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

Yang, Kai; Kwan, Alexander L. C.; Boone, John M.

doi:10.1118/1.2737263

Cited by 40 publications

(45 citation statements)

References 25 publications

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“…Also, S * n,k is equal to S n,k if x k−1 = x k in Eq. (12). Otherwise, S * n,k is forced to be consistent with q n,k , a n (x k ), and b n (x k ).…”

Section: Iib Nlcsc Lag Algorithmmentioning

confidence: 99%

“…The state variable update functions, F 1 and F 2 in Eqs. (12) and (15) enforce this constraint by calculating a consistent S * n,k , with…”

Section: Iib1 Stored Charge Constraintmentioning

confidence: 99%

“…For these methods, a suitable model of the lag decay is fit to the a-Si FP detector data with a multiexponential 12 signal or a power function. 1 Typically, the a-Si FP is modeled as a linear time-invariant (LTI) system, which is described by an impulse response function (IRF).…”

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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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“…Also, S * n,k is equal to S n,k if x k−1 = x k in Eq. (12). Otherwise, S * n,k is forced to be consistent with q n,k , a n (x k ), and b n (x k ).…”

Section: Iib Nlcsc Lag Algorithmmentioning

confidence: 99%

“…The state variable update functions, F 1 and F 2 in Eqs. (12) and (15) enforce this constraint by calculating a consistent S * n,k , with…”

Section: Iib1 Stored Charge Constraintmentioning

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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“…There are several groups currently researching dedicated breast CT at the University of California at Davis, [16][17][18][19][20][21][22] Duke University, [23][24][25][26][27][28][29][30][31] University of Rochester, [32][33][34][35][36] University of Massachusetts Medical School, [37][38][39] and University of Texas M.D. Anderson Cancer Center.…”

Section: Iib Breast Ct Data Acquisition and Image Processingmentioning

confidence: 99%

“…The gantry rotates over 360°in the horizontal plane around the breast and acquires 500 conebeam projection images in 16.6 s. Each breast is scanned separately and the mean fibroglandular radiation dose delivered to the breast was constrained to be the same as twoview mammography. [16][17][18][19][20][21][22] The low-dose acquisition of the breast data as well as the cone-beam geometry of the CT system results in image degradation due to scatter radiation and considerable quantum noise. Therefore correction of scatter and noise is a necessary part of this project because the nonuniformity of the background may cause the denser tissue ͑fibroglandular tissue͒ values to be lower than the less-dense ͑adipose tissue͒ values and vice versa.…”

Section: Iib Breast Ct Data Acquisition and Image Processingmentioning

confidence: 99%

Methodology for generating a 3D computerized breast phantom from empirical data

Li¹,

Segars²,

Tourassi³

et al. 2009

Medical Physics

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116

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The initial process for creating a flexible three-dimensional computer-generated breast phantom based on empirical data is described. Dedicated breast computed-tomography data were processed to suppress noise and scatter artifacts in the reconstructed image set. An automated algorithm was developed to classify the breast into its primary components. A preliminary phantom defined using subdivision surfaces was generated from the segmented data. To demonstrate potential applications of the phantom, simulated mammographic image data were acquired of the phantom using a simplistic compression model and an analytic projection algorithm directly on the surface model. The simulated image was generated using a model for a polyenergetic cone-beam projection of the compressed phantom. The methods used to create the breast phantom generate resulting images that have a high level of tissue structure detail available and appear similar to actual mammograms. Fractal dimension measurements of simulated images of the phantom are comparatively similar to measurements from images of real human subjects. A realistic and geometrically defined breast phantom that can accurately simulate imaging data may have many applications in breast imaging research.

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Location and direction dependence in the 3D MTF for a high‐resolution CT system

Hernandez

Mahesh

et al. 2021

Medical Physics

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Purpose The purpose of this study was to quantify location and direction‐dependent variations in the 3D modulation transfer function (MTF) of a high‐resolution CT scanner with selectable focal spot sizes and resolution modes. Methods The Aquilion Precision CT scanner (Canon Medical Systems) has selectable 0.25 mm or 0.5 mm detectors (by binning) in both the axial (x‐y) and detector array width (z) directions. For the x‐y and z orientations, detectors are configured (x–y) = 0.5 mm/(z) = 0.5 mm for normal resolution (NR), 0.25/0.5 mm for high resolution (HR), and 0.25/0.25 mm for super high resolution (SHR). Six focal spots (FS1‐FS6) range in size from 0.4 (x‐y) × 0.5 mm (z) for FS1 to 1.6 × 1.4 mm for FS6. Phantoms fabricated from spherical objects were positioned at radial distances of 0, 4.0, 7.5, 11.0, 14.5, and 18.5 cm. Axial and helical acquisitions were utilized and reconstructed using filtered back projection with the FC18 “Body,” FC30 “Bone,” and FC81 “Bone Sharp” kernels. The reconstructions were used to measure a 1D slice of the 3D MTF by oversampling the 3D ESF in the axial plane [MTF(fr); φ = 0°)], 45° out of the axial plane [MTF(fr); φ = 45°)], in the longitudinal direction [MTF(fr); φ = 80°)], and along the radial and azimuthal directions within the axial plane. Results The MTF(fr); φ = 45°) drops to 10% (f10) at 1.20, 1.45, and 2.06 mm−1 for NR, HR, and SHR, respectively, for a helical acquisition with FS1, FC30, and r = 4 cm from the isocenter. The MTF(fr); φ = 45°) includes contributions of both the axial‐plane MTF (f10 = 1.10, 2.04, and 2.01 mm−1) and the longitudinal MTF (f10 = 1.17, 1.18, and 1.82 mm−1) for the NR, HR, and SHR modes, respectively. For SHR, the axial scan mode showed a 15–25% improvement over helical mode in the longitudinal resolution. Helical pitch, ranging from 0.569 to 1.381, did not appreciably affect the 3D resolution (<2%). The radial MTFs across the axial field of view (FOV) showed dependencies on the focal spot length in z; for example, for SHR with FS2 (0.6 × 0.6 mm), f10 at r = 11 cm was within 17% of the value at r = 4 cm, but for SHR with FS3 (0.6 × 1.3), the reduction in f10 was 46% from 4 to 11 cm from the isocenter. The azimuthal MTF also decreased as r increased but less so for longer gantry rotation times and smaller focal spot dimensions in the axial plane. The longitudinal MTF was minimally affected (<11%) by position in the FOV and was principally affected by the focal spot length in the z‐dimension. Conclusions The 3D MTF was measured throughout the FOV of a high‐resolution CT scanner, quantifying the advantages of different resolution modes and focal spot sizes on the axial‐plane and longitudinal MTF. Reconstruction kernels were shown to impact axial‐plane resolution, imparting non‐isotropic 3D resolution characteristics. Focal spot size (both in x‐y and in z) and gantry rotation time play important roles in preserving the high‐resolution characteristics throughout the field of view for this new high‐resolution CT scanner technology.

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

Cited by 40 publications

References 25 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

Methodology for generating a 3D computerized breast phantom from empirical data

Location and direction dependence in the 3D MTF for a high‐resolution CT system

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