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.

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.

Objective:To investigate the validity of a computer-based method using temporal subtraction in carpal joints of patients with rheumatoid arthritis (RA), which can detect the difference in joint space between two images with the joint space difference index (JSDI).Methods:The study consisted of 43 patients with RA (39 females and 4 males) who underwent radiography at baseline and at 1-year follow-up. The joint space narrowing (JSN) of carpal joints on bilateral hand radiographs was assessed by our computer-based method, using the Sharp/van der Heijde method as the standard of reference. We compared the JSDI of joints with JSN progression in the follow-up period with that of those without JSN progression. In addition, we examined whether there is a significant difference in JSDI in terms of laterality or topology of the joint.Results:The JSDI of joints with JSN progression was significantly higher than that of those without JSN progression (Mann–Whitney U test, p < 0.001). There was no statistically signifi...

Purpose: Given the potential risk of motion artifacts, acquisition time reduction is desirable in pediatric ^99mTc‐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 ^99mTc‐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 ^99mTc‐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 ^99mTc‐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 ^99mTc‐DMSA planar imaging.