CT Image Quality Research Articles

Application of Multi-Model Adaptive Statistical Iterative Reconstruction-Veo in Ultra-Low Dose Chest CT Examination of Children in Plateau Area.

Objective To explore the application value of multi-model adaptive statistical iterative reconstruction-Veo (ASiR-V) in ultra-low dose chest CT examination of children in the plateau area. Methods The children who underwent chest CT examination in Xizang Autonomous Region People's Hospital were enrolled in this study and assigned into two groups according to the scanning conditions.Group A underwent scanning at a tube voltage of 100 kV and ASiR-V 50% reconstruction,and group B underwent scanning at a tube voltage of 80 kV and ASiR-V 0 (Group B1) and ASiR-V 50% (Group B2) reconstruction.The image quality of each group was evaluated objectively and subjectively.The radiation dose and image quality were compared between groups. Results Groups A and B showed the volume CT dose indexes of (2.33±0.62) mGy and (0.86±0.01) mGy and the dose length products of (65.01±25.12) mGy·cm and (23.55±3.38) mGy·cm,respectively,which presented differences between groups (both P<0.001).The image noise in the bilateral upper and middle lung areas in group B2 was lower than that in group B1 but higher than that in group A (all P<0.001).There was no significant difference in image quality score of the lung window among groups (all P>0.05).Groups A,B1,and B2 had no significant differences in ascending aorta (P=0.538) or liver CT value (P=0.175) in the mediastinal window.The signal-to-noise ratios and contrast-to-noise ratios of ascending aorta and liver in group B2 were higher than those in group B1 (all P<0.001) and lower than those in group A (all P<0.05).The image quality score of the mediastinal window followed a descending order of group A>group B2>group B1 (all P<0.001). Conclusion ASiR-V combined with low tube voltage can effectively reduce the radiation dose and guarantee the image quality of chest CT of children in the plateau area.

CT radiation dose reduction with tin filter for localisation/characterisation level image quality in PET-CT: a phantom study

BackgroundThe tin filter has allowed radiation dose reduction in some standalone diagnostic computed tomography (CT) applications. Yet, ‘low-dose’ CT scans are commonly used in positron emission tomography (PET)-CT for lesion localisation/characterisation (L/C), with higher noise tolerated. Thus, dose reductions permissible with the tin filter at this image quality level may differ. The aim was to determine the level of CT dose reduction permitted with the tin filter in PET-CT, for comparable image quality to the clinical reference standard (CRS) L/C CT images acquired with standard filtration.Materials and methodsA whole-body CT phantom was scanned with standard filtration in CRS protocols, using 120 kV with 20mAs-ref for bone L/C (used in 18F-Sodium Fluoride (NaF) PET-CT) and 40mAs-ref for soft tissue L/C (used in 18F-Fluorodeoxyglucose (FDG) PET-CT), followed by tin filter scans at 100 kV (Sn100kV) and 140 kV (Sn140kV) with a range of mAs settings. For each scan, effective dose (ED) in an equivalent-sized patient was calculated, and image quality determined in 5 different tissues through quantitative (contrast-to-noise ratio) and qualitative (visual) analyses. The relative dose reductions which could be achieved with the tin filter for comparable image quality to CRS images were calculated.ResultsQuantitative analysis demonstrated dose savings of 50–76% in bone, 27–51% in lung and 8–61% in soft tissue with use of the tin filter at Sn100kV. Qualitative analysis demonstrated dose reductions using Sn100kV in general agreement with the dose reductions indicated by quantitative analysis. Overall, CT dose reductions of around 85% were indicated for NaF bone PET-CT, allowing whole-body CT at just 0.2mSv ED, and a 30–40% CT dose reduction for FDG PET-CT using Sn100kV (1.7-2.0mSv), providing comparable image quality to current CRS images with standard filtration. Sn140kV demonstrated limited value in CT dose reduction.ConclusionsLarge CT dose reductions can be made using the tin filter at Sn100kV, when imaging bone, lung and soft tissue at L/C level CT image quality in PET-CT. As well as reducing the risk of inducing a cancer in later life, such dose reductions may also impact PET-CT practice, such as justifying cross-sectional over planar imaging or justifying PET-CT in younger patients.

Low-dose lung CT: Optimizing diagnostic radiation dose – A phantom study

Background/purposeTo investigate a quantitative method for assessing image quality of low dose lung computed tomography (CT) and find the lowest exposure dose providing diagnostic images. MethodsAxial volumetric lung CT acquisitions (256 slice scanner) were performed on three different sized anthropomorphic phantoms at different dose levels. The maximum steepness of sigmoid curves fitted to line density profiles was measured at lung-to-pleura interfaces. For each phantom, image sharpness was calculated as the median of 468 measurements from 39 different locations. Diagnostic image quality for the adult and paediatric phantom was rated by three radiologists using 4-point Likert scales. The image sharpness cut-off for obtaining adequate image quality was determined from qualitative ratings. ResultsAdequate diagnostic image quality was reached at a median steepness of 713 HU/mm in the adult phantom with a corresponding CTDIvol of 0.14 mGy and an effective dose of 0.13 mSv at a dose level of 100 kVp and 10 mA. In the paediatric phantom diagnostic image quality was reached at a median steepness of 1139 HU/mm with a corresponding CTDIvol of 0.13 mGy and an effective dose of 0.08 mSv at a dose level of 100 kVp and 10 mA. ConclusionsDetermination of image sharpness on line density profiles can be used as quantitative measure for image quality of lung CT. Sufficient-quality lung CT can be achieved at effective radiation doses of 0.13 mSv (adult phantom) and 0.08 mSv (paediatric phantom). These findings suggest that substantial dose reduction is feasible without compromising diagnostic accuracy.

Application of Adaptive Search Window-Based Nonlocal Total Variation Filter in Low-Dose Computed Tomography Images: A Phantom Study

Computed tomography (CT) imaging using low-dose radiation effectively reduces radiation exposure; however, it introduces noise amplification in the resulting image. This study models an adaptive nonlocal total variation (NL-TV) algorithm that efficiently reduces noise in X-ray-based images and applies it to low-dose CT images. In this study, an AAPM CT performance phantom is used, and the resulting image is obtained by applying an annotation filter and a high-pitch protocol. The adaptive NL-TV filter was designed by applying the optimal window value calculated by confirming the difference between Gaussian filtering and the basic NL-TV approach. For quantitative image quality evaluation parameters, contrast-to-noise ratio (CNR), coefficient of variation (COV), and sigma value were used to confirm the noise reduction effectiveness and spatial resolution value. The CNR and COV values in low-dose CT images using the adaptive NL-TV filter, which performed an optimization process, improved by approximately 1.29 and 1.45 times, respectively, compared with conventional NL-TV. In addition, the adaptive NL-TV filter was able to acquire spatial resolution data that were similar to a CT image without applying noise reduction. In conclusion, the proposed NL-TV filter is feasible and effective in improving the quality of low-dose CT images.

Spatial resolution enhancement using deep learning improves chest disease diagnosis based on thick slice CT

CT is crucial for diagnosing chest diseases, with image quality affected by spatial resolution. Thick-slice CT remains prevalent in practice due to cost considerations, yet its coarse spatial resolution may hinder accurate diagnoses. Our multicenter study develops a deep learning synthetic model with Convolutional-Transformer hybrid encoder-decoder architecture for generating thin-slice CT from thick-slice CT on a single center (1576 participants) and access the synthetic CT on three cross-regional centers (1228 participants). The qualitative image quality of synthetic and real thin-slice CT is comparable (p = 0.16). Four radiologists’ accuracy in diagnosing community-acquired pneumonia using synthetic thin-slice CT surpasses thick-slice CT (p < 0.05), and matches real thin-slice CT (p > 0.99). For lung nodule detection, sensitivity with thin-slice CT outperforms thick-slice CT (p < 0.001) and comparable to real thin-slice CT (p > 0.05). These findings indicate the potential of our model to generate high-quality synthetic thin-slice CT as a practical alternative when real thin-slice CT is preferred but unavailable.

Contrast-enhanced thin-slice abdominal CT with super-resolution deep learning reconstruction technique: evaluation of image quality and visibility of anatomical structures.

To compare image quality and visibility of anatomical structures on contrast-enhanced thin-slice abdominal CT images reconstructed using super-resolution deep learning reconstruction (SR-DLR), deep learning-based reconstruction (DLR), and hybrid iterative reconstruction (HIR) algorithms. This retrospective study included 54 consecutive patients who underwent contrast-enhanced abdominal CT. Thin-slice images (0.5mm thickness) were reconstructed using SR-DLR, DLR, and HIR. Objective image noise and contrast-to-noise ratio (CNR) for liver parenchyma relative to muscle were assessed. Two radiologists independently graded image quality using a 5-point rating scale for image noise, sharpness, artifact/blur, and overall image quality. They also graded the visibility of small vessels, main pancreatic duct, ureters, adrenal glands, and right adrenal vein on a 5-point scale. SR-DLR yielded significantly lower objective image noise and higher CNR than DLR and HIR (P < .001). The visual scores of SR-DLR for image noise, sharpness, and overall image quality were significantly higher than those of DLR and HIR for both readers (P < .001). Both readers scored significantly higher on SR-DLR than on HIR for visibility for all structures (P < .01), and at least one reader scored significantly higher on SR-DLR than on DLR for visibility for all structures (P < .05). SR-DLR reduced image noise and improved image quality of thin-slice abdominal CT images compared to HIR and DLR. This technique is expected to enable further detailed evaluation of small structures.

Combination of edge enhancement and cold diffusion model for low dose CT image denoising.

Since the quality of low dose CT (LDCT) images is often severely affected by noise and artifacts, it is very important to maintain high quality CT images while effectively reducing the radiation dose. In recent years, the representation of diffusion models to produce high quality images and stable trainability has attracted wide attention. With the extension of the cold diffusion model to the classical diffusion model, its application has greater flexibility. Inspired by the cold diffusion model, we proposes a low dose CT image denoising method, called CECDM, based on the combination of edge enhancement and cold diffusion model. The LDCT image is taken as the end point (forward) of the diffusion process and the starting point (reverse) of the sampling process. Improved sobel operator and Convolution Block Attention Module are added to the network, and compound loss function is adopted. The experimental results show that CECDM can effectively remove noise and artifacts from LDCT images while the inference time of a single image is reduced to 0.41 s. Compared with the existing LDCT image post-processing methods, CECDM has a significant improvement in all indexes.

Low Dose CT Image Denoising: A Comparative Study of Deep Learning Models and Training Strategies

Article Low Dose CT Image Denoising: A Comparative Study of Deep Learning Models and Training Strategies Heng Zhao 1, Like Qian 1, Yaqi Zhu 1 and Dingcheng Tian 1,2,∗ 1 Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo 315211, China 2 College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110016, China ∗ Correspondence: 2310520@stu.neu.edu.cn Received: 8 August 2024; Revised: 10 October 2024; Accepted: 14 October 2024; Published: 5 November 2024 Abstract: Low-dose computed tomography (LDCT) denoising is an important topic in CT image research. Compared with normal-dose CT images, LDCT can reduce the radiation dose of X-rays, decreasing the radiation burden on the human body, which is beneficial to human health. However, quantum noise caused by low-dose rays will reduce the quality of CT images, thereby decreasing the accuracy of clinical diagnosis. In recent years, deep learning-based denoising methods have shown promising advantages in this field. Researchers have proposed some optimized models for low-dose CT image denoising. These methods have enhanced the application of low-dose CT image denoising from different aspects. From the perspective of experimental research, this paper investigates and evaluates some top deep learning models proposed in the field of low-dose image denoising in recent years, with the aim of determining the best models and training strategies for this task. We conducted experiments on seven deep learning models (REDCNN, EDCNN, QAE, OCTNet, UNet, WGAN, CTformer) on the AAPM dataset and the Piglet dataset. Our research shows that UNet has the best denoising effect among the models, obtaining PSNR = 33.06 (AAPM dataset) and PSNR = 31.21 (Piglet dataset), and good generalization capacity is also observed. However, UNet has a large number of parameters, and the time it takes to process an image is about 8 ms, while EDCNN takes about 4.8 ms to process an image, and its average PSNR is ranked second after UNet. EDCNN strikes a balance between denoising performance and processing efficiency, making it ideal for low-dose CT image denoising tasks.

Interscan reproducibility of computed tomography derived coronary plaque volume measurements

BackgroundCoronary computed tomography angiography (CCTA) enables detailed quantification and characterization of coronary atherosclerotic plaques, offering diagnostic and prognostic value. Interscan reproducibility studies on plaque volume measurements are limited. This study aims to assess the interscan reproducibility of coronary plaque quantification and the implications of clinical and technical characteristics on interscan reproducibility. MethodsCCTA was performed twice in 101 patients with known coronary artery disease at a 1-h interval. The scans were conducted using identical CCTA acquisition protocols. Coronary plaque volumes were quantified using a semi-automated software and performed on a per-lesion, per-vessel, and per-patient level. ResultsMedian plaque volumes were comparable between the first and second CCTA scan. Interscan correlation was high for total plaque (TP), non-calcified plaque (NCP), and calcified plaque (CP) across all analyses (Pearson's coefficient 0.93–0.99), but lower for low-density non-calcified plaque (LD-NCP) volume measurements (Pearson's coefficient 0.74–0.77). Bland-Altman analyses demonstrated higher interscan agreement on a per-patient level compared to on per-vessel and per-lesion level. Interscan reproducibility on CP volumes was affected by CT image quality with narrower LoA in scans with the highest image quality score (p ​= ​0.003), or lowest image reconstructive iteration level (p ​< ​0.001). Limits of agreement were significantly narrower for TP, NCP, and CP volumes in LAD-lesions and vessels compared to non-LAD lesions and vessels (p ​≤ ​0.001). ConclusionOverall reproducibility of repeated CCTA derived plaque measurements by a semi-automated software was modest, and was influenced by image quality, image reconstruction settings, and lesion location.

Benefits of front coating crystalline scintillator screens for phase-contrast synchrotron micro-tomography.

Transparent crystalline scintillators such as cerium-doped YAG or LuAG are widely used in X-ray imaging for the indirect detection of X-rays. The application of reflective coatings on the front side to improve the optical gain is common practice for flat panel detectors with CsI or Gd2O2S powder scintillators but still largely unknown for crystalline scintillators such as LuAG. This work shows experimentally and quantitatively how a black and reflective coating on the X-ray side of a 2 mm LuAG:Ce scintillator improves the image quality compared to a 2 mm LuAG:Ce scintillator without a coating. The measurements have been done for two different distances, with 2 m and 29.7 m on the BM18 beamline of the European Synchrotron. The Modulation Transfer Function (MTF) and the Signal-to-Noise-Ratio (SNR2) power spectrum as well as contrast-to-noise ratio are used for comparing image quality. Propagation-based phase contrast strongly enhances the SNR2 amplitudes (gain ≈10 from 2 m to 29.7 m object-detector distance) of the raw images' spectrum independent of the scintillator coating. For both detector positions, the reflective coating is able to raise SNR2 by up to 80% through the improved optical gain, while black coating does the opposite (decrease SNR2 by 20%) with respect to no coating. With the tested optical setups, changes in MTF /sharpness between the coatings are minor. Comparing CNR2 in CT scans of a multi-material sample, in this case an electric motor, we observe the reflective coating yielding better material contrast for plastic and air. Application and effect of Wiener-deconvolution, along with Paganin-type phase retrieval, are also discussed in the context of CT image quality.

Assessment of cervical spine CT by an image quality audit using qualitative and quantitative methods.

To study the feasibility and assess the correlation of qualitative and quantitative methods for an image quality (IQ) audit of a Cervical spine CT. Five radiologists retrospectively performed a blinded visual grading analysis (VGA) on 20 studies (10 from Protocol 1 and 10 from Protocol 2), using the RANZCR CT IQ Self-Audit worksheet. A Visual Grading Analysis Score (VGAS) and Area under the curve using Visual Grading Characteristics (AUCVGC) were the figures of merit. Quantitative metrics for noise and contrast were correlated to the qualitative assessment. No statistically significant difference was observed in the IQ, VGASProtocol1 = 0.65, 95% CI [0.54, 0.75] and VGASProtocol 2 = 0.73, 95% CI [0.67, 0.79] and AUCVGC = 0.548, 95% CI [0.40, 0.69]. Protocol 2 indicated a statistically significant average dose reduction of 35% in CTDIvol (P = 0.020) and a higher noise; however, the difference was statistically insignificant. There was a moderate correlation between the manual noise measurements in soft tissue and air (P = 0.035) and a strong correlation between the manual and automated noise measurements (P < 0.001). The contrast resolution-based quantitative parameter, EdgeGradientSoft, correlated to the qualitative scores (P = 0.031). Validated VGA tools can be used for IQ audits; however, tailoring the image criteria and rating scale to the clinical practice is suggested. The use of contrast-based IQ metrics showed encouraging results, and further larger-scale studies are needed to explore their potential use in quality management.

Dual-energy computed tomography: pediatric considerations.

Multidetector computed tomography (CT) has revolutionized medicine and is now a fundamental aspect of modern radiology. Hardware and software advancements have significantly improved CT accessibility, image quality, and acquisition times. While considerable attention has been directed towards the potential risks of ionizing radiation from CT scans in children, recent concerns regarding the possible short- and long-term risks related to magnetic resonance imaging (MRI) conducted under general anesthesia have generated fresh interest in novel pediatric CT applications and techniques that allow imaging of awake patients at low radiation doses. Among these novel techniques, dual-energy CT (DECT) stands out for its ability to provide enhanced diagnostic information, reduce radiation doses further, and facilitate faster scans, making it a highly promising tool in pediatric radiology. This manuscript explores the current role of DECT in pediatric imaging, emphasizing its technical foundations, hardware configurations, and various reconstruction techniques. We discuss advanced post-processing techniques, such as material decomposition algorithms and virtual monoenergetic imaging, highlighting their clinical advantages in improving diagnostic accuracy and patient outcomes. Furthermore, the paper reviews the clinical applications of DECT in evaluating pulmonary perfusion, cardiovascular assessments, and oncologic imaging in pediatric patients.

Excluding significant coronary artery disease in patients planned for TAVR using CT: Diagnostic accuracy of newest generation dual source photon counting scanner compared to coronary angiography

Abstract Introduction Patients with significant aortic stenosis routinely undergo CT for evaluation of the aortic root prior to interventional aortic valve replacement (TAVR). Assessment of CAD is usually performed by invasive coronary angiography. We assessed the diagnostic accuracy of the latest generation dual source photon counting scanner for exclusion of relevant CAD in a contemporary TAVR cohort. Methods Patients referred for cardiac CT prior to transcatheter aortic valve implantation were screened for inclusion this analysis. All cardiac CT exams were performed by dual source photon counting scanner (NAEOTOM alpha, Siemens healthineers, Germany). CT acquisitions were performed using retrospectively triggered spiral acquisition (30-70% of the cardiac cycle) without ultra-high resolution. For each patient, several reconstructions were rendered (thin slice reconstruction with 0.4 mm slice thickness: best diastolic phase, best systolic phase, 200-350 ms folllowing R-wave in 50 ms intervals as well as 0.6 mm slice thickness multiphase reconstruction 0-90% of the cardiac cycle in 10 % increments). Assessment of significant CAD was performed in CT and invasive angiography on a per-vessel analysis and a per-patient analysis using 2 thresholds: ≥ 50% stenosis and ≥ 70% stenosis. Assessment of stenoses in CT was performed using multiplanar reconstructions (MPR) and curved MPR and only in vessels &amp;gt; 1.5mm diameter. Assessment of stenosis in invasive angiography was performed using the projection with the most relevant lumen reduction. Image quality in CT was assessed on a likert scale from 1 to 4 (1=excellent, 2=minor artifacts, 3=major artifacts still assessable, 4=non-assessable). Results In this analysis 31 consecutive patients were included (mean age 79.5y, 64.5% males, 35.5% females). Mean heart rate was 66.5 bpm and 19% were in atrial fibrillation during CT acquisition. Mean image quality was 1.4. 13 Patients had no relevant CAD (stenosis ≥50%) in invasive angiography and in 18 patients at least 1 vessel was deemed to have ≥ 50% stenosis in invasive angiography. On a per vessel analysis, sensitivity, specificity and accuracy of cardiac CT to exclude stenoses using 50% and 70% thresholds were 98%, 93% and 94% vs. 97%, 93% and 94%, respectively. Negative likelihood ratio was 0.02 vs. 0.03 respectively. On a per-patient level, sensitivity and specificity were 100%. Out of 31 patients, only 2 vessels in 2 patients were deemed non-assessable. 3 patients with previous PCI as well as 3 patients with previous CABG were included in this analysis. Only 1 false negative vessel was reported in a very distal LAD segment. Conclusion Using newest generation dual source CT with photon counting, CT allowed highly reliable diagnostic workup regarding the presence of CAD in this TAVR cohort. The use of CT to assess CAD in TAVR patients seems clinically feasible especially in patients without previous percutaneous revascularisation.

Interscan reproducibility of computed tomography derived coronary plaque volume measurements using a semi-automated analysis software

Abstract Background Coronary computed tomography angiography (CCTA) enables detailed quantification and characterization of coronary atherosclerotic plaque, offering diagnostic and prognostic value. Despite the growing demand for accurate plaque analysis, studies on interscan reproducibility of automated plaque quantification are limited in number and size. Moreover, impact of clinical and technical factors on the reproducibility of plaque assessment tools are scarce. Purpose The objective of this study was to assess the interscan reproducibility of CCTA-derived plaque volume measurements and the implications of clinical and technical characteristics on interscan reproducibility. Methods A total of 101 patients with known coronary artery disease underwent two CCTA scans within hour interval using identical CT acquisition protocol. Coronary plaque volume measurements were quantified on a per-patient level using a contemporary semi-automated plaque analysis software. Results There was no statistically significant difference in median plaque volumes between the first and second scan. The direct correlation was excellent for volumes of non-calcified plaque (NCP), calcified plaque (CP), and total plaque (TP) across all analyses (Pearson’s coefficient 0.96 to 0.99), while moderate for low-density non-calcified plaque (LD-NCP) volumes (Pearson’s coefficient 0.77). Bland Altman analyses demonstrated high interscan agreement with narrow absolute and relative limits of agreement (LoA) across plaque subtypes, except for LD-NCP volumes (Table 1). Interscan reproducibility on CP volumes was affected by CT image quality with narrower LoA in scans with the highest image quality score (p = 0.003), or lowest image reconstructive iteration level (p &amp;lt;0.001). Limits of agreement were significantly narrower for NCP, CP, and TP volumes in lesions located in LAD compared to non-LAD lesions (p &amp;lt;0.001). However, no significant difference in LoA was present when comparing patients according to BMI (&amp;lt;27 and ≥27 kg/m2), heart rate (&amp;lt;65 and ≥65 beats per minute), or Agatston score (&amp;lt;193 and ≥193). Conclusions This comprehensive interscan reproducibility study revealed good agreement in the measurement of plaque subtypes. Adding new insight to existing literature, we have demonstrated that factors such as image quality, image reconstructive algorithms, and lesion location affect the reproducibility of plaque volume measurements. CT-derived plaque quantification using a semi-automated plaque analysis software holds potential for monitoring disease progression or regression in patients with CAD, provided high CCTA image quality.

Pediatric contrast-enhanced chest CT on a photon-counting detector CT: radiation dose and image quality compared to energy-integrated detector CT.

Photon counting detector (PCD) CT benefits from reduced noise compared with conventional energy-integrating detector (EID) CT, which should translate to improved image quality and reduced radiation exposure for pediatric patients undergoing chest CT with IV contrast. To determine the differences in radiation exposure and image quality of PCD CT and EID CT in pediatric chest CT with intravenous (IV) contrast. In this institutional review board-approved retrospective observational study, 20 scan pairs (20 PCD CT; 20 EID CT) for children who underwent chest CT with IV contrast on both a PCD CT (Siemens NAEOTOM Alpha) and an EID CT (Siemens SOMATOM Definition Edge or Force) within 12months were reviewed independently by three pediatric radiologists for three subjective quality features on 5-point Likert scales: overall quality, small structure delineation, and motion artifact. Objective measures of image quality (image noise, signal-to-noise ratio, and contrast-to-noise ratio) were assessed by a single radiologist in several locations in the chest through region of interest measurement of Hounsfield units (HU) and standard deviation. Patient-related and radiation exposure parameters were collected for each scan and summarized with median and interquartile range (IQR). The Wilcoxon rank-sum test was utilized to compare groups. A P < 0.05 indicated statistical significance. Inter-observer agreement of subjective image quality metrics was analyzed using weighted kappa. Age (14.2years vs 13.8years, P= 0.15), height (P= 0.13), weight (P= 0.21), and BMI (P = 0.24) did not significantly differ between groups. There were 10 male and 3 female patients. Compared to EID CT, PCD CT showed lower radiation exposure parameters including volumetric CT dose index, 1.7mGy (IQR 1.1-2.4mGy) vs 3.8mGy (IQR 2.0-4.7mGy) (P< 0.01), and size-specific dose estimate, 2.6mGy (IQR 1.8-3.1mGy) vs 5.0mGy (IQR 3.3-6.2mGy) (P< 0.01). Objective image quality of lung parenchyma was improved on the PCD CT scanner, including image noise 119.5 HU (IQR 95.4-135.7 HU) vs 143.1 HU (IQR 125.4-169.8 HU) (P < 0.01), signal-to-noise ratio (SNR) -6.1 (IQR -8.4 to -4.8) vs -4.9 (IQR -5.6 to -3.8) (P= 0.01), and contrast-to-noise ratio -63.9 (-84.1 to -57.5) vs -60.5 (-76.3 to -52.5) (P = 0.01). Motion artifact was improved on the PCD CT scanner (P< 0.01). No significant differences in overall image quality or small structure delineation were identified (P= 0.06 and P= 0.31). PCD CT pediatric chest CT had significantly reduced radiation exposure, improved image quality, and reduced motion artifact compared with EID CT.

Dual-Energy CT as a Well-Established CT Modality to Reduce Contrast Media Amount: A Systematic Review from the Computed Tomography Subspecialty Section of the Italian Society of Radiology.

Background: Our study aims to provide an overview of existing evidence regarding the image quality of dual-energy CT (DECT) employing reduced contrast media (CM) volumes, in comparison to single-energy CT (SECT) with standard CM loads. The advantages, indications, and possible applications of DECT were investigated from the perspective of providing better patient care, minimizing CM volume and managing CM shortage. Methods: In this systematic review (PRISMA methodology), PubMed and WOS were searched from January 2010 to January 2023 by two independent reviewers. The scan and CM characteristics, radiation dose, and results of quantitative (contrast to noise ratio, CNR, and signal to noise ratio, SNR) and qualitative assessment of image quality were collected. Sixty non-duplicated records eligible for full-text screening were examined. Results: Finally, 22 articles (1818 patients) were included. The average CM reduction with DECT ranged between 43.4 ± 11%. Despite the wide variability in CT scan protocols, no differences were found in radiation doses between DECT and SECT. Conclusions: DECT scanners allow the employment of lower CM volumes with equal or better image quality evaluated by quantitative and qualitative analyses and similar dose radiation compared to SECT. Using image reconstructions at low monochromatic energy levels, DECT increases iodine conspicuity and attenuation contributing to CM containment measures.

Image Quality and Lesion Detectability of Low-Concentration Iodine Contrast and Low Radiation Hepatic Multiphase CT Using a Deep-Learning-Based Contrast-Boosting Model in Chronic Liver Disease Patients.

This study investigated the image quality and detectability of double low-dose hepatic multiphase CT (DLDCT, which targeted about 30% reductions of both the radiation and iodine concentration) using a vendor-agnostic deep-learning-based contrast-boosting model (DL-CB) compared to those of standard-dose CT (SDCT) using hybrid iterative reconstruction. The CT images of 73 patients with chronic liver disease who underwent DLDCT between June 2023 and October 2023 and had SDCT were analyzed. Qualitative analysis of the overall image quality, artificial sensation, and liver contour sharpness on the arterial and portal phase, along with the hepatic artery clarity was conducted by two radiologists using a 5-point scale. For quantitative analysis, the image noise, signal-to-noise ratio, and contrast-to-noise ratio were measured. The lesion conspicuity was analyzed using generalized estimating equation analysis. Lesion detection was evaluated using the jackknife free-response receiver operating characteristic figures-of-merit. Compared with SDCT, a significantly lower effective dose (16.4 ± 7.2 mSv vs. 10.4 ± 6.0 mSv, 36.6% reduction) and iodine amount (350 mg iodine/mL vs. 270 mg iodine/mL, 22.9% reduction) were utilized in DLDCT. The mean overall arterial and portal phase image quality scores of DLDCT were significantly higher than SDCT (arterial phase, 4.77 ± 0.45 vs. 4.93 ± 0.24, AUCVGA 0.572 [95% CI, 0.507-0.638]; portal phase, 4.83 ± 0.38 vs. 4.92 ± 0.26, AUCVGA 0.535 [95% CI, 0.469-0.601]). Furthermore, DLDCT showed significantly superior quantitative results for the lesion contrast-to-noise ratio (7.55 ± 4.55 vs. 3.70 ± 2.64, p < 0.001) and lesion detectability (0.97 vs. 0.86, p = 0.003). In patients with chronic liver disease, DLDCT using DL-CB can provide acceptable image quality without impairing the detection and evaluation of hepatic focal lesions compared to SDCT.

Computed tomography image quality in patients with primary hepatocellular carcinoma: intraindividual comparison of contrast agent concentrations.

This study aimed to assess the impact of the different concentrations of iodine contrast agents used on the quality of computed tomography (CT) images obtained intraindividually in hepatocellular carcinoma patients. In this retrospective study, data from a cohort of 29 patients diagnosed with primary hepatocellular carcinoma who had undergone two preoperative CT-enhanced examinations within a 3-month timeframe were analyzed. Each patient was randomly assigned to receive either a low-concentration contrast agent (300 mg I/mL iohexol) or a high-concentration contrast agent (350 mg I/mL iohexol) for the first scan and the alternative contrast agent for the second scan. CT images of different liver regions of each patient were compared between low-and high-concentration scans using their before-and-after control design. Subjective image quality scores for portal vein images were also assessed. The findings of this study indicate that patients in the high-concentration group presented significantly elevated CT values across various anatomical regions, including the liver parenchyma, abdominal aorta, and hepatic portal vein, compared to those in the low-concentration group (p < 0.05). Moreover, the high-concentration group demonstrated superior subjective image ratings (p < 0.05). Nevertheless, there was no statistically significant difference in the CT values observed in liver cancer parenchyma scans at different phases between the two groups (p > 0.05). In summary, using a high-concentration iodine contrast agent is efficient in enhancing the visual clarity of the liver parenchyma, the aorta, and the portal vein in individuals diagnosed with primary hepatocellular carcinoma.

Fast reconstruction 3D computed tomography image of stacked cell under faster scanning by dual-branch cross-fusion flat bottom network

Abstract Cone beam computed tomography (CBCT) fast scanning and reconstruction is a key step to achieve rapid detection of internal defects in batteries. In this work, we have achieved a faster CT scanning just in 5 seconds by reducing the X-ray exposure time in sparse view CT. However, the CT data is extremely incomplete by faster scanning; the existing reconstruction methods are difficult to reconstruct a high quality three-dimensional (3D) CT image of stacked cells. To address this issue, we propose a 3D CT image reconstruction network, which can reconstruct higher quality CT images from low quality 3D volume data. The input data of the reconstruction network is not 2D projection data, but 3D volume data. In this network, a high and low resolution dual-branch cross-fusion flat bottom structure is designed. The high resolution flat bottom branch aims to preserve detailed information, while the low resolution flat bottom branch focuses on capturing more semantic information. Cross-fusion between these branches mitigates the loss of semantic details. Additionally, the auxiliary loss function, the main loss function, and the 3D attention module are designed to enhance semantic accuracy and the learning performance of the network. The 3D training data is collected under a fast scanning strategy spanning 5-60 seconds. During the training phase, we use clipping block technology to cut the 3D volume data, enabling direct training on the 3D volume data. Our experimental results demonstrate that our 3D reconstruction network outperforms mainstream algorithms under this faster scanning strategy, which is able to reconstruct higher quality 3D CT images just in 15 seconds. Ablation experiments confirm the positive impact of the dual-branch cross-fusion flat bottom structure, attention module, and loss functions on improving the quality of 3D CT images.

Image quality of photon-counting detector CT for visualization of maxillofacial anatomy in comparison with energy-integrating detector CT and intraoperative C-arm CBCT

BackgroundAccurate diagnostic imaging is crucial for managing facial fractures, which are a common global occurrence. This study aimed to compare the image quality of Photon Counting Detector CT (PCD-CT) with state-of-the-art Energy Integrating Detector CT (EID-CT) and intraoperative C-arm CBCT (CBCT) in visualizing maxillofacial anatomy using a cadaveric sheep head model. MethodsThree fresh sheep heads were used, with surgical interventions simulating metal implants in two of them. The specimens were imaged using PCD-CT, EID-CT, and CBCT, following which quantitative assessments of signal-to-noise ratio, sharpness, and artifacts were conducted. A visual grading study was performed by six observers, using criteria focusing on the mandible, orbit, and soft tissues. Statistical analyses included Friedman tests for comparing modalities and Kendall’s W and Gwet’s AC1 for assessing inter- and intrarater agreement. ResultsPCD-CT demonstrated a significantly higher signal-to-noise ratio (p = 0.03) and bone sharpness (p < 0.001) compared to CBCT. In visual grading, PCD-CT outperformed CBCT, but not EID-CT, particularly in delineating mandibular and orbital structures. EID-CT and PCD-CT showed slightly more severe hypodense artifacts (p = 0.01) but were comparable in streak artifact presentation. The interrater and intrarater agreements indicated consistent evaluations across and within observers. ConclusionPCD-CT exhibits superior image quality over CBCT in key parameters essential for maxillofacial imaging, while no apparent improvement was shown compared to state-of-the-art EID-CT. PCD-CT offers enhanced visualization of critical anatomical structures, suggesting its potential as a preferred modality in managing maxillofacial trauma. The findings in this study align with limited existing research on PCD-CT, underscoring its promise for advanced diagnostic imaging in maxillofacial applications.