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BACKGROUND: The rapid development and complexity of new x-ray computed tomography (CT) technologies and the need for evidence-based optimization of image quality with respect to radiation and contrast media dose call for an updated approach towards CT performance evaluation. AIMS: This report offers updated testing guidelines for testing CT systems with an enhanced focus on the operational performance including iterative reconstructions and automatic exposure control (AEC) techniques. MATERIALS AND METHODS: The report was developed based on a comprehensive review of best methods and practices in the scientific literature. The detailed methods include the assessment of 1) CT noise (magnitude, texture, nonuniformity, inhomogeneity), 2) resolution (task transfer function under varying conditions and its scalar reflections), 3) task-based performance (detectability, estimability), and 4) AEC performance (spatial, noise, and mA concordance of attenuation and exposure modulation). The methods include varying reconstruction and tube current modulation conditions, standardized testing protocols, and standardized quantities and metrology to facilitate tracking, benchmarking, and quantitative comparisons. RESULTS: The methods, implemented in cited publications, are robust to provide a representative reflection of CT system performance as used operationally in a clinical facility. The methods include recommendations for phantoms and phantom image analysis. DISCUSSION: In line with the current professional trajectory of the field toward quantitation and operational engagement, the stated methods offer quantitation that is more predictive of clinical performance than specification-based approaches. They can pave the way to approach performance testing of new CT systems not only in terms of acceptance testing (i.e., verifying a device meets predefined specifications), but also system commissioning (i.e., determining how the system can be used most effectively in clinical practice). CONCLUSION: We offer a set of common testing procedures that can be utilized towards the optimal clinical utilization of CT imaging devices, benchmarking across varying systems and times, and a basis to develop future performance-based criteria for CT imaging.
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Sociedades Médicas , Tomografía Computarizada por Rayos X/métodos , Medios de Contraste , Guías como Asunto , Procesamiento de Imagen Asistido por Computador , Control de Calidad , Dosis de Radiación , Tomografía Computarizada por Rayos X/instrumentación , Tomografía Computarizada por Rayos X/normasRESUMEN
We investigate the use of energy dispersive x-ray coherent scatter computed tomography (ED-CSCT) as a non-invasive diagnostic method to differentiate between type I and type II breast calcifications. This approach is sensitive to the differences of composition and internal crystal structure of different types of microcalcifications. The study is carried out by simulating a CSCT system with a scanning pencil beam, considering a polychromatic x-ray source and an energy-resolving photon counting detector. In a first step, the multidimensional angle and energy distributed CSCT data is reduced to the projection-space distributions of only a few components, corresponding to the expected target composition: adipose, glandular tissue, weddellite (calcium oxalate) for type I calcifications, and hydroxyapatite for type II calcifications. The maximum-likelihood estimation of scatter components algorithm used, operating in the projection space, takes into account the polychromatic source, the detector response function and the energy dependent attenuation. In the second step, component images are reconstructed from the corresponding estimated component projections using filtered backprojection. In a preliminary step the coherent scatter differential cross sections for hydroxyapatite and weddellite minerals were determined experimentally. The classification of type I or II calcifications is done using the relative contrasts of their components as the criterion. Simulation tests were carried out for different doses and energy resolutions for multiple realizations. The results were analyzed using relative/receiver operating characteristic methodology and show good discrimination ability at medium and higher doses. The noninvasive CSCT technique shows potential to further improve the breast diagnostic accuracy and reduce the number of breast biopsies.
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Mama/diagnóstico por imagen , Calcinosis/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , Algoritmos , Mama/patología , Calcinosis/clasificación , Calcinosis/patología , Femenino , Humanos , Rayos XRESUMEN
Coherent scatter computed tomography (CSCT) is a reconstructive x-ray imaging technique that yields the spatially resolved coherent-scatter cross section of the investigated object revealing structural information of tissue under investigation. In the original CSCT proposals the reconstruction of images from coherently scattered x-rays is done at each scattering angle separately using analytic reconstruction. In this work we develop a maximum likelihood estimation of scatter components algorithm (ML-ESCA) that iteratively reconstructs images using a few material component basis functions from coherent scatter projection data. The proposed algorithm combines the measured scatter data at different angles into one reconstruction equation with only a few component images. Also, it accounts for data acquisition statistics and physics, modeling effects such as polychromatic energy spectrum and detector response function. We test the algorithm with simulated projection data obtained with a pencil beam setup using a new version of MC-GPU code, a Graphical Processing Unit version of PENELOPE Monte Carlo particle transport simulation code, that incorporates an improved model of x-ray coherent scattering using experimentally measured molecular interference functions. The results obtained for breast imaging phantoms using adipose and glandular tissue cross sections show that the new algorithm can separate imaging data into basic adipose and water components at radiation doses comparable with Breast Computed Tomography. Simulation results also show the potential for imaging microcalcifications. Overall, the component images obtained with ML-ESCA algorithm have a less noisy appearance than the images obtained with the conventional filtered back projection algorithm for each individual scattering angle. An optimization study for x-ray energy range selection for breast CSCT is also presented.
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Mama/patología , Calcinosis/diagnóstico por imagen , Modelos Teóricos , Fantasmas de Imagen , Tomografía Computarizada por Rayos X/métodos , Algoritmos , Mama/efectos de la radiación , Femenino , Humanos , Funciones de Verosimilitud , Método de Montecarlo , Dispersión de Radiación , Rayos XRESUMEN
PURPOSE: To devise a new methodology for CT image quality evaluation in order to assess the dose reduction potential of new iterative reconstruction algorithms (IRA). METHODS: Because of the nonlinear behavior of IRA, the authors propose a task-based methodology consisting of measuring the detectability of small, low contrast signals at random locations. The authors test, via simulations, a phantom design that facilitates human and numerical observer studies in such conditions. The setup allows for the random selection of regions of interest (ROI) around each signal, so that the relative signal location is unknown if the ROIs are shown separately. With such a setup one can perform signal detectability measurements with a variety of image reading arrangements and data analysis methods. In this work, the authors demonstrate the use of the localization relative operating characteristic method. The phantom design also allows for efficient image evaluation utilizing an automatic signal search technique and a recently developed nonparametric data analysis method using the exponential transformation of the free response characteristic curve. RESULTS: The authors present the application of these methods by performing a comparison between the filtered back projection (FBP) algorithm and a polychromatic iterative image reconstruction algorithm. In this generic illustration of the image evaluation framework, the expected improved performance of the IRA over FBP is confirmed. CONCLUSIONS: The results demonstrate the ability of these methods to determine signal detectability indices with good accuracy with only a small number, of the order of a few tens, of image samples.
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Procesamiento de Imagen Asistido por Computador/métodos , Interpretación de Imagen Radiográfica Asistida por Computador/métodos , Tomografía Computarizada por Rayos X , Algoritmos , Simulación por Computador , Humanos , Distribución Normal , Fantasmas de Imagen , Distribución de Poisson , Curva ROC , Reproducibilidad de los Resultados , Procesamiento de Señales Asistido por ComputadorRESUMEN
PURPOSE: The authors compared the performance of five protocols intended to reduce dose to the breast during computed tomography (CT) coronary angiography scans using a model observer unknown-location signal-detectability metric. METHODS: The authors simulated CT images of an anthropomorphic female thorax phantom for a 120 kV reference protocol and five "dose reduction" protocols intended to reduce dose to the breast: 120 kV partial angle (posteriorly centered), 120 kV tube-current modulated (TCM), 120 kV with shielded breasts, 80 kV, and 80 kV partial angle (posteriorly centered). Two image quality tasks were investigated: the detection and localization of 4-mm, 3.25 mg/ml and 1-mm, 6.0 mg/ml iodine contrast signals randomly located in the heart region. For each protocol, the authors plotted the signal detectability, as quantified by the area under the exponentially transformed free response characteristic curve estimator (ÂFE), as well as noise and contrast-to-noise ratio (CNR) versus breast and lung dose. In addition, the authors quantified each protocol's dose performance as the percent difference in dose relative to the reference protocol achieved while maintaining equivalent ÂFE. RESULTS: For the 4-mm signal-size task, the 80 kV full scan and 80 kV partial angle protocols decreased dose to the breast (80.5% and 85.3%, respectively) and lung (80.5% and 76.7%, respectively) with ÂFE=0.96, but also resulted in an approximate three-fold increase in image noise. The 120 kV partial protocol reduced dose to the breast (17.6%) at the expense of increased lung dose (25.3%). The TCM algorithm decreased dose to the breast (6.0%) and lung (10.4%). Breast shielding increased breast dose (67.8%) and lung dose (103.4%). The 80 kV and 80 kV partial protocols demonstrated greater dose reductions for the 4-mm task than for the 1-mm task, and the shielded protocol showed a larger increase in dose for the 4-mm task than for the 1-mm task. In general, the CNR curves indicate a similar relative ranking of protocol performance as the corresponding ÂFE curves, however, the CNR metric overestimated the performance of the shielded protocol for both tasks, leading to corresponding underestimates in the relative dose increases compared to those obtained when using the ÂFE metric. CONCLUSIONS: The 80 kV and 80 kV partial angle protocols demonstrated the greatest reduction to breast and lung dose, however, the subsequent increase in image noise may be deemed clinically unacceptable. Tube output for these protocols can be adjusted to achieve a more desirable noise level with lesser breast dose savings. Breast shielding increased breast and lung dose when maintaining equivalent ÂFE. The results demonstrated that comparisons of dose performance depend on both the image quality metric and the specific task, and that CNR may not be a reliable metric of signal detectability.
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Mama/efectos de la radiación , Angiografía Coronaria/instrumentación , Fantasmas de Imagen , Dosis de Radiación , Protección Radiológica/instrumentación , Femenino , HumanosRESUMEN
PURPOSE: To develop an efficient nonparametric method for evaluation the detectability of signals at unknown locations in images, as a mean for image quality assessment. METHODS: We use the free-response methodology that allows the image observer to mark and score all locations found as suspicious in an image, summarizing these results in a free-response operating characteristic (FROC) curve. However, unlike the relative (or receiver) operating characteristic (ROC), or the localization ROC (LROC), the FROC curve has an undefined, theoretically infinite, right side limit. Therefore area under the FROC curves cannot be directly used as an overall performance index, as the area under the curve is for ROC or LROC. We circumvent this drawback by using a transformation of the abscissa that leads to a finite integration range. By applying an exponential transformation we derive a nonparametric estimator for such a metric, and we study its properties by deriving analytical expressions for the mean and standard deviation in conditions of scores independence. RESULTS: A comparative study with other related nonparametric estimators for ROC, LROC, and alternative FROC (AFROC) method is presented. CONCLUSIONS: The new nonparametric estimator has sensitivity and scalability properties that make it particularly advantageous for signal detectability evaluation in phantom experiments using model observers.
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Diagnóstico por Imagen/métodos , Procesamiento de Señales Asistido por Computador , Algoritmos , Simulación por Computador , Interpretación Estadística de Datos , Humanos , Modelos Estadísticos , Fantasmas de Imagen , Curva ROC , Estadística como Asunto , Estadísticas no ParamétricasRESUMEN
A signal detection model is presented that combines a signal model and a noise model providing mathematical descriptions of the frequency of appearance of the signals, and of the signal-like features naturally occurring in the background. We derive expressions for the likelihood functions for the whole ensemble of observed suspicious locations, in various possible combinations of signals and false signal candidates. As a result, this formalism is able to describe several new types of detection tests using likelihood ratio statistics. We have a global image abnormality test and an individual signal detection test. The model also provides an alternative mechanism in which is selected the combination of signal and noise features candidates that has the maximum likelihood. These tests can be analyzed with a variety of operating characteristic curves (ROC, LROC, FROC, etc.). In the mathematical formalism of the model, all the details characterizing the suspicious features are reduced to a single scalar function, which we name the signal specificity function, representing the frequency that a signal takes a certain value relative to the frequency of having a false signal with the same value in an image of given size. The signal specificity function ranks the degree of suspiciousness of the features found, and can be used to unify into a single score all the suspicious feature characteristics, and then apply the usual decision conventions as in the Swensson's detection model [Med. Phys. 23, 1709-1725 (1996)]. We present several examples in which these tests are compared. We also show how the signal specificity function can be used to model various degrees of accuracy of the observer's knowledge about image noise and signal statistical properties. Aspects concerning modeling of the human observer are also discussed.
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Procesamiento de Imagen Asistido por Computador/métodos , Procesamiento de Señales Asistido por Computador , Algoritmos , Simulación por Computador , Interpretación Estadística de Datos , Diagnóstico por Imagen/métodos , Reacciones Falso Positivas , Humanos , Funciones de Verosimilitud , Modelos Estadísticos , Modelos Teóricos , Variaciones Dependientes del Observador , Curva ROC , Reproducibilidad de los ResultadosRESUMEN
In this paper we review several results of the nonparametric receiver operating characteristic (ROC) analysis and present an extension to the nonparametric localization ROC (LROC) analysis. Equations for the estimation of the area under the characteristic curve and for the variance calculations are derived. Expressions for the choice of the optimal ratio between the number of signal-absent and signal-present image samples are also presented. The results can be applied both with continuous or discrete scoring scales. The simulation studies carried out validate the theoretical derivations and show that the LROC analysis is considerably more sensitive than the ROC analysis.
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Algoritmos , Procesamiento de Imagen Asistido por Computador , Curva ROC , Tecnología Radiológica/métodosRESUMEN
In this paper is investigated the use of the scan statistic for evaluating the detectability of small nodules in medical images. The scan-statistic method is often used in applications in which random fields must be searched for abnormal local features. Several results of the detection with localization theory are reviewed and a generalization is presented using the noise nodule distribution obtained by scanning arbitrary areas. One benefit of the noise nodule model is that it enables determination of the scan-statistic distribution by using only a few image samples in a way suitable both for simulation and experimental setups. Also, based on the noise nodule model, the case of multiple targets per image is addressed and an image abnormality test using the likelihood ratio and an alternative test using multiple decision thresholds are derived. The results obtained reveal that in the case of low contrast nodules or multiple nodules the usual test strategy based on a single decision threshold underperforms compared with the alternative tests. That is a consequence of the fact that not only the contrast or the size, but also the number of suspicious nodules is a clue indicating the image abnormality. In the case of the likelihood ratio test, the multiple clues are unified in a single decision variable. Other tests that process multiple clues differently do not necessarily produce a unique ROC curve, as shown in examples using a test involving two decision thresholds. We present examples with two-dimensional time-of-flight (TOF) and non-TOF PET image sets analysed using the scan statistic for different search areas, as well as the fixed position observer.
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Diagnóstico por Imagen/estadística & datos numéricos , Algoritmos , Fenómenos Biofísicos , Biofisica , Humanos , Procesamiento de Imagen Asistido por Computador , Funciones de Verosimilitud , Modelos Estadísticos , Variaciones Dependientes del Observador , Tomografía de Emisión de Positrones/estadística & datos numéricosRESUMEN
In this paper we propose a comprehensive energy-based scatter correction approach for positron emission tomography (PET). We take advantage of the marked difference between the energy spectra of the unscattered and scattered photons, and use the detailed energy information that comes with the list-mode data for the estimation of the scattered events distribution in the data space. Also, inside the maximum-likelihood expectation maximization (ML-EM) image reconstruction algorithm, we introduce energy-dependent factors that individualize the correction terms for each event, given its position and energy information. The central piece of our approach is the two-dimensional detector energy response model represented as a linear combination of four components, each one representing a particular state a PET event can be found in: both photons unscattered, the second scattered while the first not, the first photon scattered while the second not and both photons scattered. For a set of events collected in the vicinity of a point in the projection space, the coefficient of each component is determined by applying a statistical estimator. As a result we obtain the number of scattered events that are in the given set. The model also gives us the variation of scatter fraction with the photon pair energies for that particular position in the data space. A simulation study that demonstrates the proposed methods is presented.
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Algoritmos , Procesamiento de Imagen Asistido por Computador/métodos , Tomografía Computarizada de Emisión de Fotón Único/instrumentación , Tomografía Computarizada de Emisión de Fotón Único/métodos , Simulación por Computador , Humanos , Procesamiento de Imagen Asistido por Computador/instrumentación , Modelos Biológicos , Modelos Estadísticos , Fantasmas de Imagen , Dispersión de RadiaciónRESUMEN
The purpose of this paper is to determine the benefit that can be achieved in image quality for a time-of-flight (TOF) fully three-dimensional (3-D) whole-body positron emission tomography (PET) scanner. We simulate a 3-D whole-body time-of-flight PET scanner with a complete modeling of spatial and energy resolutions. The scanner is based on LaBr3 Anger-logic detectors with which 300ps timing resolution has been achieved. Multiple simulations were performed for 70-cm long uniform cylinders with 27-cm and 35-cm diameters, containing hot spheres (22, 17, 13, and 10-mm diameter) in a central slice and 10-mm diameter hot spheres in a slice at 1/4 axial FOV. Image reconstruction was performed with a list-mode iterative TOF algorithm and data were analyzed after attenuation and scatter corrections for timing resolutions of 300, 600, 1000 ps and non-TOF for varying count levels. The results show that contrast recovery improves slightly with TOF (NEMA NU2-2001 analysis), and improved timing resolution leads to a faster convergence to the maximum contrast value. Detectability for 10-mm diameter hot spheres estimated using a nonprewhitening matched filter (NPW SNR) also improves nonlinearly with TOF. The gain in image quality using contrast and noise measures is proportional to the object diameter and inversely proportional to the timing resolution of the scanner. The gains in NPW SNR are smaller, but they also increase with increasing object diameter and improved timing resolution. The results show that scan times can be reduced in a TOF scanner to achieve images similar to those from a non-TOF scanner, or improved image quality achieved for same scan times.