Updated on 2024/05/04

写真a

 
ICHIKAWA Shota
 
Organization
Academic Assembly Institute of Medicine and Dentistry Health Sciences Assistant Professor
Faculty of Medicine School of Health Sciences Radiological Technology Assistant Professor
Title
Assistant Professor
External link

Degree

  • Doctor of Health Sciences ( 2024.3   Hokkaido University )

  • Master of Health Sciences ( 2017.3   Hokkaido University )

  • Bachelor of Health Sciences ( 2015.3   Hokkaido University )

Research Interests

  • Medical AI

  • Medical Image Analysis

  • CT Perfusion

Research Areas

  • Life Science / Radiological sciences

  • Life Science / Medical systems

Research History (researchmap)

  • Niigata University   Faculty of Medicine School of Health Sciences Radiological Technology   Assistant Professor

    2023.4

      More details

  • Niigata University   Academic Assembly Institute of Medicine and Dentistry Health Sciences   Assistant Professor

    2023.4

      More details

  • Kurashiki Central Hospital   Department of Radiological Technology   Radiological Technologists

    2017.4 - 2023.3

      More details

Research History

  • Niigata University   Radiological Technology, School of Health Sciences, Faculty of Medicine   Assistant Professor

    2023.4

  • Niigata University   Health Sciences, Institute of Medicine and Dentistry, Academic Assembly   Assistant Professor

    2023.4

Education

  • Hokkaido University   Graduate School of Health Sciences   Ph.D. course

    2021.4 - 2024.3

      More details

  • Hokkaido University   Graduate School of Health Sciences   Master course

    2015.4 - 2017.3

      More details

  • Hokkaido University   Department of Health Sciences, School of Medicine   放射線技術科学専攻

    2011.4 - 2015.3

      More details

    Country: Japan

    researchmap

Professional Memberships

  • Medical Imaging and Information Sciences

    2022.6

      More details

  • Japanese Society of CT Technology

    2021.4

      More details

  • European Society of Radiology

    2019.8

      More details

  • Japan Association of Radiological Technologists

    2017.5

      More details

  • Japanese Society of Radiological Technology

    2017.5

      More details

Qualification acquired

  • Radiological Technologist

 

Papers

  • Development of a Method for Estimating the Angle of Lumbar Spine X-ray Images Using Deep Learning with Pseudo X-ray Images Generated from Computed Tomography Reviewed

    Ryuma Moriya, Takaaki Yoshimura, Minghui Tang, Shota Ichikawa, Hiroyuki Sugimori

    Applied Sciences   2024.4

     More details

    Publishing type:Research paper (scientific journal)  

    DOI: 10.3390/app14093794

    researchmap

  • Development of a Patient–Radiopharmaceutical Matching System Using Smartphone to Prevent Misadministration: Feasibility Study Reviewed

    Sato Mitsuru, Hoshino Hiromitsu, Shimizu Masataka, Daisaki Hiromitsu, Ichikawa Shota, Kondo Tatsuya, Okamoto Masashi

    RADIOISOTOPES   73 ( 1 )   69 - 80   2024.3

     More details

    Language:Japanese   Publisher:Japan Radioisotope Association  

    We designed a patient–radiopharmaceutical matching system for preventing misadministration using smartphone, and developed a deep-learning model to identify radiopharmaceutical containers as an elemental technology for the system. As a result of the ResNet18 transfer learning and 10-fold cross-validation, this model achieved 100% accuracy in classifying 15 different radiopharmaceutical containers. The feasibility of the proposed system was proven.

    DOI: 10.3769/radioisotopes.73.69

    researchmap

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

    Shota Ichikawa, Hiroyuki Sugimori

    Journal of computer assisted tomography   2024.2

     More details

    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

    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

    researchmap

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

    Makoto Ozaki, Shota Ichikawa, Masaaki Fukunaga, Hiroyuki Yamamoto

    Radiological physics and technology   2023.10

     More details

    Language:English   Publishing type:Research paper (scientific journal)  

    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

    researchmap

  • Development of a Mammography Calcification Detection Algorithm Using Deep Learning with Resolution-Preserved Image Patch Division Reviewed

    Miu Sakaida, Takaaki Yoshimura, Minghui Tang, Shota Ichikawa, Hiroyuki Sugimori

    Algorithms   16 ( 10 )   483 - 483   2023.10

     More details

    Publishing type:Research paper (scientific journal)   Publisher: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

    researchmap

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

    Soichiro Inomata, Takaaki Yoshimura, Minghui Tang, Shota Ichikawa, Hiroyuki Sugimori

    Sensors   23 ( 14 )   6580 - 6580   2023.7

     More details

    Publishing type:Research paper (scientific journal)   Publisher: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

    researchmap

  • Deep learning‐based body weight from scout images can be an alternative to actual body weight in CT radiation dose management Reviewed International journal

    Shota Ichikawa, Hideki Itadani, Hiroyuki Sugimori

    Journal of Applied Clinical Medical Physics   24 ( 8 )   e14080   2023.6

     More details

    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

    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

    PubMed

    researchmap

  • Development of Chest X-ray Image Evaluation Software Using the Deep Learning Techniques Reviewed

    Kousuke Usui, Takaaki Yoshimura, Shota Ichikawa, Hiroyuki Sugimori

    Applied Sciences   13 ( 11 )   6695 - 6695   2023.5

     More details

    Publishing type:Research paper (scientific journal)   Publisher: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

    researchmap

  • Acquisition time reduction in pediatric 99m Tc‐DMSA planar imaging using deep learning Reviewed International journal

    Shota Ichikawa, Hiroyuki Sugimori, Koki Ichijiri, Takaaki Yoshimura, Akio Nagaki

    Journal of Applied Clinical Medical Physics   24 ( 6 )   e13978   2023.4

     More details

    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

    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

    PubMed

    researchmap

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

    Shota Ichikawa, Hideki Itadani, Hiroyuki Sugimori

    Radiological physics and technology   16 ( 1 )   127 - 134   2023.1

     More details

    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

    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

    PubMed

    researchmap

  • Toward automatic reformation at the orbitomeatal line in head computed tomography using object detection algorithm Reviewed International journal

    Shota Ichikawa, Hideki Itadani, Hiroyuki Sugimori

    Physical and Engineering Sciences in Medicine   45 ( 3 )   835 - 845   2022.7

     More details

    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)   Publisher: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

    PubMed

    researchmap

  • A deep-learning method using computed tomography scout images for estimating patient body weight Reviewed International journal

    Shota Ichikawa, Misaki Hamada, Hiroyuki Sugimori

    Scientific Reports   11 ( 1 )   15627 - 15627   2021.12

     More details

    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)   Publisher: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

    PubMed

    researchmap

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

    Shota Ichikawa, Hiroyuki Yamamoto, Takumi Morita

    La radiologia medica   126 ( 6 )   795 - 803   2021.6

     More details

    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher: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

    PubMed

    researchmap

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

    Shota Ichikawa, Misaki Hamada, Daiki Watanabe, Osamu Ito, Takafumi Moriya, Hiroyuki Yamamoto

    Emergency Radiology   28 ( 2 )   309 - 315   2021.4

     More details

    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher: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

    PubMed

    researchmap

  • CT dose management of adult patients with unknown body weight using an effective diameter Reviewed International journal

    Masaaki Fukunaga, Kosuke Matsubara, Shota Ichikawa, Hideki Mitsui, Hiroyuki Yamamoto, Tosiaki Miyati

    European Journal of Radiology   135   109483 - 109483   2021.2

     More details

    Language:English   Publishing type:Research paper (scientific journal)   Publisher: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

    PubMed

    researchmap

  • Pulmonary Artery/Vein Separation Using Single-Phase Computed Tomography Reviewed International journal

    Shota Ichikawa, Hiroyuki Yamamoto, Osamu Ito, Masaaki Fukunaga

    Journal of Thoracic Imaging   35 ( 3 )   173 - 178   2020.5

     More details

    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher: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

    PubMed

    researchmap

  • Scatter Radiation Intensities during Transforaminal Lumbar Interbody Fusion Using a Mobile C-arm System Reviewed

    Masaaki Fukunaga, Kosuke Matsubara, Yasutaka Takei, Hideki Mitsui, Hiroaki Kameiyama, Takanao Tanaka, Shota Ichikawa

    Japanese Journal of Radiological Technology   76 ( 6 )   572 - 578   2020

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

    Kazuki Kato, Nobutoshi Yasojima, Kenichi Tamura, Shota Ichikawa, Kenneth Sutherl, Masaru Kato, Jun Fukae, Kazuhide Tanimura, Yuki Tanaka, Taichi Okino, Yutong Lu, Tamotsu Kamishima

    Scientific Reports   9 ( 1 )   2019.12

     More details

    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media {LLC}  

    DOI: 10.1038/s41598-019-44747-6

    researchmap

  • Quantification of hand synovitis in rheumatoid arthritis: Arterial mask subtraction reinforced with mutual information can improve accuracy of pixel-by-pixel time-intensity curve shape analysis in dynamic MRI Reviewed International journal

    Yuto Kobayashi, Tamotsu Kamishima, Hiroyuki Sugimori, Shota Ichikawa, Atsushi Noguchi, Michihito Kono, Toshitake Iiyama, Kenneth Sutherl, Tatsuya Atsumi

    Journal of Magnetic Resonance Imaging   48 ( 3 )   687 - 694   2018.9

     More details

    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Wiley  

    DOI: 10.1002/jmri.25995

    researchmap

  • Radiographic temporal subtraction analysis can detect finger joint space narrowing progression in rheumatoid arthritis with clinical low disease activity Reviewed International journal

    Taichi Okino, Tamotsu Kamishima, Kenneth Lee Sutherl, Jun Fukae, Akihiro Narita, Shota Ichikawa, Kazuhide Tanimura

    Acta Radiologica   59 ( 4 )   460 - 467   2018.4

     More details

    Language:English   Publishing type:Research paper (scientific journal)   Publisher:{SAGE} Publications  

    DOI: 10.1177/0284185117721262

    researchmap

  • Quantitative knee cartilage measurement at MR imaging of patients with anterior cruciate ligament tear Reviewed International journal

    Kazuki Kato, Tamotsu Kamishima, Eiji Kondo, Tomohiro Onodera, Shota Ichikawa

    Radiological Physics and Technology   10 ( 4 )   431 - 438   2017.12

     More details

    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media {LLC}  

    DOI: 10.1007/s12194-017-0415-4

    CiNii Article

    researchmap

    Other Link: https://search.jamas.or.jp/link/ui/2018321665

  • Computer-Based Radiographic Quantification of Joint Space Narrowing Progression Using Sequential Hand Radiographs: Validation Study in Rheumatoid Arthritis Patients from Multiple Institutions Reviewed International journal

    Shota Ichikawa, Tamotsu Kamishima, Kenneth Sutherl, Jun Fukae, Kou Katayama, Yuko Aoki, Takanobu Okubo, Taichi Okino, Takahiko Kaneda, Satoshi Takagi, Kazuhide Tanimura

    Journal of Digital Imaging   30 ( 5 )   648 - 656   2017.10

     More details

    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media {LLC}  

    DOI: 10.1007/s10278-017-9970-9

    researchmap

  • Cartilage quantification using contrast-enhanced MRI in the wrist of rheumatoid arthritis: cartilage loss is associated with bone marrow edema Reviewed International journal

    Motoshi Fujimori, Satoko Nakamura, Kiminori Hasegawa, Kunihiro Ikeno, Shota Ichikawa, Kenneth Sutherl, Tamotsu Kamishima

    The British Journal of Radiology   90 ( 1077 )   20170167 - 20170167   2017.8

     More details

    Language:English   Publishing type:Research paper (scientific journal)   Publisher:British Institute of Radiology  

    DOI: 10.1259/bjr.20170167

    CiNii Article

    researchmap

  • Semi-Automated Quantification of Finger Joint Space Narrowing Using Tomosynthesis in Patients with Rheumatoid Arthritis Reviewed International journal

    Shota Ichikawa, Tamotsu Kamishima, Kenneth Sutherl, Hideki Kasahara, Yuka Shimizu, Motoshi Fujimori, Nobutoshi Yasojima, Yohei Ono, Takahiko Kaneda, Takao Koike

    Journal of Digital Imaging   30 ( 3 )   369 - 375   2017.6

     More details

    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media {LLC}  

    DOI: 10.1007/s10278-017-9949-6

    researchmap

  • A reliability study using computer-based analysis of finger joint space narrowing in rheumatoid arthritis patients Reviewed International journal

    Katsuya Hatano, Tamotsu Kamishima, Kenneth Sutherl, Masaru Kato, Ikuma Nakagawa, Shota Ichikawa, Keisuke Kawauchi, Shota Saitou, Masaya Mukai

    Rheumatology International   37 ( 2 )   189 - 195   2017.2

     More details

    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media {LLC}  

    DOI: 10.1007/s00296-016-3588-y

    researchmap

  • Radiographic quantifications of joint space narrowing progression by computer-based approach using temporal subtraction in rheumatoid wrist Reviewed International journal

    Shota Ichikawa, Tamotsu Kamishima, Kenneth Sutherl, Takanobu Okubo, Kou Katayama

    The British Journal of Radiology   89 ( 1057 )   20150403 - 20150403   2016.1

     More details

    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:British Institute of Radiology  

    DOI: 10.1259/bjr.20150403

    CiNii Article

    CiNii Books

    researchmap

  • Performance of computer-based analysis using temporal subtraction to assess joint space narrowing progression in rheumatoid patients Reviewed International journal

    Shota Ichikawa, Tamotsu Kamishima, Kenneth Sutherl, Takanobu Okubo, Kou Katayama

    Rheumatology International   36 ( 1 )   101 - 108   2016.1

     More details

    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media {LLC}  

    DOI: 10.1007/s00296-015-3349-3

    researchmap

▶ display all

MISC

  • 心臓CT画像からの深層学習によるセグメンテーションを用いた大動脈弁自動推定法の検討

    猪股 壮一郎, 吉村 高明, 唐 明輝, 市川 翔太, 杉森 博行

    日本放射線技術学会雑誌   79 ( 9 )   1028 - 1029   2023.9

     More details

    Language:Japanese   Publisher:(公社)日本放射線技術学会  

    researchmap

  • マンモグラフィにおける石灰化識別のための半教師あり学習の適用と評価

    境田みう, 唐明輝, 杉森博行, 吉村高明, 市川翔太

    北海道放射線技術雑誌(Web)   95   2023

  • 造影心臓CT画像を用いた深層学習による大動脈弁自動抽出法の検討

    猪股壮一郎, 唐明輝, 吉村高明, 市川翔太, 杉森博行

    北海道放射線技術雑誌(Web)   95   2023

  • 頭部単純CT撮影における物体検出技術を用いた多段面再構成画像の自動生成

    市川 翔太, 山本 浩之, 板谷 英樹, 杉森 博行

    日本放射線技術学会総会学術大会予稿集   78回   165 - 166   2022.3

     More details

    Language:Japanese   Publisher:(公社)日本放射線技術学会  

    researchmap

  • 頭部単純CT撮影における物体検出技術を用いた多段面再構成画像の自動生成

    市川 翔太, 山本 浩之, 板谷 英樹, 杉森 博行

    日本放射線技術学会総会学術大会予稿集   78回   165 - 166   2022.3

     More details

    Language:Japanese   Publisher:(公社)日本放射線技術学会  

    J-GLOBAL

    researchmap

  • Deep Learning Reconstructionを用いた頭部単純CT撮影の物理特性評価

    杉原智洋, 福永正明, 山本浩之, 伊藤修, 市川翔太, 山口雄貴

    中四国放射線医療技術フォーラムプログラム抄録集   18th   2022

  • 頭部CT AngiographyにおけるDeep Learning画像再構成法を用いた血管描出能の評価

    尾崎誠, 市川翔太, 市川翔太, 福永正明, 守屋隆史, 伊藤修, 山本浩之

    日本放射線技術学会雑誌   78 ( 9 )   1077 - 1077   2022

     More details

    Language:Japanese   Publisher:(公社)日本放射線技術学会  

    J-GLOBAL

    researchmap

  • 急性期脳梗塞に対するCT検査フローの変革~Abierto Reading Support Solutionへの期待~

    市川翔太

    Innervision   36 ( 6 )   14 - 15   2021

     More details

    Language:Japanese   Publisher:(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)

    J-GLOBAL

    researchmap

  • 畳み込みニューラルネットワークを用いた胸部CTスカウト画像からの性別・体格推定

    市川翔太, 市川翔太, 山本浩之, 杉森博行

    中四国放射線医療技術フォーラムプログラム抄録集   17th   2021

  • 肺癌術前患者における3D-CTによる肺容積測定ソフトウェアの比較

    市川翔太, 山本浩之

    日本診療放射線技師会誌   66 ( 9 )   1010 - 1010   2019

     More details

    Language:Japanese   Publisher:(公社)日本診療放射線技師会  

    J-GLOBAL

    researchmap

  • 移動型脊椎外科用イメージングにおける室内散乱線分布の測定

    福永正明, 福永正明, 松原孝祐, 光井英樹, 亀井山弘晃, 市川翔太, 竹井泰孝

    日本放射線技術学会総会学術大会予稿集   75th   303 - 303   2019

     More details

    Language:Japanese   Publisher:(公社)日本放射線技術学会  

    J-GLOBAL

    researchmap

  • 2つの線量計算ソフトウェアにおけるCT線量指標および臓器線量の比較

    山口雄貴, 福永正明, 市川翔太, 山本浩之, 長木昭男, 長木昭男, 光井英樹

    中四国放射線医療技術フォーラムプログラム抄録集   15th   2019

  • 小児胸部X線撮影における金属付加フィルタによる線量低減

    市尻航輝, 福永正明, 市川翔太, 宮田潤也

    中四国放射線医療技術フォーラムプログラム抄録集   15th   2019

  • 人の目とソフトウェアにおける関節リウマチ患者の微小関節裂隙狭小化の検出能の比較

    加藤一樹, 本郷七瀬, 八十嶋伸敏, 田村賢一, 市川翔太, SUTHERLAND Kenneth, 神島保

    北海道医学雑誌   94 ( 1 )   57 - 57   2019

     More details

    Language:Japanese   Publisher:北海道医学会  

    J-GLOBAL

    researchmap

  • 当院におけるCT読影補助の現状分析と異常所見学習ツールの作成

    日村栞菜, 市川翔太, 山本浩之, 熊代正行, 渡辺大輝

    日本診療放射線技師会誌   66 ( 9 )   1051 - 1051   2019

     More details

    Language:Japanese   Publisher:(公社)日本診療放射線技師会  

    J-GLOBAL

    researchmap

  • 全自動解析ソフトウェアによるリウマチ患者の関節裂隙狭小化進行評価

    田中悠貴, 近藤麻菜, 市川翔太, 神島保, 高橋英治, 古崎章, 天崎吉晴

    北海道医学雑誌   93 ( 2 )   123 - 123   2018

     More details

    Language:Japanese   Publisher:北海道医学会  

    J-GLOBAL

    researchmap

  • ダイナミックMRIによる手関節リウマチ滑膜炎の定量化

    小林勇渡, 田口愛望, 杉森博之, 神島保, 市川翔太, SUTHERLAND Kenneth, 野口淳史, 河野通仁, 渥美達也

    北海道医学雑誌   93 ( 2 )   122 - 123   2018

     More details

    Language:Japanese   Publisher:北海道医学会  

    J-GLOBAL

    researchmap

  • 頭部CT Perfusionにおける自作ソフトウェアを用いた体動補正処理の臨床的有用性

    市川翔太, 山本浩之

    日本診療放射線技師会誌   65 ( 9 )   1010 - 1010   2018

     More details

    Language:Japanese   Publisher:(公社)日本診療放射線技師会  

    J-GLOBAL

    researchmap

  • Assessment of Knee Cartilage Volume Using an Original Software for Conventional MRI(和訳中)

    宍戸 駿, 神島 保, 加藤 一樹, 市川 翔太, 近藤 英司, 小野寺 智洋

    日本放射線技術学会雑誌   73 ( 9 )   867 - 867   2017.9

     More details

    Language:English   Publisher:(公社)日本放射線技術学会  

    J-GLOBAL

    researchmap

  • 従来型MRIに対応した独自のソフトウエアを用いた膝軟骨体積の評価(Assessment of Knee Cartilage Volume Using an Original Software for Conventional MRI)

    宍戸 駿, 神島 保, 加藤 一樹, 市川 翔太, 近藤 英司, 小野寺 智洋

    日本放射線技術学会雑誌   73 ( 9 )   867 - 867   2017.9

     More details

    Language:English   Publisher:(公社)日本放射線技術学会  

    J-GLOBAL

    researchmap

  • 関節リウマチ患者の関節腔狭隘化を定量的に評価するためのcomputer-based temporal subtraction法(Quantitative Assessment of Joint Space Narrowing Progression in the Feet of Rheumatoid Arthritis Patients Using a Computer-based Temporal Subtraction Method)

    田中 悠貴, 千葉 拓貴, 神島 保, 市川 翔太, 沖野 太一, 深江 淳, 青木 悠子, 谷村 一秀

    日本放射線技術学会雑誌   73 ( 9 )   832 - 832   2017.9

     More details

    Language:English   Publisher:(公社)日本放射線技術学会  

    J-GLOBAL

    researchmap

  • 関節リウマチにおける造影ダイナミックMRIを用いた滑膜炎定量評価

    小林勇渡, 市川翔太, 神島保, 杉森博行, 野口淳史, 河野通仁, 渥美達也

    北海道医学雑誌   92 ( 2 )   108 - 109   2017

     More details

    Language:Japanese   Publisher:北海道医学会  

    J-GLOBAL

    researchmap

  • 関節リウマチ患者の関節裂隙狭小化進行評価を可能にするオリジナルソフトウェア開発-非専門医による再現性の調査-

    波多野克哉, 市川翔太, 神島保, 加藤將, 中川育磨, 渥美達也, 齋藤翔太, 川内敬介, SUTHERLAND Kenneth, 貴志孝行, 寺江聡, 向井正也

    北海道医学雑誌   92 ( 2 )   109 - 109   2017

     More details

    Language:Japanese   Publisher:北海道医学会  

    J-GLOBAL

    researchmap

  • 単純X線写真におけるリウマチ患者の関節裂隙狭小化の変化-経時差分技術を用いた検討

    佐々木亮祐, 神島保, 市川翔太, SUTHERLAND Kenneth, 大久保学宣, 片山耕

    日本放射線技術学会雑誌   70 ( 9 )   1054 - 1054   2014

     More details

    Language:Japanese   Publisher:(公社)日本放射線技術学会  

    J-GLOBAL

    researchmap

  • 単純X線写真に対する経時差分技術を用いたリウマチ患者の関節裂隙狭小化評価-手根部関節への応用

    市川翔太, 神島保, 佐々木亮祐, SUTHERLAND Kenneth, 大久保学宣, 片山耕

    日本放射線技術学会雑誌   70 ( 9 )   1054 - 1055   2014

     More details

    Language:Japanese   Publisher:(公社)日本放射線技術学会  

    J-GLOBAL

    researchmap

▶ display all

Presentations

  • シンポジウム2 「臨床へのチャレンジ」 頭頸部CT領域におけるAIを用いた臨床への挑戦

    第20回CTテクノロジーフォーラム  2021.12 

     More details

  • 拠点大学院生の研究計画 深層学習を用いた医用画像データからの体格指標予測に関する研究

    保健医療分野におけるAI研究開発加速に向けた人材育成産学協働プロジェクト 第一回合同シンポジウム  2021.10 

     More details

  • シンポジウム 「臨床で活躍する達人たちのルーティン」

    JSCT2021 特定非営利活動法人日本CT技術学会第9回学術大会  2021.10 

     More details

  • 頭部CT perfusionの意義を再考する~急性期脳梗塞を中心に~

    第131回高速X線CT研究会  2021.5 

     More details

  • 急性期脳梗塞に対するCT検査フローの変革~ Abierto Reading Support Solutionへの期待 ~

    Canon Advanced Imaging Seminar 2021  2021.3 

     More details

Awards

  • 研究奨励賞・技術新人賞 医療情報分野

    2024.4   公益社団法人 日本放射線技術学会   Prediction of body weight from chest radiographs using deep learning with a convolutional neural network

     More details

  • 北海道大学大学院保健科学院長賞

    2024.3   北海道大学  

     More details

  • Shape your skills

    2023.3   European Congress of Radiology 2023   Reducing acquisition time in pediatric 99mTc-DMSA planar imaging using deep learning

     More details

  • Shape your skills

    2020.7   European Congress of Radiology 2020   Usefulness of Bayesian estimation algorithm for simulated low-dose cerebral computed tomography perfusion in patients with acute ischemic stroke

     More details

  • 北海道大学大学院保健科学院長賞

    2017.3   北海道大学  

     More details

  • 北海道大学大学院保健科学院修士課程研究発表賞(Best Presentation Award)

    2017.3   北海道大学  

     More details

  • クラーク賞

    2015.3   公益財団法人北海道大学クラーク記念財団  

     More details

  • 北海道大学医学部保健学科長賞

    2015.2   北海道大学  

     More details

  • 新渡戸賞

    2012.7   北海道大学  

     More details

▶ display all

Research Projects

  • 人工知能画像解析による救急医療を想定した患者体重即時推定システムの構築

    Grant number:23K19828

    2023.8 - 2025.3

    System name:科学研究費助成事業

    Research category:研究活動スタート支援

    Awarding organization:日本学術振興会

    市川 翔太

      More details

    Grant amount:\2600000 ( Direct Cost: \2000000 、 Indirect Cost:\600000 )

    researchmap

 

Teaching Experience

  • 保健学総合

    2023
    Institution name:新潟大学

  • 医用画像処理工学演習

    2023
    Institution name:新潟大学

  • スタディスキルズ (放射)

    2023
    Institution name:新潟大学