Display stabilization is a technique by which a feature of interest in a cine image sequence is tracked and then shifted to remain approximately stationary on the display device. Prior simulations indicate that display stabilization with high playback rates ( 30 f/s) can significantly improve detectability of low-contrast features in coronary angiograms. Display stabilization may also help to improve the accuracy of intra-coronary device placement. We validated our automated tracking algorithm by comparing the inter-frame difference (jitter) between manual and automated tracking of 150 coronary x-ray image sequences acquired on a digital cardiovascular X-ray imaging system with CsI/a-Si flat panel detector. We find that the median (50%) inter-frame jitter between manual and automatic tracking is 1.41 pixels or less, indicating a jump no further than an adjacent pixel. This small jitter implies that automated tracking and manual tracking should yield similar improvements in the performance of most visual tasks. We hypothesize that cardiologists would perceive a benefit in viewing the stabilized display as an addition to the standard playback of cine recordings. A benefit of display stabilization was identified in 87 of 101 sequences (86%). The most common tasks cited were evaluation of stenosis and determination of stent and balloon positions. We conclude that display stabilization offers perceptible improvements in the performance of visual tasks by cardiologists.
Fluoroscopic images are degraded by scattering of x-rays from within the patient and by veiling glare in the image intensifier. Both of these degradations are well described by a response function applied to primary intensity. We can automatically estimate the parameters of the response function with the aid of a reference object placed in the imaging field. Subtraction of the true reference signal yields artifacts unless proper scatter-glare correction is performed. We adjust the scatter-glare parameters in order to minimize these artifacts. We demonstrate this technique using an anthropomorphic phantom plus additional scattering material. Root mean square error in densitometric measurements of an x-ray phantom is reduced by 54 percent compared with no correction and by 36 percent compared with subtraction of uniform scatter measured under a beam stop.
Layer decomposition is a promising technique for background removal and noise reduction in coronary angiograms. Our layer decomposition algorithm decomposes a projection image sequence into multiple 2D layers undergoing translation, rotation, and scaling. We apply this layer decomposition algorithm to simulated angiograms containing stenotic vessels with and without thrombus. We constructed 85 pairs of simulated angiographic sequences by embedding each of 5 simulated vessels (with and without thrombus) in 17 clinical angiograms. We computed the response of a matched eye filter applied to (1) one raw image of each sequence at the time of minimal motion (RAW), (2) a layered digital subtraction angiography (LDSA) image of the same frame, and (3) the time-averaged vessel layer image (LAYER). We find that on average the LAYER and LDSA images have higher signal-to-noise ration and larger area under the receiver- operator characteristic curves (AUC) than the raw images.
Low-contrast features such as thrombus, dissection, and even stents can be difficult to detect in coronary x-ray images or angiograms. For these reasons we propose to improve the clinical visualization of low-contrast structures using layer decomposition. Our method for layer decomposition models the cone-beam projections through the chest as a set of superposed layers moving with translation, rotation, and scaling. We solve for the layer motions using phase correlation methods. We solve for the layer densities by averaging along moving trajectories and subtracting new layer densities from previous layer estimates. We apply layer decomposition to clinical coronary angiograms with and without contrast material. The reconstructed vessel layer represents a motion-compensated temporal average of structures co-moving with the vessel. Subtraction of background layers from the original image sequence yields a tracked background-subtracted sequence which has no vessel-motion artifacts and almost no increase in noise, unlike standard background substraction techniques. Layer decomposition improves vessel definition and visibility of low-contrast objects in cine x-ray image sequences.
Accurate placement and expansion of coronary stents is hindered by the fact that most stents are only slightly radiopaque, and hence difficult to see in a typical coronary x-rays. We propose a new technique for improved image guidance of multiple coronary stents deployment using layer decomposition of cine x-ray images of stented coronary arteries. Layer decomposition models the cone-beam x-ray projections through the chest as a set of superposed layers moving with translation, rotation, and scaling. Radiopaque markers affixed to the guidewire or delivery balloon provide a trackable feature so that the correct vessel motion can be measured for layer decomposition. In addition to the time- averaged layer image, we also derive a background-subtracted image sequence which removes moving background structures. Layer decomposition of contrast-free vessels can be used to guide placement of multiple stents and to assess uniformity of stent expansion. Layer decomposition of contrast-filled vessels can be used to measure residual stenosis to determine the adequacy of stent expansion. We demonstrate that layer decomposition of a clinical cine x-ray image sequence greatly improves the visibility of a previously deployed stent. We show that layer decomposition of contrast-filled vessels removes background structures and reduces noise.
We present a method for decomposition of angiographic image sequences into moving layers undergoing translation, rotation, and scaling. We first describe a regularization method for scatter-glare correction which can be used to obtain good estimates of projected x-ray attenuation coefficient. We then compute a set of weighted correlation functions to determine the motion of each layer, and compute the layer densities in the spatial domain by averaging along moving trajectories. We demonstrate the utility of our method by successfully decomposing simulated angiograms into moving layers. We also demonstrate visually acceptable layer decomposition of actual angiograms.
Videodensitometric measurement of blood flow requires injection of spatially varying contrast density with minimal perturbation of the flow. Constant injection does not yield sufficient spatial gradients for flow measurement during peak flow and diastole, whereas pulsed injection superposes a time- varying perturbation on the measured flow. In order to avoid these drawbacks, we propose to pre-load the catheter injection tubing with spatially varying contrast density. This allows us to generate spatially localized contrast profiles in phantoms and to inject temporally varying contrast profiles in vivo without pulsatile injection. In phantom experiments we obtain better spatial localization of bolus using pre-loading than by pulsed injection of pure contrast material. Preliminary results indicate improvement in accuracy of blood flow measurements using pre-loaded bolus.
Blood flow rate is an important parameter for functional evaluation of vascular disease. Instantaneous blood flow measurements from digital cerebral angiograms can be performed during endovascular interventional procedures providing radiologists with real-time minimally invasive flow measurements. Most published videodensitometric techniques assume a specific radial profile of velocity. Such assumptions can cause errors if the velocity profile differs from the model, changes during the heart cycle or if contrast concentration is not radially uniform. All of these conditions are typical in clinical practice. We propose to divide the vessel inside the region of interest into a number of narrow laminae and its X-ray image into corresponding narrow bands. Flow inside each lamina is assumed to be plug and parallel to the lamina. Blood flow velocities within each band are be computed using existing angiographic techniques and are used to compute flow velocities within each laminae and within the entire vessel. We evaluated the new approach on simulated and phantom angiograms. It improved accuracy of measurements of plug-flow algorithms from simulated angiograms. The results obtained during the evaluation of the technique on phantom angiograms are inconclusive. The proposed algorithm extends regular videodensitometric flow measurement techniques to allow for radially dependent flow profiles.
Fluoroscopic images are degraded by scattering of x-rays from within the patient and by veiling glare in the image intensifier. Both of these degradations are well described by a response function applied to the primary intensity. If the response function is known, than an estimate of the primary component of the image can be computed by applying the inverse operation. However, the response function is actually variable, with dependence on such factors as patient thickness and imaging geometry. We describe a technique for estimating a parameterized response function so that a good estimate of the subject density profile can be recovered even if the response function parameters are not known in advance. Our method uses a partially absorbing filter with spatially varying density as a reference object which enables us to compute good estimates of the parameterized response function. We use simulated images to evaluate our method for a wide range of conditions. Our simulation results show that this technique can greatly reduce densitometric errors in fluoroscopic images.
Blood flow rate is an important parameter for functional evaluation of vascular disease. Instantaneous blood flow measurements from digital cerebral angiograms can be performed during endovascular interventional procedures providing interventional radiologists with minimally invasive real-time flow measurements. Distance-density curve matching (DDCM) methods are a promising class of videodensitometric techniques. However, published techniques have a relatively low theoretical maximum of measurable flow rate and sensitivity to noise and image artifacts. We investigate the use of alternative difference metrics along with curve fitting and extrapolation. These modifications can potentially reduce the influence of noise, image defects and flow irregularities. Extrapolation of difference profiles may overcome the theoretical limit for maximum measurable flow rate. The proposed methods were evaluated using both simulated angiograms and angiograms obtained by imaging a flow phantom under clinically realistic flow and contrast injection conditions. Our results indicate that under the conditions of constant flow the proposed modifications yield some improvement in both accuracy and reliability of instantaneous flow rate measurements. These improvements were the most noticeable during the early contrast wash-out phase, when the published DDCM methods were observed to fail.
The goal of this work is to demonstrate the feasibility of 3D imaging of coronary stents using fluoroscopy. This technique could potentially provide an inexpensive and non- invasive alternative to stent inspection by intravascular ultrasound. The major difficulty to e overcome is real or apparent motion of the stent between successive views. We solve this problem by tracking a feature point ont he stent prior to performing the 3D reconstruction. We shifted the images to eliminate this apparent motion, then reconstructed the 3D stent image using iterative backprojection. The stent cross-sectional images are successfully reconstructed with spatial resolution of approximately 0.4 mm. This successful reconstruction of a coronary stent in vitro demonstrates the feasibility of 3D imaging of coronary stents using fluoroscopy.
The purpose of this work is to evaluate the potential of motion-compensated temporal filtering (MCTF) for reduction of noise in B-mode echocardiograms. MCTF is expected to reduce noise with less blurring of the signal than would be obtained from spatial filtering or direct averaging of sequential frames. We perform motion estimation using the assumption of constant brightness for moving features in the image sequences.We transform the images using the assumption of motion-compensated sequence. We then perform direct averaging of a variable number of frames. Filtered images are obtained using direct averaging, MCTF using estimated motion, MCTF using known motion, and a biased 2D least mean squares filter (TDLMS). We compare signal-to-noise ratios (SNR) for each of these methods. Degradation of signal accuracy by blurring is evaluated independently by computing the correlation coefficient between the original and filtered signals. The signal-to-noise ratios and signal accuracy obtained from MCTF are consistently better than obtained from direct averaging of the images. The application of biased TDLMS to the same frames yields even higher SNR in many cases but may also increase the signal blurring.
We propose a novel technique for estimation of image noise amplitude without a priori signal information. Knowledge of the normalized noise distribution is used to construct an approximate Wiener filter parametrized by the estimated noise amplitude. For a given noise amplitude, the resulting signal estimate is subtracted from the image to produce a sample noise estimate. The estimated noise amplitude is varied in order to maximize the probability that the noise estimate is a sample of the known noise distribution with the estimated variance. Probability is measured by the (chi) 2 distribution. The technique is tested for several images by adding stationary zero-mean Gaussian noise with varying amplitude. The variation of estimated versus added noise variance is very nearly linear with unit slope for all of the images tested. The estimated noise variance for images with no added noise is generally small compared to the signal power unless the signal power spectrum is nearly white.
X-ray fluoroscopic images are degraded by x-ray scattering within the subject and veiling glare in the image intensifer. Densitometric accuracy is further degraded by beam hardening. Scattering, veiling glare, or both are modeled as a blurred representation of the primary image plus an offset. If the image can be represented by convolution of the primary with a known response function, then an estimate of the primary component of the image can be computed by deconvolution. We describe a technique for estimating a parameterized response function so that a good estimate of the subject density profile can be recovered even if the response function parameters are not known in advance. This is important for x-ray imaging (particularly fluoroscopy) since the acquisition parameters are variable. A reference object designed to be uncorrelated with the subject is imaged in superposition with the subject. The unknown parameters are then adjusted to minimize a cost function subject to the constraint that the correlation between the known reference density and the estimated subject density be zero. The method can be extended to include a correction for beam hardening.
Conventional vessel tracking and segmentation techniques identify the positions and two- dimensional structure of arteries in each frame of the angiographic sequence, but cannot distinguish the artery and background contributions to the intensity. We report a new technique for motion-compensated estimation of artery and background structures in coronary angiograms. The image within a region of interest is modeled as consisting of a sum of two independently moving layers, one of which contains the artery and one consisting of only background structures. The density of each of these layers is solved under two assumptions: (1) within each layer, the density varies from frame to frame only by rigid translation, and (2) the sum of the densities of the two layers equals the actual image density. This technique can be used to enhance image sequences by subtracting the component of the background whose temporal variation is entirely due to rigid translation. The feasibility of this technique is demonstrated on synthetic and clinical image sequences.
New algorithms for motion estimation from sequential images are applied to M-mode echocardiograms. Motion is estimated by finding a transformation which relates an initial and final image. The transformation includes a 1D displacement field and modifications in image intensity. The displacements and intensity modifications are adjusted iteratively using the method of convex projections applied to linearized constraint equations. Preliminary results indicate that this method is effective in estimating motion from M-mode images. Computed velocity vectors are approximately tangent to the visible heart wall boundary trajectories. Motion computed from a single reference time appears to provide a means for tracking individual heart wall boundaries.
An important aspect of interventional neuroradiological procedures is the ability to access and interact with digital angiographic images to select and clinically evaluate intravascular therapeutic treatment. These issues can be adequately addressed and successfully accomplished using PACS. PACS provides the key technologies needed to access, display, and analyze sequential angiographic images. A dedicated neuroradiology PACS network consisting of workstations for the review of diagnostic data, quantitative analysis of arterial blood flow, and therapeutic assessment has been established at UCLA. The PACS provides not only a reliable and efficient infrastructure for on-line image retrieval and delivery of all digitally acquired angiographic images, but could also serve as a network supercomputing resource for computationally intensive calculations of hemodynamic parameters in the cerebral vasculature. In summary we have developed an innovative application of PACS in neuroangiography. It offers: (1) automatic optimized image display upon acquisition; (2) automatic retrieval of archived cases for a current patient; and, (3) a rapid, streamlined user interface for the quantitation of hemodynamic flow phenomena in normal and diseased cerebral vessels.
The intensity of medical images often represents a quantity which is conserved during motion. Hence the motion which occurs between sequential images can be viewed as a coordinate transformation. If edge effects can be neglected, the form of the transformation can be determined from the generalized moments of the two images. The equations which transform arbitrary generalized moments from an initial image to a target image are expressed as a function of the displacement field. The apparent displacement field or optical flow is then computed by the method of convex projections, utilizing the functional derivatives of the linearized moment equations. Smoothness is ensured by using sinusoidal moments and building up the solution from low to high spatial frequencies. The technique is demonstrated using simple examples and actual medical images. It is expected that this method will be useful for analysis of heart motion and blood flow.
A method is presented to automatically determine the three-dimensional geometry of a stationary vessel tree from orthogonal biplanar digital angiographic image sequences, without a priori knowledge or user interaction. Vessels are identified unambiguously by their position in each projection and by comparison of time-density curves. Single-plane angiographic sequences are used to illustrate the technique, simulating a single slice of three-dimensional data. Projections are taken in each direction, yielding one row and one column of data which are used to reconstruct the vessel geometry. It is demonstrated that overlapping vascular regions can be resolved by temporal processing of angiographic image sequences, although high-quality reconstruction has not yet been achieved.
A system for display and analysis of neurological digital angiographic images is described. Images are archived along with patient data in a central database. The angiographic images can be displayed simultaneously with images from other modalities for study of anatomy. Digital videodensitometric techniques are used to calculate geometric and dynamic parameters from the angiographic image sequences.
Two types of workstation are being developed at UCLA for neuroradiology a display workstation and a therapeutic workstation. The display station is used for review of images derived from neuroradiology examinations whereas the therapeutic station is for image analysis. The therapeutic workstation will be used to compare angiograms with CT and MR images and to calculate quantitative information from image sets. Parameters obtained by image analysis will be useful for planning and evaluation of interventional procedures. Current emphasis is on development of analysis tools for digital subtraction angiography. Digital densitometry and parametric imaging routines are being developed for analysis of DSA images of blood flow (with contrast injection) taken with a GE Digital Flouricon 5000 system. These routines include determination of vessel geometry regional blood volume flow rate and velocity. The starting point for software development is the CALIPSO (CALifornia Image Processing SOftware) package developed at UCLA for the Macintosh II. A Stellar GS2000 graphics mini-supercomputer will ultimately be used to allow rapid manipulation of images. The workstation will be connected to various imaging modalities through an Ethernet network. 1.