Digital flat panel a-Si x-ray detectors can exhibit image lag of several percent. The image lag can limit the temporal
resolution of the detector, and introduce artifacts into CT reconstructions. It is believed that the majority of image lag is
due to defect states, or traps, in the a-Si layer. Software methods to characterize and correct for the image lag exist, but
they may make assumptions such as the system behaves in a linear time-invariant manner. The proposed method of
reducing lag is a hardware solution that makes few additional hardware changes. For pulsed irradiation, the proposed
method inserts a new stage in between the readout of the detector and the data collection stages. During this stage the
photodiode is operated in a forward bias mode, which fills the defect states with charge. Parameters of importance are
current per diode and current duration, which were investigated under light illumination by the following design
parameters: 1.) forward bias voltage across the photodiode and TFT switch, 2.) number of rows simultaneously forward
biased, and 3.) duration of the forward bias current. From measurements, it appears that good design criteria for the
particular imager used are 8 or fewer active rows, 2.9V (or greater) forward bias voltage, and a row frequency of 100
kHz or less. Overall, the forward bias method has been found to reduce first frame lag by as much as 95%. The panel
was also tested under x-ray irradiation. Image lag improved (94% reduction), but the temporal response of the
scintillator became evident in the turn-on step response.
C-Arm CT systems suffer from artifacts due to truncated projections caused by a finite detector size. One method used to mitigate the truncation artifacts is projection extrapolation without <i>a priori</i> knowledge. This work focuses on estimating the 0<sup>th</sup> and 1<sup>st</sup> moments of an image, which can be used to extrapolate a set of truncated projections. If some projections are not truncated, then accurate estimation of the moments can be achieved using only those projections. The more difficult case arises when all projections are truncated by some amount. For this case we make simplifying assumptions and fit the truncated projections with elliptical profiles. From this fit, we estimate the 0<sup>th</sup> and 1<sup>st</sup> moments of the original image. These estimated moments are then used to perform an extrapolation of the truncated projections, where each projection meets a constraint based on the 0<sup>th</sup> and 1<sup>st</sup> moments (moment extrapolation). This work focuses on how accurate moment estimates must be for moment extrapolation to be effective. The algorithm was tested on simulated and real data for the head, thorax, and abdomen, and those results were compared to symmetric mirroring by Ohnesorge et al., another extrapolation technique that requires no <i>a priori</i> knowledge. Overall, moment estimation and mass extrapolation alleviates a large amount of image artifact, and can improve on other extrapolation techniques. For the real CT head and abdominal data, the average reconstruction error for mass extrapolation was 48% less than the reconstruction error for symmetric mirroring.
C-arm CT first emerged as a useful high-contrast imaging modality in the late 1990s, using an XRII as the large area x-ray detector. To date, the C-arm approach to intra-procedural 3D imaging has primarily been used for high-contrast imaging tasks. The emerging goal for these systems is to extend the imaging range into the area of soft-tissue, and it is thought that digital flat-panel detectors may help. Flat panels replace the analog image intensifier, the camera optics, the pickup tube and the analog-to-digital converter with an all-digital detector. Flat panel detectors have a linear response, do not require distortion correction, do not suffer from veiling glare or blooming, and have higher dynamic range that current XRIIs. On the other hand, XRIIs have greater flexibility in FOV, and could support higher frame rates at high resolution, thereby reducing the effects of view aliasing. We have experience with a typical XRII-based C-arm imaging system and a new high-end C-arm equipped with a large flat-panel detector. Initial investigations show that when projection pixel size, acquisition geometry and focal spot size are matched, the flat-panel-based system produces reconstructions with improved MTF, primarily due to the additional interpolation step required for XRII warp correction. Investigations of artifact levels and comparison with <i>in vivo</i> CT images are presented.