Coronary computed tomography angiography (CCTA) is routinely used for anatomical assessment of coronary artery disease (CAD). However, invasive measurement of fractional flow reserve (FFR) is the current gold standard for the diagnosis of hemodynamically significant CAD. CT-derived FFRCT and CT perfusion are two emerging techniques that can provide a functional assessment of CAD for risk stratification and clinical decision making. Several clinical studies have shown that the diagnostic performance of concomitant CCTA and functional CT assessment for detecting hemodynamically significant CAD is at least non-inferior to that of other routinely used imaging modalities. This article aims to review the current clinical evidence and recent developments in functional CT techniques.

Objective: This study aimed to validate a deep learning-based fully automatic calcium scoring (coronary artery calcium [CAC]_auto) system using previously published cardiac computed tomography (CT) cohort data with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard. Materials and Methods: We developed the CAC_auto system using 100 co-registered, non-enhanced and contrast-enhanced CT scans. For the validation of the CAC_auto system, three previously published CT cohorts (n = 2985) were chosen to represent different clinical scenarios (i.e., 2647 asymptomatic, 220 symptomatic, 118 valve disease) and four CT models. The performance of the CAC_auto system in detecting coronary calcium was determined. The reliability of the system in measuring the Agatston score as compared with CAC_hand was also evaluated per vessel and per patient using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The agreement between CAC_auto and CAC_hand based on the cardiovascular risk stratification categories (Agatston score: 0, 1-10, 11-100, 101-400, > 400) was evaluated. Results: In 2985 patients, 6218 coronary calcium lesions were identified using CAC_hand. The per-lesion sensitivity and false-positive rate of the CAC_auto system in detecting coronary calcium were 93.3% (5800 of 6218) and 0.11 false-positive lesions per patient, respectively. The CAC_auto system, in measuring the Agatston score, yielded ICCs of 0.99 for all the vessels (left main 0.91, left anterior descending 0.99, left circumflex 0.96, right coronary 0.99). The limits of agreement between CAC_auto and CAC_hand were 1.6 ± 52.2. The linearly weighted kappa value for the Agatston score categorization was 0.94. The main causes of false-positive results were image noise (29.1%, 97/333 lesions), aortic wall calcification (25.5%, 85/333 lesions), and pericardial calcification (24.3%, 81/333 lesions). Conclusion: The atlas-based CAC_auto empowered by deep learning provided accurate calcium score measurement as compared with manual method and risk category classification, which could potentially streamline CAC imaging workflows.

Objective: To investigate the accuracy of the Agatston score obtained with the ultra-high-pitch (UHP) acquisition mode using tin-filter spectral shaping (Sn150 kVp) and a kVp-independent reconstruction algorithm to reduce the radiation dose. Materials and Methods: This prospective study included 114 patients (mean ± standard deviation, 60.3 ± 9.8 years; 74 male) who underwent a standard 120 kVp scan and an additional UHP Sn150 kVp scan for coronary artery calcification scoring (CACS). These two datasets were reconstructed using a standard reconstruction algorithm (120 kVp + Qr36d, protocol A; Sn150 kVp + Qr36d, protocol B). In addition, the Sn150 kVp dataset was reconstructed using a kVp-independent reconstruction algorithm (Sn150 kVp + Sa36d, protocol C). The Agatston scores for protocols A and B, as well as protocols A and C, were compared. The agreement between the scores was assessed using the intraclass correlation coefficient (ICC) and the Bland-Altman plot. The radiation doses for the 120 kVp and UHP Sn150 kVp acquisition modes were also compared. Results: No significant difference was observed in the Agatston score for protocols A (median, 63.05; interquartile range [IQR], 0-232.28) and C (median, 60.25; IQR, 0-195.20) (p = 0.060). The mean difference in the Agatston score for protocols A and C was relatively small (-7.82) and with the limits of agreement from -65.20 to 49.56 (ICC = 0.997). The Agatston score for protocol B (median, 34.85; IQR, 0-120.73) was significantly underestimated compared with that for protocol A (p < 0.001). The UHP Sn150 kVp mode facilitated an effective radiation dose reduction by approximately 30% (0.58 vs. 0.82 mSv, p < 0.001) from that associated with the standard 120 kVp mode. Conclusion: The Agatston scores for CACS with the UHP Sn150 kVp mode with a kVp-independent reconstruction algorithm and the standard 120 kVp demonstrated excellent agreement with a small mean difference and narrow agreement limits. The UHP Sn150 kVp mode allowed a significant reduction in the radiation dose.

Objective: To evaluate the prognostic implications of preoperative magnetic resonance imaging (MRI) features of hepatocellular carcinoma (HCC) with a focus on those with targetoid appearance based on the Liver Imaging Reporting and Data System (LI-RADS), as well as known microvascular invasion (MVI) features. Materials and Methods: This retrospective study included 242 patients (190 male; mean age, 57.1 years) who underwent surgical resection of a single HCC (≤ 5 cm) as well as preoperative gadoxetic acid-enhanced MRI between January 2012 and March 2015. LI-RADS category was assigned, and the LR-M category was further classified into two groups according to rim arterial-phase hyperenhancement (APHE). The imaging features associated with MVI were also assessed. The overall survival (OS), recurrence-free survival (RFS), and their associated factors were evaluated. Results: Among the 242 HCCs, 190 (78.5%), 25 (10.3%), and 27 (11.2%) were classified as LR-4/5, LR-M with rim APHE, and LR-M without rim APHE, respectively. LR-M with rim APHE (vs. LR-4/5; hazard ratio [HR] for OS, 5.48 [p = 0.002]; HR for RFS, 2.09 [p = 0.042]) and tumor size (per cm increase; HR for OS, 6.04 [p = 0.009]; HR for RFS, 1.77 [p = 0.014]) but not MVI imaging features (p > 0.05) were independent factors associated with OS and RFS. Compared to the 5-year OS and RFS rates in the LR-4/5 group (93.9% and 66.8%, respectively), the LR-M with rim APHE group had significantly lower rates (68.0% and 45.8%, respectively, both p < 0.05), while the LR-M without rim APHE group did not significantly differ in the survival rates (91.3% and 80.2%, respectively, both p > 0.05). Conclusion: Further classification of LR-M according to the presence of rim APHE may help predict the postoperative prognosis of patients with a single HCC.

Objective: To determine whether volumetric CT texture analysis (CTTA) using fully automatic tumor segmentation can help predict recurrence-free survival (RFS) in patients with intrahepatic mass-forming cholangiocarcinomas (IMCCs) after surgical resection. Materials and Methods: This retrospective study analyzed the preoperative CT scans of 89 patients with IMCCs (64 male; 25 female; mean age, 62.1 years; range, 38-78 years) who underwent surgical resection between January 2005 and December 2016. Volumetric CTTA of IMCCs was performed in late arterial phase images using both fully automatic and semi-automatic liver tumor segmentation techniques. The time spent on segmentation and texture analysis was compared, and the first-order and second-order texture parameters and shape features were extracted. The reliability of CTTA parameters between the techniques was evaluated using intraclass correlation coefficients (ICCs). Intra- and interobserver reproducibility of volumetric CTTAs were also obtained using ICCs. Cox proportional hazard regression were used to predict RFS using CTTA parameters and clinicopathological parameters. Results: The time spent on fully automatic tumor segmentation and CTTA was significantly shorter than that for semi-automatic segmentation: mean ± standard deviation of 1 minutes 37 seconds ± 50 seconds vs. 10 minutes 48 seconds ± 13 minutes 44 seconds (p < 0.001). ICCs of the texture features between the two techniques ranged from 0.215 to 0.980. ICCs for the intraobserver and interobserver reproducibility using fully automatic segmentation were 0.601-0.997 and 0.177-0.984, respectively. Multivariable analysis identified lower first-order mean (hazard ratio [HR], 0.982; p = 0.010), larger pathologic tumor size (HR, 1.171; p < 0.001), and positive lymph node involvement (HR, 2.193; p = 0.014) as significant parameters for shorter RFS using fully automatic segmentation. Conclusion: Volumetric CTTA parameters obtained using fully automatic segmentation could be utilized as prognostic markers in patients with IMCC, with comparable reproducibility in significantly less time compared with semi-automatic segmentation.

Ovarian cystadenofibroma is a benign ovarian tumor that is characterized by a consistent percentage of masses, which remain indeterminate in ultrasonography and require magnetic resonance (MR) investigation; they may mimic borderline or malignant lesions. Three main morphologic patterns, resembling different ovarian neoplasms, can be identified in cystadenofibromas: multilocular solid lesions, unilocular cystic lesions with parietal thickening, and purely cystic masses. However, a cystoadenofibroma has typical features, such as T2-weighted hypointensity associated with no restrictions in diffusion-weighted imaging (the so-called "dark-dark appearance") and progressive post-contrast enhancement (type I perfusion curve). The purpose of this study was to review the features of ovarian cystadenofibromas in MR imaging and to suggest pearls and pitfalls regarding their correct diagnosis.

This is a narrative review of various treatment modalities for advanced hepatocellular carcinoma (HCC), with a focus on recent updates in radiological treatments, as well as novel treatment concepts related to immune checkpoint inhibitors and combination therapies with locoregional treatments. Interventional radiologists have made efforts toward developing alternative and/or combination treatments for first-line systemic treatment of patients with advanced HCC. Locoregional treatments with or without systemic therapy may be considered in the selected patients. Various treatment modalities for advanced HCC are emerging, and several randomized controlled trials, including those of combination treatments with immunotherapy, are ongoing.

Objective: To analyze the computational fluid dynamics (CFD) of femoral artery pseudoaneurysm (FAP), identify a suitable location and timing for percutaneous thrombin injection (PTI) based on this analysis, and report our clinical experience with the procedure. Materials and Methods: CFD can be used to analyze the hemodynamics of the human body. An analysis using CFD recommended that the suitable location of the needle tip for PTI is at the center of the aneurysm sac and the optimal timing for starting PTI is during the early inflow phase of blood into the sac. Since 2011, seven patients (three male and four female; median age, 60 years [range, 43-75 years]) with FAP were treated with PTI based on the devised suitable location and time. Prior to the procedure, color Doppler ultrasonography was performed to determine the location and timing of the thrombin injection. Results: The technical success rate of the PTI was 100%. The amount of thrombin used for the procedure ranged from 200 IU to 1000 IU (median, 500 IU). None of the patients experienced any symptoms or signs of embolic complications during the procedure. Follow-up CT images did not reveal any embolism in the lower extremities and showed complete thrombosis of the pseudoaneurysm. Conclusion: Based on our study of CFD, PTI administered centrally in the FAP during early inflow, as seen on color Doppler, can be an effective technique.

Objective: To prospectively investigate the feasibility of shear wave elastography (SWE) as a new quantitative and objective method for evaluating the stiffness of the gastrocnemius medialis (GM) muscle during passive stretching in patients with Parkinson's disease (PD). Materials and Methods: SWE of the GM muscle was performed in 28 patients with PD [13 female and 15 male; mean age ± standard deviation (SD): 63.0 ± 8.5 years] and 12 healthy controls (5 female and 7 male; mean age ± SD: 59.3 ± 6.4 years) during passive ankle rotation. A Young's modulus-ankle angle curve was constructed. The GM slack angle and baseline Young's modulus (E₀) were compared between the markedly symptomatic and mildly symptomatic sides of patients with PD, and healthy controls. Additionally, the correlation between the GM slack angle and the severity of rigidity, and the observer reproducibility of SWE in determining the GM slack angle were evaluated. Results: The GM slack angle was smaller on both the markedly and mildly symptomatic sides in patients with PD than in healthy controls (mean ± SD of -29.13° ± 3.79° and -25.65° ± 3.39°, respectively, vs. -21.22° ± 3.52°; p < 0.001 and p = 0.006, respectively). Additionally, in patients with PD, the GM slack angle on the markedly symptomatic side was smaller than that on the mildly symptomatic side (p = 0.003). The E0 value was lower on both the markedly and mildly symptomatic sides in patients with PD than in healthy controls (mean ± SD of 10.11 ± 2.85 kPa and 10.08 ± 1.88 kPa, respectively, vs. 12.23 ± 1.02 kPa; p = 0.012 and p < 0.001, respectively). However, no significant difference was found between the markedly and mildly symptomatic sides in patients with PD (p = 0.634). A negative linear relationship was observed between the GM slack angle and lower limb rigidity score on the markedly symptomatic side in patients with PD (r = -0.719; p < 0.001). The intraclass correlation coefficients for observer reproducibility of SWE ranged from 0.880 to 0.951. Conclusion: The slack angle determined by SWE may be a useful quantitative and reproducible method for evaluating muscle stiffness in patients with PD.

Objective: The purpose of this study was to assess whether a deep learning (DL) algorithm could enable simultaneous noise reduction and edge sharpening in low-dose lumbar spine CT. Materials and Methods: This retrospective study included 52 patients (26 male and 26 female; median age, 60.5 years) who had undergone CT-guided lumbar bone biopsy between October 2015 and April 2020. Initial 100-mAs survey images and 50-mAs intraprocedural images were reconstructed by filtered back projection. Denoising was performed using a vendor-agnostic DL model (ClariCT.AI^TM, ClariPI) for the 50-mAS images, and the 50-mAs, denoised 50-mAs, and 100-mAs CT images were compared. Noise, signal-to-noise ratio (SNR), and edge rise distance (ERD) for image sharpness were measured. The data were summarized as the mean ± standard deviation for these parameters. Two musculoskeletal radiologists assessed the visibility of the normal anatomical structures. Results: Noise was lower in the denoised 50-mAs images (36.38 ± 7.03 Hounsfield unit [HU]) than the 50-mAs (93.33 ± 25.36 HU) and 100-mAs (63.33 ± 16.09 HU) images (p < 0.001). The SNRs for the images in descending order were as follows: denoised 50-mAs (1.46 ± 0.54), 100-mAs (0.99 ± 0.34), and 50-mAs (0.58 ± 0.18) images (p < 0.001). The denoised 50-mAs images had better edge sharpness than the 100-mAs images at the vertebral body (ERD; 0.94 ± 0.2 mm vs. 1.05 ± 0.24 mm, p = 0.036) and the psoas (ERD; 0.42 ± 0.09 mm vs. 0.50 ± 0.12 mm, p = 0.002). The denoised 50-mAs images significantly improved the visualization of the normal anatomical structures (p < 0.001). Conclusion: DL-based reconstruction may enable simultaneous noise reduction and improvement in image quality with the preservation of edge sharpness on low-dose lumbar spine CT. Investigations on further radiation dose reduction and the clinical applicability of this technique are warranted.

Recent advances in the molecular and genetic characterization of central nervous system (CNS) tumors have ushered in a new era of tumor classification, diagnosis, and prognostic assessment. In this emerging and rapidly evolving molecular genetic era, imaging plays a critical role in the preoperative diagnosis and surgical planning, molecular marker prediction, targeted treatment planning, and post-therapy assessment of CNS tumors. This review provides an overview of the current imaging methods relevant to the molecular genetic classification of CNS tumors. Specifically, we focused on 1) the correlates between imaging features and specific molecular genetic markers and 2) the post-therapy imaging used for therapeutic assessment.

Objective: Central nervous system involvement in coronavirus disease 2019 (COVID-19) has been increasingly reported. We performed a systematic review and meta-analysis to evaluate the incidence of radiologically demonstrated neurologic complications and detailed neuroimaging findings associated with COVID-19. Materials and Methods: A systematic literature search of MEDLINE/PubMed and EMBASE databases was performed up to September 17, 2020, and studies evaluating neuroimaging findings of COVID-19 using brain CT or MRI were included. Several cohort-based outcomes, including the proportion of patients with abnormal neuroimaging findings related to COVID-19 were evaluated. The proportion of patients showing specific neuroimaging findings was also assessed. Subgroup analyses were also conducted focusing on critically ill COVID-19 patients and results from studies that used MRI as the only imaging modality. Results: A total of 1394 COVID-19 patients who underwent neuroimaging from 17 studies were included; among them, 3.4% of the patients demonstrated COVID-19-related neuroimaging findings. Olfactory bulb abnormalities were the most commonly observed (23.1%). The predominant cerebral neuroimaging finding was white matter abnormality (17.6%), followed by acute/subacute ischemic infarction (16.0%), and encephalopathy (13.0%). Significantly more critically ill patients had COVID-19-related neuroimaging findings than other patients (9.1% vs. 1.6%; p = 0.029). The type of imaging modality used did not significantly affect the proportion of COVID-19-related neuroimaging findings. Conclusion: Abnormal neuroimaging findings were occasionally observed in COVID-19 patients. Olfactory bulb abnormalities were the most commonly observed finding. Critically ill patients showed abnormal neuroimaging findings more frequently than the other patient groups. White matter abnormalities, ischemic infarctions, and encephalopathies were the common cerebral neuroimaging findings.

Objective: To assess the feasibility of quantitatively assessing pancreatic steatosis using magnetic resonance imaging (MRI) and its correlation with obesity and metabolic risk factors in pediatric patients. Materials and Methods: Pediatric patients (≤ 18 years) who underwent liver fat quantification MRI between January 2016 and June 2019 were retrospectively included and divided into the obesity and control groups. Pancreatic proton density fat fraction (P-PDFF) was measured as the average value for three circular regions of interest (ROIs) drawn in the pancreatic head, body, and tail. Age, weight, laboratory results, and mean liver MRI values including liver PDFF (L-PDFF), stiffness on MR elastography, and T2^* values were assessed for their correlation with P-PDFF using linear regression analysis. The associations between P-PDFF and metabolic risk factors, including obesity, hypertension, diabetes mellitus (DM), and dyslipidemia, were assessed using logistic regression analysis. Results: A total of 172 patients (male:female = 125:47; mean ± standard deviation [SD], 13.2 ± 3.1 years) were included. The mean P-PDFF was significantly higher in the obesity group than in the control group (mean ± SD, 4.2 ± 2.5% vs. 3.4 ± 2.4%; p = 0.037). L-PDFF and liver stiffness values showed no significant correlation with P-PDFF (p = 0.235 and p = 0.567, respectively). P-PDFF was significantly associated with obesity (odds ratio 1.146, 95% confidence interval 1.006-1.307, p = 0.041), but there was no significant association with hypertension, DM, and dyslipidemia. Conclusion: MRI can be used to quantitatively measure pancreatic steatosis in children. P-PDFF is significantly associated with obesity in pediatric patients.

Double outlet right ventricle (DORV) is a relatively common congenital heart disease in which both great arteries are connected completely or predominantly to the morphologic RV. Unlike other congenital heart diseases, DORV demonstrates various anatomic and hemodynamic subtypes, mimicking ventricular septal defect, tetralogy of Fallot, transposition of the great arteries, and functional single ventricle. Because different surgical strategies are applied to different subtypes of DORV with ventricular septal defects, a detailed assessment of intracardiac anatomy should be performed preoperatively. Due to high spatial and contrast resolutions, cardiac CT can provide an accurate characterization of various intracardiac morphologic features of DORV. In this pictorial essay, major anatomic factors affecting surgical decision-making in DORV with ventricular septal defects were comprehensively reviewed using three-dimensional cardiac CT data. In addition, the surgical procedures available for these patients and major postoperative complications are described.

Objective: Muscle quantity and quality can be measured with an automated system on CT. However, the effects of contrast phases on the muscle measurements have not been established, which we aimed to investigate in this study. Materials and Methods: Muscle quantity was measured according to the skeletal muscle area (SMA) measured by a convolutional neural network-based automated system at the L3 level in 89 subjects undergoing multiphasic abdominal CT comprising unenhanced phase, arterial phase, portal venous phase (PVP), or delayed phase imaging. Muscle quality was analyzed using the mean muscle density and the muscle quality map, which comprises normal and low-attenuation muscle areas (NAMA and LAMA, respectively) based on the muscle attenuation threshold. The SMA, mean muscle density, NAMA, and LAMA were compared between PVP and other phases using paired t tests. Bland-Altman analysis was used to evaluate the inter-phase variability between PVP and other phases. Based on the cutoffs for low muscle quantity and quality, the counts of individuals who scored lower than the cutoff values were compared between PVP and other phases. Results: All indices showed significant differences between PVP and other phases (p < 0.001 for all). The SMA, mean muscle density, and NAMA increased during the later phases, whereas LAMA decreased during the later phases. Bland-Altman analysis showed that the mean differences between PVP and other phases ranged -2.1 to 0.3 cm² for SMA, -12.0 to 2.6 cm² for NAMA, and -2.2 to 9.9 cm² for LAMA.The number of patients who were categorized as low muscle quantity did not significant differ between PVP and other phases (p ≥ 0.5), whereas the number of patients with low muscle quality significantly differed (p ≤ 0.002). Conclusion: SMA was less affected by the contrast phases. However, the muscle quality measurements changed with the contrast phases to greater extents and would require a standardization of the contrast phase for reliable measurement.

Objective: With the recent development of various MRI-conditional cardiac implantable electronic devices (CIEDs), the accurate identification and characterization of CIEDs have become critical when performing MRI in patients with CIEDs. We aimed to develop and evaluate a deep learning-based algorithm (DLA) that performs the detection and characterization of parameters, including MRI safety, of CIEDs on chest radiograph (CR) in a single step and compare its performance with other related algorithms that were recently developed. Materials and Methods: We developed a DLA (X-ray CIED identification [XCID]) using 9912 CRs of 958 patients with 968 CIEDs comprising 26 model groups from 4 manufacturers obtained between 2014 and 2019 from one hospital. The performance of XCID was tested with an external dataset consisting of 2122 CRs obtained from a different hospital and compared with the performance of two other related algorithms recently reported, including PacemakerID (PID) and Pacemaker identification with neural networks (PPMnn). Results: The overall accuracies of XCID for the manufacturer classification, model group identification, and MRI safety characterization using the internal test dataset were 99.7% (992/995), 97.2% (967/995), and 98.9% (984/995), respectively. These were 95.8% (2033/2122), 85.4% (1813/2122), and 92.2% (1956/2122), respectively, with the external test dataset. In the comparative study, the accuracy for the manufacturer classification was 95.0% (152/160) for XCID and 91.3% for PPMnn (146/160), which was significantly higher than that for PID (80.0%,128/160; p < 0.001 for both). XCID demonstrated a higher accuracy (88.1%; 141/160) than PPMnn (80.0%; 128/160) in identifying model groups (p < 0.001). Conclusion: The remarkable and consistent performance of XCID suggests its applicability for detection, manufacturer and model identification, as well as MRI safety characterization of CIED on CRs. Further studies are warranted to guarantee the safe use of XCID in clinical practice.