Spectral computed tomography (CT) is a powerful diagnostic tool offering quantitative material decomposition results that enhance clinical imaging by providing physiologic and functional insights. Iodine, a widely used contrast agent, improves visualization in various clinical contexts. However, accurately detecting low-concentration iodine presents challenges in spectral CT systems, particularly crucial for conditions like pancreatic cancer assessment. In this study, we present preliminary results from our hybrid spectral CT instrumentation which includes clinical-grade hardware (rapid kVp-switching x-ray tube, dual-layer detector). This combination expands spectral datasets from two to four channels, wherein we hypothesize improved quantification accuracy for low-dose and low-iodine concentration cases. We modulate the system duty cycle to evaluate its impact on quantification noise and bias. We evaluate iodine quantification performance by comparing two hybrid weighting strategies alongside rapid kVp-switching. This evaluation is performed with a polyamide phantom containing seven iodine inserts ranging from 0.5 to 20mg/mL. In comparison to alternative methodologies, the maximum separation configuration, incorporating data from both the 80kVp, low photon energy detector layer and the 140kVp, high photon energy detector layer produces spectral images containing low quantitative noise and bias. This study presents initial evaluations on a hybrid spectral CT system, leveraging clinical hardware to demonstrate the potential for enhanced precision and sensitivity in spectral imaging. This research holds promise for advancing spectral CT imaging performance across diverse clinical scenarios.
In radiation treatment planning (RTP), CT reconstruction that combats projection truncation artifacts induced by the patient being positioned partially outside the scan field-of-view (FOV) needs to maintain high geometric and Hounsfield Unit (HU) accuracy outside the scan FOV. A new image reconstruction method has been proposed for clinical helical CT simulation scans. This method generates support images using the Discrete Algebraic Reconstruction Technique to accurately estimate patient contours outside the scan FOV and then uses support images to guide the projection extension. The proposed method improved geometric accuracy in objects outside the scan FOV compared to a more conventional method and kept the geometric distortion within 5 mm under very severe truncation. It also demonstrated HU accuracy in objects outside the scan FOV within 2.5% for a variety of soft tissues and 15% for bone tissues on a typical electron density phantom. Images of three radiotherapy patient cases reconstructed with the proposed method exhibited clearly defined, naturally looking patient contours, including the recovery of skinfold outside the scan FOV. The proposed method shows the potential for providing clinically desirable extended FOV images for a variety of patient setups in RTP.
Percutaneous ablation procedures have been increasingly utilized to non-invasively treat tumors, such as hepatocellular carcinoma, by heating tumor cells beyond the lethal threshold. Intraprocedural temperature monitoring via spectral CT thermometry with a sensitivity less than 3 °C can reduce local recurrence rates by ensuring the tumor and its surrounding safety margin reach lethal temperatures. Because temperature sensitivity is reliant on noise, the effect of additional denoising, radiation dose, slice thickness, and iterative reconstruction levels on temperature sensitivity was evaluated on physical density slices utilized to generate temperature maps. Three different denoising algorithms (total variation, bilateral filtering, and non-local means) were applied to input images prior to generating physical density maps. Differences in noise in physical density and temperature sensitivity were calculated for each combination of parameters. All three denoising algorithms did not significantly affect quantification with an average difference of 1 x 10-4 g/mL from standard reconstructions, while generally non-local means denoising performed best with noise decreasing to 2 x 10-4 g/mL. The reduction in noise corresponded to temperature sensitivity decreasing from 15 ± 4 °C with standard reconstructions to 3 ± 2 °C with non-local means denoising at 2 mGy with 2 mm slices. Overall, temperature sensitivity at low radiation doses improved to clinically satisfactory levels with additional denoising. These accurate temperature maps from spectral CT thermometry will enable real-time, non-invasive temperature monitoring to ensure critical structures are not thermally damaged and the entire tumor and safety margin reach the lethal threshold, reducing local recurrences.
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.
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).
Peter Noël, Thomas Köhler, Alexander Fingerle, Kevin Brown, Stanislav Zabic, Daniela Münzel, Bernhard Haller, Thomas Baum, Martin Henninger, Reinhard Meier, Ernst Rummeny, Martin Dobritz
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
and noise.
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.
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