Iterative CT Reconstruction Research Articles

Exact gram filtering and efficient backprojection for iterative CT reconstruction.

Forward and backprojections are the basis of all model-based iterative reconstruction (MBIR) methods. However, computing these accurately is time-consuming. In this paper, we present a method for MBIR in parallel X-ray beam geometry that utilizes a Gram filter to efficiently implement forward andbackprojection. We propose using voxel-basis and modeling its footprint in a box spline framework to calculate the Gram filter exactly and improve the performance of backprojection. In the special case of parallel X-ray beam geometry, the forward and backprojection can be implemented by an estimated Gram filter efficiently if the sinogram signal is bandlimited. In this paper, a specialized sinogram interpolation method is proposed to eliminate the bandlimited prerequisite and thus improve the reconstruction accuracy. We build on this idea by utilizing the continuity of the voxel-basis' footprint, which provides a more accurate sinogram interpolation and further improves the efficiency and quality of backprojection. In addition, the detector blur effect can be efficiently accounted for in our method to better handle realisticscenarios. The proposed method is tested on both phantom and real computed tomography (CT) images under different resolutions, sinogram sampling steps, and noise levels. The proposed method consistently outperforms other state-of-the-art projection models in terms of speed and accuracy for both backprojection andreconstruction. We proposed a iterative reconstruction methodology for 3D parallel-beam X-ray CT reconstruction. Our experimental results demonstrate that the proposed methodology is accurate, fast, and reproducible, and outperforms alternative state-of-the-art projection models on both backprojection and reconstruction resultssignificantly.

Feasibility of dual-energy CBCT by spectral filtration of a dual-focus CNT x-ray source.

Cone beam computed tomography (CBCT) is now widely used in dentistry and growing areas of medical imaging. The presence of strong metal artifacts is however a major concern of using CBCT especially in dentistry due to the presence of highly attenuating dental restorations, fixed appliances, and implants. Virtual monoenergetic images (VMIs) synthesized from dual energy CT (DECT) datasets are known to reduce metal artifacts. Although several techniques exist for DECT imaging, they in general come with significantly increased equipment cost and not available in dental clinics. The objectives of this study were to investigate the feasibility of developing a low-cost dual energy CBCT (DE-CBCT) by retrofitting a regular CBCT scanner with a carbon nanotube (CNT) x-ray source with dual focal spots and corresponding low-energy (LE) and high-energy (HE) spectral filters. A testbed with a CNT field emission x-ray source (NuRay Technology, Chang Zhou, China), a flat panel detector (Teledyne, Waterloo, Canada), and a rotating object stage was used for this feasibility study. Two distinct polychromatic x-ray spectra with the mean photon energies of 66.7keV and 86.3keV were produced at a fixed 120kVp x-ray tube voltage by using Al+Au and Al+Sn foils as the respective LE and HE filters attached to the exist window of the x-ray source. The HE filter attenuated the x-ray photons more than the LE filter. The calculated post-object air kerma rate of the HE beam was 31.7% of the LE beam. An anthropomorphic head phantom (RANDO, Nuclear Associates, Hicksville, NY) with metal beads was imaged using the testbed and the images were reconstructed using an iterative volumetric CT reconstruction algorithm. The VMIs were synthesized using an image-domain basis materials decomposition method with energy ranging from 30 to 150keV. The results were compared to the reconstructed images from a single energy clinical dental CBCT scanner (CS9300, Carestream Dental, Atlanta, GA). A significant reduction of the metal artifacts was observed in the VMI images synthesized at high energies compared to those from the same object imaged by the clinical dental CBCT scanner. The ability of the CNT x-ray source to generate the output needed to compensate the reduction of photon flux due to attenuation from the spectral filters and to maintain the CT imaging time was evaluated. The results demonstrated the feasibility of DE-CBCT imaging using the proposed approach. Metal artifact reduction was achieved in VMIs synthesized. The x-ray output needed for the proposed DE-CBCT can be generated by a fixed-anode CNT x-ray source.

Iterative CT reconstruction based on ADMM using shearlet sparse regularization.

The total variation (TV) method favors solutions with the piece-wise constant assumption of the desired image from sparse-view sampling, for example, simple geometric images with flat intensity. When the phantoms become more complex and contain complicated textures, for example, high-resolution phantom and lung CT images, the images reconstructed by TV regularization may lose their contrast and fine structures. One of the optimally sparse transforms for images, the shearlet transform, has C2 without discontinuities on C2 curves, giving excellent sensitive directional information as compared with other wavelet transform approaches. Here, we developed a Shearlet-Sparse Regularization (SSR) algorithm solved with the Alternating Direction Method of Multipliers (ADMM) to overcome this limitation. With the strengthened characteristics of SSR, we performed one simulation experiment and two real experiments using a NeuViz 64 X-ray CT scanning system to measure the performance and properties of proposed algorithm. The results demonstrate that the SSR method exhibits the advantage of providing high-quality directional information and contrast as compared with TV.

A CT reconstruction method based on constrained data fidelity range estimation.

For the CT iterative reconstruction, choosing the parameters of different regularization terms has been a difficult problem. Transforming the reconstruction problem into constrained optimization can solve this problem, but determining the constraint range and accurately solving it remains a challenge. This paper proposes a CT reconstruction method based on constrained data fidelity term, which estimates the distribution of the constraint function by Taylor expansion to determine the constraint range. We respectively use Douglas-Rachford splitting (DRS) and Projection-based primal-dual algorithm (PPD) to split the reconstruction problem and solve the data fidelity subproblem. This method can accurately estimate the constrained range of data fidelity terms to ensure reconstruction accuracy and use different regularization terms for reconstruction without parameter adjustment. Three regularization terms are used for reconstruction experiments, and simulation results show that the proposed method can converge stably, and its reconstruction quality is better than the filtered back-projection.

Model-based iterative reconstruction in CT of paranasal sinuses in cystic fibrosis

To assess image quality and dose-reduction efficacy of model-based iterative reconstruction (MBIR) in computed tomography (CT) of the paranasal sinus (CTPNS) compared with adaptive statistical iterative reconstruction (ASIR) in cystic fibrosis (CF) patients. Unenhanced CTPNS studies performed in adult CF patients from 2014 to 2020 were included. MBIR and ASIR were used and compared. Two radiologists assessed the CT images blindly and randomly. Quantitative assessment of noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) was performed in the maxillary sinus, sphenoid body, temporalis, and background air. Qualitative assessment performed included image sharpness, noise and contrast. Thirty-seven MBIR and 45 ASIR CT PNS studies were included. MBIR achieved a 74% effective median dose reduction (0.039 mSv) compared with ASIR (0.147 mSv). Measured noise was significantly lower in all regions using MBIR (p<0.001) with superior SNR (p<0.001). MBIR had higher CNR compared to ASIR (4.567 versus 2.03, p<0.001). MBIR images were sharper and less noisy, with equal contrast. Cohen's weighted kappa value was 0.824 for qualitative analysis, indicating good inter-rater agreement. Both methods had 100% diagnostic acceptability. MBIR produces high-quality CTPNS images at a significantly lower dose compared with ASIR. It is the preferred imaging surveillance method in CF patients and may have roles in other patient cohorts.

CT Noise-Reduction Methods for Lower-Dose Scanning: Strengths and Weaknesses of Iterative Reconstruction Algorithms and New Techniques.

Iterative reconstruction (IR) algorithms are the most widely used CT noise-reduction method to improve image quality and have greatly facilitated radiation dose reduction within the radiology community. Various IR methods have different strengths and limitations. Because IR algorithms are typically nonlinear, they can modify spatial resolution and image noise texture in different regions of the CT image; hence traditional image-quality metrics are not appropriate to assess the ability of IR to preserve diagnostic accuracy, especially for low-contrast diagnostic tasks. In this review, the authors highlight emerging IR algorithms and CT noise-reduction techniques and summarize how these techniques can be evaluated to help determine the appropriate radiation dose levels for different diagnostic tasks in CT. In addition to advanced IR techniques, we describe novel CT noise-reduction methods based on convolutional neural networks (CNNs). CNN-based noise-reduction techniques may offer the ability to reduce image noise while maintaining high levels of image detail but may have unique drawbacks. Other novel CT noise-reduction methods are being developed to leverage spatial and/or spectral redundancy in multiphase or multienergy CT. Radiologists and medical physicists should be familiar with these different alternatives to adapt available CT technology for different diagnostic tasks. The scope of this article is (a) to review the clinical applications of IR algorithms as well as their strengths, weaknesses, and methods of assessment and (b) to explore new CT image reconstruction and noise-reduction techniques that promise to facilitate radiation dose reduction. ©RSNA, 2021.

Comparison of CT image quality between the AIDR 3D and FIRST iterative reconstruction algorithms: an assessment based on phantom measurements and clinical images

Modern CT iterative reconstruction algorithms are transitioning from a statistical-based to model-based approach. However, increasing complexity does not ensure improved image quality for all indications, and thorough characterization of new algorithms is important to understand their potential clinical impacts. This study performs both quantitative and qualitative analyses of image quality to compare Canon’s statistical-based Adaptive Iterative Dose Reduction 3D (AIDR 3D) algorithm to its model-based algorithm, Forward-projected model-based Iterative Reconstruction SoluTion(FIRST). A phantom was used to measure the task-specific modulation transfer function (MTFTask), the noise power spectrum (NPS), and the low-contrast object-specific CNR (CNRLO) for each algorithm using three dose levels and the convolution algorithm (kernel) appropriate for abdomen, lung, and brain imaging. Additionally, MTFTask was measured at four contrast levels, and CNRLO was measured for two object sizes. Lastly, three radiologists participated in a preference study to compare clinical image quality for three study types: non-contrast abdomen, pulmonary embolism (PE), and lung screening. Nine questions related to the appearance of anatomical features or image quality characteristics were scored for twenty exams of each type. The behavior of both algorithms depended strongly on the kernel selected. Phantom measurements suggest that FIRST should be beneficial over AIDR 3D for abdomen imaging, but do not suggest a clear overall benefit to FIRST for lung or brain imaging; metrics suggest performance may be equivalent to or slightly favor AIDR 3D, depending on the size of the object being imaged and whether spatial resolution or low-contrast resolution is more important for the task at hand. Overall, radiologists strongly preferred AIDR 3D for lung screening, slightly preferred AIDR 3D for non-contrast abdomen, and had no preference for PE. FIRST was superior for the reduction of metal artifacts. Radiologist preference may be influenced by changes to noise texture.

Manifold Reconstruction of Differences: A Model-Based Iterative Statistical Estimation Algorithm With a Data-Driven Prior.

Manifold learning using deep neural networks been shown to be an effective tool for building sophisticated prior image models that can be applied to noise reduction in low-close CT. A manifold is a low-dimensional space that captures most of the variation in a class of data (e.g. chest CT images). This nonlinear reduction in dimensionality is one way to build up a sophisticated model of the features and structures present in image data. This nonlinear dimensionality reduction tends to learn features associated with the signal rather than the noise when provided with a large training dataset. However, the internal workings operation are typically not easy to understand. The results can be highly nonlinear and object-dependent. After training the model is fixed and during reconstruction is typically no check for fitness between the image estimates and the measured data. We propose a new iterative CT reconstruction algorithm, called Manifold Reconstruction of Differences (MRoD), which combines physical and statistical models with a data-driven prior based on manifold learning. The MRoD algorithm involves estimating a manifold component, approximating common features among all patients, and the difference component which has the freedom to fit the measured data. By applying a sparsity-promoting penalty to the difference image rather than a hard constraint to the manifold, the MRoD algorithm is able to reconstruct features which are not present in the training data. The difference component itself may be independently useful. While the manifold captures typical patient features (e.g. healthy anatomy), the difference image highlights patient-specific elements (e.g. pathology). In this work, we present a description of an optimization framework which combines trained manifold-based modules with physical modules. We present a simulation study using anthropomorphic lung data showing that the MRoD algorithm can both isolate differences between a particular patient and the typical distribution, but also provide significant noise reduction with less bias than a typical penalized likelihood estimator in composite manifold plus difference reconstructions.

Reducing motion artifact in sequential-scan dual-energy CT imaging by incorporating deformable registration within joint statistical image reconstruction

Dual-energy computed tomography (DECT) has been widely used to reconstruct basis components. In previous studies, ou DECT algorithm has shown high accuracy in stopping power ratio (SPR) estimation of fixed objects for proton radiotherapy planning. However, patient movement between sequential data acquisitions may lead to severe motion artifacts in the component images. In order to reduce or eliminate the motion artifacts in clinical applications, we combine a deformable registration method with an accurate joint statistical iterative reconstruction algorithm, dual-energy alternating minimization (DEAM). Image registration is a process of geometrically aligning two or more images. We implement a multi-modality symmetric deformable registration method based on Advanced Normalization Tools (ANTs) to automatically align the scans we acquire for the same patient. The precalculated registration mapping and its inverse are then embedded into each iteration of the DEAM algorithm. The performance of warped DEAM is quantitatively assessed. Theoretically, the performance of warped DEAM on moved patients should be comparable to the performance of the original DEAM algorithm on fixed objects. The warped DEAM algorithm reduces motion artifacts while preserving the accuracy of the iterative joint statistical CT reconstruction algorithm, which enables us to reconstruct accurate results from sequentially scanned dual-energy patient data.

Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography.

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.

CT iterative vs deep learning reconstruction: comparison of noise and sharpness.

To compare image noise and sharpness of vessels, liver, and muscle in lower extremity CT angiography between "adaptive statistical iterative reconstruction-V" (ASIR-V) and deep learning reconstruction "TrueFidelity" (TFI). Thirty-seven patients (mean age, 65.2 years; 32 men) with lower extremity CT angiography were enrolled between November and December 2019. Images were reconstructed with two ASIR-V (blending factor of 80% and 100% (AV-100)) and three TFI (low-, medium-, and high-strength-level (TF-H) settings). Two radiologists evaluated these images for vessels (aorta, femoral artery, and popliteal artery), liver, and psoas muscle. For quantitative analyses, conventional indicators (CT number, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)) and blur metric values (indicating the degree of image sharpness) of selected regions of interest were determined. For qualitative analyses, the degrees of quantum mottle and blurring were assessed. The higher the blending factor in ASIR-V or the strength in TFI, the lower the noise, the higher the SNR and CNR values, and the higher the blur metric values in all structures. The SNR and CNR values of TF-H images were significantly higher than those of AV-80 images and similar to those of AV-100 images. The blur metric values in TFI images were significantly lower than those in ASIR-V images (p < 0.001), indicating increased sharpness. Among all the investigated image procedures, the overall qualitative image quality was best in TF-H images. TF-H was the most balanced image in terms of image noise and sharpness among the examined image combinations. • Deep learning image reconstruction "TrueFidelity" is superior to iterative reconstruction "ASIR-V" regarding image noise and sharpness. • The high-strength "TrueFidelity" approach generated the best image quality among the examined image reconstruction procedures. • In iterative and deep learning CT image reconstruction, the higher the blending and strength factors, the lower the image noise and the poorer the image sharpness.

Influence of X-ray computed tomography (CT) exposure and reconstruction parameters on positron emission tomography (PET) quantitation

BackgroundThe CT of PET CT provides diagnostic information, anatomic localisation and attenuation correction (AC). When only AC is required, very lose dose CT is desirable. CT iterative reconstruction (IR) improves image quality with lower exposures however there is little data on very low dose IR CT for AC of PET. This work assesses the impact of CT exposure and reconstruction algorithm on PET voxel values.MethodAn anthropomorphic torso phantom was filled with physiologically typical [18]F concentrations in heart, liver and background compartments. A 17-mm-diameter right lung “tumour” filled with [18]F was included (surrounding lung contained no 18[F]). PET was acquired followed by 24 CT acquisitions with varying CT exposures (15–50 mAs, 80–120 kVp, pitch 0.671 or 0.828). Each CT was reconstructed twice using filtered back projection (FBP) or IR and these used for AC of PET. The reference PET reconstruction (RR) used CT acquired at 50 mAs, 120 kVp, pitch 0.828, IR, all others were test PET reconstructions (TR). Regions of interest (ROIs) were drawn in the liver, soft tissue and over “tumour” on each TR and compared with the RR. Voxel values in each TR were compared to the RR using a paired t test and by calculating which and what proportion of voxels in each TR differed by a quantitatively significant difference (QSD) from the RR.ResultsTRs reconstructed using lower dose CTs underestimated mean and maximum ROI activity relative to the RR; greater with IR than FBP. Once CT dose index (CTDI) increased to 1 mGy, differences were less than QSD. On voxel analysis, all TRs were significantly different to the RR (p < 0.0001). TRs reconstructed at the lowest CT exposure with IR had 6% of voxels that differed by greater than QSD. Differences were reduced with increasing CTDI and FBP reconstruction. Voxels which exceeded the QSD were spatially localised to regions of high activity, interfaces between different attenuation and areas of CT beam hardening.ConclusionsVery low dose CT exposures are feasible for accurate PET AC. Scanner- and reconstruction-specific validation should be employed prior very low dose CT AC for PET.

Usefulness of model-based iterative reconstruction in low-dose lumbar spine CT after spine surgery with metal implant: a phantom study.

The major problems of computed tomography (CT) imaging include radiation exposure and severe artifacts caused by operative implants. To evaluate the usefulness of the metal artifact reduction algorithm and model-based iterative reconstruction (MBIR) in postoperative low-dose (LD) spine CT. A CT torso phantom was scanned at standard-dose (SD) and LD settings. The CT images were reconstructed by three methods: hybrid iterative reconstruction (HIR); metal artifact reduction; and MBIR. The radiation dose of the phantom imaging was evaluated by volume CT dose index (mGy), dose length product (DLP, mGy × cm), and effective dose (mSv). The image quality of the six images was visually evaluated and analyzed using Scheffe's paired comparison method. The average preference of each method was calculated based on the comparative scores. The task transfer function (TTF) and noise power spectrum for HIR and MBIR were also measured. The respective radiation-dose-related parameters of the SD and LD conditions were: volume CT dose index = 10.2 and 1.2 mGy; DLP = 277.9 and 33.9 mGy × cm; and effective dose = 4.2 and 0.5 mSv. The average preference for diagnostic acceptability of MBIR at LD was not significantly different from the other reconstructions of SD data. MBIR successfully reduced metal artifacts in the LD condition. The 10% TTF was higher for HIR at SD and higher for MBIR at LD. MBIR is useful for LD spine CT after spine surgery with metal implant.

Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: A phantom study

PurposeWe aimed to thoroughly characterize image quality of a novel deep learning image reconstruction (DLIR), and investigate its potential for dose reduction in abdominal CT in comparison with filtered back-projection (FBP) and a partial model-based iterative reconstruction (ASiR-V). MethodsWe scanned a phantom at three dose levels: regular (7 mGy), low (3 mGy) and ultra-low (1 mGy). Images were reconstructed using DLIR (low, medium and high levels) and ASiR-V (0% = FBP, 50% and 100%). Noise and contrast-dependent spatial resolution were characterized by computing noise power spectra and target transfer functions, respectively. Detectability indexes of simulated acute appendicitis or colonic diverticulitis (low contrast), and calcium-containing urinary stones (high contrast) (|ΔHU| = 50 and 500, respectively) were calculated using the nonprewhitening with eye filter model observer. ResultsAt all dose levels, increasing DLIR and ASiR-V levels both markedly decreased noise magnitude compared with FBP, with DLIR low and medium maintaining noise texture overall. For both low- and high-contrast spatial resolution, DLIR not only maintained, but even slightly enhanced spatial resolution in comparison with FBP across all dose levels. Conversely, increasing ASiR-V impaired low-contrast spatial resolution compared with FBP. Overall, DLIR outperformed ASiR-V in all simulated clinical scenarios. For both low- and high-contrast diagnostic tasks, increasing DLIR substantially enhanced detectability at any dose and contrast levels for any simulated lesion size. ConclusionsUnlike ASiR-V, DLIR substantially reduces noise while maintaining noise texture and slightly enhancing spatial resolution overall. DLIR outperforms ASiR-V by enabling higher detectability of both low- and high-contrast simulated abdominal lesions across all investigated dose levels.

Effects of different levels of CT iterative reconstruction on low-contrast detectability and radiation dose in patients of different sizes: an anthropomorphic phantom study.

The purpose of this study was to verify the maintenance of low-contrast detectability at different CT dose reduction levels, in patients of different sizes, as a consequence of the application of iterative reconstruction at different strengths combined with tube current modulation. Anthropomorphic abdominal phantoms of two sizes (small and large) were imaged at a fixed noise with iterative algorithm ASIR-V percentages in the range between 0 and 70% and corresponding dose reductions in the range of 0-83%. A total of 1400 images with and without liver low-contrast simulated lesions were evaluated by five radiologists, using the receiver operating characteristics (ROC) paradigm and evaluating the area under the ROC curve (AUC). The human observer results were then compared with AUC obtained with a channelized Hotelling observer (CHO). CNR values were also calculated. For the small phantom, the AUC values lie between 0.90 and 0.93 for human evaluations of images acquired without iterative reconstruction, with 30% ASIR-V and with 50% ASIR-V. The AUC decreased significantly to 0.81 (p = 0.0001) at 70% ASIR-V. The CHO results were in coherence with human observer scores. Also, similar results were observed for the large size phantom. CNR values were stable for the different ASIR-V percentages. The iterative algorithm maintained the low-contrast detectability up to a dose reduction of about 70%, following application of a 50% ASIR-V combined with automatic tube current modulation, regardless of the phantom size. At further dose reductions using greater iterative percentages, a significant decrease in detectability was observed.

Iterative model-based CT reconstruction algorithm: the background and added clinical value

Iterative model-based CT reconstruction algorithm: the background and added clinical value

CT colonography with spectral filtration and advanced modeled iterative reconstruction in the third-generation dual-source CT: image quality, radiation dose and performance in clinical utility

To evaluate image quality, radiation dose and its diagnostic performance in clinical utility of CT colonography (CTC) applying spectral filtration and advanced modeled iterative reconstruction (ADMIRE) techniques in third-generation dual-source CT. A total of 125 patients for screening or diagnostic purposes underwent CTC at 120kVp standard dose (120kVp-STD) with filtered-back projection reconstruction (FBP) in supine position, then at a tin-filtered 150 kVp low dose (Sn150kVp-LD) and a tin-filtered 100 kVp ultra-low dose (Sn100kVp-ULD) with ADMIRE reconstruction in prone position. Radiation metrics were recorded. Objective and subjective image qualities were compared, and the diagnostic performance was assessed for both colonic and extracolonic findings using CTC reporting and data system (C-RADS). The effective dose was significantly lower for Sn150kVp-LD and Sn100kVp-ULD than 120kVp-STD protocol, resulting in 22.5% and 87.5% reductions (1.55±0.30 and 0.25±0.07 mSv vs. 2.00±0.52 mSv; both p<0.01), respectively. Image noise and signal-to-noise ratio were improved significantly for Sn150kVp-LD with ADMIRE compared with 120kVp-STD, both of which had similar excellent 2D and 3D subjective image quality with equivalent diagnostic performance. Sn100kVp-ULD with ADMIRE had decreased subjective image quality and significant different C-RADS extracolonic-score (E-score) compared with 120kVp-STD, however, C-RADS colonic-score (C-score) of that showed no significantly difference. Sn150kVp and Sn100kVp with ADMIRE reconstruction provide an alternative low dose CTC strategy and could be feasible in clinical screening or diagnostic scenarios.

Performance evaluation of two iterative reconstruction algorithms, MBIR and ASIR, in low radiation dose and low contrast dose abdominal CT in children.

The adverse effect of low-dose CT on image quality may be mitigated using iterative reconstructions. The purpose of this study was to evaluate the performance of the full model-based iterative reconstruction (MBIR) and adaptive statistical reconstruction (ASIR) algorithms in low radiation dose and low contrast dose abdominal contrast-enhanced CT (CECT) in children. A total of 59 children (32 males and 27 females) undergoing low radiation dose (100kVp) and low contrast dose (270 mgI/ml) abdominal CECT were enrolled. The median age was 4.0years (ranging from 0.3 to 13years). The raw data were reconstructed with MBIR, ASIR and filtered back-projection (FBP) algorithms into 6 groups (MBIR, 100%ASIR, 80%ASIR, 60%ASIR, 40%ASIR and FBP). The CT numbers, standard deviations, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of liver, pancreas, kidney and abdominal aorta were measured. Two radiologists independently evaluated the subjective image quality including the overall image noise and structure display ability on a 4-point scale with 3 being clinically acceptable. The measurements among the reconstruction groups were compared using one-way ANOVA. The overall image noise score and display ability were 4.00 ± 0.00 and 4.00 ± 0.00 with MBIR and 3.27 ± 0.33 and 3.25 ± 0.43 with ASIR100%, respectively, which met the diagnostic requirement; other reconstructions couldn't meet the diagnostic requirements. Compared with FBP images, the noise of MBIR images was reduced by 62.86-65.73% for the respective organs (F = 48.15-80.47, P < 0.05), and CNR increased by 151.38-170.69% (F = 22.94-38.02, P < 0.05). MBIR or ASIR100% improves the image quality of low radiation dose and contrast dose abdominal CT in children to meet the diagnostic requirements, and MBIR has the best performance.

View-driven compressed sensing method of CT image reconstruction

In order to significantly reducing the X-ray dose of CT examination, this study focuses on reconstructing the tomographic images from sparse projection data with sufficient accuracy. Aiming at two-dimensional (2D) image reconstruction of fan-beam projection under circular scanning, this paper proposes the view-driven system model and designs a CT iterative image reconstruction method by combining the iterative algorithm and the compressed sensing theory. The simulation results show that the algorithm has high numerical accuracy, low computational complexity, less memory overhead, and strong engineering practicability under the condition of ultra-sparse projection data (no more than 20 view angles for fan-beam scanning in the range of [0,2π)).

Computationally Efficient System Matrix Calculation Techniques in Computed Tomography Iterative Reconstruction.

Background:Relative to classical methods in computed tomography, iterative reconstruction techniques enable significantly improved image qualities and/or lowered patient doses. However, the computational speed is a major concern for these iterative techniques. In the present study, we present a method for fast system matrix calculation based on the line integral model (LIM) to speed up the computations without compromising the image quality. In addition, we develop a hybrid line–area integral model (AIM) that highlights the advantages of both LIM and AIMs.Methods:The contributing detectors for a given pixel and a given projection view, and the length of corresponding intersection lines with pixels, are calculated using our proposed algorithm. For the hybrid method, the respective narrow-angle fan beam was modeled by multiple equally spaced lines. The computed system matrix was evaluated in the context of reconstruction using the simultaneous algebraic reconstruction technique (SART) as well as maximum likelihood expectation maximization (MLEM).Results:The proposed LIM offers a considerable reduction in calculation times compared to the standard Siddon algorithm: 2.9 times faster. Differences in root mean square error and peak signal-to-noise ratio were not significant between the proposed LIM and the Siddon algorithm for both SART and MLEM reconstruction methods (P > 0.05). Meanwhile, the proposed hybrid method resulted in significantly improved image qualities relative to LIM and the Siddon algorithm (P < 0.05), though computations were 4.9 times more intensive than the proposed LIM.Conclusion:We have proposed two fast algorithms to calculate the system matrix. The first is based on LIM and was faster than the Siddon algorithm, with matched image quality, whereas the second method is a hybrid LIM–AIM that achieves significantly improved images though with its computational requirements.