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

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 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. 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.

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

PURPOSE: 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. METHODS: 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. RESULTS: 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%. CONCLUSIONS: 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.

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

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

A patient set-up protocol based on partially blocked cone-beam CT.

Three-dimensional x-ray cone-beam CT (CBCT) is being increasingly used in radiation therapy. Since the whole treatment course typically lasts several weeks, the repetitive x-ray imaging results in large radiation dose delivered on the patient. In the current radiation therapy treatment, CBCT is mainly used for patient set-up, and a rigid transformation of the CBCT data from the planning CT data is also assumed. For an accurate rigid registration, it is not necessary to acquire a full 3D image. In this paper, we propose a patient set-up protocol based on partially blocked CBCT. A sheet of lead strips is inserted between the x-ray source and the scanned patient. From the incomplete projection data, only several axial slices are reconstructed and used in the image registration for patient set-up. Since the radiation is partially blocked, the dose delivered onto the patient is significantly reduced, with an additional benefit of reduced scatter signals. The proposed approach is validated using experiments on two anthropomorphic phantoms. As x-ray beam blocking ratio increases, more dose reduction is achieved, while the patient set-up error also increases. To investigate this tradeoff, two lead sheets with different strip widths are implemented, which correspond to radiation dose reduction of approximately 6 and approximately 11, respectively. We compare the registration results using the partially blocked CBCT with those using the regular CBCT. Both lead sheets achieve high patient set-up accuracies. It is seen that, using the lead sheet with radiation dose reduction by a factor of approximately 11, the patient set-up error is still less than 1mm in translation and less than 0.2 degrees in rotation. The comparison of the reconstructed images also shows that the image quality of the illuminated slices in the partially blocked CBCT is much improved over that in the regular CBCT.

An efficient estimation method for reducing the axial intensity drop in circular cone-beam CT.

Reconstruction algorithms for circular cone-beam (CB) scans have been extensively studied in the literature. Since insufficient data are measured, an exact reconstruction is impossible for such a geometry. If the reconstruction algorithm assumes zeros for the missing data, such as the standard FDK algorithm, a major type of resulting CB artifacts is the intensity drop along the axial direction. Many algorithms have been proposed to improve image quality when faced with this problem of data missing; however, development of an effective and computationally efficient algorithm remains a major challenge. In this work, we propose a novel method for estimating the unmeasured data and reducing the intensity drop artifacts. Each CB projection is analyzed in the Radon space via Grangeat's first derivative. Assuming the CB projection is taken from a parallel beam geometry, we extract those data that reside in the unmeasured region of the Radon space. These data are then used as in a parallel beam geometry to calculate a correction term, which is added together with Hu's correction term to the FDK result to form a final reconstruction. More approximations are then made on the calculation of the additional term, and the final formula is implemented very efficiently. The algorithm performance is evaluated using computer simulations on analytical phantoms. The reconstruction comparison with results using other existing algorithms shows that the proposed algorithm achieves a superior performance on the reduction of axial intensity drop artifacts with a high computation efficiency.

Noninvasive temperature estimation based on the energy of backscattered ultrasound.

Hyperthermia has been used as a cancer treatment in which tumors are elevated to cytotoxic temperatures to aid in their control. A noninvasive method for volumetrically determining temperature distribution during treatment would greatly enhance the ability to uniformly heat tumors at therapeutic levels. Ultrasound is an attractive modality for this purpose. We investigated changes in backscattered energy (CBE) from pulsed ultrasound with temperature. Our predicted changes in backscattered energy were matched by in vitro measurements in samples of bovine liver, turkey breast, and pork rib muscle. We studied CBE in tissue regions with multiple scatterers, of isolated individual scatterers, and in collections of individual scatterers. The latter appears to have the most potential. We measured the CBE with a focused circular transducer with a center frequency of 7.5 MHz. The standard deviation of the CBE of 75-125 scattering regions from 0.3 to 0.5 cm3 volumes increased nearly monotonically from 37 degrees C to 50 degrees C in each tissue type. Although the slopes were different, the curve for each type of tissue was well matched by a second-degree polynomial, with a correlation coefficient of 0.99 in each case. Thus the use of the CBE of ultrasound for temperature estimation may have clinical promise with a convenient, low cost modality. Because our approach exploits the inhomogeneities present in tissue, we believe that if it is successful in vitro, it holds promise for in vivo application.