Using Patient-Specific Contrast Enhancement Optimizer Simulation Software During the Transcatheter Aortic Valve Implantation-Computed Tomography Angiography in Patients With Aortic Stenosis.

OBJECTIVES: This study assessed whether patient-specific contrast enhancement optimizer simulation software (p-COP) can reduce the contrast material (CM) dose compared with the conventional body weight (BW)-tailored scan protocol during transcatheter aortic valve implantation-computed tomography angiography (TAVI-CTA) in patients with aortic stenosis. METHODS: We used the CM injection protocol selected by the p-COP in group A (n = 30). p-COP uses an algorithm that concerns data on an individual patient's cardiac output. Group B (n = 30) was assigned to the conventional BW-tailored CM injection protocol group. We compared the CM dose, CM amount, injection rate, and computed tomography (CT) values in the abdominal aorta between the 2 groups and classified them as acceptable (>280 Hounsfield units (HU)) or unacceptable (<279 HU) based on the optimal CT value and visualization scores for TAVI-CTA. We used the Mann-Whitney U test to compare patient characteristics and assess the interpatient variability of subjects in both groups. RESULTS: Group A received 56.2 mL CM and 2.6 mL/s of injection, whereas group B received 76.9 mL CM and 3.4 mL/s of injection (P < 0.01). The CT value for the abdominal aorta at the celiac level was 287.0 HU in group A and 301.7HU in group B (P = 0.46). The acceptable (>280 HU) and unacceptable (<280 HU) CT value rates were 22 and 8 patients in group A and 24 and 6 patients in group B, respectively (P = 0.76). We observed no significant differences in the visualization scores between groups A and B (visualization score = 3, P = 0.71). CONCLUSION: The utilization of p-COP may decrease the CM dosage and injection rate by approximately 30% in individuals with aortic stenosis compared with the body-weight-tailored scan protocol during TAVI-CTA.

Performance of Ultra-High-Resolution Computed Tomography in Super High-Resolution Mode at the Routine Radiation Dose: Phantom Study.

OBJECTIVE: Using a chest phantom, we compared the image quality of ultra-high-resolution computed tomography (U-HRCT) images acquired in super high-resolution (SHR) and normal resolution (NR) mode and at the routine radiation dose. The detector size was 0.25 and 0.5 mm, respectively. METHODS: A chest phantom was scanned on a U-HRCT scanner. The scan parameters were tube voltage 120 kV and volume CT dose index 13.0 mGy, the routine radiation dose for conventional scans. The rotation time was 0.5 s/rot, the number of matrices was 512 in NR and 1024 in SHR mode. For physical evaluation, the modulation transfer function was measured on the spherical simulated nodule, and the noise power spectrum on the cylindrical water phantom. A CT value profile curve was created using an in-house simulated bronchial phantom. For visual evaluation, 3 radiologists and 3 radiology technologists evaluated overall image quality using a 4-grade scale (grade 1, poor; and grade 4, excellent). RESULTS: The 10% of modulation transfer function was 13.5 lp/cm in NR and 14.9 lp/cm in SHR mode ( P <0.01). ƒ peak was 5.6 lp/cm in NR and 8.8 lp/cm in SHR mode ( P <0.01), and the peak of noise power spectrum shifted. On the profile curves, the CT value at the edge changed in NR but not in SHR mode. The overall image quality was grade 3.0 ± 0.7 in SHR and grade 2.0 ± 0.7 in NR mode ( P <0.01). CONCLUSIONS: The image quality of SHR mode with U-HRCT was superior to that of NR mode at the routine radiation dose.

Prediction of Aortic Contrast Enhancement on Dynamic Hepatic Computed Tomography-Performance Comparison of Machine Learning Methods and Simulation Software.

OBJECTIVES: The aim of this study was to compare prediction ability between ensemble machine learning (ML) methods and simulation software for aortic contrast enhancement on dynamic hepatic computed tomography. METHODS: We divided 339 human hepatic dynamic computed tomography scans into 2 groups. One group consisted of 279 scans used to create cross-validation data sets, the other group of 60 scans were used as test data sets. To evaluate the effect of the patient characteristics on enhancement, we calculated changes in the contrast medium dose per enhancement of the abdominal aorta in the hepatic arterial phase. The parameters for ML were the patient sex, age, height, body weight, body mass index, and cardiac output. We trained 9 ML regressors by applying 5-fold cross-validation, integrated the predictions of all ML regressors for ensemble learning and the simulations, and used the training and test data to compare their Pearson correlation coefficients. RESULTS: Comparison of different ML methods showed that the Pearson correlation coefficient for the real and predicted contrast medium dose per enhancement of the abdominal aorta was highest with ensemble ML (r = 0.786). It was higher than that obtained with the simulation software (r = 0.350). With ensemble ML, the Bland-Altman limit of agreement [mean difference, 5.26 Hounsfield units (HU); 95% limit of agreement, -112.88 to 123.40 HU] was narrower than that obtained with the simulation software (mean difference, 11.70 HU; 95% limit of agreement, -164.71 to 188.11 HU). CONCLUSION: The performance for predicting contrast enhancement of the abdominal aorta in the hepatic arterial phase was higher with ensemble ML than with the simulation software.

Usefulness of the patient-specific contrast enhancement optimizer simulation software during the whole-body computed tomography angiography.

To evaluate whether the patient-specific contrast enhancement optimizer simulation software (p-COP) is useful for predicting contrast enhancement during whole-body computed tomography angiography (WBCTA). We randomly divided the patients into two groups using a random number table. We used the contrast material (CM) injection protocol selected by p-COP in group A (n = 52). The p-COP used an algorithm including data on the individual patient's cardiac output. Group B (n = 50) was assigned to the conventional CM injection protocol based on body weight. We compared the CT number in the abdominal aorta at the celiac artery level between the two groups and classified them as acceptable (> 280 HU) and unacceptable (< 279 HU) based on the optimal CT number for the WBCTA scans. To evaluate the difference in both injection protocols, we compared the visual inspection of the images of the artery of Adamkiewicz in both protocols. The CM dosage and injection rate in group A were significantly lower than those in group B (480.8 vs. 501.1 mg I/kg and 3.1 vs. 3.3 ml/s, p < 0.05). The CT number of the abdominal aorta at the celiac level was 382.4 ± 62.3 HU in group A and 363.8 ± 71.3 HU in group B (p = 0.23). CM dosage and injection rate were positively correlated to cardiac output for group A (r = 0.80, p < 0.05) and group B (r = 0.16, p < 0.05). The number of patients with an acceptable CT number was higher in group A [46/6 (86.7%)] than in group B [43/7 (71.4%)], but not significant (p = 0.71). The visualization rate for the Adamkiewicz artery was not significantly different between groups A and B (p = 0.89). The p-COP was useful for predicting contrast enhancement during WBCTA with a lower CM dosage and a lower contrast injection rate than that based on the body weight protocol. In patients with lower cardiac output a reduction in contrast injection rate and CM dosage did not lead to a reduced imaging quality, thus particularly in this group CM dosage can be reduced by p-COP.

Diagnostic value of deep learning reconstruction for radiation dose reduction at abdominal ultra-high-resolution CT.

OBJECTIVES: We evaluated lower dose (LD) hepatic dynamic ultra-high-resolution computed tomography (U-HRCT) images reconstructed with deep learning reconstruction (DLR), hybrid iterative reconstruction (hybrid-IR), or model-based IR (MBIR) in comparison with standard-dose (SD) U-HRCT images reconstructed with hybrid-IR as the reference standard to identify the method that allowed for the greatest radiation dose reduction while preserving the diagnostic value. METHODS: Evaluated were 72 patients who had undergone hepatic dynamic U-HRCT; 36 were scanned with the standard radiation dose (SD group) and 36 with 70% of the SD (lower dose [LD] group). Hepatic arterial and equilibrium phase (HAP, EP) images were reconstructed with hybrid-IR in the SD group, and with hybrid-IR, MBIR, and DLR in the LD group. One radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise. The overall image quality was assessed by 3 other radiologists; they used a 5-point confidence scale ranging from 1 (unacceptable) to 5 (excellent). Superiority and equivalence with prespecified margins were assessed. RESULTS: With respect to the image noise, in the HAP and EP, LD DLR and LD MBIR images were superior to SD hybrid-IR images; LD hybrid-IR images were neither superior nor equivalent to SD hybrid-IR images. With respect to the quality scores, only LD DLR images were superior to SD hybrid-IR images. CONCLUSIONS: DLR preserved the quality of abdominal U-HRCT images even when scanned with a reduced radiation dose. KEY POINTS: • Lower dose DLR images were superior to the standard-dose hybrid-IR images quantitatively and qualitatively at abdominal U-HRCT. • Neither hybrid-IR nor MBIR may allow for a radiation dose reduction at abdominal U-HRCT without compromising the image quality. • Because DLR allows for a reduction in the radiation dose and maintains the image quality even at the thinnest slice section, DLR should be applied to abdominal U-HRCT scans.

Incidence and factor analysis of laryngohyoid fractures in hanging individuals-computed tomography study.

OBJECTIVES: Although laryngohyoid fracture indicates the applied neck pressure and is an important finding in hanging individuals, the reported rate varies widely and its true incidence remains controversial. We used computed tomography (CT) studies to investigate the incidence of laryngohyoid fracture in hanging individuals and identify factors contributing to such fractures. METHODS: Considered for inclusion in this study were 107 attempted or successful hanging individuals subjected to CT studies between 2005 and 2019. After excluding 19 whose images were inadequate for evaluation, 88 subjects were included. Body suspension was complete in 20, partial in 49, and unknown in 19; 54 (61.4%) individuals died. Two radiologists performed image analysis and recorded the presence and site of laryngohyoid fractures. Multiple logistic regression analysis was used for factor analysis of laryngohyoid fractures; it included the gender, the age (< or ≧ 40 years), the type of suspension (complete or incomplete), and the outcome (death or survival). RESULTS: Of the 88 subjects, 35 (39.8%) presented with laryngohyoid fractures on CT images; the superior horn of the thyroid cartilage was fractured in 32 (91.4%) of the 35. Age was the only factor significantly related to laryngohyoid fracture (odds ratio = 2.85, 95% confidence interval = 1.08-7.52). CONCLUSIONS: In hanging individuals, the incidence of laryngohyoid fracture on CT images was 39.8%. The superior horn of the thyroid cartilage was the most frequent fracture site. KEY POINTS: • The incidence of laryngohyoid fracture on CT images of hanging individuals was almost 40%; the superior horn of the thyroid cartilage was the most frequent fracture site. • In older hanging individuals, attention must be paid to laryngohyoid fractures on CT images.

Accuracy of thin-slice model-based iterative reconstruction designed for brain CT to diagnose acute ischemic stroke in the middle cerebral artery territory: a multicenter study.

PURPOSE: Model-based iterative reconstruction (MBIR) yields higher spatial resolution and a lower image noise than conventional reconstruction methods. We hypothesized that thin-slice MBIR designed for brain CT could improve the detectability of acute ischemic stroke in the middle cerebral artery (MCA) territory. METHODS: Included were 41 patients with acute ischemic stroke in the MCA territory; they were seen at 4 medical centers. The controls were 39 subjects without acute stroke. Images were reconstructed with hybrid IR and with MBIR designed for brain CT at slice thickness of 2 mm. We measured the image noise in the ventricle and compared the contrast-to-noise ratio (CNR) in the ischemic lesion. We analyzed the ability of reconstructed images to detect ischemic lesions using receiver operating characteristics (ROC) analysis; 8 observers read the routine clinical hybrid IR with 5 mm-thick images, while referring to 2 mm-thick hybrid IR images or MBIR images. RESULTS: The image noise was significantly lower on MBIR- than hybrid IR images (1.2 vs. 3.4, p < 0.001). The CNR was significantly higher with MBIR than hybrid IR (6.3 vs. 1.6, p < 0.001). The mean area under the ROC curve was also significantly higher on hybrid IR plus MBIR than hybrid IR (0.55 vs. 0.48, p < 0.036). Sensitivity, specificity, and accuracy were 41.2%, 88.8%, and 65.7%, respectively, for hybrid IR; they were 58.8%, 86.1%, and 72.9%, respectively, for hybrid IR plus MBIR. CONCLUSION: The additional thin-slice MBIR designed for brain CT may improve the detection of acute MCA stroke.

Utility of Radial Scanning for the Identification of Arterial Hypervascularity of Hepatocellular Carcinoma on Gadoxetic Acid-Enhanced Magnetic Resonance Images.

OBJECTIVES: This study aimed to compare the accuracy of assessing the arterial hypervascularity of hepatocellular carcinoma (HCC) on dynamic computed tomography (CT) scans and gadoxetic acid (EOB)-enhanced magnetic resonance imaging (MRI) scans performed with radial sampling. METHODS: We studied the images of 40 patients with hypervascular HCC. A radiologist recorded the standard deviation of the attenuation (or the signal intensity [SI]) in subcutaneous fat tissue as the image noise (N) and calculated the contrast-to-noise ratio (CNR) as follows: (CNR) = (n-ROIT - n-ROIL)/N, where n-ROIT is the mean attenuation (or SI) of the tumor divided by the mean attenuation (or SI) of the aorta and n-ROIL is the mean attenuation (or SI) of the liver parenchyma divided by the mean attenuation (or SI) of the aorta. RESULTS: The CNR was significantly higher on EOB-enhanced MRI than on dynamic CT scans. CONCLUSIONS: For the assessment of HCC vascularity, EOB-enhanced MRI scans acquired with radial sampling were more accurate than dynamic CT images.

Advanced CT techniques for assessing hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the sixth-most common cancer in the world, and hepatic dynamic CT studies are routinely performed for its evaluation. Ongoing studies are examining advanced imaging techniques that may yield better findings than are obtained with conventional hepatic dynamic CT scanning. Dual-energy CT-, perfusion CT-, and artificial intelligence-based methods can be used for the precise characterization of liver tumors, the quantification of treatment responses, and for predicting the overall survival rate of patients. In this review, the advantages and disadvantages of conventional hepatic dynamic CT imaging are reviewed and the general principles of dual-energy- and perfusion CT, and the clinical applications and limitations of these technologies are discussed with respect to HCC. Finally, we address the utility of artificial intelligence-based methods for diagnosing HCC.

Possibility of Deep Learning in Medical Imaging Focusing Improvement of Computed Tomography Image Quality.

Deep learning (DL), part of a broader family of machine learning methods, is based on learning data representations rather than task-specific algorithms. Deep learning can be used to improve the image quality of clinical scans with image noise reduction. We review the ability of DL to reduce the image noise, present the advantages and disadvantages of computed tomography image reconstruction, and examine the potential value of new DL-based computed tomography image reconstruction.

Individual Optimization of Contrast Media Injection Protocol at Hepatic Dynamic Computed Tomography Using Patient-Specific Contrast Enhancement Optimizer.

OBJECTIVE: We developed a patient-specific contrast enhancement optimizer (p-COP) that can exploratorily calculate the contrast injection protocol required to obtain optimal enhancement at target organs using a computer simulator. Appropriate contrast media dose calculated by the p-COP may minimize interpatient enhancement variability. Our study sought to investigate the clinical utility of p-COP in hepatic dynamic computed tomography (CT). METHODS: One hundred thirty patients (74 men, 56 women; median age, 65 years) undergoing hepatic dynamic CT were randomly assigned to 1 of 2 contrast media injection protocols using a random number table. Group A (n = 65) was injected with a p-COP-determined iodine dose (developed by Higaki and Awai, Hiroshima University, Japan). In group B (n = 65), a standard protocol was used. The variability of measured CT number (SD) between the 2 groups of aortic and hepatic enhancement was compared using the F test. In the equivalence test, the equivalence margins for aortic and hepatic enhancement were set at 50 and 10 Hounsfield units (HU), respectively. The rate of patients with an acceptable aortic enhancement (250-350 HU) for the diagnosis of hypervascular liver tumors was compared using the χ test. RESULTS: The mean ± SD values of aortic and hepatic enhancement were 311.0 ± 39.9 versus 318.7 ± 56.5 and 59.0 ± 11.5 versus 58.6 ± 11.8 HU in groups A and B, respectively. Although the SD for aortic enhancement was significantly lower in group A (P = 0.006), the SD for hepatic enhancement was not significantly different (P = 0.871). The 95% confidence interval for the difference in aortic and hepatic enhancement between the 2 groups was within the range of the equivalence margins. The number of patients with acceptable aortic enhancement was significantly greater in group A than in group B (P < 0.01). CONCLUSIONS: The p-COP software reduced interpatient variability in aortic enhancement and obtained acceptable aortic enhancement at a significantly higher rate compared with the standard injection protocol for hepatic dynamic CT.

Minimizing individual variations in arterial enhancement on coronary CT angiographs using "contrast enhancement optimizer": a prospective randomized single-center study.

OBJECTIVES: To investigate the clinical utility of our newly developed contrast enhancement optimizer (CEO) software for coronary CT angiography (CCTA). METHODS: We randomly assigned 295 patients (168 males, 127 females, median age 71 years) undergoing CCTA to one of two contrast media injection protocols. Group A (n = 150) was injected with a CEO-selected iodine dose based on patient factors. In group B (n = 145), we used our standard protocol (245 mg I/kg). We recorded the CT number in the ascending aorta and determined whether the CT number was equivalent in groups A and B. For the equivalence test, we adopted 75 Hounsfield units (HU) as the equivalence margin. The standard deviation in the CT number and the rate of patients with an acceptable CT number were compared using the F test and the chi-square test, respectively. RESULTS: The iodine dose in group A was significantly smaller than that in group B (235.7 vs. 253.6 mg I/kg, p < 0.001). The CT number of the ascending aorta was 428.6 ± 55.5 HU in group A and 436.1 ± 68.7 HU in group B; the 95% confidence interval for the difference between the groups was -4.3 HU to 16.9 HU and within the range of the predetermined equivalence margins. In group A, the variance was significantly smaller than that in group B (p = 0.009). The number of patients with an acceptable CT number was significantly higher in group A than in group B (84.7% vs. 71.7%, p = 0.007). CONCLUSIONS: The use of our CEO for CCTA studies yielded optimal aortic contrast enhancement in significantly more patients than the standard protocol based on the body weight. KEY POINTS: • With our contrast enhancement optimizer (CEO) software, optimal and stable aortic enhancement can be obtained on coronary CT angiography scans irrespective of patient factors. • Management of contrast media becomes more appropriate by the CEO software. • The CEO software can control contrast enhancement at different tube voltage levels.

Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT.

OBJECTIVES: Deep learning reconstruction (DLR) is a new reconstruction method; it introduces deep convolutional neural networks into the reconstruction flow. This study was conducted in order to examine the clinical applicability of abdominal ultra-high-resolution CT (U-HRCT) exams reconstructed with a new DLR in comparison to hybrid and model-based iterative reconstruction (hybrid-IR, MBIR). METHODS: Our retrospective study included 46 patients seen between December 2017 and April 2018. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and calculated the contrast-to-noise ratio (CNR) for the aorta, portal vein, and liver. The overall image quality was assessed by two other radiologists and graded on a 5-point confidence scale ranging from 1 (unacceptable) to 5 (excellent). The difference between CT images subjected to hybrid-IR, MBIR, and DLR was compared. RESULTS: The image noise was significantly lower and the CNR was significantly higher on DLR than hybrid-IR and MBIR images (p < 0.01). DLR images received the highest and MBIR images the lowest scores for overall image quality. CONCLUSIONS: DLR improved the quality of abdominal U-HRCT images. KEY POINTS: • The potential degradation due to increased noise may prevent implementation of ultra-high-resolution CT in the abdomen. • Image noise and overall image quality for hepatic ultra-high-resolution CT images improved with deep learning reconstruction as compared to hybrid- and model-based iterative reconstruction.

Correction to: Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT.

The original version of this article, published on 11 April 2019, unfortunately, contained a mistake. The following correction has therefore been made in the original: The image in Fig. 3c was wrong. The corrected figure is given below. The original article has been corrected.

Deep learning-based image restoration algorithm for coronary CT angiography.

OBJECTIVES: The purpose of this study was to compare the image quality of coronary computed tomography angiography (CTA) subjected to deep learning-based image restoration (DLR) method with images subjected to hybrid iterative reconstruction (IR). METHODS: We enrolled 30 patients (22 men, 8 women) who underwent coronary CTA on a 320-slice CT scanner. The images were reconstructed with hybrid IR and with DLR. The image noise in the ascending aorta, left atrium, and septal wall of the ventricle was measured on all images and the contrast-to-noise ratio (CNR) in the proximal coronary arteries was calculated. We also generated CT attenuation profiles across the proximal coronary arteries and measured the width of the edge rise distance (ERD) and the edge rise slope (ERS). Two observers visually evaluated the overall image quality using a 4-point scale (1 = poor, 4 = excellent). RESULTS: On DLR images, the mean image noise was lower than that on hybrid IR images (18.5 ± 2.8 HU vs. 23.0 ± 4.6 HU, p < 0.01) and the CNR was significantly higher (p < 0.01). The mean ERD was significantly shorter on DLR than on hybrid IR images, whereas the mean ERS was steeper on DLR than on hybrid IR images. The mean image quality score for hybrid IR and DLR images was 2.96 and 3.58, respectively (p < 0.01). CONCLUSIONS: DLR reduces the image noise and improves the image quality at coronary CTA. KEY POINTS: • Deep learning-based image restoration is a new technique that employs the deep convolutional neural network for image quality improvement. • Deep learning-based restoration reduces the image noise and improves image quality at coronary CT angiography. • This method may allow for a reduction in radiation exposure.

Neointimal formation after carotid artery stenting: phantom and clinical evaluation of model-based iterative reconstruction (MBIR).

OBJECTIVES: The objective of this study was to investigate the usefulness of model-based iterative reconstruction (IR) for detecting neointimal formations after carotid artery stenting. METHODS: In a cervical phantom harbouring carotid artery stents, we placed simulated neointimal formations measuring 0.40, 0.60, 0.80 and 1.00 mm along the stent wall. The thickness of in-stent neointimal formations was measured on images reconstructed with filtered-back projection (FBP), hybrid IR (AIDR 3D), and model-based IR (FIRST). The clinical study included 43 patients with carotid stents. Cervical computed tomography (CT) images obtained on a 320-slice scanner were reconstructed with AIDR 3D and FIRST. Five blinded observers visually graded the likelihood of neointimal formations on AIDR 3D and AIDR 3D plus FIRST images. Carotid ultrasound images were the reference standard. We analysed results of visual grading by using a Jack-knife type receiver observer characteristics analysis software. RESULTS: In the phantom study, the difference between the measured and the true diameter of the neointimal formations was smaller on FIRST than FBP or AIDR 3D images. In the clinical study, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of AIDR 3D were 58%, 88%, 83%, 67% and 73%, respectively. For AIDR 3D plus FIRST images they were 84%, 78%, 80%, 82% and 81%, respectively. The mean area under the curve was significantly higher on AIDR 3D plus FIRST than AIDR 3D images (0.82 vs 0.72; p < 0.01). CONCLUSIONS: The model-based IR algorithm helped to improve diagnostic performance for the detection of neointimal formations after carotid artery stenting. KEY POINTS: • Neointimal formations can be visualised more accurately with model-based IR. • Model-based IR improves the detection of neointimal formations after carotid artery stenting. • Model-based IR is suitable for follow up after carotid artery stenting.

Clinical staging of upper urinary tract urothelial carcinoma for T staging: Review and pictorial essay.

Upper urinary tract urothelial carcinoma is staged using the TNM classification of malignant tumors. Preoperative TNM is important for treatment planning. Computed tomography urography is now widely used for clinical survey of upper urinary tract carcinoma because of its diagnostic accuracy. Computed tomography urography is recommended as the first-line imaging procedure in several guidelines. Several reports stated that computed tomography urography is also useful for staging. However, no educational and practical reviews detailing the T staging of upper urinary tract urothelial carcinomas using imaging are available. We discuss the scanning protocol, T staging using computed tomography urography, limitations, magnetic resonance imaging, computed tomography comparison and pitfalls in imaging of upper urinary tract urothelial carcinoma. A recent study reported the high diagnostic accuracy of computed tomography urography with respect to T3 or higher stage tumors. To date, images that show a Tis-T2 stage have not been reported, but various studies are ongoing. Although magnetic resonance imaging has lower spatial resolution than computed tomography urography, magnetic resonance imaging can be carried out without radiation exposure or contrast agents. Magnetic resonance imaging also offers the unique ability of diffusion-weighted imaging without contrast agent use. Some researchers reported that diffusion-weighted imaging is useful not only for detecting lesions, but for predicting the T stage and tumor grade. We recommend the appropriate use of computed tomography and magnetic resonance while considering the limitations of each modality and the pitfalls in upper urinary tract urothelial carcinoma imaging.

Effect of Patient Characteristics on Vessel Enhancement in Pediatric Chest Computed Tomography Angiography.

INTRODUCTION: To evaluate the effect of sex, age, height, cardiac output (CO), total body weight (TBW), body surface area (BSA), and lean body weight (LBW) on vessel enhancement of the ascending aorta in pediatric chest computed tomography angiography (c-CTA). MATERIALS AND METHODS: This retrospective study received institutional review board approval; parental prior informed consent for inclusion was obtained for all patients. All 50 patients were examined using our routine protocol; iodine (600 mg/kg) was the contrast medium (CM). Unenhanced and contrast-enhanced scans were obtained. We calculated the CM volume per vessel enhancement and performed univariate and multivariate linear regression analysis of the relationship between CM volume per vessel enhancement and each of the body parameters. RESULTS: All patient characteristics were significantly related to CM volume per vessel enhancement (P < .05). Multivariate linear regression analysis revealed a significant correlation between CM volume per vessel enhancement and TBW, BSA, and LBW, but not the patient sex, age, CO, and height. The LBW model for CM volume per vessel enhancement yielded the highest determination coefficient (R2 = .913) and the lowest Akaike Information Criterion (400.324). CONCLUSIONS: Our findings support the delivery of an iodine dose adjusted to the LBW at c-CTA.

Evaluation of raw-data-based and calculated electron density for contrast media with a dual-energy CT technique.

OBJECTIVES: The aim of the current study is to evaluate the accuracy and the precision of raw-data-based relative electron density (REDraw) and the calibration-based RED (REDcal) at a range of low-RED to high-RED for tissue-equivalent phantom materials by comparing them with reference RED (REDref) and to present the difference of REDraw and REDcal for the contrast medium using dual-energy CT (DECT). METHODS: The REDraw images were reconstructed by raw-data-based decomposition using DECT. For evaluation of the accuracy of the REDraw, REDref was calculated for the tissue-equivalent phantom materials based on their specified density and elemental composition. The REDcal images were calculated using three models: Lung-Bone model, Lung-Ti model and Lung-Ti (SEMAR) model which used single-energy metal artifact reduction (SEMAR). The difference between REDraw and REDcal was calculated. RESULTS: In the titanium rod core, the deviations of REDraw and REDcal (Lung-Bone model, Lung-Ti model and Lung-Ti model with SEMAR) from REDref were 0.45%, 50.8%, 15.4% and 15.0%, respectively. The largest differences between REDraw and REDcal (Lung-Bone model, Lung-Ti model and Lung-Ti model with SEMAR) in the contrast medium phantom were 8.2%, -23.7%, and 28.7%, respectively. However, the differences between REDraw and REDcal values were within 10% at 20 mg/ml. The standard deviation of the REDraw was significantly smaller than the REDcal with three models in the titanium and the materials that had low CT numbers. CONCLUSION: The REDcal values could be affected by beam hardening artifacts and the REDcal was less accurate than REDraw for high-Z materials as titanium. ADVANCES IN KNOWLEDGE: The raw-data-based reconstruction method could reduce the beam hardening artifact compared with image-based reconstruction and increase the accuracy for the RED estimation in high-Z materials, such as titanium and iodinated contrast medium.

Quantification of the salivary volume flow rate in the parotid duct using the time-spatial labeling inversion pulse (Time-SLIP) technique at MRI: A feasibility study.

BACKGROUND: We developed a method to quantify the volume flow rate (VFR) using the time-spatial labeling inversion pulse (Time-SLIP) technique to evaluate salivary function. PURPOSE/HYPOTHESIS: To investigate the accuracy of quantification of the salivary VFR using the Time-SLIP technique in phantoms and to examine the feasibility of its use in human subjects. STUDY TYPE: This was a prospective phantom and volunteer study. POPULATION/SUBJECTS/PHANTOM/SPECIMEN/ANIMAL MODEL: A phantom and 23 normal volunteers who fasted at least 2 hours study was performed. FIELD STRENGTH/SEQUENCE: Flow images of the phantom and the parotid duct of 23 volunteers were acquired on a 3T-MRI scanner using the Time-SLIP technique. ASSESSMENT: Hypothesizing that flow aggregates in the conducting duct, we measured the VFR on flow images. In the phantom study, the actual VFR (slow, medium, fast flow) was controlled by an automatic pump system and the measured VFR was compared with the actual VFR on flow images. In the human study we injected citric acid into the mouth of healthy volunteers to stimulate saliva secretion and recorded the VFR. STATISTICAL TESTS: As this study was a feasibility study, statistical tests were not performed. RESULTS: In the phantom study, the VFR at slow, medium, and fast flow was 5.7 ± 0.4 (SD), 8.4 ± 0.3, and 12.2 ± 1.1 mm3 /sec, respectively. The error between the measured and actual VFR values was 2.8-3.7%. Salivary flow in the parotid duct was visualized in 22 of the 23 volunteers. The mean VFR was 8760 mm3 /10 min. DATA CONCLUSION: When salivary flow was stimulated with citric acid in normal volunteers, the salivary VFR could be obtained using the Time-SLIP technique. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:928-935.