Metal artifacts have been a challenge in computed tomography (CT) for nearly four decades. Despite intensive research in this area, challenges still exist in commercial metal artifact reduction (MAR) solutions. MAR is particularly important for radiation therapy and proton therapy treatment planning because metal artifacts not only degrade the outline of tumors and sensitive organs, but also introduce errors in stopping power estimation, compromising dose prediction accuracy. In this study, we developed a MAR approach that combines hardware and algorithmic innovations to systematically tackle the challenge of metal artifacts in radiation therapy. We propose to operate the X-ray tube at exceptionally high voltage and the detector DAS with adaptive triggering rate to prevent photon starvation in the CT raw data, followed by physics-based sinogram domain precorrection and model-based iterative reconstruction to correct the metal artifacts. We performed an end-to-end simulation of the integrated MAR approach with advanced hardware and algorithmic solutions. We simulated 700mAs/140 kVp and 550mAs/180 kVp CT scans, 984 views, with and without adaptive triggering, of an image volume based on the Visible Human Project CT data set, and after inserting two Titanium hip prostheses. The results demonstrated that the proposed MAR scheme can effectively eliminate metal artifacts and improve the accuracy of proton therapy planning. The dosimetric evaluation showed that with the proposed MAR solution, the error in range calculation was reduced from 7 mm to <1 mm.
Artifacts resulting from metal objects have been a persistent problem in CT images over the last four decades. A common
approach to overcome their effects is to replace corrupt projection data with values synthesized from an interpolation
scheme or by reprojection of a prior image. State-of-the-art correction methods, such as the interpolation- and
normalization-based algorithm NMAR, often do not produce clinically satisfactory results. Residual image artifacts remain
in challenging cases and even new artifacts can be introduced by the interpolation scheme. Metal artifacts continue to be
a major impediment, particularly in radiation and proton therapy planning as well as orthopedic imaging. A new solution
to the long-standing metal artifact reduction (MAR) problem is deep learning, which has been successfully applied to
medical image processing and analysis tasks. In this study, we combine a convolutional neural network (CNN) with the
state-of-the-art NMAR algorithm to reduce metal streaks in critical image regions. Training data was synthesized from CT
simulation scans of a phantom derived from real patient images. The CNN is able to map metal-corrupted images to
artifact-free monoenergetic images to achieve additional correction on top of NMAR for improved image quality. Our
results indicate that deep learning is a novel tool to address CT reconstruction challenges, and may enable more accurate
tumor volume estimation for radiation therapy planning.
In this study, we propose to use patient-specific x-ray fluence control to reduce the radiation dose to sensitive organs
while still achieving the desired image quality (IQ) in the region of interest (ROI). The mA modulation profile is
optimized view by view, based on the sensitive organs and the ROI, which are obtained from an ultra-low-dose
volumetric CT scout scan . We use a clinical chest CT scan to demonstrate the feasibility of the proposed concept: the
breast region is selected as the sensitive organ region while the cardiac region is selected as IQ ROI. Two groups of
simulations are performed based on the clinical CT dataset: (1) a constant mA scan adjusted based on the patient
attenuation (120 kVp, 300 mA), which serves as baseline; (2) an optimized scan with aggressive bowtie and ROI
centering combined with patient-specific mA modulation. The results shows that the combination of the aggressive
bowtie and the optimized mA modulation can result in 40% dose reduction in the breast region, while the IQ in the
cardiac region is maintained. More generally, this paper demonstrates the general concept of using a 3D scout scan for
optimal scan planning.
Radiation exposure during CT imaging has drawn growing concern from academia, industry as well as the general public. Sinusoidal tube current modulation has been available in most commercial products and used routinely in clinical practice. To further exploit the potential of tube current modulation, Sperl et al. proposed a Computer-Assisted Scan Protocol and Reconstruction (CASPAR) scheme  that modulates the tube current based on the clinical applications and patient specific information. The purpose of this study is to accelerate the CASPAR scheme to make it more practical for clinical use and investigate its dose benefit for different clinical applications. The Monte Carlo simulation in the original CASPAR scheme was substituted by the dose reconstruction to accelerate the optimization process. To demonstrate the dose benefit, we used the CATSIM package generate the projection data and perform standard FDK reconstruction. The NCAT phantom at thorax position was used in the simulation. We chose three clinical cases (routine chest scan, coronary CT angiography with and without breast avoidance) and compared the dose level with different mA modulation schemes (patient specific, sinusoidal and constant mA) with matched image quality. The simulation study of three clinical cases demonstrated that the patient specific mA modulation could significantly reduce the radiation dose compared to sinusoidal modulation. The dose benefits depend on the clinical application and object shape. With matched image quality, for chest scan the patient specific mA profile reduced the dose by about 15% compared to the sinusoid mA modulation; for the organ avoidance scan the dose reduction to the breast was over 50% compared to the constant mA baseline.
Computerized Tomography (CT) is a powerful radiographic imaging technology but the health risk due to the exposure of x-ray radiation has drawn wide concern. In this study, we propose to use kVp modulation to reduce the radiation dose and achieve the personalized low dose CT. Two sets of simulation are performed to demonstrate the effectiveness of kVp modulation and the corresponding calibration. The first simulation used the helical body phantom (HBP) that is an elliptical water cylinder with high density bone inserts. The second simulation uses the NCAT phantom to emulate the practical use of kVp modulation approach with region of interest (ROI) selected in the cardiac region. The kVp modulation profile could be optimized view by view based on the knowledge of patient attenuation. A second order correction is applied to eliminate the beam hardening artifacts. To simplify the calibration process, we first generate the calibration vectors for a few representative spectra and then acquire other calibration vectors with interpolation. The simulation results demonstrate the beam hardening artifacts in the images with kVp modulation can be eliminated with proper beam hardening correction. The results also show that the simplification of calibration did not impair the image quality: the calibration with the simplified and the complete vectors both eliminate the artifacts effectively and the results are comparable. In summary, this study demonstrates the feasibility of kVp modulation and gives a practical way to calibrate the high order beam hardening artifacts.
Over the past decade, there has been abundant research on future cardiac CT architectures and corresponding reconstruction algorithms. Multiple cardiac CT concepts have been published, including third-generation single-source CT with wide-cone coverage, dual-source CT, and electron-beam CT, etc. In this paper, we apply a Radon space analysis method to two multi-beamline architectures: triple-source CT and semi-stationary ring-source CT. In our studies, we have considered more than thirty cardiac CT architectures and triple-source CT was identified as a promising solution, offering approximately a three-fold advantage in temporal resolution, which can significantly reduce motion artifacts due to the moving heart and lungs. In this work, we describe a triple-source CT architecture with all three beamlines (i.e. source-detector pairs) limited to the cardiac field of view in order to eliminate the radiation dose outside the cardiac region. We also demonstrate the capability of performing full field of view imaging when desired, by shifting the detectors. Ring-source dual-rotating-detector CT is another architecture of interest, which offers the opportunity to provide high temporal resolution using a full-ring stationary source. With this semi-stationary architecture, we found that the azimuthal blur effect can be greater than in a fully-rotating CT system. We therefore propose novel scanning modes to reduce the azimuthal blur in ring-source rotating detector CT. Radon space analysis method proves to be a useful method in CT system architecture study.
In a previous study, we proposed a helical scanning configuration with triple X-ray sources symmetrically positioned
and established its reconstruction algorithm. Although symmetrically positioned sources are convenient in practice,
artifacts can be produced in a reconstructed image if the physical sources are not equally spaced. In this work, we
develop an exact backprojection filtration (BPF) type algorithm for the configuration with unequally spaced triple
sources to reduce the artifacts. Similar to the Tam-Danielsson window, we define the minimum detection window as the
region bounded by the most adjacent turns of two helices. The sum of the heights of the three consequent minimum
detection windows is equal to that of the traditional Tam-Danielsson window for a single source. Furthermore, we prove
that inter-helix PI-lines satisfy the existence and uniqueness properties (i.e., through any point inside the triple helices,
there exists one and only one inter-helix PI-line for any pair of helices). The proposed algorithm is of the
backprojection-filtration (BPF) type and can be implemented in three steps: 1) differentiation of the cone-beam
projection from each source; 2) weighted backprojection of the derivates on the inter-helix PI-arcs; 3) inverse Hilbert
transformation along one of the three inter-helix PI-lines. Numerical simulations with 3D Shepp-Logan phantoms are
performed to validate the algorithm. We also demonstrate that artifacts are generated when the algorithm for the
symmetric configuration is applied to the unequally spaced helices setting.
Multiple source helical cone-beam scanning is a promising technique for dynamic volumetric CT/micro-CT. In the previous studies, we had proposed a helical cone-beam scanning mode with triple x-ray source and detector assemblies that are symmetrically arranged, and proved the property of minimum detection windows under this configuration. Moreover, we had established an exact backprojection filtration (BFP) reconstruction algorithm for this setting. In this paper, we perform simulation studies for this reconstruction algorithm with 3D Shepp-Logan and Defrise phantoms. The implementation of the BFP algorithm in the planar detector geometry consists of three steps. First, the cone-beam projection from each of the three sources is differentiated respectively. Second, the derivates on the three inter-helix PI-arcs are summed up with weights to form the backprojection. Third, inverse Hilbert transformations are performed along each of the three inter-helix PI-lines. The reconstructed images validate the proposed algorithm. Furthermore, this work can be generalized to the case of multiple source helical cone-beam CT.