Computed Tomography Reconstruction Algorithm Research Articles

Phase-contrast visualization of human tissues using superimposed wavefront imaging of diffraction-enhanced x-rays.

Phase-contrast computed tomography (CT) using high-brilliance, synchrotron-generated x-rays enable three-dimensional (3D) visualization of microanatomical structures within biological specimens, offering exceptionally high-contrast images of soft tissues. Traditional methods for phase-contrast CT; however, necessitate a gap between the subject and the x-ray camera, compromising spatial resolution due to penumbral blurring. Our newly developed technique, Superimposed Wavefront Imaging of Diffraction-enhanced x-rays (SWIDeX), leverages a Laue-case Si angle analyzer affixed to a scintillator to convert x-rays to visible light, capturing second-order differential phase contrast images and effectively eliminating the distance to the x-ray camera. This innovation achieves superior spatial resolution over conventional methods. In this paper, the imaging principle and CT reconstruction algorithm based on SWIDeX are presented in detail and compared with conventional analyzer-based imaging (ABI). It also shows the physical setup of SWIDeX that provides the resolution preserving second-order differential images for reconstruction. We compare the spatial resolution and the sensitivity of SWIDeX to conventional ABI. To demonstrate high-spatial resolution achievable by SWIDeX, the internal structures of four human tissues-ductal carcinoma in situ, normal stomach, normal pancreas, and intraductal papillary mucinous neoplasm of the pancreas-were visualized using an imaging system configured at the Photon Factory's BL14B beamline under the High Energy Accelerator Research Organization (KEK). Each tissue was thinly sliced after imaging, stained with hematoxylin and eosin (H&E) for conventional microscope-based pathology. A comparison of SWIDeX-CT and pathological images visually demonstrates the effectiveness of SWIDeX-CT for biological tissue imaging. SWIDeX could generate clearer 3D images than existing analyzer-based phase-contrast methods and accurately delineate tissue structures, as validated against histopathological images. SWIDeX can visualize important 3D structures in biological soft tissue with high spatial resolution and can be an important tool for providing information between the disparate scales of clinical and pathological imaging.

Fast computational approach with prior dimension reduction for three-dimensional chemical component analysis using CT data of spectral imaging.

Spectral image (SI) measurement techniques, such as X-ray absorption fine structure (XAFS) imaging and scanning transmission electron microscopy (STEM) with energy-dispersive X-ray spectroscopy (EDS) or electron energy loss spectroscopy (EELS), are useful for identifying chemical structures in composite materials. Machine-learning techniques have been developed for automatic analysis of SI data, and their usefulness has been proven. Recently, an extended measurement technique combining SI with a computed tomography (CT) technique (CT-SI), such as CT-XAFS and STEM-EDS/EELS tomography, was developed to identify the three-dimensional (3D) structures of chemical components. CT-SI analysis can be conducted by combining CT reconstruction algorithms and chemical component analysis based on machine learning techniques. However, this analysis incurs high computational costs owing to the size of the CT-SI datasets. To address this problem, this study proposed a fast computational approach for 3D chemical component analysis in an unsupervised learning setting. The primary idea for reducing the computational cost involved compressing the CT-SI data prior to CT computation and performing 3D reconstruction and chemical component analysis on the compressed data. The proposed approach significantly reduced the computational cost without losing information about the 3D structure and chemical components. We experimentally evaluated the proposed approach using synthetic and real CT-XAFS data, which demonstrated that our approach achieved a significantly faster computational speed than the conventional approach while maintaining analysis performance. As the proposed procedure can be implemented with any CT algorithm, it is expected to accelerate 3D analyses with sparse regularized CT algorithms in noisy and sparse CT-SI datasets.

Model-driven CT reconstruction algorithm for nano-resolution x-ray phase contrast imaging

Abstract The low-density imaging performance of a zone plate-based nano-resolution hard x-ray computed tomography (CT) system can be significantly improved by incorporating a grating-based Lau interferometer. Due to the diffraction, however, the acquired nano-resolution phase signal may suffer splitting problem, which impedes the direct reconstruction of phase contrast CT (nPCT) images. To overcome, a new model-driven nPCT image reconstruction algorithm is developed in this study. In it, the diffraction procedure is mathematically modeled into a matrix B , from which the projections without signal splitting can be generated invertedly. Furthermore, a penalized weighted least-square model with total variation (PWLS-TV) is employed to denoise these projections, from which nPCT images with high accuracy are directly reconstructed. Numerical experiments demonstrate that this new algorithm is able to work with phase projections having any splitting distances. Moreover, results also reveal that nPCT images of higher signal-to-noise-ratio (SNR) could be reconstructed from projections having larger splitting distances. In summary, a novel model-driven nPCT image reconstruction algorithm with high accuracy and robustness is verified for the Lau interferometer-based hard x-ray nano-resolution phase contrast imaging.

Evaluation of four computed tomography reconstruction algorithms using a coronary artery phantom.

Despite advancements in coronary computed tomography angiography (CTA), challenges in positive predictive value and specificity remain due to limited spatial resolution. The purpose of this experimental study was to investigate the effect of 2nd generation deep learning-based reconstruction (DLR) on the quantitative and qualitative image quality in coronary CTA. A vessel model with stepwise non-calcified plaque was scanned using 320-detector CT. Image reconstruction was performed using four techniques: hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), DLR, and 2nd generation DLR. The luminal peak CT number, contrast-to-noise ratio (CNR), and edge rise slope (ERS) were quantitatively evaluated via profile curve analysis. Two observers qualitatively graded the graininess, lumen sharpness, and overall lumen visibility on the basis of the degree of confidence for the stenosis severity using a five-point scale. The image noise with HIR, MBIR, DLR, and 2nd generation DLR was 23.0, 21.0, 16.9, and 9.5 HU, respectively. The corresponding CNR (25% stenosis) was 15.5, 15.9, 22.1, and 38.3, respectively. The corresponding ERS (25% stenosis) was 203.2, 198.6, 228.9, and 262.4 HU/mm, respectively. Among the four reconstruction methods, the 2nd generation DLR achieved the significantly highest CNR and ERS values. The score of 2nd generation DLR in all evaluation points (graininess, sharpness, and overall lumen visibility) was higher than those of the other methods (overall vessel visibility score, 2.6±0.5, 3.8±0.6, 3.7±0.5, and 4.6±0.5 with HIR, MBIR, DLR, and 2nd generation DLR, respectively). 2nd generation DLR provided better CNR and ERS in coronary CTA than HIR, MBIR, and previous-generation DLR, leading to the highest subjective image quality in the assessment of vessel stenosis.

A priority-based self-guided serial–parallel genetic algorithm for low-dose computed tomography

Computed tomography (CT) is a non-destructive evaluation technique to know the internal structure of the objects under scan. It has numerous applications in engineering as well as in the medical field. The prime objective of this manuscript is to reduce the radiation dose to the object under scanning and to reconstruct the low-noise CT images, even in limited-view projections data scenarios. The present manuscript proposes a novel priority-based self-guided serial-parallel hybrid genetic algorithm for low-dose CT reconstruction. The current algorithm combines serial and parallel processing elements to reduce the loss of diversity in the population and increase the convergence rate. The proposed algorithm uses a novel priority-based self-guided competition operator. It consists of two approaches for maintaining exploration and exploitation tradeoffs. Here, the first approach uses serial processing, whereas the second approach uses parallel processing. The algorithm also uses priority-based hybrid crossover (PHCO) and adaptive priority-based mutation operator (PMO) to generate the most suitable offspring in every generation. Experimental results reveal that the presented algorithm produces satisfactory results with limited-view projections. The presented algorithm also outperforms other limited-view CT reconstruction algorithms.

Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction

Objective. In the realm of utilizing artificial intelligence (AI) for medical image analysis, the paradigm of ‘signal-image-knowledge’ has remained unchanged. However, the process of ‘signal to image’ inevitably introduces information distortion, ultimately leading to irrecoverable biases in the ‘image to knowledge’ process. Our goal is to skip reconstruction and build a diagnostic model directly from the raw data (signal). Approach. This study focuses on computed tomography (CT) and its raw data (sinogram) as the research subjects. We simulate the real-world process of ‘human-signal-image’ using the workflow ‘CT-simulated data- reconstructed CT,’ and we develop a novel AI predictive model directly targeting raw data (RCTM). This model comprises orientation, spatial, and global analysis modules, embodying the fusion of local to global information extraction from raw data. We selected 1994 patients with retrospective cases of solid lung nodules and modeled different types of data. Main results. We employed predefined radiomic features to assess the diagnostic feature differences caused by reconstruction. The results indicated that approximately 14% of the features had Spearman correlation coefficients below 0.8. These findings suggest that despite the increasing maturity of CT reconstruction algorithms, they still introduce perturbations to diagnostic features. Moreover, our proposed RCTM achieved an area under the curve (AUC) of 0.863 in the diagnosis task, showcasing a comprehensive superiority over models constructed from secondary reconstructed CTs (0.840, 0.822, and 0.825). Additionally, the performance of RCTM closely resembled that of models constructed from original CT scans (0.868, 0.878, and 0.866). Significance. The diagnostic and therapeutic approach directly based on CT raw data can enhance the precision of AI models and the concept of ‘signal-to-image’ can be extended to other types of imaging. AI diagnostic models tailored to raw data offer the potential to disrupt the traditional paradigm of ‘signal-image-knowledge’, opening up new avenues for more accurate medical diagnostics.

Image reconstruction based on nonlinear diffusion model for limited-angle computed tomography

The problem of limited-angle computed tomography (CT) imaging reconstruction has a wide range of practical applications. Due to various factors such as high x-ray absorption, structural characteristics of the scanned object, and equipment limitations, it is often impractical to obtain a complete angular scan, resulting in limited-angle scan data. In this paper, we propose an iterative image reconstruction algorithm for limited-angle CT. The algorithm carries out a traditional CT reconstruction and a nonlinear diffusion process alternatively. Specifically, a subtle partial differential equation is constructed to guide the nonlinear diffusion process to eliminate limited-angle artifacts in the reconstructed image. Numerical experiments on both analytic data and real data validate the efficacy of the proposed nonlinear diffusion reconstruction algorithm. Furthermore, a linear diffusion reconstruction algorithm which combines a traditional CT reconstruction algorithm and a linear diffusion process is also presented in the paper.

Simulation-Assisted Augmentation of Missing Wedge and Region-of-Interest Computed Tomography Data.

This study reports a strategy to use sophisticated, realistic X-ray Computed Tomography (CT) simulations to reduce Missing Wedge (MW) and Region-of-Interest (RoI) artifacts in FBP (Filtered Back-Projection) reconstructions. A 3D model of the object is used to simulate the projections that include the missing information inside the MW and outside the RoI. Such information augments the experimental projections, thereby drastically improving the reconstruction results. An X-ray CT dataset of a selected object is modified to mimic various degrees of RoI and MW problems. The results are evaluated in comparison to a standard FBP reconstruction of the complete dataset. In all cases, the reconstruction quality is significantly improved. Small inclusions present in the scanned object are better localized and quantified. The proposed method has the potential to improve the results of any CT reconstruction algorithm.

A novel multi-agent genetic algorithm for limited-view computed tomography

Computed tomography (CT) is a non-invasive testing technique to generate the internal image of any object. It has various applications belonging to medical imaging and other engineering applications. The present article proposes a novel multiagent genetic algorithm based on hill-climbing for limited-view computed tomography (MAGAH-CT). The MAGAH combines a multi-agent system based on a genetic algorithm (MAGA) and a hill-climbing approach. The MAGAH uses multiple operators to refine the agent population during evolution. These operators are neighborhood-based competition, hybrid crossover, adaptive mutation, and self-learning. MAGAH-CT explores better solutions in neighborhood-based competition and cooperation operators. The self-learning operator learns through the agent’s population generated by adaptive mutation and hybrid crossover operator, ensuring the results’ continuity. The proposed algorithm has been tested on a Shepp-Logan head phantom with limited view projection data. The algorithm can accurately reconstruct the low-noise CT image using limited data constraints. Experimental results reveal that the presented MAGAH-CT algorithm is efficient and computationally reasonable. The presented algorithm is suitable for low-noise CT reconstruction and out-performs other limited-view CT reconstruction algorithms.

CT Reconstruction Algorithm and Low Contrast Detectability of Phantom Study: A Systematic Review and Meta-Analysis.

For almost three decades, computed tomography (CT) has been extensively used in medical diagnosis, which led researchers to conduct linking of CT dose exposure with image quality. In this study, a systematic review and a meta-analysis study were conducted on CT phantom for resolution study especially based on the low contrast detectability (LCD). Furthermore, the association between the CT parameter such as tube voltage and the type of reconstruction algorithm, the amount of phantom scanning affecting the image quality and the exposure dose were also investigated in this study. We utilize PubMed, ScienceDirect, Google Scholar and Scopus databases to search related published articles from the year 2011 until 2020. The notable keywords comprise "computed tomography", "CT phantom", and "low contrast detectability". Of 52 articles, 20 articles are within the inclusion criteria in this systematic review. The dichotomous outcomes were chosen to represent the results in terms of risk ratio as per meta-analysis study. Notably, the noise in iterative reconstruction (IR) reduced by 24%, 33% and 36% with the use of smooth, medium and sharp filters, respectively. Furthermore, adaptive iterative dose reduction (AIDR 3D) improved image quality and the visibility of smaller less dense objects compared to filtered back-projection. Most of the researchers used 120 kVp tube voltage to scan phantom for quality assurance study. Hence, optimizing primary factors such as tube potential reduces the dose exposure significantly, and the optimized IR technique could substantially reduce the radiation dose while maintaining the image quality.

Initial Clinical Images From a Second-Generation Prototype Silicon-Based Photon-Counting Computed Tomography System

To demonstrate the feasibility andpotential of using a second-generation prototype photon-counting computed tomography (CT) system to provide simultaneous high spatial resolution images and high spectral resolution material information across a range of routine imaging tasks using clinical patient exposure levels. The photon-counting system employs an innovative silicon-based photon-counting detector to provide a balanced approach to ultra-high-resolution spectral CT imaging. An initial cohort of volunteer subjects was imaged using the prototype photon-counting system. Acquisition technique parameters and radiation dose exposures were guided by routine clinical exposure levels used at the institution. Images were reconstructed in native slice thickness using an early version of a spectral CT reconstruction algorithm Samples of images across a range of clinical tasks were selected and presented for review. Clinical cases are presented across inner ear, carotid angiography, chest, and musculoskeletal imaging tasks. Initial reconstructed images illustrate ultra-high spatial resolution imaging. The fine detail of small structures and pathologies is clearly visualized, and structural boundaries are well delineated. The prototype system additionally provides concomitant spectral information with high spatial resolution. This initial study demonstrates that routine imaging at clinically appropriate patient exposure levels is feasible using a novel deep-silicon photon-counting detector CT system. Furthermore, a deep-silicon detector may provide a balanced approach to photon-counting CT, providing high spatial resolution imaging with simultaneous high-fidelity spectral information.

Dose-efficiency quantification of computed tomography systems using a model-observer.

Recent advances in computed tomography (CT) technology have considerably improved the quality of CT images and reduced radiation exposure in patients. At present, however, there is no generally accepted figure of merit (FOM) for comparing the dose efficiencies of CT systems. (i) To establish an FOM that characterizes the quality of CT images in relation to the radiation dose by means of a mathematical model observer and (ii) to evaluate the new FOM on different CT systems and image reconstruction algorithms. Images of a homogeneous phantom with four low-contrast inserts were acquired using three different CT systems at three dose levels and a representative protocol for CT imaging of low-contrast objects in the abdomen. The images were reconstructed using filtered-back projection and iterative algorithms. A channelized hotelling observer with difference-of-Gaussian channels was applied to compute the detectability ( ). This was done for each insert and each of the considered imaging conditions from square regions of interest (ROIs) that were (semi-)automatically centered on the inserts. The estimated detectabilities ( ) were averaged in the first step over the three dose levels ( ), and subsequently over the four contrast inserts ( ). All calculation steps included a dedicated assessment of the related uncertainties following accepted metrological guidelines. The determined detectabilities ( ) varied considerably with the contrast and diameter of the four inserts, as well as with the radiation doses and reconstruction algorithms used for image generation ( =1.3-5.5). Thus, the specification of a single detectability as an FOM is not well suited for comprehensively characterizing the dose efficiency of a CT system. A more comprehensive and robust characterization was provided by the averaged detectabilities and, in particular, . Our analysis reveals that the model observer analysis is very sensitive to the exact position of the ROIs. The presented automatable software approach yielded with the weighted detectability an objective FOM to benchmark different CT systems and reconstruction algorithms in a robust and reliable manner. An essential advantage of the proposed model-observer approach is that uncertainties in the FOM can be provided, which is an indispensable prerequisite for type testing.

Adaptive-weighted high order TV algorithm for sparse-view CT reconstruction.

With the development of low-dose computed tomography (CT), incomplete data reconstruction has been widely concerned. The total variation (TV) minimization algorithm can accurately reconstruct images from sparse or noisy data. However, the traditional TV algorithm ignores the direction of structures in images, leading to the loss of edge information and block artifacts when the object is not piecewise constant. Since the anisotropic information can facilitate preserving the edge and detail information in images, we aim to improve the TV algorithm in terms of reconstruction accuracy via this approach. In this paper, we propose an adaptive-weighted high order total variation (awHOTV) algorithm. We construct the second order TV-norm using the second order gradient, adapt the anisotropic edge property between neighboring image pixels, adjust the local image-intensity gradient to keep edge information, and design the corresponding Chambolle-Pock (CP) solving algorithm. Implementing the proposed algorithm, comprehensive studies are conducted in the ideal projection data experiment where the Structural similarity (SSIM), Root Mean Square Error (RMSE), Contrast to noise ratio (CNR), and modulation transform function (MTF) curves are utilized to evaluate the quality of reconstructed images in statism, structure, spatial resolution, and contrast, respectively. In the noisy data experiment, we further use the noise power spectrum (NPS) curve to evaluate the reconstructed images and compare it with other three algorithms. We use the 2D slice in the XCAT phantom, 2D slice in TCIA Challenge data and FORBILD phantom as simulation phantoms and use real bird data for real verification. The results show that, compared with the traditional TV and FBP algorithms, the awHOTV has better performance in terms of RMSE, SSIM, and Pearson correlation coefficient (PCC) under the projected data with different sparsity. In addition, the awHOTV algorithm is robust against the noise of different intensities. The proposed awHOTV method can reconstruct the images with high accuracy under sparse or noisy data. The awHOTV solves the strip artifacts caused by sparse data in the FBP method. Compared with the TV method, the awHOTV can effectively suppress block artifacts and has good detail protectionability.

Spectral CT reconstruction via low-rank representation and structure preserving regularization

Objective: With the development of computed tomography (CT) imaging technology, it is possible to acquire multi-energy data by spectral CT. Being different from conventional CT, the X-ray energy spectrum of spectral CT is cut into several narrow bins which leads to the result that only a part of photon can be collected in each individual energy channel.This can severely degrade the image qualities. To address this problem, we propose a spectral CT reconstruction algorithm based on low-rank representation and structure preserving regularization in this paper. Approach: To make full use of the prior knowledge about both the inter-channel correlation and the sparsity in gradient domain of inner-channel data, this paper combines a low-rank correlation descriptor with a structure extraction operator as priori regularization terms for spectral CT reconstruction. Furthermore, a split-Bregman based iterative algorithm is developed to solve the reconstruction model. Finally, we propose a multi-channel adaptive parameters generation strategy according to CT values of each individual energy channel. Main results: Experimental results on numerical simulations and real mouse data indicate that the proposed algorithm achieves higher accuracy on both reconstruction and material decomposition than the methods based on simultaneous algebraic reconstruction technique (SART), total variation minimization (TVM), total variation with low-rank (LRTV), and spatial-spectral cube matching frame (SSCMF). Compared with SART, our algorithm improves the feature similarity (FSIM) by 40.4% on average for numerical simulation reconstruction, whereas TVM, LRTV, and SSCMF correspond to 26.1%, 28.2%, and 29.5%, respectively. Significance: We outline a multi-channel reconstruction algorithm tailored for spectral CT. The qualitative and quantitative comparisons present a significant improvement of image quality, indicating its promising potential in spectral CT imaging.

Projection Matrix Based Iterative Reconstruction Algorithm for Robotic CT

Robotic computed tomography (CT) consists of two separate manipulators which carry the source and detector, respectively. Various errors occur in motion due to lack of rigid connection, which may change scanning trajectory of this system. Some methods aimed at recovering geometry relation have occurred. However, these methods are relied on circular scanning trajectory. Projection matrix is the matrix form of geometry definition, which contains geometric parameters in the matrix and transforms geometry calculation into homogeneous matrix calculation. It is suitable for robotic CT because of its irrelevance between angles. Besides, iteration reconstruction algorithm makes no assumptions on scanning trajectories, which is suitable for arbitrary scanning trajectories. Hence, this study combines iteration reconstruction algorithm with the concept of projection matrix to fill the gap. In this study, we established a transformation between projection matrix and ray function, and construct corresponding projection model. Projection matrix based iterative reconstruction algorithm was then developed and validated by simulation and experiment. Uncooperative rotation of manipulators, jitter in motion and non-circular motion trajectories were simulated. FDK, projection matrix-based FDK and projection matrix-based iterative method were compared. Real experiments were also conducted. The results showed projection matrix based iterative reconstruction could lead to better image quality against motion error. In summary, this study provided a high-precision reconstruction approach for robotic CT.

Dual Plug and Play Multi-Energy Computed Tomography Reconstruction Algorithm

Dual Plug and Play Multi-Energy Computed Tomography Reconstruction Algorithm

A new limited-angle CT reconstruction algorithm based on the local anisotropic total variation restoration of continuity

A new limited-angle computed tomography(CT) reconstruction algorithm based on local detail information restoration in the CT image projection domain was proposed in this paper. The lost projection information could be repaired with the new algorithm called local anisotropic total variation repair method of sinogram (LATV). According to the continuity of local gradient in sinogram, local high frequency characteristics can be improved which are blurred by conventional TVM. A series of numerical reconstruction experiments were conducted to validate the new algorithm. The overall quality of images reconstructed by new algorithm was better than conventional TVM algorithm when quantitatively evaluated. Then, a local region with rich details was selected in order to evaluate the reconstruction effect of the internal details. From the perspective of three image quality evaluation parameters, it was shown that the local high frequency details were well repaired. Finally, the reconstruction quality was evaluated by actual CT experimental data. The new method also performed better than conventional method. Therefore, the new LATV algorithm proposed in this paper may be a new method suitable for limited-angle CT reconstruction problems.

Implications of computed tomography reconstruction algorithms on coronary atheroma quantification: Comparison with intravascular ultrasound

BackgroundAdvances in coronary computed tomography angiography (CCTA) reconstruction algorithms are expected to enhance the accuracy of CCTA plaque quantification. We aim to evaluate different CCTA reconstruction approaches in assessing vessel characteristics in coronary atheroma using intravascular ultrasound (IVUS) as the reference standard. MethodsMatched cross-sections (n ​= ​7241) from 50 vessels in 15 participants with chronic coronary syndrome who prospectively underwent CCTA and 3-vessel near-infrared spectroscopy-IVUS were included. Twelve CCTA datasets per patient were reconstructed using two different kernels, two slice thicknesses (0.75 ​mm and 0.50 ​mm) and three different strengths of advanced model-based iterative reconstruction (IR) algorithms. Lumen and vessel wall borders were manually annotated in every IVUS and CCTA cross-section which were co-registered using dedicated software. Image quality was sub-optimal in the reconstructions with a sharper kernel, so these were excluded. Intraclass correlation coefficient (ICC) and repeatability coefficient (RC) were used to compare the estimations of the 6 CT reconstruction approaches with those derived by IVUS. ResultsSegment-level analysis showed good agreement between CCTA and IVUS for assessing atheroma volume with approach 0.50/5 (slice thickness 0.50 ​mm and highest strength 5 ADMIRE IR) being the best (total atheroma volume ICC: 0.91, RC: 0.67, p ​< ​0.001 and percentage atheroma volume ICC: 0.64, RC: 14.06, p ​< ​0.001). At lesion-level, there was no difference between the CCTA reconstructions for detecting plaques (accuracy range: 0.64–0.67; p ​= ​0.23); however, approach 0.50/5 was superior in assessing IVUS-derived lesion characteristics associated with plaque vulnerability (minimum lumen area ICC: 0.64, RC: 1.31, p ​< ​0.001 and plaque burden ICC: 0.45, RC: 32.0, p ​< ​0.001). ConclusionCCTA reconstruction with thinner slice thickness, smooth kernel and highest strength advanced IR enabled more accurate quantification of the lumen and plaque at a segment-, and lesion-level analysis in coronary atheroma when validated against intravascular ultrasound. Clinicaltrials.gov (NCT03556644)

Effectiveness of orthopedic metal artifact reduction among patients undergoing computed tomography at a tertiary setting – A cross-sectional study

Background: In computed tomography (CT), metal implants increase the inconsistencies between the measured data and the linear assumption of the radon transform made by the analytic CT reconstruction algorithm. Aims and Objectives: The aims of this study was to evaluate the effectiveness of orthopedic metal artifact reduction (O-MAR) among patients undergoing CT at a tertiary setting. Materials and Methods: A cross-sectional observational study was conducted on 30 subjects attending the department of radiology for CT scan for 1 year from December 2018 to 2019. The CT scan was performed in 64 detector row (PHILIPS INGENUITY CORE 128 slice) scanner. The patient was asked for the presence of metallic objects/history of implants, and the topo gram confirms it. Image noise was considered as the primary outcome variable. The statistical analysis was performed using the coGuide software and P&lt;0.005. Results: All the thirty participants were analyzed finally. Image noise in plain image type was 79.5 SD of Hounsfield units, and in O-MAR image type, it was 44.01 SD of Hounsfield units denoting a higher percentage of SD in plain than O-MAR images. Conclusion: The O-MAR application helped in improving image quality of CT for patients in the aspect of metal artifact reduction and soft-tissue profile. However, it can also improve diagnostic quality in the evaluation of patients with severe metallic artifacts by decreasing the negative impacts of orthopedic metals.

Impact of an artificial intelligence deep-learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study.

BackgroundRecently, computed tomography (CT) manufacturers have developed deep‐learning‐based reconstruction algorithms to compensate for the limitations of iterative reconstruction (IR) algorithms, such as image smoothing and the spatial resolution's dependence on contrast and dose levels.PurposeTo assess the impact of an artificial intelligence deep‐learning reconstruction (AI‐DLR) algorithm on image quality and dose reduction compared with a hybrid IR algorithm in chest CT for different clinical indications.MethodsAcquisitions on the CT American College of Radiology (ACR) 464 and CT Torso CTU‐41 phantoms were performed at five dose levels (CTDIvol: 9.5/7.5/6/2.5/0.4 mGy) used for chest CT conditions. Raw data were reconstructed using filtered backprojection, two levels of IR (iDose4 levels 4 (i4) and 7 (i7)), and five levels of AI‐DLR (Precise Image; Smoother, Smooth, Standard, Sharp, Sharper). Noise power spectrum (NPS), task‐based transfer function, and detectability index (d′) were computed: d′‐modeled detection of a soft tissue mediastinal nodule (low‐contrast soft tissue chest nodule within the mediastinum [LCN]), ground‐glass opacity (GGO), or high‐contrast pulmonary (HCP) lesion. The subjective image quality of chest anthropomorphic phantom images was independently evaluated by two radiologists. They assessed image noise, image smoothing, contrast between vessels and fat in the mediastinum for mediastinal images, visual border detection between bronchus and lung parenchyma for parenchymal images, and overall image quality using a commonly used four‐ or five‐point scale.ResultsFrom Standard to Smoother levels, on average, the noise magnitude decreased (for all dose levels: −66.3% ± 0.5% for mediastinal images and −63.1% ± 0.1% for parenchymal images), the average NPS spatial frequency decreased (for all dose levels: −35.3% ± 2.2% for mediastinal images and −13.3% ± 2.2% for parenchymal images), and the detectability (d′) of the three lesions increased. The opposite pattern was found from Standard to Sharper levels. From Smoother to Sharper levels, the spatial resolution increased for the low‐contrast polyethylene insert and the opposite for the high‐contrast air insert. Compared to the i4 used in clinical practice, d′ values were higher using Smoother (mean for all dose levels: 338.7% ± 29.4%), Smooth (103.4% ± 11.2%), and Standard (34.1% ± 6.6%) levels for the LCN on mediastinal images and Smoother (169.5% ± 53.2% for GGO and 136.9% ± 1.6% for HCP) and Smooth (36.4% ± 22.1% and 24.1% ± 0.9%, respectively) levels for parenchymal images. Radiologists considered the images satisfactory for clinical use at these levels, but adaptation to the dose level of the protocol is required.ConclusionWith AI‐DLR, the smoothest levels reduced the noise and improved the detectability of chest lesions but increased the image smoothing. The opposite was found with the sharpest levels. The choice of level depends on the dose level and type of image: mediastinal or parenchymal.