Publications by authors named "H Sugimori"

Background: No research has been conducted on the use of deep learning for breastfeeding support.

Research Aim: This study aims to develop a nipple trauma evaluation system using deep learning.

Methods: We used an exploratory data analysis approach to develop a deep-learning model for medical imaging.

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Article Synopsis
  • The study assessed the accuracy and consistency of T1, T2*, and proton density values from quantitative parameter mapping (QPM) using the ISMRM/NIST MRI system phantom and compared findings with computer simulations.
  • Researchers compared QPM-derived relaxation times and proton density against reference values from the phantom and traditional methods to validate their results.
  • The results indicated a strong correlation between QPM values and reference measurements, with simulations aligning closely with actual scan variations, suggesting minimal influence from factors other than noise.
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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.

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Background: Challenges persist in achieving automatic and efficient inflammation quantification using dynamic contrast-enhanced (DCE) MRI in rheumatoid arthritis (RA) patients.

Purpose: To investigate an automatic artificial intelligence (AI) approach and an optimized dynamic MRI protocol for quantifying disease activity in RA in whole hands while excluding arterial pixels.

Study Type: Retrospective.

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