Fluoroscopic images belong to the classes of low contrast and high noise. Simply lowering radiation dose will render the images unreadable. Feature enhancement filters can reduce patient dose by acquiring images at low dose settings and then digitally restoring them to the original quality. In this study, a stent contrast enhancement filter is developed to selectively improve the contrast of stent contour without dramatically boosting the image noise including quantum noise and clinical background noise. Gabor directional filter banks are implemented to detect the edges and orientations of the stent. A high orientation resolution of 9° is used. To optimize the use of the information obtained from Gabor filters, a computerized Monte Carlo simulation followed by ROC study is used to find the best nonlinear operator. The next stage of filtering process is to extract symmetrical parts in the stent. The global and local symmetry measures are used. The information gathered from previous two filter stages are used to generate a stent contour map. The contour map is then scaled and added back to the original image to get a contrast enhanced stent image. We also apply a spatio-temporal channelized Hotelling observer model and other numerical measures to characterize the response of the filters and contour map to optimize the selections of parameters for image quality. The results are compared to those filtered by an adaptive unsharp masking filter previously developed. It is shown that stent enhancement filter can effectively improve the stent detection and differentiation in the interventional fluoroscopy.
Low exposure X-ray fluoroscopy is used to guide some complicate interventional procedures. Due to the inherent high levels of noise, improving the visibility of some interventional devices such as stent will greatly benefit those interventional procedures. Stent, which is made up of tiny steel wires, is also suffered from contrast dilutions of large flat panel detector pixels. A novel adaptive unsharp masking filter has been developed to improve stent contrast in real-time applications. In unsharp masking processing, the background is estimated and subtracted from the original input image to create a foreground image containing objects of interest. A background estimator is therefore critical in the unsharp masking processing. In this specific study, orientation filter kernels are used as the background estimator. To make the process simple and fast, the kernels average along a line of pixels. A high orientation resolution of 18° is used. A nonlinear operator is then used to combine the information from the images generated from convolving the original background and noise only images with orientation filters. A computerized Monte Carlo simulation followed by ROC study is used to identify the best nonlinear operator. We then apply the unsharp masking filter to the images with stents present. It is shown that the locally adaptive unsharp making filter is an effective filter for improving stent visibility in the interventional fluoroscopy. We also apply a spatio-temporal channelized human observer model to quantitatively optimize and evaluate the filter.
Parallel imaging using multiple coils and sub-sampled k-space data is a promising fast MR image acquisition technique. We used detection studies and perceptual difference models on image data with ¼ sampling to evaluate three different reconstruction methods: a regularization method developed by Ying and Liang of UIUC, a simplified regularization method, and an iterative method. We also included images obtained from a full complement of k-space data as "gold standard" images. Detection studies were performed using a simulated dark tumor added on MR images of bovine liver. We found that human detection depended strongly on the reconstruction methods used, with the simplified regularization and UIUC methods achieving better performance than the iterative method. We also evaluated images using detection with a Channlized Hotelling Observer (CHO) model and with a Perceptual Difference Model (PDM). Both predicted the same trends as observed in the human detection studies. We are encouraged that PDM gives trends similar to that for detection studies. Its ease of use and applicability to a variety of MR imaging situations make it attractive for evaluating image quality in a variety of MR studies.
Flat panel detectors have a large number of parameters that affect fluoroscopy image quality. Scintillator thickness is very important and can be changed in fabrication. In general, with increasing thickness, there is a degradation of MTF with spatial blurring but improved conversion efficiency. This design trade-off should be optimized for visualization. Using quantitative experimental and techniques, we simulated three detector models, including a direct detector and two indirect detectors with different scintillator thickness (160 and 210 mg/cm2) and displayed each "acquired" pixel directly on the screen without further processing in a sequence of fluoroscopy images. To measure image quality, we investigated detection of two interventional devices: stents and guide wires. Human observers and a channelized human observer model both demonstrated that detection depended on detector scintillator thickness and the type of interventional device. Detection performance was improved with the thicker scintillator, especially at low exposure. A simulated direct detector gave less blurring and even better detection performance for stent detection. The thick indirect detector gave contrast sensitivities equal to those for the direct detector for the case of guide wire detection. An ideal observer model gave trends similar to those for human observers, even though it does not account for many features of human viewing of image sequences and gives extraordinarily high detection SNR values because it uses all images in a sequence.
Pixel size is of great interest in flat-panel detector design. To visualize small interventional devices such as a stent in angiographic x-ray fluoroscopy, pixels should be small to limit contrast dilution from partial-area and large to collect sufficient x-rays for an adequate signal-to-noise ratio (SNR). Using quantitative experimental and modeling techniques, we investigated the optimal pixel sizes for visualization of a stent created from 50 μm diameter wires. Image quality was evaluated by the ability of subjects to perform two tasks: detect the presence of a stent and discriminate a partially deployed stent from a fully deployed one. With regard to detection, for the idealized direct detector, the 100 μm pixel size resulted in maximum measured contrast sensitivity. For an idealized indirect detector, with a scintillating layer, the maximal measured contrast sensitivity was obtained at 200 μm pixel size. The channelized human observer model predicted a peak at 150 and 170 μm, for idealized direct and indirect detectors, respectively. Stent deployment is more sensitive to pixel size than stent detection, resulting in a steeper drop in performance with large pixels. With regard to stent deployment detection, smaller even pixel sizes are favored for both detector types. With increasing exposures, the model predicts a smaller optimal pixel size because the noise penalty is reduced.
Although physical measurements such as detective quantum efficiency (DQE) and modulation transfer function (MTF) provide insights, quantitative optimization of x-ray flat panel detectors requires consideration of image quality as perceived by humans. Using experiments and human observer models, we quantified image quality as the ability to detect targets such as stents and guidewires used in interventional angiography. We realistically simulated direct and indirect flat panel detectors over a range of exposures to create realistic fluoroscopy sequences. We performed objective, m-alternative adaptive forced choice experiments and applied models of human detection to fit almost all experiments and predict results for similar tasks and processing. With regard to pixel size, the best size at low fluoroscopic exposures for detecting a 400 μm guide wire with a realistic, indirect detector was at about 200 μm and depended upon such device parameters as electronic noise. For both indirect and direct detectors at higher exposures, noise was not limiting, and a small pixel was desirable. With regard to binning, we determined that binning is desirable at low exposures even for detection of small objects such as a guide wire. A new alternate binning method was found to have superior image quality by utilizing the ability of humans to temporally fuse alternating images binned at different orientations. Comparing to magnification of analog image intensifiers, we determined that flat panel images can be digitally magnified without loss of image quality at a decreased average exposure.