To evaluate whether super-resolution deep learning reconstruction (SR-DLR) improves two-dimensional (2D) brain fluid-attenuated inversion recovery (FLAIR) image quality while preserving automated white matter hyperintensity (WMH) volumetry, compared with Gaussian-filtered reconstruction (GA) and denoising DLR (dDLR). Thirty-six healthy volunteers underwent 3T axial 2D FLAIR. Images were reconstructed using GA, dDLR, and SR-DLR (twofold in-plane upscaling). Quantitative metrics (noise, SNR, CNR, sharpness) were measured in standardized regions of interest. Two radiologists independently scored qualitative image quality. In a WMH-positive subgroup, WMH volumes were obtained using a transformer-based U-Net segmentation model on GA, dDLR, SR-DLR, and a high-resolution acquisition (HR-dDLR). Quantitative image-quality metrics were compared using one-way ANOVA, WMH volumes using paired t-tests, and interobserver agreement using weighted kappa. SR-DLR demonstrated the lowest image noise and the highest SNR, CNR, and sharpness compared with GA and dDLR (all p < 0.001). Qualitative scores for noise, sharpness, and overall image quality were significantly higher for SR-DLR (p < 0.001), with perfect inter-observer agreement for sharpness and overall quality. Mean WMH volumes did not differ significantly across reconstructions, and SR-DLR volumes closely matched those of HR-dDLR. SR-DLR substantially improves 2D brain FLAIR image quality at 3T by reducing noise and increasing SNR, CNR, and sharpness while preserving WMH volumetry consistent with high-resolution reference standards.

OBJECTIVE:To evaluate the impact of Super-Resolution Deep Learning Reconstruction (SR-DLR) (Canon Medical Systems Corporation) on image quality and myocardial hemodynamic parameters in dynamic myocardial computed tomography (CT) perfusion compared with filtered-back projection (FBP), hybrid iterative reconstruction (IR), and normal-resolution deep learning reconstruction (NR-DLR). METHODS:This prospective single-center study included 25 patients (mean age ± SD, 65 ± 10; 21 men) who underwent dynamic myocardial CT perfusion. For qualitative analysis, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were assessed, while qualitative analysis included overall image quality and lesion visibility. Myocardial blood flow (MBF) at rest and stress, as well as coronary flow reserve (CFR) were analyzed. Image quality and hemodynamic parameters were compared across 4 reconstruction methods. RESULTS:SR-DLR achieved the lowest image noise (20.33 ± 2.45 HU), significantly lower than FBP (145.20 ± 74.81 HU), hybrid IR (47.19 ± 10.02 HU), and NR-DLR (22.92 ± 2.63 HU) ( P < 0.001). In rest imaging, SR-DLR showed significantly higher SNR (6.71 ± 1.88) and CNR (15.41 ± 5.48) compared with other reconstruction methods ( P < 0.001). Similar improvements were observed in stress imaging, with SR-DLR providing significantly enhanced SNR and CNR compared with all other methods. The mean CFR was 2.75 ± 1.88 for SR-DLR, 2.75 ± 1.99 for NR-DLR, 2.74 ± 2.44 for hybrid IR, and 2.56 ± 3.17 for FBP, with no statistically significant differences observed in any pairwise comparisons. Qualitative analysis showed that SR-DLR achieved the highest overall image quality and lesion visibility, significantly outperforming FBP and comparable to hybrid IR and NR-DLR. CONCLUSIONS:SR-DLR and NR-DLR significantly enhanced image quality by reducing noise and improving SNR and CNR while maintaining hemodynamic quantification.

Background:Contrast media-related adverse events pose serious risks, not only due to allergic reactions but also for patients with renal failure, older age, heart failure, and diabetes mellitus. Various efforts have been made to maximize vascular contrast enhancement (CE) using low-concentration contrast media. This study aimed to compare quantitative and qualitative image characteristics between vendor-agnostic and vendor-specific CE boost methods in low-concentration contrast medium settings against standard-concentration contrast medium settings. Methods:This retrospective study including 160 patients compared two concentrations of iodinated contrast media-350 mg I/mL (standard-concentration group) and 270 mg I/mL (low-concentration group) who underwent contrast-enhanced abdominal computed tomography (CT). Vendor-agnostic and vendor-specific boost methods were applied to the CT images of the low-concentration group. Quantitative [image noise, CT attenuation, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), noise power spectrum, and image sharpness] and qualitative (Likert scale rating by two radiologists) measures of image quality were evaluated in abdominal vessels and liver lesions. Results:The present study included 80 patients in the standard-concentration and 80 patients in the low-concentration group, respectively. There were no significant differences in age (P=0.75), sex (P=0.99), and body mass index (P=0.98) between the two groups. Both vendor-agnostic and vendor-specific CE-boost methods significantly reduced image noise (P<0.001) and improved CT attenuation, SNR, and CNR in both vessels and liver lesions (all P<0.001) in the low-concentration group compared to the corresponding values in the standard-concentration group. The vendor-specific boost method was more effective in suppressing high-frequency noise (P<0.001), whereas the vendor-agnostic boost method yielded sharper images (0.31±0.02 vs. 0.29±0.01; P<0.001). Subjective image analysis revealed significantly higher image quality (P<0.001) for both boost methods compared with standard-concentration contrast medium groups. Conclusions:Although vendor-agnostic and vendor-specific CE-boost methods effectively enhanced image quality and CT attenuation in low-concentration contrast medium settings, subjective image analysis revealed lower ratings due to increased artificial appearance. The vendor-agnostic method provided superior image sharpness, whereas the vendor-specific method more effectively reduced structured noise and achieved higher subjective ratings for overall image quality, artificial suppression, and natural image appearance.

To assess the image quality and radiation dose between reduced-dose CT with deep learning reconstruction (DLR) using SilverBeam filter and standard dose with iterative reconstruction (IR) in abdominopelvic CT. In total, 182 patients (mean age ± standard deviation, 63 ± 14 years; 100 men) were included. Standard-dose scanning was performed with a tube voltage of 100 kVp, automatic tube current modulation, and IR reconstruction, whereas reduced-dose scanning was performed with a tube voltage of 120 kVp, a SilverBeam filter, and DLR. Additionally, a contrast-enhanced (CE)-boost image was obtained for reduced-dose scanning. Radiation dose, objective, and subjective image analyses were performed in each body mass index (BMI) category. The radiation dose for SilverBeam with DLR was significantly lower than that of standard dose with IR, with an average reduction in the effective dose of 59.0% (1.87 vs. 4.57 mSv). Standard dose with IR (10.59 ± 1.75) and SilverBeam with DLR (10.60 ± 1.08) showed no significant difference in image noise (p = 0.99). In the obese group (BMI > 25 kg/m2), there were no significant differences in SNRs of the liver, pancreas, and spleen between standard dose with IR and SilverBeam with DLR. SilverBeam with DLR + CE-boost demonstrated significantly better SNRs and CNRs, compared with standard dose with IR and SilverBeam with DLR. DLR combined with silver filter is effective for routine abdominopelvic CT, achieving a clearly reduced radiation dose while providing image quality that is non-inferior to standard dose with IR.

This study aimed to evaluate the quantitative and qualitative performances of ultra-low-dose computed tomography (CT) with deep learning image reconstruction (DLR) compared with those of hybrid iterative reconstruction (IR) for preoperative paranasal sinus (PNS) imaging. This retrospective analysis included 132 patients who underwent non-contrast ultra-low-dose sinus CT (0.03 mSv). Images were reconstructed using hybrid IR and DLR. Objective image quality metrics, including image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), noise power spectrum (NPS), and no-reference perceptual image sharpness, were assessed. Two board-certified radiologists independently performed subjective image quality evaluations. The ultra-low-dose CT protocol achieved a low radiation dose (effective dose: 0.03 mSv). DLR showed significantly lower image noise (28.62 ± 4.83 Hounsfield units) compared to hybrid IR (140.70 ± 16.04, p < 0.001), with DLR yielding smoother and more uniform images. DLR demonstrated significantly improved SNR (22.47 ± 5.82 vs 9.14 ± 2.45, p < 0.001) and CNR (71.88 ± 14.03 vs 11.81 ± 1.50, p < 0.001). NPS analysis revealed that DLR reduced the noise magnitude and NPS peak values. Additionally, DLR demonstrated significantly sharper images (no-reference perceptual sharpness metric: 0.56 ± 0.04) compared to hybrid IR (0.36 ± 0.01). Radiologists rated DLR as superior in overall image quality, bone structure visualization, and diagnostic confidence compared to hybrid IR at ultra-low-dose CT. DLR significantly outperformed hybrid IR in ultra-low-dose PNS CT by reducing image noise, improving SNR and CNR, enhancing image sharpness, and maintaining critical anatomical visualization, demonstrating its potential for effective preoperative planning with minimal radiation exposure. Question Ultra-low-dose CT for paranasal sinuses is essential for patients requiring repeated scans and functional endoscopic sinus surgery (FESS) planning to reduce cumulative radiation exposure. Findings DLR outperformed hybrid IR in ultra-low-dose paranasal sinus CT. Clinical relevance Ultra-low-dose CT with DLR delivers sufficient image quality for detailed surgical planning, effectively minimizing unnecessary radiation exposure to enhance patient safety.

This study aimed to evaluate the diagnostic performance, image quality, and radiation dose among ultralow-dose protocol with deep learning reconstruction (DLR), ultralow-dose computed tomography (CT) with iterative reconstruction (IR), and conventional-dose protocols for detecting intracranial hemorrhage. This retrospective study enrolled 93 patients (median age: 67 years; interquartile range [IQR]: 59–76 years; 61 males). A conventional-dose CT was obtained using 120 kVp, 123–188 mA and IR. Follow-up ultralow-dose CT was obtained using 120 kVp, 50 mA with IR and DLR. Qualitative assessments and quantitative assessments were conducted. The diagnostic performance for detecting intracranial hemorrhage was assessed. An approximately 84.0

Heart auscultation is a simple and inexpensive first-line diagnostic test for the early screening of heart abnormalities. A phonocardiogram (PCG) is a digital recording of an analog heart sound acquired using an electronic stethoscope. A computerized algorithm for PCG analysis can aid in detecting abnormal signal patterns and support the clinical use of auscultation. It is important to detect fundamental components, such as the first and second heart sounds (S1 and S2), to accurately diagnose heart abnormalities. In this study, we developed a fully convolutional hybrid fusion network to identify S1 and S2 locations in PCG. It enables timewise, high-level feature fusion from dimensionally heterogeneous features: 1D envelope and 2D spectral features. For the fusion of heterogeneous features, we proposed a novel convolutional multimodal factorized bilinear pooling approach that enables high-level fusion without temporal distortion. We experimentally demonstrated the benefits of the comprehensive interpretation of heterogeneous features, with the proposed method outperforming other state-of-the-art PCG segmentation methods. To the best of our knowledge, this is the first study to interpret heterogeneous features through a high level of feature fusion in PCG analysis.

The recently introduced super-resolution (SR) deep learning image reconstruction (DLR) is potentially effective in reducing noise level and enhancing the spatial resolution. We aimed to investigate whether SR-DLR has advantages in the overall image quality and intensity homogeneity on coronary computed tomography (CT) angiography with four different approaches: filtered-back projection (FBP), hybrid iterative reconstruction (IR), DLR, and SR-DLR. Sixty-three patients (mean age, 61 ± 11 years; range, 18–81 years; 40 men) who had undergone coronary CT angiography between June and October 2022 were retrospectively included. Image noise, signal to noise ratio, and contrast to noise ratio were quantified in both proximal and distal segments of the major coronary arteries. The left ventricle myocardium contrast homogeneity was analyzed. Two independent reviewers scored overall image quality, image noise, image sharpness, and myocardial homogeneity. Image noise in Hounsfield units (HU) was significantly lower (P < 0.001) for the SR-DLR (11.2 ± 2.0 HU) compared to those associated with other image reconstruction methods including FBP (30.5 ± 10.5 HU), hybrid IR (20.0 ± 5.4 HU), and DLR (14.2 ± 2.5 HU) in both proximal and distal segments. SR-DLR significantly improved signal to noise ratio and contrast to noise ratio in both the proximal and distal segments of the major coronary arteries. No significant difference was observed in the myocardial CT attenuation with SR-DLR among different segments of the left ventricle myocardium (P = 0.345). Conversely, FBP and hybrid IR resulted in inhomogeneous myocardial CT attenuation (P < 0.001). Two reviewers graded subjective image quality with SR-DLR higher than other image reconstruction techniques (P < 0.001). SR-DLR improved image quality, demonstrated clearer delineation of distal segments of coronary arteries, and was seemingly accurate for quantifying CT attenuation in the myocardium.

Radiomics has gained popularity as a quantitative analysis method for medical images. However, computed tomography (CT) scans are performed using various parameters, such as X-ray dose and reconstruction kernels, which is a fundamental reason for the lack of reproducibility of radiomic features. This study evaluated whether the proposed network improves the reproducibility of radiomic features across various CT protocols and reconstruction kernels. We set five CT scan protocols and two reconstruction kernels to create various noise settings for the obtained CT images with an abdominal phantom. We developed an enhanced hierarchical feature synthesis (EHFS) network to improve the reproducibility of radiomic features across various CT protocols and reconstruction kernels. Eight hundred and nineteen radiomic features were extracted, including first-order, second-order, and wavelet features. Reproducibility was assessed using Lin’s concordance correlation coefficient (CCC) on internal and external testing. We considered a radiomic feature with CCC $\ge0.85$ as a high-agreement feature. As a result, the average number of reproducible features increased in all protocols, from 241 ± 38 to 565 ± 11 in internal testing. In external testing, consisting of a new phantom and unseen protocol, 239 ± 74 reproducible features were in source images and 324 ± 16 were in generated images. The EHFS network is a novel approach to improving the reproducibility of radiomic features. It outperforms existing methods in reproducibility and generalization, as demonstrated by comprehensive experiments on both internal and external datasets. Our deep-learning-based CT image conversion could be a solution for standardization in ongoing radiomics research.

OBJECTIVE:This study evaluated the effects of scanning position and contrast medium injection rate on pulmonary CT perfusion (CTP) images in healthy dogs.ANIMALS:7 healthy Beagles.METHODS:Experiments involved 4 conditions: dorsal and sternal recumbency at 2.5 mL/s (first) and sternal recumbency with additional rates of 1.5 and 3.5 mL/s (second). Various parameters, including the initial time of venous enhancement (Tv), peak time of arterial enhancement (PTa), and peak enhancement values of the artery, were measured. The PTa to Tv interval was calculated. Perfusion mapping parameters (pulmonary blood flow, pulmonary blood volume, mean transit time, time to maximum, and time to peak) were determined in different lung regions (left and right dorsal, middle, and ventral).RESULTS:There are significant variations in most perfusion mapping parameters based on the pulmonary parenchymal location. Dorsal recumbency had a lower peak value of arterial enhancement than sternal recumbency. Pulmonary blood flow in the dorsal region and mean transit time and time to maximum in all regions showed no significant differences based on position. Pulmonary blood volume and time to peak varied with scanning position. The PTa to Tv interval did not differ based on the injection rate, but the injection time at 1.5 mL/s was longer than at other rates. All perfusion mapping parameters of the ventral region increased with higher injection rates.CLINICAL RELEVANCE:The recommended CTP imaging approach in dogs is a low injection rate of 1.5 mL/s in the sternal recumbency. This study provides reference ranges for perfusion parameters based on the pulmonary parenchymal location, contributing to the acquisition and application of pulmonary CTP images for differential diagnosis in small-breed dogs.

Purpose:We aimed to compare the efficacy of the double contrast enhancement (CE)-boost technique with that of conventional methods to improve vascular contrast attenuation in lower-extremity computed tomography (CT) angiography. Approach:This retrospective study enrolled 45 patients (mean age, 70 years; range, 26 to 90 years; 30 males). To generate the CE-boost image, the degree of CE was determined by subtracting the post-contrast CT images from the pre-contrast CT images. The double CE-boost technique involves the application of this CE process twice. Both objective assessments (CT attenuation, noise level, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR], and image sharpness) and subjective quality evaluations were conducted on three types of images (conventional, CE-boost, and double CE-boost images). Results:Double CE-boost images demonstrated significantly reduced noise in Hounsfield units (HUs) compared with conventional and CE-boost images ( p < 0.001 ). CT attenuation values (HUs) were substantially higher in all different locations of the lower extremity with double CE-boost images ( 834.49 ± 140.73 ), as opposed to conventional ( 399.63 ± 62.01 ) and CE-boost images ( 572.66 ± 93.61 ). The SNR and CNR were notably improved in the double CE-boost image compared with both conventional and CE-boost images. Image sharpness analysis of the popliteal artery ( p = 0.828 ), anterior tibial artery ( p = 0.671 ), and dorsalis pedis artery ( p = 0.281 ) revealed consistency across conventional, CE-boost, and double CE-boost images. Subjective image analysis indicated superior ratings for the double CE-boost compared with other types. Conclusions:The implementation of the double CE-boost technique improves image quality by decreasing image noise, increasing CT attenuation, and improving SNR, CNR, and subjective assessment compared with CE-boost and conventional imaging.

Objectives: To investigate the usefulness of super-resolution deep learning reconstruction (SR-DLR) with cardiac option in the assessment of image quality in patients with stent-assisted coil embolization, coil embolization, and flow-diverting stent placement compared with other image reconstructions. Methods: This single-centre retrospective study included 50 patients (mean age, 59 years; range, 44-81 years; 13 men) who were treated with stent-assisted coil embolization, coil embolization, and flow-diverting stent placement between January and July 2023. The images were reconstructed using filtered back projection (FBP), hybrid iterative reconstruction (IR), and SR-DLR. The objective image analysis included image noise in the Hounsfield unit (HU), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and full width at half maximum (FWHM). Subjectively, two radiologists evaluated the overall image quality for the visualization of the flow-diverting stent, coil, and stent. Results: The image noise in HU in SR-DLR was 6.99 +/- 1.49, which was significantly lower than that in images reconstructed with FBP (12.32 +/- 3.01) and hybrid IR (8.63 +/- 2.12) (P < .001). Both the mean SNR and CNR were significantly higher in SR-DLR than in FBP and hybrid IR (P < .001 and P < .001). The FWHMs for the stent (P < .004), flow-diverting stent (P < .001), and coil (P < .001) were significantly lower in SR-DLR than in FBP and hybrid IR. The subjective visual scores were significantly higher in SR-DLR than in other image reconstructions (P < .001). Conclusions: SR-DLR with cardiac option is useful for follow-up imaging in stent-assisted coil embolization and flow-diverting stent placement in terms of lower image noise, higher SNR and CNR, superior subjective image analysis, and less blooming artifact than other image reconstructions. Advances in knowledge: SR-DLR with cardiac option allows better visualization of the peripheral and smaller cerebral arteries. SR-DLR with cardiac option can be beneficial for CT imaging of stent-assisted coil embolization and flow-diverting stent.

Research question: We test whether a plane shoulder radiograph can be used together with deep learning methods to identify patients with rotator cuff tears as opposed to using an MRI in standard of care. Findings: By integrating convolutional block attention modules into a deep neural network, our model demonstrates high accuracy in detecting patients with rotator cuff tears, achieving an average AUC of 0.889 and an accuracy of 0.831. Meaning: This study validates the efficacy of our deep learning model to accurately detect rotation cuff tears from radiographs, offering a viable pre-assessment or alternative to more expensive imaging techniques such as MRI.

Objectives This study aimed to assess the impact of super-resolution deep learning reconstruction (SR-DLR) on coronary CT angiography (CCTA) image quality and blooming artifacts from coronary artery stents in comparison to conventional methods, including hybrid iterative reconstruction (HIR) and deep learning-based reconstruction (DLR).Methods A retrospective analysis included 66 CCTA patients from July to November 2022. Major coronary arteries were evaluated for image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Stent sharpness was quantified using 10%-90% edge rise slope (ERS) and 10%-90% edge rise distance (ERD). Qualitative analysis employed a 5-point scoring system to assess overall image quality, image noise, vessel wall, and stent structure.Results SR-DLR demonstrated significantly lower image noise compared to HIR and DLR. SNR and CNR were notably higher in SR-DLR. Stent ERS was significantly improved in SR-DLR, with mean ERD values of 0.70 +/- 0.20 mm for SR-DLR, 1.13 +/- 0.28 mm for HIR, and 0.85 +/- 0.26 mm for DLR. Qualitatively, SR-DLR scored higher in all categories.Conclusions SR-DLR produces images with lower image noise, leading to improved overall image quality, compared with HIR and DLR. SR-DLR is a valuable image reconstruction algorithm for enhancing the spatial resolution and sharpness of coronary artery stents without being constrained by hardware limitations.Advances in knowledge The overall image quality was significantly higher in SR-DLR, resulting in sharper coronary artery stents compared to HIR and DLR.

Computed tomography (CT) imaging is vital for diagnosing and monitoring diseases in both humans and animals, yet radiation exposure remains a significant concern, especially in animal imaging. Low-dose CT (LDCT) minimizes radiation exposure but often compromises image quality due to a reduced signal-to-noise ratio (SNR). Recent advancements in deep learning, particularly with CycleGAN, offer promising solutions for denoising LDCT images, though challenges in preserving anatomical detail and image sharpness persist. This study introduces a novel framework tailored for animal LDCT imaging, integrating deep learning techniques within the CycleGAN architecture. Key components include BlurPool for mitigating high-resolution image distortion, PixelShuffle for enhancing expressiveness, hierarchical feature synthesis (HFS) networks for feature retention, and spatial channel squeeze excitation (scSE) blocks for contrast reproduction. Additionally, a multi-scale discriminator enhances detail assessment, supporting effective adversarial learning. Rigorous experimentation on veterinary CT images demonstrates our framework’s superiority over traditional denoising methods, achieving significant improvements in noise reduction, contrast enhancement, and anatomical structure preservation. Extensive evaluations show that our method achieves a precision of 0.93 and a recall of 0.94. This validates our approach’s efficacy, highlighting its potential to enhance diagnostic accuracy in veterinary imaging. We confirm the scSE method’s critical role in optimizing performance, and robustness to input variations underscores its practical utility.

Coronary artery procedures are primarily performed based on X-ray angiography images. However, coronary arteries in X-ray images are often partially broken, complicating diagnoses and procedures owing to lack of visibility. In this paper, we propose a fully automatic method to restore locally broken parts of coronary arteries in X-ray images without using any external information, such as computed tomography images. To this end, we design a new multi-scale generative adversarial network and a vesselness-loss function. The proposed method is optimized for focus on elongated structures and can be utilized in various clinical applications. The proposed method is evaluated and compared with four other existing methods using the performance metrics, PSNR, MSE, and SSIM, and the result shows 34.3, 0.18, and 0.91 averages, respectively for each metric. Based on the performance result, the blocked regions are plausibly reconstructed into such original shapes of blood vessels, which can aid in image-based guiding catheter manipulation during coronary artery procedures. Eventually, the proposed method can be utilized in various clinical applications, e.g., image-based planning and guidance of coronary procedures and prior simulation of results.

Diffusion-weighted MRI is a highly sensitive to alteration in the movement of water molecules and allows the assessment of various pathologies. Interpretation of the changes can be obscured by signal-dropout due to tissue-air susceptibility difference at the boundaries of nasal cavities, especially in the prefrontal region. The head tilting (chin-up) method during the brain scan could be considered as an accessible way without additional hardware- or sequence to prevent those susceptibility-induced signal-dropout. We demonstrated DWI with head-tilting (up to 20°) resulted in had less severe signal dropout and improved image quality than normal scan in the prefrontal cortex.

BACKGROUND:The reference protocol for the quantification of coronary artery calcium (CAC) should be updated to meet the standards of modern imaging techniques.PURPOSE:To assess the influence of filtered-back projection (FBP), hybrid iterative reconstruction (IR), and three levels of deep learning reconstruction (DLR) on CAC quantification on both in vitro and in vivo studies.MATERIAL AND METHODS:In vitro study was performed with a multipurpose anthropomorphic chest phantom and small pieces of bones. The real volume of each piece was measured using the water displacement method. In the in vivo study, 100 patients (84 men; mean age = 71.2 ± 8.7 years) underwent CAC scoring with a tube voltage of 120 kVp and image thickness of 3 mm. The image reconstruction was done with FBP, hybrid IR, and three levels of DLR including mild (DLRmild), standard (DLRstd), and strong (DLRstr).RESULTS:In the in vitro study, the calcium volume was equivalent (P = 0.949) among FBP, hybrid IR, DLRmild, DLRstd, and DLRstr. In the in vivo study, the image noise was significantly lower in images that used DLRstr-based reconstruction, when compared images other reconstructions (P < 0.001). There were no significant differences in the calcium volume (P = 0.987) and Agatston score (P = 0.991) among FBP, hybrid IR, DLRmild, DLRstd, and DLRstr. The highest overall agreement of Agatston scores was found in the DLR groups (98%) and hybrid IR (95%) when compared to standard FBP reconstruction.CONCLUSION:The DLRstr presented the lowest bias of agreement in the Agatston scores and is recommended for the accurate quantification of CAC.

BACKGROUND:In coronary computed tomography angiography (CCTA), the main issue of image quality is noise in obese patients, blooming artifacts due to calcium and stents, high-risk coronary plaques, and radiation exposure to patients.OBJECTIVE:To compare the CCTA image quality of deep learning-based reconstruction (DLR) with that of filtered back projection (FBP) and iterative reconstruction (IR).METHODS:This was a phantom study of 90 patients who underwent CCTA. CCTA images were acquired using FBP, IR, and DLR. In the phantom study, the aortic root and the left main coronary artery in the chest phantom were simulated using a needleless syringe. The patients were classified into three groups according to their body mass index. Noise, the signal-to-noise ratio (SNR), and the contrast-to-noise ratio (CNR) were measured for image quantification. A subjective analysis was also performed for FBP, IR, and DLR.RESULTS:According to the phantom study, DLR reduced noise by 59.8% compared to FBP and increased SNR and CNR by 121.4% and 123.6%, respectively. In a patient study, DLR reduced noise compared to FBP and IR. Furthermore, DLR increased the SNR and CNR more than FBP and IR. In terms of subjective scores, DLR was higher than FBP and IR.CONCLUSION:In both phantom and patient studies, DLR effectively reduced image noise and improved SNR and CNR. Therefore, the DLR may be useful for CCTA examinations.

Background and purpose This study aimed to investigate the potential of contrast enhancement (CE)-boost technique in the head and neck computed tomography (CT) angiography in terms of the objective and subjective image quality. Materials and methods Consecutive patients who underwent head and neck CT angiography between May 2022 and July 2022 were included. The CE-boost images were generated by combining the subtracted iodinated image and contrast-enhanced image. The objective image analysis was compared for each image with and without CE-boost technique using the CT attenuation, image noise, signal-to-noise-ratio (SNR), contrast-to-noise-ratio (CNR), and image sharpness (full width at half width maximum, FWHM). The subjective image analysis was evaluated by two independent experienced radiologists in the following aspects: the overall image quality, motion artifact, vascular delineation, and vessel sharpness. Results A total of 65 patients (mean age, 59.48 ± 13.71 years; range, 24–87 years; 36 women) were included. The CT attenuation of the vertebrobasilar arteries was significantly (p < 0.001) higher in the images obtained using CE-boost technique than in conventional images. Image noise was significantly (p < 0.001) lower for CE-boost images (6.09 ± 1.93) than for conventional images (7.79 ± 1.73). Moreover, CE-boost technique yielded higher SNR (64.43 ± 17.17 vs. 121.37 ± 38.77, p < 0.001) and CNR (56.90 ± 18.79 vs. 116.65 ± 57.44, p < 0.001) than conventional images. CE-boost resulted in shorter FWHM than conventional images (p < 0.001). Higher subjective image quality scores were also demonstrated by the CE-boost than images without CE-boost technique. Conclusions In both objective and subjective image analysis, the CE-boost technique provided higher image quality without increasing the flow rate and concentration of contrast media in the head and neck CT angiography. Furthermore, the vessel completeness and delineation were superior in CE-boost images than in conventional images.