In CT imaging, a variety of applications exist where reconstructions are SNR and/or resolution limited. However, if the
measured data provide redundant information, composite image data with high SNR can be computed. Generally, these
composite image volumes will compromise spectral information and/or spatial resolution and/or temporal resolution.
This brings us to the idea of transferring the high SNR of the composite image data to low SNR (but high resolution)
‘source’ image data.
It was shown that the SNR of CT image data can be improved using iterative reconstruction  .We present a novel
iterative reconstruction method enabling optimal dose usage of redundant CT measurements of the same body region.
The generalized update equation is formulated in image space without further referring to raw data after initial
reconstruction of source and composite image data. The update equation consists of a linear combination of the previous
update, a correction term constrained by the source data, and a regularization prior initialized by the composite data.
The efficiency of the method is demonstrated for different applications: (i) Spectral imaging: we have analysed material
decomposition data from dual energy data of our photon counting prototype scanner: the material images can be
significantly improved transferring the good noise statistics of the 20 keV threshold image data to each of the material
images. (ii) Multi-phase liver imaging: Reconstructions of multi-phase liver data can be optimized by utilizing the noise
statistics of combined data from all measured phases (iii) Helical reconstruction with optimized temporal resolution:
splitting up reconstruction of redundant helical acquisition data into a short scan reconstruction with Tam window
optimizes the temporal resolution The reconstruction of full helical data is then used to optimize the SNR. (iv) Cardiac
imaging: the optimal phase image (‘best phase’) can be improved by transferring all applied over radiation into that
In all these cases, we show that - at constant patient dose - SNR can efficiently be transferred from the composite data to
the source data while maintaining spatial, temporal and contrast resolution properties of the source data.
Based on a phantom study with a realistic coronary vessel phantom, we investigated if motion compensated cardiac CT
reconstruction can improve best phase image quality with respect to motion artifacts and patency of coronary vessel
lumen. Basically, tracking based methods (with and without improvement of temporal resolution) deriving the motion
fields by a registration-like procedure are compared to optimization based methods optimizing objective functions while
minimizing artifact levels (e.g. Motion Artifact Metric Optimization (MAM) Reconstruction).
Using the MAM technique, the motion field is iteratively calculated with a steepest descent update equation minimizing
a motion artifact metric.
We evaluated patency of the vessel lumen, the normalized cross correlation (NCC) of the respective reconstruction data
with the ground truth data and a best phase improvement index correlating the motion compensated reconstruction data
to the non-compensated FDK-based reconstruction data. It will be shown that the MAM technique is superior to the
tracking methods. The latter proved to be more or less susceptible to template matching and, or erroneous template size.
The value of MAM is also demonstrated evaluating clinical data. In particular it is beneficial for patients with high heart
rates as well as for dose optimized scan protocols because it does not need over-radiation.
In this paper, a 4D iterative reconstruction scheme is developed to improve noise characteristics and/or reduce radiation
exposure in multi-slice cardiac CT. In our implementation, image volume datasets are reconstructed at adjacent temporal
positions with respect to an optimized cardiac phase (best phase). Nonlinear regularization priors operate on a 4D cube
surrounding each voxel in 4D space, reducing image noise while maintaining temporal and spatial image sharpness.
The temporal resolution of image data is maintained despite the usage of temporal data that can substantially exceed the
reconstruction range of the 'best phase' reconstruction. Consequently, the noise statistics is significantly improved
because non-correlated image data at different temporal positions are utilized.
To reduce the high computational load, the iterative regularization in 4D can be transferred into image space. Raw data
based Iterative Reconstruction reducing artifacts due to the non-exactness of the backprojector is decoupled from
regularization and restricted to only those projection data belonging to the 'best phase' reconstruction.
Finally, the image formation is achieved by a normalized combination of the low frequency part of the raw data based
Iterative Reconstruction at cardiac best phase and the high frequency part of the image based regularization image at best
We demonstrate the potential of noise reduction on basis of clinical cardiac CT data. As an example for cardiac dual
source CT (DSCT) data a noise reduction up to 70% was achieved. Even in case of a very high and irregular heart beat
with an average heart rate of 115 bpm the high temporal resolution of DSCT could be maintained.
Metal implants in the field of measurement lead to strong artifacts in CT images and reduce the image quality and
the diagnostic value severely. We introduce frequency split metal artifact reduction (FSMAR), a conceptually
new MAR method which is designed to reduce metal artifacts and preserve details and edges of structures even
close to metal implants. There are many MAR methods which simply replace unreliable parts of the projection
data by inpainting. FSMAR is a combination of an inpainting-based MAR method with a frequency split
approach. Normalized metal artifact reduction (NMAR) is chosen as the inpainting-based MAR method in
this work. The high frequencies of the original image, where all rawdata were used for the reconstruction, are
combined with an NMAR-corrected image. NMAR uses a normalization step to reduce metal artifacts without
introducing severe new artifacts. Algorithms using a frequency split were already used in CT for example to
reduce cone-beam artifacts. FSMAR is tested for patient datasets with different metal implants. The study
includes patients with hip prostheses, a neuro coil, and a spine fixation. All datasets were scanned with modern
clinical dual source CT scanners. In contrast to other MAR methods, FSMAR yields images without the usual
blurring close to metal implants.
It is well known that, in CT reconstruction, Maximum A Posteriori (MAP) reconstruction based on a Poisson noise
model can be well approximated by Penalized Weighted Least Square (PWLS) minimization based on a data dependent
Gaussian noise model. We study minimization of the PWLS objective function using the Gradient Descent (GD) method,
and show that if an exact inverse of the forward projector exists, the PWLS GD update equation can be translated into an
update equation which entirely operates in the image domain. In case of non-linear regularization and arbitrary noise
model this means that a non-linear image filter must exist which solves the optimization problem. In the general case of
non-linear regularization and arbitrary noise model, the analytical computation is not trivial and might lead to image
filters which are computationally very expensive. We introduce a new iteration scheme in image space, based on a
regularization filter with an anisotropic noise model. Basically, this approximates the statistical data weighting and
regularization in PWLS reconstruction. If needed, e.g. for compensation of the non-exactness of backprojector, the
image-based regularization loop can be preceded by a raw data based loop without regularization and statistical data
weighting. We call this combined iterative reconstruction scheme Adaptive Iterative Reconstruction (AIR). It will be
shown that in terms of low-contrast visibility, sharpness-to-noise and contrast-to-noise ratio, PWLS and AIR
reconstruction are similar to a high degree of accuracy. In clinical images the noise texture of AIR is also superior to the
more artificial texture of PWLS.
In this paper, a novel regularization approach for (non-statistical) iterative reconstruction is developed. In our
implementation, the update equation of iterative reconstruction is based on Filtered Backprojection (FBP) and the
solution is stabilized using nonlinear regularization priors. It is well known that the usage of nonlinear regularization
priors can reduce image noise at the same time preserving image sharpness . The final noise level can be adjusted by
dedicated choice of regularization priors, regularization strength and the total number of iterations. In contrast to
conventional CT using convolution kernels, image characteristics can not be further manipulated. This might cause
artificial image texture.
We present a new class of (non-local) 3D-regularization priors, which gives us control over image characteristics similar
to that obtained with conventional CT convolution kernels. In addition, efficient noise reduction at constant sharpness is
obtained. Due to the manipulation of the low-frequency components of the regularization filter, the filter is non-local.
The regularization strength becomes a 3D-matrix with contrast-dependent entries, which gives us control over contrastdependent
sharpness. The contrast edges are estimated using a 3D Laplacian kernel. High contrast edges get a low
regularization weight and vice versa. We demonstrate the potential of noise reduction on basis of clinical CT data. Also,
it is shown, that radiation exposure to the patient can be reduced by 60% in general purpose radiological CT applications
and cardiac CT at the same time maintaining image quality. Moreover, for a 128-slice detector with 0.6 mm collimation,
it is shown, that cone-beam and spiral artifacts caused by non-exact image reconstruction can be fairly removed. Putting
all together our iterative reconstruction approach substantially improves image quality in cone-beam CT, and thus has
the potential to enter routine clinical CT.
We present a method for spatio-temporal filtration of dynamic CT data, to increase the signal-to-noise ratio (SNR) of
image data at the same time maintaining image quality, in particular spatial and temporal sharpness of the images.
Alternatively, the radiation dose applied to the patient can be reduced at the same time maintaining the noise level and
the image sharpness. In contrast to classical methods, which generally operate on the three spatial dimensions of image
data, noise statistics is improved by extending the filtration to the temporal dimension. Our approach is based on
nonlinear and anisotropic diffusion filters, which are based on a model of heat diffusion adapted to medical CT data.
Bilateral filters are a special class of diffusion filters, which do not need iteration to reach a convergence image, but
represent the fixed point of a dedicated diffusion filter.
Spatio-temporal, anisotropic bilateral filters are developed and applied to dynamic CT image data. The potential was
evaluated using data from perfusion CT and cardiac dual source CT (DSCT) data, respectively. It was shown, that in
perfusion CT, SNR can be improved by a factor of 4 at the same radiation dose. On basis of clinical data it was shown,
that alternatively the radiation dose to the patient can be reduced by a factor of at least 2. A more accurate evaluation of
the perfusion parameters blood flow, blood volume and time-to-peak is supported.
In DSCT noise statistics can be improved using more projection data than needed for image reconstruction, however, as
a consequence the temporal resolution is significantly impaired. Due to the anisotropy of the spatio-temporal bilateral
filter temporal contrast edges between adjacent time samples are preserved, at the same time substantially smoothing
image data in homogeneous regions. Also temporal contrast edges are preserved, maintaining the very high temporal
resolution of DSCT acquisitions (~ 80 ms). CT examinations of the heart require careful dose management to reduce the
radiation dose burden to the patient. The use of spatio-temporal diffusion filters allows for dose reduction at the same
noise level, at the same time preserving spatial and temporal image resolution. Our approach can be extended to any
imaging method, that is based on dynamic data, as an efficient tool for edge-preserving noise reduction.
We present new acquisition modes of a recently introduced dual-source computed tomography (DSCT) system equipped with two X-ray tubes and two corresponding detectors, mounted onto the rotating gantry with an angular offset of typically <i>90°</i>. Due to the simultaneous acquisition of complementary data, the minimum exposure time is reduced by a factor of two compared to a
single-source CT system (SSCT). The correspondingly improved temporal resolution is beneficial for cardiac CT. Also, maximum table feed per rotation in a spiral mode can be increased by a factor of 2 compared to SSCT, which provides benefits both for cardiac CT and non-cardiac CT. In an ECG-triggered mode the entire cardiac volume can be scanned within a fraction of one cardiac
RR-cycle. At a rotation time of <i>0.28s</i> using a detector with <i>64×0.6 mm</i> beam collimation, the scan time of the entire heart is less than <i>0.3s</i> at a temporal resolution of <i>75 ms</i>. It will be shown, that the extremely fast cardiac scan reduces the patient dose to a theoretical lowest limit: for a <i>120 kV</i> scan the dose level for a typical cardiac CT scan is well below <i>2 mSv</i>. Using further protocol
optimization (scan range adaptation, 100kV), the radiation dose can be reduced below <i>1mSv</i>.
In cardiac CT temporal resolution is directly related to the gantry rotation time of 3<sup>rd</sup> generation CT scanners. This time
cannot be substantially reduced below current standards of 0.33 s - 0.35 s due to mechanical limitations. As an
alternative we present a dual source CT (DSCT) system. The system is equipped with two X-ray tubes and two
corresponding detectors that are mounted onto the rotating gantry with an angular offset of 90°. Due to the simultaneous
data acquisition and the angular offset, complementary quarter-scan data are measured at the same phase in the cardiac
cycle. Hence, the exposure time of any image slice is reduced by a factor of two and the temporal resolution is
improved by the same factor. In contrast to single source cardiac CT with multi-segment image reconstruction, the
temporal resolution does not depend on the heart rate.
Since multi-segment reconstruction techniques applied in single source cardiac CT, which limit the table speed, are no
longer needed, faster volume coverage in cardiac spiral imaging can be achieved. As a consequence of these concepts,
patient dose in cardiac CT can be significantly reduced.
ECG correlated image reconstruction is based on 3D backprojection of the Feldkamp type. Data truncation coming from
the fact that one detector (A) covers the entire scan field of view (50 cm in diameter), while the other detector (B) is
restricted to a smaller, central field of view (26 cm in diameter), has to be treated.
We evaluate temporal resolution and dose efficiency by means of phantom scans and computer simulations. We present
first patient scans to illustrate the performance of DSCT for ECG correlated cardiac imaging.