• Computed tomography angiography
• Coronary artery disease
• Image reconstruction
• Radiation dose

• Z210013 the Beijing Natural Science Foundation
• 82325026 the National Science Fund for Distinguished Young Scholars
• 2023-I2M-C&T-A-004 the CAMS Innovation Fund for Medical Science
• 2022-PUMCH B-027 the National High Level Hospital Clinical Research Funding

• artificial intelligence
• computed tomography
• deep learning
• image reconstruction
• tumor staging

• CT, reconstruction
• Deep learning
• Sparse-view
• Thoraco-abdominal scans

• Deep Learning
• Humans
• Tomography, X-Ray Computed/methods
• Image Processing, Computer-Assisted/methods
• Signal-To-Noise Ratio
• Algorithms
• Neural Networks, Computer
• Radiographic Image Interpretation, Computer-Assisted/methods

• Humans
• COVID-19/diagnostic imaging
• Male
• Female
• Tomography, X-Ray Computed/methods
• Middle Aged
• Retrospective Studies
• Aged
• Radiographic Image Interpretation, Computer-Assisted/methods
• Signal-To-Noise Ratio
• Adult
• Pneumonia, Viral/diagnostic imaging
• SARS-CoV-2
• Pandemics
• Lung/diagnostic imaging
• Coronavirus Infections/diagnostic imaging
• Aged, 80 and over
• Radiography, Thoracic/methods
• Betacoronavirus

• computer-assisted
• deep learning
• diagnostic imaging
• image processing
• lung
• multidetector computed tomography

• Abdominal aortic aneurysms
• Adaptive statistical iterative reconstruction
• Dual-energy computed tomography angiography
• Endoleak
• Endovascular aneurysm repair
• Image reconstruction, deep learning model
• Virtual monoenergetic images

• Humans
• Endoleak/diagnostic imaging
• Radiography, Dual-Energy Scanned Projection/methods
• Radiographic Image Interpretation, Computer-Assisted/methods
• Retrospective Studies
• Tomography, X-Ray Computed/methods
• Signal-To-Noise Ratio

• CT
• abdomen
• deep learning image reconstruction
• image quality
• iterative reconstruction
• radiation dose

• Humans
• Deep Learning
• Quality Improvement
• Retrospective Studies
• Radiation Dosage
• Radiographic Image Interpretation, Computer-Assisted/methods
• Tomography, X-Ray Computed/methods
• Image Processing, Computer-Assisted

• Dual-energy computed tomography
• Endovascular aortic aneurysm repair
• Iterative reconstructions
• Stent
• Virtual monoenergetic images

• Artificial intelligence
• CCTA
• Coronary computed tomography angiography
• Deep learning
• Image reconstruction

• cardiovascular risk
• coronary artery calcium score
• coronary artery disease
• image reconstruction

• Artificial intelligence iterative reconstruction
• PET/CT
• Standard-dose
• Ultra-low-dose

• Humans
• Prospective Studies
• Radiation Dosage
• Reference Values
• Retrospective Studies
• Software
• Tomography, X-Ray Computed/adverse effects
• Tomography, X-Ray Computed/methods

A rare and valuable Palaeolithic wooden point, presumably belonging to a hunting weapon, was found in the Ljubljanica River in Slovenia in 2008. In order to prevent complete decay, the waterlogged wooden artefact had to undergo conservation treatment, which usually involves some expected deformations of structure and shape. To investigate these changes, a series of surface-based 3D models of the artefact were created before, during and after the conservation process. Unfortunately, the surface-based 3D models were not sufficient to understand the internal processes inside the wooden artefact (cracks, cavities, fractures). Since some of the surface-based 3D models were taken with a microtomographic scanner, we decided to create a volumetric 3D model from the available 2D tomographic images. In order to have complete control and greater flexibility in creating the volumetric 3D model than is the case with commercial software, we decided to implement our own algorithm. In fact, two algorithms were implemented for the construction of surface-based 3D models and for the construction of volumetric 3D models, using (1) unsegmented 2D images CT and (2) segmented 2D images CT. The results were positive in comparison with commercial software and new information was obtained about the actual state and causes of the deformation of the artefact. Such models could be a valuable aid in the selection of appropriate conservation and restoration methods and techniques in cultural heritage research.

• Algorithms
• Artifacts
• Imaging, Three-Dimensional/methods
• Rivers
• Tomography, X-Ray Computed/methods

Radiation-related cancer risk is an object of concern in CT of trauma patients, as these represent a young population. Different radiation reducing methods, including iterative reconstruction (IR), and spilt bolus techniques have been introduced in the recent years in different large scale trauma centers.

To compare image quality in human cadaver exposed to thoracoabdominal computed tomography using IR and standard filtered back-projection (FBP) at different dose levels.

Ten cadavers were scanned at full dose and a dose reduction in CTDIvol of 5 mGy (low dose 1) and 7.5 mGy (low dose 2) on a Siemens Definition Flash 128-slice computed tomography scanner. Low dose images were reconstructed with FBP and Sinogram affirmed iterative reconstruction (SAFIRE) level 2 and 4. Quantitative image quality was analyzed by comparison of contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). Qualitative image quality was evaluated by use of visual grading regression (VGR) by four radiologists.

Readers preferred SAFIRE reconstructed images over FBP at a dose reduction of 40% (low dose 1) and 56% (low dose 2), with significant difference in overall impression of image quality. CNR and SNR showed significant improvement for images reconstructed with SAFIRE 2 and 4 compared to FBP at both low dose levels.

Iterative image reconstruction, SAFIRE 2 and 4, resulted in equal or improved image quality at a dose reduction of up to 56% compared to full dose FBP and may be used a strong radiation reduction tool in the young trauma population.

• X-ray
• computed tomography
• image processing
• iterative image reconstruction
• radiation dose

Noise power spectrum (NPS) is a commonly used performance metric to evaluate noise‐reduction techniques (NRT) in imaging systems. The images reconstructed with and without an NRT can be compared via their NPS to better understand the NRT's effects on image noise. However, when comparing NPSs, simple visual assessments or a comparison of NPS peaks or medians are often used. These assessments make it difficult to objectively evaluate the effect of noise reduction across all spatial frequencies. In this work, we propose a new noise reduction profile (NRP) to facilitate a more complete and objective evaluation of NPSs for a range of NRTs used specifically in computed tomography (CT).

The homogeneous section of the ACR or Catphan phantoms was scanned on different CT scanners equipped with the following NRTs: AIDR3D, AiCE, ASiR, ASiR‐V, TrueFidelity, iDose, SAFIRE, and ADMIRE. The images were then reconstructed with all strengths of each NRT in reference to the baseline filtered back projection (FBP) images. One set of the baseline FBP images was also processed with PixelShine, an NRT based on artificial intelligence. The NPSs of the images before and after noise reduction were calculated in both the xy‐plane and along the z‐direction. The difference in the logarithmic scale between each NPS (baseline FBP and NRT) was then calculated and deemed the NRP. Furthermore, the relationship between the NRP and NPS peak positions was mathematically analyzed.

Each NRT has its own unique NRP. By comparing the NPS and NRP for each NRT, it was found that NRP is related to the peak shift of NPS. Additionally, under the assumption that the NPS has one peak and is differentiable, a relationship was mathematically derived between the slope of the NRP at the peak position of the NPS before noise reduction and the shift of the NPS peak position after noise reduction.

A new metric, NRP, was proposed based on NPS to objectively evaluate and compare methods for noise reduction in CT. The NRP can be used to compare the effects of various NRTs on image noise in both the xy‐plane and z‐direction. It also enables unbiased assessment of the detailed noise reduction properties of each NRT over all relevant spatial frequencies.

• image reconstruction
• noise power spectrum
• noise reduction

• Coronary CT angiography
• Coronary artery disease
• Image reconstruction
• Model-based adaptive filter

• Adversarial training
• Coronary CT angiography
• Cycle consistency
• Low-dose CT
• Unsupervised learning
• Wavelet transform

• Algorithms
• Humans
• Image Processing, Computer-Assisted
• Neural Networks, Computer
• Signal-To-Noise Ratio
• Tomography, X-Ray Computed

• CT
• central frequency ratio
• noise magnitude ratio
• noise power spectrum
• noise reduction

• Computer tomography
• Contrast media
• Iodine
• Monoenergetic images
• Spectral imaging

• deep learning
• image quality
• protocol optimization
• reconstruction

To compare performance of visual and quantitative analyses for detecting myocardial ischaemia from single- and dual-energy computed tomography (CT) in patients with suspected coronary artery disease (CAD).

Eighty-four patients with suspected CAD were scheduled for dual-energy cardiac CT at rest (CTA) and pharmacological stress (CTP). Myocardial CT perfusion was analysed visually and using three parameters: mean attenuation density (MA), transmural perfusion ratio (TPR) and myocardial perfusion reserve index (MPRI), on both single-energy CT and CT-based iodine images. Significant CAD was defined in AHA-segments by concomitant myocardial hypoperfusion identified visually or quantitatively (parameter < threshold) and coronary stenosis detected by CTA. Single-photon emission CT and invasive coronary angiography were used as reference. Perfusion-parameter cut-off values were calculated in a randomly-selected subgroup of 30 patients.

The best-performing thresholds for TPR, MPRI and MA were 0.96, 23 and 0.5 for single-energy CT and 0.97, 47 and 0.3 for iodine imaging. For both CT-imaging modalities, TPR yielded the highest area under receiver operating characteristic curve (AUC) (0.99 and 0.97 for single-energy CT and iodine imaging, respectively, in vessel-based analysis) compared to visual analysis, MA and MPRI. Visual interpretation on iodine imaging resulted in higher AUC compared to that on single-energy CT in per-vessel (AUC: 0.93 vs 0.86, respectively) and per-patient (0.94 vs 0.93) analyses.

Transmural perfusion ratio on both CT-imaging modalities is the best-performing parameter for detecting myocardial ischaemia compared to visual method and other perfusion parameters. Visual analysis on CT-based iodine imaging outperforms that on single-energy CT.

• CT myocardial perfusion imaging
• Coronary artery disease
• Dual-energy CT-based iodine imaging
• Myocardial ischaemia
• Transmural perfusion ratio

• Adaptive statistical iterative reconstruction (ASiR)
• Computed tomography (CT)
• Noise power spectrum (NPS)
• Sagittal view
• Spine
• z-axis spatial resolution

• Convolutional neural network
• Coronary atherosclerotic stenosis
• Coronary computed tomographic angiography
• Deep learning

• Artificial Intelligence
• Coronary Angiography
• Coronary Artery Disease/diagnostic imaging
• Coronary Stenosis/diagnostic imaging
• Coronary Vessels
• Deep Learning
• Humans
• Predictive Value of Tests
• Retrospective Studies
• Tomography, X-Ray Computed

To systematically evaluate the influence of acquisition settings in conjunction with raw-data based iterative image reconstruction (IR) on lung densitometry based on multi-row detector computed tomography (CT) in an anthropomorphic chest phantom.

Ten porcine heart-lung explants were mounted in an ex vivo chest phantom shell, six with highly and four with low attenuating chest wall. CT (Somatom Definition Flash, Siemens Healthineers) was performed at 120kV_p and 80kV_p, each combined with current-time products of 120, 60, 30, and 12mAs, and was reconstructed with filtered back projection (FBP) and IR (Safire, Siemens Healthineers). Mean lung density (LD), air density (AD) and noise were measured by semi-automated region-of interest (ROI) analysis, with 120kV_p/120 mAs serving as the standard of reference.

Using IR, noise in lung parenchyma was reduced by ~ 31% at high attenuating chest wall and by ~ 22% at low attenuating chest wall compared to FBP, respectively (p<0.05). IR induced changes in the order of ±1 HU to mean absolute LD and AD compared to corresponding FBP reconstructions which were statistically significant (p<0.05).

Densitometry is influenced by acquisition parameters and reconstruction algorithms to a degree that may be clinically negligible. However, in longitudinal studies and clinical research identical protocols and potentially other measures for calibration may be required.

To investigate the feasibility of a low tube voltage (80 kVp) protocol with low concentration contrast media (CM) (iodixanol 320 mgl/ml) as compared with a high tube voltage (100 kVp) protocol with high concentration CM (iomeprol 400 mgl/ml) in coronary CT angiography (CCTA) for patients with body mass index less than 30.

A total of 93 patients were randomly assigned into three groups and underwent CCTA as follows: Group A) 100 kVp, 100–350 mAs, 400 mgl/ml CM at 4ml/s, and reconstructed with filtered back projection; Group B and C) 80 kVp, 100–450 mAs, 320 mgl/ml CM at 4 ml/s and 5 ml/s, respectively and reconstructed with iterative reconstruction. Objective and subjective image quality (IQ) was analyzed.

The image noise, intravascular attenuation, signal-to-noise ratio and contrast-to-noise ratio of major coronary arteries did not differ significantly among three groups. Subjective IQ analyses on vascular attenuation and image noise did not differ significantly, either (all of p > 0.05). Qualitative IQ of Group B and C was non-inferior to that of Group A. Substantial reduction of radiation exposure was achieved in group B (2.60 ± 0.48 mSv) and C (2.72 ± 0.54 mSv), compared with group A (3.58 ± 0.67 mSv) (p < 0.05).

CCTA at 80 kVp with 320 mgl/ml CM and iterative reconstruction is feasible, achieving radiation dose reduction, while preserving IQ.

To assess the feasibility of applying a deep learning-based denoising technique to coronary CT angiography (CCTA) along with iterative reconstruction for additional noise reduction.

We retrospectively enrolled 82 consecutive patients (male:female = 60:22; mean age, 67.0 ± 10.8 years) who had undergone both CCTA and invasive coronary artery angiography from March 2017 to June 2018. All included patients underwent CCTA with iterative reconstruction (ADMIRE level 3, Siemens Healthineers). We developed a deep learning based denoising technique (ClariCT.AI, ClariPI), which was based on a modified U-net type convolutional neural net model designed to predict the possible occurrence of low-dose noise in the originals. Denoised images were obtained by subtracting the predicted noise from the originals. Image noise, CT attenuation, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were objectively calculated. The edge rise distance (ERD) was measured as an indicator of image sharpness. Two blinded readers subjectively graded the image quality using a 5-point scale. Diagnostic performance of the CCTA was evaluated based on the presence or absence of significant stenosis (≥ 50% lumen reduction).

Objective image qualities (original vs. denoised: image noise, 67.22 ± 25.74 vs. 52.64 ± 27.40; SNR [left main], 21.91 ± 6.38 vs. 30.35 ± 10.46; CNR [left main], 23.24 ± 6.52 vs. 31.93 ± 10.72; all p < 0.001) and subjective image quality (2.45 ± 0.62 vs. 3.65 ± 0.60, p < 0.001) improved significantly in the denoised images. The average ERDs of the denoised images were significantly smaller than those of originals (0.98 ± 0.08 vs. 0.09 ± 0.08, p < 0.001). With regard to diagnostic accuracy, no significant differences were observed among paired comparisons.

Application of the deep learning technique along with iterative reconstruction can enhance the noise reduction performance with a significant improvement in objective and subjective image qualities of CCTA images.

• Computed tomography angiography
• Coronary artery disease
• Deep learning
• Multidetector computed tomography

• Aged
• Female
• Humans
• Imaging, Three-Dimensional/instrumentation
• Imaging, Three-Dimensional/methods
• Male
• Middle Aged
• Phantoms, Imaging
• Quality Improvement
• Radiographic Image Enhancement/instrumentation
• Radiographic Image Enhancement/methods
• Radiographic Image Interpretation, Computer-Assisted/instrumentation
• Radiographic Image Interpretation, Computer-Assisted/methods
• Retrospective Studies
• Signal-To-Noise Ratio
• Tomography, X-Ray Computed/instrumentation
• Tomography, X-Ray Computed/methods

• contrast agent
• coronary CT angiography
• iodine intake
• radiation dose
• tube voltage

• 80kVp
• Iterative reconstruction
• contrast agent
• image quality
• portal venography
• radiation dose

The circular‐edge technique using a low‐contrast cylindrical object is commonly used to measure the modulation transfer functions (MTFs) in computed tomography (CT) images reconstructed with iterative reconstruction (IR) algorithms. This method generally entails averaging multiple images of the cylinder to reduce the image noise. We suspected that the cylinder edge shape depicted in the IR images might exhibit slight deformation with respect to the true shape because of the intrinsic nonlinearity of IR algorithms. Image averaging can reduce the image noise, but does not effectively improve the deformation of the edge shape; thereby causing errors in the MTF measurements. We address this issue and propose a method to correct the MTF. We scanned a phantom including cylindrical objects with a CT scanner (Ingenuity Elite, Philips Healthcare). We obtained cylinder images with iterative model reconstruction (IMR) algorithms. The images suggested that the depicted edge shape deforms and fluctuates depending on slice positions. Because of this deformation, image averaging can potentially cause additional blurring. We define the deformation function D that describes the additional blurring, and obtain D by analyzing multiple images. The MTF measured by the circular‐edge method (referred to as MTF') can be thought of as the multiplication of the true MTF by the Fourier transformation (FT) of D. We thus obtain the corrected MTF (MTF_corrected) by dividing MTF' by the FT of D. We validate our correction method by comparing the calculated images based on the convolution theorem using MTF' and MTF_corrected with the actual images obtained with the scanner. The calculated image using MTF_corrected is more similar to the actual image compared with the image calculated using MTF', particularly in edge regions. We describe a pitfall in MTF measurement using the circular‐edge technique with image averaging, and suggest a method to correct it.

• circular-edge technique
• computed tomography
• iterative reconstruction algorithm
• modulation transfer function
• spatial resolution

To assess the effects of iterative model reconstruction (IMR) on image quality for demonstrating non-calcific high-risk plaque characteristics of coronary arteries.

This study included 66 patients (53 men and 13 women; aged 39–76 years; mean age, 55 ± 13 years) having single-vessel disease with predominantly non-calcified plaques evaluated using prospective electrocardiogram-gated 256-slice CT angiography. Paired image sets were created using two types of reconstruction: hybrid iterative reconstruction (HIR) and IMR. Plaque characteristics were compared using the two algorithms. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the images and the CNR between the plaque and adjacent adipose tissue were also compared between the two reformatted methods.

Seventy-seven predominantly non-calcified plaques were detected. Forty plaques showed napkin-ring sign with the IMR reformatted method, while nineteen plaques demonstrated napkin-ring sign with HIR. There was no statistically significant difference in the presentation of positive remodeling, low attenuation plaque, and spotty calcification between the HIR and IMR reconstructed methods (all p > 0.5); however, there was a statistically significant difference in the ability to discern the napkin-ring sign between the two algorithms (χ² = 12.12, p < 0.001). The image noise of IMR was lower than that of HIR (10 ± 2 HU versus 12 ± 2 HU; p < 0.01), and the SNR and CNR of the images and the CNR between plaques and surrounding adipose tissues on IMR were better than those on HIR (p < 0.01).

IMR can significantly improve image quality compared with HIR for the demonstration of coronary artery and atherosclerotic plaques using a 256-slice CT.

• Atherosclerosis
• Computed tomography angiography
• Coronary artery disease
• Image reconstruction
• Multidetector computed tomography

• CT
• Chest
• Dictionary learning
• Dose reduction
• Sub-millisievert

• Angiography, coronary
• CT angiography
• Coronary artery disease
• Medical imaging
• Trial protocols

We evaluated the diagnostic accuracy of myocardial blood flow (MBF) and perfusion reserve (MPR) measured from low-dose dynamic contrast-enhanced (DCE) imaging with a whole-heart coverage CT scanner for detecting functionally significant coronary artery disease (CAD).

Twenty one patients with suspected or known CAD had rest and dipyridamole stress MBF measurements with CT and SPECT myocardial perfusion imaging (MPI), and lumen narrowing assessment with coronary angiography (catheter and/or CT based) within 6 weeks. SPECT MBF measurements and coronary angiography were used together as reference to determine the functional significance of coronary artery stenosis. In each CT MPI study, DCE images of the whole heart were acquired with breath-hold using a low-dose acquisition protocol to generate MBF maps. Binomial logistic regression analysis was used to determine the diagnostic accuracy of CT-measured MBF and MPR (ratio of stress to rest MBF) for assessing functionally significant coronary stenosis.

Mean stress MBF and MPR in ischemic segments were lower than those in non-ischemic segments (1.37 ± 0.34 vs. 2.14 ± 0.64 ml/min/g; 1.56 ± 0.41 vs. 2.53 ± 0.70; p < 0.05 for all). The receiver operating characteristic curve analysis revealed that MPR (AUC 0.916, 95%CI: 0.885–0.947) had a superior power than stress MBF (AUC 0.869, 95%CI: 0.830–0.909) for differentiating non-ischemic and ischemic myocardial segments (p = 0.045). On a per-vessel and per-segment analysis, concomitant use of MPR and stress MBF thresholds further improved the diagnostic accuracy compared to MPR or stress MBF alone for detecting obstructive coronary lesions (per-vessel: 93.4% vs. 83.6% and 88.5%, respectively; per-segment: 90.0% vs. 83.7% and 83.1%, respectively). The estimated effective dose of a rest and stress CT MPI study was 3.04 and 3.19 mSv respectively.

Quantitative rest and stress myocardial perfusion measurement with a large-coverage CT scanner improves the diagnostic accuracy for detecting functionally significant coronary stenosis.

• CT perfusion
• Coronary artery disease
• Large-coverage CT scanner
• Myocardial perfusion reserve
• Quantitative myocardial perfusion measurement

• Aged
• Algorithms
• Computed Tomography Angiography/instrumentation
• Computed Tomography Angiography/methods
• Computed Tomography Angiography/standards
• Coronary Angiography/instrumentation
• Coronary Angiography/methods
• Coronary Angiography/standards
• Coronary Vessels/diagnostic imaging
• Coronary Vessels/physiology
• Female
• Humans
• Male
• Middle Aged
• Vascular Calcification/diagnostic imaging
• Vascular Calcification/pathology

• National Research Foundation (NRF) grant funded by the Korea government (MEST)

• Computed tomography-derived fractional flow reserve
• Coronary artery bypass grafting
• Coronary computed tomography angiography
• Left main or 3-vessel coronary artery disease
• SYNTAX score
• Stable coronary artery disease

• Pulmonary embolism
• Radiation
• Tomography

• Adult
• Aged
• Aged, 80 and over
• Computed Tomography Angiography/methods
• Contrast Media
• Emergency Service, Hospital
• Female
• Humans
• Iohexol/analogs & derivatives
• Male
• Middle Aged
• Prospective Studies
• Pulmonary Embolism/diagnostic imaging
• Radiation Dosage
• Radiographic Image Interpretation, Computer-Assisted/methods
• Signal-To-Noise Ratio

• adaptive statistical iterative reconstruction
• model-based iterative reconstruction
• submillisievert coronary CT angiography

• Abdominal imaging
• Dual-source CT
• Radiation dose reduction
• Sinogram-affirmed iterative reconstruction

• Female
• Humans
• Image Processing, Computer-Assisted/methods
• Male
• Middle Aged
• Radiation Dosage
• Radiographic Image Interpretation, Computer-Assisted/methods
• Radiography, Abdominal/methods
• Retrospective Studies
• Signal-To-Noise Ratio
• Tomography, X-Ray Computed/methods

• adversarial neural network
• cycle consistency
• deep learning
• low-dose CT
• multiphase coronary CT angiography
• unsupervised learning