Although photon counting systems have shown strong clinical potential, this technology has not yet been fully evaluated or optimized for specific clinical applications. The purpose of this study was to develop a framework for realistic virtual clinical trials (VCTs) in photon counting CT (PCCT) imaging. We developed a photon counting CT simulator based on the geometry and physics of an existing research prototype scanner. The developed simulator models primary, scatter, and noise signals, detector responses, vendor-specific bowtie filters and X-ray spectra, axial/helical trajectories, vendor-specific acquisition modes, and multiple energy thresholds per detector pixel. The simulation procedure is accelerated by parallel processing using multiple GPUs. The generated projection images can be reconstructed using generic reconstruction algorithms as well as a commercial reconstruction software (ReconCT Siemens). A computational model of a physical Mercury phantom was imaged at multiple energy thresholds (25 and 75 keV) and dose levels (36, 72, 144, and 216 mAs). Noise magnitude was measured in the simulated images and compared against noise measurements in a real scan acquired with a research prototype photon counting scanner (Siemens Healthcare). The results showed that our simulator was capable of synthesizing realistic photon counting CT data. The simulator can be combined with realistic 4D high-resolution XCAT phantoms with intra-organ heterogeneities to conduct VCTs for specific clinical applications. This framework can greatly facilitate the evaluation, optimization, and eventual clinical use of PCCT.
This study evaluates the capabilities of a whole-body photon counting CT system to differentiate between four
common kidney stone materials, namely uric acid (UA), calcium oxalate monohydrate (COM), cystine (CYS),
and apatite (APA) ex vivo. Two different x-ray spectra (120 kV and 140 kV) were applied and two acquisition
modes were investigated. The macro-mode generates two energy threshold based image-volumes and two energy
bin based image-volumes. In the chesspattern-mode four energy thresholds are applied. A virtual low energy
image, as well as a virtual high energy image are derived from initial threshold-based images, while considering
their statistically correlated nature. The energy bin based images of the macro-mode, as well as the virtual
low and high energy image of the chesspattern-mode serve as input for our dual energy evaluation. The dual
energy ratio of the individually segmented kidney stones were utilized to quantify the discriminability of the
different materials. The dual energy ratios of the two acquisition modes showed high correlation for both applied
spectra. Wilcoxon-rank sum tests and the evaluation of the area under the receiver operating characteristics
curves suggest that the UA kidney stones are best differentiable from all other materials (AUC = 1.0), followed
by CYS (AUC ≈ 0.9 compared against COM and APA). COM and APA, however, are hardly distinguishable
(AUC between 0.63 and 0.76). The results hold true for the measurements of both spectra and both acquisition
modes.
The energy resolving capabilities of Photon Counting Detectors (PCD) in Computed Tomography (CT) facilitate energy-sensitive measurements. The provided image-information can be processed with Dual Energy and Multi Energy algorithms. A research PCD-CT firstly allows acquiring images with a close to clinical configuration of both the X-ray tube and the CT-detector. In this study, two algorithms (Material Decomposition and Virtual Non-Contrast-imaging (VNC)) are applied on a data set acquired from an anesthetized rabbit scanned using the PCD-CT system. Two contrast agents (CA) are applied: A gadolinium (Gd) based CA used to enhance contrasts for vascular imaging, and xenon (Xe) and air as a CA used to evaluate local ventilation of the animal's lung. Four different images are generated: a) A VNC image, suppressing any traces of the injected Gd imitating a native scan, b) a VNC image with a Gd-image as an overlay, where contrast enhancements in the vascular system are highlighted using colored labels, c) another VNC image with a Xe-image as an overlay, and d) a 3D rendered image of the animal's lung, filled with Xe, indicating local ventilation characteristics. All images are generated from two images based on energy bin information. It is shown that a modified version of a commercially available dual energy software framework is capable of providing images with diagnostic value obtained from the research PCD-CT system.
Osteoporosis is a degenerative bone disease usually diagnosed at the manifestation of fragility fractures, which severely endanger the health of especially the elderly. To ensure timely therapeutic countermeasures, noninvasive and widely applicable diagnostic methods are required. Currently the primary quantifiable indicator for bone stability, bone mineral density (BMD), is obtained either by DEXA (Dual-energy X-ray absorptiometry) or qCT (quantitative CT). Both have respective advantages and disadvantages, with DEXA being considered as gold standard. For timely diagnosis of osteoporosis, another CT-based method is presented. A Dual Energy CT reconstruction workflow is being developed to evaluate BMD by evaluating lumbar spine (L1-L4) DE-CT images. The workflow is ROI-based and automated for practical use. A dual energy 3-material decomposition algorithm is used to differentiate bone from soft tissue and fat attenuation. The algorithm uses material attenuation coefficients on different beam energy levels. The bone fraction of the three different tissues is used to calculate the amount of hydroxylapatite in the trabecular bone of the corpus vertebrae inside a predefined ROI. Calibrations have been performed to obtain volumetric bone mineral density (vBMD) without having to add a calibration phantom or to use special scan protocols or hardware. Accuracy and precision are dependent on image noise and comparable to qCT images. Clinical indications are in accordance with the DEXA gold standard. The decomposition-based workflow shows bone degradation effects normally not visible on standard CT images which would induce errors in normal qCT results.
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 [1]. 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.
The purpose of this study was to compare lesion detection in images reconstructed using standard filtered
back projection (FBP) with those reconstructed using a new CT reconstruction algorithm called Iterative
Reconstruction in Image Space (IRIS). Detection performance was experimentally measured using a 2-
AFC software package that computes the lesion intensity corresponding to a detection accuracy of 92%
(i.e., I92%). Abdominal images were acquired on a Siemens Somaton Definition Flash CT scanner and
reconstructed at four slice thickness values ranging from 1.5 mm to 10 mm. Detection of three lesion sizes
was investigated, whose diameters ranged from 5 mm to 10 mm. AFC experiments were performed using
FBP and IRIS reconstructed images that were presented to observers in a random manner. For any lesion in
a given image, we obtained an Enhancement Factor (EF) defined as the I92% using FBP divided by the
corresponding I92% using IRIS. In 9 out of 12 paired results, EF values were significantly greater than 1.0,
and in the remaining three cases, EF values were approximately 1.0. EF was independent of CT image slice
thickness, with an average value of 1.17 ± 0.12. Values of EF increased with decreasing lesion size, and
were about 20% greater for 5 mm lesions than 10 mm lesions. The results of this pilot study show that IRIS
improved lesion detection compared to conventional FBP, with an average increase in signal to noise ratio
of 17%. For the smallest lesions, improvements in signal to noise ratio approached 30%. Our results
suggest that radiation dose reductions of one third might be achievable for abdominal imaging without any
loss in signal to noise ratio.
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