In recent years, simulations of the blood flow and the wall mechanics in the vascular system with patient-specific boundary conditions by using computational fluid dynamics (CFD) and computational solid mechanics (CSM) have gained significant interest. A common goal of such simulations is to help predict the development of vascular diseases over time. However, the validity of such simulations and therefore the validity of the predictions are often questioned by physicians. The aim of the research reported in this paper is to validate CFD simulations performed on patient-specific models of abdominal aorta aneurysms (AAAs) using patient-specific blood velocity inflow profiles. Patient-specific AAA geometries were derived from images originating from Computed Tomography (CT) or Magnetic Resonance (MR) imaging. Patient-specific flow profiles were measured with Phase-Contrast MR imaging (Quantitative flow, Qflow). Such profiles, determined at the inflow site of the AAA, were used as inflow boundary condition for CFD simulations. Qflow images that were taken on a number of planes along the AAA were used for the validation of the simulation results. To compare the measured with the simulated flow we have generated synthetic Qflow images from the simulated velocities on cut-planes positioned and oriented according to the planes of the validation images. The comparison of the real with the simulated flow profiles was performed visually and by quantitatively comparing flow values on cross sections of the AAA in the measured and the synthetic Qflow images. In a preliminary study on two patients we found a reasonable agreement between the measured and the simulated flow profiles.
Finite element wall stress simulations on patient-specific models of abdominal aortic aneurysm (AAA) may provide a better rupture risk predictor than the currently used maximum transverse diameter. Calcifications in the wall of AAA lead to a higher maximum wall stress and thus may lead to an elevated rupture risk. The reported material properties for calcifications and the material properties actually used for simulations show great variation. Previous studies have focused on simplified modelling of the calcification shapes within a realistic aneurysm shape. In this study we use an accurate representation of the calcification geometry and a simplified model for the AAA. The objective of this approach is to investigate the influence of the calcification geometry, the material properties and the modelling approach for the computed peak wall stress. For four realistic calcification shapes from standard clinical CT images of AAA, we performed simulations with three distinct modelling approaches, at five distinct elasticity settings. The results show how peak wall stress is sensitive to the material properties of the calcifications. For relatively elastic calcifications, the results from the different modelling approaches agree. Also, for relatively elastic calcifications the computed wall stress in the tissue surrounding the calcifications shows to be insensitive to the exact calcification geometry. For stiffer calcifications the different modelling approaches and the different geometries lead to significantly different results. We conclude that an important challenge for future research is accurately estimating the material properties and the rupture potential of the AAA wall including calcifications.
Finite element method based patient-specific wall stress in
abdominal aortic aneurysm (AAA) may provide a more accurate rupture
risk predictor than the currently used maximum transverse diameter.
In this study, we have investigated the sensitivity of the wall
stress in AAA with respect to geometrical variations. We have
acquired MR and CT images for four patients with AAA. Three
individual users have delineated the AAA vessel wall contours on the
image slices. These contours were used to generate synthetic feature images for a deformable model based segmentation method. We investigated the reproducibility and the influence of the user variability on the wall stress. For sufficiently smooth models of the AAA wall, the peak wall stress is reproducible for three out of the four AAA geometries. The 0.99 percentiles of the wall stress show
excellent reproducibility for all four AAAs. The variations induced by user variability are larger than the errors caused by the segmentation variability. The influence of the user variability appears to be similar for MR and CT. We conclude that the peak wall stress in AAA is sensitive to small geometrical variations. To increase reproducibility it appears to be best not to allow too much geometrical detail in the simulations. This could be achieved either by using a sufficiently smooth geometry representation or by using a more robust statistical parameter derived from the wall stress distribution.
In recent years, the assessment of patient-specific hemodynamic information of the cardiovascular system has become an important issue. It is believed that this information will improve the diagnosis and treatment of cardiovascular diseases. Realistic patient geometries and flow velocities acquired from image data can nowadays be used as input for computational fluid dynamics (CFD) simulations of the blood flow through the cardiovascular system. Results obtained from these simulations have to be comprehensively visualized so that the physician can understand them and draw diagnostic and/or therapeutic conclusions. The aim of the research reported in this paper is to provide methods for the combined comprehensive visualization of the anatomical information segmented from image data with the hemodynamic information acquired by CFD simulations based on these image data. Several methods are known for the visualization of the blood flow velocity, e.g. flow streamlines, particle traces or simple cut planes through the vessel with a color-coded overlay of the flow velocity. To make these flow visualizations more understandable for the physician, we have developed methods to generate combined visualizations of the simulated blood flow velocity and the patient’s anatomy segmented from the image data. First results of these methods show that the perception of CFD simulation results of blood flow is much better when it is combined with anatomical information of surrounding structures. Physicians reacted very enthusiastically during presentations of results of our new visualization methods. Results will be demonstrated at the conference.
Recent advances in Magnetic Resonance Imaging allow fast recording of contrast enhanced myocardial perfusion scans. MR perfusion scans are made by recording, during a period of 20-40 seconds a number of short-axis slices through the myocardium. The scanning is triggered by the patient's ECG typically resulting in one set of slices per heart beat. For the perfusion analysis, the myocardial boundaries must be traced in all images Currently this is done manually, a tedious procedure, prone to inter- and intra-observer variability. In this paper a method for automatic detection of myocardial boundaries is proposed. This results in a considerable time reduction of the analysis and is an important step towards automatic analysis of cardiac MR perfusion scans. The most important consideration in the proposed approach is the use of not only spatial-intensity information, but also intensity-time and shape information to realize a robust segmentation. The procedure was tested on a total of 30 image sequences from 14 different scans. From 26 out of 30 sequences the myocardial boundaries were correctly found. The remaining 4 sequences were of very low quality and would most likely not be used for analysis.
Magnetic Resonance Imaging (MRI) is a powerful technique for imaging cardiovascular diseases. The introduction of cardiovascular MRI into clinical practice is however hampered by the lack of efficient and accurate image analysis methods. This paper focuses on the evaluation of blood perfusion in the myocardium (the heart muscle) from MR images, using contrast-enhanced ECG-triggered MRI. We have developed an automatic quantitative analysis method, which works as follows. First, image registration is used to compensate for translation and rotation of the myocardium over time. Next, the boundaries of the myocardium are detected and for each position within the myocardium a time-intensity profile is constructed. The time interval during which the contrast agent passes for the first time through the left ventricle and the myocardium is detected and various parameters are measured from the time-intensity profiles in this interval. The measured parameters are visualized as color overlays on the original images. Analysis results are stored, so that they can later on be compared for different stress levels of the heart. The method is described in detail in this paper and preliminary validation results are presented.
A specially designed phantom consisting of a 3D array of 427 accurately manufactured spheres together with a point-based registration algorithm was used to detect distortion described by polynomial orders 1-4. More than thirty 3D gradient echo (FFE) and multi-slice spin echo (SE) phantom scans were acquired with a Philips 1.5T Gyroscan. Distortion was measured as a function of: readout gradient strength (0.72<= G<SUB>r</SUB> <=1.7mT/m), TR/TE/flip angle, shim settings, and temporal distortion change for 11 weekly scans for the FFE sequence and TR/TE/slice gap for SE. Precision measurements for linear distortion were: scale <=0.03%, shear <=0.04 degrees. Linear distortion in the readout dependent directions increased with decreased readout strength (r>0.93). There was a significantly higher (p<0.01) sagittal shear for 5 SE scans compared with 5 FFE ones with the same G<SUB>r</SUB> - possibly because of slice selection. Different shim settings produced only linear distortion change: up to 2% scale and 1 degree shear. There was negligible distortion change over time: scale < 0.1%, shear <= 0.05 degrees. There was a decrease in distortion as a function of polynomial order (r>0.9, n=33), 75% of the distortion was either first or second order.
Three-dimensional magnetic resonance medical images may contain scanner- and patient-induced geometric distortion. For qualitative diagnosis, geometric errors of a few millimeters are often tolerated. However, quantitative applications such as image-guided neurosurgery and radiotherapy can require an accuracy of a millimeter or better. We have developed a method to accurately measure scanner-induced geometric distortion and to correct the MR images for this type of distortion. The method involves a number of steps. First, a specially designed phantom is scanned that contains a large number of reference structures on positions with a manufacturing error of less than 0.05 mm. Next, the positions of the reference structures are automatically detected in the scanned images and a higher-order polynomial distortion-correction transformation is estimated. Then the patient is scanned and the transformation is applied to correct the patient images for the detected distortion. The distortion-correction method is explained in detail in this paper. The accuracy of the method has been measured with synthetically generated phantom scans that contain an exactly-known amount and type of distortion. The reproducibility of the method has been measured by applying it to a series of consecutive phantom scans. Validation results are briefly described in this paper, a more-detailed description is given in another submission to SPIE Medical Imaging 2001.
Almost all present-day lossy image-compression methods are so-called waveform coders, i.e. they attempt to approximate the original image waveform as closely as possible with the available number of bits. At medium-to-low compression ratios this can usually be achieved quite well, but at high compression ratios clearly visible artefacts may be introduced into the coded images. During the compression of medical x-ray images we noticed that the data that suffer most from a high degree of compression are the noise-like components present in these images. The loss of these components is found to depreciate the perceived image quality. This paper proposes a method for modeling the noise components removed due to lossy compression and for regenerating these components during the decompression of the image.
In previous papers, we presented an overlapped transform-coding method for efficient data compression of medical x-ray image series. The proposed method is a lossy compression method. The distortion that is introduced by the compression is determined by the step size with which the transform coefficients are quantized. The number of bits produced per image depends on the amount of detail in the image. In principle, highly detailed images produce higher bit rates than less detailed images. For applications in which only a small number of images are recorded, the amount of time needed to store or transmit these images may not be an essential factor. In that case, some fluctuation in the bit rate may be tolerable. But for applications in which image series have to be stored or transmitted in real time, a constant bit rate is often preferred. In this paper, we explain how compression at a constant bit rate can be achieved. We propose a bit-rate control method that is capable of realizing a constant number of bits per image with a homogeneous distribution of the quantization errors over the coded image.
In previous papers, we presented an overlapped transform-coding method for efficient data compression of medical x-ray image series. In this paper, we address two improvements of this method. Firstly, we applied the method to so-called enhanced x-ray images, i.e. to images of which the middle and high frequencies had been emphasized. In this paper, we explain how to code raw instead of enhanced images under the constraint that enhancement of the coded raw images does not lead to a clear visibility of coding artefacts. Secondly, the coding artefacts introduced at high compression ratios by the previously published method are more clearly visible in the dark than in the bright areas of an image. In this paper, we explain what provisions can be added to our data-compression system to achieve a better balance between the perceptual image quality in dark and bright areas.
Component-based color video signals usually consist of one luminance (Y) and two chrominance or color-difference (U and V) components, which are obtained by multiplying the R, G and B components produced by the video camera by a 3 X 3 matrix. In order to compensate for the nonlinearity of the TV monitor on which the video signal will be displayed, the R, G, and B signals are usually first gamma-corrected before the matrix operation is applied. Due to this gamma correction, the Y component does not represent exactly the real luminance L of the recorded scene, and part of the real luminance information is carried by the U and V components. The introduction of errors into these chrominance components by, for example, video coding will therefore lead to perceivable errors in the luminance produced by the TV monitor on which the coded signal is eventually displayed. In this paper, we present a simple but effective method for avoiding this crosstalk of chrominance errors into the luminance. This method can be incorporated in most compression systems.
The potential of overlapped transform coding for data compression of medical x-ray image series has been investigated and a comparison with conventional block-based transform coding was made. We found that overlapped transform coding clearly produces less blocking artifacts than conventional block-based transform coding at compression ratios in the range 8 - 16.
The HDTV studios of the future will require magnetic recorders for the acquisition, storage, editing and broadcasting of HDTV material. Inside these studios, the HDTV signal will probably be a digital signal. Due to the high bit rate of digital HDTV, recorders without bit- rate reduction will be mechanically complex and these recorders will have a limited playing time. The complexity of the recorder can be decreased, and the playing time can be increased, by reducing the bit rate of the HDTV signal before it is recorded on tape. This paper first discusses the constraints on bit-rate reduction for professional HDTV recording and then goes on to describe the results obtained with a bit-rate reduction method called motion-adaptive intraframe transform coding.
The application of subband coding to video signals is hindered by the fact that these signals usually have the interlaced format. For proper preservation of the motion in an interlaced video signal, field-based subband coding is preferred. In areas of the video frames without motion, however, frame-based subband coding leads to a lower bit rate than field-based coding. This paper proposes a method to combine the advantages of field-based and frame- based subband coding. The method is called motion-adaptive subband coding. It first divides each video frame into moving and non-moving areas. The moving areas are processed with field-based subband coding, the non-moving ones with frame-based subband coding. Special attention is paid to the transitions between moving and non-moving areas. It is shown that the proposed motion-adaptive subband coding system has a higher performance in terms of bit rate versus picture quality than purely field-based or frame-based subband coding.
Gradual introduction of HDTV is considered to be important. In this paper, a bit-rate reduction system is introduced, which decreases the bit rate of digital HDTV from 664 Mbit/s to about 80 Mbit/s, while ensuring compatibility with TV. The system is based on first subband splitting the interlaced HDTV signal into an interlaced compatible TV signal and three surplus signals. Then, the compatible TV signal is coded with intraframe DCT coding, whereas the surplus signals are coded with quantization, variable-length coding and runlength coding.