Akie Katsuki

Quantitative validation of data-driven motion correction for brain PET using phantom with motion generator system

Yuto Kamitaka, Muneyuki Sakata, Keiichi Oda, Akie Katsuki et autres

BACKGROUND: Head motion during brain positron emission tomography (PET) degrades image quality and quantitative accuracy. Therefore, a data-driven motion correction (MC) method utilizing ultrafast list-mode reconstruction technology has been proposed and shown to considerably improve image quality. However, reproducing accurate actual motions …

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Use of relative Patlak plot Ki′ images as an alternative to standard Patlak plot Ki images in clinical practice

Masatoyo Nakajo, Hirofumi Kawakami, Yutaro Kiyao, Mitsuho Hirahara et autres

BACKGROUND: The kinetic rate constant (Ki), derived from the Patlak slope, reflects 18F-FDG uptake and supports disease assessment. Standard Patlak Ki imaging requires prolonged dynamic acquisition and full arterial input function (IF), limiting clinical feasibility. The relative Patlak plot omits the early-phase …

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The Utility of a Modified Data-Driven Motion Correction Method Incorporating Computed Tomography Positional Data for Positron Emission Tomography

Hayato Odagiri, Hiroshi Watabe, Kentaro Takanami, Hirofumi Kawakami et autres

In positron emission tomography/computed tomography (PET/CT), long image acquisition time often causes patient motion and misalignment between the PET and CT images. This misalignment can compromise the accuracy of attenuation correction and quantitative values, thus affecting diagnostic reliability. Recently, data-driven motion correction …

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Verification of the effect of data-driven brain motion correction on PET imaging

Hayato Odagiri, Hiroshi Watabe, Kentaro Takanami, K. Akimoto et autres

INTRODUCTION: Brain positron emission tomography/computed tomography (PET/CT) scans are useful for identifying the cause of dementia by evaluating glucose metabolism in the brain with F-18-fluorodeoxyglucose or Aβ deposition with F-18-florbetaben. However, since imaging time ranges from 10 to 30 minutes, movements during …

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Development of Pericardial Fat Count Images Using a Combination of Three Different Deep-Learning Models: Image Translation Model From Chest Radiograph Image to Projection Image of Three-Dimensional Computed Tomography

Takaaki Matsunaga, Atsushi K. Kono, Hidetoshi Matsuo, Kaoru Kitagawa et autres

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Development of pericardial fat count images using a combination of three different deep-learning models

Takaaki Matsunaga, Atsushi K. Kono, Hidetoshi Matsuo, Kaoru Kitagawa et autres

Rationale and Objectives: Pericardial fat (PF), the thoracic visceral fat surrounding the heart, promotes the development of coronary artery disease by inducing inflammation of the coronary arteries. For evaluating PF, this study aimed to generate pericardial fat count images (PFCIs) from chest …

The efficacy of 18F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors

Masatoyo Nakajo, Aya Takeda, Akie Katsuki, Megumi Jinguji et autres

Objective: To examine whether the machine-learning approach using 18-fludeoxyglucose positron emission tomography (18F-FDG-PET)-based radiomic and deep-learning features is useful for predicting the pathological risk subtypes of thymic epithelial tumors (TETs). Methods: This retrospective study included 79 TET [27 low-risk thymomas (types A, …

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3D Inception U-Net for Aorta Segmentation using Computed Tomography Cardiac Angiography

Savitha Rani Ravichandran, Balaji Nataraj, Su Huang, Zhiliang Qin et autres

Computed Tomography Coronary Angiography (CTCA) is an effective imaging technique used for diagnosis and surgical planning. Segmentation of the aorta from the CTCA can be used clinically for interpretation, aortic valve measurement for intervention and identification of coronary structures. The process of …

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Medical Image Segmentation with Stochastic Aggregated Loss in a Unified U-Net

Phi Xuan Nguyen, Zhongkang Lu, Weimin Huang, Su Huang et autres

Automatic segmentation of medical images, such as computed tomography (CT) or magnetic resonance imaging (MRI), plays an essential role in efficient clinical diagnosis. While deep learning have gained popularity in academia and industry, more works have to be done to improve the …

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