High image noise in low-dose fluoroscopic x-ray often necessitates additional radiographic-dose exposures to patients to
include as part of the medical records. We present an image registration based approach for the generation of highquality
images from a sequence of low-dose x-ray fluoroscopy exposures. Image subregions in consecutively acquired
fluoroscopy frames are registered to subregions in a pre-selected reference frame using a two-dimensional
transformation model. Frames neighboring the reference image are resampled using a smooth deformation field
generated by interpolation of the individual subregion deformations. Motion-corrected neighboring frames are then
combined with the reference frame using a weighted, frequency-specific multi-resolution combination method. Using
this method, image noise (localized standard deviation) was reduced by 38% in phantom data and by 29% in clinical
barium swallow examinations. We demonstrate an effective method for generating a simulated radiographic-dose x-ray
image from a set of consecutively acquired low-dose fluoroscopy images. The significant improvement in image quality
indicates the potential of this approach to lower average patient dose by substantially reducing the need for additional
exposures for patient records.
Digital tomosynthesis (DTS) is emerging as an advanced imaging technique that enables volumetric slice imaging with a detector typically used for projection radiography. An understanding of the interactions between DTS acquisition parameters and characteristics of the reconstructed slice images is required for optimizing the acquisition protocols of various clinical applications. This paper presents our investigation of the effects and interactions of acquisition parameters, including sweep angle, number of projections, and dose, on clinically relevant image-quality metrics. Metrics included the image characteristics of in-slice resolution, depth resolution, image noise level, and presence of ripple.
Phantom experiments were performed to characterize the relationship between the acquisition parameters and image quality. Results showed that the depth resolution was mainly dependent on sweep angle. Visibility of ripple was determined by the projection density (number of projections divided by sweep angle), as well as properties of the imaged object. Image noise was primarily dependent on total dose and not significantly affected by the number of projections. These experimental and theoretical results were confirmed using anthropomorphic phantoms and also used to develop clinical acquisition protocols. Assessment of phantom and clinical images obtained with these protocols revealed that the use of acquisition protocols optimized for a given clinical exam enables rapid, low-dose, high quality DTS imaging for diverse clinical applications including abdomen, hand, shoulder, spine, and chest.
We conclude that DTS acquisition parameters have a significant effect on image quality and should be tailored for the imaged anatomy and desired clinical application. Relationships developed in this work will guide the selection of acquisition protocols to improve image quality and clinical utility of DTS for a wide variety of clinical exams.
The detection of coronary calcifications with CT is generally accepted as a useful method for predicting early onset of coronary artery disease. Film-screen X-ray and fluoroscopy have also been shown to have high predictive value for coronary disease diagnosis, but have minimal sensitivity. Recently, flat-panel detectors capable of dual-energy techniques have enabled the separation of soft-tissue and bone from images. Clinical studies report substantially improved sensitivity for the detection of coronary calcifications using these techniques. However, heart motion causes minor artefacts from misregistration of both calcified and soft-tissue structures, resulting in inconsistent detection of calcifications. This research examines whether cardiac gating improves the reliability of calcification detection. Single-energy, gated, and non-gated dual-energy imaging techniques are examined in a dynamic phantom model.
A gating system was developed to synchronize two dual-energy exposures to a specified phase of the cardiac cycle. The performance and repeatability of the gating system was validated with the use of a cyclical phantom. An anthropomorphic phantom was developed to simulate both cardiac and soft-tissue motion, and generate ECG-like output signals. The anthropomorphic phantom and motion artefact accuracy was verified by comparison with clinical images of patients with calcifications. The ability of observers to detect calcifications in non-gated, and gated techniques was compared through the use of an ROC experiment.
Gating visibly reduces the effect of motion artifacts in the dual-energy images. Without gating, motion artefacts cause greater variability in calcification detection. Comparison of the average area-under-the-curve of the ROC curves show that gating significantly increases the accuracy of calcification detection.
The effects of motion and gating on DE cardiac calcification detection have been demonstrated and characterized in a phantom model that mimics the clinical scenario for dual-energy examinations. There exists significant potential for reliable cardiac calcification detection with gated dual-energy radiography.
With growing clinical acceptance of dual-energy chest radiography, there is increased interest in the application of dual-energy techniques to other clinical areas. This paper describes the creation and experimental validation of a poly-energetic signal-propagation model for technique optimization of new dual-energy clinical applications. The model is verified using phantom experiments simulating typical abdominal radiographic applications such as Intravenous Urography (IVU) and the detection of pelvic and sacral bone lesions or kidney stones in the presence of bowel gas.
The model is composed of a spectral signal propagation component and an image-processing component. The spectral propagation component accepts detector specifications, X-ray spectra, phantom and imaging geometry as inputs, and outputs the detected signal and estimated noise. The image-processing module performs dual-energy logarithmic subtraction and returns figures-of-merit such as contrast and contrast-to-noise ratio (CNR), which are evaluated in conjunction with Monte Carlo calculations of dose.
Phantoms assembled from acrylic, aluminum, and iodinated contrast-agent filled tubes were imaged using a range of kVp's and dose levels. Simulated and experimental results were compared by dose, clinical suitability, and system limitations in order to yield technique recommendations that optimize one or more figures-of-merit.
The model accurately describes phantom images obtained in a low scatter environment. For the visualization of iodinated vessels in the abdomen and the detection of pelvic bone lesions, both simulated and experimental results indicate that dual-energy techniques recommended by the model yield significant improvements in CNR without significant increases in patient dose as compared to conventional techniques. For example the CNR of iodinated vessels can be doubled using two-thirds of the dose of a standard exam. Alternatively, in addition to a standard dose image, the clinician can obtain a dual-energy bone image with greater than 8-fold increase in CNR, with the addition of just 15% higher dose. It is expected that this tool will enable the rapid clinical utilization of new applications of dual-energy radiography.
Dual-energy (DE) chest radiography with a digital flat panel (DFP) shows significant potential for increased sensitivity and specificity of pulmonary nodule detection. DFP-based DE produces significantly better image quality compared to Computed Radiography (CR) due to high detective quantum efficiency (DQE) and wide energy separation. We developed novel noise reduction filtering that significantly improves image quality at a given dose level, thereby allowing considerable additional dose reduction compared to CR. The algorithm segments images into structures, which are processed using anisotropic smoothing and sharpening, and non-structures, which are processed using isotropic smoothing. A fraction of the original image is blended with the processed image to obtain an image with improved noise characteristics. DE decomposed radiographs were obtained at film equivalent of 400 speed chest exam dose for 12 patients (set A) and at twice the dose for 7 other patients (set C). Images from set A were filtered using our algorithm to form set B. Images were evaluated by four radiologists using a noise rating scale. A two-sample t-test showed no significant difference in ratings between B and C, while significant differences were found between A and B, and A and C. Therefore, our algorithm enables effective patient dose reduction while maintaining perceptual image quality.
Our laboratory uses image perception studies to optimize the acquisition and processing of image sequences from x-ray fluoroscopy and interventional MRI (iMRI) both of which are used to guide complex minimally invasive treatments of cancer and vascular disease. Fluoroscopy consists of high frame rate, quantum-limited image sequences. Since it accounts for over half of the diagnostic population x-ray dose, we attempt to reduce dose by optimizing image acquisition and filtering. We quantify image quality using human detection experiments and modeling. Human spatio-temporal processing greatly affects results. For example, spatial noise reduction filtering is significantly more effective on image sequences than on single image frames where it gives relatively little improvement due to the deleterious effect of spatial noise correlation. At CWRU, we use iMRI to guide a radio-frequency probe used for the thermal ablation of cancer. Improving the speed and accuracy of insertion to the target will reduce patient risk and discomfort. We are investigating keyhole imaging whereby one updates only a portion of the Fourier domain at each time step, producing a fast, approximate image sequence. To optimize the very large number of techniques and parameters, we use a perceptual difference model that quantifies the degrading effects introduced by fast MR imaging, including the blurring of interventional devices. Preliminary studies show that a perpendicular frequency encoding direction provides superior image quality in the region of interest compared to other keyhole stripe orientations. Together these two applications illustrate that image perception studies can impact the design of medical imaging systems.
The use of x-ray fluoroscopy in complex interventional procedures can result in high patient dose leading to severe skin injuries. Simply reducing exposure degrades image quality. One solution is to acquire images at reduced exposures and digitally filter to reduce noise and restore image quality. We quantitatively evaluated image quality improvement from a bi-directional multi-stage (BMS) median spatio-temporal filter. Improvements were assessed using forced-choice measurement of detection and discrimination. Targets were vertical projected cylinders mimicking interventional devices such as catheters. Poisson white noise was added on uniform backgrounds to simulate low-dose x-ray fluoroscopy. The BMS filter improved detection by 20% and discrimination by 31% giving estimated dose savings of 31% and 42%, respectively. Minimal spatial and temporal blurring of targets was observed in filtered sequences.
One potential method to lower x-ray fluoroscopy dose without compromising image quality is to acquire images at a decreased exposure rate and digitally filter to reduce noise. In both single image frames and image sequences, we investigated the effect of noise-reduction spatial filtering on the detection of stationary cylinders that mimicked arteries, catheters, and guide wires in x-ray imaging. We simulated ideal edge- preserving spatial filters by filtering the noise only and then adding targets for detection. Fitters used were three different center-weighted averagers that reduce pixel noise variance by factors of 0.75, 0.5, and 0.25. Detection performance in unfiltered and spatially filtered noisy image sequences and single frames was measured using a reference/test, 9-alternative, adaptive forced-choice method. Performance level was fixed and results were obtained in the form of signal contrast sensitivity. In single images, the effect of filtering on detection was insignificant at all filtering levels. On the other hand, filtering in image sequences improved detectability by as much as 23%, yielding a potential x-ray dose savings of 34%. Comparing results with the prewhitening matched filter model indicated that human observers have improved detection efficiency in spatially filtered image sequences, as compared to white-noise sequences. We conclude that edge-preserving spatial filtering is more effective in sequences than in single frames. Such filtering can potentially improve image quality in noisy image sequences such as x-ray fluoroscopy.
We developed a new, interspersed, adaptive forced-choice method of general applicability, and used it to study perception in x-ray fluoroscopy. We measured detectability of low-contrast objects in noisy image sequences and determined x-ray dose levels for equivalent detectability of test (typically pulsed fluoroscopy at 15 acq/sec, hereafter called pulsed-15) as compared to reference (conventional fluoroscopy at 30 acq/sec, hereafter called pulsed-30). We interspersed reference and test in order to reduce effects of subject effort and attention. After 200 total presentations, we obtained absolute detectability of reference and test and an equivalent perception dose ratio (EPDR) for test as compared to reference. For this technically demanding application, implementation features such as real-time creation of noisy image frames and fast maximum-likelihood estimation of detectability were critical. We derived parameter uncertainties and proved applicability with Monte Carlo simulations and experiments. The interspersed, reference/test method lowered experimental standard deviations due to the removal of day-to-day variations in absolute detectability. Reliability of comparisons of subject response times was also improved. A variety of results in x-ray fluoroscopy has been obtained with this new method. Examples are a dose savings of pulsed- 15 as compared to pulsed-30, a saturation of the detectability response as one increases the number of frames in the display loop, effects of temporal filtering, and effects of motion.
We have performed a large variety of perception experiments aimed at issues in x-ray fluoroscopy. These include effects of image acquisition rate, digital temporal filtering, motion, and x-ray system motion blur. In this report, two model structures were considered. Both were non-prewhitening matched filter models modified to include a spatio-temporal visual system contrast sensitivity function. The first model used a spatial template following temporal integration and the second used a spatio-temporal template. The first model best described all data. However, it did not describe all motion experiments as accurately as the second. Our conclusion was that given an experiment, we should use the simplest model which most accurately describes similar experiments. Models will help plan critical experiments, allow one to concisely describe results, and predict similar experiments. Both models will be used to further our ultimate goal of using quantitative image quality studies to minimize dose and maximize image quality in x-ray fluoroscopy.
Temporal noise-reduction filtering of image sequences is commonly applied in medical imaging and other applications, and a common assessment technique is to measure the reduction in display noise variance. Theoretically and experimentally, we demonstrate
that this is inadequate because it does not account for the interaction with the human observer. Using a new forced-choice method, we compare detectability of low-contrast objects and find a noise level for an unfiltered sequence that gives the same detectability as the filtered sequence. We report the equivalent detectability noise variance ratio, or EDVR. For a digital low-pass filter that reduces the bandwidth by 1/2, display noise reduction predicts an EDVR of 0.5. The measured value averaged over three subjects,
0.9360.19, compares favorably with the 0.85 predicted from a theoretical human observer model, and both are very close to the value of 1.0 expected for no filtering. Hence, the effective, perceived noise is relatively unchanged by temporal low-pass filtering. The computational observer model successfully evaluates a simple low-pass temporal filter, and we anticipate that it can be used to predict the observer response to other image enhancement filters.