Machine learning-based estimation of patient body weight from radiation dose metrics in computed tomography.

PURPOSE: Currently, precise patient body weight (BW) at the time of diagnostic imaging cannot always be used for radiation dose management. Various methods have been explored to address this issue, including the application of deep learning to medical imaging and BW estimation using scan parameters. This study develops and evaluates machine learning-based BW prediction models using 11 features related to radiation dose obtained from computed tomography (CT) scans. METHODS: A dataset was obtained from 3996 patients who underwent positron emission tomography CT scans, and training and test sets were established. Dose metrics and descriptive data were automatically calculated from the CT images or obtained from Digital Imaging and Communications in Medicine metadata. Seven machine-learning models and three simple regression models were employed to predict BW using features such as effective diameter (ED), water equivalent diameter (WED), and mean milliampere-seconds. The mean absolute error (MAE) and correlation coefficient between the estimated BW and the actual BW obtained from each BW prediction model were calculated. RESULTS: Our results found that the highest accuracy was obtained using a light gradient-boosting machine model, which had an MAE of 1.99 kg and a strong positive correlation between estimated and actual BW (ρ = 0.972). The model demonstrated significant predictive power, with 73% of patients falling within a ±5% error range. WED emerged as the most relevant dose metric for BW estimation, followed by ED and sex. CONCLUSIONS: The proposed machine-learning approach is superior to existing methods, with high accuracy and applicability to radiation dose management. The model's reliance on universal dose metrics that are accessible through radiation dose management software enhances its practicality. In conclusion, this study presents a robust approach for BW estimation based on CT imaging that can potentially improve radiation dose management practices in clinical settings.

Deep learning-based correction for time truncation in cerebral computed tomography perfusion.

Cerebral computed tomography perfusion (CTP) imaging requires complete acquisition of contrast bolus inflow and washout in the brain parenchyma; however, time truncation undoubtedly occurs in clinical practice. To overcome this issue, we proposed a three-dimensional (two-dimensional + time) convolutional neural network (CNN)-based approach to predict missing CTP image frames at the end of the series from earlier acquired image frames. Moreover, we evaluated three strategies for predicting multiple time points. Seventy-two CTP scans with 89 frames and eight slices from a publicly available dataset were used to train and test the CNN models capable of predicting the last 10 image frames. The prediction strategies were single-shot prediction, recursive multi-step prediction, and direct-recursive hybrid prediction.Single-shot prediction predicted all frames simultaneously, while recursive multi-step prediction used prior predictions as input for subsequent steps, and direct-recursive hybrid prediction employed separate models for each step with prior predictions as input for the next step. The accuracies of the predicted image frames were evaluated in terms of image quality, bolus shape, and clinical perfusion parameters. We found that the image quality metrics were superior when multiple CTP images were predicted simultaneously rather than recursively. The bolus shape also showed the highest correlation (r = 0.990, p < 0.001) and the lowest variance (95% confidence interval, -453.26-445.53) in the single-shot prediction. For all perfusion parameters, the single-shot prediction had the smallest absolute differences from ground truth. Our proposed approach can potentially minimize time truncation errors and support the accurate quantification of ischemic stroke.

Functional Morphologic Changes of the Heel Fat Pad and Plantar Fascia in Patients With Heel Pain During Weightbearing and Nonweightbearing.

Background: This study aimed to investigate the thickness changes of the heel fat pad and the plantar fascia associated with loading and unloading in healthy individuals and patients with heel pain and reveal the differences between them. Methods: The study included adult male participants with (n = 9) and without (n = 26) heel pain. The participants placed their right foot on an evaluation apparatus with a polymethylpentene resin board (PMP), while their left foot was positioned on a weighing scale used to adjust the loading weight. The heel fat pad was differentiated into superficial Microchamber and deep Macrochamber layers. These layers and plantar fascia thickness were measured using an ultrasonographic imaging device at loading phase ranging from 0% to 100% of their body weight and unloading phase from 100% to 0%. Additionally, the study examined the thickness change ratios of the superficial and deep heel fat pad layers when the load increased from 0% (unload) to 100% (full load). Results: In healthy individuals and patients with heel pain, no significant thickness changes were observed in the Microchamber layer of the heel fat pad or the plantar fascia during loading and unloading evaluations. However, significant thickness changes were observed in the Macrochamber layer of the heel fat pad, and the pattern of change differed between the loading and unloading phases. Additionally, patients with heel pain showed differences in the thickness change and thickness change ratios of the microchamber and macrochamber layers of the heel fat pad during both loading and unloading phases. The thickness of the plantar fascia did not show significant differences between both groups. Conclusion: Compared with healthy individuals, in our relatively small study, patients with heel pain had greater deep fat pad compression in loading and less recovery after load removal. This finding suggests that these patients have different intrinsic fat pad function and related morphology than those without heel pain. Level of Evidence: Level III, retrospective cohort study.

Estimating Body Weight From Measurements From Different Single-Slice Computed Tomography Levels: An Evaluation of Total Cross-Sectional Body Area Measurements and Deep Learning.

OBJECTIVE: This study aimed to evaluate the correlation between the estimated body weight obtained from 2 easy-to-perform methods and the actual body weight at different computed tomography (CT) levels and determine the best reference site for estimating body weight. METHODS: A total of 862 patients from a public database of whole-body positron emission tomography/CT studies were retrospectively analyzed. Two methods for estimating body weight at 10 single-slice CT levels were evaluated: a linear regression model using total cross-sectional body area and a deep learning-based model. The accuracy of body weight estimation was evaluated using the mean absolute error (MAE), root mean square error (RMSE), and Spearman rank correlation coefficient ( ρ ). RESULTS: In the linear regression models, the estimated body weight at the T5 level correlated best with the actual body weight (MAE, 5.39 kg; RMSE, 7.01 kg; ρ = 0.912). The deep learning-based models showed the best accuracy at the L5 level (MAE, 6.72 kg; RMSE, 8.82 kg; ρ = 0.865). CONCLUSIONS: Although both methods were feasible for estimating body weight at different single-slice CT levels, the linear regression model using total cross-sectional body area at the T5 level as an input variable was the most favorable method for single-slice CT analysis for estimating body weight.

Improving the depiction of small intracranial vessels in head computed tomography angiography: a comparative analysis of deep learning reconstruction and hybrid iterative reconstruction.

This study aimed to evaluate the ability of deep learning reconstruction (DLR) compared to that of hybrid iterative reconstruction (IR) to depict small vessels on computed tomography (CT). DLR and two types of hybrid IRs were used for image reconstruction. The target vessels were the basilar artery (BA), superior cerebellar artery (SCA), anterior inferior cerebellar artery (AICA), and posterior inferior cerebellar artery (PICA). The peak value, ΔCT values defined as the difference between the peak value and background, and full width at half maximum (FWHM), were obtained from the profile curves. In all target vessels, the peak and ΔCT values of DLR were significantly higher than those of the two types of hybrid IR (p < 0.001). Compared to that associated with hybrid IR, the FWHM of DLR was significantly lower in the SCA (p < 0.001), AICA (p < 0.001), and PICA (p < 0.001). In conclusion, DLR has the potential to improve visualization of small vessels.

Globo-series Gb4 activates ERK and promotes the proliferation of osteoblasts.

OBJECTIVES: Globo-series Gb4 (globoside) is involved in the immune system and disease pathogenesis. We recently reported that systemic Gb4 deficiency in mice led to decreased bone formation due to a reduction in osteoblast number. However, it remains unclear whether Gb4 expressed in osteoblasts promotes their proliferation. Therefore, we investigated the role of Gb4 in osteoblast proliferation in vitro. METHODS: We examined osteoblast proliferation in Gb3 synthase knockout mice lacking Gb4. We investigated the effects of Gb4 synthase knockdown in the mouse osteoblast cell line MC3T3-E1 on its proliferation. Furthermore, we administered Gb4 to MC3T3-E1 cells in which Gb4 was suppressed by a glucosylceramide synthase (GCS) inhibitor and evaluated its effects on their proliferation. To elucidate the mechanisms by which Gb4 promotes osteoblast proliferation, the phosphorylated extracellular signal-regulated kinases 1 and 2 (ERK1/2) levels were measured in MC3T3-E1 cells. RESULTS: Osteoblast proliferation was lower in Gb3 synthase knockout mice lacking Gb4 than in wild-type mice. Proliferation was inhibited by Gb4 synthase knockdown in MC3T3-E1 cells. Furthermore, the administration of Gb4 to MC3T3-E1 cells, in which a GCS inhibitor suppressed Gb4, promoted their proliferation. Moreover, it increased the phosphorylated ERK1/2 levels in MC3T3-E1 cells. CONCLUSIONS: Our results suggest that Gb4 expressed in osteoblasts promotes their proliferation through ERK1/2 activation.

Estimation of Left and Right Ventricular Ejection Fractions from cine-MRI Using 3D-CNN.

Cardiac function indices must be calculated using tracing from short-axis images in cine-MRI. A 3D-CNN (convolutional neural network) that adds time series information to images can estimate cardiac function indices without tracing using images with known values and cardiac cycles as the input. Since the short-axis image depicts the left and right ventricles, it is unclear which motion feature is captured. This study aims to estimate the indices by learning the short-axis images and the known left and right ventricular ejection fractions and to confirm the accuracy and whether each index is captured as a feature. A total of 100 patients with publicly available short-axis cine images were used. The dataset was divided into training:test = 8:2, and a regression model was built by training with the 3D-ResNet50. Accuracy was assessed using a five-fold cross-validation. The correlation coefficient, MAE (mean absolute error), and RMSE (root mean squared error) were determined as indices of accuracy evaluation. The mean correlation coefficient of the left ventricular ejection fraction was 0.80, MAE was 9.41, and RMSE was 12.26. The mean correlation coefficient of the right ventricular ejection fraction was 0.56, MAE was 11.35, and RMSE was 14.95. The correlation coefficient was considerably higher for the left ventricular ejection fraction. Regression modeling using the 3D-CNN indicated that the left ventricular ejection fraction was estimated more accurately, and left ventricular systolic function was captured as a feature.

Deep learning-based body weight from scout images can be an alternative to actual body weight in CT radiation dose management.

PURPOSE: Accurate body weight measurement is essential to promote computed tomography (CT) dose optimization; however, body weight cannot always be measured prior to CT examination, especially in the emergency setting. The aim of this study was to investigate whether deep learning-based body weight from chest CT scout images can be an alternative to actual body weight in CT radiation dose management. METHODS: Chest CT scout images and diagnostic images acquired for medical checkups were collected from 3601 patients. A deep learning model was developed to predict body weight from scout images. The correlation between actual and predicted body weight was analyzed. To validate the use of predicted body weight in radiation dose management, the volume CT dose index (CTDIvol ) and the dose-length product (DLP) were compared between the body weight subgroups based on actual and predicted body weight. Surrogate size-specific dose estimates (SSDEs) acquired from actual and predicted body weight were compared to the reference standard. RESULTS: The median actual and predicted body weight were 64.1 (interquartile range: 56.5-72.4) and 64.0 (56.3-72.2) kg, respectively. There was a strong correlation between actual and predicted body weight (ρ = 0.892, p < 0.001). The CTDIvol and DLP of the body weight subgroups were similar based on actual and predicted body weight (p < 0.001). Both surrogate SSDEs based on actual and predicted body weight were not significantly different from the reference standard (p = 0.447 and 0.410, respectively). CONCLUSION: Predicted body weight can be an alternative to actual body weight in managing dose metrics and simplifying SSDE calculation. Our proposed method can be useful for CT radiation dose management in adult patients with unknown body weight.

Acquisition time reduction in pediatric 99m Tc-DMSA planar imaging using deep learning.

PURPOSE: Given the potential risk of motion artifacts, acquisition time reduction is desirable in pediatric 99m Tc-dimercaptosuccinic acid (DMSA) scintigraphy. The aim of this study was to evaluate the performance of predicted full-acquisition-time images from short-acquisition-time pediatric 99m Tc-DMSA planar images with only 1/5th acquisition time using deep learning in terms of image quality and quantitative renal uptake measurement accuracy. METHODS: One hundred and fifty-five cases that underwent pediatric 99m Tc-DMSA planar imaging as dynamic data for 10 min were retrospectively collected for the development of three deep learning models (DnCNN, Win5RB, and ResUnet), and the generation of full-time images from short-time images. We used the normalized mean squared error (NMSE), peak signal-to-noise ratio (PSNR), and structural similarity index metrics (SSIM) to evaluate the accuracy of the predicted full-time images. In addition, the renal uptake of 99m Tc-DMSA was calculated, and the difference in renal uptake from the reference full-time images was assessed using scatter plots with Pearson correlation and Bland-Altman plots. RESULTS: The predicted full-time images from the deep learning models showed a significant improvement in image quality compared to the short-time images with respect to the reference full-time images. In particular, the predicted full-time images obtained by ResUnet showed the lowest NMSE (0.4 [0.4-0.5] %) and the highest PSNR (55.4 [54.7-56.1] dB) and SSIM (0.997 [0.995-0.997]). For renal uptake, an extremely high correlation was achieved in all short-time and three predicted full-time images (R2  > 0.999 for all). The Bland-Altman plots showed the lowest bias (-0.10) of renal uptake in ResUnet, while short-time images showed the lowest variance (95% confidence interval: -0.14, 0.45) of renal uptake. CONCLUSIONS: Our proposed method is capable of producing images that are comparable to the original full-acquisition-time images, allowing for a reduction of acquisition time/injected dose in pediatric 99m Tc-DMSA planar imaging.

Prediction of body weight from chest radiographs using deep learning with a convolutional neural network.

Accurate body weights are not necessarily available in routine clinical practice. This study aimed to investigate whether body weight can be predicted from chest radiographs using deep learning. Deep-learning models with a convolutional neural network (CNN) were trained and tested using chest radiographs from 85,849 patients. The CNN models were evaluated by calculating the mean absolute error (MAE) and Spearman's rank correlation coefficient (ρ). The MAEs of the CNN models were 2.63 kg and 3.35 kg for female and male patients, respectively. The predicted body weight was significantly correlated with the actual body weight (ρ = 0.917, p < 0.001 for females; ρ = 0.915, p < 0.001 for males). The body weight was predicted using chest radiographs by applying deep learning. Our method is potentially useful for radiation dose management, determination of the contrast medium dose, and estimation of the specific absorption rate in patients with unknown body weights.

Comparison of foot posture and foot muscle morphology between lifesaver athletes and healthy adults.

This study aimed to compare the foot muscle morphology and foot posture between healthy adults and lifesavers in sandy beach sports. The participants included 15 lifesaver athletes and 15 healthy adults. Using a non-contact three-dimensional foot measurement device, the foot length, width, and arch height of the right foot were measured while standing and sitting without back support, and the transverse arch length ratio and arch height index were subsequently calculated. Muscle cross-sectional area was measured using an ultrasound imaging device. Muscle cross-sectional areas, arch height, foot width, arch height index, and transverse arch length ratio were larger in the lifesaver than in the healthy adult group. Lifesavers had higher arches and more developed intrinsic and extrinsic muscles than healthy adults. Performing physical activity while barefoot on sandy beaches may effectively develop the foot intrinsic and extrinsic muscles and raise the arch.

Toward automatic reformation at the orbitomeatal line in head computed tomography using object detection algorithm.

Consistent cross-sectional imaging is desirable to accurately detect lesions and facilitate follow-up in head computed tomography (CT). However, manual reformation causes image variations among technologists and requires additional time. We therefore developed a system that reformats head CT images at the orbitomeatal (OM) line and evaluated the system performance using real-world clinical data. Retrospective data were obtained for 681 consecutive patients who underwent non-contrast head CT. The datasets were randomly divided into one of three sets for training, validation, or testing. Four landmarks (bilateral eyes and external auditory canal) were detected with the trained You Look Only Once (YOLO)v5 model, and the head CT images were reformatted at the OM line. The precision, recall, and mean average precision at the intersection over union threshold of 0.5 were computed in the validation sets. The reformation quality in testing sets was evaluated by three radiological technologists on a qualitative 4-point scale. The precision, recall, and mean average precision of the trained YOLOv5 model for all categories were 0.688, 0.949, and 0.827, respectively. In our environment, the mean implementation time was 23.5 ± 2.4 s for each case. The qualitative evaluation in the testing sets showed that post-processed images of automatic reformation had clinically useful quality with scores 3 and 4 in 86.8%, 91.2%, and 94.1% for observers 1, 2, and 3, respectively. Our system demonstrated acceptable quality in reformatting the head CT images at the OM line using an object detection algorithm and was highly time efficient.

Ultrasonic Evaluation of the Heel Fat Pad under Weight-Bearing Conditions Using a Polymethylpentene Resin Plate: Part 1.

To evaluate the two-layer structure of the heel fat pad (HFP) from non-weight-bearing to full-weight-bearing conditions, we developed an instrument that assesses these changes from the sole through a polymethylpentene resin plate (PMP) with ultrasonography. For actual use, we investigated the influence on measured values and ultrasonogram appearance by interposing the PMP. Additionally, as the PMP may be bent under weight-bearing conditions, its influence on the measured values was investigated. First, two examiners measured the distances inside the phantom with and without a PMP. Second, ultrasonograms were obtained from 40 healthy adults with and without a PMP, and the thicknesses of the whole layer and the two layers of the HFP were measured using the same ultrasonogram. For each experiment, reproducibility was investigated. Third, the distances inside the phantom were measured and compared through the bent PMP, which models the weight-bearing condition, and the flat PMP. The reproducibility of the measurements was equivalent with and without the PMP interposed. Potential bias in measured values arising from deformation of PMP under weight-bearing conditions was not detected. Overall, the PMP's interposition and the bending of the PMP might not influence the measured values and reproducibility of the measurements.

A deep-learning method using computed tomography scout images for estimating patient body weight.

Body weight is an indispensable parameter for determination of contrast medium dose, appropriate drug dosing, or management of radiation dose. However, we cannot always determine the accurate patient body weight at the time of computed tomography (CT) scanning, especially in emergency care. Time-efficient methods to estimate body weight with high accuracy before diagnostic CT scans currently do not exist. In this study, on the basis of 1831 chest and 519 abdominal CT scout images with the corresponding body weights, we developed and evaluated deep-learning models capable of automatically predicting body weight from CT scout images. In the model performance assessment, there were strong correlations between the actual and predicted body weights in both chest (ρ = 0.947, p < 0.001) and abdominal datasets (ρ = 0.869, p < 0.001). The mean absolute errors were 2.75 kg and 4.77 kg for the chest and abdominal datasets, respectively. Our proposed method with deep learning is useful for estimating body weights from CT scout images with clinically acceptable accuracy and potentially could be useful for determining the contrast medium dose and CT dose management in adult patients with unknown body weight.

Comparison of a Bayesian estimation algorithm and singular value decomposition algorithms for 80-detector row CT perfusion in patients with acute ischemic stroke.

PURPOSE: A variety of postprocessing algorithms for CT perfusion are available, with substantial differences in terms of quantitative maps. Although potential advantages of a Bayesian estimation algorithm are suggested, direct comparison with other algorithms in clinical settings remains scarce. We aimed to compare performance of a Bayesian estimation algorithm and singular value decomposition (SVD) algorithms for the assessment of acute ischemic stroke using an 80-detector row CT perfusion. METHODS: CT perfusion data of 36 patients with acute ischemic stroke were analyzed using the Vitrea implemented a standard SVD algorithm, a reformulated SVD algorithm and a Bayesian estimation algorithm. Correlations and statistical differences between affected and contralateral sides of quantitative parameters (cerebral blood volume [CBV], cerebral blood flow [CBF], mean transit time [MTT], time to peak [TTP] and delay) were analyzed. Agreement of the CT perfusion-estimated and the follow-up diffusion-weighted imaging-derived infarct volume were evaluated by nonparametric Passing-Bablok regression analysis. RESULTS: CBF and MTT of the Bayesian estimation algorithm were substantially different and showed a better correlation with the standard SVD algorithm (ρ = 0.78 and 0.80, p < 0.001) than with the reformulated SVD algorithm (ρ = 0.59 and 0.39, p < 0.001). There is no significant difference in MTT only when using the reformulated SVD algorithm (p = 0.217). Regarding the regression lines, the slope and intercept were nearly ideal with the Bayesian estimation algorithm (y = 2.42 x-6.51; ρ = 0.60, p < 0.001) in comparison with the SVD algorithms. CONCLUSIONS: The Bayesian estimation algorithm can lead to a better performance compared with the SVD algorithms in the assessment of acute ischemic stroke because of better delineation of abnormal perfusion areas and accurate estimation of infarct volume.

CT dose management of adult patients with unknown body weight using an effective diameter.

PURPOSE: To assess the usefulness of effective diameter (Deff) for CT dose management of adult patients with unknown body weight. METHODS: A total of 642 adult patients whose height and weight had been measured before CT examination (chest CT using Aquilion Prime SP, 428 patients; chest CT using Biograph mCT, 100 patients; and abdominal CT using Aquilion Prime SP, 114 patients) were retrospectively examined between April 2018 and September 2019. The Deff was automatically calculated from the lateral diameter on a CT localizer radiograph by a dose management software (Radimetrics). In order to determine the correlation between body weight and Deff, we compared volume CT dose index and dose length product between patients with body weight between 50 and 70 kg and those with Deff equivalent to body weight between 50 and 70 kg. Correlation analysis was performed by Pearson's product-moment correlation, and statistical analyses were performed by using t-test. RESULTS: The correlation coefficient values between body weight and Deff were 0.920 for chest CT using Aquilion Prime SP, 0.929 for chest CT using Biograph mCT, and 0.805 for abdominal CT using Aquilion Prime SP. In both chest and abdominal CT scans, there were no significant differences in volume CT dose index and dose length product between patients with body weight between 50 and 70 kg and those with Deff equivalent to body weight between 50 and 70 kg. CONCLUSIONS: The Deff may be useful as a somatometric parameter for CT dose management of adult patients with unknown body weight.

Optimal slice thickness of brain computed tomography using a hybrid iterative reconstruction algorithm for identifying hyperdense middle cerebral artery sign of acute ischemic stroke.

PURPOSE: To determine the optimal slice thickness of brain non-contrast computed tomography using a hybrid iterative reconstruction algorithm to identify hyperdense middle cerebral artery sign in patients with acute ischemic stroke. METHODS: We retrospectively enrolled 30 patients who had presented hyperdense middle cerebral artery sign and 30 patients who showed no acute ischemic change in acute magnetic resonance imaging. Reformatted axial images at an angle of the orbitomeatal line in slice thicknesses of 0.5, 1, 3, 5, and 7 mm were generated. Optimal slice thickness for identifying hyperdense middle cerebral artery sign was evaluated by a receiver operating characteristics curve analysis and area under the curve (AUC). RESULTS: The mean AUC value of 0.5-mm slice (0.921; 95% confidence interval (95% CI), 0.868 to 0.975) was significantly higher than those of 3-mm (0.791; 95% CI, 0.686 to 0.895; p = 0.041), 5-mm (0.691; 95% CI, 0.583 to 0.799, p < 0.001), and 7-mm (0.695; 95% CI, 0.593 to 0.797, p < 0.001) slices, whereas it was equivalent to that of 1-mm slice (0.901; 95% CI, 0.837 to 0.965, p = 0.751). CONCLUSION: Thin slice thickness of ≤ 1 mm has a better diagnostic performance for identifying hyperdense artery sign on brain non-contrast computed tomography with a hybrid iterative reconstruction algorithm in patients with acute ischemic stroke.

[Scatter Radiation Intensities during Transforaminal Lumbar Interbody Fusion Using a Mobile C-arm System].

The purpose of this study was to measure the scatter radiation intensity during transforaminal lumbar interbody fusion using a mobile C-arm system (Arcadis Orbic 3D; Siemens) and minimize radiation exposure. Dosimetry was performed with anterior-posterior and lateral continuous fluoroscopy, and cone beam computed tomography (CT). A scaffold tower (L: 300 cm×W: 200 cm×H: 150 cm) was built with radiation-resistant paper cylinders at intervals of 50 cm and plastic joints over the bed, and 100 optically stimulated luminescence dosimeters (nanoDot; Nagase Landauer) were placed on each joint. A human torso phantom from head to pelvis (Kyoto Kagaku) was positioned on the bed in a prone position. The scatter radiation dose in a lateral view was highest on the X-ray tube side at the height of 100 cm (170.5 µGy/min). The scatter radiation dose increased significantly on the X-ray tube side during lateral continuous fluoroscopy. Continuous change of surgeons' standing positions is important to minimize radiation exposure received by a specific surgeon.

Pulmonary Artery/Vein Separation Using Single-Phase Computed Tomography: Feasibility and the Influence of Patient Characteristics on Vessel Enhancement.

PURPOSE: The purpose of this article was to verify the usefulness and feasibility of a single-phase scan for pulmonary artery/vein separation using a bolus-tracking technique and to evaluate the influence of patient characteristics on differentiation of computed tomography (CT) values between arteries and veins. MATERIAL AND METHODS: A total of 79 patients (60 male individuals and 19 female individuals, mean age 70 y) with suspected lung cancers or metastasis underwent contrast-enhanced chest CT and ultrasonic echocardiography. The CT values of the pulmonary arteries and veins were measured, and the difference in CT values was calculated. The relationships between the difference in CT values and age, weight, height, body surface area, body mass index, cardiac output, cardiac index, trigger time, trigger CT value, and pulmonary transit time were investigated using univariate linear regression analysis. RESULTS: The CT values were 352.8±87.3 HU and 494.6±76.5 HU for the pulmonary arteries and veins, respectively (P<0.001). A significant but weak correlation was seen between the difference in CT values and the height (r=0.24), trigger time (r=0.35), cardiac index (r=-0.25), and pulmonary transit time (r=0.53) (P<0.05). There was no significant correlation between the difference in CT values and the remaining values. CONCLUSION: The single-phase scan protocol using a bolus-tracking technique is feasible to differentiate CT values between pulmonary arteries and veins. The influence of patient characteristics on the differentiation of CT values lacks impact. Thus, the suggested protocol may be suitable independent of these factors after further validation.

Detection of Fine Radiographic Progression in Finger Joint Space Narrowing Beyond Human Eyes: Phantom Experiment and Clinical Study with Rheumatoid Arthritis Patients.

The visual assessment of joint space narrowing (JSN) on radiographs of rheumatoid arthritis (RA) patients such as the Genant-modified Sharp score (GSS) is widely accepted but limited by its subjectivity and insufficient sensitivity. We developed a software application which can assess JSN quantitatively using a temporal subtraction technique for radiographs, in which the chronological change in JSN between two radiographs was defined as the joint space difference index (JSDI). The aim of this study is to prove the superiority of the software in terms of detecting fine radiographic progression in finger JSN over human observers. A micrometer measurement apparatus that can adjust arbitrary joint space width (JSW) in a phantom joint was developed to define true JSW. We compared the smallest detectable changes in JSW between the JSDI and visual assessment using phantom images. In a clinical study, 222 finger joints without interval score change on GSS in 15 RA patients were examined. We compared the JSDI between joints with and without synovial vascularity (SV) on power Doppler ultrasonography during the follow-up period. True JSW difference was correlated with JSDI for JSW differences ranging from 0.10 to 1.00 mm at increments of 0.10 mm (R2 = 0.986 and P < 0.001). Rheumatologists were difficult to detect JSW difference of 0.30 mm or less. The JSDI of finger joints with SV was significantly higher than those without SV (P = 0.030). The software can detect fine differences in JSW that are visually unrecognizable.