Iterative reconstruction techniques for computed tomography part 2: initial results in dose reduction and image quality

Reduction of contrast medium for transcatheter aortic valve replacement planning using a spectral detector CT: a prospective clinical trial

INTRODUCTION

This study investigated the use of dual-energy spectral detector computed tomography (CT) and virtual monoenergetic imaging (VMI) reconstructions in pre-interventional transcatheter aortic valve replacement (TAVR) planning. We aimed to determine the minimum required contrast medium (CM) amount to maintain diagnostic CT imaging quality for TAVR planning.

METHODS

In this prospective clinical trial, TAVR candidates received a standardized dual-layer spectral detector CT protocol. The CM amount (Iohexol 350 mg iodine/mL, standardized flow rate 3 mL/s) was reduced systematically after 15 patients by 10 mL, starting at 60 mL (institutional standard). We evaluated standard, and 40- and 60-keV VMI reconstructions. For image quality, we measured signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and diameters in multiple vessel sections (i.e., aortic annulus: diameter, perimeter, area; aorta/arteries: minimal diameter). Mixed regression models (MRM), including interaction terms and clinical characteristics, were used for comparison.

RESULTS

Sixty consecutive patients (mean age, 79.4 ± 7.5 years; 28 females, 46.7%) were included. In pre-TAVR CT, the CM reduction to 40 mL is possible without affecting the image quality (MRM: SNR: -1.1, p = 0.726; CNR: 0.0, p = 0.999). VMI 40-keV reconstructions showed better results than standard reconstructions with significantly higher SNR (+ 6.04, p < 0.001). Reduction to 30 mL CM resulted in a significant loss of quality (MRM: SNR: -12.9, p < 0.001; CNR: -13.9, p < 0.001), regardless of the reconstruction. Across the reconstructions, we observed no differences in the metric evaluation (p > 0.914).

CONCLUSION

Among TAVR candidates undergoing pre-interventional CT at a dual-layer spectral detector system, applying 40 mL CM is sufficient to maintain diagnostic image quality. VMI 40-keV reconstructions improve the vessel attenuation and are recommended for evaluation.

CLINICAL RELEVANCE STATEMENT

Contrast medium reduction to 40 mL in pre-interventional transcatheter aortic valve replacement CT using dual-energy CT maintains image quality, while 40-keV virtual monoenergetic imaging reconstructions enhance vessel attenuation. These results offer valuable recommendations for interventional transcatheter aortic valve replacement evaluation and potentially improve nephroprotection in patients with compromised renal function.

KEY POINTS

• Patients undergoing transcatheter aortic valve replacement (TAVR), requiring pre-interventional CT, are often multimorbid with impaired renal function. • Using a spectral detector dual-layer CT, contrast medium reduction to 40 mL is feasible, maintaining diagnostic image quality. • The additional application of virtual monoenergetic image reconstructions with 40 keV improves vessel attenuation significantly in clinical practice.

Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review

Background

The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further. Hence, the purpose of the study is to review the influence of DLIR on Radiation dose (RD), Image noise (IN), and outcomes of the studies compared with IR and FBP in Head and Chest CT examinations.

Methods

We performed a detailed search in PubMed, Scopus, Web of Science, Cochrane Library, and Embase to find the articles reported using DLIR for Head and Chest CT examinations between 2017 to 2023. Data were retrieved from the short-listed studies using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines.

Results

Out of 196 articles searched, 15 articles were included. A total of 1292 sample size was included. 14 articles were rated as high and 1 article as moderate quality. All studies compared DLIR to IR techniques. 5 studies compared DLIR with IR and FBP. The review showed that DLIR improved IQ, and reduced RD and IN for CT Head and Chest examinations.

Conclusions

DLIR algorithm have demonstrated a noted enhancement in IQ with reduced IN for CT Head and Chest examinations at lower dose compared with IR and FBP. DLIR showed potential for enhancing patient care by reducing radiation risks and increasing diagnostic accuracy.

Deep learning enables time-efficient soft tissue enhancement in CBCT: Proof-of-concept study for dentomaxillofacial applications

PURPOSE

The use of iterative and deep learning reconstruction methods, which would allow effective noise reduction, is limited in cone-beam computed tomography (CBCT). As a consequence, the visibility of soft tissues is limited with CBCT. The study aimed to improve this issue through time-efficient deep learning enhancement (DLE) methods.

METHODS

Two DLE networks, UNIT and U-Net, were trained with simulated CBCT data. The performance of the networks was tested with three different test data sets. The quantitative evaluation measured the structural similarity index measure (SSIM) and the peak signal-to-noise ratio (PSNR) of the DLE reconstructions with respect to the ground truth iterative reconstruction method. In the second assessment, a dentomaxillofacial radiologist assessed the resolution of hard tissue structures, visibility of soft tissues, and overall image quality of real patient data using the Likert scale. Finally, the technical image quality was determined using modulation transfer function, noise power spectrum, and noise magnitude analyses.

RESULTS

The study demonstrated that deep learning CBCT denoising is feasible and time efficient. The DLE methods, trained with simulated CBCT data, generalized well, and DLE provided quantitatively (SSIM/PSNR) and visually similar noise-reduction as conventional IR, but with faster processing time. The DLE methods improved soft tissue visibility compared to the conventional Feldkamp-Davis-Kress (FDK) algorithm through noise reduction. However, in hard tissue quantification tasks, the radiologist preferred the FDK over the DLE methods.

CONCLUSION

Post-reconstruction DLE allowed feasible reconstruction times while yielding improvements in soft tissue visibility in each dataset.

Iterative Metal Artifact Reduction in Head and Neck CT Facilitates Tumor Visualization of Oral and Oropharyngeal Cancer Obscured by Artifacts From Dental Hardware

RATIONALE AND OBJECTIVES

The purpose of this study was to evaluate the diagnostic utility of iterative metal artifact reduction (iMAR) in computed tomography (CT)-imaging of oral and oropharyngeal cancers when obscured by dental hardware artifacts and to determine the most appropriate iMAR settings for this purpose.

MATERIALS AND METHODS

The study retrospectively enrolled 27 patients (8 female, 19 male; mean age 64±12.7years) with histologically confirmed oral or oropharyngeal cancer obscured by dental artifacts in contrast-enhanced CT. Raw CT data were reconstructed with ascending iMAR strengths (levels 1/2/3/4/5) and one reconstruction without iMAR (level 0). For subjective analysis, two blinded radiologists rated tumor visualization and artifact severity on a five-point Likert scale. For objective analysis, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artifact index (AI) were determined.

RESULTS

iMAR reconstructions improved the subjective image quality of tumor edge and contrast, and the objective parameters of tumor SNR and CNR, reaching their optimum at iMAR levels 4 and 5 (P<.001). AI decreased with iMAR reconstructions reaching its minimum at iMAR level 5 (P<.001). Tumor detection rates increased 2.4-fold with iMAR 5, 2.1-fold with iMAR 4, and 1.9-fold with iMAR 3 compared to reconstructions without iMAR. Disadvantages such as algorithm-induced artifacts increased significantly with higher iMAR strengths (P<.05), reaching a maximum with iMAR 5.

CONCLUSION

iMAR significantly improves CT imaging of oral and oropharyngeal cancers, as confirmed by both subjective and objective measures, with best results at highest iMAR strengths.

Evolution of CT radiation dose in pediatric patients undergoing hybrid 2-[18F]FDG PET/CT between 2007 and 2021

OBJECTIVES

To evaluate the evolution of CT radiation dose in pediatric patients undergoing hybrid 2-[18F]fluoro-2-deoxy-D-glucose (2-[18F]FDG) PET/CT between 2007 and 2021.

METHODS AND MATERIALS

Data from all pediatric patients aged 0-18 years who underwent hybrid 2-[18F]FDG PET/CT of the body between January 2007 and May 2021 were reviewed. Demographic and imaging parameters were collected. A board-certified radiologist reviewed all CT scans and measured image noise in the brain, liver, and adductor muscles.

RESULTS

294 scans from 167 children (72 females (43%); median age: 14 (IQR 10-15) years; BMI: median 17.5 (IQR 15-20.4) kg/m2) were included. CT dose index-volume (CTDIvol) and dose length product (DLP) both decreased significantly from 2007 to 2021 (both p < 0.001, Spearman's rho coefficients -0.46 and -0.35, respectively). Specifically, from 2007 to 2009 to 2019-2021 CTDIvol and DLP decreased from 2.94 (2.14-2.99) mGy and 309 (230-371) mGy*cm, respectively, to 0.855 (0.568-1.11) mGy and 108 (65.6-207) mGy*cm, respectively. From 2007 to 2021, image noise in the brain and liver remained constant (p = 0.26 and p = 0.06), while it decreased in the adductor muscles (p = 0.007). Peak tube voltage selection (in kilovolt, kV) of CT scans shifted from high kV imaging (140 or 120kVp) to low kV imaging (100 or 80kVp) (p < 0.001) from 2007 to 2021.

CONCLUSION

CT radiation dose in pediatric patients undergoing hybrid 2-[18F]FDG PET/CT has decreased in recent years equaling approximately one-third of the initial amount.

ADVANCES IN KNOWLEDGE

Over the past 15 years, CT radiation dose decreased considerably in pediatric patients undergoing hybrid imaging, while objective image quality may not have been compromised.

Artificial intelligence in cardiac computed tomography

Artificial Intelligence (AI) is a broad discipline of computer science and engineering. Modern application of AI encompasses intelligent models and algorithms for automated data analysis and processing, data generation, and prediction with applications in visual perception, speech understanding, and language translation. AI in healthcare uses machine learning (ML) and other predictive analytical techniques to help sort through vast amounts of data and generate outputs that aid in diagnosis, clinical decision support, workflow automation, and prognostication. Coronary computed tomography angiography (CCTA) is an ideal union for these applications due to vast amounts of data generation and analysis during cardiac segmentation, coronary calcium scoring, plaque quantification, adipose tissue quantification, peri-operative planning, fractional flow reserve quantification, and cardiac event prediction. In the past 5 years, there has been an exponential increase in the number of studies exploring the use of AI for cardiac computed tomography (CT) image acquisition, de-noising, analysis, and prognosis. Beyond image processing, AI has also been applied to improve the imaging workflow in areas such as patient scheduling, urgent result notification, report generation, and report communication. In this review, we discuss algorithms applicable to AI and radiomic analysis; we then present a summary of current and emerging clinical applications of AI in cardiac CT. We conclude with AI's advantages and limitations in this new field.

Quantitative Analysis for Lung Disease on Thin-Section CT

Thin-section computed tomography (CT) is widely employed not only for assessing morphology but also for evaluating respiratory function. Three-dimensional images obtained from thin-section CT provide precise measurements of lung, airway, and vessel volumes. These volumetric indices are correlated with traditional pulmonary function tests (PFT). CT also generates lung histograms. The volume ratio of areas with low and high attenuation correlates with PFT results. These quantitative image analyses have been utilized to investigate the early stages and disease progression of diffuse lung diseases, leading to the development of novel concepts such as pre-chronic obstructive pulmonary disease (pre-COPD) and interstitial lung abnormalities. Quantitative analysis proved particularly valuable during the COVID-19 pandemic when clinical evaluations were limited. In this review, we introduce CT analysis methods and explore their clinical applications in the context of various lung diseases. We also highlight technological advances, including images with matrices of 1024 × 1024 and slice thicknesses of 0.25 mm, which enhance the accuracy of these analyses.

Dose reduction and image enhancement in micro-CT using deep learning

BACKGROUND

In preclinical settings, micro-computed tomography (CT) provides a powerful tool to acquire high resolution anatomical images of rodents and offers the advantage to in vivo non-invasively assess disease progression and therapy efficacy. Much higher resolutions are needed to achieve scale-equivalent discriminatory capabilities in rodents as those in humans. High resolution imaging however comes at the expense of increased scan times and higher doses. Specifically, with preclinical longitudinal imaging, there are concerns that dose accumulation may affect experimental outcomes of animal models.

PURPOSE

Dose reduction efforts under the ALARA (as low as reasonably achievable) principles are thus a key point of attention. However, low dose CT acquisitions inherently induce higher noise levels which deteriorate image quality and negatively impact diagnostic performance. Many denoising techniques already exist, and deep learning (DL) has become increasingly popular for image denoising, but research has mostly focused on clinical CT with limited studies conducted on preclinical CT imaging. We investigate the potential of convolutional neural networks (CNN) for restoring high quality micro-CT images from low dose (noisy) images. The novelty of the CNN denoising frameworks presented in this work consists of utilizing image pairs with realistic CT noise present in the input as well as the target image used for the model training; a noisier image acquired with a low dose protocol is matched to a less noisy image acquired with a higher dose scan of the same mouse.

METHODS

Low and high dose ex vivo micro-CT scans of 38 mice were acquired. Two CNN models, based on a 2D and 3D four-layer U-Net, were trained with mean absolute error (30 training, 4 validation and 4 test sets). To assess denoising performance, ex vivo mice and phantom data were used. Both CNN approaches were compared to existing methods, like spatial filtering (Gaussian, Median, Wiener) and iterative total variation image reconstruction algorithm. Image quality metrics were derived from the phantom images. A first observer study (n = 23) was set-up to rank overall quality of differently denoised images. A second observer study (n = 18) estimated the dose reduction factor of the investigated 2D CNN method.

RESULTS

Visual and quantitative results show that both CNN algorithms exhibit superior performance in terms of noise suppression, structural preservation and contrast enhancement over comparator methods. The quality scoring by 23 medical imaging experts also indicates that the investigated 2D CNN approach is consistently evaluated as the best performing denoising method. Results from the second observer study and quantitative measurements suggest that CNN-based denoising could offer a 2-4× dose reduction, with an estimated dose reduction factor of about 3.2 for the considered 2D network.

CONCLUSIONS

Our results demonstrate the potential of DL in micro-CT for higher quality imaging at low dose acquisition settings. In the context of preclinical research, this offers promising future prospects for managing the cumulative severity effects of radiation in longitudinal studies.

Impact of radiation dose reduction and iterative image reconstruction on CT-guided spine biopsies

This study aimed to systematically evaluate the impact of dose reduction on image quality and confidence for intervention planning and guidance regarding computed tomography (CT)-based intervertebral disc and vertebral body biopsies. We retrospectively analyzed 96 patients who underwent multi-detector CT (MDCT) acquired for the purpose of biopsies, which were either derived from scanning with standard dose (SD) or low dose (LD; using tube current reduction). The SD cases were matched to LD cases considering sex, age, level of biopsy, presence of spinal instrumentation, and body diameter. All images for planning (reconstruction: "IMR1") and periprocedural guidance (reconstruction: "iDose4") were evaluated by two readers (R1 and R2) using Likert scales. Image noise was measured using attenuation values of paraspinal muscle tissue. The dose length product (DLP) was statistically significantly lower for LD scans regarding the planning scans (SD: 13.8 ± 8.2 mGy*cm, LD: 8.1 ± 4.4 mGy*cm, p < 0.01) and the interventional guidance scans (SD: 43.0 ± 48.8 mGy*cm, LD: 18.4 ± 7.3 mGy*cm, p < 0.01). Image quality, contrast, determination of the target structure, and confidence for planning or intervention guidance were rated good to perfect for SD and LD scans, showing no statistically significant differences between SD and LD scans (p > 0.05). Image noise was similar between SD and LD scans performed for planning of the interventional procedures (SD: 14.62 ± 2.83 HU vs. LD: 15.45 ± 3.22 HU, p = 0.24). Use of a LD protocol for MDCT-guided biopsies along the spine is a practical alternative, maintaining overall image quality and confidence. Increasing availability of model-based iterative reconstruction in clinical routine may facilitate further radiation dose reductions.

Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects

Filtered back projection (FBP) has been the standard CT image reconstruction method for 4 decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in several clinical applications. However, with faster and more advanced CT scanners, FBP has become increasingly obsolete. Higher image noise and more artifacts are especially noticeable in lower-dose CT imaging using FBP. This performance gap was partly addressed by model-based iterative reconstruction (MBIR). Yet, its "plastic" image appearance and long reconstruction times have limited widespread application. Hybrid iterative reconstruction partially addressed these limitations by blending FBP with MBIR and is currently the state-of-the-art reconstruction technique. In the past 5 years, deep learning reconstruction (DLR) techniques have become increasingly popular. DLR uses artificial intelligence to reconstruct high-quality images from lower-dose CT faster than MBIR. However, the performance of DLR algorithms relies on the quality of data used for model training. Higher-quality training data will become available with photon-counting CT scanners. At the same time, spectral data would greatly benefit from the computational abilities of DLR. This review presents an overview of the principles, technical approaches, and clinical applications of DLR, including metal artifact reduction algorithms. In addition, emerging applications and prospects are discussed.

The Impact of Novel Reconstruction Algorithms on Calcium Scoring: Results on a Dedicated Cardiac CT Scanner

Contemporary reconstruction algorithms yield the potential of reducing radiation exposure by denoising coronary computed tomography angiography (CCTA) datasets. We aimed to assess the reliability of coronary artery calcium score (CACS) measurements with an advanced adaptive statistical iterative reconstruction (ASIR-CV) and model-based adaptive filter (MBAF2) designed for a dedicated cardiac CT scanner by comparing them to the gold-standard filtered back projection (FBP) calculations. We analyzed non-contrast coronary CT images of 404 consecutive patients undergoing clinically indicated CCTA. CACS and total calcium volume were quantified and compared on three reconstructions (FBP, ASIR-CV, and MBAF2+ASIR-CV). Patients were classified into risk categories based on CACS and the rate of reclassification was assessed. Patients were categorized into the following groups based on FBP reconstructions: 172 zero CACS, 38 minimal (1-10), 87 mild (11-100), 57 moderate (101-400), and 50 severe (400<). Overall, 19/404 (4.7%) patients were reclassified into a lower-risk group with MBAF2+ASIR-CV, while 8 additional patients (27/404, 6.7%) shifted downward when applying stand-alone ASIR-CV. The total calcium volume with FBP was 7.0 (0.0-133.25) mm³, 4.0 (0.0-103.5) mm³ using ASIR-CV, and 5.0 (0.0-118.5) mm³ with MBAF2+ASIR-CV (all comparisons p < 0.001). The concomitant use of ASIR-CV and MBAF2 may allow the reduction of noise levels while maintaining similar CACS values as FBP measurements.

Impact of low-dose computed tomography (LDCT) screening on lung cancer-related mortality

BACKGROUND

Lung cancer is the most common cause of cancer-related death in the world, however lung cancer screening has not been implemented in most countries at a population level. A previous Cochrane Review found limited evidence for the effectiveness of lung cancer screening with chest radiography (CXR) or sputum cytology in reducing lung cancer-related mortality, however there has been increasing evidence supporting screening with low-dose computed tomography (LDCT).  OBJECTIVES: To determine whether screening for lung cancer using LDCT of the chest reduces lung cancer-related mortality and to evaluate the possible harms of LDCT screening.

SEARCH METHODS

We performed the search in collaboration with the Information Specialist of the Cochrane Lung Cancer Group and included the Cochrane Lung Cancer Group Trial Register, Cochrane Central Register of Controlled Trials (CENTRAL, the Cochrane Library, current issue), MEDLINE (accessed via PubMed) and Embase in our search. We also searched the clinical trial registries to identify unpublished and ongoing trials. We did not impose any restriction on language of publication. The search was performed up to 31 July 2021.  SELECTION CRITERIA: Randomised controlled trials (RCTs) of lung cancer screening using LDCT and reporting mortality or harm outcomes.  DATA COLLECTION AND ANALYSIS: Two review authors were involved in independently assessing trials for eligibility, extraction of trial data and characteristics, and assessing risk of bias of the included trials using the Cochrane RoB 1 tool. We assessed the certainty of evidence using GRADE. Primary outcomes were lung cancer-related mortality and harms of screening. We performed a meta-analysis, where appropriate, for all outcomes using a random-effects model. We only included trials in the analysis of mortality outcomes if they had at least 5 years of follow-up. We reported risk ratios (RRs) and hazard ratios (HRs), with 95% confidence intervals (CIs) and used the I2 statistic to investigate heterogeneity.  MAIN RESULTS: We included 11 trials in this review with a total of 94,445 participants. Trials were conducted in Europe and the USA in people aged 40 years or older, with most trials having an entry requirement of ≥ 20 pack-year smoking history (e.g. 1 pack of cigarettes/day for 20 years or 2 packs/day for 10 years etc.). One trial included male participants only. Eight trials were phase three RCTs, with two feasibility RCTs and one pilot RCT. Seven of the included trials had no screening as a comparison, and four trials had CXR screening as a comparator. Screening frequency included annual, biennial and incrementing intervals. The duration of screening ranged from 1 year to 10 years. Mortality follow-up was from 5 years to approximately 12 years.  None of the included trials were at low risk of bias across all domains. The certainty of evidence was moderate to low across different outcomes, as assessed by GRADE. In the meta-analysis of trials assessing lung cancer-related mortality, we included eight trials (91,122 participants), and there was a reduction in mortality of 21% with LDCT screening compared to control groups of no screening or CXR screening (RR 0.79, 95% CI 0.72 to 0.87; 8 trials, 91,122 participants; moderate-certainty evidence). There were probably no differences in subgroups for analyses by control type, sex, geographical region, and nodule management algorithm. Females appeared to have a larger lung cancer-related mortality benefit compared to males with LDCT screening. There was also a reduction in all-cause mortality (including lung cancer-related) of 5% (RR 0.95, 95% CI 0.91 to 0.99; 8 trials, 91,107 participants; moderate-certainty evidence).  Invasive tests occurred more frequently in the LDCT group (RR 2.60, 95% CI 2.41 to 2.80; 3 trials, 60,003 participants; moderate-certainty evidence). However, analysis of 60-day postoperative mortality was not significant between groups (RR 0.68, 95% CI 0.24 to 1.94; 2 trials, 409 participants; moderate-certainty evidence).  False-positive results and recall rates were higher with LDCT screening compared to screening with CXR, however there was low-certainty evidence in the meta-analyses due to heterogeneity and risk of bias concerns. Estimated overdiagnosis with LDCT screening was 18%, however the 95% CI was 0 to 36% (risk difference (RD) 0.18, 95% CI -0.00 to 0.36; 5 trials, 28,656 participants; low-certainty evidence). Four trials compared different aspects of health-related quality of life (HRQoL) using various measures. Anxiety was pooled from three trials, with participants in LDCT screening reporting lower anxiety scores than in the control group (standardised mean difference (SMD) -0.43, 95% CI -0.59 to -0.27; 3 trials, 8153 participants; low-certainty evidence). There were insufficient data to comment on the impact of LDCT screening on smoking behaviour.  AUTHORS' CONCLUSIONS: The current evidence supports a reduction in lung cancer-related mortality with the use of LDCT for lung cancer screening in high-risk populations (those over the age of 40 with a significant smoking exposure). However, there are limited data on harms and further trials are required to determine participant selection and optimal frequency and duration of screening, with potential for significant overdiagnosis of lung cancer. Trials are ongoing for lung cancer screening in non-smokers.

The evaluation of the reduction of radiation dose via deep learning-based reconstruction for cadaveric human lung CT images

To compare the quality of CT images of the lung reconstructed using deep learning-based reconstruction (True Fidelity Image: TFI ™; GE Healthcare) to filtered back projection (FBP), and to determine the minimum tube current-time product in TFI without compromising image quality. Four cadaveric human lungs were scanned on CT at 120 kVp and different tube current-time products (10, 25, 50, 75, 100, and 175 mAs) and reconstructed with TFI and FBP. Two image evaluations were performed by three independent radiologists. In the first experiment, using the same tube current-time product, a side-by-side TFI and FBP comparison was performed. Images were evaluated with regard to noise, streak artifacts, and overall image quality. Overall image quality was evaluated in view of whole image quality. In the second experiment, CT images reconstructed using TFI and FBP with five different tube current-time products were displayed in random order, which were evaluated with reference to the 175 mAs-FBP image. Images were scored with regard to normal structure, abnormal findings, noise, streak artifacts, and overall image quality. Median scores from three radiologists were statistically analyzed. Quantitative evaluation of noise was performed by setting regions of interest (ROIs) in air. In first experiment, overall image quality was improved, and noise was decreased in images of TFI compared to that of FBP for all tube current-time products. In second experiment, scores of all evaluation items except for small vessels in images of 25 mAs-TFI were almost the same as that of 175 mAs-FBP (all p > 0.31). Using TFI instead of FBP, at least 85% radiation dose reduction could be possible without any degradation in the image quality.

Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study

While the recent advancements of computed tomography (CT) technology have contributed in reducing radiation dose and image noise, an objective evaluation of image quality in patient scans has not yet been established. In this study, we present a patient-specific CT image quality evaluation method that includes fully automated measurements of noise level, structure sharpness, and alteration of structure. This study used the CT images of 120 patients from four different CT scanners reconstructed with three types of algorithm: filtered back projection (FBP), vendor-specific iterative reconstruction (IR), and a vendor-agnostic deep learning model (DLM, ClariCT.AI, ClariPi Inc.). The structure coherence feature (SCF) was used to divide an image into the homogeneous (RH) and structure edge (RS) regions, which in turn were used to localize the regions of interests (ROIs) for subsequent analysis of image quality indices. The noise level was calculated by averaging the standard deviations from five randomly selected ROIs on RH, and the mean SCFs on RS was used to estimate the structure sharpness. The structure alteration was defined by the standard deviation ratio between RS and RH on the subtraction image between FBP and IR or DLM, in which lower structure alterations indicate successful noise reduction without degradation of structure details. The estimated structure sharpness showed a high correlation of 0.793 with manually measured edge slopes. Compared to FBP, IR and DLM showed 34.38% and 51.30% noise reduction, 2.87% and 0.59% lower structure sharpness, and 2.20% and -12.03% structure alteration, respectively, on an average. DLM showed statistically superior performance to IR in all three image quality metrics. This study is expected to contribute to enhance the CT protocol optimization process by allowing a high throughput and quantitative image quality evaluation during the introduction or adjustment of lower-dose CT protocol into routine practice.

Low-Dose Abdominal CT for Evaluating Suspected Appendicitis: Recommendations for CT Imaging Techniques and Practical Issues

A vast disparity exists between science and practice for CT radiation dose. Despite high-level evidence supporting the use of low-dose CT (LDCT) in diagnosing appendicitis, a recent survey showed that many care providers were still concerned that the low image quality of LDCT may lead to incorrect diagnoses. For successful implementation of LDCT practice, it is important to inform and educate the care providers not only of the scientific discoveries but also of concrete guidelines on how to overcome more practical matters. Here, we discuss CT imaging techniques and other practical issues for implementing LDCT practice.

Optimization of a customized simultaneous algebraic reconstruction technique algorithm for phase-contrast breast computed tomography

Objective. To introduce the optimization of a customized GPU-based simultaneous algebraic reconstruction technique (cSART) in the field of phase-contrast breast computed tomography (bCT). The presented algorithm features a 3D bilateral regularization filter that can be tuned to yield optimal performance for clinical image visualization and tissues segmentation. Approach. Acquisitions of a dedicated test object and a breast specimen were performed at Elettra, the Italian synchrotron radiation (SR) facility (Trieste, Italy) using a large area CdTe single-photon counting detector. Tomographic images were obtained at 5 mGy of mean glandular dose, with a 32 keV monochromatic x-ray beam in the free-space propagation mode. Three independent algorithms parameters were optimized by using contrast-to-noise ratio (CNR), spatial resolution, and noise texture metrics. The results obtained with the cSART algorithm were compared with conventional SART and filtered back projection (FBP) reconstructions. Image segmentation was performed both with gray scale-based and supervised machine-learning approaches. Main results. Compared to conventional FBP reconstructions, results indicate that the proposed algorithm can yield images with a higher CNR (by 35% or more), retaining a high spatial resolution while preserving their textural properties. Alternatively, at the cost of an increased image ‘patchiness’, the cSART can be tuned to achieve a high-quality tissue segmentation, suggesting the possibility of performing an accurate glandularity estimation potentially of use in the realization of realistic 3D breast models starting from low radiation dose images. Significance. The study indicates that dedicated iterative reconstruction techniques could provide significant advantages in phase-contrast bCT imaging. The proposed algorithm offers great flexibility in terms of image reconstruction optimization, either toward diagnostic evaluation or image segmentation.

Model-Based Iterative Reconstruction (MBIR) for ASPECT Scoring in Acute Stroke Patients Selection: Comparison to rCBV and Follow-Up Imaging

Background: To compare a model-based iterative reconstruction (MBIR) versus a hybrid iterative reconstruction (HIR) for initial and final Alberta Stroke Program Early Ct Score (ASPECT) scoring in acute ischemic stroke (AIS). We hypothesized that MBIR designed for brain computed tomography (CT) could perform better than HIR for ASPECT scoring. Methods: Among patients who had undergone CT perfusion for AIS between April 2018 and October 2019 with a follow-up imaging within 7 days, we designed a cohort of representative ASPECTS. Two readers assessed regional-cerebral-blood-volume-ASPECT (rCBV-ASPECTS) on the initial exam and final-ASPECTS on the follow-up non-contrast-CT (NCCT) in consensus. Four readers performed independently MBIR and HIR ASPECT scoring on baseline NCCT. Results: In total, 294 hemispheres from 147 participants (average age of 69.59 ± 15.63 SD) were analyzed. Overall raters’ agreement between rCBV-map and MBIR and HIR ranged from moderate to moderate (κ = 0.54 to κ = 0.57) with HIR and moderate to substantial (κ = 0.52 to κ = 0.74) with MBIR. Overall raters’ agreement between follow-up imaging and HIR/MBIR ranged from moderate to moderate (κ = 0.55 to κ = 0.59) with HIR and moderate to almost perfect (κ = 0.48 to κ = 0.82) with MBIR. Conclusions: ASPECT scoring with MBIR more closely matched with initial and final infarct extent than classical HIR NCCT reconstruction.

A PHANTOM EVALUATION OF THE USE OF CT AUTOMATIC TUBE CURRENT MODULATION WITH LOW TUBE POTENTIALS FOR IODINATED CONTRAST STUDIES

This paper aimed to investigate effects of different tube voltage and image quality settings on radiation dose and image quality for patients undergoing computed tomography iodinated contrast studies using automatic tube current modulation system and to recommend settings to achieve improved radiation dose and image quality values. A Pagoda phantom with an additional rod of iodine contrast was scanned using different tube voltages and noise index (NI) settings. Size-specific dose estimate (SSDE) and image quality (noise, contrast, contrast-to-noise ratio (CNR) and figure of merit (FOM)) were analysed. Values of SSDE were maintained with similar NI settings. Contrast and CNR were higher for lower tube voltage settings. Better FOM values can be achieved with higher NI settings with the lower kVs. To achieve better CNR and SSDE compared with the standard setting of 120 kV, a 80 kV with an NI setting of 15 was recommended.

Task-specific spatial resolution properties of iterative and deep learning-based reconstructions in computed tomography: Comparison using tasks assuming small and large enhanced vessels

PURPOSE

The present study aims to evaluate TTFs of deep-learning-based image reconstruction (DLIR) and iterative reconstruction (IR) in computed tomography (CT) using a conventional task with a rod object with a diameter of 30 mm and a newly-proposed task with a wire of 1 mm in diameter, simulating large and small enhanced vessels, respectively.

METHODS

The rod or wire phantom made of a material equivalent to diluted iodine that exhibits about 270 Hounsfield unit (HU) was placed inside a 30-cm water phantom. In-plane and z-directional TTFs were measured for the rod using the circular edge (CE) and plane edge (PE) methods, respectively. By using the wire (iodine wire: IW), in-plane and z-directional TTFs were measured using Fourier transform (IW method). TTFs of filtered back projection (FBP), IR, and DLIR of a 256-row CT system and FBP and IR of a 64-row CT system were evaluated with CT dose indices of 10 and 5 mGy.

RESULTS

For DLIR and IR, TTFs measured using the IW method were notably lower than those using the CE (or PE) method; moreover, they were also lower than those of corresponding FBP, indicating that the small enhanced vessels with a diameter of about 1 mm would be blurred with both DLIR and IR.

CONCLUSIONS

The proposed IW method has turned out to be effective to evaluate TTFs for small enhanced vessels, which have not been properly evaluated by the CE or PE method conventionally recommended.

Deep Learning Reconstruction Shows Better Lung Nodule Detection for Ultra-Low-Dose Chest CT

Background Ultra-low-dose (ULD) CT could facilitate the clinical implementation of large-scale lung cancer screening while minimizing the radiation dose. However, traditional image reconstruction methods are associated with image noise in low-dose acquisitions. Purpose To compare the image quality and lung nodule detectability of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-V (ASIR-V) in ULD CT. Materials and Methods Patients who underwent noncontrast ULD CT (performed at 0.07 or 0.14 mSv, similar to a single chest radiograph) and contrast-enhanced chest CT (CECT) from April to June 2020 were included in this prospective study. ULD CT images were reconstructed with filtered back projection (FBP), ASIR-V, and DLIR. Three-dimensional segmentation of lung tissue was performed to evaluate image noise. Radiologists detected and measured nodules with use of a deep learning-based nodule assessment system and recognized malignancy-related imaging features. Bland-Altman analysis and repeated-measures analysis of variance were used to evaluate the differences between ULD CT images and CECT images. Results A total of 203 participants (mean age ± standard deviation, 61 years ± 12; 129 men) with 1066 nodules were included, with 100 scans at 0.07 mSv and 103 scans at 0.14 mSv. The mean lung tissue noise ± standard deviation was 46 HU ± 4 for CECT and 59 HU ± 4, 56 HU ± 4, 53 HU ± 4, 54 HU ± 4, and 51 HU ± 4 in FBP, ASIR-V level 40%, ASIR-V level 80% (ASIR-V-80%), medium-strength DLIR, and high-strength DLIR (DLIR-H), respectively, of ULD CT scans (P < .001). The nodule detection rates of FBP reconstruction, ASIR-V-80%, and DLIR-H were 62.5% (666 of 1066 nodules), 73.3% (781 of 1066 nodules), and 75.8% (808 of 1066 nodules), respectively (P < .001). Bland-Altman analysis showed the percentage difference in long diameter from that of CECT was 9.3% (95% CI of the mean: 8.0, 10.6), 9.2% (95% CI of the mean: 8.0, 10.4), and 6.2% (95% CI of the mean: 5.0, 7.4) in FBP reconstruction, ASIR-V-80%, and DLIR-H, respectively (P < .001). Conclusion Compared with adaptive statistical iterative reconstruction-V, deep learning image reconstruction reduced image noise, increased nodule detection rate, and improved measurement accuracy on ultra-low-dose chest CT images. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee in this issue.

Impact of dose reduction and iterative model reconstruction on multi-detector CT imaging of the brain in patients with suspected ischemic stroke

Non-contrast cerebral computed tomography (CT) is frequently performed as a first-line diagnostic approach in patients with suspected ischemic stroke. The purpose of this study was to evaluate the performance of hybrid and model-based iterative image reconstruction for standard-dose (SD) and low-dose (LD) non-contrast cerebral imaging by multi-detector CT (MDCT). We retrospectively analyzed 131 patients with suspected ischemic stroke (mean age: 74.2 ± 14.3 years, 67 females) who underwent initial MDCT with a SD protocol (300 mAs) as well as follow-up MDCT after a maximum of 10 days with a LD protocol (200 mAs). Ischemic demarcation was detected in 26 patients for initial and in 64 patients for follow-up imaging, with diffusion-weighted magnetic resonance imaging (MRI) confirming ischemia in all of those patients. The non-contrast cerebral MDCT images were reconstructed using hybrid (Philips “iDose4”) and model-based iterative (Philips “IMR3”) reconstruction algorithms. Two readers assessed overall image quality, anatomic detail, differentiation of gray matter (GM)/white matter (WM), and conspicuity of ischemic demarcation, if any. Quantitative assessment included signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) calculations for WM, GM, and demarcated areas. Ischemic demarcation was detected in all MDCT images of affected patients by both readers, irrespective of the reconstruction method used. For LD imaging, anatomic detail and GM/WM differentiation was significantly better when using the model-based iterative compared to the hybrid reconstruction method. Furthermore, CNR of GM/WM as well as the SNR of WM and GM of healthy brain tissue were significantly higher for LD images with model-based iterative reconstruction when compared to SD or LD images reconstructed with the hybrid algorithm. For patients with ischemic demarcation, there was a significant difference between images using hybrid versus model-based iterative reconstruction for CNR of ischemic/contralateral unaffected areas (mean ± standard deviation: SD_IMR: 4.4 ± 3.1, SD_iDose: 3.5 ± 2.3, P < 0.0001; LD_IMR: 4.6 ± 2.9, LD_iDose: 3.2 ± 2.1, P < 0.0001).  In conclusion, model-based iterative reconstruction provides higher CNR and SNR without significant loss of image quality for non-enhanced cerebral MDCT.

Analysis and Evaluation of a Deep Learning Reconstruction Approach with Denoising for Orthopedic MRI

PURPOSE

To evaluate two settings (noise reduction of 50% or 75%) of a deep learning (DL) reconstruction model relative to each other and to conventional MR image reconstructions on clinical orthopedic MRI datasets.

MATERIALS AND METHODS

This retrospective study included 54 patients who underwent two-dimensional fast spin-echo MRI for hip (n = 22; mean age, 44 years ± 13 [standard deviation]; nine men) or shoulder (n = 32; mean age, 56 years ± 17; 17 men) conditions between March 2019 and June 2020. MR images were reconstructed with conventional methods and the vendor-provided and commercially available DL model applied with 50% and 75% noise reduction settings (DL 50 and DL 75, respectively). Quantitative analytics, including relative anatomic edge sharpness, relative signal-to-noise ratio (rSNR), and relative contrast-to-noise ratio (rCNR) were computed for each dataset. In addition, the image sets were randomized, blinded, and presented to three board-certified musculoskeletal radiologists for ranking based on overall image quality and diagnostic confidence. Statistical analysis was performed with a nonparametric hypothesis comparing derived quantitative metrics from each reconstruction approach. In addition, inter- and intrarater agreement analysis was performed on the radiologists' rankings.

RESULTS

Both denoising settings of the DL reconstruction showed improved edge sharpness, rSNR, and rCNR relative to the conventional reconstructions. The reader rankings demonstrated strong agreement, with both DL reconstructions outperforming the conventional approach (Gwet agreement coefficient = 0.98). However, there was lower agreement between the readers on which DL reconstruction denoising setting produced higher-quality images (Gwet agreement coefficient = 0.31 for DL 50 and 0.35 for DL 75).

CONCLUSION

The vendor-provided DL MRI reconstruction showed higher edge sharpness, rSNR, and rCNR in comparison with conventional methods; however, optimal levels of denoising may need to be further assessed.Keywords: MRI Reconstruction Method, Deep Learning, Image Analysis, Signal-to-Noise Ratio, MR-Imaging, Neural Networks, Hip, Shoulder, Physics, Observer Performance, Technology Assessment Supplemental material is available for this article. © RSNA, 2021.

Conditional Generative Adversarial Networks for low-dose CT image denoising aiming at preservation of critical image content

X-ray Computed Tomography (CT) is an imaging modality where patients are exposed to potentially harmful ionizing radiation. To limit patient risk, reduced-dose protocols are desirable, which inherently lead to an increased noise level in the reconstructed CT scans. Consequently, noise reduction algorithms are indispensable in the reconstruction processing chain. In this paper, we propose to leverage a conditional Generative Adversarial Networks (cGAN) model, to translate CT images from low-to-routine dose. However, when aiming to produce realistic images, such generative models may alter critical image content. Therefore, we propose to employ a frequency-based separation of the input prior to applying the cGAN model, in order to limit the cGAN to high-frequency bands, while leaving low-frequency bands untouched. The results of the proposed method are compared to a state-of-the-art model within the cGAN model as well as in a single-network setting. The proposed method generates visually superior results compared to the single-network model and the cGAN model in terms of quality of texture and preservation of fine structural details. It also appeared that the PSNR, SSIM and TV metrics are less important than a careful visual evaluation of the results. The obtained results demonstrate the relevance of defining and separating the input image into desired and undesired content, rather than blindly denoising entire images. This study shows promising results for further investigation of generative models towards finding a reliable deep learning-based noise reduction algorithm for low-dose CT acquisition.

Generative adversarial networks improve interior computed tomography angiography reconstruction

In interior computed tomography (CT), the x-ray beam is collimated to a limited field-of-view (FOV) (e.g. the volume of the heart) to decrease exposure to adjacent organs, but the resulting image has a severe truncation artifact when reconstructed with traditional filtered back-projection (FBP) type algorithms. In some examinations, such as cardiac or dentomaxillofacial imaging, interior CT could be used to achieve further dose reductions. In this work, we describe a deep learning (DL) method to obtain artifact-free images from interior CT angiography. Our method employs the Pix2Pix generative adversarial network (GAN) in a two-stage process: (1) An extended sinogram is computed from a truncated sinogram with one GAN model, and (2) the FBP reconstruction obtained from that extended sinogram is used as an input to another GAN model that improves the quality of the interior reconstruction. Our double GAN (DGAN) model was trained with 10 000 truncated sinograms simulated from real computed tomography angiography slice images. Truncated sinograms (input) were used with original slice images (target) in training to yield an improved reconstruction (output). DGAN performance was compared with the adaptive de-truncation method, total variation regularization, and two reference DL methods: FBPConvNet, and U-Net-based sinogram extension (ES-UNet). Our DGAN method and ES-UNet yielded the best root-mean-squared error (RMSE) (0.03 ± 0.01), and structural similarity index (SSIM) (0.92 ± 0.02) values, and reference DL methods also yielded good results. Furthermore, we performed an extended FOV analysis by increasing the reconstruction area by 10% and 20%. In both cases, the DGAN approach yielded best results at RMSE (0.03 ± 0.01 and 0.04 ± 0.01 for the 10% and 20% cases, respectively), peak signal-to-noise ratio (PSNR) (30.5 ± 2.6 dB and 28.6 ± 2.6 dB), and SSIM (0.90 ± 0.02 and 0.87 ± 0.02). In conclusion, our method was able to not only reconstruct the interior region with improved image quality, but also extend the reconstructed FOV by 20%.

Classification of moving coronary calcified plaques based on motion artifacts using convolutional neural networks: a robotic simulating study on influential factors

BACKGROUND

Motion artifacts affect the images of coronary calcified plaques. This study utilized convolutional neural networks (CNNs) to classify the motion-contaminated images of moving coronary calcified plaques and to determine the influential factors for the classification performance.

METHODS

Two artificial coronary arteries containing four artificial plaques of different densities were placed on a robotic arm in an anthropomorphic thorax phantom. Each artery moved linearly at velocities ranging from 0 to 60 mm/s. CT examinations were performed with four state-of-the-art CT systems. All images were reconstructed with filtered back projection and at least three levels of iterative reconstruction. Each examination was performed at 100%, 80% and 40% radiation dose. Three deep CNN architectures were used for training the classification models. A five-fold cross-validation procedure was applied to validate the models.

RESULTS

The accuracy of the CNN classification was 90.2 ± 3.1%, 90.6 ± 3.5%, and 90.1 ± 3.2% for the artificial plaques using Inception v3, ResNet101 and DenseNet201 CNN architectures, respectively. In the multivariate analysis, higher density and increasing velocity were significantly associated with higher classification accuracy (all P < 0.001). The classification accuracy in all three CNN architectures was not affected by CT system, radiation dose or image reconstruction method (all P > 0.05).

CONCLUSIONS

The CNN achieved a high accuracy of 90% when classifying the motion-contaminated images into the actual category, regardless of different vendors, velocities, radiation doses, and reconstruction algorithms, which indicates the potential value of using a CNN to correct calcium scores.

Optimizing iterative reconstruction for quantification of calcium hydroxyapatite with photon counting flat-detector computed tomography: a cardiac phantom study

Purpose: Coronary artery calcium (CAC) scoring with computed tomography (CT) has been proposed as a screening tool for coronary artery disease, but concerns remain regarding the radiation dose of CT CAC scoring. Photon counting detectors and iterative reconstruction (IR) are promising approaches for patient dose reduction, yet the preservation of CAC scores with IR has been questioned. The purpose of this study was to investigate the applicability of IR for quantification of CAC using a photon counting flat-detector. Approach: We imaged a cardiac rod phantom with calcium hydroxyapatite (CaHA) inserts with different noise levels using an experimental photon counting flat-detector CT setup to simulate the clinical CAC scoring protocol. We applied filtered back projection (FBP) and two IR algorithms with different regularization strengths. We compared the air kerma values, image quality parameters [noise magnitude, noise power spectrum, modulation transfer function (MTF), and contrast-to-noise ratio], and CaHA quantification accuracy between FBP and IR. Results: IR regularization strength influenced CAC scores significantly ( p < 0.05 ). The CAC volumes and scores between FBP and IRs were the most similar when the IR regularization strength was chosen to match the MTF of the FBP reconstruction. Conclusion: When the regularization strength is selected to produce comparable spatial resolution with FBP, IR can yield comparable CAC scores and volumes with FBP. Nonetheless, at the lowest radiation dose setting, FBP produced more accurate CAC volumes and scores compared to IR, and no improved CAC scoring accuracy at low dose was demonstrated with the utilized IR methods.

Low-dose MDCT: evaluation of the impact of systematic tube current reduction and sparse sampling on quantitative paraspinal muscle assessment

Background

Wasting disease entities like cachexia or sarcopenia are associated with a decreasing muscle mass and changing muscle composition. For valid and reliable disease detection and monitoring diagnostic techniques offering quantitative musculature assessment are needed. Multi-detector computed tomography (MDCT) is a broadly available imaging modality allowing for muscle composition analysis. A major disadvantage of using MDCT for muscle composition assessment is the radiation exposure. In this study we evaluated the performance of different methods of radiation dose reduction for paravertebral muscle composition assessment.

Methods

MDCT scans of eighteen subjects (6 males, age: 71.5±15.9 years, and 12 females, age: 71.0±8.9 years) were retrospectively simulated as if they were acquired at 50%, 10%, 5%, and 3% of the original X-ray tube current or number of projections (i.e., sparse sampling). Images were reconstructed with a statistical iterative reconstruction (SIR) algorithm. Paraspinal muscles (psoas and erector spinae muscles) at the level of L4 were segmented in the original-dose images. Segmentations were superimposed on all low-dose scans and muscle density (MD) extracted.

Results

Sparse sampling derived mean MD showed no significant changes (P=0.57 and P=0.22) down to 5% of the original projections in the erector spinae and psoas muscles, respectively. All virtually reduced tube current series showed significantly different (P>0.05) mean MD in the psoas and erector spinae muscles as compared to the original dose except for the images of 5% of the original tube current in the erector spinae muscle.

Conclusions

Our findings demonstrated the possibility of considerable radiation dose reduction using MDCT scans for assessing the composition of the paravertebral musculature. The sparse sampling approach seems to be promising and a potentially superior technique for dose reduction as compared to tube current reduction.

Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compared to partial model-based iterative reconstruction

OBJECTIVES

To compare deep learning (True Fidelity, TF) and partial model based Iterative Reconstruction (ASiR-V) algorithm for image texture, low contrast lesion detectability and potential dose reduction.

METHODS

Anthropomorphic phantoms (mimicking non-overweight and overweight patient), containing lesions of 6 mm in diameter with 20HU contrast, were scanned at five different dose levels (2,6,10,15,20 mGy) on a CT system, using clinical routine protocols for liver lesion detection. Images were reconstructed using ASiR-V 0% (surrogate for FBP), 60 % and TF at low, medium and high strength. Noise texture was characterized by computing a normalized Noise Power Spectrum filtered by an eye filter. The similarity against FBP texture was evaluated using peak frequency difference (PFD) and root mean square deviation (RMSD). Low contrast detectability was assessed using a channelized Hotelling observer and the area under the ROC curve (AUC) was used as figure of merit. Potential dose reduction was calculated to obtain the same AUC for TF and ASiR-V.

RESULTS

FBP-like noise texture was more preserved with TF (PFD from -0.043mm-1 to -0.09mm-1, RMSD from 0.12mm-1 to 0.21mm-1) than with ASiR-V (PFD equal to 0.12 mm-1, RMSD equal to 0.53mm-1), resulting in a sharper image. AUC was always higher with TF than ASIR-V. In average, TF compared to ASiR-V, enabled a radiation dose reduction potential of 7%, 25 % and 33 % for low, medium and high strength respectively.

CONCLUSION

Compared to ASIR-V, TF at high strength does not impact noise texture and maintains low contrast liver lesions detectability at significant lower dose.

Influence of tube voltage, tube current and newer iterative reconstruction algorithms in CT perfusion imaging in rabbit liver VX2 tumors

PURPOSE

We aimed to explore the influence of tube voltage, current and iterative reconstruction (IR) in computed tomography perfusion imaging (CTPI) and to compare CTPI parameters with microvessel density (MVD).

METHODS

Hepatic CTPI with three CTPI protocols (protocol A, tube voltage/current 80 kV/40 mAs; protocol B, tube voltage/current 80 kV/80 mAs; protocol C: tube voltage/current 100 kV/80 mAs) were performed in 25 rabbit liver VX2 tumor models, and filtered back projection (FBP) and IR were used for reconstruction of raw data. Hepatic arterial perfusion (HAP), hepatic portal perfusion (HPP), total perfusion (TP), hepatic arterial perfusion index (HPI), blood flow (BF) and blood volume (BV) of VX2 tumor and normal hepatic parenchyma were measured. Image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were quantified and radiation dose was recorded. MVD was counted using CD34 stain and compared with CTPI parameters.

RESULTS

The highest radiation dose was found in protocol C, followed by protocols B and A. IR lowered image noise and improved SNR and CNR in all three protocols. There was no statistical difference between HAP, HPP, TP, HPI, BF and BV of VX2 tumor and normal hepatic parenchyma among the three protocols (P > 0.05) with FBP or IR reconstruction, and no statistical difference between IR and FBP reconstruction (P > 0.05) in either protocol. MVD had a positive linear correlation with HAP, TP, BF, with best correlation observed with HAP; MVD of VX2 tumor showed no or poor correlation with HPI and BV.

CONCLUSION

CTPI parameters are not affected by tube voltage, current or reconstruction algorithm; HAP can best reflect MVD, but no correlation exists between BV and MVD.

Weakly-supervised progressive denoising with unpaired CT images

Although low-dose CT imaging has attracted a great interest due to its reduced radiation risk to the patients, it suffers from severe and complex noise. Recent fully-supervised methods have shown impressive performances on CT denoising task. However, they require a huge amount of paired normal-dose and low-dose CT images, which is generally unavailable in real clinical practice. To address this problem, we propose a weakly-supervised denoising framework that generates paired original and noisier CT images from unpaired CT images using a physics-based noise model. Our denoising framework also includes a progressive denoising module that bypasses the challenges of mapping from low-dose to normal-dose CT images directly via progressively compensating the small noise gap. To quantitatively evaluate diagnostic image quality, we present the noise power spectrum and signal detection accuracy, which are well correlated with the visual inspection. The experimental results demonstrate that our method achieves remarkable performances, even superior to fully-supervised CT denoising with respect to the signal detectability. Moreover, our framework increases the flexibility in data collection, allowing us to utilize any unpaired data at any dose levels.

Low-Dose MDCT of Patients With Spinal Instrumentation Using Sparse Sampling: Impact on Metal Artifacts

OBJECTIVE. The purpose of our study was to evaluate simulated sparse-sampled MDCT combined with statistical iterative reconstruction (SIR) for low-dose imaging of patients with spinal instrumentation. MATERIALS AND METHODS. Thirty-eight patients with implanted hardware after spinal instrumentation (24 patients with short- or long-term instrumentation-related complications [i.e., adjacent segment disease, screw loosening or implant failure, or postoperative hematoma or seroma] and 14 control subjects with no complications) underwent MDCT. Scans were simulated as if they were performed with 50% (P50), 25% (P25), 10% (P10), and 5% (P5) of the projections of the original acquisition using an in-house-developed SIR algorithm for advanced image reconstructions. Two readers performed qualitative image evaluations of overall image quality and artifacts, image contrast, inspection of the spinal canal, and diagnostic confidence (1 = high, 2 = medium, and 3 = low confidence). RESULTS. Although overall image quality decreased and artifacts increased with reductions in the number of projections, all complications were detected by both readers when 100% of the projections of the original acquisition (P100), P50, and P25 imaging data were used. For P25 data, diagnostic confidence was still high (mean score ± SD: reader 1, 1.2 ± 0.4; reader 2, 1.3 ± 0.5), and interreader agreement was substantial to almost perfect (weighted Cohen κ = 0.787-0.855). The mean volumetric CT dose index was 3.2 mGy for P25 data in comparison with 12.6 mGy for the original acquisition (P100 data). CONCLUSION. The use of sparse sampling and SIR for low-dose MDCT in patients with spinal instrumentation facilitated considerable reductions in radiation exposure. The use of P25 data with SIR resulted in no missed complications related to spinal instrumentation and allowed high diagnostic confidence, so using only 25% of the projections is probably enough for accurate and confident diagnostic detection of major instrumentation-related complications.

Deep learning-based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms

Objectives

To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT).

Methods

Cerebral NCCT acquisitions of 50 consecutive patients were reconstructed using DLR, Hybrid-IR and MBIR with a clinical CT system. Image quality, in terms of six subjective characteristics (noise, sharpness, grey-white matter differentiation, artefacts, natural appearance and overall image quality), was scored by five observers. As objective metrics of image quality, the noise magnitude and signal-difference-to-noise ratio (SDNR) of the grey and white matter were calculated. Mean values for the image quality characteristics scored by the observers were estimated using a general linear model to account for multiple readers. The estimated means for the reconstruction methods were pairwise compared. Calculated measures were compared using paired t tests.

Results

For all image quality characteristics, DLR images were scored significantly higher than MBIR images. Compared to Hybrid-IR, perceived noise and grey-white matter differentiation were better with DLR, while no difference was detected for other image quality characteristics. Noise magnitude was lower for DLR compared to Hybrid-IR and MBIR (5.6, 6.4 and 6.2, respectively) and SDNR higher (2.4, 1.9 and 2.0, respectively). Reconstruction times were 27 s, 44 s and 176 s for Hybrid-IR, DLR and MBIR respectively.

Conclusions

With a slight increase in reconstruction time, DLR results in lower noise and improved tissue differentiation compared to Hybrid-IR. Image quality of MBIR is significantly lower compared to DLR with much longer reconstruction times.

Key Points

• Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction.

• Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction.

• Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-020-07668-x.

Efficient radiation dose reduction in whole-brain CT perfusion imaging using a 3D GAN: Performance and clinical feasibility

Dose reduction in cerebral CT perfusion (CTP) imaging is desirable but is accompanied by an increase in noise that can compromise the image quality and the accuracy of image-based haemodynamic modelling used for clinical decision support in acute ischaemic stroke. The few reported methods aimed at denoising low-dose CTP images lack practicality by considering only small sections of the brain or being computationally expensive. Moreover, the prediction of infarct and penumbra size and location - the chief means of decision support for treatment options - from denoised data has not been explored using these approaches. In this work, we present the first application of a 3D generative adversarial network (3D GAN) for predicting normal-dose CTP data from low-dose CTP data. Feasibility of the approach was tested using real data from 30 acute ischaemic stroke patients in conjunction with low dose simulation. The 3D GAN model was applied to 64^3 voxel patches extracted from two different configurations of the CTP data- frame-based and stacked. The method led to whole-brain denoised data being generated for haemodynamic modelling within 90 seconds. Accuracy of the method was evaluated using standard image quality metrics and the extent to which the clinical content and lesion characteristics of the denoised CTP data were preserved. Results showed an average improvement of 5.15-5.32 dB PSNR and 0.025-0.033 SSIM for CTP images and 2.66-3.95 dB PSNR and 0.036-0.067 SSIM for functional maps at 50% and 25% of normal dose using GAN model in conjunction with a stacked data regime for image synthesis. Consequently, the average lesion volumetric error reduced significantly (p-value < 0.05) by 18-29% and dice coefficient improved significantly by 15-22%. We conclude that GAN-based denoising is a promising practical approach for reducing radiation dose in CTP studies and improving lesion characterisation.

A prospective study on the use of ultralow-dose computed tomography with iterative reconstruction for the follow-up of patients liver and renal abscess

Background

Radiation dose reduction is a major concern in patients who undergo computed tomography (CT) to follow liver and renal abscess.

Objectives

The purpose of this study is to investigate the feasibility of ultralow-dose CT with iterative reconstruction (IR) to follow patients with liver and renal abscess.

Methods

This prospective study included 18 patients who underwent ultralow-dose CT with IR to follow abscesses (liver abscesses in 10 patients and renal abscesses in 8 patients; ULD group). The control group consisted of 14 patients who underwent follow-up standard-dose CT for liver abscesses during the same period. The objective image noise was evaluated by measuring standard deviation (SD) in the liver and subcutaneous fat to select a specific IR for qualitative analysis. Two radiologists independently evaluated subjective image quality, noise, and diagnostic confidence to evaluate abscess using a five-point Likert scale. Qualitative parameters were compared between the ULD and control groups with the Mann-Whitney U test.

Results

The mean CT dose index volume and dose length product of standard-dose CT were 8.7 ± 1.8 mGy and 555.8 ± 142.8 mGy·cm, respectively. Mean dose reduction of ultralow-dose CT was 71.8% compared to standard-dose CT. After measuring SDs, iDose level 5, which showed similar SD to standard-dose CT in both the subcutaneous fat and liver (P = 0.076, and P = 0.124), was selected for qualitative analysis. Ultralow-dose CT showed slightly worse subjective image quality (P < 0.001 for reader 1, and P = 0.005 for reader 2) and noise (P = 0.004 for reader 1, and P = 0.001 for reader 2) than standard-dose CT. However, the diagnostic confidence of ultralow-dose CT for evaluating abscess was comparably excellent to standard-dose CT (P = 0.808 for reader 1, and P = 0.301 for reader 2).

Conclusions

Ultralow-dose CT with IR can be used in the follow-up of liver and renal abscess with comparable diagnostic confidence.

Imaging pediatric acute head trauma using 100-kVp low dose CT with adaptive statistical iterative reconstruction (ASIR-V) in single rotation on a 16 cm wide-detector CT

OBJECTIVE

To demonstrate the ability of achieving low dose and high-quality head CT images for children with acute head trauma using 100 kVp and adaptive statistical iterative reconstruction (ASIR-V) algorithm in single rotation on a 16 cm wide-detector system.

MATERIALS AND METHODS

We retrospectively analyzed the CT dose index (CTDI) and image quality of 104 children aged 0-6 years with acute head trauma (1 hour -3 days) in two groups: Group 1(n = 50) on a 256-row CT with single rotation at a reduced-dose of 100 kVp/240 mA and reconstructed using ASIR-V at 70%level; Group 2(n = 54) on a 64-row CT with multiple rotations at a standard dose of 120 kVp/ 180mA and reconstructed using a conventional filtered back-projection (FBP). Both groups used the 0.5 s/r axial scan mode. CT dose index (CTDI) and quantitative image quality measurements were compared using the Student t test; qualitative image quality comparison was carried out using Mann-Whitney rank test and the inter-reviewer agreement was evaluated using Kappa test.

RESULTS

The exposure time was 0.5 s for Group 1 and 3.27±0.29 s for Group 2. The CTDI in Group 1 was 9.74±0.86mGy, 36.38%lower than the 15.31mGy in Group 2 (p < 0.001). Group 1 and Group 2 had similar artifact index (2.06±1.06 vs. 2.37±1.18) in the cerebellar hemispheres, and similar contrast-to-noise ratio (2.32±0.83 vs. 1.69±0.68), (1.47±0.72 vs. 1.10±0.43) respectively for cerebellum and thalamus (p > 0.05). Image quality was acceptable for diagnosis, and motion artifacts were reduced in Group 1 (p < 0.001).

CONCLUSION

Single rotation CT with 100 kVp and 70%ASIR-V on 16 cm wide-detector CT reduces radiation dose and motion artifacts for children with acute head trauma without compromising diagnostic quality as compared with standard dose protocol. Thus, it provides a novel imaging method in management of pediatric acute head trauma.

Scoring Osteoarthritis Reliably in Large Joints and the Spine Using Whole-Body CT: OsteoArthritis Computed Tomography-Score (OACT-Score)

A standardized method to assess structural osteoarthritis (OA) burden thorough the body lacks from literature. Such a method can be valuable in developing personalized treatments for OA. We developed a reliable scoring system to evaluate OA in large joints and the spine-the OsteoArthritis Computed Tomography (OACT) score, using a convenience sample of 197 whole-body low-dose non-contrast CTs. An atlas, containing example images as reference points for training and scoring, are presented. Each joint was graded between 0-3. The total OA burden was calculated by summing scores of individual joints. Intra- and inter-observer reliability was tested 25 randomly selected scans (N = 600 joints). Intra-observer reliability and inter-observer reliability between three observers was assessed using intraclass correlation coefficient (ICC) and square-weighted kappa statistics. The square-weighted kappa for intra-observer reliability for OACT-score at joint-level ranged from 0.79 to 0.95; the ICC for the total OA grade was 0.97 (95%-CI, 0.94 to 0.99). Square-weighted kappa for interobserver reliability ranged from 0.48 to 0.95; the ICC for the total OA grade was 0.95 (95%-CI, 0.90 to 0.98). The OACT score, a new reproducible CT-based grading system reflecting OA burden in large joints and the spine, has a satisfactory reproducibility. The atlas can be used for research purposes, training, educational purposes and systemic grading of OA on CT-scans.

On the optimization of bone SPECT/CT in terms of image quality and radiation dose

INTRODUCTION

The purpose of this study was to present the optimization process of CT parameters to reduce patient exposure during bone SPECT/CT without affecting the quality of SPECT images with attenuation correction (AC).

MATERIAL AND METHODS

A fillable phantom reflecting realistic bone scintigraphy conditions was developed and acquired on an AnyScan SC. SPECT/CT scans were carried out with different x-ray tube current values (10, 20, 30, 40, 50, 60, 70, 90, 110, 130, 150, and 200 mA) at three different high-voltage values (80, 100, and 120 kV). The contrast (C) and coefficients of variation (CV) in the SPECT images as well as the signal-to-noise ratio (SNR) and noise (SD_CT ) in the CT images with CTDI_vol were measured. An optimal acquisition protocol that obtained SPECT/CT images with no artifacts on both CT and SPECT images, acceptable C, SNR, CV, and SD_CT values, and the largest reduction in patient exposure compared to the reference acquisition procedure was sought.

RESULTS

The optimal set of parameters for bone SPECT/CT was determined based on a phantom study. It has been implemented in clinical practice. Two groups of patients were examined according to the baseline and optimized protocols, respectively. The new SPECT/CT protocol substantially reduced patients' radiation exposure compared to the old protocol while maintaining the required diagnostic quality of SPECT and CT images.

CONCLUSIONS

In the study, we present a methodology that finds a compromise between diagnostic information and patient exposure during bone SPECT/CT procedures.

Technological developments of X-ray computed tomography over half a century: User's influence on protocol optimization

Since the introduction of Computed Tomography (CT), technological improvements have been impressive. At the same time, the number of adjustable acquisition and reconstruction parameters has increased substantially. Overall, these developments led to improved image quality at a reduced radiation dose. However, many parameters are interrelated and part of automated algorithms. This makes it more complicated to adjust them individually and more difficult to comprehend their influence on CT protocol adjustments. Moreover, the user's influence in adapting protocol parameters is sometimes limited by the manufacturer's policy or the user's knowledge. As a consequence, optimization can be a challenge. A literature search in Embase, Medline, Cochrane, and Web of Science was performed. The literature was reviewed with the objective to collect information regarding technological developments in CT over the past five decades and the role of the associated acquisition and reconstruction parameters in the optimization process.

Low-dose MDCT: evaluation of the impact of systematic tube current reduction and sparse sampling on the detection of degenerative spine diseases

OBJECTIVES

To investigate potential radiation dose reduction for multi-detector computed tomography (MDCT) exams of the spine by using sparse sampling and virtually lowered tube currents combined with statistical iterative reconstruction (SIR).

METHODS

MDCT data of 26 patients (68.9 ± 11.7 years, 42.3% males) were retrospectively simulated as if the scans were acquired at 50%, 10%, 5%, and 3% of the original X-ray tube current or number of projections, using SIR for image reconstructions. Two readers performed qualitative image evaluation considering overall image quality, artifacts, and contrast and determined the number and type of degenerative changes. Scoring was compared between readers and virtual low-dose and sparse-sampled MDCT, respectively.

RESULTS

Image quality and contrast decreased with virtual lowering of tube current and sparse sampling, but all degenerative changes were correctly detected in MDCT with 50% of tube current as well as MDCT with 50% of projections. Sparse-sampled MDCT with only 10% of initial projections still enabled correct identification of all degenerative changes, in contrast to MDCT with virtual tube current reduction by 90% where non-calcified disc herniations were frequently missed (R1: 23.1%, R2: 21.2% non-diagnosed herniations). The average volumetric CT dose index (CTDI_vol) was 1.4 mGy for MDCT with 10% of initial projections, compared with 13.8 mGy for standard-dose imaging.

CONCLUSIONS

MDCT with 50% of original tube current or projections using SIR still allowed for accurate diagnosis of degenerative changes. Sparse sampling may be more promising for further radiation dose reductions since no degenerative changes were missed with 10% of initial projections.

KEY POINTS

• Most common degenerative changes of the spine can be diagnosed in multi-detector CT with 50% of tube current or number of projections. • Sparse-sampled multi-detector CT with only 10% of initial projections still enables correct identification of degenerative changes, in contrast to imaging with 10% of original tube current. • Sparse sampling may be a promising option for distinct lowering of radiation dose, reducing the CTDI_vol from 13.8 to 1.4 mGy in the study cohort.

Evaluation of an integrated 3D-printed phantom for coronary CT angiography using iterative reconstruction algorithm

INTRODUCTION

3D-printed imaging phantoms are now increasingly available and used for computed tomography (CT) dose optimisation study and image quality analysis. The aim of this study was to evaluate the integrated 3D-printed cardiac insert phantom when evaluating iterative reconstruction (IR) algorithm in coronary CT angiography (CCTA) protocols.

METHODS

The 3D-printed cardiac insert phantom was positioned into a chest phantom and scanned with a 16-slice CT scanner. Acquisitions were performed with CCTA protocols using 120 kVp at four different tube currents, 300, 200, 100 and 50 mA (protocols A, B, C and D, respectively). The image data sets were reconstructed with a filtered back projection (FBP) and three different IR algorithm strengths. The image quality metrics of image noise, signal-noise ratio (SNR) and contrast-noise ratio (CNR) were calculated for each protocol.

RESULTS

Decrease in dose levels has significantly increased the image noise, compared to FBP of protocol A (P < 0.001). As a result, the SNR and CNR were significantly decreased (P < 0.001). For FBP, the highest noise with poor SNR and CNR was protocol D with 19.0 ± 1.6 HU, 18.9 ± 2.5 and 25.1 ± 3.6, respectively. For IR algorithm, the highest strength (AIDR3Dstrong ) yielded the lowest noise with excellent SNR and CNR.

CONCLUSIONS

The use of IR algorithm and increasing its strengths have reduced noise significantly and thus increased the SNR and CNR when compared to FBP. Therefore, this integrated 3D-printed phantom approach could be used for dose optimisation study and image quality analysis in CCTA protocols.

Computed tomography medical image reconstruction on affordable equipment by using Out-Of-Core techniques

BACKGROUND AND OBJECTIVE

As Computed Tomography scans are an essential medical test, many techniques have been proposed to reconstruct high-quality images using a smaller amount of radiation. One approach is to employ algebraic factorization methods to reconstruct the images, using fewer views than the traditional analytical methods. However, their main drawback is the high computational cost and hence the time needed to obtain the images, which is critical in the daily clinical practice. For this reason, faster methods for solving this problem are required.

METHODS

In this paper, we propose a new reconstruction method based on the QR factorization that is very efficient on affordable equipment (standard multicore processors and standard Solid-State Drives) by using Out-Of-Core techniques.

RESULTS

Combining both affordable hardware and the new software proposed in our work, the images can be reconstructed very quickly and with high quality. We analyze the reconstructions using real Computed Tomography images selected from a dataset, comparing the QR method to the LSQR and FBP. We measure the quality of the images using the metrics Peak Signal-To-Noise Ratio and Structural Similarity Index, obtaining very high values. We also compare the efficiency of using spinning disks versus Solid-State Drives, showing how the latter performs the Input/Output operations in a significantly lower amount of time.

CONCLUSIONS

The results indicate that our proposed me thod and software are valid to efficiently solve large-scale systems and can be applied to the Computed Tomography reconstruction problem to obtain high-quality images.

Low-Dose Computed Tomographic Scans for Postoperative Evaluation of Craniomaxillofacial Fractures: A Pilot Clinical Study

Computed tomographic scans are frequently obtained following craniomaxillofacial fracture reconstruction. The additive radiation from such scans is not trivial; cumulative radiation exposure poses stochastic health risks. In this article, the authors postulate that a low-dose computed tomography protocol provides adequate image quality for postoperative evaluation of reconstructed craniomaxillofacial fractures. This study included patients for whom a computed tomographic scan was indicated following craniomaxillofacial fracture repair at a Level I trauma center. Postoperative craniomaxillofacial computed tomography was performed using a low-dose protocol, rather than standard protocols. A craniomaxillofacial surgeon and a radiologist interpreted the images to determine whether they were of sufficient quality. It was decided a priori that any inadequate low-dose computed tomography would require repeated scanning using standard protocols. The primary endpoint was the need for repeated computed tomography. In addition, the clarity of clinically significant anatomical landmarks on the images was graded on a five-point Likert scale. Twenty patients were scanned postoperatively using the low-dose protocol. Mean radiation dose (total dose-length product) from the low-dose protocol was 71 mGy · cm versus 532 mGy · cm for the preoperative computed tomographic scans that were obtained using conventional protocols (p < 0.001). All 20 low-dose computed tomographic scans were determined to provide satisfactory image quality. No patients required repeated computed tomography secondary to poor image quality. Low-dose computed tomography received high image-quality scores. A low-dose computed tomography protocol that delivers approximately 7.5-fold less radiation than the standard protocols was found to be adequate for postoperative evaluation of craniomaxillofacial fractures. Larger prospective studies may be warranted. CLINICAL QUESTION/LEVEL OF EVIDENCE:: Therapeutic, IV.

Cardiac computed tomography angiography in the paediatric population: Expert consensus from the Filiale de cardiologie pédiatrique et congénitale (FCPC) and the Société française d'imagerie cardiaque et vasculaire diagnostique et interventionnelle (SFICV)

This paper aims to provide a paediatric cardiac computed tomography angiography expert panel consensus based on the opinions of experts from the Société française d'imagerie cardiaque et vasculaire diagnostique et interventionnelle (SFICV) and the Filiale de cardiologie pédiatrique congénitale (FCPC). This expert panel consensus includes recommendations for indications, patient preparation, computed tomography angiography radiation dose reduction techniques and postprocessing techniques. We think that to realize its full potential and to avoid pitfalls, cardiac computed tomography angiography in children with congenital heart disease requires training and experience. Moreover, paediatric cardiac computed tomography angiography protocols should be standardized to acquire optimal images in this population with the lowest radiation dose possible, to prevent unnecessary radiation exposure. We also provide a suggested structured report and a list of acquisition protocols and technical parameters in relation to specific vendors.

Pediatric cardiac computed tomography angiography: Expert consensus from the Filiale de Cardiologie Pédiatrique et Congénitale (FCPC) and the Société Française d'Imagerie Cardiaque et Vasculaire diagnostique et interventionnelle (SFICV)

This article was designed to provide a pediatric cardiac computed tomography angiography (CCTA) expert panel consensus based on opinions of experts of the Société Française d'Imagerie Cardiaque et Vasculaire diagnostique et interventionnelle (SFICV) and of the Filiale de Cardiologie Pédiatrique Congénitale (FCPC). This expert panel consensus includes recommendations for indications, patient preparation, CTA radiation dose reduction techniques, and post-processing techniques. The consensus was based on data from available literature (original papers, reviews and guidelines) and on opinions of a group of specialists with extensive experience in the use of CT imaging in congenital heart disease. In order to reach high potential and avoid pitfalls, CCTA in children with congenital heart disease requires training and experience. Moreover, pediatric cardiac CCTA protocols should be standardized to acquire optimal images in this population with the lowest radiation dose possible to prevent unnecessary radiation exposure. We also provided a suggested structured report and a list of acquisition protocols and technical parameters in relation to specific vendors.

Quantification of metal artifacts in computed tomography: methodological considerations

Numerous methods for artifact quantification in computed tomography (CT) imaging have been suggested. This study evaluated their utility with regards to correspondence with visual artifact perception and reproducibility. Two titanium rods (5 and 10 mm) were examined with 25 different scanning- and image-reconstruction parameters resulting in different types and extents of artifacts. Four radiologists evaluated every image against each other using an in-house developed software. Rating was repeated two times (2,400 comparisons = 2 times × 4 readers × 300 comparisons). Rankings were combined to obtain a reference ranking. Proposed approaches for artifact quantification include manual measurement of attenuation, standard deviation and noise and sophisticated algorithm-based approaches within the image- and frequency-domain. Two radiologists conducted manual measurements twice while the aforementioned algorithms were implemented within the Matlab-Environment allowing for automated image analysis. The reference ranking was compared to all aforementioned methods for artifact quantification to identify suited approaches. Besides visual analysis, Kappa-statistics and intraclass correlation coefficients (ICC) were used. Intra- and Inter-reader agreements of visual artifact perception were excellent (ICC 0.85-0.92). No quantitative method was able to represent the exact ranking of visually perceived artifacts; however, ICC for manual measurements were low (ICC 0.25-0.97). The method that showed best correspondence and reproducibility used a Fourier-transformed linear ROI and lower-end frequency bins. Automated measurements of artifact extent should be preferred over manual measurements as the latter show a limited reproducibility. One method that allows for automated quantification of such artefacts is made available as an electronic supplement.

Comparisons of Hounsfield Unit Linearity between Images Reconstructed using an Adaptive Iterative Dose Reduction (AIDR) and a Filter Back-Projection (FBP) Techniques

Background:

The HU linearity is an essential parameter in a quantitative imaging and the treatment planning systems of radiotherapy.

Objective:

This study aims to evaluate the linearity of Hounsfield unit (HU) in applying the adaptive iterative dose reduction (AIDR) on CT scanner and its comparison to the filtered back-projection (FBP).

Material and Methods:

In this experimental phantom study, a TOS-phantom was scanned using a Toshiba Alexion 6 CT scanner. The images were reconstructed using the FBP and AIDR. Measurements of HU and noise values were performed on images of the “HU linearity” module of the TOS-phantom. The module had five embedded objects, i.e., air, polypropylene, nylon, acrylic, and Delrin. On each object, a circle area of 4.32 cm² was drawn and used to measure HU and noise values. The R² of the relation between mass densities vs. HU values was used to measure HU linearities at four different tube voltages. The Mann-Whitney U test was used to compare unpaired data and p-value < 0.05 was considered statistically significant.

Results:

The AIDR method produced a significant smaller image noise than the FBP method (p-value < 0.05). There were no significant differences in HU values of images reconstructed using FBP and AIDR methods (p-value > 0.05). The HU values acquired by the methods showed the same linearity marked by coinciding linear lines with the same R² value (> 0.999).

Conclusion:

AIDR methods produce the HU linearity as FBP methods with a smaller image noise level.