Article

GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation

Medical Physics (Impact Factor: 2.64). 04/2010; 37(4):1757-60. DOI: 10.1118/1.3371691
Source: PubMed

ABSTRACT

Cone-beam CT (CBCT) plays an important role in image guided radiation therapy (IGRT). However, the large radiation dose from serial CBCT scans in most IGRT procedures raises a clinical concern, especially for pediatric patients who are essentially excluded from receiving IGRT for this reason. The goal of this work is to develop a fast GPU-based algorithm to reconstruct CBCT from undersampled and noisy projection data so as to lower the imaging dose.
The CBCT is reconstructed by minimizing an energy functional consisting of a data fidelity term and a total variation regularization term. The authors developed a GPU-friendly version of the forward-backward splitting algorithm to solve this model. A multigrid technique is also employed.
It is found that 20-40 x-ray projections are sufficient to reconstruct images with satisfactory quality for IGRT. The reconstruction time ranges from 77 to 130 s on an NVIDIA Tesla C1060 (NVIDIA, Santa Clara, CA) GPU card, depending on the number of projections used, which is estimated about 100 times faster than similar iterative reconstruction approaches. Moreover, phantom studies indicate that the algorithm enables the CBCT to be reconstructed under a scanning protocol with as low as 0.1 mA s/projection. Comparing with currently widely used full-fan head and neck scanning protocol of approximately 360 projections with 0.4 mA s/projection, it is estimated that an overall 36-72 times dose reduction has been achieved in our fast CBCT reconstruction algorithm.
This work indicates that the developed GPU-based CBCT reconstruction algorithm is capable of lowering imaging dose considerably. The high computation efficiency in this algorithm makes the iterative CBCT reconstruction approach applicable in real clinical environments.

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    • "This introduces negative or zero values into the raw data and consequently causes artifacts in the reconstructed CT images (Nuyts et al 2013). An alternative approach is to reduce the number of projection views (Sidky and Pan 2008, Yu and Wang 2009, Sidky et al 2010, Jia et al 2010), which decreases the relative effects of the electronic noise while the number of total photons (or total radiation dose) remains the same, but can introduce aliasing artifacts from under-sampling (Long et al 2013). There are two straightforward ways of acquiring a CT scan with sparse view protocols. "
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    ABSTRACT: For PET/CT systems, PET image reconstruction requires corresponding CT images for anatomical localization and attenuation correction. In the case of PET respiratory gating, multiple gated CT scans can offer phase-matched attenuation and motion correction, at the expense of increased radiation dose. We aim to minimize the dose of the CT scan, while preserving adequate image quality for the purpose of PET attenuation correction by introducing sparse view CT data acquisition.We investigated sparse view CT acquisition protocols resulting in ultra-low dose CT scans designed for PET attenuation correction. We analyzed the tradeoffs between the number of views and the integrated tube current per view for a given dose using CT and PET simulations of a 3D NCAT phantom with lesions inserted into liver and lung. We simulated seven CT acquisition protocols with {984, 328, 123, 41, 24, 12, 8} views per rotation at a gantry speed of 0.35 s. One standard dose and four ultra-low dose levels, namely, 0.35 mAs, 0.175 mAs, 0.0875 mAs, and 0.043 75 mAs, were investigated. Both the analytical Feldkamp, Davis and Kress (FDK) algorithm and the Model Based Iterative Reconstruction (MBIR) algorithm were used for CT image reconstruction. We also evaluated the impact of sinogram interpolation to estimate the missing projection measurements due to sparse view data acquisition. For MBIR, we used a penalized weighted least squares (PWLS) cost function with an approximate total-variation (TV) regularizing penalty function. We compared a tube pulsing mode and a continuous exposure mode for sparse view data acquisition. Global PET ensemble root-mean-squares-error (RMSE) and local ensemble lesion activity error were used as quantitative evaluation metrics for PET image quality.With sparse view sampling, it is possible to greatly reduce the CT scan dose when it is primarily used for PET attenuation correction with little or no measureable effect on the PET image. For the four ultra-low dose levels simulated, sparse view protocols with 41 and 24 views best balanced the tradeoff between electronic noise and aliasing artifacts. In terms of lesion activity error and ensemble RMSE of the PET images, these two protocols, when combined with MBIR, are able to provide results that are comparable to the baseline full dose CT scan. View interpolation significantly improves the performance of FDK reconstruction but was not necessary for MBIR. With the more technically feasible continuous exposure data acquisition, the CT images show an increase in azimuthal blur compared to tube pulsing. However, this blurring generally does not have a measureable impact on PET reconstructed images.Our simulations demonstrated that ultra-low-dose CT-based attenuation correction can be achieved at dose levels on the order of 0.044 mAs with little impact on PET image quality. Highly sparse 41- or 24- view ultra-low dose CT scans are feasible for PET attenuation correction, providing the best tradeoff between electronic noise and view aliasing artifacts. The continuous exposure acquisition mode could potentially be implemented in current commercially available scanners, thus enabling sparse view data acquisition without requiring x-ray tubes capable of operating in a pulsing mode.
    Full-text · Article · Sep 2015 · Physics in Medicine and Biology
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    • "While the image quality is substantially enhanced as compared to the conventional FDK algorithm, those iterative image reconstruction algorithms also have their shortcomings. For example, fine structures can be over-smoothed in the image reconstructed directly by total variation (TV) minimization [6] [15] [18] and residual motion [4] is still observed in the image reconstructed by prior image constraint compressive sensing (PICCS) [17] since the motion are not explicitly considered between different phases. Due to a large cone angle used in data acquisition, scatter signals are detected in CBCT projections in addition to the primary signal. "

    Full-text · Dataset · May 2015
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    • "While the image quality is substantially enhanced as compared to the conventional FDK algorithm, those iterative image reconstruction algorithms also have their shortcomings. For example, fine structures can be over-smoothed in the image reconstructed directly by total variation (TV) minimization [6] [15] [18] and residual motion [4] is still observed in the image reconstructed by prior image constraint compressive sensing (PICCS) [17] since the motion are not explicitly considered between different phases. Due to a large cone angle used in data acquisition, scatter signals are detected in CBCT projections in addition to the primary signal. "
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