Computed Tomography Algorithm Research Articles

Evaluation of a prototype metal artifact reduction algorithm for cone beam CT in patients undergoing radioembolization

Metal artifacts notoriously pose significant challenge in computed tomography (CT), leading to inaccuracies in image formation and interpretation. Artifact reduction tools have been designed to improve cone beam computed tomography (CBCT) image quality by reducing artifacts caused by certain high-density materials. Metal artifact reduction (MAR) tools are specific algorithms that are applied during image reconstruction to minimize or eliminate artifacts degrading CBCT images. The purpose of the study is to evaluate the effect of a MAR algorithm on image quality in CBCT performed for evaluating patients before transarterial radioembolization (TARE). We retrospectively included 40 consecutive patients (aged 65 ± 13 years; 23 males) who underwent 45 CBCT examinations (Allura FD 20, XperCT Roll protocol, Philips Healthcare, Best, The Netherlands) in the setting of evaluation for TARE between January 2017 and December 2018. Artifacts caused by coils, catheters, and surgical clips were scored subjectively by four readers on a 5-point scale (1 = artifacts affecting diagnostic information to 5 = no artifacts) using a side-by-side display of uncorrected and MAR-corrected images. In addition, readers scored tumor visibility and vessel discrimination. MAR-corrected images were assigned higher scores, indicating better image quality. The differences between the measurements with and without MAR were most impressive for coils with a mean improvement of 1.6 points (95%CI [1.5 1.8]) on the 5-point likert scale, followed by catheters 1.4 points (95%CI [1.3 1.5]) and clips 0.7 points (95%CI [0.3 1.1]). Improvements for other artifact sources were consistent but relatively small (below 0.25 points on average). Interrater agreement was good to perfect (Kendall’s W coefficient = 0.68–0.95) and was higher for MAR-corrected images, indicating that MAR improves diagnostic accuracy. A metal artifact reduction algorithm can improve diagnostic and interventional accuracy of cone beam CT in patients undergoing radioembolization by reducing artifacts caused by diagnostic catheters and coils, lowering interference of metal artifacts with adjacent major structures, and improving tumor visibility.

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.

Advanced hybrid attention-based deep learning network with heuristic algorithm for adaptive CT and PET image fusion in lung cancer detection

Lung cancer is one of the most deadly diseases in the world. Lung cancer detection can save the patient's life. Despite being the best imaging tool in the medical sector, clinicians find it challenging to interpret and detect cancer from Computed Tomography (CT) scan data. One of the most effective ways for the diagnosis of certain malignancies like lung tumours is Positron Emission Tomography (PET) imaging. So many diagnosis models have been implemented nowadays to diagnose various diseases. Early lung cancer identification is very important for predicting the severity level of lung cancer in cancer patients. To explore the effective model, an image fusion-based detection model is proposed for lung cancer detection using an improved heuristic algorithm of the deep learning model. Firstly, the PET and CT images are gathered from the internet. Further, these two collected images are fused for further process by using the Adaptive Dilated Convolution Neural Network (AD-CNN), in which the hyperparameters are tuned by the Modified Initial Velocity-based Capuchin Search Algorithm (MIV-CapSA). Subsequently, the abnormal regions are segmented by influencing the TransUnet3+. Finally, the segmented images are fed into the Hybrid Attention-based Deep Networks (HADN) model, encompassed with Mobilenet and Shufflenet. Therefore, the effectiveness of the novel detection model is analyzed using various metrics compared with traditional approaches. At last, the outcome evinces that it aids in early basic detection to treat the patients effectively.

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.

Natural Language Processing Algorithm Used for Staging Pulmonary Oncology from Free-Text Radiological Reports: “Including PET-CT and Validation Towards Clinical Use”

Natural language processing (NLP) can be used to process and structure free text, such as (free text) radiological reports. In radiology, it is important that reports are complete and accurate for clinical staging of, for instance, pulmonary oncology. A computed tomography (CT) or positron emission tomography (PET)-CT scan is of great importance in tumor staging, and NLP may be of additional value to the radiological report when used in the staging process as it may be able to extract the T and N stage of the 8th tumor–node–metastasis (TNM) classification system. The purpose of this study is to evaluate a new TN algorithm (TN-PET-CT) by adding a layer of metabolic activity to an already existing rule-based NLP algorithm (TN-CT). This new TN-PET-CT algorithm is capable of staging chest CT examinations as well as PET-CT scans. The study design made it possible to perform a subgroup analysis to test the external validation of the prior TN-CT algorithm. For information extraction and matching, pyContextNLP, SpaCy, and regular expressions were used. Overall TN accuracy score of the TN-PET-CT algorithm was 0.73 and 0.62 in the training and validation set (N = 63, N = 100). The external validation of the TN-CT classifier (N = 65) was 0.72. Overall, it is possible to adjust the TN-CT algorithm into a TN-PET-CT algorithm. However, outcomes highly depend on the accuracy of the report, the used vocabulary, and its context to express, for example, uncertainty. This is true for both the adjusted PET-CT algorithm and for the CT algorithm when applied in another hospital.

Dual-energy micro-focus computed tomography based on the energy-angle correlation of inverse Compton scattering source.

Inverse Compton scattering (ICS) source can produce quasi-monoenergetic micro-focus X-rays ranging from keV to MeV level, with potential applications in the field of high-resolution computed tomography (CT) imaging. ICS source has an energy-angle correlated feature that lower photon energy is obtained at larger emission angle, thus different photon energies are inherently contained in each ICS pulse, which is especially advantageous for dual- or multi-energy CT imaging. This study proposes a dual-energy micro-focus CT scheme based on the energy-angle correlation of ICS source and tests its function using numerical simulations. In this scheme, high- and low-energy regions are chosen over the angular direction of each ICS pulse, and dual-energy projections of the object are obtained by an angularly-splicing scanning method. The field-of-view (FOV) of ICS source is extended simultaneously through this scanning method, thus the scale of the imaging system can be efficiently reduced. A dedicated dual-energy CT algorithm is developed to reconstruct the monoenergetic attenuation coefficients, electron density, and effective atomic number distributions of the object. A test object composed of different materials (carbon, aluminium, titanium, iron and copper) and line pairs with different widths (15/24/39/60 μm) is imaged by the proposed dual-energy CT scheme using numerical simulations, and high-fidelity monoenergetic attenuation coefficient, electron density, and effective atomic number distributions are obtained. All the line pairs are well identified, and the contrast ratio of the 15 μm lines is 22%, showing good accordance with the theoretical predictions. The proposed dual-energy CT scheme can reconstruct fine inner structures and material compositions of the object simultaneously, opening a new possibility for the application of ICS source in the field of non-destructive testing.

Fuel temperature and injection pressure influence on the cold start GDI sprays

Cold start in gasoline direct injection engines (GDI) is a critical issue that significantly impacts fuel consumption and emissions. Therefore, it is essential to investigate and improve the spray and air-fuel mixing processes during cold starts. This study employed a complimentary set of optical diagnostic techniques, including line-of-sight(extinction, Schlieren, and long-distance microscopy) and 3D computed tomography (CT), to characterize and understand the cold-start spray dynamics under various fuel temperature and injection pressure conditions. The experiments were conducted in a constant volume spray vessel and the fuel temperature was varied using a coolant circulator, with temperatures reaching as low as -7 °C to simulate cold-start conditions. The cold fuel exhibited longer liquid/vapor penetration lengths compared to hot fuel under low injection pressure conditions. This deterioration in spray characteristics was attributed to the attenuated fuel evaporation and reduced entrainment of ambient air. The 3D spray visualization obtained through the CT algorithm, particularly the cut plane images, revealed that plumes with low fuel temperatures had narrower individual plume widths, resulting in minimized plume-to-plume interaction. Microscopic imaging further confirmed this observation which showed separate plumes in the near-nozzle region for cold fuel conditions. Meanwhile, hot fuel under high injection pressure conditions exhibited complete plume collapsing, leading to a significant amount of liquid fuel remaining in the spray core. The liquid penetration reached 70 mm during the injection period, potentially can cause wall wetting on the piston top or cylinder wall. Based on the experimental findings, this study suggests the application of multiple injections with a moderate level of injection pressure for optimized engine performance and reduced emissions during cold starts.

Volumetric flame reconstructions in an optical engine cylinder involving refraction and blockage

Practical applications of computed tomography (CT) in optical engines require an advanced algorithm that can correct the light refraction via optical windows and reconstruct the 3D signal field partially blocked by structural obstacles. In this work, an advanced CT algorithm is designed for optical engines to simultaneously eliminate the imaging distortion by refraction and diminish the reconstruction errors using partial signal blocking. By combining the pinhole model and Snell’s law, the ray tracings from discretized 3D voxels in the measurement domain to 2D pixels in the imaging planes are accurately calculated, thus restoring the distortion in recorded projections. Besides, by deciding the locations and numbers of voxels that actually participate in iterative CT calculation, the iterative update process of voxel intensity becomes independent of the blocked rays, reducing the reconstruction errors. The algorithm is then numerically validated by reconstructing a simulated signal phantom inside an optical cylinder with a lightproof obstacle between the phantom and a recording camera, which imitates the refraction and blocking conditions in practical optical engines. Moreover, experimental demonstration is performed by reconstructing practical premixed flames inside optical engines. Both the simulation and the experiment present significantly enhanced flame chemiluminescence reconstruction by applying the optimized CT algorithm compared to the original algorithm utilized in open space applications.

Development and validation of a CT algorithm for the identification of nonperforated duodenal bulb ulcer

PurposeTo assess the value of multiplanar computed tomography (CT) in the diagnosis of nonperforated duodenal bulb ulcer (NPDBU). MethodWe retrospectively analyzed data from 135 patients with NPDBU (ulcer group) and 150 patients with a normal duodenal bulb (control group) who underwent contrast-enhanced abdominal CT and were diagnosed via upper endoscopy from January 2018 to February 2022. The clinical and CT features were compared between the two groups. Independent prognostic factors for diagnosing NPDBU were determined using binary logistic regression analysis. An external validation cohort to determine the model’s efficiency comprised 80 patients from another center. ResultsGastrointestinal bleeding was more frequent in patients with NPDBU than in those without (p < 0.001). No significant differences in age and sex were observed between the groups (all p > 0.05). The duodenal bulbar wall was significantly thicker in the ulcer group than in the control group, as determined using CT (p < 0.001). Irregular mucosal surface, layered enhancement, and blurred fat space around the duodenal bulb were more common in the ulcer group than in the control group (all p < 0.001). Binary logistic regression analysis revealed that gastrointestinal bleeding, wall thickness of ≥ 4.85 mm, irregular mucosal surface, and blurred peripheral fat space were the most significant variations associated with NPDBU, with an area under the curve (AUC) of 0.974. The external validation cohort had an AUC of 0.916. ConclusionsCareful multiplanar CT interpretation suggests the underlying presence of NPDBU and allows timely endoscopic verification and appropriate treatment.

Spatial resolution, noise properties, and detectability index of a deep learning reconstruction algorithm for dual-energy CT of the abdomen.

Iterative reconstruction (IR) has increasingly replaced traditional reconstruction methods in computed tomography (CT). The next paradigm shift in image reconstruction is likely to come from artificial intelligence, with deep learning reconstruction (DLR) solutions already entering the clinic. An enduring disadvantage to IR has been a change in noise texture, which can affect diagnostic confidence. DLR has demonstrated the potential to overcome this issue and has recently become available for dual-energy CT. To evaluate the spatial resolution, noise properties, and detectability index of a commercially available DLR algorithm for dual-energy CT of the abdomen and compare it to single-energy (SE) CT. An oval 25cmx35cm custom-made phantom was scanned on a GE Revolution CT scanner (GE Healthcare, Waukesha, WI) at two dose levels (13 and 5mGy) and two iodine concentrations (8 and 2mg/mL), using three typical abdominal scan protocols: dual-energy (DE), SE 80kV (SE-80kV) and SE 120kV (SE-120kV). Reconstructions were performed with three strengths of IR (ASiR-V: AR0%, AR50%, AR100%) and three strengths of DLR (TrueFidelity: low, medium, high). The DE acquisitions were reconstructed as mono-energetic images between 40 and 80keV. The noise power spectrum (NPS), task transfer function (TTF), and detectability index (d') were determined for the reconstructions following the recommendations of AAPM Task Group 233. Noise magnitude reductions (relative to AR0%) for the SE protocols were on average (-29%, -21%) for (AR50%, TF-M), while for DE-70keV were (-28%, -43%). There was less reduction in mean frequency (fav ) for DLR than for IR, with similar results for SE and DE imaging. There was, however, a substantial change in the NPS shape when using DE with DLR, quantifiable by a marked reduction in the peak frequency (fpeak ) that was absent in SE mode. All protocols and reconstructions (including AR0%) exhibited slight to moderate shifts towards lower spatial frequencies at the lower dose (<12% in fav ). Spatial resolution was consistently superior for DLR compared to IR for SE but not for DE. All protocols and reconstructions (including AR0%) showed decreased resolution with reduced dose and iodine concentration, with less decrease for DLR compared to IR. DLR displayed a higher d' than IR. The effect of energy was large: d' increased with lower keV, and SE-80kV had higher d' than SE-120kV. Using DE with DLR could provide higher d' than SE-80kV at the higher dose but not at lower dose. DE imaging with DLR maintained spatial resolution and reduced noise magnitude while displaying less change in noise texture than IR. The d' was also higher with DLR than IR, suggesting superiority in detectability of iodinated contrast. Despite these trends being consistent with those previously established for SE imaging, there were some noteworthy differences. For DE imaging there was no improvement in resolution compared to IR and a change in noise texture. DE imaging with low keV and DLR had superior detectability to SE DLR at the high dose but was not better than SE-80kV at low dose.

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.

SAMCOR: A robust and precise co-registration algorithm for brain CT and MR imaging

ObjectiveCurrent methods for co-registration of computed tomography (CT) and magnetic resonance (MR) brain imaging employ optimization-based cost functions of advanced statistical metrics to align datasets (e.g. mutual information, MI). Yet the inherent differences between these modalities still lead to occasional failures of registration. To address these limitations, we developed a novel approached termed sequence adaptive multimodal co-registration (SAMCOR), which is robust to intensity, feature, and scanner-based differences. MethodsSAMCOR was validated using 152 CT and MRI datasets from subjects implanted with intracranial electrodes for evaluation of medically-refractory epilepsy. Alignment outcomes were compared against five current open-source co-registration algorithms and a widely used commercial neurosurgical navigation system and classified as aligned/not-aligned relative to manually co-registered datasets. ResultsSAMCOR was the only method to align all 152 datasets (100%), while success rates for existing algorithms ranged from 3% to 82%. Given the time-intensive nature of manual verification, we further developed a metric to automatically quantify co-registration accuracy using Dice Similarity Coefficients (DSC). We applied a binary classification analysis to evaluate this DSC metric performance against traditional mutual information (MI) metrics for predicting alignment outcomes. The DSC metric demonstrated significantly higher positive and negative predictive values (0.93 and 0.97, respectively) than MI (0.79 and 0.87, respectively). The DSC classifier accuracy was 94.7% (vs 83.2% for MI). ConclusionsOur novel approaches for CT-MRI co-registration and quantitative alignment outcome analysis demonstrate significant advancements of the state-of-the-art for intermodal image co-registration with clinical relevance in patients with implanted intracranial devices.

Dual-domain sparse-view CT reconstruction with Transformers

Purpose:Computed Tomography (CT) has been widely used in the medical field. Sparse-view CT is an effective and feasible method to reduce the radiation dose. However, the conventional filtered back projection (FBP) algorithm will suffer from severe artifacts in sparse-view CT. Iterative reconstruction algorithms have been adopted to remove artifacts, but they are time-consuming due to repeated projection and back projection and may cause blocky effects. To overcome the difficulty in sparse-view CT, we proposed a dual-domain sparse-view CT algorithm CT Transformer (CTTR) and paid attention to sinogram information. Methods:CTTR treats sinograms as sentences and enhances reconstructed images with sinogram’s characteristics. We qualitatively evaluate the CTTR, an iterative method TVM-POCS, a convolutional neural network based method FBPConvNet in terms of a reduction in artifacts and a preservation of details. Besides, we also quantitatively evaluate these methods in terms of RMSE, PSNR and SSIM. Results:We evaluate our method on the Lung Image Database Consortium image collection with different numbers of projection views and noise levels. Experiment studies show that, compared with other methods, CTTR can reduce more artifacts and preserve more details on various scenarios. Specifically, CTTR improves the FBPConvNet performance of PSNR by 0.76dB with 30 projections. Conclusions:The performance of our proposed CTTR is better than the method based on CNN in the case of extremely sparse views both on visual results and quantitative evaluation. Our proposed method provides a new idea for the application of Transformers to CT image processing.

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.

Development of an anthropomorphic multimodality pelvic phantom for quantitative evaluation of a deep-learning-based synthetic computed tomography generation technique.

PurposeThe objective of this study was to fabricate an anthropomorphic multimodality pelvic phantom to evaluate a deep‐learning‐based synthetic computed tomography (CT) algorithm for magnetic resonance (MR)‐only radiotherapy.MethodsPolyurethane‐based and silicone‐based materials with various silicone oil concentrations were scanned using 0.35 T MR and CT scanner to determine the tissue surrogate. Five tissue surrogates were determined by comparing the organ intensity with patient CT and MR images. Patient‐specific organ modeling for three‐dimensional printing was performed by manually delineating the structures of interest. The phantom was finally fabricated by casting materials for each structure. For the quantitative evaluation, the mean and standard deviations were measured within the regions of interest on the MR, simulation CT (CTsim), and synthetic CT (CTsyn) images. Intensity‐modulated radiation therapy plans were generated to assess the impact of different electron density assignments on plan quality using CTsim and CTsyn. The dose calculation accuracy was investigated in terms of gamma analysis and dose‐volume histogram parameters.ResultsFor the prostate site, the mean MR intensities for the patient and phantom were 78.1 ± 13.8 and 86.5 ± 19.3, respectively. The mean intensity of the synthetic image was 30.9 Hounsfield unit (HU), which was comparable to that of the real CT phantom image. The original and synthetic CT intensities of the fat tissue in the phantom were −105.8 ± 4.9 HU and −107.8 ± 7.8 HU, respectively. For the target volume, the difference in D 95% was 0.32 Gy using CTsyn with respect to CTsim values. The V 65Gy values for the bladder in the plans using CTsim and CTsyn were 0.31% and 0.15%, respectively.ConclusionThis work demonstrated that the anthropomorphic phantom was physiologically and geometrically similar to the patient organs and was employed to quantitatively evaluate the deep‐learning‐based synthetic CT algorithm.

Comparison of a Deep Learning-Based Reconstruction Algorithm with Filtered Back Projection and Iterative Reconstruction Algorithms for Pediatric Abdominopelvic CT.

ObjectiveTo compare a deep learning-based reconstruction (DLR) algorithm for pediatric abdominopelvic computed tomography (CT) with filtered back projection (FBP) and iterative reconstruction (IR) algorithms.Materials and MethodsPost-contrast abdominopelvic CT scans obtained from 120 pediatric patients (mean age ± standard deviation, 8.7 ± 5.2 years; 60 males) between May 2020 and October 2020 were evaluated in this retrospective study. Images were reconstructed using FBP, a hybrid IR algorithm (ASiR-V) with blending factors of 50% and 100% (AV50 and AV100, respectively), and a DLR algorithm (TrueFidelity) with three strength levels (low, medium, and high). Noise power spectrum (NPS) and edge rise distance (ERD) were used to evaluate noise characteristics and spatial resolution, respectively. Image noise, edge definition, overall image quality, lesion detectability and conspicuity, and artifacts were qualitatively scored by two pediatric radiologists, and the scores of the two reviewers were averaged. A repeated-measures analysis of variance followed by the Bonferroni post-hoc test was used to compare NPS and ERD among the six reconstruction methods. The Friedman rank sum test followed by the Nemenyi-Wilcoxon-Wilcox all-pairs test was used to compare the results of the qualitative visual analysis among the six reconstruction methods.ResultsThe NPS noise magnitude of AV100 was significantly lower than that of the DLR, whereas the NPS peak of AV100 was significantly higher than that of the high- and medium-strength DLR (p < 0.001). The NPS average spatial frequencies were higher for DLR than for ASiR-V (p < 0.001). ERD was shorter with DLR than with ASiR-V and FBP (p < 0.001). Qualitative visual analysis revealed better overall image quality with high-strength DLR than with ASiR-V (p < 0.001).ConclusionFor pediatric abdominopelvic CT, the DLR algorithm may provide improved noise characteristics and better spatial resolution than the hybrid IR algorithm.

Potential of radiomics and artificial intelligence in myeloma imaging : Development of automatic, comprehensive, objective skeletal analyses from whole-body imaging data

Multiple myeloma can affect the complete skeleton, which makes whole-body imaging necessary. With the current assessment of these complex datasets by radiologists, only asmall part of the accessible information is assessed and reported. Depending on the question and availability, computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) is performed and the results are then visually examined by radiologists. Acombination of automatic skeletal segmentation using artificial intelligence and subsequent radiomics analysis of each individual bone have the potential to provide automatic, comprehensive, and objective skeletal analyses. Afew automatic skeletal segmentation algorithms for CT already show promising results. In addition, first studies indicate correlations between radiomics features of bone and bone marrow with established disease markers and therapy response. Artificial intelligence (AI) and radiomics algorithms for automatic skeletal analysis from whole-body imaging are currently in an early phase of development.

Diagnostic performance and image quality of deep learning image reconstruction (DLIR) on unenhanced low-dose abdominal CT for urolithiasis.

Patients with urolithiasis undergo radiation overexposure from computed tomography (CT) scans. Improvement of image reconstruction is necessary for radiation dose reduction. To evaluate a deep learning-based reconstruction algorithm for CT (DLIR) in the detection of urolithiasis at low-dose non-enhanced abdominopelvic CT. A total of 75 patients who underwent low-dose abdominopelvic CT for urolithiasis were retrospectively included. Each examination included three reconstructions: DLIR; filtered back projection (FBP); and hybrid iterative reconstruction (IR; ASiR-V 70%). Image quality was subjectively and objectively assessed using attenuation and noise measurements in order to calculate the signal-to-noise ratio (SNR), absolute contrast, and contrast-to-noise ratio (CNR). Attenuation of the largest stones were also compared. Detectability of urinary stones was assessed by two observers. Image noise was significantly reduced with DLIR: 7.2 versus 17 and 22 for ASiR-V 70% and FBP, respectively. Similarly, SNR and CNR were also higher compared to the standard reconstructions. When the structures had close attenuation values, contrast was lower with DLIR compared to ASiR-V. Attenuation of stones was also lowered in the DLIR series. Subjective image quality was significantly higher with DLIR. The detectability of all stones and stones >3 mm was excellent with DLIR for the two observers (intraclass correlation [ICC] = 0.93 vs. 0.96 and 0.95 vs. 0.99). For smaller stones (<3 mm), results were different (ICC = 0.77 vs. 0.86). For low-dose abdominopelvic CT, DLIR reconstruction exhibited image quality superior to ASiR-V and FBP as well as an excellent detection of urinary stones.

FREQUENCY RESPONSE AND DISTORTION PROPERTIES OF RECONSTRUCTION ALGORITHMS IN COMPUTED TOMOGRAPHY

Denoising reconstruction techniques can introduce nonlinear properties into computed tomography (CT) systems. These nonlinear algorithms introduce distortion which affects the assessment of the resolution of the system. The purpose of the present study was to decouple and investigate amplitude modulation and waveform distortion in reconstruction algorithms in CT. The methodology developed by Wells, J. R. and Dobbins, J. T. III [Frequency response and distortion properties of nonlinear image processing algorithms and the importance of imaging context. Med. Phys. 40, 091906 (2013)] was adapted to CT reconstruction algorithms. The CT simulating program ASTRA Toolbox© for MATLAB™ was used for the reconstruction of the sinusoidal wave functions. Filtered back projection and the simultaneous iterative reconstruction technique were investigated with simple nonlinear mechanisms: a median filter and a non-negative constraint, respectively. The native reconstruction algorithms were not free from nonlinear waveform distortion, however, none of the metrics showed any dependence on the contrast-to-noise ratio (CNR). Furthermore, the algorithms including nonlinear mechanisms showed a clear and specific CNR dependence, indicating the necessity for distortion analysis in nonlinear CT reconstruction.

QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications

As the COVID-19 pandemic impacts global populations, computed tomography (CT) lung imaging is being used in many countries to help manage patient care as well as to rapidly identify potentially useful quantitative COVID-19 CT imaging biomarkers. Quantitative COVID-19 CT imaging applications, typically based on computer vision modeling and artificial intelligence algorithms, include the potential for better methods to assess COVID-19 extent and severity, assist with differential diagnosis of COVID-19 versus other respiratory conditions, and predict disease trajectory. To help accelerate the development of robust quantitative imaging algorithms and tools, it is critical that CT imaging is obtained following best practices of the quantitative lung CT imaging community. Toward this end, the Radiological Society of North America's (RSNA) Quantitative Imaging Biomarkers Alliance (QIBA) CT Lung Density Profile Committee and CT Small Lung Nodule Profile Committee developed a set of best practices to guide clinical sites using quantitative imaging solutions and to accelerate the international development of quantitative CT algorithms for COVID-19. This guidance document provides quantitative CT lung imaging recommendations for COVID-19 CT imaging, including recommended CT image acquisition settings for contemporary CT scanners. Additional best practice guidance is provided on scientific publication reporting of quantitative CT imaging methods and the importance of contributing COVID-19 CT imaging datasets to open science research databases.