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

Starman, Jared; Star‐Lack, Josh; Virshup, G. F.; Shapiro, Edward G.; Fahrig, Rebecca

doi:10.1118/1.3574873

Cited by 19 publications

(47 citation statements)

References 23 publications

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“…4,6,13 δ(k) is the impulse function of magnitude 1.0, and b 0 δ(k) represents the portion of the input signal that is unaffected by lag. The coefficients b n will be referred to as the lag coefficients and the exponential rates a n will be referred to as the lag rates.…”

Section: Iia Lti Lag Theorymentioning

confidence: 99%

“…Determination of the lag coefficients and lag rates from a step response is discussed further in Ref. 6. S n,k is a state variable that contains the contribution from previous inputs, where each input is recursively weighted by e −a n .…”

Section: Iia Lti Lag Theorymentioning

confidence: 99%

“…For a LTI system, the IRF should be independent of the technique used, but large differences between the responses determined from the RSRF and FSRF have been shown for both a-Si FPs and image intensifiers using cesium iodide (CsI) scintillators. 6,14,15 It has also been shown that the IRF is dependent on the exposure level of the step function used. Taken together, there is uncertainty in what is the best technique to use for lag correction using a LTI model.…”

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confidence: 99%

“…For CBCT reconstructions, the different trapping histories cause shading artifacts for parts of the object that correspond to detector pixels that experience large dynamic range changes. 6 Several hardware approaches exist to remove lag from an a-Si FP detector. Typically, the hardware methods involve waiting an extra amount of time for the lag signal to decay, 3 which slows data acquisition, or involve making the lag signal into a completely uniform offset signal that can then be subtracted out.…”

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confidence: 99%

“…13 Problems with temporal IRF deconvolution have been identified. 6 First, there are many ways to measure the IRF, such as measured directly from the response to a single frame of radiation 2 or derived from the response to a much longer period of irradiation (e.g., a step function). The rising stepresponse function (RSRF) looks at the a-Si FP response to the onset of x-ray exposure, while the falling step-response function (FSRF) is the response after a long irradiation has stopped.…”

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confidence: 99%

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

et al. 2012

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Purpose: 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. Methods: 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 stepresponse 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. Results: 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. Conclusions: 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.

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Section: Iia Lti Lag Theorymentioning

confidence: 99%

Section: Iia Lti Lag Theorymentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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

et al. 2012

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Acuros CTS: A fast, linear Boltzmann transport equation solver for computed tomography scatter – Part II: System modeling, scatter correction, and optimization

et al. 2018

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Acuros CTS is a promising new tool for fast and accurate scatter correction for CBCT imaging. By carefully modeling the imaging chain and optimizing several parameters, we achieved high correction accuracies with computation times compatible with the clinical workflow. The improvement in image quality enables better soft-tissue visualization and potentially enables applications such as adaptive radiotherapy.

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Slot‐scan dual‐energy bone densitometry using motorized X‐ray systems

et al. 2021

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Purpose We investigate the feasibility of slot‐scan dual‐energy (DE) bone densitometry on motorized radiographic equipment. This approach will enable fast quantitative measurements of areal bone mineral density (aBMD) for opportunistic evaluation of osteoporosis. Methods We investigated DE slot‐scan protocols to obtain aBMD measurements at the lumbar spine (L‐spine) and hip using a motorized x‐ray platform capable of synchronized translation of the x‐ray source and flat‐panel detector (FPD). The slot dimension was 5 × 20 cm2. The DE slot views were processed as follows: (1) convolution kernel‐based scatter correction, (2) unfiltered backprojection to tile the slots into long‐length radiographs, and (3) projection‐domain DE decomposition, consisting of an initial adipose–water decomposition in a bone‐free region followed by water–CaHA decomposition with adjustment for adipose content. The accuracy and reproducibility of slot‐scan aBMD measurements were investigated using a high‐fidelity simulator of a robotic x‐ray system (Siemens Multitom Rax) in a total of 48 body phantom realizations: four average bone density settings (cortical bone mass fraction: 10–40%), four body sizes (waist circumference, WC = 70–106 cm), and three lateral shifts of the body within the slot field of view (FOV) (centered and ±1 cm off‐center). Experimental validations included: (1) x‐ray test‐bench feasibility study of adipose–water decomposition and (2) initial demonstration of slot‐scan DE bone densitometry on the robotic x‐ray system using the European Spine Phantom (ESP) with added attenuation (polymethyl methacrylate [PMMA] slabs) ranging 2 to 6 cm thick. Results For the L‐spine, the mean aBMD error across all WC settings ranged from 0.08 g/cm2 for phantoms with average cortical bone fraction wcortical = 10% to ∼0.01 g/cm2 for phantoms with wcortical = 40%. The L‐spine aBMD measurements were fairly robust to changes in body size and positioning, e.g., coefficient of variation (CV) for L1 with wcortical = 30% was ∼0.034 for various WC and ∼0.02 for an obese patient (WC = 106 cm) changing lateral shift. For the hip, the mean aBMD error across all phantom configurations was about 0.07 g/cm2 for a centered patient. The reproducibility of hip aBMD was slightly worse than in the L‐spine (e.g., in the femoral neck, the CV with respect to changing WC was ∼0.13 for phantom realizations with wcortical = 30%) due to more challenging scatter estimation in the presence of an air–tissue interface within the slot FOV. The aBMD of the hip was therefore sensitive to lateral positioning of the patient, especially for obese patients: e.g., the CV with respect to patient lateral shift for femoral neck with WC = 106 cm and wcortical = 30% was 0.14. Empirical evaluations confirmed substantial reduction in aBMD errors with the proposed adipose estimation procedure and demonstrated robust aBMD measurements on the robotic x‐ray system, with aBMD errors of ∼0.1 g/cm2 across all three simulated ESP vertebrae and all added PMMA attenuator settings. Conc...

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

Cited by 19 publications

References 23 publications

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

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

Acuros CTS: A fast, linear Boltzmann transport equation solver for computed tomography scatter – Part II: System modeling, scatter correction, and optimization

Slot‐scan dual‐energy bone densitometry using motorized X‐ray systems

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