掲載種別：研究論文（学術雑誌）  

DOI： 10.3390/app14093794

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記述言語：日本語   出版者・発行元：公益社団法人 日本アイソトープ協会  

放射性医薬品の誤投与は防ぐ必要がある。本研究では，スマートフォンを用いた誤投与防止のための患者–放射性医薬品照合システム開発を考案し，その要素技術である放射性医薬品の容器を識別する深層学習モデルを開発した。ResNet18の転移学習および10分割交差検証を実施した結果，15種類の放射性医薬品容器をすべて正確に分類できた。本研究により，提案システムの実現可能性が証明された。

DOI： 10.3769/radioisotopes.73.69

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担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

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.

DOI： 10.1097/RCT.0000000000001587

PubMed

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

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.

DOI： 10.1007/s12194-023-00749-8

PubMed

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掲載種別：研究論文（学術雑誌）   出版者・発行元：MDPI AG  

Convolutional neural networks (CNNs) in deep learning have input pixel limitations, which leads to lost information regarding microcalcification when mammography images are compressed. Segmenting images into patches retains the original resolution when inputting them into the CNN and allows for identifying the location of calcification. This study aimed to develop a mammographic calcification detection method using deep learning by classifying the presence of calcification in the breast. Using publicly available data, 212 mammograms from 81 women were segmented into 224 × 224-pixel patches, producing 15,049 patches. These were visually classified for calcification and divided into five subsets for training and evaluation using fivefold cross-validation, ensuring image consistency. ResNet18, ResNet50, and ResNet101 were used for training, each of which created a two-class calcification classifier. The ResNet18 classifier achieved an overall accuracy of 96.0%, mammogram accuracy of 95.8%, an area under the curve (AUC) of 0.96, and a processing time of 0.07 s. The results of ResNet50 indicated 96.4% overall accuracy, 96.3% mammogram accuracy, an AUC of 0.96, and a processing time of 0.14 s. The results of ResNet101 indicated 96.3% overall accuracy, 96.1% mammogram accuracy, an AUC of 0.96, and a processing time of 0.20 s. This developed method offers quick, accurate calcification classification and efficient visualization of calcification locations.

DOI： 10.3390/a16100483

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掲載種別：研究論文（学術雑誌）   出版者・発行元：MDPI AG  

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.

DOI： 10.3390/s23146580

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担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

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.

DOI： 10.1002/acm2.14080

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掲載種別：研究論文（学術雑誌）   出版者・発行元：MDPI AG  

Although the widespread use of digital imaging has enabled real-time image display, images in chest X-ray examinations can be confirmed by the radiologist’s eyes. Considering the development of deep learning (DL) technology, its application will make it possible to immediately determine the need for a retake, which is expected to further improve examination throughput. In this study, we developed software for evaluating chest X-ray images to determine whether a repeat radiographic examination is necessary, based on the combined application of DL technologies, and evaluated its accuracy. The target population was 4809 chest images from a public database. Three classification models (CLMs) for lung field defects, obstacle shadows, and the location of obstacle shadows and a semantic segmentation model (SSM) for the lung field regions were developed using a fivefold cross validation. The CLM was evaluated using the overall accuracy in the confusion matrix, the SSM was evaluated using the mean intersection over union (mIoU), and the DL technology-combined software was evaluated using the total response time on this software (RT) per image for each model. The results of each CLM with respect to lung field defects, obstacle shadows, and obstacle shadow location were 89.8%, 91.7%, and 91.2%, respectively. The mIoU of the SSM was 0.920, and the software RT was 3.64 × 10−2 s. These results indicate that the software can immediately and accurately determine whether a chest image needs to be re-scanned.

DOI： 10.3390/app13116695

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担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

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.

DOI： 10.1002/acm2.13978

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担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

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.

DOI： 10.1007/s12194-023-00697-3

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担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Science and Business Media {LLC}  

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.

DOI： 10.1007/s13246-022-01153-z

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担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Science and Business Media {LLC}  

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.

DOI： 10.1038/s41598-021-95170-9

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担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Science and Business Media {LLC}  

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.

DOI： 10.1007/s11547-020-01316-6

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担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Science and Business Media {LLC}  

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.

DOI： 10.1007/s10140-020-01864-4

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Elsevier {BV}  

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.

DOI： 10.1016/j.ejrad.2020.109483

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担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Ovid Technologies (Wolters Kluwer Health)  

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.

DOI： 10.1097/rti.0000000000000455

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記述言語：日本語   掲載種別：研究論文（学術雑誌）   出版者・発行元：(公社)日本放射線技術学会  

DOI： 10.6009/jjrt.2020_jsrt_76.6.572

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その他リンク： https://search-tp.jamas.or.jp/index.php?module=Default&action=Link&pub_year=2020&ichushi_jid=J01192&link_issn=&doc_id=20200706440002&doc_link_id=1390848250120172544&url=https%3A%2F%2Fcir.nii.ac.jp%2Fcrid%2F1390848250120172544&type=CiNii&icon=https%3A%2F%2Fjk04.jamas.or.jp%2Ficon%2F00003_3.gif

記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Science and Business Media {LLC}  

DOI： 10.1038/s41598-019-44747-6

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Wiley  

DOI： 10.1002/jmri.25995

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：{SAGE} Publications  

DOI： 10.1177/0284185117721262

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Science and Business Media {LLC}  

DOI： 10.1007/s12194-017-0415-4

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その他リンク： https://search.jamas.or.jp/link/ui/2018321665

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Science and Business Media {LLC}  

DOI： 10.1007/s10278-017-9970-9

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：British Institute of Radiology  

DOI： 10.1259/bjr.20170167

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担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Science and Business Media {LLC}  

DOI： 10.1007/s10278-017-9949-6

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Science and Business Media {LLC}  

DOI： 10.1007/s00296-016-3588-y

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担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：British Institute of Radiology  

Objective: To investigate the validity of a computerbased 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 significant difference in the JSDI between the left and right carpal joints, which was analysed for five different joints altogether and each joint separately (Mann-Whitney U test, p > 0.05). There was statistically significant difference in JSDI among different joints (Kruskal-Wallis test, p = 0.003). Conclusion: These results suggest that our computerbased method may be useful to recognize the JSN progression on radiographs of rheumatoid wrists. Advances in knowledge: The computer-based temporal subtraction method can detect the JSN progression in the wrist, which is the single most commonly involved site in RA.

DOI： 10.1259/bjr.20150403

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担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Springer Science and Business Media {LLC}  

DOI： 10.1007/s00296-015-3349-3

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記述言語：日本語   出版者・発行元：(公社)日本放射線技術学会  

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記述言語：日本語   出版者・発行元：(株)インナービジョン  

静注血栓溶解(rt-PA)療法や血栓回収療法の適応拡大が進む急性期脳梗塞の治療は、治療開始までの時間短縮に加え、画像による組織評価を基にした治療選択が重要になっている。キヤノンメディカルシステムズの読影支援ソリューション「Abierto Reading Support Solution(Abierto RSS)」は、さまざまな解析アプリケーションを用いて画像情報を迅速、適切に処理して治療判断をサポートする。当院における急性期脳梗塞の画像診断フローとAbierto RSSの運用について報告する。(著者抄録)

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記述言語：日本語   出版者・発行元：(公社)日本診療放射線技師会  

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記述言語：日本語   出版者・発行元：(公社)日本放射線技術学会  

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記述言語：日本語   出版者・発行元：北海道医学会  

J-GLOBAL

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記述言語：日本語   出版者・発行元：(公社)日本診療放射線技師会  

J-GLOBAL

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記述言語：日本語   出版者・発行元：北海道医学会  

J-GLOBAL

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記述言語：日本語   出版者・発行元：北海道医学会  

J-GLOBAL

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記述言語：日本語   出版者・発行元：(公社)日本診療放射線技師会  

J-GLOBAL

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記述言語：英語   出版者・発行元：(公社)日本放射線技術学会  

J-GLOBAL

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記述言語：英語  

J-GLOBAL

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記述言語：英語  

J-GLOBAL

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記述言語：日本語   出版者・発行元：北海道医学会  

J-GLOBAL

researchmap

記述言語：日本語   出版者・発行元：北海道医学会  

J-GLOBAL

researchmap

記述言語：日本語   出版者・発行元：(公社)日本放射線技術学会  

J-GLOBAL

researchmap

記述言語：日本語   出版者・発行元：(公社)日本放射線技術学会  

J-GLOBAL

researchmap

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