Ultra-low dose CT scanning produces non-ideal data with many problems when the number of photons reaching the detector is very small. One such problem is the bias introduced by clipping of negative measurement values prior to the log operation. This paper proposes a correction method for this clipping-induced bias, in particular for the case when access to the original un-clipped measurements is no longer available.
Computed tomography (CT) imaging of the thorax is a common application of CT in radiology. Most of these scans are performed with a helical scan protocol. A significant number of images suffer from motion artefacts due to the inability of the patients to hold their breath or due to hiccups or coughing. Some images become nondiagnostic while others are simply degraded in quality. In order to correct for these artefacts a motion compensated reconstruction for non-periodic motion is required. <p> </p>For helical CT scans with a pitch smaller or equal to one the redundancy in the helical projection data can be used to generate images at the identical spatial position for multiple time points. As the scanner moves across the thorax during the scan, these images do not have a fixed time point, but a well-defined temporal distance inbetween the images. Using image based registration a motion vector field can be estimated based on these images. The motion artefacts are corrected in a subsequent motion compensated reconstruction. The method is tested on mathematical phantom data (reconstruction) and clinical lung scans (motion estimation and reconstruction).
The objective of this study was to investigate the improvement in diagnostic quality of an iterative model–based reconstruction (IMBR) algorithm for low-tube-voltage (80-kVp) and low-tube-current in abdominal computed tomography angiography (CTA). A total of 11 patients were imaged on a 256-slice multidetector computed tomography for visualization of the aorta. For all patients, three different reconstructions from the low-tube-voltage data are generated: filtered backprojection (FBP), IMBR, and a mixture of both IMBR+FBP. To determine the diagnostic value of IMBR-based reconstructions, the image quality was assessed. With IMBR-based reconstructions, image noise could be significantly reduced, which was confirmed by a highly improved contrast-to-noise ratio. In the image quality assessment, radiologists were able to reliably detect more third-order and higher aortic branches in the IMBR reconstructions compared to FBP reconstructions. The effective dose level was, on average, 3.0 mSv for 80-kVp acquisitions. Low-tube-voltage CTAs significantly improve vascular contrast as presented by others; however, this effect in combination with IMBR enabled yet another substantial improvement of diagnostic quality. For IMBR, a significant improvement of image quality and a decreased radiation dose at low-tube-voltage can be reported.
Model observers were created and compared to human observers for the detection of low contrast targets in computed tomography (CT) images reconstructed with an advanced, knowledge-based, iterative image reconstruction method for low x-ray dose imaging. A 5-channel Laguerre-Gauss Hotelling Observer (CHO) was used with internal noise added to the decision variable (DV) and/or channel outputs (CO). Models were defined by parameters: (k1) DV-noise with standard deviation (std) proportional to DV std; (k2) DV-noise with constant std; (k3) CO-noise with constant std across channels; and (k4) CO-noise in each channel with std proportional to CO variance. Four-alternative forced choice (4AFC) human observer studies were performed on sub-images extracted from phantom images with and without a “pin” target. Model parameters were estimated using maximum likelihood comparison to human probability correct (PC) data. PC in human and all model observers increased with dose, contrast, and size, and was much higher for advanced iterative reconstruction (IMR) as compared to filtered back projection (FBP). Detection in IMR was better than FPB at 1/3 dose, suggesting significant dose savings. Model(k1,k2,k3,k4) gave the best overall fit to humans across independent variables (dose, size, contrast, and reconstruction) at fixed display window. However Model(k1) performed better when considering model complexity using the Akaike information criterion. Model(k1) fit the extraordinary detectability difference between IMR and FBP, despite the different noise quality. It is anticipated that the model observer will predict results from iterative reconstruction methods having similar noise characteristics, enabling rapid comparison of methods.
This report develops a new strategy for the acceleration of a maximum likelihood (ML) iterative reconstruction
algorithm for CT, by selecting a starting image which is closer to the solution of the ML algorithm than the
commonly used filtered backprojection image. The starting image is obtained by passing both the acquired
projection data and the reconstructed volume though a novel
de-noising algorithm which uses the same image
penalty function as the ML reconstruction. Clinical examples suggest that a savings of 5-10 iterations of the
separable paraboloidal surrogates algorithm per volume is possible when using this new acceleration strategy.
Thin-slice images reconstructed from helical multi-slice CT scans typically display artifacts known as windmill
artifacts, which arise from not satisfying the Nyquist sampling criteria in the patient longitudinal direction.
Since these are essentially aliasing artifacts, they can be reduced or removed by trading off resolution, either
globally (by reconstructing thicker slices) or locally (by local smoothing of the strong gradients). The obvious
drawback to this approach is the associated loss in resolution. Another approach is to utilize an x-ray tube with
the capability to modulate the focal spot in the z-direction, to effectively improve the sampling rate.
This work presents a new method for windmill artifact reduction based on total variation minimization in the
image domain, which is capable of removing windmill artifacts while at the same time preserving the resolution
of anatomic structures within the images. This is a big improvement over previous reconstruction methods that
sacrifice resolution, and it provides practically the same benefits as a z-switching x-ray tube with a much simpler
impact to the overall CT system.
This paper describes the image quality improvements achieved by developing a new fully physical imaging chain.
The key enablers for this imaging chain are a new scatter correction technique and an analytic computation of
the beam hardening correction for each detector. The new scatter correction technique uses off-line Monte Carlo
simulations to compute a large database of scatter kernels representative of a large variety of patient shapes
and an on-line combination of those based on the attenuation profile of the patient in the measured projections.
In addition, profiles of scatter originating from the wedge are estimated and subtracted. The beam hardening
coefficients are computed using analytic simulations of the full beam path of each individual ray through the
scanner. Due to the new approach, scatter and beam hardening are computed from first principles with no
further tuning factors, and are thus straight forward to adapt to any patient and scan geometry. Using the new
fully physical imaging chain unprecedented image quality was achieved. This is demonstrated with a special
scatter phantom. With current image correction techniques this phantom typically shows position dependent
inhomogeneity and streak artifacts resulting from the impact of scattered radiation. With the new imaging
chain these artifacts are almost completely eliminated, independent of position and scanning mode (kV). Further
preliminary patient studies show that in addition to fully guaranteeing an absolute Hounsfield scale in arbitrary
imaging conditions, the new technique also strongly sharpens object boundaries such as the edges of the liver.
In multi-slice cone beam CT imaging, there are artifacts known as windmill artifacts. These artifacts are due
to not satisfying the Nyquist criteria in the patient longitudinal direction. This paper quantifies and compares
these artifacts as a function of the number of rows, pitch, collimation, and image thickness of the CT scanner.
Scanners with rows of 16, 64 and 128 are measured and compared with simulated data, using both Helical and
Axial scanning modes. In addition three focal spot switching modes are compared: the traditional within image
plane mode; diagonal mode; and quad mode. All images are compared via four criteria: artifacts, MTF, SSP
Results show that the frequency of the artifact, or number of blades on the windmill and magnitude of each
blade, is dependent on the rate at which the rows are crossed for an image. For example, for a given pitch,
doubling the rows doubles the frequency of the artifact, with each artifact approximately the magnitude. A
similar result can be obtained by keeping the number of rows constant and varying the pitch. The artifact
disappears as the Nyquist criteria is satisfied by either increasing the slice thickness or incorporating one of the
focal spot switching modes that switch in the patient longitudinal direction. For a given MTF and SSP, the
diagonal focal spot switching mode has slightly more noise while the other two are approximately equal. The
artifact varies with the quad mode being the best and traditional mode being the worse.