KEYWORDS: Cones, Angiography, Reconstruction algorithms, 3D image enhancement, 3D acquisition, Computed tomography, 3D image processing, Image restoration, Tomography, In vivo imaging, Numerical simulations, Image segmentation, Data acquisition, Metals, 3D image reconstruction
Currently, clinical acquisition of IV 3D-DSA requires two separate scans: one mask scan without contrast
medium and a filled scan with contrast injection. Having two separate scans adds radiation dose to the patient
and increases the likelihood of suffering inadvertent patient motion induced mis-registration and the associated
mis-registraion artifacts in IV 3D-DSA images. In this paper, a new technique, SMART-RECON is introduced
to generate IV 3D-DSA images from a single Cone Beam CT (CBCT) acquisition to eliminate the mask scan.
Potential benefits of eliminating mask scan would be: (1) both radiation dose and scan time can be reduced by
a factor of 2; (2) intra-sweep motion can be eliminated; (3) inter-sweep motion can be mitigated. Numerical
simulations were used to validate the algorithm in terms of contrast recoverability and the ability to mitigate
limited view artifacts.
In this work, a newly developed reconstruction algorithm, Synchronized MultiArtifact Reduction with Tomographic RECONstruction (SMART-RECON), was applied to C-arm cone beam CT perfusion (CBCTP) imaging. This algorithm contains a special rank regularizer, designed to reduce limited-view artifacts associated with super- short scan reconstructions. As a result, high temporal sampling and temporal resolution image reconstructions were achieved using an interventional C-arm x-ray system. The algorithm was evaluated in terms of the fidelity of the dynamic contrast update curves and the accuracy of perfusion parameters through numerical simulation
studies. Results shows that, not only were the dynamic curves accurately recovered (relative root mean square error ∈ [3%, 5%] compared with [13%, 22%] for FBP), but also the noise in the final perfusion maps was dramatically reduced. Compared with filtered backprojection, SMART-RECON generated CBCTP maps with much improved capability in differentiating lesions with perfusion deficits from the surrounding healthy brain tissues.
KEYWORDS: Image processing, Denoising, Data acquisition, Angiography, Medical imaging, Arteries, Algorithm development, In vivo imaging, Optimization (mathematics), Image quality
In this work we developed a novel denoising algorithm for DSA image series. This algorithm takes advantage of the low rank nature of the DSA image sequences to enable a dramatic reduction in radiation and/or contrast doses in DSA imaging. Both spatial and temporal regularizers were introduced in the optimization algorithm to further reduce noise. To validate the method, in vivo animal studies were conducted with a Siemens Artis Zee biplane system using different radiation dose levels and contrast concentrations. Both conventionally processed DSA images and the DSA images generated using the novel denoising method were compared using absolute noise standard deviation and the contrast to noise ratio (CNR). With the application of the novel denoising algorithm for DSA, image quality can be maintained with a radiation dose reduction by a factor of 20 and/or a factor of 2 reduction in contrast dose. Image processing is completed on a GPU within a second for a 10s DSA data acquisition.
Time-resolved CT imaging methods play an increasingly important role in clinical practice, particularly, in the diagnosis and treatment of vascular diseases. In a time-resolved CT imaging protocol, it is often necessary to irradiate the patients for an extended period of time. As a result, the cumulative radiation dose in these CT applications is often higher than that of the static CT imaging protocols. Therefore, it is important to develop new means of reducing radiation dose for time-resolved CT imaging. In this paper, we present a novel statistical model based iterative reconstruction method that enables the reconstruction of low noise time-resolved CT images at low radiation exposure levels. Unlike other well known statistical reconstruction methods, this new method primarily exploits the intrinsic low dimensionality of time-resolved CT images to regularize the reconstruction. Numerical simulations were used to validate the proposed method.
Statistical Image Reconstruction (SIR) often involves a balance of two requirements: the first requirement is enforcing a minimal difference between the forward projection of the reconstructed image with the measured projection data and the second requirement enforcing some kind of image smoothness, which depends on the specific selection of regularizer, to reduce the noise in the reconstructed image. The needed delicate balance between these two requirements in the numerical implementations often slow down the reconstruction speed due to either a degradation in convergence rate of the algorithm or a degradation of parallellizability of the numerical implementation algorithms. In this work, a general numerical implementation strategy has been proposed to allow the SIR algorithms to be implemented in two decoupled and alternating steps. The first step using SIR without any regularizer which allows for the use of the well-known ordered subset (OS) strategy to accelerate the image reconstruction. The second step solves a denoising problem without involving the data fidelity term. The alternation of these two decoupled steps enable one to perform SIR with both high convergence rate and high parallellizability. The total variation norm of the image has been used as an example of regularizers to illustrate the proposed numerical implementation strategy. Numerical simulations have been performed to validate the proposed algorithm. The noise-spatial resolution tradeoff curve and convergence speed of the algorithm have been investigated and compared against the conventional gradient descent based implementation strategy.
This paper provides a fast and patient-specific scatter artifact correction method for cone-beam computed tomography (CBCT) used in image-guided interventional procedures. Due to increased irradiated volume of interest in CBCT imaging, scatter radiation has increased dramatically compared to 2D imaging, leading to a degradation of image quality. In this study, we propose a scatter artifact correction strategy using an analytical convolution-based model whose free parameters are estimated using a rough estimation of scatter profiles from the acquired cone-beam projections. It was evaluated using Monte Carlo simulations with both monochromatic and polychromatic X-ray sources. The results demonstrated that the proposed method significantly reduced the scatter-induced shading artifacts and recovered CT numbers.
In the current workflow of ischemic stroke management, it is highly desirable to obtain perfusion information with the
C-arm CBCT system in the interventional room. Due to hardware limitations, the data acquisition speed of the current Carm
CBCT systems is relatively slow and only 7 time frames are available for a 45 s perfusion study. In this study, a
novel temporal recovery method was proposed to recover contrast enhancement curves in C-arm CBCT perfusion
studies. The proposed temporal recovery problem is a constrained optimization problem. Two numerical methods were
used to solve the proposed problem. The feasibility of proposed temporal recovery method was validated with numerical
experiments. Both solvers can achieve a satisfactory solution for the temporal recovery problem, while the result of the
Bregman algorithm is more accurate than that from the CG. In vivo animal studies were used to demonstrated the
improvement of the proposed method in C-arm CBCT perfusion. A stoked canine model was scanned with both C-arm
CBCT and diagnostic CT. Perfusion defects can be clearly indentified from the cerebral blood flow (CBF) map of
diagnostic CT perfusion. Without the temporal recovery technique, these defects can hardly be identified from the CBCT CBF map. After applying the proposed temporal recovery method, the CBCT CBF map well correlates with the CBF
map from diagnostic CT.
Statistical iterative reconstruction methods have come to the forefront of CT research in recent years, as they
have the ability to incorporate the statistical fluctuations in CT measurements into the image reconstruction
process. While statistical iterative reconstruction methods have been found to be beneficial in CT imaging, they
have not been extensively investigated or applied in other new and promising CT imaging techniques, such as
x-ray differential phase contrast computed tomography (DPC-CT). The purpose of this study is to investigate
and apply statistical image reconstruction to DPC-CT to reduce streaking artifacts caused by strong small-angle
scattering objects.
In this work we applied dose reduction using the prior image constrained compress sensing (DR-PICCS) method
on a C-arm cone beam CT system. DR-PICCS uses a smoothed image as the prior image. After applying DRPICCS,
the final image will have noise variance inherited from the prior image and spatial resolution from the
projection data. In order to investigate the dose reduction of DR-PICCS, three different dose levels were used in
C-arm scans of animal subjects using a Siemens Zeego C-arm system under an IACUC protocol. Image volumes
were reconstructed using the standard FBP and DR-PICCS algorithms(total of 160 images). These images were
randomly mixed and presented to three experienced interventional radiologists(each having more than twenty
years reading experience) to review and score using a five-point scale. After statistical significance testing, the
results show that DR-PICCS can achieve more than 60% dose reduction while keeping the same image quality.
And if we compare FBP and DR-PICCS at the same dose level the results show that DR-PICCS will generate
higher quality images.
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