Development of a collimator blurring compensation method using fine angular sampling projection data in SPECT

Previously the Orthogonal Polynomial Expansion on the Disk (OPED) algorithm was presented. Further, in prototype experiments in combination with the CT D`or geometry feasibility was demonstrated. In this study we implemented OPED with a clinical Scanner, and evaluated the potential using phantom studies. All studies were acquired on a Siemens Somatom 64 (Erlangen, Germany) scanner, where raw projection data were reconstructed with the conventional FBP reconstruction and the OPED algorithm. OPED allows one to use fan beam geometry directly without any additional procedures such as interpolation or rebinning if using the CT D`or geometry. In particular, OPED describes an approximation of the image function as a sum of polynomials using Chebychev polynomials. For performance evaluation, the Catphan phantom 600 was imaged. OPED Images where reconstructed using C++ and MATLAB® .We measured uniformity, MTF and CNR for different dose levels and compared these to standard FBP images reconstructions with different filter kernels. The integration and interpretation of the MDCT projection data for the OPED algorithm was accomplished. Reconstruction time is about 6 s on Quad-Core 3 GHz Intel Xeon processor. Typical artifacts are reduced when applying OPED. Using OPED the MTF maintains constant over the whole FOV. Uniformity and CNR are equal compared to FBP. Advantages of OPED were demonstrated by applying the algorithm to projections images from a clinical MDCT scanner. In the future, we see OPED applications for low-dose or limited angle geometries to reduce the radiation dose while improving diagnostic quality of the reconstructed slices.

The authors developed and evaluated a technique of compressing raw projection data at computed tomography (CT). Raw projection data acquired at CT were compressed and decompressed and then used for image reconstruction. For comparison, original images were compressed by comparable ratios. Projection data files were more compressible than image files. Projection data compression is a promising, efficient method to reduce data file size and thus to facilitate retrospective image reconstruction.

The aim of this work is to calculate, directly from raw projection data, clear and concise images characterising the spatial and temporal distribution of labelled compounds. Conventionally, image reconstruction and the calculation of parametric images are performed separately. By combining both processes, low noise parametric images are obtained, bypassing the expensive computations of image reconstruction. The calculation of the images is performed by restricting the pixel time activity curves to a positive linear sum of predefined time characteristics. The weights in this sum are calculated directly from the PET projection data, using an iterative algorithm derived from a Maximum Likelihood (ML) iterative algorithm commonly used for tomographic reconstruction. Combining reconstruction and parametric estimation also produces a computational advantage. The ability of the algorithm to extract known kinetic components from the raw data is assessed, using data from both a phantom experiment and from clinical studies. The calculated parametric images indicate differential kinetic behaviour and have been used to aid in the identification of tissues which exhibit variations in the handling of labelled compounds.

Recent advances in compressed sensing (CS) enable accurate CT image reconstruction from highly undersampled and noisy projection measurements, due to the sparsifiable feature of most CT images using total variation (TV). These novel reconstruction methods have demonstrated advantages in clinical applications where radiation dose reduction is critical, such as onboard cone-beam CT (CBCT) imaging in radiation therapy. The image reconstruction using CS is formulated as either a constrained problem to minimize the TV objective within a small and fixed data fidelity error, or an unconstrained problem to minimize the data fidelity error with TV regularization. However, the conventional solutions to the above two formulations are either computationally inefficient or involved with inconsistent regularization parameter tuning, which significantly limit the clinical use of CS-based iterative reconstruction. In this paper, we propose an optimization algorithm for CS reconstruction which overcomes the above two drawbacks. The data fidelity tolerance of CS reconstruction can be well estimated based on the measured data, as most of the projection errors are from Poisson noise after effective data correction for scatter and beam-hardening effects. We therefore adopt the TV optimization framework with a data fidelity constraint. To accelerate the convergence, we first convert such a constrained optimization using a logarithmic barrier method into a form similar to that of the conventional TV regularization based reconstruction but with an automatically adjusted penalty weight. The problem is then solved efficiently by gradient projection with an adaptive Barzilai-Borwein step-size selection scheme. The proposed algorithm is referred to as accelerated barrier optimization for CS (ABOCS), and evaluated using both digital and physical phantom studies. ABOCS directly estimates the data fidelity tolerance from the raw projection data. Therefore, as demonstrated in both digital Shepp-Logan and physical head phantom studies, consistent reconstruction performances are achieved using the same algorithm parameters on scans with different noise levels and∕or on different objects. On the contrary, the penalty weight in a TV regularization based method needs to be fine-tuned in a large range (up to seven times) to maintain the reconstructed image quality. The improvement of ABOCS on computational efficiency is demonstrated in the comparisons with adaptive-steepest-descent-projection-onto-convex-sets (ASD-POCS), an existing CS reconstruction algorithm also using constrained optimization. ASD-POCS alternatively minimizes the TV objective using adaptive steepest descent (ASD) and the data fidelity error using projection onto convex sets (POCS). For similar image qualities of the Shepp-Logan phantom, ABOCS requires less computation time than ASD-POCS in MATLAB by more than one order of magnitude. We propose ABOCS for CBCT reconstruction. As compared to other published CS-based algorithms, our method has attractive features of fast convergence and consistent parameter settings for different datasets. These advantages have been demonstrated on phantom studies.

We investigated a subtraction-based reprojection approach to reduce CT metal artifacts due to I-125 seeds and evaluated the clinical implications in postimplant dosimetry for prostate permanent implant brachytherapy. The raw projection data were used to reduce metal artifacts due to I-125 seeds. CT images of the metal parts only were separated from the original CT images by setting the threshold for pixel value to that of the I-125 seeds. Using these images, sinograms of CT images with and without seeds were obtained by inverse Radon transform (iRT), and the sinogram of the metal image was subtracted from that of the original image. Finally, the image was reconstructed using the sinogram by Radon transform (RT). This technique was applied to a prostate phantom and to a patient undergoing prostate permanent implant brachytherapy. Metal artifacts from I-125 seeds were reduced in both the phantom and patient studies. This technique decreased the density of the inner region of seeds but enhanced the density of the seed edge, thereby facilitating the identification of seed number, orientation, and location. This method reduces metal artifacts from I-125 seeds, and has potential for decreasing the time required for and improving the accuracy of postimplant dosimetry.

PurposeLung cancer screening with low-dose CT has recently been approved for reimbursement, heralding the arrival of such screening services worldwide. Computer-Aided Detection (CAD) tools offer the potential to assist radiologists in detecting nodules in these screening exams. In lung screening, as in all CT exams, there is interest in further reducing radiation dose. However, the effects of continued dose reduction on CAD performance are not fully understood. In this work, we investigated the effect of reducing radiation dose on CAD lung-nodule detection performance in a screening population.MethodsThe raw projection data files were collected from 481 patients who underwent low-dose screening CT exams at our institution as part of the National Lung Screening Trial (NLST). All scans were performed on a multi-detector scanner (Sensation 64, Siemens Healthcare, Forchheim Germany) according to the NLST protocol, which called for a fixed tube current scan of 25 effective mAs for standard-sized patients and 40 effective mAs for larger patients. The raw projection data were input to a reduced-dose simulation software to create simulated reduced-dose scans corresponding to 50% and 25% of the original protocols. All raw data files were reconstructed at the scanner with 1 mm slice thickness and B50 kernel. The lungs were segmented semi-automatically, and all images and segmentations were input to an in-house CAD algorithm trained on higher-dose scans (75–300 mAs). CAD findings were compared to a reference standard generated by an experienced reader. Nodule- and patient-level sensitivities were calculated along with false positives per scan, all of which were evaluated in terms of the relative change with respect to dose. Nodules were subdivided based on size and solidity into categories analogous to the LungRADS assessment categories, and sub-analyses were performed.ResultsFrom the 481 patients in this study, 82 had at least one nodule (prevalence of 17%) and 399 did not (83%). A total of 118 nodules were identified. 27 nodules (23%) corresponded to LungRADS category 4 based on size and composition, while 18 (15%) corresponded to LungRADS category 3 and 73 (61%) corresponded to LungRADS category 2. For solid nodules ≥ 8 mm, patient-level median sensitivities were 100% at all three dose levels, and mean sensitivities were 72%, 63%, and 63% at original, 50%, and 25% dose respectively. Overall mean patient-level sensitivities for nodules ranging from 3 to 45 mm were 38%, 37%, and 38% at original, 50%, and 25% dose due to the prevalence of smaller nodules and non-solid nodules in our reference standard. The mean false-positive rates were 3, 5, and 13 per case.ConclusionsCAD sensitivity decreased very slightly for larger nodules as dose was reduced, indicating that reducing the dose to 50% of original levels may be investigated further for use in CT screening. However, the effect of dose was small relative to the effect of the nodule size and solidity characteristics. The number of false positives per scan increased substantially at 25% dose, illustrating the importance of tuning CAD algorithms to very challenging, high-noise screening exams.

Applications of X-ray computed tomography (CT) for porosity characterization of engineering materials often involve an extended data analysis workflow that includes CT reconstruction of raw projection data, binarization, labeling and mesh extraction. It is often desirable to map the porosity in larger samples but the computational challenge of reducing gigabytes of raw data to porosity information poses a critical bottleneck. In this work, we describe algorithms and implementation of an end-to-end porosity mapping code "Tomo2Mesh" that processes raw projection data from a synchrotron CT instrument into a porosity measurement and visualization within minutes on a single high-performance workstation equipped with GPU.

Purpose: To develop an automatic method for metal artifact reduction (MAR) from small objects such as brachytherapy (BT) seeds. Method and Materials: A phantom made of agar (water‐like) and consisting of 6 slices of 5 mm in which 75 seeds (activity at background level) were implanted for imaging purposes. The phantom was scanned on a helical CT scanner (Siemens Somatom) to produce continuous 1 mm and 3 mm slices of the full phantom. The proposed method is based on the interpolation of missing projections by directly using raw CT data (sinogram). First, an initial image was reconstructed from the raw projection data. Then, the metal objects segmented from the reconstructed image were re‐projected into the same sinogram. The Steger method was used to precisely determine the position and edges of the seed traces in raw CT data. By combining the use of Steger detection and re‐projections, the missing projections were finally detected and further replaced by interpolation of non‐missing neighbouring projections. Results: In both phantom experiment and patient studies, the missing projections have been well detected and the artifacts caused by metallic objects in the image reconstructed using the corrected sinogram have been significantly reduced. The performance of the algorithm has been also proven after a quantitative evaluation by comparing the intensity uniformity between the uncorrected and corrected phantom images. Conclusion: An efficient algorithm for MAR in seed brachytherapy was developed. The challenge of detecting and correcting artifacts from 60 to 120 tiny objects in sinogram space has been successfully demonstrated. The detected traces can be further processed to extract, from a large sample of points, the position and orientation of each seed with high precision. This should enable a more accurate use of advance brachytherapy dose calculations, such as Monte Carlo simulations.

This paper presents a procedure for estimating an accurate model of the CT imaging process including spectral effects. As raw projection data are typically unavailable to the end-user, we adopt a post-processing approach that utilizes the reconstructed images themselves. This approach includes errors from x-ray scatter and the nonidealities of the built-in soft tissue correction into the beam characteristics, which is crucial to beam hardening correction algorithms that are designed to be applied directly to CT reconstructed images. We formulate this approach as a quadratic programming problem and propose two different methods, dimension reduction and regularization, to overcome ill conditioning in the model. For the regularization method we use a statistical procedure, Cross Validation, to select the regularization parameter. We have constructed step-wedge phantoms to estimate the effective beam spectrum of a GE CT-I scanner. Using the derived spectrum, we computed the attenuation ratios for the wedge phantoms and found that the worst case modeling error is less than 3% of the corresponding attenuation ratio. We have also built two test (hybrid) phantoms to evaluate the effective spectrum. Based on these test phantoms, we have shown that the effective beam spectrum provides an accurate model for the CT imaging process. Last, we used a simple beam hardening correction experiment to demonstrate the effectiveness of the estimated beam profile for removing beam hardening artifacts. We hope that this estimation procedure will encourage more independent research on beam hardening corrections and will lead to the development of application-specific beam hardening correction algorithms.

High quality cone-beam tomography (CBCT) reconstruction requires accurately estimating and subtracting the (often) large amount of scatter from the raw projection data. Although considerable attention has been paid to scatter correction algorithm development over the past several years, there still exists the need for a practical, general-purpose tool that is accurate, fast, and requires minimal calibration. Here, we introduce 3D VSHARP® which utilizes a finite element solver of the Linear Boltzmann Transport Equation (LBTE) to accurately and rapidly simulate photon transport through a model of the object being scanned and then scale and subtract the estimated scatter from raw projections. 3D VSHARP has been incorporated into the commercially available reconstruction software development toolkit, CST (Varex Imaging, Salt Lake City, UT) enabling scatter correction to be applied to arbitrary scanner configurations and geometries as part of an entire reconstruction pipeline. To set parameters for 3D VSHARP, the user chooses from a library of files that describe key physical aspects of the CT system, including its x-ray spectrum, detector response, and, if they exist, bowtie filter, and anti-scatter grid. The object model, which characterizes the spatial distribution of the atomic number and density of the scanned object, is automatically generated from the first-pass reconstruction which may, if desired, include CST’s existing kernel-based scatter correction 2D VSHARP®. We describe the new correction tool and show example reconstructions. High accuracy of scatter correction and excellent image quality were achieved with total reconstruction times on the order of 1 minute.

Metallic implants are known to generate bright and dark streaking artifacts in x-ray computed tomography (CT) images. These artifacts cause loss of information, reduces the resolution, forms noise which hinders diagnostic capability. The reduction of metal artifact is of immense importance in the present scenario. We propose seeded watershed segmentation-interpolation based sinogram correction method to reduce the metal artifacts caused by metallic implants. We tend to find projection bins affected by the metallic objects in the raw projection data and to replace the corrupted values by appropriate estimates. The novelty of proposed method lies in segmentation of metal part from the CT image by seeded watershed segmentation method, using IlastiK tool. Proposed method is experimented using dataset pertaining to different clinical cases. Solution is studied for correctness by quantitatively as well as qualitatively. The result presents significant improvement in artifact reduction aiding to better diagnostic ability.

Virtual nodule insertion paves the way towards the development of standardized databases of hybrid CT images with known lesions. The purpose of this study was to assess three methods (an established and two newly developed techniques) for inserting virtual lung nodules into CT images. Assessment was done by comparing virtual nodule volume and shape to the CT-derived volume and shape of synthetic nodules. 24 synthetic nodules (three sizes, four morphologies, two repeats) were physically inserted into the lung cavity of an anthropomorphic chest phantom (KYOTO KAGAKU). The phantom was imaged with and without nodules on a commercial CT scanner (SOMATOM Definition Flash, Siemens) using a standard thoracic CT protocol at two dose levels (1.4 and 22 mGy CTDIvol). Raw projection data were saved and reconstructed with filtered back-projection and sinogram affirmed iterative reconstruction (SAFIRE, strength 5) at 0.6 mm slice thickness. Corresponding 3D idealized, virtual nodule models were co-registered with the CT images to determine each nodule’s location and orientation. Virtual nodules were voxelized, partial volume corrected, and inserted into nodule-free CT data (accounting for system imaging physics) using two methods: projection-based Technique A, and image-based Technique B. Also a third Technique C based on cropping a region of interest from the acquired image of the real nodule and blending it into the nodule-free image was tested. Nodule volumes were measured using a commercial segmentation tool (iNtuition, TeraRecon, Inc.) and deformation was assessed using the Hausdorff distance. Nodule volumes and deformations were compared between the idealized, CT-derived and virtual nodules using a linear mixed effects regression model which utilized the mean, standard deviation, and coefficient of variation (, and of the regional Hausdorff distance. Overall, there was a close concordance between the volumes of the CT-derived and virtual nodules. Percent differences between them were less than 3% for all insertion techniques and were not statistically significant in most cases. Correlation coefficient values were greater than 0.97. The deformation according to the Hausdorff distance was also similar between the CT-derived and virtual nodules with minimal statistical significance in the () for Techniques A, B, and C. This study shows that both projection-based and image-based nodule insertion techniques yield realistic nodule renderings with statistical similarity to the synthetic nodules with respect to nodule volume and deformation. These techniques could be used to create a database of hybrid CT images containing nodules of known size, location and morphology.

Communication between nurse and patient can have a profound impact on the achievement of effective health education practice. This relationship is explored using raw project data (Macleod Clark et al., 1992) of nurse-patient interactions.

Virtual water accounting for building: case study for E-town, Beijing

Kinetic parametric PET imaging provides valuable information about physiological and biochemical processes. Direct reconstruction of parametric images combines kinetic modelling and image reconstruction to estimate parametric images from raw dynamic PET projection data. Compared to indirect methods, direct methods lead to improved parametric images, often with lower bias and variance. However, raw dynamic PET data may not always be available, and even when it is, there are complex scanner-specific data corrections (e.g. scatter, attenuation and normalisation) as well as the need for a fully 3D forward and back projector. In this proof-ofconcept work we apply our synthesized PET image reconstruction methodology to exploit the benefits of direct parametric reconstruction methodology when only previously reconstructed dynamic PET images are available. The synthesised reconstruction framework creates a new reconstruction problem from standard reconstructed images (which have already used scannerspecific software with all data corrections accounted for). Our synthesised approach has already shown benefits for spatial resolution recovery [1] and post-reconstruction image enhancement with anatomical guidance via the kernel method and the deep image prior [2]. This present work now proposes the synthesised reconstruction framework (which generates synthetic dynamic PET sinograms from dynamic PET images) to perform direct estimation of kinetic parameters from the synthetic data. This allows the direct parametric reconstruction methodology to be applied even when there is no access to the original raw PET data nor the forward-or-back projectors.