Cone-Beam CT image contrast and attenuation-map linearity improvement (CALI) for brain stereotactic radiosurgery procedures.

A Contrast and Attenuation-map Linearity Improvement (CALI) framework is proposed for cone-beam CT (CBCT) images used for brain stereotactic radiosurgery (SRS). The proposed framework is tailored to improve soft tissue contrast of a new point-of-care image-guided SRS system that employs a challenging half cone beam geometry, but can be readily reproduced on any CBCT platform. CALI includes a pre- and post-processing step. In pre-processing we apply a shading and beam hardening artifact correction to the projections, and in post-processing step we correct the dome/capping artifact on reconstructed images caused by the spatial variations in X-ray energy generated by the bowtie-filter. The shading reduction together with the beam hardening and dome artifact correction algorithms aim to improve the linearity and accuracy of the CT-numbers (CT#). The CALI framework was evaluated using CatPhan to quantify linearity, contrast-to-noise (CNR), and CT# accuracy, as well as subjectively on patient images acquired on a clinical system. Linearity of the reconstructed attenuation-map was improved from 0.80 to 0.95. The CT# mean absolute measurement error was reduced from 76.1 to 26.9 HU. The CNR of the acrylic insert in the sensitometry module was improved from 1.8 to 7.8. The resulting clinical brain images showed substantial improvements in soft tissue contrast visibility, revealing structures such as ventricles which were otherwise undetectable in the original clinical images obtained from the system. The proposed reconstruction framework also improved CT# accuracy compared to the original images acquired on the system. For frameless image-guided SRS, improving soft tissue visibility can facilitate evaluation of MR to CBCT co-registration. Moreover, more accurate CT# may enable the use of CBCT for daily dose delivery measurements.

Simultaneous deblurring and iterative reconstruction of CBCT for image guided brain radiosurgery.

One of the limiting factors in cone-beam CT (CBCT) image quality is system blur, caused by detector response, x-ray source focal spot size, azimuthal blurring, and reconstruction algorithm. In this work, we develop a novel iterative reconstruction algorithm that improves spatial resolution by explicitly accounting for image unsharpness caused by different factors in the reconstruction formulation. While the model-based iterative reconstruction techniques use prior information about the detector response and x-ray source, our proposed technique uses a simple measurable blurring model. In our reconstruction algorithm, denoted as simultaneous deblurring and iterative reconstruction (SDIR), the blur kernel can be estimated using the modulation transfer function (MTF) slice of the CatPhan phantom or any other MTF phantom, such as wire phantoms. The proposed image reconstruction formulation includes two regularization terms: (1) total variation (TV) and (2) nonlocal regularization, solved with a split Bregman augmented Lagrangian iterative method. The SDIR formulation preserves edges, eases the parameter adjustments to achieve both high spatial resolution and low noise variances, and reduces the staircase effect caused by regular TV-penalized iterative algorithms. The proposed algorithm is optimized for a point-of-care head CBCT unit for image-guided radiosurgery and is tested with CatPhan phantom, an anthropomorphic head phantom, and 6 clinical brain stereotactic radiosurgery cases. Our experiments indicate that SDIR outperforms the conventional filtered back projection and TV penalized simultaneous algebraic reconstruction technique methods (represented by adaptive steepest-descent POCS algorithm, ASD-POCS) in terms of MTF and line pair resolution, and retains the favorable properties of the standard TV-based iterative reconstruction algorithms in improving the contrast and reducing the reconstruction artifacts. It improves the visibility of the high contrast details in bony areas and the brain soft-tissue. For example, the results show the ventricles and some brain folds become visible in SDIR reconstructed images and the contrast of the visible lesions is effectively improved. The line-pair resolution was improved from 12 line-pair/cm in FBP to 14 line-pair/cm in SDIR. Adjusting the parameters of the ASD-POCS to achieve 14 line-pair/cm caused the noise variance to be higher than the SDIR. Using these parameters for ASD-POCS, the MTF of FBP and ASD-POCS were very close and equal to 0.7 mm-1 which was increased to 1.2 mm-1 by SDIR, at half maximum.

Accelerated Compressed Sensing Based CT Image Reconstruction.

In X-ray computed tomography (CT) an important objective is to reduce the radiation dose without significantly degrading the image quality. Compressed sensing (CS) enables the radiation dose to be reduced by producing diagnostic images from a limited number of projections. However, conventional CS-based algorithms are computationally intensive and time-consuming. We propose a new algorithm that accelerates the CS-based reconstruction by using a fast pseudopolar Fourier based Radon transform and rebinning the diverging fan beams to parallel beams. The reconstruction process is analyzed using a maximum-a-posterior approach, which is transformed into a weighted CS problem. The weights involved in the proposed model are calculated based on the statistical characteristics of the reconstruction process, which is formulated in terms of the measurement noise and rebinning interpolation error. Therefore, the proposed method not only accelerates the reconstruction, but also removes the rebinning and interpolation errors. Simulation results are shown for phantoms and a patient. For example, a 512 × 512 Shepp-Logan phantom when reconstructed from 128 rebinned projections using a conventional CS method had 10% error, whereas with the proposed method the reconstruction error was less than 1%. Moreover, computation times of less than 30 sec were obtained using a standard desktop computer without numerical optimization.

Adaptively Tuned Iterative Low Dose CT Image Denoising.

Improving image quality is a critical objective in low dose computed tomography (CT) imaging and is the primary focus of CT image denoising. State-of-the-art CT denoising algorithms are mainly based on iterative minimization of an objective function, in which the performance is controlled by regularization parameters. To achieve the best results, these should be chosen carefully. However, the parameter selection is typically performed in an ad hoc manner, which can cause the algorithms to converge slowly or become trapped in a local minimum. To overcome these issues a noise confidence region evaluation (NCRE) method is used, which evaluates the denoising residuals iteratively and compares their statistics with those produced by additive noise. It then updates the parameters at the end of each iteration to achieve a better match to the noise statistics. By combining NCRE with the fundamentals of block matching and 3D filtering (BM3D) approach, a new iterative CT image denoising method is proposed. It is shown that this new denoising method improves the BM3D performance in terms of both the mean square error and a structural similarity index. Moreover, simulations and patient results show that this method preserves the clinically important details of low dose CT images together with a substantial noise reduction.

On estimating the directionality distribution in pedicle trabecular bone from micro-CT images.

Our interest in the trabecular alignment within bone stems from the need to better understand the manner in which it can affect ultrasound propagation, particularly in pedicles. Within long bones it is well established that trabecular structures are aligned in an organized manner associated with the direction of load distribution; however, for smaller bones there are limited alignment studies. To investigate the directionality distribution in a quantitative manner we used a micro-CT to obtain three-dimensional (3D) structural data and developed analytical methods based on the special properties of Gabor filters. Implementation of these techniques has been developed and tested on a variety of simulated images as well as on 3D structures whose geometry is well-defined. To test the use of this technique we compared the results obtained on vertebral body trabecular bone with visual directionality and previous measurements by others. The method has been applied to six human pedicle samples in two orthogonal planes with results that provide reasonable proof-of-principle evidence that the method is well suited for estimating the directionality distribution within pedicle bones.

Optimal image reconstruction for detection and characterization of small pulmonary nodules during low-dose CT.

OBJECTIVES: To optimize the slice thickness/overlap parameters for image reconstruction and to study the effect of iterative reconstruction (IR) on detectability and characterization of small non-calcified pulmonary nodules during low-dose thoracic CT. MATERIALS AND METHODS: Data was obtained from computer simulations, phantom, and patient CTs. Simulations and phantom CTs were performed with 9 nodules (5, 8, and 10 mm with 100, -630, and -800 HU). Patient data were based on 11 ground glass opacities (GGO) and 9 solid nodules. For each analysis the nodules were reconstructed with filtered back projection and IR algorithms using 10 different combinations of slice thickness/overlap (0.5-5 mm). The attenuation (CT#) and the contrast to noise ratio (CNR) were measured. Spearman's coefficient was used to correlate the error in CT# measurements and slice thickness. Paired Student's t test was used to measure the significance of the errors. RESULTS: CNR measurements: CNR increases with increasing slice thickness/overlap for large nodules and peaks at 4.0/2.0 mm for smaller ones. Use of IR increases the CNR of GGOs by 60 %. CT# measurements: Increasing slice thickness/overlap above 3.0/1.5 mm results in decreased CT# measurement accuracy. CONCLUSION: Optimal detection of small pulmonary nodules requires slice thickness/overlap of 4.0/2.0 mm. Slice thickness/overlap of 2.0/2.0 mm is required for optimal nodule characterization. IR improves conspicuity of small ground glass nodules through a significant increase in nodule CNR. KEY POINTS: • Slice thickness/overlap affects the accuracy of pulmonary nodule detection and characterization. • Slice thickness ≥3 mm increases the risk of misclassifying small nodules. • Optimal nodule detection during low-dose CT requires 4.0/2.0-mm reconstructions. • Optimal nodule characterization during low-dose CT requires 2.0/2.0-mm reconstructions. • Iterative reconstruction improves the CNR of ground glass nodules by 60 %.

Non-local total variation based low-dose Computed Tomography denoising.

Radiation dose of X-ray Computed Tomography (CT) imaging has raised a worldwide health concern. Therefore, low-dose CT imaging has been of a huge interest in the last decade. However, lowering the radiation dose degrades the image quality by increasing the noise level, which may reduce the diagnostic performance of the images. As a result, image denoising is one of the fundamental tasks in low-dose CT imaging. One of the state of art denoising methods, which has been successfully used in this area, is Total Variation (TV) denoising. Nevertheless, if the parameters of the TV denoising are not optimally adjusted or the algorithm is not stopped in an appropriate point, some of the small structures will be removed by this method. Here, we provide a solution to this problem by proposing a modified nonlocal TV method, called probabilistic NLTV (PNLTV). Denoising performance of PNLTV is improved by using better weights and an appropriate stopping criterion based on statistics of image wavelet coefficients. Non-locality allows the algorithm to preserve the image texture, which combined with the proposed stopping criterion enables PNLTV to keep fine details unchanged.