Software Phantom Research Articles

WE-E-217BCD-02: Update on the Upcoming ACR Digital Mammography QC Manual.

We propose a new metric called Distance Discordance (DD), which is defined as the distance between two anatomic points from two moving images, which are co-located on some reference image, when deformed onto another reference image. To demonstrate the concept of DD, we created a reference software phantom which contains two objects. The first object (1) consists of a hollow box with a fixed size core and variable wall thickness. The second object (2) consists of a solid box of fixed size and arbitrary location. 7 different variations of the fixed phantom were created. Each phantom was deformed onto every other phantom using two B-Spline DIR algorithms available in Elastix and Plastimatch. Voxels were sampled from the reference phantom [1], which were also deformed from moving phantoms [2…6], and we find the differences in their corresponding location on phantom [7]. Each voxel results in a distribution of DD values, which we call distance discordance histogram (DDH). We also demonstrate this concept in 8 Head & Neck patients. The two image registration algorithms produced two different DD results for the same phantom image set. The mean values of the DDH were slightly lower for Elastix (0-1.28 cm) as compared to the values produced by Plastimatch (0-1.43 cm). The combined DDH for the H&N patients followed a lognormal distribution with a mean of 0.45 cm and std. deviation of 0.42 cm. The proposed distance discordance (DD) metric is an easily interpretable, quantitative tool that can be used to evaluate the effect of inter-patient variability on the goodness of the registration in different parts of the patient anatomy. Therefore, it can be utilized to exclude certain images based on their DDH characteristics. In addition, this metric does not rely on 'ground truth' or the presence of contoured structures.

Reduction of irregular breathing artifacts in respiration‐correlated CT images using a respiratory motion model

Respiration-correlated CT (RCCT) images produced with commonly used phase-based sorting of CT slices often exhibit discontinuity artifacts between CT slices, caused by cycle-to-cycle amplitude variations in respiration. Sorting based on the displacement of the respiratory signal yields slices at more consistent respiratory motion states and hence reduces artifacts, but missing image data (gaps) may occur. The authors report on the application of a respiratory motion model to produce an RCCT image set with reduced artifacts and without missing data. Input data consist of CT slices from a cine CT scan acquired while recording respiration by monitoring abdominal displacement. The model-based generation of RCCT images consists of four processing steps: (1) displacement-based sorting of CT slices to form volume images at 10 motion states over the cycle; (2) selection of a reference image without gaps and deformable registration between the reference image and each of the remaining images; (3) generation of the motion model by applying a principal component analysis to establish a relationship between displacement field and respiration signal at each motion state; (4) application of the motion model to deform the reference image into images at the 9 other motion states. Deformable image registration uses a modified fast free-form algorithm that excludes zero-intensity voxels, caused by missing data, from the image similarity term in the minimization function. In each iteration of the minimization, the displacement field in the gap regions is linearly interpolated from nearest neighbor nonzero intensity slices. Evaluation of the model-based RCCT examines three types of image sets: cine scans of a physical phantom programmed to move according to a patient respiratory signal, NURBS-based cardiac torso (NCAT) software phantom, and patient thoracic scans. Comparison in physical motion phantom shows that object distortion caused by variable motion amplitude in phase-based sorting is visibly reduced with model-based RCCT. Comparison of model-based RCCT to original NCAT images as ground truth shows best agreement at motion states whose displacement-sorted images have no missing slices, with mean and maximum discrepancies in lung of 1 and 3 mm, respectively. Larger discrepancies correlate with motion states having a larger number of missing slices in the displacement-sorted images. Artifacts in patient images at different motion states are also reduced. Comparison with displacement-sorted patient images as a ground truth shows that the model-based images closely reproduce the ground truth geometry at different motion states. Results in phantom and patient images indicate that the proposed method can produce RCCT image sets with reduced artifacts relative to phase-sorted images, without the gaps inherent in displacement-sorted images. The method requires a reference image at one motion state that has no missing data. Highly irregular breathing patterns can affect the method's performance, by introducing artifacts in the reference image (although reduced relative to phase-sorted images), or in decreased accuracy in the image prediction of motion states containing large regions of missing data.

4D blood flow visualization fusing 3D and 4D MRA image sequences

To present and evaluate the feasibility of a novel automatic method for generating 4D blood flow visualizations fusing high spatial resolution 3D and time-resolved (4D) magnetic resonance angiography (MRA) datasets. In a first step, the cerebrovascular system is segmented in the 3D MRA dataset and a surface model is computed. The hemodynamic information is extracted from the 4D MRA dataset and transferred to the surface model using rigid registration where it can be visualized color-coded or dynamically over time. The presented method was evaluated using software phantoms and 20 clinical datasets from patients with an arteriovenous malformation. Clinical evaluation was performed by comparison of Spetzler-Martin scores determined from the 4D blood flow visualizations and corresponding digital subtraction angiographies. The performed software phantom validation showed that the presented method is capable of producing reliable visualization results for vessels with a minimum diameter of 2 mm for which a mean temporal error of 0.27 seconds was achieved. The clinical evaluation based on 20 datasets comparing the 4D visualization to DSA images revealed an excellent interrater reliability. The presented method enables an improved combined representation of blood flow and anatomy while reducing the time needed for clinical rating.

Evaluation of deformable image registration and a motion model in CT images with limited features

Deformable image registration (DIR) is increasingly used in radiotherapy applications and provides the basis for a previously described model of patient-specific respiratory motion. We examine the accuracy of a DIR algorithm and a motion model with respiration-correlated CT (RCCT) images of software phantom with known displacement fields, physical deformable abdominal phantom with implanted fiducials in the liver and small liver structures in patient images. The motion model is derived from a principal component analysis that relates volumetric deformations with the motion of the diaphragm or fiducials in the RCCT. Patient data analysis compares DIR with rigid registration as ground truth: the mean ± standard deviation 3D discrepancy of liver structure centroid positions is 2.0 ± 2.2 mm. DIR discrepancy in the software phantom is 3.8 ± 2.0 mm in lung and 3.7 ± 1.8 mm in abdomen; discrepancies near the chest wall are larger than indicated by image feature matching. Marker's 3D discrepancy in the physical phantom is 3.6 ± 2.8 mm. The results indicate that visible features in the images are important for guiding the DIR algorithm. Motion model accuracy is comparable to DIR, indicating that two principal components are sufficient to describe DIR-derived deformation in these datasets.

Optimized generation of high resolution breast anthropomorphic software phantoms

The authors present an efficient method for generating anthropomorphic software breast phantoms with high spatial resolution. Employing the same region growing principles as in their previous algorithm for breast anatomy simulation, the present method has been optimized for computational complexity to allow for fast generation of the large number of phantoms required in virtual clinical trials of breast imaging. The new breast anatomy simulation method performs a direct calculation of the Cooper's ligaments (i.e., the borders between simulated adipose compartments). The calculation corresponds to quadratic decision boundaries of a maximum a posteriori classifier. The method is multiscale due to the use of octree-based recursive partitioning of the phantom volume. The method also provides user-control of the thickness of the simulated Cooper's ligaments and skin. Using the proposed method, the authors have generated phantoms with voxel size in the range of (25-1000 μm)(3)∕voxel. The power regression of the simulation time as a function of the reciprocal voxel size yielded a log-log slope of 1.95 (compared to a slope of 4.53 of our previous region growing algorithm). A new algorithm for computer simulation of breast anatomy has been proposed that allows for fast generation of high resolution anthropomorphic software phantoms.

A novel breast software phantom for biomechanical modeling of elastography

In developing breast imaging technologies, testing is done with phantoms. Physical phantoms are normally used but their size, shape, composition, and detail cannot be modified readily. These difficulties can be avoided by creating a software breast phantom. Researchers have created software breast phantoms using geometric and/or mathematical methods for applications like image fusion. The authors report a 3D software breast phantom that was built using a mechanical design tool, to investigate the biomechanics of elastography using finite element modeling (FEM). The authors propose this phantom as an intermediate assessment tool for elastography simulation; for use after testing with commonly used phantoms and before clinical testing. The authors design the phantom to be flexible in both, the breast geometry and biomechanical parameters, to make it a useful tool for elastography simulation. The authors develop the 3D software phantom using a mechanical design tool based on illustrations of normal breast anatomy. The software phantom does not use geometric primitives or imaging data. The authors discuss how to create this phantom and how to modify it. The authors demonstrate a typical elastography experiment of applying a static stress to the top surface of the breast just above a simulated tumor and calculate normal strains in 3D and in 2D with plane strain approximations with linear solvers. In particular, they investigate contrast transfer efficiency (CTE) by designing a parametric study based on location, shape, and stiffness of simulated tumors. The authors also compare their findings to a commonly used elastography phantom. The 3D breast software phantom is flexible in shape, size, and location of tumors, glandular to fatty content, and the ductal structure. Residual modulus, maps, and profiles, served as a guide to optimize meshing of this geometrically nonlinear phantom for biomechanical modeling of elastography. At best, low residues (around 1-5 KPa) were found within the phantom while errors were elevated (around 10-30 KPa) at tumor and lobule boundaries. From our FEM analysis, the breast phantom generated a superior CTE in both 2D and in 3D over the block phantom. It also showed differences in CTE values and strain contrast for deep and shallow tumors and showed significant change in CTE when 3D modeling was used. These changes were not significant in the block phantom. Both phantoms, however, showed worsened CTE values for increased input tumor-background modulus contrast. Block phantoms serve as a starting tool but a next level phantom, like the proposed breast phantom, will serve as a valuable intermediate for elastography simulation before clinical testing. Further, given the CTE metrics for the breast phantom are superior to the block phantom, and vary for tumor shape, location, and stiffness, these phantoms would enhance the study of elastography contrast. Further, the use of 2D phantoms with plane strain approximations overestimates the CTE value when compared to the true CTE achieved with 3D models. Thus, the use of 3D phantoms, like the breast phantom, with no approximations, will assist in more accurate estimation of modulus, especially valuable for 3D elastography systems.

DTI segmentation via the combined analysis of connectivity maps and tensor distances

We describe a novel approach to extract the neural tracts of interest from a diffusion tensor image (DTI). Compared to standard streamline tractography, existing probabilistic methods are able to capture fiber paths that deviate from the main tensor diffusion directions. At the same time, tensor clustering methods are able to more precisely delimit the border of the bundle. To the best of our knowledge, we propose the first algorithm which combines the advantages supplied by probabilistic and tensor clustering approaches. The algorithm includes a post-processing step to limit partial-volume related segmentation errors. We extensively test the accuracy of our algorithm on different configurations of a DTI software phantom for which we systematically vary the image noise, the number of gradients, the geometry of the fiber paths and the angle between adjacent and crossing fiber bundles. The reproducibility of the algorithm is supported by the segmentation of the corticospinal tract of nine patients. Additional segmentations of the corticospinal tract, the arcuate fasciculus, and the optic radiations are in accordance with anatomical knowledge. The required user interaction is comparable to that of streamline tractography, which allows for an uncomplicated integration of the algorithm into the clinical routine.

Real-time prompt gamma monitoring in spot-scanning proton therapy using imaging through a knife-edge-shaped slit

In this paper we report on Monte Carlo simulations to investigate real-time monitoring of the track depth profile in particle therapy by measuring prompt gamma ray emissions: a high sensitivity imaging system employing a knife-edge-shaped slit combined with a position-sensitive gamma detector was evaluated. Calculations to test this new concept were performed for a head-sized software phantom. Clear spatial correlation is shown between the distribution of gamma rays detected with energies above 1.5 MeV and the distribution of prompt gamma rays emitted from the phantom. The number of neutrons originating from nuclear reactions in the phantom that are detected at these high energies is small. Most importantly it is shown that under common therapy conditions enough data may be collected during one spot-step (of the order of 10 ms) to locate the distal dose edge with a 1σ accuracy of better than 1 mm. This indicates that simple slit cameras have high potential for accurate real-time particle therapy adjustment and may become a practical way to improve particle therapy accuracy.

Motion Correction in Dual Gated Cardiac PET Using Mass-Preserving Image Registration

Respiratory and cardiac motion leads to image degradation in positron emission tomography (PET) studies of the human heart. In this paper we present a novel approach to motion correction based on dual gating and mass-preserving hyperelastic image registration. Thereby, we account for intensity modulations caused by the highly nonrigid cardiac motion. This leads to accurate and realistic motion estimates which are quantitatively validated on software phantom data and carried over to clinically relevant data using a hardware phantom. For patient data, the proposed method is first evaluated in a high statistic (20 min scans) dual gating study of 21 patients. It is shown that the proposed approach properly corrects PET images for dual-cardiac as well as respiratory-motion. In a second study the list mode data of the same patients is cropped to a scan time reasonable for clinical practice (3 min). This low statistic study not only shows the clinical applicability of our method but also demonstrates its robustness against noise obtained by hyperelastic regularization.

Fast Monte Carlo-simulator with full collimator and detector response modelling for SPECT

Monte Carlo (MC)-simulations have proved to be a valuable tool in studying SPECT-reconstruction algorithms. Despite their popularity, the use of Monte Carlo-simulations is still often limited by their large computation demand. This is especially true in situations where full collimator and detector modelling with septal penetration, scatter and X-ray fluorescence needs to be included. This paper presents a rapid and simple MC-simulator, which can effectively reduce the computation times. The simulator was built on the convolution-based forced detection principle, which can markedly lower the number of simulated photons. Full collimator and detector response look-up tables are pre-simulated and then later used in the actual MC-simulations to model the system response. The developed simulator was validated by comparing it against (123)I point source measurements made with a clinical gamma camera system and against (99m)Tc software phantom simulations made with the SIMIND MC-package. The results showed good agreement between the new simulator, measurements and the SIMIND-package. The new simulator provided near noise-free projection data in approximately 1.5min per projection with (99m)Tc, which was less than one-tenth of SIMIND's time. The developed MC-simulator can markedly decrease the simulation time without sacrificing image quality.

Direction-dependency of binary tomographic reconstruction algorithms

In this work we study the relation between the quality of a binary tomographic reconstruction and the choice of angles of the projections. We conduct experiments on a set of software phantoms by reconstructing them from different projection sets using three different discrete tomography reconstruction algorithms, and compare the accuracy of the corresponding reconstructions with suitable approaches. To validate our results for possible real-world applications, we conduct the experiments by adding random noise of different characteristics to the simulated projection data, and by applying small topological changes on the phantom images as well. In addition, we also discuss some consequences of the angle-selection dependency and possible practical applications arising from the field of non-destructive testing, too.

Development and characterization of an anthropomorphic breast software phantom based upon region-growing algorithm

We present a novel algorithm for computer simulation of breast anatomy for generation of anthropomorphic software breast phantoms. A realistic breast simulation is necessary for preclinical validation of volumetric imaging modalities. The anthropomorphic software breast phantom simulates the skin, regions of adipose and fibroglandular tissue, and the matrix of Cooper's ligaments and adipose compartments. The adipose compartments are simulated using a seeded region-growing algorithm; compartments are grown from a set of seed points with specific orientation and growing speed. The resulting adipose compartments vary in shape and size similar to real breasts; the adipose region has a compact coverage by adipose compartments of various sizes, while the fibroglandular region has fewer, more widely separated adipose compartments. Simulation parameters can be selected to cover the breadth of variations in breast anatomy observed clinically. When simulating breasts of the same glandularity with different numbers of adipose compartments, the average compartment volume was proportional to the phantom size and inversely proportional to the number of simulated compartments. The use of the software phantom in clinical image simulation is illustrated by synthetic digital breast tomosynthesis images of the phantom. The proposed phantom design was capable of simulating breasts of different size, glandularity, and adipose compartment distribution. The region-growing approach allowed us to simulate adipose compartments with various size and shape. Qualitatively, simulated x-ray projections of the phantoms, generated using the proposed algorithm, have a more realistic appearance compared to previous versions of the phantom. A new algorithm for computer simulation of breast anatomy has been proposed that improved the realism of the anthropomorphic software breast phantom.

Post-processing central k-space subtraction for high-resolution arterial peripheral MR angiography

Peripheral MR angiography requires high resolution and arterial contrast. Neither can be obtained simultaneously due to the short arterial phase of the contrast agent. To improve temporal resolution, keyhole imaging was developed, which combines high resolution and arterial k-spaces at the time of image acquisition. Here, a related approach is introduced for image post-processing in the Fourier domain. It is demonstrated that simple substitution of the central k-space with low-resolution data leads to severe distortion. Hence, a dedicated calculation scheme is necessary for composite k-space post-processing. A solution is presented for high-resolution arterial peripheral MR angiography that uses subtraction of venous intensities from the central high-resolution k-space. The calculations in the Fourier domain do not require interpolations between the different resolutions. High-resolution steady-state MR angiography, which exhibits contrast-enhanced arteries and veins at an isotropic resolution of 0.65 mm, and standard resolution arterial first-pass MR angiography were combined to obtain images with the resolution of the steady-state images and arterial contrast. Numerical simulations on software phantoms are presented. The operation of the method is demonstrated in five patients.

Projection Selection Dependency in Binary Tomography

It has already been shown that the choice of projection angles can significantly influence the quality of reconstructions in discrete tomography. In this contribution we summarize and extend the previous results by explaining and demonstrating tile effects of projection selection dependency, in a set of experimental software tests. We perform reconstructions of software phantoms, by using different binary tomography reconstruction algorithms, from different equiangular and non-equiangular projections sets, under various conditions (i.e., when the objects to be reconstructed undergo slight topological changes, or the projection data is affected by noise) and compare the results with suitable approaches. Based on our observations, we reveal regularities in the resulting data and discuss possible consequences of such projection selection dependency in binary tomography.

Variance-reduction normalization technique for a compton camera system

For an artifact-free dataset, pre-processing (known as normalization) is needed to correct inherent non-uniformity of detection property in the Compton camera which consists of scattering and absorbing detectors. The detection efficiency depends on the non-uniform detection efficiency of the scattering and absorbing detectors, different incidence angles onto the detector surfaces, and the geometry of the two detectors. The correction factor for each detected position pair which is referred to as the normalization coefficient, is expressed as a product of factors representing the various variations. The variance-reduction technique (VRT) for a Compton camera (a normalization method) was studied. For the VRT, the Compton list-mode data of a planar uniform source of 140 keV was generated from a GATE simulation tool. The projection data of a cylindrical software phantom were normalized with normalization coefficients determined from the non-uniformity map, and then reconstructed by an ordered subset expectation maximization algorithm. The coefficient of variations and percent errors of the 3-D reconstructed images showed that the VRT applied to the Compton camera provides an enhanced image quality and the increased recovery rate of uniformity in the reconstructed image.

Segmentation of fiber tracts based on an accuracy analysis on diffusion tensor software phantoms

Due to its unique sensitivity to tissue microstructure, one of the primary applications of diffusion-weighted magnetic resonance imaging is the reconstruction of neural fiber pathways by means of fiber-tracking algorithms. In this work, we make use of realistic diffusion-tensor software phantoms in order to carry out an analysis of the precision of streamline tractography by systematically varying certain properties of the simulated image data (noise, tensor anisotropy, and image resolution) as well as certain fiber-tracking parameters (number of seed points and step length). Building upon the gained knowledge about the precision of the analyzed fiber-tracking algorithm, we proceed by suggesting a fuzzy segmentation algorithm for diffusion tensor images which better estimates the precise spatial extent of a tracked fiber bundle. The presented segmentation algorithm utilizes information given by the estimated main diffusion direction in a voxel and the respective uncertainty, and its validity is confirmed by both qualitative and quantitative analyses.

Evaluation of an improved algorithm for producing realistic 3D breast software phantoms: Application for mammography

This work presents an improved algorithm for the generation of 3D breast software phantoms and its evaluation for mammography. The improved methodology has evolved from a previously presented 3D noncompressed breast modeling method used for the creation of breast models of different size, shape, and composition. The breast phantom is composed of breast surface, duct system and terminal ductal lobular units, Cooper's ligaments, lymphatic and blood vessel systems, pectoral muscle, skin, 3D mammographic background texture, and breast abnormalities. The key improvement is the development of a new algorithm for 3D mammographic texture generation. Simulated images of the enhanced 3D breast model without lesions were produced by simulating mammographic image acquisition and were evaluated subjectively and quantitatively. For evaluation purposes, a database with regions of interest taken from simulated and real mammograms was created. Four experienced radiologists participated in a visual subjective evaluation trial, as they judged the quality of the simulated mammograms, using the new algorithm compared to mammograms, obtained with the old modeling approach. In addition, extensive quantitative evaluation included power spectral analysis and calculation of fractal dimension, skewness, and kurtosis of simulated and real mammograms from the database. The results from the subjective evaluation strongly suggest that the new methodology for mammographic breast texture creates improved breast models compared to the old approach. Calculated parameters on simulated images such as beta exponent deducted from the power law spectral analysis and fractal dimension are similar to those calculated on real mammograms. The results for the kurtosis and skewness are also in good coincidence with those calculated from clinical images. Comparison with similar calculations published in the literature showed good agreement in the majority of cases. The improved methodology generated breast models with increased realism compared to the older model as shown in evaluations of simulated images by experienced radiologists. It is anticipated that the realism will be further improved using an advanced image simulator so that simulated images may be used in feasibility studies in mammography.

Interactive navigation of segmented MR angiograms using simultaneous curved planar and volume visualizations

Interactive visualization is required to inspect and monitor the automatic segmentation of vessels derived from contrast-enhanced magnetic resonance angiography (CE-MRA). A dual-view visualization scheme consisting of curved planar reformation (CPR) and direct volume rendering (DVR) was developed for this purpose and tested. A dual view visualization scheme was developed using the vessel pathline for both camera position and rotation in 3D, greatly reducing the degrees of freedom (DOF) required for navigation. Pathline-based navigation facilitates coupling of the CPR and DVR views, as local position and orientation can be matched precisely. The new technique was compared to traditional techniques in a user study. Layperson users were required to perform a visual search task that involves checking for (minor) errors in segmentations of MRA data from a software phantom. The task requires the user to examine both views. Pathline-based navigation and coupling of CPR and DVR provide user speed performance improvements in a vessel inspection task. Interactive MRA visualization with this method, where rotational degrees of freedom were reduced, had no negative effect. The DOF reduction achieved by the new navigation technique is beneficial to user performance. The technique is promising and merits comprehensive evaluation in a realistic clinical setting.

SU-GG-I-112: Evaluation of Deformable Registration in CT Images with Limited Feature Visibility

Purpose: Applications of deformable image registration (DIR) often assume deformations to be correct everywhere in the patient volume of interest, but prior studies of DIR accuracy have been limited mostly to high‐contrast features in lung. This study examines DIR accuracy with respiration‐correlated CT (RCCT) images of software and physical deformable phantoms and with patient images in abdomen. Methods and Materials: DIR uses a fast free‐form algorithm, which minimizes an energy function consisting of pixel grayscale similarity and smoothness terms. We evaluate its accuracy in 3 types of RCCT images: an anthropomorphic software phantom that models diaphragmatic contraction and chest expansion and whose known voxel displacements provide ground truth; a physical deformable abdominal phantom with implanted radiopaque markers; and patient data in which the motion between end expiration (EE) and end inspiration (EI) images of small low‐contrast features in or near the liver are compared to rigid registration as an approximate ground truth. Results: The mean 3D vector EE‐to‐EI discrepancy between DIR and ground truth displacement in software phantom is 4.5 mm in lung and 5.0 mm in abdomen. This is larger than that observed by comparing visible features in images alone. Mean 3D vector discrepancy is 6.1mm in physical phantom. In both phantoms, larger discrepancy appears to correlate with regions that lack visible features. In patient images, visible features in liver show good agreement between DIR prediction and ground truth, with mean 3D vector EE‐to‐EI discrepancy of 2.0 mm. Conclusions: Visible internal features in CTimages are important for guiding deformable registration. Radiotherapy applications that assume DIR is accurate throughout the volume may underestimate errors in regions lacking visible features. Results in patient images show that reasonable results can be obtained for features in abdomen. Research supported by NIH/NCI grant R01CA126993.

High-quality 3-D coronary artery imaging on an interventional C-arm x-ray system

Three-dimensional (3-D) reconstruction of the coronary arteries during a cardiac catheter-based intervention can be performed from a C-arm based rotational x-ray angiography sequence. It can support the diagnosis of coronary artery disease, treatment planning, and intervention guidance. 3-D reconstruction also enables quantitative vessel analysis, including vessel dynamics from a time-series of reconstructions. The strong angular undersampling and motion effects present in gated cardiac reconstruction necessitate the development of special reconstruction methods. This contribution presents a fully automatic method for creating high-quality coronary artery reconstructions. It employs a sparseness-prior based iterative reconstruction technique in combination with projection-based motion compensation. The method is tested on a dynamic software phantom, assessing reconstruction accuracy with respect to vessel radii and attenuation coefficients. Reconstructions from clinical cases are presented, displaying high contrast, sharpness, and level of detail. The presented method enables high-quality 3-D coronary artery imaging on an interventional C-arm system.