PurposeSpatio-temporal variability in clinical fluoroscopy and cine angiography images combined with nonlinear image processing prevents the application of traditional image quality measurements in the cardiac catheterization laboratory. We aimed to develop and validate methods to measure human observer impressions of the image quality.ApproachMulti-frame images of the thorax of a euthanized pig were acquired to provide an anatomical background. The detector dose was varied from 6 to 200 nGy (increments 2×), and 0.6 and 1.0 mm focal spots were used. Two coronary stents with/without 0.5 mm separation and a synthetic right coronary artery (RCA) with hemispherical defects were embedded into the background images as test objects. The quantitative observer (n=17) performance was measured using a two-alternating forced-choice test of whether stents were separated and by a count of visible right coronary artery defects. Qualitative impressions of noise, spatial resolution, and overall image quality were measured using a visual analog scale (VAS). A paired t-test and multinomial logistic regression model were used to identify statistically significant factors affecting the observer’s impression image quality.ResultsThe proportion of correct detection of stent separation and the number of reported right coronary artery defects changed significantly with detector dose increment in the 6 to 100 nGy (p<0.05). Although a trend favored the 0.6 versus 1.0 mm focal spot for these quantitative assessments, this was insignificant. Visual analog scale measurements changed significantly with detector dose increments in the range of 24 to 100 nGy and focal spot size (p<0.05). The application of multinomial logistic regression analysis to observer VAS scores demonstrated sensitivity matching of the paired t-test applied to quantitative observer performance measurements.ConclusionsBoth quantitative and qualitative measurements of observer impression of the image quality were sensitive to image quality changes associated with changing the detector dose and focal spot size. These findings encourage future work that uses qualitative image quality measurements to assess clinical fluoroscopy and angiography image quality.
In addition to low-energy-threshold images (TLIs), photon-counting detector (PCD) computed tomography (CT) can generate virtual monoenergetic images (VMIs) and iodine maps. Our study sought to determine the image type that maximizes iodine detectability. Adult abdominal phantoms with iodine inserts of various concentrations and lesion sizes were scanned on a PCD-CT system. TLIs, VMIs at 50 keV, and iodine maps were generated, and iodine contrast-to-noise ratio (CNR) was measured. A channelized Hotelling observer was used to determine the area under the receiver-operating-characteristic curve (AUC) for iodine detectability. Iodine map CNR (0.57 ± 0.42) was significantly higher (P < 0.05) than for TLIs (0.46 ± 0.26) and lower (P < 0.001) than for VMIs at 50 keV (0.74 ± 0.33) for 0.5 mgI/cc and a 35-cm phantom. For the same condition and an 8-mm lesion, iodine detectability from iodine maps (AUC = 0.95 ± 0.01) was significantly lower (P < 0.001) than both TLIs (AUC = 0.99 ± 0.00) and VMIs (AUC = 0.99 ± 0.01). VMIs at 50 keV had similar detectability to TLIs and both outperformed iodine maps. The lowest detectable iodine concentration was 0.5 mgI/cc for an 8-mm lesion and 1.0 mgI/cc for a 4-mm lesion.
Multiple efforts have been made in x-ray angiography to transition from traditional image quality metrics to mathematical observer models. Recent works have successfully implemented the channelized Hotelling observer (CHO) model for x-ray angiography systems. However, in these works the channel selection process is ambiguous and limits to identifying a range of frequencies and other channel parameters that are believed to represent the most relevant features of the imaging tasks. This channel selection rationale can be sufficient for certain simple scenarios but it might not be enough for more complex ones. On the other hand, it has been shown that besides dealing with the well-known bias caused by a finite number of samples, there is also another source of bias in the estimation of the detectability index in x-ray angiography. Such source of bias has been attributed to nonrandom differences in noise between images acquired at different time points, also referred as temporally variable nonstationary noise. This work proposes a task-specific automated method for optimal channel selection and corrects for the influence of bias due to temporally variable nonstationary noise, particular from x-ray angiography systems. The proposed method is computationally inexpensive, provides time efficient selection of optimal channels, and contributes to minimize bias, all of these without significantly compromising the accuracy of the detectability index estimation. This method for channel optimization can be readily adapted to other imaging modalities.
Photon counting detector (PCD) based multi-energy CT is able to generate different types of images such as virtual monoenergetic images (VMIs) and material specific images (e.g., iodine maps) in addition to the conventional single energy images. The purpose of this study is to determine the image type that has optimal iodine detection and to determine the lowest detectable iodine concentration using a PCD-CT system. A 35 cm body phantom with iodine inserts of 4 concentrations and 2 sizes was scanned on a research PCD-CT system. For each iodine concentration, 80 repeated scans were performed and images were reconstructed for each energy threshold. In addition, VMIs at different keVs and iodine maps were also generated. CNR was measured for each type of images. A channelized Hotelling observer was used to assess iodine detectability after being validated with human observer studies, with area under the ROC curve (AUC) as a figure of merit. The agreement between model and human observer performance indicated that model observer could serve as an effective approach to determine optimal image type for the clinical practice and to determine the lowest detectable iodine concentration. Results demonstrated that for all size and concentration combinations, VMI at 70 keV had similar performance as that of threshold low images, both of which outperformed the iodine map images. At the AUC value of 0.8, iodine concentration as low as 0.2 mgI/cc could be detected for an 8 mm object and 0.5 mgI/cc for a 4 mm object with a 5 mm slice thickness.
Our institution routinely uses limited-angle cone-beam CT (CBCT) from a C-arm with 3D capabilities to diagnose and treat cardiovascular and orthopedic diseases in both adult and pediatric patients. While CBCT contributes to qualitative and quantitative assessment of both normal and abnormal patient anatomy, it also contributes substantially to patient radiation dose. Reducing the dose associated with CBCT exams while maintaining clinical utility can be considered to be of benefit to patients for whom CBCT is routinely used and may extend its adoption to clinical tasks and patient populations where the dose is currently considered prohibitive. In this work we developed and validated a method to simulate low-dose CBCT images from standard-dose projection images. The method was based on adding random noise to real projection images. The method was validated using an anthropomorphic thorax phantom of variable size with a custom-made insert containing iodine contrast rods of variable concentration. Images reconstructed from the low-dose simulations were compared to the actually acquired lower-dose images. Subtraction images of the simulated and acquired lower-dose images demonstrated a lack of residual structure patterns, indicating that differences between the image sets were consistent with random noise only. Noise power spectrum (NPS) and iodine signal-difference-to-noise ratio (SDNR) showed good agreement between simulated and acquired lower-dose images for dose levels between 70% and 30% of the routine dose. The average difference in iodine SDNR between simulated and acquired low-dose images was below 5% for all dose levels and phantom sizes. This work demonstrates the feasibility of accurately simulating low-dose CBCT based on real images acquired using standard dose and degrading the images by adding noise.
Evaluation of flat-panel angiography equipment through conventional image quality metrics is limited by the scope of standard spatial-domain image quality metric(s), such as contrast-to-noise ratio and spatial resolution, or by restricted access to appropriate data to calculate Fourier domain measurements, such as modulation transfer function, noise power spectrum, and detective quantum efficiency. Observer models have been shown capable of overcoming these limitations and are able to comprehensively evaluate medical-imaging systems. We present a spatial domain-based channelized Hotelling observer model to calculate the detectability index (DI) of our different sized disks and compare the performance of different imaging conditions and angiography systems. When appropriate, changes in DIs were compared to expectations based on the classical Rose model of signal detection to assess linearity of the model with quantum signal-to-noise ratio (SNR) theory. For these experiments, the estimated uncertainty of the DIs was less than 3%, allowing for precise comparison of imaging systems or conditions. For most experimental variables, DI changes were linear with expectations based on quantum SNR theory. DIs calculated for the smallest objects demonstrated nonlinearity with quantum SNR theory due to system blur. Two angiography systems with different detector element sizes were shown to perform similarly across the majority of the detection tasks.
DICOM Index Tracker (DIT) is an integrated platform to harvest rich information available from Digital Imaging and Communications in Medicine (DICOM) to improve quality assurance in radiology practices. It is designed to capture and maintain longitudinal patient-specific exam indices of interests for all diagnostic and procedural uses of imaging modalities. Thus, it effectively serves as a quality assurance and patient safety monitoring tool. The foundation of DIT is an intelligent database system which stores the information accepted and parsed via a DICOM receiver and parser. The database system enables the basic dosimetry analysis. The success of DIT implementation at Mayo Clinic Arizona calls for the DIT deployment at the enterprise level which requires significant improvements. First, for geographically distributed multi-site implementation, the first bottleneck is the communication (network) delay; the second is the scalability of the DICOM parser to handle the large volume of exams from different sites. To address this issue, DICOM receiver and parser are separated and decentralized by site. To facilitate the enterprise wide Quality Assurance (QA), a notable challenge is the great diversities of manufacturers, modalities and software versions, as the solution DIT Enterprise provides the standardization tool for device naming, protocol naming, physician naming across sites. Thirdly, advanced analytic engines are implemented online which support the proactive QA in DIT Enterprise.
An actively cooled charge couple device detector in combination with a 4 mm focal length lens (camera) was used to evaluate the luminance and perceived contrast properties of a liquid crystal display (LCD). The circular field of view (FOV) of the camera occupied an angular range (θ) of ±42.5° from normal in all directions. Uniform field images corresponding to 17 equally spaced grayscale values in the 8 bit digital driving level (DDL) range of the display system were acquired. The 12 bit grayscale digital images produced by the camera were converted to luminance (cd/m2) units via the measured DDL vs. luminance response of the camera. The Barten model of the grayscale response of the human visual system was used to compute the perceived contrast of the display within the angular FOV of the camera and throughout the 8-bit DDL range of the display. 1D profiles were extracted from the 2D measurements and compared to measurements acquired from a similar display using a Fourier-optics-based luminance meter and published methods. The results of the two methods generally agreed to within 5%. Greater discrepancy was realized for the lowest portion of the DDL range. The photographic methods used were straightforward and resulted in accurate display assessment measurements over a FOV that is relevant for the clinical use of LCDs.
The American Association of Physicists in Medicine Task Group 18 (TG18) has recently developed guidelines for objective performance evaluation of medical displays. This paper reports on the first multi-institutional trial focusing on the implementation and clinical verification of the TG18 methodology for performance testing of medical image display devices in use at different clinical centers. A minimum of two newly-installed PACS display devices were tested at each institution. The devices represented a broad spectrum of makes and models of 1-5 megapixel CRT and LCD display devices. They were all either new or in clinical use for primary diagnosis with acceptable performance at the time of testing. The TG18 test patterns were loaded on all the systems. Visual and quantitative tests were performed according to the guidelines for assessing specific display quality characteristics including geometrical distortion, reflection, luminance response, luminance uniformity, resolution, noise, veiling glare, color uniformity, and display artifacts. The results were collected in a common database. For each test, the results and their variability were compared to the recommended acceptance criteria. The findings indicated that TG18 tests and guidelines can easily be implemented in clinical settings. Most recommended criteria were deemed appropriate, while small minor modifications were suggested.
The purpose of this work was to determine the effects of various incident x-ray beam spectra on the measured noise power spectrum (NPS) of a computed radiography (CR) image acquisition device. A CR phosphor was uniformly exposed to 1 mR with x-ray beams whose peak tube potentials were 70, 95, and 120 kVp that were filtered by various thicknesses of a 'patient equivalent phantom' (PEP; 2% aluminum, 98% acrylic by thickness), aluminum, and copper. From the uniform exposure images, NPS curves were calculated and their integral values were computed. The integral noise values were found to vary substantially as a function of x-ray beam spectral content. A simple x-ray beam and filter model that accounted for the shape of the filtered x-ray spectra and the mass energy absorption coefficient of the storage phosphor verified the qualitative behavior of the integral NPS values corresponding to changes in the incident x-ray beam used. The x-ray beam and filter combinations of 70 kVp and 10.1 cm of PEP filtration and 120 kVp and 20.2 cm of PEP filtration were chosen as standard techniques for evaluating clinical imaging systems. These two combinations represent a relatively low, clinically relevant CR noise (integral NPS equals 2.6 X 10-6 mm) technique and a relatively high, clinically relevant CR noise (integral NPS equals 3.3 X 10-6 mm) technique.
KEYWORDS: Modulation transfer functions, Image quality, Calibration, Signal to noise ratio, Lithium, Absorbance, Quality measurement, Software, Image analysis, Data modeling
A semi-automated, quantitative film digitizer quality control program that is based on the computer analysis of the image data from a single digitized test film was developed. This program includes measurements of the geometric accuracy, optical density performance, signal to noise ratio, and presampled modulation transfer function. The variability of the measurements was less than plus or minus 5%. Measurements were made on a group of two clinical and two laboratory laser film digitizers during a trial period of approximately four months. Quality control limits were established based on clinical necessity, vendor specifications and digitizer performance. During the trial period, one of the digitizers failed the performance requirements and was corrected by calibration.
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