Edward Shapiro

Multiple-gain-ranging readout method to extend the dynamic range of amorphous silicon flat-panel imagers

The dynamic range of many flat panel imaging systems are fundamentally limited by the dynamic range of the charge amplifier and readout signal processing. We developed two new flat panel readout methods that achieve extended dynamic range by changing the read out charge amplifier feedback capacitance dynamically and on a real-time basis. In one method, the feedback capacitor is selected automatically by a level sensing circuit, pixel-by-pixel, based on its exposure level. Alternatively, capacitor selection is driven externally, such that each pixel is read out two (or more) times, each time with increased feedback capacitance. Both methods allow the acquisition of X-ray image data with a dynamic range approaching the fundamental limits of flat panel pixels. Data with an equivalent bit depth of better than 16 bits are made available for further image processing. Successful implementation of these methods requires careful matching of selectable capacitor values and switching thresholds, with the imager noise and sensitivity characteristics, to insure X-ray quantum limited operation over the whole extended dynamic range. Successful implementation also depends on the use of new calibration methods and image reconstruction algorithms, to insure artifact free rebuilding of linear image data by the downstream image processing systems. The multiple gain ranging flat panel readout method extends the utility of flat panel imagers and paves the way to new flat panel applications, such as cone beam CT. We believe that this method will provide a valuable extension to the clinical application of flat panel imagers.

40 x 30 cm flat-panel imager for angiography, R&F, and cone-beam CT applications

Preliminary results are presented from the PaxScan 4030A; a 40x30cm, 2048 x 1536 landscape, flat panel imager, with 194um pixel pitch. This imager builds on our experience with the PaxScan 2520, a 127um real-time flat panel detector capable of both high-resolution radiography and low dose fluoroscopy. While the PS2520 has been applied in C-arms, neuroangiography, cardiac imaging and small area radiographic units, the larger active area of the PaxScan 4030A addresses the broader applications of angiography, general RF however, a number of innovations have been incorporated into the 4030A to increase its versatility. The most obvious change is that the data interface between the receptor and command processor has been reduced to one very flexible and thin fiber-optic cable. A second new feature for the 4030A is the use of split datalines. Split datalines facilitate scanning the two halves of the array in parallel, cutting the readout time in half and increasing the time window for pulsed x-ray delivery to 15ms at 30fps. In addition, split datalines result in lower noise, which, coupled with the larger signal of the 194um pixels, enables high quality imaging at lower fluoroscopy doses rates.

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 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.

Multidetector-row CT with a 64–row amorphous silicon flat panel detector

A unique 64-row flat panel (FP) detector has been developed for sub-second multidetector-row CT (MDCT). The intent was to explore the image quality achievable with relatively inexpensive amorphous silicon (a-Si) compared to existing diagnostic scanners with discrete crystalline diode detectors. The FP MDCT system is a bench-top design that consists of three FP modules. Each module uses a 30 cm x 3.3 cm a-Si array with 576 x 64 photodiodes. The photodiodes are 0.52 mm x 0.52 mm, which allows for about twice the spatial resolution of most commercial MDCT scanners. The modules are arranged in an overlapping geometry, which is sufficient to provide a full-fan 48 cm diameter scan. Scans were obtained with various detachable scintillators, e.g. ceramic Gd2O2S, particle-in-binder Gd2O2S:Tb and columnar CsI:Tl. Scan quality was evaluated with a Catphan-500 performance phantom and anthropomorphic phantoms. The FP MDCT scans demonstrate nearly equivalent performance scans to a commercial 16-slice MDCT scanner at comparable 10 - 20 mGy/100mAs doses. Thus far, a high contrast resolution of 15 lp/cm and a low contrast resolution of 5 mm @ 0.3 % have been achieved on 1 second scans. Sub-second scans have been achieved with partial rotations. Since the future direction of MDCT appears to be in acquiring single organ coverage per scan, future efforts are planned for increasing the number of detector rows beyond the current 64- rows.

Flat panel CT detectors for sub-second volumetric scanning

This paper explores the potential of flat panel detectors in sub-second CT scanning applications. Using a PaxScan 4030CB with 600um thick CsI(Tl), a central section of the panel (16 to 32 rows), was scanned at frame rates up to 1000fps. Using this platform, fundamental issues related to high speed scanning were characterized. The offset drift of the imager over 60 seconds was found to be less than 0.014 ppm/sec relative to full scale. The gain stability over a 10 hour period is better than +/- .45%, which is at the resolution limit of the measurement. Two different types of lag measurements were performed in order to separate the photodiode array lag from the CsI afterglow. The panel lag was found to be 0.41% 1st frame and 0.054% 25th frame at 1000fps. The CsI(Tl) afterglow, however, is roughly an order of magnitude higher, dominating the lag for sub-second scans. At 1000fps the 1st frame lag due to afterglow was 3.3% and the 25th frame lag was 0.34%. Both the lag and afterglow are independent of signal level and each follows a simple power law evolution versus time. Reconstructions of anatomical phantoms and the CATPHAN 500 phantom are presented. With a 2 second, 1200 projection scan of the CATPHAN phantom at 600fps in 32 slice mode, using 120kVp and CTDI100 of 43.2mGy, 0.3% contrast resolution for a 6mm diameter target, can be visualized. In addition, 15lp/cm spatial resolution was achieved with a 2mm slice and a central CTDI100 of 10.8mGy.

Asymmetric scatter kernels for software-based scatter correction of gridless mammography

Scattered radiation remains one of the primary challenges for digital mammography, resulting in decreased image contrast and visualization of key features. While anti-scatter grids are commonly used to reduce scattered radiation in digital mammography, they are an incomplete solution that can add radiation dose, cost, and complexity. Instead, a software-based scatter correction method utilizing asymmetric scatter kernels is developed and evaluated in this work, which improves upon conventional symmetric kernels by adapting to local variations in object thickness and attenuation that result from the heterogeneous nature of breast tissue. This fast adaptive scatter kernel superposition (fASKS) method was applied to mammography by generating scatter kernels specific to the object size, x-ray energy, and system geometry of the projection data. The method was first validated with Monte Carlo simulation of a statistically-defined digital breast phantom, which was followed by initial validation on phantom studies conducted on a clinical mammography system. Results from the Monte Carlo simulation demonstrate excellent agreement between the estimated and true scatter signal, resulting in accurate scatter correction and recovery of 87% of the image contrast originally lost to scatter. Additionally, the asymmetric kernel provided more accurate scatter correction than the conventional symmetric kernel, especially at the edge of the breast. Results from the phantom studies on a clinical system further validate the ability of the asymmetric kernel correction method to accurately subtract the scatter signal and improve image quality. In conclusion, software-based scatter correction for mammography is a promising alternative to hardware-based approaches such as anti-scatter grids.

Lung cancer, respiratory 3D motion imaging, with a 19 focal spot kV x-ray tube and a 60 fps flat panel imager

The combinations of a 60 fps kV x-ray flat panel imager, a 19 focal spot kV x-ray tube enabled by a steered electron beam, plus SART or SIRT sliding reconstruction via GPUs, allow real time 6 fps 3D-rendered digital tomosynthesis tracking of the respiratory motion of lung cancer lesions. The tube consists of a “U” shaped vacuum chamber with 19 tungsten anodes, spread uniformly over 3 sides of a 30 cm x 30 cm square, each attached to a cylindrical copper heat sink cooled by flowing water. The beam from an electron gun was steered and focused onto each of the 19 anodes in a predetermined sequence by a series of dipole, quadrupole and solenoid magnets. The imager consists of 0.194 mm pixels laid out in 1576 rows by 2048 columns, binned 4x4 to achieve 60 fps projection image operation with 16 bits dynamic range. These are intended for application with free breathing patients during ordinary linac C-arm radiotherapy with modest modifications to typical system hardware or to standard clinical treatment delivery protocols. The sliding digital tomosynthesis reconstruction is completed after every 10 projection images acquired at 60 fps, but using the last 19 such projection images for each such reconstruction at less than 8 mAs exposure per 3D rendered frame. Comparisons, to “ground truth” optical imaging and to diagnostic 4D CT (10 phase) images, are being used to determine the accuracy and limitations of the various versions of this new “19 projection image x-ray tomosynthesis fluorooscopy” motion tracking technique.

SU-FF-I-27: Can a Flat Panel Cone Beam CT Imager Pass the American College of Radiology CT Accreditation Requirements for Head Scans?

Purpose: To evaluate the performance of a flat panel cone‐beam CT system with the American College of Radiology (ACR) CT Accreditation Program. Methods and Materials: A bench‐top flat panel (FP) cone‐beam CT system using the Varian Medical Systems 4030CB panel and G424/B130 x‐ray tube with a rotating stage were used. The FP was operated in the dynamic gain mode with a frame rate of 15 fps and a scan time of 42 seconds and 625 views. The source and detector geometry were such that a full‐fan head size 25 cm field‐of‐view was reconstructed. The Gammex Inc. 464 ACR accreditation phantom and ACT automated evaluation software were used to form an unbiased pass/fail determination of the ACR head scan criteria. Standard CTDIdosimetry measurements were performed with a 16 cm diameter PMMA phantom and Victoreen 660 survey meter with 10 cm ionization chamber.Results: The ACR phantom has 4 sections, which are to evaluate a number of performance criteria. The scans were performed at a measured CTDI100 of 45 mGy absorbed dose in air. These results are from the first attempt at using the ACR phantom on the FP CBCT system. The image quality was sufficient to pass the majority of the criteria, but failed to pass the CT number accuracy by 1 HU on the acrylic sample and by 1–2 HU on the CT number uniformity test. Conclusion: The results indicate that the CT number accuracy and uniformity are areas that can be improved with better software beam hardening and scatter corrections. Also these scans were taken without an anti scatter grid, which if used should improve CT number uniformity. Therefore, there are significant indications that we can pass all categories of the ACR head accreditation tests. Additional test are then to be performed on a C‐arm based system.

WE‐D‐332‐01: Advances in Sub‐Second CT Scanning with a 64‐Row Amorphous Silicon Flat Panel Imager

Purpose: To increase the data acquisition speed and detection limits of amorphous silicon flat panels for use in a low cost multidetector‐row CT (MDCT) scanner with diagnostic image quality. Method and Materials: A bench‐top sub‐second flat panel (FP) multidetector‐row CTsystem has been developed using three 64‐row FP detectors. Each FP is 30 cm × 3.3 cm in active area with 576 × 64 pixels that are 0.52 mm per side. A high degree of parallel processing is used to speed the data acquisition from the panels. Dynamic gain operation of the ASIC readout amplifiers has been used to improve noise performance over the previous fixed gain mode. The system has been tested with various detachable scintillators and scans of performance and anthropomorphic phantoms are compared with their diagnostic MDCT scans. Results: The 64‐row FP MDCT system can achieve full rotation 660 projection scans in 1 seconds. A 0.3 second partial rotation scan can be achieved with 32 rows by row binning. The image quality of 20 cm diameter performance phantom scans is comparable to a commercial MDCT scanner with similar technique/dose. Medium sized body scans are nearly comparable except for slight artifacts due to panel overlaps and lag. Large body phantom scans have improved with increased dynamic range provided by the readout ASICs dynamic gain mode. Conclusion: The results indicate the potential for FP MDCT to be used as a less expensive and less complex alternative to crystalline silicon detectors on MDCT scanners. There is pressure to increase the number of MDCT rows beyond 64 in cardiac imaging to achieve single organ coverage in one scan rotation. The use of larger area FP detectors to achieve greater than 256 rows exists and the sub second speed can be achieved with compensations and a high degree of parallel processing.