseases in both adult and pediatric patients. While CBCT contributes to qualitative and quantitative assessment of both normal and abnormal patient anatomy, it also contributes substantially to patient radiation dose. Reducing the dose associated with CBCT exams while maintaining clinical utility can be considered to be of benefit to patients for whom CBCT is routinely used and may extend its adoption to clinical tasks and patient populations where the dose is currently considered prohibitive. In this work we developed and validated a method to simulate low-dose CBCT images from standard-dose projection images. The method was based on adding random noise to real projection images. The method was validated using an anthropomorphic thorax phantom of variable size with a custom-made insert containing iodine contrast rods of variable concentration. Images reconstructed from the low-dose simulations were compared to the actually acquired lower-dose images. Subtraction images of the simulated and acquired lower-dose images demonstrated a lack of residual structure patterns, indicating that differences between the image sets were consistent with random noise only. Noise power spectrum (NPS) and iodine signal-difference-to-noise ratio (SDNR) showed good agreement between simulated and acquired lower-dose images for dose levels between 70% and 30% of the routine dose. The average difference in iodine SDNR between simulated and acquired low-dose images was below 5% for all dose levels and phantom sizes. This work demonstrates the feasibility of accurately simulating low-dose CBCT based on real images acquired using standard dose and degrading the images by adding noise.
Evaluation of flat-panel angiography equipment through conventional image quality metrics is limited by the scope of standard spatial-domain image quality metric(s), such as contrast-to-noise ratio and spatial resolution, or by restricted access to appropriate data to calculate Fourier domain measurements, such as modulation transfer function, noise power spectrum, and detective quantum efficiency. Observer models have been shown capable of overcoming these limitations and are able to comprehensively evaluate medical-imaging systems. We present a spatial domain-based channelized Hotelling observer model to calculate the detectability index (DI) of our different sized disks and compare the performance of different imaging conditions and angiography systems. When appropriate, changes in DIs were compared to expectations based on the classical Rose model of signal detection to assess linearity of the model with quantum signal-to-noise ratio (SNR) theory. For these experiments, the estimated uncertainty of the DIs was less than 3%, allowing for precise comparison of imaging systems or conditions. For most experimental variables, DI changes were linear with expectations based on quantum SNR theory. DIs calculated for the smallest objects demonstrated nonlinearity with quantum SNR theory due to system blur. Two angiography systems with different detector element sizes were shown to perform similarly across the majority of the detection tasks.
KEYWORDS: Image quality, 3D modeling, 3D image reconstruction, 3D image processing, 3D acquisition, Angiography, Calibration, X-rays, Data acquisition, Reconstruction algorithms
The use of a C-arm radiographic system for 3D reconstruction of opacified vasculature presents several computational and engineering challenges. Factors that may lead to inconsistency at the projection data set and subsequent reconstruction errors include image noise, variations in vessel opacification during the acquisition, and inaccurate determination of the imaging geometry. We have utilized simulations to study the effect of these factors on 3D reconstruction with algebraic reconstruction technique (ART) in order to identify possible artifacts and loss of image quality in the 3D image. Corrective measures designed to counter artifacts such as smoothing, averaging, and use of constraints with ART have been developed and validated. These studies have made it possible to identify the causes of artifacts in preliminary in vivo applications, and to estimate the tolerance for imperfections in data acquisition. Moreover, these works have established modifications to the reconstruction procedure for reducing image artifacts and improving image quality.
Typical digital subtraction angiography (DSA) acquisition rates are often inadequate for visualizing and analyzing fast-moving flow patterns. Therefore, an interpolation method that captures the angiographic flow pattern was developed. The temporal change of gray value in each pixel along a blood vessel records the flow movement at that location. Thus, temporal interpolation was performed on a pixel-by-pixel basis. To generate each interpolated image, a polynomial interpolation was applied to six sequential images. To validate the interpolation technique, a flow phantom was imaged with a high acquisition frame rate, and interpolation was done in a lower frame rate and compared to the acquired data. The interpolated images were also compared to results from linear interpolation and cubic spline interpolation. Clinical utility was illustrated on DSA images of cerebral vasculature with aneurysms. Image sequences of 60 frame/s were generated from DSA images acquired at 7.5 frame/s. The results show improved flow pattern visualization, especially flow head locations in blood vessels. This interpolation method has also been applied to dynamic 3D reconstruction from biplane DSA projections. In this application, the method was used to offset temporal discrepancies between biplane projection pairs and contrast injections, making dynamic 3D reconstruction possible.
A method for 3D cone beam reconstruction of cerebral vasculature (both morphology and grayscale) from a limited number (less than 10) of digital subtraction angiographic (DSA) projections obtained with a standard biplane C-arm x-ray system is described. The reconstruction method includes geometric calibration of the source and detector orientation, spatial image distortion correction, and algebraic reconstruction technique (ART) with non- negativity constraint. Accuracy of voxel gray scale values estimated by ART is enhanced by determination of weights based on the intersection volume between a pyramidal ray and cubic voxel. The reconstruction is accelerated by retaining only the vessel containing voxels and distributed computing. Reconstruction of a phantom containing fiducial markers at known 3D locations demonstrated that the reconstructed geometry is accurate to less than a pixel width. Reconstruction is also obtained from an anatomic skull phantom with an embedded cerebral vasculature reproduction that includes an aneurysm. Three dimensional reconstruction exhibited the necessary details, both structural and grayscale.
The exact weighting function in 3D image reconstruction from 2D projections with cone beam geometry is obtained as the volume of intersection of a pyramidal ray with a cubic voxel. This intersection yields a convex polyhedron whose faces are formed by either the side of the pyramid or the voxel face. For each face of a voxel, we maintain a vertex link map. When one of the four pyramidal ray planes clips the voxel, we obtain a new face and a set of new vertices, while updating existing faces and their vertex link maps. Progressively clipping the voxel by the necessary ray planes yields the intersection polyhedron, whose faces and vertices are provided by the face list and its associated vertex link maps. To generate the weight, the volume of the polyhedron is calculated by dividing the polyhedron into tetrahedrons, whose volumes are summed. The exact calculated weights were used to reconstruct 3D vascular images from simulated data using a ROI (region of interest) limited ART (algebraic reconstruction technique). Comparing the results to those obtained from length approximation indicates that more accurate reconstruction could be achieved from the weights calculated with the new method.
An algorithm has been developed to correct spatial distortion in image intensifier-based projections for three-dimensional (3D) angiographic reconstruction. A piecewise spatial warping technique is used to calculate a set of correction lookup tables which store the row and column subpixel spatial shifts, based on a reference grid image. Pixel amplitudes in the corrected image are determined from bilinear interpolation of the four surrounding pixels in the observed image. The method has been tested using an x-ray imaging chain with a 30-cm image intensifier positioned at various angular orientations and x-ray source distances. Prior to distortion correction, the maximum error between observed and expected reference point locations was found to be 14 mm. After correction, the maximum and mean errors were 0.23 mm and 0.053 mm, respectively.
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