Sinogram Image Research Articles

Development of Computed Tomography Scanner Application: Prototype Model Approach

The development of software application for image reconstruction system is essential in the research study of computed tomography (CT). An experimental prototype was the research tool used in developing a series of useful prototype to evaluate the reconstruction methods. A CT scanner software prototype was created using the MATLAB image processing toolbox together with the Graphical User Interface (GUI) and Graphical User Interface Development Environment (GUIDE). The prototype model was selected to guide the development of the prototype application and mainly to provide a platform to update a system from the available knowledge. The prototype model was capable in creating a CT scanner synthetic datasets, display a sinogram image from the synthetic datasets, construct images using back projection technique and displaying cross-section images. Two types of images were acquired from this research, the first type was the unfiltered and blurred image and the second type was the filtered and clear image. This research also focused on two-dimensional parallel-beam and fan-beam geometry configuration; the principle of transforming fan-beam datasets into parallel-beam datasets was carried out using the classical rebinning concept.

Cylinder fitting from a sinogram image for dimensional CT metrology

Cylinder fitting from a sinogram image for dimensional CT metrology

In-Situ ISAR Imaging of Wind Turbines

Measurement and processing methodologies are developed to capture the in-situ inverse synthetic aperture radar (ISAR) image of an operating wind turbine. In-situ measurement data of a small three-blade wind turbine are collected using an ultrawideband radar, and the measured signatures are compared with simulation results based on physical optics. The backscattering phenomenology is examined in the sinogram, spectrogram, and ISAR image domains. It is shown that the resulting ISAR images provide revealing geometrical insight into the blade scattering phenomenology which is not as apparent in either the range profile or Doppler spectrogram alone. Subsequently, the same methodologies are applied to generate the in-situ ISAR imagery of an 18-blade windmill and a 1.7-MW utility-class wind turbine.

A method for improving measurement accuracy of cylinders in dimensional CT metrology

Measurement quality of industrial cone beam X-ray computed tomography (CT) is influenced by many types of artefacts, therefore, industrial-level accuracy is difficult to attain. In order to avoid the influences of these artefacts, this paper proposes a new method for measuring cylindrical surface that is a common geometric structure in mechanical parts. The proposed method fits cylindrical surface from a sinogram image directly. Compared with a standard way for dimensional CT metrology, higher measurement accuracy and less measurement time can be achieved. Mainly, the method consists of edge points detection of a sinogram image and cylindrical surface fitting. The performance of the method is evaluated by use of both simulation data and actual data.

An adaptive nonlocal filtering for low-dose CT in both image and projection domains

Abstract An important problem in low-dose CT is the image quality degradation caused by photon starvation. There are a lot of algorithms in sinogram domain or image domain to solve this problem. In view of strong self-similarity contained in the special sinusoid-like strip data in the sinogram space, we propose a novel non-local filtering, whose average weights are related to both the image FBP (filtered backprojection) reconstructed from restored sinogram data and the image directly FBP reconstructed from noisy sinogram data. In the process of sinogram restoration, we apply a non-local method with smoothness parameters adjusted adaptively to the variance of noisy sinogram data, which makes the method much effective for noise reduction in sinogram domain. Simulation experiments show that our proposed method by filtering in both image and projection domains has a better performance in noise reduction and details preservation in reconstructed images.

Ring artifact correction for X-ray computed tomography

In this paper, a novel preprocessing technique is proposed for the correction of ring artifact in X-ray CT image. By fitting each projection in sinogram image with piecewise polynomial, a set of candidate correction factors for mis-calibrated detector is created and then the factor with maximum probability occurrence is picked out to rectify the projection data. Principle and procedure of the method is illustrated. Tests under different condition show that, sparse or continuous ring artifact in the CT image can both be well rectified effectively, while spatial resolution of the image preserved. CT images for multi-material object can also be corrected by the method.

X射线CT环形伪影去除方法

In this paper, a novel preprocessing technique is proposed for the correction of ring artifact in X-ray CT image. By fitting each projection in sinogram image with piecewise polynomial, a set of candidate correction factors for mis-calibrated detector is created and then the factor with maximum probability occurrence is picked out to rectify the projection data. Principle and procedure of the method is illustrated. Tests under different condition show that, sparse or continuous ring artifact in the CT image can both be well rectified effectively, while spatial resolution of the image preserved. CT images for multi-material object can also be corrected by the method.

SR-NLM: A sinogram restoration induced non-local means image filtering for low-dose computed tomography

Radiation dose has raised significant concerns to patients and operators in modern X-ray computed tomography (CT) examinations. A simple and cost-effective means to perform a low-dose CT scan is to lower the milliampere-seconds (mAs) as low as reasonably achievable in data acquisition. However, the associated image quality with lower-mAs scans (or low-dose scans) will be unavoidably degraded due to the excessive data noise, if no adequate noise control is applied during image reconstruction. For image reconstruction with low-dose scans, sinogram restoration algorithms based on modeling the noise properties of measurement can produce an image with noise-induced artifact suppression, but they often suffer noticeable resolution loss. As an alternative technique, the noise-reduction algorithms via edge-preserving image filtering can yield an image without noticeable resolution loss, but they often do not completely eliminate the noise-induced artifacts. With above observations, in this paper, we present a sinogram restoration induced non-local means (SR-NLM) image filtering algorithm to retain the CT image quality by fully considering the advantages of the sinogram restoration and image filtering algorithms in low-dose image reconstruction. Extensive experimental results show that the present SR-NLM algorithm outperforms the existing methods in terms of cross profile, noise reduction, contrast-to-ratio measure, noise-resolution tradeoff and receiver operating characteristic (ROC) curves.

Center of rotation automatic measurement for fan-beam CT system based on sinogram image features

For fan-beam X-ray CT system, projection center of rotation (COR) is one of the most important parameters which should be strictly controlled with high precision to ensure the accuracy of the image reconstruction. In our research, a novel method is proposed to locate COR for fan-beam CT system based on sinogram features. In this method, getting a data set with regular symmetric shape by averaging the original sinogram along the direction of column, and another data set by flipping the averaged data along the direction of row, the cross correlation operation is applied to these two data sets and finally the position of COR is determined by locating the peak values of the cross correlation function. In our proposed method, only the sinogram of the scanned slice is used for locating COR based on the image cross correlation without the calibration sample. The experimental results prove that it is easy to implement with high accuracy and anti-noise ability.

SU-E-T-486: Comparison of TLD Measured Dose and MVCT Reconstructed Dose for Post-Mastectomy Chest Wall Irradiation with TomoTherapy

Purpose: To determine the accuracy of TomoTherapy's delivery verification planned adaptive (DVPA) dose reconstruction software in post‐ mastectomy radiation therapy (PMRT) calculations to the chest wall. Methods: Archived treatment data for 5 patients treated at MBPCC were restored to the DVPA analysis software. This data included daily pretreatment MVCT images as well as exit detector transit dosimetry data collected during treatment. These were used to calculate the delivery verification (DV) sinogram for each treatment fraction. The DV sinogram and MVCT image for each fraction were used to reconstruct the delivereddose for that fraction using a convolution/superposition algorithm. Point doses at four TLD locations were previously acquired for 15 treatment fractions for each patient. The DVPA and TLDdoses for each patient TLD for each fraction and compared to our clinically acceptable criterion of 5% agreement. Results: The mean difference for all the data was −3.2 ± 0.28% (4.7%). Approximately 66% of the data points agreed within 5%. The largest difference between measured and calculated doses was −17.3%. While the mean difference was within 5%, the large standard deviation of the data demonstrates the potential for large differences in agreement for individual fraction data. Conclusions: Patient motion after the MVCT can have a large effect on the calculation of radiological path‐length used by the database technique, especially near the surface. This uncertainty may be responsible for the differences seen in this work. Future evaluations of the DVPA software will include anthropomorphic phantom studies. Since the motion variable is removed in the treatment of the phantom it is expected that the dose reconstruction will compare well to TLDdoses.

A wavelet multiscale denoising algorithm for magnetic resonance (MR) images

Based on the Radon transform, a wavelet multiscale denoising method is proposed for MR images. The approach explicitly accounts for the Rician nature of MR data. Based on noise statistics we apply the Radon transform to the original MR images and use the Gaussian noise model to process the MR sinogram image. A translation invariant wavelet transform is employed to decompose the MR ‘sinogram’ into multiscales in order to effectively denoise the images. Based on the nature of Rician noise we estimate noise variance in different scales. For the final denoised sinogram we apply the inverse Radon transform in order to reconstruct the original MR images. Phantom, simulation brain MR images, and human brain MR images were used to validate our method. The experiment results show the superiority of the proposed scheme over the traditional methods. Our method can reduce Rician noise while preserving the key image details and features. The wavelet denoising method can have wide applications in MRI as well as other imaging modalities.

Statistical Sinogram Smoothing for Low-Dose CT With Segmentation-Based Adaptive Filtering

As is known, noises in calibrated and log-transformed projection data of low-mA (or low-dose) CT protocol follow approximately a non-stationary Gaussian distribution. In this study, we further demonstrate that some isolated noise points would not satisfy the above observation. Hence, we propose a noise reduction scheme which includes isolated data removal and segmentation-based filtering. In this scheme, an isolated data removal algorithm is first adopted to remove isolated data such that the remaining sinogram data approximately follows a Gaussian distribution. Secondly, image segmentation technique is employed for segmenting sinogram image into different segments in which pixels with similar intensities are grouped, and the segmentation-based adaptive statistical sinogram smoothing technique is proposed with different smoothness parameters applied to different segments for adaptively filtering. The effectiveness of the proposed method is validated by both computer simulations and experimental studies. The gain of the proposed approach over other methods is quantified by noise-resolution tradeoff curves.

Tomographic Imaging of Surface Deformation From Scarce Measurements via Sinogram Recovery

Imaging from limited data is a common practice in many industrial tomography applications where sensor design often assumes an irregular approach with low number of measurements. In this paper, we propose a novel algorithm that reduces the task of hard-field imaging from incomplete data to a sinogram recovery problem. The algorithm utilises a novel angular interpolation scheme, employing the sinusoidal Hough transform, to identify sinusoidal traces in the sinogram and estimate missing sinogram samples along such traces. Together with the detailed theory behind the algorithm, we present its performance with experimental data obtained from a photonic guided path tomography system. The targeted subject function is the induced deformation in the quasi-planar surface of a flexible ~1 m2 sensor, fixed to an underlying soft-foam mat. The set of 32 independent measurements generated by the system are presented in a severely sparse 91times180 sinogram image. The sinogram is then recovered to a degree suitable for standard tomographic algorithms for hard-field data inversion, such as filtered back-projection.

Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray computed tomography

Reconstructing low-dose X-ray computed tomography (CT) images is a noise problem. This work investigated a penalized weighted least-squares (PWLS) approach to address this problem in two dimensions, where the WLS considers first- and second-order noise moments and the penalty models signal spatial correlations. Three different implementations were studied for the PWLS minimization. One utilizes a Markov random field (MRF) Gibbs functional to consider spatial correlations among nearby detector bins and projection views in sinogram space and minimizes the PWLS cost function by iterative Gauss-Seidel algorithm. Another employs Karhunen-Loeve (KL) transform to de-correlate data signals among nearby views and minimizes the PWLS adaptively to each KL component by analytical calculation, where the spatial correlation among nearby bins is modeled by the same Gibbs functional. The third one models the spatial correlations among image pixels in image domain also by a MRF Gibbs functional and minimizes the PWLS by iterative successive over-relaxation algorithm. In these three implementations, a quadratic functional regularization was chosen for the MRF model. Phantom experiments showed a comparable performance of these three PWLS-based methods in terms of suppressing noise-induced streak artifacts and preserving resolution in the reconstructed images. Computer simulations concurred with the phantom experiments in terms of noise-resolution tradeoff and detectability in low contrast environment. The KL-PWLS implementation may have the advantage in terms of computation for high-resolution dynamic low-dose CT imaging

Transverse tomography with incoherent optical reconstruction

A thin layer of an object can be imaged by reconstruction from a so-called sinogram. It is produced by an X-ray fan beam rotating around the object while a recording film is moved in a direction perpendicular to the plane of the fan beam. Before reconstruction the sinogram image is convoluted according to a special function to remove artifacts consisting of spurious shadows between different object elements. The reconstruction is done from the convoluted sinogram by means of a back projector, which operates according to a principle that is the reverse of the recording of the original sinogram. Tomograms of phantoms, pork chops and the head of a dog show that the process is capable of high spatial resolution but is limited by low contrast.