As an important clinical task, evaluating the placement of multiple coronary stents requires fine judgments
of distance between stents. However, making these judgments is limited by low system resolution, noise,
low contrast of the deployed stent, and stent motion during the cardiac cycle. We use task performance as a
figure of merit for optimizing image display parameters. In previous work, we described our simulation
procedure in detail, and also reported results of human observers for a visual task involving discrimination
of 4 gap sizes under various frame rates and number of frames. Here, we report the results of three spatial
model observers (i.e. NPW, NPWE, and PWMF) and two temporal sensitivity functions (i.e. transient and
sustained) for the same task. Under signal known exactly conditions, we find that model observers can be
used to predict human observers in terms of discrimination accuracy by adding internal noise.
The placement of multiple coronary stents requires fine judgments of distance between a deployed stent
and stent/guidewire assembly. The goal of this deployment is to achieve continuous and gapless coverage
between them. However, making these judgments is difficult because of limited system resolution, noise,
relatively low contrast of the deployed stent, and stent motion during the cardiac cycle. In this work, we
extend our previous work by investigating wider range of conditions associated with this task. The present
studies consider number of frames and frame rate separately, and include stabilization of the stents as a way
to quantify the performance effects of stent motion. We find that (1) stabilization reduces the uncertainty
when detecting the gap size; (2) observer performance increases with the number of frames; (3) the effect
of display frame rate is highly dependent on the motion of the target.
Evaluating the placement of multiple coronary stents requires fine judgments of distance between two or more deployed
stents in order to determine if there is continuous coverage without a gap or overlap between the two. These judgments
are made difficult by limited system resolution, noise, relatively low contrast of the deployed stent, and stent motion
during the cardiac cycle. In this work, we assess the effect of frame rate and number of frames used in a sequence on the
detection accuracy of gaps between the stents. Both of these factors can be used to reduce patient dose. We use real X-ray
coronary angiograms as backgrounds along with stents imaged separately with Lucite for similar beam attenuation.
Stents and simulated guidewires are embedded in the angiograms by adding optical densities after scatter subtraction.
Realistic motion is rendered by manually synchronizing the stent densities to vascular features in each image. We find
no significant difference the different frame rates or sequence lengths, indicating potential savings in dose.
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.
KEYWORDS: Angiography, Arteries, Eye, Digital imaging, Image filtering, Signal to noise ratio, Motion estimation, Image processing, Signal processing, Computer simulations
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 applied three different model observers (non-prewhitening matched filter with an eye filter, Hotelling and channelized Hotelling) to predict the effect of JPEG image compression on human visual detection of a simulated lesion (clinically known as thrombus) in single frame digital x-ray coronary angiograms. Since the model observers' absolute performance is better than human, model performance was degraded to match human performance by injecting internal noise proportional to the external noise. All three model-observers predicted reasonably well the degradation in human performance as a function of JPEG image compression, although the NPWEW and the channelized Hotelling models (with internal noise proportional to the external noise) were better predictors than the Hotelling model.
Eigler et al (1994) proposed an optimized display for coronary angiograms where each image of the sequence is digitally shifted so that the feature of interest within an artery remains fixed at the center of the screen and the background moves (stabilized display). We measure the effect of JPEG and CREW (a wavelet-based software) image compression on the detectability of a simulated morphological feature (filling defect) for the stabilized display and compare it to the conventional moving artery display. Our results show that 15:1 compressed JPEG for the stabilized display and the moving artery display does not significantly degrade human performance but a 19:1 CREW did. The stabilized display significantly improved performance with respect to the conventional moving artery display for the uncompressed and the 15:1 JPEG but not for the 19:1 CREW.
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.
Quantitative coronary angiography (QCA) diameter measurements are important in determining the extent of coronary artery disease progression and course of treatment in a patient. Traditional QCA techniques filter the X-ray angiographic image in order to enhance the edge profiles. We investigated a new method of obtaining an edge enhancement filter based on the power spectrum of an ensemble of view specific background images for X-ray angiographic images. The band-pass filter is obtained from the power spectra of a particular view imaged with (1) background only and (2) contrast filled arteries plus background. We tested our band-pass filter by measuring the diameters of a coronary artery phantom. The angiograms were filtered with a Sobel kernel to highlight the edges. The same angiograms were band-pass filtered and then Sobel filtered to see if our band-pass filter had any effect on the accuracy of the artery diameter measurement. The mean absolute percent error of the diameter measures decreased with the use of the band-pass filter 14.2% plus or minus 16.5% (n equals 57). Two- way analysis of variance was not statistically significant between the diameter measures (0.5 - 5.0 mm) of the Sobel only filtered image compared to the band-pass edge enhancement plus Sobel filtering.
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.
Quantitative coronary angiography (QCA) diameter measurements have been used as an endpoint measurement in clinical studies involving therapies to reduce coronary atherosclerosis. The accuracy and precision of the QCA measure can affect the sample size and study conclusions of a clinical study. Measurements using x-ray test phantoms can underestimate the precision and accuracy of the actual arteries in clinical digital angiograms because they do not contain complex patient structures. Determining the clinical performance of QCA algorithms under clinical conditions is difficult because: (1) no gold standard test object exists in clinical images, (2) phantom images do not have any structured background noise. We purpose the use of computer simulated arteries as a replacement for traditional angiographic test phantoms to evaluate QCA algorithm performance.
Model observers have been compared to human performance detecting low contrast signals in a variety of computer generated background including white noise, correlated noise, lumpy backgrounds, and two component noise. The purpose of the present paper is to extend this work by comparing a cumber of previously proposed model observers to human visual detection performance in real anatomic backgrounds. Human and model observer performance are compared as a function of increasing added white noise. Our results show that three of the four models are good predictors of human performance.
Many receiver operating characteristic (ROC) studies rely on establishing 'truth' about lesion absence/presence on the agreement of a panel of experts. In addition, in the consensus committee methodology, images where the members of the committee did not reach any agreement about the lesion absence/presence are discarded from the ROC study. But how reliable are 'gold standards' established by these expert committees. And does discarding images where no agreement was reached bias the spectrum of difficulty of the test image set for the ROC study. Computer simulated lesions of different strengths were embedded in real x-ray coronary angiogram background in order to measure the agreement among the decisions of members of the committee as a function of signal strength, to establish the accuracy of the decisions of the consensus expert committee and to compare it to individual more inexperienced readers.
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.
Experiments on visual detection in computer simulated noise (e.g. white noise) show that random variations from location to location in the image (due to noise) degrade human performance. Psychophysical experiments of visual detection of signals superimposed on a known deterministic background ('mask') show that human performance can be degraded by the presence of a high contrast deterministic background through divisive inhibition. The purpose of this paper is to perform a psychophysical experiment to determine the relative importance of these two sources of performance degradation (random background variations and contrast masking effects) in human visual detection in natural medical image backgrounds. The results show that both contrast masking and random background variations degrade human performance for detecting signals in natural medical image backgrounds. These results suggest that current observer models which do not include a source of degradation due to the deterministic presence of the background might need to model such effects in order to reliably predict human visual detection in natural medical image backgrounds.
Single-frame quantitative coronary angiography (QCA) requires a human operator to select one frame for analysis out of typically 90 or more acquired in each sequence. QCA measurements for the same arterial segment differ from frame to frame due to a variety of factors including: rapid coronary motion, overlapping arteries, patient scatter, and structure noise from imaging the ribs, spine and diaphragm. These factors make frame selection a source of diagnostic variability. Test images were generated by radiographically projecting 3- D model arteries of known diameters onto patient coronary angiograms. Our objective is to eliminate subjective selection of a single frame and improve the overall accuracy of QCA by utilizing all of the image frames in a sequence. Diameters of the artery within each image were measured with an automated edge detection algorithm. Our approach to improve the accuracy of the diameter measure is to search for the coronary artery edge in both the spatial (individual image frame) and temporal domain (all frames in the sequence that show the edge).
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.
We investigate human visual detection and contrast discrimination of a moving Gabor signal in spatiotemporal white noise. We measure performance as a function of signal contrast for detection and contrast discrimination in a 4 alternative forced choice task. Observers were instructed and trained to maintain their gaze on a fixation point at all times during the experiment. The effect of signal contrast on human detection and contrast discrimination performance (d') for a moving signal in spatiotemporal noise is similar to that found for the case of a stationary signal in spatial noise. It can be described by a linear function with a positive x-intercept for detection and a 0 intercept for contrast discrimination. The difference in x-intercepts for the detection and contrast discrimination tasks are consistent with signal uncertainty. The improvement in performance with increasing number of frames is different for the detection and contrast discrimination tasks. Results show performance improvement with number of frames that saturates much later (750 - 800 msec) than would be expected from the early temporal filters (100 - 150 msec). Observers are more efficient detecting a stationary signal than a moving signal (when no eye tracking is allowed) in spatiotemporal noise. In an additional experiment where the signal interframe displacement was increased, observer performance (d') decreased with increasing interframe signal displacements dropping 50% for an interframe displacement of 70 min. of arc showing that human performance for detection of a moving signal is affected by the specific characteristics of the signal motion.
KEYWORDS: Signal detection, Interference (communication), Arteries, Visualization, Image segmentation, Signal to noise ratio, Angiography, Medical imaging, Detection theory, Motion detection
This paper investigates signal detectability in a fixed structured background as a function of signal contrast, additive white noise and feature motion. We use a 4 AFC (alternative forced choice) detection task where the signal appeared at the center of one of four identical, clearly visible, simulated cylindrical artery segments. All four segments moved identically relative to the background in 32-frame image sequences displayed at 15 frames per second. The background in one condition was uniform and in a second condition was structured noise consisting of a single frame randomly selected from a group of clinical x-ray coronary angiograms. We studied two display formats, the 'moving artery' display in which the background was stationary and the cylinders moved back and forth, simulating the motion of the coronary arteries, and the 'stabilized artery' in which each frame of the sequence was translated to keep the cylinders stationary, while the background moved back and forth. The signal to be detected was a disk superimposed at the center of one of the simulated cylinders. Signal energy and the variance of additive Gaussian spatiotemporal white noise were manipulated. For each level of additive white noise the threshold signal energy for detection (at the 82% correct performance level) was determined. There was no time limit to reach decision. For all conditions the threshold signal energy increased linearly with added white noise variance, with a positive y-intercept. The presence of the structured background increased both the y-intercept and the slope of this relationship between threshold energy and added white noise variance. Thus, the presence of the structured background had a multiplicative effect, as well as an additive effect, on the degradation of performance due to added white noise. The multiplicative effect might be modeled by an increase in induced internal noise (noise proportional to the external noise) with the presence of the structured background. Such an effect, if it occurs in the setting of clinical coronary angiography, would cause changes in radiation exposure (and thus quantum noise) to affect visual perception more than expected from experiments with white noise alone. One possible mechanism for this effect may be that the added random noise interferes with the observer's use of spatiotemporal correlations to 'subtract' or 'read around' the structured noise.
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.
Image quality associated with image compression has been either arbitrarily evaluated through visual inspection, loosely defined in terms of some subjective criteria such as image sharpness or blockiness, or measured by arbitrary measures such as the mean square error between the uncompressed and compressed image. The present paper psychophysically evaluated the effect of three different compression algorithms (JPEG, full-frame, and wavelet) on human visual detection of computer-simulated low-contrast lesions embedded in real medical image noise from patient coronary angiogram. Performance identifying the signal present location as measure by d' index of detectability decreased for all three algorithms by approximately 30% and 62% for the 16:1 and 30:1 compression rations respectively. We evaluated the ability of two previously proposed measures of image quality, mean square error (MSE) and normalized nearest neighbor difference (NNND), to determine the best compression algorithm. The MSE predicted significantly higher image quality for the JPEG algorithm in the 16:1 compression ratio and for both JPEG and full-frame for the 30:1 compression ratio. The NNND predicted significantly high image quality for the full-frame algorithm for both compassion rations. These findings suggest that these two measures of image quality may lead to erroneous conclusions in evaluations and/or optimizations if image compression algorithms.
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.
KEYWORDS: Signal detection, Visualization, Human vision and color perception, Visual process modeling, Interference (communication), Image processing, Medical imaging, Detection theory, Signal processing, Signal to noise ratio
An observer's ability to detect low contrast features (signals)
within an image is an important measure of image quality. A theory
exists for describing the relationship between measurable image
parameters and the detectability of simple visual signals such as
squares or disks in single images. This signal detection theory
has been successfully applied to many practical visual tasks
yielding fundamental re'ationships between noise, contrast, and the
effect on detectability of intensifying screen/x-ray film
combinations in conventional radiology2, and quantization noise,3
image processing,4 and window/level settings5'6 in digital
displays.
We are aware of no studies examining signal detectability in
dynamically displayed medical images, despite the importance of
these displays for many imaging modalities. Examples of dynamic
displays in medical imaging include x-ray fluoroscopy, cardiac
cineangiography, real-time two-dimensional ultrasound (2D-Echo),
rapid-sequence nuclear magnetic resonance imaging (cine MRI),
radioisotope ventriculography, and ultrafast computed tomography
(UFCT) . The goal of the present study was to quantify the
psychophysical parameters which affect observer performance in
dynamically displayed sequences of noisy images.
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