Cone-beam CT (CBCT) is being increasingly used in modern radiation therapy. However, as compared to
conventional CT, the degraded image quality of CBCT hampers its applications in radiation therapy. Due to the
large volume of x-ray illumination, scatter is considered as one of the fundamental limitations of CBCT image
quality. Many scatter correction algorithms have been proposed in the literature, while drawbacks still exist. In
this work, we propose a correction algorithm which is particularly useful in radiation therapy. Since the same
patient is scanned repetitively during one radiation treatment course, we measure the scatter distribution in
one scan, and use the measured scatter distribution to estimate and correct scatter in the following scans. A
partially blocked CBCT is used in the scatter measurement scan. The x-ray beam blocker has a strip pattern,
such that the whole-field scatter distribution can be estimated from the detected signals in the shadow region and
the patient rigid transformation can be determined from the reconstructed image using the illuminated detector
projection data. From the derived patient transformation, the measured scatter is then modified accordingly and
used for scatter correction in the following regular CBCT scans. The proposed method has been evaluated using
Monte Carlo simulations and physical experiments on an anthropomorphic chest phantom. The results show
a significant suppression of scatter artifacts using the proposed method. Using the reconstruction in a narrow
collimator geometry as a reference, the comparison also shows that the proposed method reduces reconstruction
error from 13.2% to 3.8%. The proposed method is attractive in applications where a high CBCT image quality
is critical, for example, dose calculation in adaptive radiation therapy.
Recently, we proposed a scatter correction method for x-ray imaging using primary modulation. A primary
modulator with spatially variant attenuating materials is inserted between the x-ray source and the object to
make the scatter and part of the primary distributions strongly separate in the Fourier domain. Linear filtering
and demodulation techniques suffice to extract and correct the scatter for this modified system. The method has
been verified by computer simulations and preliminary experimental results on a simple object. In this work, we
improve performance by using a new primary modulator with a higher modulation frequency and by refining the
algorithm. The improved method is evaluated experimentally using a pelvis phantom. The imaging parameters
are chosen to match the Varian Acuity CT simulator, where scatter correction has been shown to be challenging
due to complicated artifact patterns. The results using our approach are compared with those without scatter
correction, and with scatter estimated and corrected using a slit measurement as a pre-scan. The comparison
shows that the primary modulation method greatly reduces the scatter artifacts and improves image contrast.
Using only one single scan, this method achieves CT HU accuracy comparable to that obtained using a slit measurement as a pre-scan.
Accurate prediction of reconstructed noise in computed tomography (CT) images is important for purposes of
system design, optimization and evaluation. A large body of work describes noise prediction methods for CT,
the vast majority of which assume stationarity of both noise and signal processes. Consequently, these methods
are usually applied to and evaluated using simple phantoms, and only a portion of the image is scrutinized.
In this work, we derive a practical method for reconstructing CT noise variance maps for arbitrary objects.
Photon Poisson noise and system electronic noise are considered. The final formula has the same structure as
that of the filtered backprojection (FBP) formula, but with different weighting factors and convolution kernels.
The algorithm is verified using computer simulations of the Shepp-Logan phantom, and a good match is found
between the predicted noise map from one single noisy scan and the measured noise using 128 noisy scans.
As compared to other proposed noise models, our complementary work provides a method of noise prediction
by simple adaptation of FBP reconstruction algorithms. The result is a tool that can be useful for system
optimization and evaluation tasks as well as the design of reconstruction filters.
Scatter correction is an active research topic in cone beam computed tomography (CBCT) because CBCT (especially flat-panel detector (FPD) based) systems have large scatter-to-primary ratios. Scatter produces artifact and contrast reduction, and is difficult to model accurately. Direct measurement using a beam blocker array provides accurate scatter estimates. However, since the blocker array also blocks primary radiation, imaging requires a second (or subsequent) scan without the blocker array in place. This approach is inefficient in terms of scanning time and patient dose. To combine accurate scatter estimation and reconstruction into one single scan, a new approach based on an array of moving blockers has been developed. The blocker array moves from projection to projection, such that every detector pixel is not consecutively blocked during the data acquisition, and the missing primary data in the blocker shadows are estimated by interpolation. Using different blocker array trajectories, the algorithm has been evaluated through software phantom studies using Monte Carlo simulations and image processing techniques. Results show that this approach is able to greatly reduce the effect of scatter in the reconstruction. By properly choosing blocker distance and primary data interpolation method, the mean square error of the reconstructed image decreases from 32.3% to 1.13%, and the induced visual artifacts are significantly reduced when a raster-scanning blocker array trajectory is used. Further analysis also shows that artifact arises mostly due to inaccurate scatter estimates, rather than due to interpolation of the primary data.