Influence of contrast enhancement at the contrast injection location for the arm or leg in neonatal and infant patients during cardiac computed tomography.

INTRODUCTION AND OBJECTIVES: Obtaining CCTA images with optimal injection location such as the arm or leg is important to avoid the artifacts caused by the CM. This study compares the computed tomography (CT) numbers and visualization scores of the three-dimensional (3D) images of the lumens of the blood vessels in the arm or leg during cardiac computed tomography angiography (CCTA) in neonatal and infant patients. PATIENTS OR MATERIALS AND METHODS: Between January 2017 and January 2020, 253 consecutive patients were considered for inclusion. We used the estimated propensity scores as a function of the demographic data, including age, body weight, and injection location (right or left side) in the arm (n = 58) and leg (n = 58) of neonatal and infant patients. We compared the mean CT numbers of the pulmonary artery, ascending aorta, and left superior vena cava; contrast-noise ratios (CNR); and visualization scores between the arm and leg as the injection locations. RESULTS: The mean CT numbers during CCTA for the arm and leg were 479.4 and 461.3 HU in the ascending aorta, 464.2 and 448.1 HU in the pulmonary artery, and 232.8 and 220.1 HU in the left superior vena cava, respectively. The mean image noise (SD) and CNR values, respectively, were 38.9 HU and 12.1 for the arm as the injection location and 39.1 HU and 12.3 for the leg as the injection location. The median visualization scores of volume rendering of the 3D images were 3.0 and 3.0 for the arm and leg injection sites, respectively. There were no significant differences in the mean CT numbers of the ascending aorta, pulmonary artery, and left superior vena cava; SD value; CNR; and visualization scores between the arm and leg injection locations. CONCLUSIONS: The CT numbers of the lumen of the blood vessel and visualization scores of the 3D images of the arm and leg injection locations are equal during CCTA in neonatal and infant patients with congenital heart disease.

Influencia del realce de contraste al inyectar un medio de contraste en el brazo o la pierna en pacientes neonatos y lactantes durante la angiografía por cardiotomografía / Influence of Contrast Enhancement at the Contrast Injection Location for the Arm or Leg in Neonatal and Infant Patients during Cardiac Computed Tomography

Introducción y objetivos: En la obtención de imágenes de angiografía por cardiotomografía (ACT) es importante escoger una ubicación adecuada para inyectar el medio de contraste (p. ej., el brazo o la pierna) a fin de evitar la formación de artefactos que este provoca. En este estudio se comparan los valores de tomografía computarizada (TC) y las puntuaciones de visualización de las imágenes tridimensionales (3D) de los lúmenes de los vasos sanguíneos del brazo y la pierna durante la ACT en pacientes neonatos y lactantes. Pacientes o materiales y métodos: Entre los meses de enero de 2017 y enero de 2020 se evaluaron 253 pacientes de forma consecutiva para determinar su inclusión en el estudio. Se utilizaron las puntuaciones de propensión estimadas en función de los datos demográficos, incluidos la edad, el peso corporal y la ubicación de la inyección (lado derecho o izquierdo) en el brazo (n=58) y la pierna (n=58) de los pacientes neonatos y lactantes. A continuación, se compararon los valores medios de TC de la arteria pulmonar, la aorta ascendente y la vena cava superior izquierda; las relaciones contraste-ruido (RCR); y las puntuaciones de visualización del brazo y la pierna como lugares de inyección. Resultados: Los valores medios de TC durante la ACT para el brazo y la pierna fueron de 479,4 y 461,3 UH en la aorta ascendente, de 464,2 y 448,1 UH en la arteria pulmonar y de 232,8 y 220,1 UH en la vena cava superior izquierda, respectivamente. Los valores medios de ruido de la imagen (DE) y de RCR fueron, respectivamente, de 38,9 y 12,1 UH para el brazo y de 39,1 y 12,3 UH para la pierna. Las puntuaciones medias de visualización de la representación del volumen de las imágenes 3D fueron de 3,0 y 3,0 para los lugares de inyección del brazo y la pierna, respectivamente.(AU)

Introduction and Objectives: Obtaining CCTA images with optimal injection location such as the arm or leg is important to avoid the artifacts caused by the CM. This study compares the computed tomography (CT) numbers and visualization scores of the three-dimensional (3D) images of the lumens of the blood vessels in the arm or leg during cardiac computed tomography angiography (CCTA) in neonatal and infant patients. Patients or Materials and Methods: Between January 2017 and January 2020, 253 consecutive patients were considered for inclusion. We used the estimated propensity scores as a function of the demographic data, including age, body weight, and injection location (right or left side) in the arm (n=58) and leg (n=58) of neonatal and infant patients. We compared the mean CT numbers of the pulmonary artery, ascending aorta, and left superior vena cava; contrast–noise ratios (CNR); and visualization scores between the arm and leg as the injection locations. Results: The mean CT numbers during CCTA for the arm and leg were 479.4 and 461.3 HU in the ascending aorta, 464.2 and 448.1 HU in the pulmonary artery, and 232.8 and 220.1 HU in the left superior vena cava, respectively. The mean image noise (SD) and CNR values, respectively, were 38.9 HU and 12.1 for the arm as the injection location and 39.1 HU and 12.3 for the leg as the injection location. The median visualization scores of volume rendering of the 3D images were 3.0 and 3.0 for the arm and leg injection sites, respectively. There were no significant differences in the mean CT numbers of the ascending aorta, pulmonary artery, and left superior vena cava; SD value; CNR; and visualization scores between the arm and leg injection locations. Conclusions: The CT numbers of the lumen of the blood vessel and visualization scores of the 3D images of the arm and leg injection locations are equal during CCTA in neonatal and infant patients with congenital heart disease.(AU)

Applying patient characteristics, stent-graft selection, and pre-operative computed tomographic angiography data to a machine learning algorithm: Is endoleak prediction possible?

INTRODUCTION: This study aims to predict endoleak after endovascular aneurysm repair (EVAR) using machine learning (ML) integration of patient characteristics, stent-graft configuration, and a selection of vessel lengths, diameters and angles measured using pre-operative computed tomography angiography (CTA). METHODS: We evaluated 1-year follow-up CT scans (arterial and delayed phases) in patients who underwent EVAR for the presence or absence of an endoleak. We also obtained data on the patient characteristics, stent-graft selection, and preoperative CT vessel morphology (diameter, length, and angle). The extreme gradient boosting (XGBoost) for the ML system was trained on 30 patients with endoleaks and 81 patients without. We evaluated 5217 items in 111 patients with abdominal aortic aneurysms, including the patient characteristics, stent-graft configuration and vascular morphology acquired using pre-EVAR abdominal CTA. We calculated the area under the curve (AUC) of our receiver operating characteristic analysis using the ML method. RESULTS: The AUC, accuracy, 95% confidence interval (CI), sensitivity, and specificity were 0.88, 0.88, 0.79-0.97, 0.85, and 0.91 for ML applying XGBoost, respectively. CONCLUSIONS: The diagnostic performance of the ML method was useful when factors such as the patient characteristics, stent-graft configuration and vessel length, diameter and angle of the vessels were considered from pre-EVAR CTA. IMPLICATIONS FOR PRACTICE: Based on our findings, we suggest that this is a potential application of ML for the interpretation of abdominal CTA scans in patients with abdominal aortic aneurysms scheduled for EVAR.

Radiation dose reduction method combining the ECG-Edit function and high helical pitch in retrospectively-gated CT angiography.

INTRODUCTION: The purpose of this study was to demonstrate that dose reduction does not compromise image quality when combining high helical pitch (HP) and the ECG-Edit function during low HP retrospectively gated computed tomography angiography (CTA). METHODS: This study made use of a pulsating cardiac phantom (ALPHA 1 VTPC). The heart rate (HR) of the cardiac phantom was changed in five intervals, every 5 beats per minute (bpm), from 40 to 60 bpm. Evaluation of a range of HR was important because data loss might occur when combining a low HR and high HP. We performed retrospectively gated CTA scans five times using a low HP (0.16) and high HP (0.24), for each of the five HR intervals, using a 64-detector row CT scanner. The CT volume dose index (CTDIvol) was recorded from the CT console of each scan. For the images with data loss, data were repaired using the ECG-Edit function. We compared the CTDIvol, estimated cardiac phantom volume, and the visualization of the coronary ladder phantom between HP 0.16, with or without repaired HP 0.24, using the ECG-Edit function. RESULTS: Data loss occurred with a HR of 40 bpm and 45 bpm when using HP 0.24. The CTDIvol was reduced by approximately 33% with HP 0.24 when compared with HP 0.16. There were no significant differences in the mean cardiac motion phantom volume and visualization scores between HP 0.16 and with and without repaired HP 0.24 using the ECG-Edit function (p < 0.05). CONCLUSION: The ECG-Edit function is potential useful for repairing the lost data in patients with a low HR, and when combined with a high HP, it is possible to reduce the radiation dose by approximately 33%. IMPLICATIONS FOR PRACTICE: The ECG-Edit function and high HP may be a viable option in pediatric CTA studies.

Effect of injection duration on contrast enhancement during cardiac computed tomography angiography in newborns and infants.

INTRODUCTION: To investigate how changing the injection duration at cardiac computed tomography angiography (CCTA) affects contrast enhancement in newborns and infants. METHODS: Included were 142 newborns and infants with confirmed congenital heart disease who underwent CCTA between January 2015 and December 2018. In group 1 (n = 71 patients), the injection duration was 8 s; in group 2 (n = 71) it was 16 s. Our findings were assessed by one-to-one matching analysis to estimate the propensity score of each patient. We compare the CT number for the pulmonary artery (PA), ascending aorta (AAO), left superior vena cava (SVC), AAO and PA enhancement ratio, and the scores for visualization between the two groups. RESULTS: In group 1, median CT number and ranges was 345 (211-591) HU in the AAO, 324 (213-567) HU in the PA, and 62 (1-70) HU in the SVC. These values were 465 (308-669) HU, 467 (295-638) HU, and 234 (67-443) HU, respectively, in group 2 (p < 0.05). The median score for volume-rendering visualization on 3D images of the CCTA was 2 in group 1 and 3 in group 2; the score for visualization of the left SVC of the maximum intensity projection images was 2 in group 1 and 3 in group 2 (p < 0.05). The CT number for the AAO and PA enhancement ratio was 15.2 in group 1 and 9.2 in group 2 (p < 0.05). CONCLUSION: The 16-sec injection protocol yielded significantly higher CT numbers for the AAO, PA, and the SVC than the 8-sec injection protocol; the visualization scores were also significantly higher in group 2. IMPLICATIONS FOR PRACTICE: In newborns and infants, the longer injection time for CCTA yields stable and higher contrast enhancement at identical CM concentrations.

Deep learning with convolutional neural network for estimation of the characterisation of coronary plaques: Validation using IB-IVUS.

INTRODUCTION: Deep learning approaches have shown high diagnostic performance in image classifications, such as differentiation of malignant tumors and calcified coronary plaque. However, it is unknown whether deep learning is useful for characterizing coronary plaques without the presence of calcification using coronary computed tomography angiography (CCTA). The purpose of this study was to compare the diagnostic performance of deep learning with a convolutional neural network (CNN) with that of radiologists in the estimation of coronary plaques. METHODS: We retrospectively enrolled 178 patients (191 coronary plaques) who had undergone CCTA and integrated backscatter intravascular ultrasonography (IB-IVUS) studies. IB-IVUS diagnosed 81 fibrous and 110 fatty or fibro-fatty plaques. We manually captured vascular short-axis images of the coronary plaques as Portable Network Graphics (PNG) images (150 × 150 pixels). The display window level and width were 100 and 700 Hounsfield units (HU), respectively. The deep-learning system (CNN; GoogleNet Inception v3) was trained on 153 plaques; its performance was tested on 38 plaques. The area under the curve (AUC) obtained by receiver operating characteristic analysis of the deep learning system and by two board-certified radiologists was compared. RESULTS: With the CNN, the AUC and the 95% confidence interval were 0.83 and 0.69-0.96, respectively; for radiologist 1 they were 0.61 and 0.42-0.80; for radiologist 2 they were 0.68 and 0.51-0.86, respectively. The AUC for CNN was significantly higher than for radiologists 1 (p = 0.04); for radiologist 2 it was not significantly different (p = 0.22). CONCLUSION: DL-CNN performed comparably to radiologists for discrimination between fatty and fibro-fatty plaque on CCTA images. IMPLICATIONS FOR PRACTICE: The diagnostic performance of the CNN and of two radiologists in the assessment of 191 ROIs on CT images of coronary plaques whose type corresponded with their IB-IVUS characterization was comparable.

Efficacy of the spiral flow generating extended tube during paediatric CCTA.

INTRODUCTION: To compare the computed tomography (CT) number for paediatric cardiac computed tomography angiography (CCTA) and visualisation score of the three-dimensional (3D) images using the conventional T-shaped extended tube (T-tube) and spiral flow-generating extended tube (spiral-tube) connected between the contrast injector and cannula. METHODS: In total, 108 patients suspected to have congenital heart disease (CHD) were considered for inclusion. We utilised the T-tube for intravenous contrast and spiral-tube in 54 patients each. Observers individually inspected randomized volume rendering images of the internal thoracic artery, each acquired from the with or without spiral-tube groups, using a four-point scale. We compared the mean CT number of the ascending aorta (AAO) and pulmonary artery (PA), contrast noise ratio (CNR), CT number for the AAO and PA enhancement ratio, and the visualisation scores between the groups. RESULTS: There were no significant differences in patient characteristics between the with or without spiral-tube groups (p > 0.05). The mean CT number ±standard deviation for the AAO and PA, and the CNR without or with spiral-tube groups were 441.2 ± 89.2 and 489.8 ± 86.1 HU for the AAO, 436.3 ± 100.6 and 475.3 ± 85.2 HU for the PA, and 9.5 ± 2.2 and 10.8 ± 2.4 for the CNR, respectively (p < 0.05). In the spiral-tube group, the CT number, CNR, and visualisations score of the 3D images were significantly higher for the AAO and PA than those in the T-tube group (p < 0.05). CONCLUSION: The spiral-tube proved to be beneficial in improving the CT number for the AAO and PA, CNR, and visualisation score compared with the conventional T-tube during paediatric CCTA. IMPLICATIONS FOR PRACTICE: The spiral-tube may allow the visualisation of smaller blood vessels than those visualised by the conventional T-tube for paediatric patients in CCTA.

Enhancement rate of venous phase to portal venous phase computed tomography and its correlation with ultrasound elastography determination of liver fibrosis.

INTRODUCTION: This study aimed to compare the correlation between the computed tomography (CT) enhancement rate of the venous to portal venous phase (VP-ER) and the extracellular volume (ECV) fraction with shear-wave ultrasound elastography (USE) findings in patients with liver fibrosis. METHODS: We included 450 patients with clinically suspected liver cirrhosis who underwent triphasic dynamic CT studies and USE. We compared the USE results with the unenhanced CT phase, with enhancement in the hepatic artery phase (HAP), portal venous phase (PVP), and venous phase (VP), and with the ECV fraction and the VP-ER. We also compared the area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the ECV fraction and VP-ER with that of the values obtained with USE. RESULTS: The VP-ER was the most highly correlated with the liver stiffness value determined with USE (Pearson's correlation coefficient: r = 0.37), followed by enhancement in the PVP (r = -0.25), CT number on unenhanced CT scans (r = -0.22), the ECV fraction (r = 0.19), enhancement in the VP (r = 0.059), and enhancement in the HAP (r = -0.023) (all p < 0.01). The VP-ER showed a significantly higher AUC than the ECV fraction (0.75 vs 0.62) when the liver stiffness was >15 kPa in USE studies (p = 0.04). CONCLUSION: Compared to the ECV fraction, the VP-ER is more useful for predicting all degrees of liver fibrosis on routine triphasic dynamic CT images. IMPLICATIONS FOR PRACTICE: Although improvement is needed, the VP-ER has a higher diagnostic ability for liver fibrosis than the ECV fraction in clinical practice.

Virtual magnetic resonance lumbar spine images generated from computed tomography images using conditional generative adversarial networks.

INTRODUCTION: The aim of this study was to generate virtual Magnetic resonance (MR) from computed tomography (CT) using conditional generative adversarial networks (cGAN). METHODS: We selected examinations from 22 adults who obtained their CT and MR lumbar spine examinations. Overall, 4 examinations were used as test data, and 18 examinations were used as training data. A cGAN was trained to generate virtual MR images from the CT images using the corresponding MR images as targets. After training, the generated virtual MR images from test data in epochs 1, 10, 50, 100, 500, and 1000 were compared with the original ones using the mean square error (MSE) and structural similarity index (SSIM). Additionally, two radiologists also performed qualitative assessments. RESULTS: The MSE of the virtual MR images decreased as the epoch of the cGANs increased from the original CT images: 8876.7 ± 1192.9 (original CT), 1567.5 ± 433.9 (Epoch 1), 1242.4 ± 442.0 (Epoch 10), 1065.8 ± 478.1 (Epoch 50), 1276.1 ± 718.9 (Epoch 100), 1046.7 ± 488.2 (Epoch 500), and 1031.7 ± 400.0 (Epoch 1000). No considerable differences were observed in the qualitative evaluation between the virtual MR images and the original ones, except in the structure of the spinal canal. CONCLUSION: Virtual MR lumbar spine images using cGANs could be a feasible technique to generate near-MR images from CT without MR examinations for evaluation of the vertebral body and intervertebral disc. IMPLICATIONS FOR PRACTICE: Virtual MR lumbar spine images using cGANs can offer virtual CT images with sufficient quality for attenuation correction for PET or dose planning in radiotherapy.

Diagnostic performance of computed tomography digital subtraction angiography of the lower extremities during haemodialysis in patients with suspected peripheral artery disease.

INTRODUCTION: With intra-arterial digital subtraction angiography (DSA) considered as the gold standard, we compared the diagnostic value of computed tomography angiography (CTA) and computed tomography-digital subtraction angiography (CT-DSA in hemodialysis (HD) patients suspected of having lower limb peripheral artery disease (PAD). METHODS: In this retrospective study, we enrolled 220 HD patients with suspected PAD. CT-DSA images were obtained by subtracting unenhanced images from enhanced images. The research team calculated the area under the curve (AUC), sensitivity, specificity, positive and negative predictive value (PPV, NPV), and recorded the diagnostic accuracy between the CTA and CT-DSA images using the DSA as gold standard. Visual evaluation of calcifications in the peripheral arteries were also compared between CTA and CT-DSA images. RESULTS: At the above-knee level, the CTA AUC [95% confidence interval (CI)] was 0.68 (CI 0.64-0.72), sensitivity and specificity were 60 and 81%, PPV and NPV were 85 and 53%, and accuracy was 67%. Below the knee, these values were 0.66 (CI 0.62-0.70), 71 and 79%, 79 and 47%, and 66%. For CT-DSA, above-knee, the AUC [95% CI] was 0.88 (CI 0.85-0.91), sensitivity and specificity were 84 and 92%, PPV and NPV were 89 and 97%, and accuracy was 93%. Below the knee, these values were 0.95 (CI 0.93-0.97), 95 and 93%, 96 and 83%, and 93%. The scores for the visualization of calcification in the peripheral arteries was significantly higher for CT-DSA than CTA (p < 0.05). CONCLUSIONS: CT-DSA helps to assess stenotic PAD with high calcification in the lower extremities of HD patients. IMPLICATIONS FOR PRACTICE: On CT-DSA images, the severity of vascular calcification can be assessed for HD patients suspected of PAD of the lower extremities.

Machine learning to identify lymph node metastasis from thyroid cancer in patients undergoing contrast-enhanced CT studies.

INTRODUCTION: We compared the diagnostic performance of morphological methods such as the major axis, the minor axis, the volume and sphericity and of machine learning with texture analysis in the identification of lymph node metastasis in patients with thyroid cancer who had undergone contrast-enhanced CT studies. METHODS: We sampled 772 lymph nodes with histology defined tissue types (84 metastatic and 688 benign lymph nodes) that were visualised on CT images of 117 patients. A support vector machine (SVM), free programming software (Python), and the scikit-learn machine learning library were used to discriminate metastatic-from benign lymph nodes. We assessed 96 texture and 4 morphological features (major axis, minor axis, volume, sphericity) that were reported useful for the differentiation between metastatic and benign lymph nodes on CT images. The area under the curve (AUC) obtained by receiver operating characteristic analysis of univariate logistic regression and SVM classifiers were calculated for the training and testing datasets. RESULTS: The AUC for all classifiers in training and testing datasets was 0.96 and 0.86, at the SVM for machine learning. When we applied conventional methods to the training and testing datasets, the AUCs were 0.63 and 0.48 for the major axis, 0.70 and 0.44 for the minor axis, 0.66 and 0.43 for the volume, and 0.69 and 0.54 for sphericity, respectively. The SVM using texture features yielded significantly higher AUCs than univariate logistic regression models using morphological features (p = 0.001). CONCLUSION: For the identification of metastatic lymph nodes from thyroid cancer on contrast-enhanced CT images, machine learning combined with texture analysis was superior to conventional diagnostic methods with the morphological parameters. IMPLICATIONS FOR PRACTICE: Our findings suggest that in patients with thyroid cancer and suspected lymph node metastasis who undergo contrast-enhanced CT studies, machine learning using texture analysis is high diagnostic value for the identification of metastatic lymph nodes.

The combined application of the contrast-to-noise index and 80 kVp for cardiac CTA scanning before atrial fibrillation ablation reduces radiation dose exposure.

INTRODUCTION: To compare the radiation dose, diagnostic accuracy, and the resultant ablation procedures using 80 and 120-kVp cardiac computed tomography angiography (CCTA) protocols with the same contrast-to-noise ratio in patients scheduled for atrial fibrillation (AF) ablation. METHODS: This retrospective study was performed following institutional review board approval. We divided 140 consecutive patients who had undergone CCTA using a 64-MDCT scanner into two equal groups. Standard deviation (SD) of the CT number was set at 25 Hounsfield units (HU) for the 120-kVp protocol. To facilitate a reduction in radiation dose it was set at 40 HU for the 80 kVp protocol. We compared the two protocols with respect to the radiation dose, the diagnostic accuracy for detecting left atrial appendage (LAA) thrombi, matching for surface registration, and the resultant ablation procedures. RESULTS: At 120 kVp, the dose length product (DLP) was 2.2 times that at 80 kVp (1269.0 vs 559.0 mGy cm, p < 0.01). The diagnostic accuracy for thrombus detection was 100% using both protocols. There was no difference between the two protocols with respect to matching for surface registration. The protocols did not differ with respect to the subsequent time required for the ablation procedures and the ablation fluoroscopy time, and the radiation dose (p = 0.54, 0.33, and 0.32, respectively). CONCLUSION: For the same CNR, the DLP at 80 kVp (559.0 mGy cm) was 56% of that delivered at 120 kVp (1269.0 mGy cm). There was no reduction in diagnostic accuracy. IMPLICATIONS FOR PRACTICE: Maintaining CNR allows for a reduction in the radiation dose without reducing the image quality.

Metal Artifact Reduction in Head CT Performed for Patients with Deep Brain Stimulation Devices: Effectiveness of a Single-Energy Metal Artifact Reduction Algorithm.

BACKGROUND AND PURPOSE: Deep brain stimulation electrodes induce massive artifacts on CT images, deteriorating the diagnostic value of examinations. We aimed to investigate the usefulness and potential limitations of a single-energy metal artifact reduction algorithm in head CT performed in patients with implanted deep brain stimulation devices. MATERIALS AND METHODS: Thirty-four patients with deep brain stimulation (bilateral, n = 28) who underwent head CT on a 320-detector row scanner and whose images were reconstructed with and without single-energy metal artifact reduction at the examinations were retrospectively included. The severity of artifacts around electrodes was assessed objectively using SDs and an artifact index. Two radiologists subjectively evaluated the severity of artifacts from electrodes, the visibility of electrode localization and surrounding structures, and overall diagnostic confidence on 4-point scales. Background image quality (GM-WM contrast and image noise) was subjectively and objectively assessed. The presence and location of artifacts newly produced by single-energy metal artifact reduction were analyzed. RESULTS: Single-energy metal artifact reduction provided lower objective and subjective metal artifacts and improved visualization of electrode localization and surrounding structures and diagnostic confidence compared with non-single-energy metal artifact reduction images, with statistical significance (all, P < .01). No significant differences were observed in GM-WM contrast and image noise (all, P ≥ .11). The new artifacts from single-energy metal artifact reduction were prominently observed in patients with bilateral deep brain stimulation at high convexity, possibly induced by deep brain stimulation leads placed under the parietal scalp. CONCLUSIONS: Single-energy metal artifact reduction substantially reduces the metal artifacts from deep brain stimulation electrodes and improves the visibility of intracranial structures without affecting background image quality. However, non-single-energy metal artifact reduction images should be simultaneously reviewed to accurately assess the entire intracranial area, particularly in patients with bilateral deep brain stimulation.

A multiparametric MRI-based machine learning to distinguish between uterine sarcoma and benign leiomyoma: comparison with 18F-FDG PET/CT.

AIM: To compare the performance of machine learning using multiparametric magnetic resonance imaging (mp-MRI) and positron-emission tomography (PET) to distinguish between uterine sarcoma and leiomyoma. MATERIALS AND METHODS: This retrospective study was approved by the institutional review board and informed consent was waived. Sixty-seven consecutive patients with uterine sarcoma or leiomyoma who underwent pelvic 3 T MRI and PET were included. Of 67 patients, 11 had uterine sarcomas and 56 had leiomyomas. Seven different parameters were measured in the tumours, from T2-weighted, T1-weighted, contrast-enhanced, and diffusion-weighted MRI, and PET. The areas under the receiver operating characteristic curves (AUC) with a leave-one-out cross-validation were used to compare the diagnostic performances of the univariate and multivariate logistic regression (LR) model with those of two board-certified radiologists. RESULTS: The AUCs of the univariate models using MRI parameters (0.68-0.8) were inferior to that of the maximum standardised uptake value (SUVmax) of PET (0.85); however, the AUC of the multivariate LR model (0.92) was superior to that of SUVmax, and comparable to that of the board-certified radiologists (0.97 and 0.89). CONCLUSION: The diagnostic performance of the machine learning using mp-MRI was superior to PET and comparable to that of experienced radiologists.

Dual-layer detector CT of chest, abdomen, and pelvis with a one-third iodine dose: image quality, radiation dose, and optimal monoenergetic settings.

AIM: To compare the image quality and radiation dose of reduced iodine dose dual-layer detector (DL) computed tomography (CT) with those of a conventional 120 kVp protocol for chest-abdomen-pelvis CT (CAP-CT). MATERIALS AND METHODS: Forty patients with renal dysfunction (estimated glomerular filtrating ratio <45 ml/min/1.73 m2) underwent reduced iodine dose CAP-CT (120 kVp, 200 mg iodine/kg) on DLCT. Virtual monochromatic images (VMI) at 40-70 keV (5 keV interval) were reconstructed retrospectively. Forty matched patients who underwent conventional CAP-CT (120 kVp, 600 mg iodine/kg, iterative reconstruction) were included as controls. The size-specific dose estimate (SSDE), image noise, CT attenuation, and contrast-to-noise ratio (CNR) were compared between the protocols. Two radiologists rated image contrast, image noise, streak artefact, and diagnostic confidence on a five-point scale. RESULTS: The SSDE of the DLCT group was approximately 20% lower than that of the 120 kVp group (15.4±1.9 versus 19.4±2.3 mGy, p<0.01). DLCT-VMI provided almost constant image noise throughout the range of energies (differences of ≤13%), with the noise being equivalent or lower than 120 kVp in the abdomen. CT attenuation and CNR gradually increased as the energy decreased, with values comparable to 120 kVp being attained at around 45-50 keV. Although streak artefact was accentuated at 40-50 keV (p<0.01), the highest scores for diagnostic confidence were assigned at 40 and 45 keV, both of which were equivalent to 120 kVp (p=1.0). CONCLUSION: For CAP-CT with a one-third iodine dose, DLCT-VMI at 40-45 keV allows for a 20% reduction in radiation dose, while preserving image quality comparable to that of conventional 120 kVp protocol.

Value of 100 kVp scan with sinogram-affirmed iterative reconstruction algorithm on a single-source CT system during whole-body CT for radiation and contrast medium dose reduction: an intra-individual feasibility study.

AIM: To perform an intra-individual investigation of the usefulness of a contrast medium (CM) and radiation dose-reduction protocol using single-source computed tomography (CT) combined with 100 kVp and sinogram-affirmed iterative reconstruction (SAFIRE) for whole-body CT (WBCT; chest-abdomen-pelvis CT) in oncology patients. MATERIALS AND METHODS: Forty-three oncology patients who had undergone WBCT under both 120 and 100 kVp protocols at different time points (mean interscan intervals: 98 days) were included retrospectively. The CM doses for the 120 and 100 kVp protocols were 600 and 480 mg iodine/kg, respectively; 120 kVp images were reconstructed with filtered back-projection (FBP), whereas 100 kVp images were reconstructed with FBP (100 kVp-F) and the SAFIRE (100 kVp-S). The size-specific dose estimate (SSDE), iodine load and image quality of each protocol were compared. RESULTS: The SSDE and iodine load of 100 kVp protocol were 34% and 21%, respectively, lower than of 120 kVp protocol (SSDE: 10.6±1.1 versus 16.1±1.8 mGy; iodine load: 24.8±4versus 31.5±5.5 g iodine, p<0.01). Contrast enhancement, objective image noise, contrast-to-noise-ratio, and visual score of 100 kVp-S were similar to or better than of 120 kVp protocol. CONCLUSION: Compared with the 120 kVp protocol, the combined use of 100 kVp and SAFIRE in WBCT for oncology assessment with an SSCT facilitated substantial reduction in the CM and radiation dose while maintaining image quality.

Negative predictive value of multiparametric MRI for prostate cancer detection: outcome of 5-year follow-up in men with negative findings on initial MRI studies.

OBJECTIVE: To assess the clinical negative predictive value (NPV) of multiparametric MRI (mp-MRI) for prostate cancer in a 5-year follow-up. MATERIALS AND METHODS: One hundred ninety-three men suspected of harboring prostate cancer with negative MRI findings were included. Patients with positive transrectal ultrasound (TRUS)-guided biopsy findings were defined as false-negative. Patients with negative initial TRUS-guided biopsy findings were followed up and only patients with negative findings by digital rectal examination, MRI, and repeat biopsy and no increase in PSA at 5-year follow-up were defined as "clinically negative". The clinical NPV of mp-MRI was calculated. For quantitative analysis, mean signal intensity on T2-weighted images and the mean apparent diffusion coefficient value on ADC maps of the initial MRI studies were compared between peripheral-zone (PZ) cancer and the normal PZ based on pathologic maps of patients who had undergone radical prostatectomy. RESULTS: The clinical NPV of mp-MRI was 89.6% for significant prostate cancer. Small cancers, prostatitis, and benign prostatic hypertrophy masking prostate cancer returned false-negative results. Quantitative analysis showed that there was no significant difference between PZ cancer and the normal PZ. CONCLUSION: The mp-MRI revealed a high clinical NPV and is a useful tool to rule out clinically significant prostate cancer before biopsy.

Low-dose abdominal CT protocols with a tube voltage setting of 100 kVp or 80 kVp: Performance of radiation dose reduction and influence on visual contrast.

AIM: To evaluate the radiation dose, image quality, and influence on visual contrast of low tube voltage abdominal computed tomography (CT) and the effects of display setting optimization. MATERIALS AND METHODS: One hundred and fifty-seven patients were randomly assigned to one of three protocols. Fifty-two patients underwent a 120 kVp protocol, and 53 and 52 patients underwent low-dose protocols with 100 and 80 kVp, respectively. The effective dose (ED), image noise, CT attenuation, and signal-to-noise ratio (SNR) of each organ of each protocol were compared using Dunnett's test. Qualitative analysis between the protocols was also performed. RESULTS: The ED of the 100 and 80 kVp protocols were 22% and 37% reduced, respectively. There were no significant differences in the SNR between the protocols (120 kVp: 13.8 ± 3.5; 100 kVp: 13.9 ± 3.3; 80 kVp: 13.5 ± 2.9; p > 0.05). However, there were significant differences in contrast familiarity between the 120 kVp images and all the other images, except 100 kVp images, with optimized display settings (p < 0.05). CONCLUSION: In abdominal CT, an 80 kVp setting offers greater radiation dose reduction than a 100 kVp setting without significant deterioration of the SNR; however, the visual contrast between the organs might be changed.

Reduction of dental metallic artefacts in CT: value of a newly developed algorithm for metal artefact reduction (O-MAR).

AIM: To evaluate the image quality of O-MAR (Metal Artifact Reduction for Orthopedic Implants) for dental metal artefact reduction. MATERIALS AND METHODS: This prospective study received institutional review board approval and written informed consent was obtained. Thirty patients who had dental implants or dental fillings were included in this study. Computed tomography (CT) images were obtained through the oral cavity and neck during the portal venous phase. The system reconstructed the O-MAR-processed images in addition to the uncorrected images. CT attenuation and image noise of the soft tissue of the oral cavity were compared between the O-MAR and the uncorrected images. Qualitative analysis was undertaken between the two image groups. RESULTS: The image noise of the O-MAR images was significantly lower than that of the uncorrected images (p < 0.01). O-MAR offered plausible attenuations of soft tissue compared with non-O-MAR. Better qualitative scores were obtained in the streaking artefacts and the degree of depiction of the oral cavity with O-MAR compared with non-O-MAR. CONCLUSION: O-MAR enables the depiction of structures in areas in which this was not previously possible due to dental metallic artefacts in qualitative image analysis. O-MAR images may have a supplementary role in addition to uncorrected images in oral diagnosis.