Detector Lag Research Articles

Geospatial analysis of spatial distribution, patterns, and relationships of health status in the belt and road initiative

The Health Silk Road plays a crucial role in the Belt and Road Initiative, and comprehending the health status within the participating countries is fundamental for fostering cooperation in public health. This paper collected five health indicators to represent the health status of the Belt and Road countries. Employing spatial statistics, the spatial patterns of health indicators and the associations with influencing factors were investigated. The utilized spatial statistics encompass spatial autocorrelation methods, geographical detector and spatial lag model. The results revealed obvious disparities and significant positive spatial autocorrelation of health indicators within the Belt and Road countries. Specifically, countries in Sub-Saharan Africa exhibited significant clustering of limited health indicators, while countries in Europe and Central Asia demonstrated significant clustering of robust health indicators. Furthermore, the health indicators exhibited significant spatial heterogeneity and association with the influencing factors. Universal health coverage, household air pollution, and the prevalence of undernourishment emerge as influential factors affecting health indicators. Overall, our findings highlighted complex influencing factors that contributed to the profound health inequalities across the Belt and Road countries. These factors should be duly considered in public health collaborations within the Belt and Road Initiative.

Carbon nanotube-based multiple source C-arm CT system: feasibility study with prototype system.

To extend the field of view while reducing dimensions of the C-arm, we propose a carbon nanotube (CNT)-based C-arm computed tomography (CT) system with multiple X-ray sources. A prototype system was developed using three CNT X-ray sources, enabling a feasibility study. Geometry calibration and image reconstruction were performed to improve the quality of image acquisition. However, the geometry of the prototype system led to projection truncation for each source and an overlap region of object area covered by each source in the two-dimensional Radon space, necessitating specific corrective measures. We addressed these problems by implementing truncation correction and applying weighting techniques to the overlap region during the image reconstruction phase. Furthermore, to enable image reconstruction with a scan angle less than 360°, we designed a weighting function to solve data redundancy caused by the short scan angle. The accuracy of the geometry calibration method was evaluated via computer simulations. We also quantified the improvements in reconstructed image quality using mean-squared error and structural similarity. Moreover, detector lag correction was applied to address the afterglow observed in the experimental data obtained from the prototype system. Our evaluation of image quality involved comparing reconstructed images obtained with and without incorporating the geometry calibration results and images with and without lag correction. The outcomes of our simulation study and experimental investigation demonstrated the efficacy of our proposed geometry calibration, image reconstruction method, and lag correction in reducing image artifacts.

Quantitative Dual-Energy Imaging of Bone Marrow Edema Using Multisource Cone-Beam CT with Model-Based Decomposition.

We investigated the feasibility of dual-energy (DE) detection of bone marrow edema (BME) using a dedicated extremity cone-beam CT (CBCT) with a unique three-source x-ray unit. The sources can be operated at different energies to enable single-scan DE acquisitions. However, they are arranged parallel to the axis of rotation, resulting in incomplete sampling and precluding the application of DE projection-domain decompositions (PDD) for beam-hardening reduction. Therefore, we propose a novel combination of a model-based "one-step" DE two-material decomposition followed by a constrained image-domain change-of-basis to obtain virtual non-calcium (VNCa) images for BME detection. DE projections were obtained using an "alternating-kV" protocol by operating the peripheral two sources of the CBCT system at low-energy (60 kV, 0.105 mAs/frame) and the central source at high-energy (100 kV, 0.028 mAs/frame), for a total of 600 frames over 216° of gantry rotation. Projections were processed with detector lag, glare and fast Monte Carlo (MC)-based iterative scatter corrections. Model-based material decomposition (MBMD) was then implemented to obtain aluminum (Al) and polyethylene (PE) volume fraction images with minimal beam-hardening. Statistical ray weights in MBMD were modified to account for regions with highly oblique sampling by the peripheral sources. To generate the VNCa maps, image-domain decomposition (IDD) constrained by the volume conservation principle (VCP) was performed to convert the Al and PE MBMD images into volume fractions of water, fat and cortical bone. Accuracy of BME detection was evaluated using physical phantom data acquired on the multi-source extremity CBCT scanner. The proposed framework estimated the volume of BME with ~10% error. The MC-based scatter corrections and the modified MBMD ray weights were essential to achieve such performance - the error without MC scatter corrections was >30%, whereas the uniformity of estimated VNCa images was 3x improved using the modified weights compared to the conventional weights. The proposed DE decomposition framework was able to overcome challenges of high scatter and incomplete sampling to achieve BME detection on a CBCT system with axially-distributed x-ray sources.

Improving dose calculation accuracy in preclinical radiation experiments using multi-energy element resolved cone-beam CT

Objective. Cone-beam CT (CBCT) in modern pre-clinical small-animal radiation research platforms provides volumetric images for image guidance and experiment planning purposes. In this work, we implemented multi-energy element-resolved (MEER) CBCT using three scans with different kVps on a SmART platform (Precision x-ray Inc.) to determine images of relative electron density (rED) and elemental composition (EC) that are needed for Monte Carlo-based radiation dose calculation. Approach. We performed comprehensive calibration tasks to achieve sufficient accuracy for this quantitative imaging purpose. For geometry calibration, we scanned a ball bearing phantom and used an analytical method together with an optimization approach to derive gantry angle specific geometry parameters. Intensity calibration and correction included the corrections for detector lag, glare, and beam hardening. The corrected CBCT projection images acquired at 30, 40, and 60 kVp in multiple scans were used to reconstruct CBCT images using the Feldkamp–Davis–Kress reconstruction algorithm. After that, an optimization problem was solved to determine images of rED and EC. We demonstrated the effectiveness of our CBCT calibration steps by showing improvements in image quality and successful material decomposition in cases with a small animal CT calibration phantom and a plastinated mouse phantom. Main results. It was found that artifacts induced by geometry inaccuracy, detector lag, glare, and beam hardening were visually reduced. CT number mean errors were reduced from 19% to 5%. In the CT calibration phantom case, median errors in H, O, and Ca fractions for all the inserts were below 1%, 2%, and 4% respectively, and median error in rED was less than 5%. Compared to the standard approach deriving material type and rED via CT number conversion, our approach improved Monte Carlo simulation-based dose calculation accuracy in bone regions. Mean dose error was reduced from 47.5% to 10.9%. Significance. The MEER-CBCT implemented on an existing CBCT system of a small animal irradiation platform achieved accurate material decomposition and significantly improved Monte Carlo dose calculation accuracy.

Measurement of the Lag Correction Factor in Low-Dose Fluoroscopic Imaging.

Lag signals occur at images sequentially acquired from a flat-panel (FP) dynamic detector in fluoroscopic imaging due to charge trapping in photodiodes and incomplete readouts. This lag signal produces various lag artifacts and prevents analyzing detector performances because the measured noise power spectrum (NPS) values are reduced. In order to design dynamic detectors, which produce low lag artifacts, accurately evaluating the detector lag through its quantitative measurement is required. A lag correction factor can be used to both examine the detector lag and correct measured NPS. To measure the lag correction factor, the standard of IEC62220-1-3 suggests a temporal power spectral density under a constant potential generator for the x-rays. However, this approach is sensitive to disturbing noise and thus becomes a problem in obtaining accurate estimates especially at low doses. The Granfors-Aufrichtig (GA) method is appropriate for noisy environments with a synchronized pulse x-ray source. However, for the x-ray source of a constant potential generator, gate-line scanning to read out charges produces a nonuniform lag signal within each image frame and thus the conventional GA method yields wrong estimates. In this paper, we first analyze the GA method and show that the method is an asymptotically unbiased estimate. Based on the GA method, we then propose three algorithms considering the scanning process and exposure leak, in which line estimates along the gate line are exploited. We extensively conducted experiments for FP dynamic detectors and compared the results with conventional algorithms.

WE‐AB‐207A‐03: A CBCT Head Scanner for Point‐Of‐Care Imaging of Intracranial Hemorrhage

Purpose:This work reports the design, development, and first technical assessment of a cone‐beam CT (CBCT) scanner developed specifically for imaging of acute intracranial hemorrhage (ICH) at the point of care, with target applications in diagnosis and monitoring of traumatic brain injury, stroke, and postsurgical hemorrhage.Methods:System design employed a task‐based image quality model to quantify the influence of factors such as additive noise and high‐gain (HG) detector readout on ICH detectability. Three bowtie filters with varying bare‐beam attenuation strength and curvature were designed to enable HG readout without detector saturation, and a polyenergetic gain correction was developed to minimize artifacts from bowtie flood‐field calibration. Image reconstruction used an iterative penalized weighted least squares (PWLS) method with artifact correction including Monte Carlo scatter estimation, Joseph‐Spital beam hardening correction, and spatiotemporal deconvolution of detector glare and lag. Radiation dose was characterized for half‐scan and full‐scan protocols at various kV, and imaging performance was assessed in a head phantom presenting simulated ICH with diameter ranging 2–12 mm.Results:The image quality model guided system design and was validated by measurements on a CBCT imaging bench. Compared to low‐gain readout without a bowtie filter, the combination of HG readout and a modest bowtie improved the contrast‐to‐noise ratio (CNR per unit square‐root dose) by 20% in the center of the image but degraded noise performance near the periphery (20% reduction in CNR). Low‐frequency bowtie artifacts (∼100 HU magnitude) were corrected by the polyenergetic gain correction. Image reconstructions on the prototype scanner demonstrate clear visibility of the smallest ICH insert (2 mm diameter) in both HG readout (with a bowtie) and dual‐gain readout (without bowtie).Conclusion:Technical assessment of the prototype scanner suggests the capability for reliable visualization of small (2 mm), low‐contrast (50 HU) ICH at <20 mGy dose and motivates translation to clinical studies, now underway.Xiaohui Wang and David Foos (co‐authors) are employees of Carestream Health. Research funding support from Carestream Health.

High-fidelity artifact correction for cone-beam CT imaging of the brain

CT is the frontline imaging modality for diagnosis of acute traumatic brain injury (TBI), involving the detection of fresh blood in the brain (contrast of 30–50 HU, detail size down to 1 mm) in a non-contrast-enhanced exam. A dedicated point-of-care imaging system based on cone-beam CT (CBCT) could benefit early detection of TBI and improve direction to appropriate therapy. However, flat-panel detector (FPD) CBCT is challenged by artifacts that degrade contrast resolution and limit application in soft-tissue imaging. We present and evaluate a fairly comprehensive framework for artifact correction to enable soft-tissue brain imaging with FPD CBCT. The framework includes a fast Monte Carlo (MC)-based scatter estimation method complemented by corrections for detector lag, veiling glare, and beam hardening.The fast MC scatter estimation combines GPU acceleration, variance reduction, and simulation with a low number of photon histories and reduced number of projection angles (sparse MC) augmented by kernel de-noising to yield a runtime of ~4 min per scan. Scatter correction is combined with two-pass beam hardening correction. Detector lag correction is based on temporal deconvolution of the measured lag response function. The effects of detector veiling glare are reduced by deconvolution of the glare response function representing the long range tails of the detector point-spread function. The performance of the correction framework is quantified in experiments using a realistic head phantom on a testbench for FPD CBCT.Uncorrected reconstructions were non-diagnostic for soft-tissue imaging tasks in the brain. After processing with the artifact correction framework, image uniformity was substantially improved, and artifacts were reduced to a level that enabled visualization of ~3 mm simulated bleeds throughout the brain. Non-uniformity (cupping) was reduced by a factor of 5, and contrast of simulated bleeds was improved from ~7 to 49.7 HU, in good agreement with the nominal blood contrast of 50 HU. Although noise was amplified by the corrections, the contrast-to-noise ratio (CNR) of simulated bleeds was improved by nearly a factor of 3.5 (CNR = 0.54 without corrections and 1.91 after correction). The resulting image quality motivates further development and translation of the FPD-CBCT system for imaging of acute TBI.

SU‐E‐J‐125: Classification of CBCT Noises in Terms of Their Contribution to Proton Range Uncertainty

Purpose:This study assesses the potential use of CBCT images in adaptive protontherapy by estimating the contribution of the main sources of noise and calibration errors to the proton range uncertainty.Methods:Measurements intended to highlight each particular source have been achieved by adapting either the testbench configuration, e.g. use of filtration, fan‐beam collimation, beam stop arrays, phantoms and detector reset light, or the sequence of correction algorithms including water precorrection. Additional Monte‐Carlo simulations have been performed to complement these measurements, especially for the beam hardening and the scatter cases. Simulations of proton beams penetration through the resulting images have then been carried out to quantify the range change due to these effects. The particular case of a brain irradiation is considered mainly because of the multiple effects that the skull bones have on the internal soft tissues.Results:On top of the range error sources is the undercorrection of scatter. Its influence has been analyzed from a comparison of fan‐beam and full axial FOV acquisitions. In this case, large range errors of about 12 mm can be reached if the assumption is made that the scatter has only a constant contribution over the projection images. Even the detector lag, which a priori induces a much smaller effect, has been shown to contribute for up to 2 mm to the overall error if its correction only aims at reducing the skin artefact. This last result can partially be understood by the larger interface between tissues and bones inside the skull.Conclusion:This study has set the basis of a more systematical analysis of the effect CBCT noise on range uncertainties based on a combination of measurements, simulations and theoretical results. With our method, even more subtle effects such as the cone‐beam artifact or the detector lag can be assessed.SBR and JOR are financed by iMagX, a public‐private partnership between the region Wallone of Belgium and IBA under convention #1217662

2D lag and signal nonlinearity correction in an amorphous silicon EPID and their impact on pretreatment dosimetric verification

This investigation provides measurements of signal lag and nonlinearity separately for the Varian aS500 electronic portal imaging device (EPID), and an algorithm to correct for these effects in 2D; their potential impact on intensity modulated radiation therapy (IMRT) verification is also investigated. The authors quantify lag, as a function of both delivered monitor units (MU) and time, by using a range of MUs delivered at a clinically used rate of 400 MU∕min. Explicit cumulative lag curves are thus determined for a range of MUs and times between the end of irradiation and the end of image acquisition. Signal nonlinearity is also investigated as a function of total MUs delivered. The family of cumulative lag curves and signal nonlinearity are then used to determine their effects on dynamic multileaf collimator (MLC) (IMRT) deliveries, and to correct for theses effects in 2D. Images acquired with an aS500 EPID and Varis Portal-Vision software were used to quantify detector lag and signal-nonlinearity. For the signal lag investigation, Portal-Vision's service monitor was used to acquire EPID images at a rate of 8 frames/s. The images were acquired during irradiation and 66 s thereafter, by inhibiting the M-holdoff-In signal of the Linac for a range of 4.5-198.5 MUs. Relative cumulative lag was calculated by integrating the EPID signal for a time after beam-off, and normalizing this to the integrated EPID signal accumulated during radiation. Signal nonlinearity was studied by acquiring 10 × 10 cm(2) open-field EPID images in "integrated image" mode for a range of 2-500 MUs, and normalized to the 100 MU case. All data were incorporated into in-house written software to create a 2D correction map for these effects, using the field's MLC file and a field-specific calculated 2D "time-map," which keeps track of the time elapsed from the last fluence delivered at each given point in the image to the end of the beam delivery. Relative cumulative lag curves reveal that the lag alone can deviate the EPID's perceived dose by as large as 6% (1 MU delivery, 60 s postirradiation). For signal nonlinearity relative to 100 MU, EPID signals per MU of 0.84 and 1.01 were observed for 2 and 500 MUs, respectively. Correction maps were applied to a 1 cm sweeping-window 14 × 14 cm(2) field and clinical head-and-neck IMRT field. A mean correction of 1.028 was implemented in the head-and-neck field, which significantly reduced lag-related asymmetries in the EPID images, and restored linearity to the EPID imager's dose response. Corrections made to the sweeping-field showed good agreement with the treatment planning system-predicted field, yielding an average percent difference of 0.05% ± 0.91%, compared to the -1.32% ± 1.02% before corrections, or 1.75% ± 1.04% when only a signal nonlinearity correction is made. Lag and signal-nonlinearity have been quantified for an aS500 EPID imager, and an effective 2D correction method has been developed which effectively removes nonlinearity and lag effects. Both of these effects were shown to negatively impact IMRT verifications. Especially fields that involve prolonged irradiation and small overall MUs should be corrected for in 2D.

A nonlinear lag correction algorithm for a‐Si flat‐panel x‐ray detectors

Detector lag, or residual signal, in a-Si flat-panel (FP) detectors can cause significant shading artifacts in cone-beam computed tomography reconstructions. To date, most correction models have assumed a linear, time-invariant (LTI) model and correct lag by deconvolution with an impulse response function (IRF). However, the lag correction is sensitive to both the exposure intensity and the technique used for determining the IRF. Even when the LTI correction that produces the minimum error is found, residual artifact remains. A new non-LTI method was developed to take into account the IRF measurement technique and exposure dependencies. First, a multiexponential (N = 4) LTI model was implemented for lag correction. Next, a non-LTI lag correction, known as the nonlinear consistent stored charge (NLCSC) method, was developed based on the LTI multiexponential method. It differs from other nonlinear lag correction algorithms in that it maintains a consistent estimate of the amount of charge stored in the FP and it does not require intimate knowledge of the semiconductor parameters specific to the FP. For the NLCSC method, all coefficients of the IRF are functions of exposure intensity. Another nonlinear lag correction method that only used an intensity weighting of the IRF was also compared. The correction algorithms were applied to step-response projection data and CT acquisitions of a large pelvic phantom and an acrylic head phantom. The authors collected rising and falling edge step-response data on a Varian 4030CB a-Si FP detector operating in dynamic gain mode at 15 fps at nine incident exposures (2.0%-92% of the detector saturation exposure). For projection data, 1st and 50th frame lag were measured before and after correction. For the CT reconstructions, five pairs of ROIs were defined and the maximum and mean signal differences within a pair were calculated for the different exposures and step-response edge techniques. The LTI corrections left residual 1st and 50th frame lag up to 1.4% and 0.48%, while the NLCSC lag correction reduced 1st and 50th frame residual lags to less than 0.29% and 0.0052%. For CT reconstructions, the NLCSC lag correction gave an average error of 11 HU for the pelvic phantom and 3 HU for the head phantom, compared to 14-19 HU and 2-11 HU for the LTI corrections and 15 HU and 9 HU for the intensity weighted non-LTI algorithm. The maximum ROI error was always smallest for the NLCSC correction. The NLCSC correction was also superior to the intensity weighting algorithm. The NLCSC lag algorithm corrected for the exposure dependence of lag, provided superior image improvement for the pelvic phantom reconstruction, and gave similar results to the best case LTI results for the head phantom. The blurred ring artifact that is left over in the LTI corrections was better removed by the NLCSC correction in all cases.

SU-E-I-104: Detector Lag Testing for Digital Radiography.

Signal lag is an inherent property of the digital detectors used for radiography. The extent and impact of signal lag can vary. In this exhibit we aim to:'¢ provide context for detector lag testing as a matter of acceptance testing for detectors used in digital radiography (DR)'¢ provide example lag data from various DR systems. Lag measurement and analysis techniques have been published but without the expected results that are useful for benchmarking.'¢bring greater awareness and understanding to the impact of detector lag for the performance of digital detectors and the appearance of image ghosting in clinical images. A standardized method was used for the quantification of lag similar to that described in the IPEM report 32 part vii [1]. Six different vended detector options were measured and compared. Lag images were visually compared and lag measurement values were normalized using the measured signal transfer properties of each system. The results are shown as normalized with exposure. The results show a wide variety of lag responses, even for the same physical detector hardware but different vendor implementations. The ultimate test for detector lag performance is whether residual 'ghost' images appear in clinical images. Some vendor systems are more prone to the appearance of lag than others. We attempt to quantify system properties at acceptance testing that can enable discriminationbetween systems which have poor or superior lag properties. A suggested easurement analysis technique which normalizes data to be linear with exposure does not appear to adequately assess the likelihood for the clinical appearance of lag. [1] Mackenzie A et al. Measurement of the Performance Characteristics of Diagnostic X-Ray Systems: Digital Imaging Systems. Institute of Physics in Medicine 2009 (IPEM Report 32, part vii). 2010.

SU-E-I-79: Correction of Dark Current and Image Lag in Multi-Source Carbon Nanotube Imaging Systems.

To correct for dark current and detector image lag in multi-source carbon nanotube (CNT) based imaging systems. CNTs can be used as a novel electron source for X-ray generation. Here a small (∼2 kV) gate voltage induces a large electric field at the CNT tip that causes field emission as opposed to thermionic emission. This enables faster switching times and allows miniaturization which aids in the development of stationary multi-source systems. These systems may encounter two key problems: dark current in non-active sources and detector lag. In damaged and perhaps end-of-life devices, sources may develop significant electron output without gate voltage, interfering with images from non-active sources. Although the output is small, it is constant, and continuous integration of detectors compared with small X-ray exposure time magnifies its contribution to the signal. A method based on binary multiplexing is developed to mitigate the dark current contributions possibly extending device lifetime. Having discrete sources, these multi-source systems have coarser angular sampling than rotating systems. This coarser sampling means that the detector signal changes more in adjacent projections than rotating systems exacerbating the effect of detector lag. Since flat panel detectors have a capacitive behavior, readout only removes some charge from pixels. As a result, a device dependent amount (6.6% in our device) of the signal from the current image contributes to the subsequent image. A fast, stable inverse filter separating detector lag has been developed. Projection data were simulated based on parameters measured from a multi-source CNT system. Incorporating dark current and image lag phenomena into reconstruction improves contrast to noise ratio. These correction methods can be easily implemented on clinical multi-source systems to improve image quality and extend the device's usable lifetime. Supported in part by Siemens Medical.

Beyond noise power in 3D computed tomography: The local NPS and off‐diagonal elements of the Fourier domain covariance matrix

To investigate the correlation and stationarity of noise in volumetric computed tomography (CT) using the local discrete noise-power spectrum (NPS) and off-diagonal elements of the covariance matrix of the discrete Fourier transform of noise-only images (denoted Σ(DFT)). Experimental conditions were varied to affect noise correlation and stationarity, the effects were quantified in terms of the NPS and Σ(DFT), and practical considerations in CT performance characterization were identified. Cone-beam CT (CBCT) images were acquired using a benchtop system comprising an x-ray tube and flat-panel detector for a range of acquisition techniques (e.g., dose and x-ray scatter) and three phantom configurations hypothesized to impart distinct effects on the NPS and Σ(DFT): (A) air, (B) a 20-cm-diameter water cylinder with a bowtie filter, and (C) the cylinder without a bowtie filter. The NPS and off-diagonal elements of the Σ(DFT) were analyzed as a function of position within the reconstructions. The local NPS varied systematically throughout the axial plane in a manner consistent with changes in fluence transmitted to the detector and view sampling effects. Variability in fluence was manifest in the NPS magnitude-e.g., a factor of ~2 variation in NPS magnitude within the axial plane for case C (cylinder without bowtie), compared to nearly constant NPS magnitude for case B (bowtie filter matched to the cylinder). View sampling effects were most prominent in case A (air) where the variance increased at greater distance from the center of reconstruction and in case C (cylinder) where the NPS exhibited correlations in the radial direction. The effects of detector lag were observed as azimuthal correlation. The cylinder (without bowtie) had the strongest nonstationarity because of the larger variability in fluence transmitted to the detector. The diagonal elements of the Σ(DFT) were equivalent to the NPS estimated from the periodogram, and the average off-diagonal elements of the Σ(DFT) exhibited amplitude of ~1% of the NPS for the experimental conditions investigated. Furthermore, the off-diagonal elements demonstrated fairly long tails of nearly constant amplitude, with magnitude somewhat reduced for experimental conditions associated with greater stationarity (viz., lower Σ(DFT) tails for cases A and B in comparison to case C). Volumetric CT exhibits nonstationarity in the NPS as hypothesized in relation to fluence uniformity and view sampling. Measurement of the NPS should seek to minimize such changes in noise correlations and include careful reporting of experimental conditions (e.g., phantom design and use of a bowtie filter) and spatial dependence (e.g., analysis at fixed radius within a phantom). Off-diagonal elements of the Σ(DFT) similarly depend on experimental conditions and can be readily computed from the same data as the NPS. This work begins to check assumptions in NPS analysis examine the extent to which NPS is an appropriate descriptor of noise correlations, and investigate the magnitude of off-diagonal elements of the Σ(DFT). While the magnitude of such off-diagonal elements appears to be low, their cumulative effect on space-variant detectability remains to be investigated-e.g., using task-specific figures of merit.

A forward bias method for lag correction of an a-Si flat panel detector

Digital a-Si flat panel (FP) x-ray detectors can exhibit detector lag, or residual signal, of several percent that can cause ghosting in projection images or severe shading artifacts, known as the radar artifact, in cone-beam computed tomography (CBCT) reconstructions. A major contributor to detector lag is believed to be defect states, or traps, in the a-Si layer of the FP. Software methods to characterize and correct for the detector lag exist, but they may make assumptions such as system linearity and time invariance, which may not be true. The purpose of this work is to investigate a new hardware based method to reduce lag in an a-Si FP and to evaluate its effectiveness at removing shading artifacts in CBCT reconstructions. The feasibility of a novel, partially hardware based solution is also examined. The proposed hardware solution for lag reduction requires only a minor change to the FP. For pulsed irradiation, the proposed method inserts a new operation step between the readout and data collection stages. During this new stage the photodiode is operated in a forward bias mode, which fills the defect states with charge. A Varian 4030CB panel was modified to allow for operation in the forward bias mode. The contrast of residual lag ghosts was measured for lag frames 2 and 100 after irradiation ceased for standard and forward bias modes. Detector step response, lag, SNR, modulation transfer function (MTF), and detective quantum efficiency (DQE) measurements were made with standard and forward bias firmware. CBCT data of pelvic and head phantoms were also collected. Overall, the 2nd and 100th detector lag frame residual signals were reduced 70%-88% using the new method. SNR, MTF, and DQE measurements show a small decrease in collected signal and a small increase in noise. The forward bias hardware successfully reduced the radar artifact in the CBCT reconstruction of the pelvic and head phantoms by 48%-81%. Overall, the forward bias method has been found to greatly reduce detector lag ghosts in projection data and the radar artifact in CBCT reconstructions. The method is limited to improvements of the a-Si photodiode response only. A future hybrid mode may overcome any limitations of this method.

Effect of detector lag on CT noise power spectra

The authors examined the effect of detector lag on the noise power spectrum (NPS) of CT images reconstructed with filtered backprojection (FBP). The authors derived an analytical expression of the NPS with detector lag, and then verified it using computer simulations with parallel beam and fan beam geometries. The dependence of the NPS on the amount of lag, location within the scanned field of view (FOV), and the number of views used in the reconstruction (samples per rotation) was investigated using constant and view dependent noise in the raw data. Detector lag introduces noise correlation in the azimuthal direction. The effect on the NPS is a frequency dependent reduction in amplitude. In small regions of the image, the effect is primarily in the frequencies corresponding to the azimuthal direction. The noise blurring and NPS filtering increases with increasing radial distance, and therefore regions at larger radial distances have lower noise power. With the same detector lag response function, the amount of noise correlation and NPS filtering decreases with increasing number of views. The shape of the NPS depends on the detector lag coefficients, location of the region, and the number of views used in the reconstruction. In general, the noise correlation caused by detector lag decreased the amplitude of the NPS.

Investigation into the optimal linear time-invariant lag correction for radar artifact removal

Detector lag, or residual signal, in amorphous silicon (a-Si) flat-panel (FP) detectors can cause significant shading artifacts in cone-beam computed tomography (CBCT) reconstructions. To date, most correction models have assumed a linear, time-invariant (LTI) model and lag is corrected by deconvolution with an impulse response function (IRF). However, there are many ways to determine the IRF. The purpose of this work is to better understand detector lag in the Varian 4030CB FP and to identify the IRF measurement technique that best removes the CBCT shading artifact. We investigated the linearity of lag in a Varian 4030CB a-Si FP operating in dynamic gain mode at 15 frames per second by examining the rising step-response function (RSRF) followed by the falling step-response function (FSRF) at ten incident exposures (0.5%-84% of a-Si FP saturation exposure). We implemented a multiexponential (N = 4) LTI model for lag correction and investigated the effects of various techniques for determining the IRF such as RSRF versus FSRF, exposure intensity, length of exposure, and spatial position. The resulting IRFs were applied to (1) the step-response projection data and (2) CBCT acquisitions of a large pelvic phantom and acrylic head phantom. For projection data, 1st and 50th frame lags were measured pre- and postcorrection. For the CBCT reconstructions, four pairs of ROIs were defined and the maximum and mean errors within each pair were calculated for the different exposures and step-response edge techniques. A nonlinearity greater than 50% was observed in the FSRF data. A model calibrated with RSRF data resulted in overcorrection of FSRF data. Conversely, models calibrated with FSRF data applied to RSRF data resulted in undercorrection of the RSRF. Similar effects were seen when LTI models were applied to data collected at different incident exposures. Some spatial variation in lag was observed in the step-response data. For CBCT reconstructions, an average error range of 3-21 HU was observed when using IRFs from different techniques. For our phantoms and FP, the lowest average error occurred for the FSRF-based techniques at exposures of 1.6 or 3.4% a-Si FP saturation, depending on the phantom used. The choice of step-response edge (RSRF versus FSRF) and exposure intensity for IRF calibration could leave large residual lag in the step-response data. For the CBCT reconstructions, IRFs derived from FSRF data at low exposure intensities (1.6 and 3.4%) best removed the CBCT shading artifact. Which IRF to use for lag correction could be selected based on the object size.

SU‐FF‐J‐02: Impact of Cross Scatter On CNR in Fluoroscopic Images Taken with a Dual Flat Panel X‐Ray Imaging System

Purpose: To examine the impact of cross scatter on contrast‐to‐noise ratio (CNR) in fluoroscopic images acquired with a dual flat‐panel x‐ray imaging system. Method and Materials: An x‐ray imaging system has been developed consisting of two tube/detector pairs mounted orthogonally; each chain includes a 40×30 cm amorphous silicon flat panel detector. A timing system was developed to offset the exposures of each imaging chain relative to the detector cycle which remained synchronous for both imagers. The amount of cross scatter present is dependent on several factors: kVp, mAs, time between exposures, detector lag, and object size. Several cylindrical phantoms that incorporate areas of varying contrast were used to perform CNR measurements. CNR was taken as the relative contrast between two ROIs divided by the noise, the average of the standard deviation of the contrast measured in 15 images. Effects of cross‐scatter were determined by comparing CNR when both tubes were fired to CNR when only one tube was fired. Additionally, the dependence of CNR on kVp and the number of scrub frames was measured. Since cross scatter is non‐uniform across the imaging plane, CNR was measured using ROIs in various regions of scatter intensity across the detector. Results: Preliminary results show that contrast and CNR can be reduced by up to factors of approximately 10 when cross scatter is present. Contrast reductions are greater at lower kVp. CNR reductions are greater at higher kVp. By inserting scrub frames and time delays, contrast and CNR can be restored to near the values observed without cross scatter. Conclusion: Cross scatter can be substantial in dual x‐ray projection imaging. It's effects vary with kVp and can be alleviated by scrub frames and tube timing. Conflict of Interest: This research is supported in part by Varian collaboration.

TU‐C‐342‐01: Image Guidance During Minimally Invasive Cardiac Interventions : Beyond Fluoroscopy

Minimally invasive cardiac procedures using endovascular access have enabled treatment of patients that previously would have required open‐heart surgery. Such procedures are typically carried out in the interventional suite, with x‐ray fluoroscopy used to guide the placement of a catheter, followed by a phase of the procedure in which x‐ray fluoroscopy provides little to no information on the treatment. C‐arm CT has been under development since the early 90's, but has now entered a new phase as the ability to visualize soft tissue and low‐contrast lesions improves. By generating 3D images utilizing the x‐ray system in the interventional suite, the error prone and time‐consuming spatio‐temporal registration with prior CT or MRI exams can be avoided. However, C‐arm CT imaging in the presence of cardiac motion remains a challenge due to the slow rotation speed of the gantry, and the slow readout rates of the flat‐panel detectors. Three main data acquisition approaches to circumvent these limitations will be discussed: multi‐sweep retrospective ECG gating for myocardial visualization, single‐ or double‐sweep with volume image fusion for left atrium and pulmonary vein visualization and single‐sweep with retrospective gating for coronary artery visualization. Approaches to improve image quality, both hardware and software, such as optimized acquisition timing, motion compensation approaches and algorithms for non‐idealities such as x‐ray scatter, detector lag, limited field‐of‐view or angular coverage, data insufficiency and noise will be presented. With these developments the ultimate goal is to accomplish faster and more accurate catheter based interventions with an equivalent success rate as surgical procedures (80–90%). The C‐arm CT technology described here could eventually impact many minimally invasive procedures such as RF ablation for atrial fibrillation and coronary stenting, and also future procedures as they become available, such as endocardial injection of stem cells for treatment of myocardial infarction.Educational Objectives:1. Learn how 3D cardiac imaging is done in the interventional suite: acquisition protocols, injection protocols, challenges and solutions.2. Learn how image quality of 3D cardiac images can be improved using hardware and algorithm approaches.

WE-D-L100E-01: Cone Beam CT Workshop

The development of practical large field-of-view flat panel x-ray detectors has lead to an explosion in cone-beam computed tomography (CT) applications. In this workshop, a number of different clinical applications for cone-beam CT will be described. Methodologies for addressing issues such as x-ray scatter, detector lag, and limited field-of-view or angular coverage will also be discussed. C-arm fluoroscopic systems have been used for image guidance in the operating room for many years. With the advent of flat panel-based C-arm systems, the availability of portable, operating room-based computed tomography systems exists. The three dimensional characteristics of CT will likely improve surgical outcomes involving surgical device implantation. The development of a portable C-arm-based CT system, for use in the operating theater as well as in other clinical settings, will be discussed. Angiographic suites are designed to optimize vascular imaging geometry. Flat panel-based angiography suites have undergone modifications to allow CT imaging for some specific vascular imaging applications. These systems are also capable of performance in orthopedic applications. The use of such systems in a number of clinical applications will be described, and corrections methods for x-ray scatter and detector lag will also be presented. The use of image guidance in radiation therapy applications has skyrocketed, and in many clinical radiation therapy systems, system-mounted cone-beam CT systems are available for use for guiding RT delivery. The use of cone-beam CT for radiation therapy applications will be described, and some of the challenges and limitations of this technology will be discussed. Overall, a sampling of cone-beam CT applications will be presented in this workshop. The overall flexibility of cone-beam CT systems to accommodate the wide range of applications described is a testimony to the long term clinical potential of cone-beam CT systems. This workshop will also describe some of the compromises and limitations which are required when cone-beam CT geometry is utilized.

Computer modeling of the spatial resolution properties of a dedicated breast CT system

Computer simulation methods were used to evaluate the spatial resolution properties of a dedicated cone-beam breast CT system. X-ray projection data of a 70 microm nickel-chromium wire were simulated. The modulation transfer function (MTF) was calculated from the reconstructed axial images at different radial positions from the isocenter to study the spatial dependency of the spatial resolution of the breast CT scanner. The MTF was also calculated in both the radial and azimuthal directions. Subcomponents of the cone beam CT system that affect the MTF were modeled in the computer simulation in a serial manner, including the x-ray focal spot distribution, gantry rotation under the condition of continuous fluoroscopy, detector lag, and detector spatial resolution. Comparison between the computer simulated and physically measured MTF values demonstrates reasonable accuracy in the simulation process, with a small systematic difference (approximately 9.5 +/- 6.4% difference, due to unavoidable uncertainties from physical measurement and system calibration). The intrinsic resolution in the radial direction determined by simulation was about 2.0 mm(-1) uniformly through the field of view. The intrinsic resolution in the azimuthal direction degrades from 2.0 mm(-1) at the isocenter to 1.0 mm(-1) at the periphery with 76.9 mm from the isocenter. The results elucidate the intrinsic spatial resolution properties of the prototype breast CT system, and suggest ways in which spatial resolution can be improved with system modification.