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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., (© 2024 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Background and Purpose: The cerebral metabolic rate of oxygen (CMRO 2 ) is considered a robust marker of the infarct core in 15 O-tracer-based PET. We aimed to delineate the infarct core in patients with acute ischemic stroke by using commonly used relative CBF (rCBF) < 30% and oxygen metabolism parameter of CMRO 2 on CT perfusion in comparison with pretreatment DWI-derived infarct core volume., Materials and Methods: Patients with acute ischemic stroke who met the inclusion criteria were recruited. The CMRO 2 and CBF maps in CT perfusion were automatically generated by using postprocessing software. The infarct core volume was quantified with relative cerebral metabolic rate of oxygen (rCMRO 2 ) <20% -30% and rCBF <30%. The optimal threshold was defined as those that demonstrated the smallest mean absolute error, lowest mean infarct core volume difference, narrowest 95% limit of agreement, and largest intraclass correlation coefficient (ICC) against the DWI., Results: This study included 76 patients (mean age ± standard deviation, 69.97 ± 12.15 years, 43 men). The optimal thresholds of rCMRO 2 <26% resulted in the lowest mean infarct core volume difference, narrowest 95% limit of agreement, and largest ICC among different thresholds. Bland-Altman analysis demonstrated a volumetric bias of 1.96 mL between DWI and rCMRO 2 <26%, whereas in cases of DWI and rCBF <30%, the bias was notably larger at 14.10 mL. The highest correlation was observed for rCMRO 2 <26% (ICC = 0.936), whereas rCBF <30% showed a slightly lower ICC of 0.934., Conclusions: CT perfusion-derived CMRO 2 is a promising parameter for estimating the infarct core volume in patients with acute ischemic stroke., (© 2024 by American Journal of Neuroradiology.)

Background: 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., Methods: 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., Results: 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)., Conclusions: 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., (© 2024. The Author(s).)

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., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

(1) Background: The intravoxel incoherent motion (IVIM) model can provide information about both molecular diffusion and blood flow for the evaluation of skeletal muscle inflammation. MRI-based fat quantification is advantageous for assessing fat infiltration in skeletal muscle. (2) Purpose: We aimed to quantitatively measure various parameters associated with IVIM diffusion-weighted imaging (DWI) and fat quantification in the muscles of patients with polymyositis and dermatomyositis using magnetic resonance imaging and to investigate the relationship between these parameters and electromyography (EMG) findings. (3) Material and methods: Data were retrospectively evaluated for 12 patients with polymyositis and dermatomyositis who underwent thigh MRI, including IVIM-DWI and fat quantification. The IVIM-derived parameters included the pure diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction ( f ). Fat fraction values were assessed using the six-point Dixon technique. Needle EMG was performed within 9 days of the MRI. (4) Results: The f values (19.02 ± 4.87%) in muscles with pathological spontaneous activity on EMG were significantly higher than those (14.60 ± 5.31) in muscles without pathological spontaneous activity ( p < 0.027). There were no significant differences in D, D*, ADC, or fat fraction between muscles with and without pathologic spontaneous activity. Significant negative correlations were observed between fat fraction and amplitude ( r = -0.402, p < 0.015) and between fat fraction and duration ( r = -0.360, p < 0.031). (5) Conclusion: The current study demonstrates that IVIM-DWI and fat quantification using 3.0 T MRI may aid in predicting EMG findings in patients with polymyositis and dermatomyositis and promote the pathophysiological study of idiopathic inflammatory myopathies.

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 (DLR mild ), standard (DLR std ), and strong (DLR str )., Results: In the in vitro study, the calcium volume was equivalent ( P  = 0.949) among FBP, hybrid IR, DLR mild , DLR std , and DLR str . In the in vivo study, the image noise was significantly lower in images that used DLR str -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, DLR mild , DLR std , and DLR str . 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 DLR str presented the lowest bias of agreement in the Agatston scores and is recommended for the accurate quantification of CAC.

Objective: Although computed tomography perfusion (CTP) is used to select and guide decision-making processes in patients with acute ischemic stroke, there is no clear standardization of the optimal threshold to predict ischemic core volume accurately. The infarct core volume with a relative cerebral blood flow(rCBF) threshold of < 30% is commonly used. We aimed to assess the volumetric agreement of the infarct core volume with different CTP parameters and thresholds using CTP software (RAPID, VITREA) and the infarct volume on diffusion-weighted imaging (DWI), with a short interval time (within 60 min) between CTP and follow-up DWI., Materials and Methods: This retrospective study included 42 acute ischemic stroke patients with occlusion of the large artery in the anterior circulation between April 2017-November 2020. RAPID identified infarct core as tissue rCBF < 20-38%. VITREA defined the infarct core as cerebral blood volume (CBV) < 26-56%. Olea Sphere was used to measure infarct core volume on DWI. The CTP-infarct core volume with different thresholds of perfusion parameters (CBF threshold vs CBV threshold) were compared with DWI-infarct core volumes., Results: The median time between CTP and DWI was 37.5min. The commonly used threshold of CBV< 41% (4.3 mL) resulted in lower median infarct core volume difference compared to the commonly used thresholds of rCBF < 30% (8.2mL). On the other hand, the optimal thresholds of CBV < 26% (-1.0mL; 95% CI, -53.9 to 58.1 mL; 0.945) resulted in the lowest median infarct core volume difference, narrowest limits of agreement, and largest interclass correlation coefficient compared with the optimal thresholds of rCBF < 38% (4.9 mL; 95% CI, -36.4 to 62.9 mL; 0.939)., Conclusion: Our study found that the both optimal and commonly used thresholds of CBV provided a more accurate prediction of the infarct core volume in patients with AIS than rCBF., Competing Interests: Declaration of Competing Interest No conflict of interest, (Copyright © 2023 Elsevier Inc. All rights reserved.)

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., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: The co-authors (J. K. Ryu and H. Shim) are employee Canon Medical Systems Korea, Seoul, Korea that is the subsidiary in Korea of Canon Medical Systems Corporation, Otawara-si, Japan. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2023 Otgonbaatar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: The demand for homogeneous and higher vascular contrast enhancement is critical to provide an appropriate interpretation of abnormal vascular findings in coronary computed tomography angiography (CTA)., Purpose: To evaluate the effect of various contrast media concentrations (Iohexol-370, Iohexol-300, Iohexol-240) and image reconstructions (filtered back projection [FBP], hybrid iterative reconstruction [IR], and deep learning reconstruction [DLR]) on coronary CTA., Material and Methods: A total of 63 patients referred for coronary CTA between July and October 2021 were enrolled in this prospective study, and they randomly received one of three contrast media. CTA images were reconstructed with FBP, hybrid IR, and DLR. The CT attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated for all three images. The images were subjectively evaluated by two radiologists in terms of overall image quality, artifacts, image noise, and vessel wall delineation on a 5-point Likert scale., Results: The application of DLR resulted in significantly lower image noise; higher CT attenuation, SNR, and CNR; and better subjective analysis among the three different concentrations of contrast media groups ( P  < 0.001). There was no significant difference in the CT attenuation of the left ventricle ( P  = 0.089) and coronary arteries ( P  = 0.072) between hybrid IR at Iohexol-300 and DLR at Iohexol-240. Furthermore, application of DLR to the Iohexol-240 significantly improved SNR and CNR; it achieved higher subjective scores compared with hybrid IR at Iohexol-300 ( P  < 0.001)., Conclusion: We suggest that using DLR with Iohexol-240 contrast media is preferable to hybrid IR with Iohexol-300 contrast media in coronary CTA.

Objective: This study aimed to investigate whether a deep learning reconstruction (DLR) method improves the image quality, stent evaluation, and visibility of the valve apparatus in coronary computed tomography angiography (CCTA) when compared with filtered back projection (FBP) and hybrid iterative reconstruction (IR) methods., Materials and Methods: CCTA images of 51 patients (mean age ± standard deviation [SD], 63.9 ± 9.8 years, 36 male) who underwent examination at a single institution were reconstructed using DLR, FBP, and hybrid IR methods and reviewed. CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and stent evaluation, including 10%-90% edge rise slope (ERS) and 10%-90% edge rise distance (ERD), were measured. Quantitative data are summarized as the mean ± SD. The subjective visual scores (1 for worst -5 for best) of the images were obtained for the following: overall image quality, image noise, and appearance of stent, vessel, and aortic and tricuspid valve apparatus (annulus, leaflets, papillary muscles, and chordae tendineae). These parameters were compared between the DLR, FBP, and hybrid IR methods., Results: DLR provided higher Hounsfield unit (HU) values in the aorta and similar attenuation in the fat and muscle compared with FBP and hybrid IR. The image noise in HU was significantly lower in DLR (12.6 ± 2.2) than in hybrid IR (24.2 ± 3.0) and FBP (54.2 ± 9.5) ( p < 0.001). The SNR and CNR were significantly higher in the DLR group than in the FBP and hybrid IR groups ( p < 0.001). In the coronary stent, the mean value of ERS was significantly higher in DLR (1260.4 ± 242.5 HU/mm) than that of FBP (801.9 ± 170.7 HU/mm) and hybrid IR (641.9 ± 112.0 HU/mm). The mean value of ERD was measured as 0.8 ± 0.1 mm for DLR while it was 1.1 ± 0.2 mm for FBP and 1.1 ± 0.2 mm for hybrid IR. The subjective visual scores were higher in the DLR than in the images reconstructed with FBP and hybrid IR., Conclusion: DLR reconstruction provided better images than FBP and hybrid IR reconstruction., Competing Interests: The co-authors (J. K. Ryu and H. Shim) are employee Canon Medical Systems Korea, Seoul, Korea that is the subsidiary in Korea of Canon Medical Systems Corporation, Otawara-si, Japan., (Copyright © 2022 The Korean Society of Radiology.)

Objective: This study aimed to evaluate chest computed tomography (CT) angiography image quality using the contrast enhancement (CE)-boost technique compared with conventional images., Methods: Forty patients who underwent contrast-enhanced chest CT were included. Combined CT angiography images of the iodinated image obtained from the subtraction of nonenhanced CT images and CT angiography images were used to generate CE-boost images. Computed tomography attenuation, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) for the right and left pulmonary arteries as the central and subsegmental arteries as peripheral vessels were assessed. Subjective image quality was rated on a 5-point scale by 2 radiologists. Image quality was assessed using a paired t test., Results: Computed tomography attenuation in the main pulmonary artery was significantly higher for the CE-boost images (311.05 ± 91.94) than for the conventional images (221.25 ± 61.21, P < 0.001). Similarly, the CE-boost images resulted in significantly higher CT attenuation in the subsegmental arteries (right, 305.34 ± 90.13; left, 313.05 ± 97.21) than in the conventional images (right, 218.45 ± 63.16; left, 223.89 ± 74.27). The CE-boost technique demonstrated marked improvement in the visualization of the peripheral pulmonary artery without the administration of a higher iodine delivery rate. The mean SNR and CNR were also significantly higher in the central and peripheral vessels in the CE-boost images than in the conventional images (P < 0.001). In the subjective analysis, the image contrast and vascular contrast edge were significantly higher for the CE-boost images than for conventional images (P < 0.001)., Conclusions: The CE-boost technique increases not only the visualization of peripheral arteries by improving vascular attenuation but also the SNR and CNR., Competing Interests: The coauthors (J.-K.R., H.S.) are employees of Canon Medical Systems Korea, Seoul, Korea, which is a subsidiary of Canon Medical Systems Corporation, Otawara-si, Japan. The remaining authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

To evaluate the ability of a commercialized deep learning reconstruction technique to depict intracranial vessels on the brain computed tomography angiography and compare the image quality with filtered-back-projection and hybrid iterative reconstruction in terms of objective and subjective measures. Forty-three patients underwent brain computed tomography angiography, and images were reconstructed using three algorithms: filtered-back-projection, hybrid iterative reconstruction, and deep learning reconstruction. The image noise, computed tomography attenuation value, signal-to-noise ratio, and contrast-to-noise ratio were measured in the bilateral cavernous segment of the internal carotid artery, vertebral artery, basilar apex, horizontal segment of the middle cerebral artery and used for the objective assessment of the image quality among the three different reconstructions. The subjective image quality score was significantly higher for the deep learning reconstruction than hybrid iterative reconstruction and filtered-back-projection images. The deep learning reconstruction markedly improved the reduction of blooming artifacts in surgical clips and coiled aneurysms. The deep learning reconstruction method generally improves the image quality of brain computed tomography angiography in terms of objective measurement and subjective grading compared with filtered-back-projection and hybrid iterative reconstruction. Especially, deep learning reconstruction is deemed advantageous for better depiction of small vessels compared to filtered-back projection and hybrid iterative reconstruction., Competing Interests: The co-authors (Jae-Kyun Ryu, Seonkyu Kim) are employees of Canon Medical Systems Korea, Seoul, Korea that is the subsidiary in Korea of Canon Medical Systems Corporation, Otawara-si, Japan. The authors declare the paper has no conflicts of interest with the company., (© 2021 The Author(s). Published by IMR Press.)