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山田 誉大(ヤマダ タカヒロ)
近畿大学病院 | 助手 |
■研究者基本情報
科研費研究者番号
J-Global ID
プロフィール
There are 26 papers* in total, one of which is the first author.
*Proceedings are excluded.(the past 5 years)
2024.04 ~ 2025.03 → 5 (first 0)
2023.04 ~ 2024.03 → 4 (first 0)
2022.04 ~ 2023.03 → 8 (first 0)
2021.04 ~ 2022.03 → 5 (first 0)
2020.04 ~ 2021.03 → 2 (first 0)
~ 2020.03 → 2 (first 1)
journal (N, IF)
・Acta Medica Kindai University (2, 0.0)
・Advanced Biomedical Engineering (2, 0.8)
・Annals of Nuclear Medicine (6, 2.5)
・Applied Sciences (1, 2.7)
・Asia Oceania Journal of Nuclear Medicine and Biology (1, 0.0)
・Biomedical Physics and Engineering Express (2, 1.3)
・BMC Medical Imaging (1, 2.9)
・European Journal of Nuclear Medicine and Molecular Imaging Physics (1, 3.0)
・European Journal of Radiology (1, 3.2)
・Japanese Journal of Radiology (2, 2.9)
・Journal of Alzheimer’s Disease (1, 3.4)
・Journal of Imaging Informatics in Medicine (1, 2.9)
・Journal of Nuclear Cardiology (1, 3.0)
・Journal of Nuclear Medicine (1, 9.1)
・Medical & Biological Engineering & Computing (1, 2.6)
・Neuroradiology (1, 2.4)
・Physics in Medicine and Biology (1, 3.3)
研究キーワード
- MRI CT dementia with Lewy bodies Alzheimer's disease 認知症 FDG-PET Amyloid PET Tau PET 薬物速度論 放射線医学 核医学動態解析 医用画像処理
■経歴
経歴
学歴
委員歴
■研究活動情報
受賞
- 2024年02月 SPIE Medical Imaging 2024 Honorable Mention Poster Award
Unsupervised lung lesion detection on FDG-PET/CT images by deep image transformation-based 2.5-dimensional local anomaly detection.受賞者: Arata Segawa;Mitsutaka Nemoto;Hayato Kaida;Yuichi Kimura;Takashi Nagaoka;Katsuhiro Mikami;Takahiro Yamada;Kohei Hanaoka;Tatsuya Tsuchitani;Kazuhiro Kitajima;Kazunari Ishii - 2022年06月 Society of Nuclear Medicine and Molecular Imaging (SNMMI) SNMMI-TS Professional Development Grant Award
Comparison between the μ-maps of different PET tracers: 18F-FDG and 18F-flutemetamol, generated by the attenuation correction method without external radiation source受賞者: Takahiro Yamada;Kohei Hanaoka;Yoshiyuki Yamakawa;Suzuka Minagawa;Atsushi Ohtani;Tetsuro Mizuta;Hayato Kaida;Kazunari Ishii - 2020年09月 電子情報通信学会 MI研究奨励賞
正常データセットの教師なし学習に基づく病変検出支援システム画像特徴量の汎用的生成に関する検討 ~ 少規模なデータセットを用いた特徴量生成の実験的検証 ~受賞者: 牛房 和之;根本 充貴;木村 裕一;永岡 隆;山田 誉大;林 直人 - 2019年06月 日本生体医工学会 Young Investigator’s Award 優秀賞
深層畳み込みオートエンコーダを用いた健常データの教師なし学習による病変認識特徴量の汎用的自動生成受賞者: 牛房 和之;根本 充貴;木村 裕一;永岡 隆;山田 誉大;林 直人 - 2018年04月 日本核医学会 2018年世界核医学メルボルン大会若手トラベルグラント
Noise Reduction Algorithm for Amyloid Imaging to Preserve the Contrast Between Gray and White Matters Using Simplified Reference Tissue Model JPN受賞者: Takahiro Yamada;Yuichi kimura;Kosuke Fujii;Shougo Watanabe;Takashi Nagaoka;Mitsutaka Nemoto;Kohei Hanaoka;Chisa Hosokawa;Kazunari Ishii - 2017年11月 日本核医学会 核医学理工分科会 研究奨励賞
PETアミロイドイメージングによるアルツハイマー病の早期画像診断のための雑音除去アルゴリズムの性能評価受賞者: 山田 誉大;藤井 康介;渡辺 翔吾;木村 裕一
論文
- Yasuyuki Kojita; Atsushi Kono; Takahiro Yamada; Minoru Yamada; Sung-Woon In; Takenori Kozuka; Hayato Kaida; Motoi Kuwahara; Yoshitaka Nagai; Kazunari IshiiEuropean Journal of Radiology 178 111597 2024年07月 [査読有り]
Abstract Purpose Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are two major demyelinating diseases affecting the central nervous system (CNS). The objective of this study is to evaluate the prevalence of pontine trigeminal nerve lesions in patients diagnosed with MS and NMOSD using MRI. Methods This retrospective study included patients diagnosed with MS or NMOSD between July 2018 and July 2023. MS patients were clinically diagnosed using the 2017 McDonald criteria, while NMOSD patients were those who met the 2015 International Panel for NMO Diagnosis (IPND) criteria and were positive for Aquaporin-4 Antibody (AQP4-Ab). Results The study included a total of 90 patients, with 45 diagnosed with MS and another 45 with NMOSD. Pontine trigeminal nerve lesions were observed in both MS and NMOSD, but were more prevalent in MS patients (20 % vs. 2 %, p = 0.008). Root entry zone (REZ) lesions were found in 4 of 45 MS patients, accounting for 9 % (95 % CI: 3 %–17 %), and were absent in the NMOSD group; however, there was no significant difference between the two groups (p = 0.12). Of the MS patients with pontine trigeminal nerve lesions, 6 out of 9 (63 %; 95 % CI, 36 %–98 %) exhibited bilateral lesions, which was significantly more prevalent compared to the NMOSD group (13 % vs. 0 %, p = 0.03). Conclusions The presence of pontine trigeminal nerve lesions, particularly when bilateral, are significantly more prevalent in MS patients than in those with NMOSD, suggesting their utility as a distinctive marker and potential diagnostic indicator specifically for MS. - Koji Sugimoto; Takahiro Yamada; Atsushi Kono; SungWoon Im; Kazunari IshiiActa Medica Kindai University 49 1 1 - 6 2024年06月 [査読有り]
Purpose: Our earlier investigation assessed the correlation between regional accumulations of a tau positron emission tomography (PET) tracer (THK5351) and cognitive dysfunction in the Alzheimer's disease (AD) continuum brain. We reported significant correlations between bilateral parietotemporal and left posterior cingulate/precuneus THK5351 accumulations and MiniMental State Examination (MMSE)/Alzheimer Disease Assessment Scale-Cognitive Subtest (ADAS) scores. Herein, we explored the relationship between these variables in non-AD pathologic brains. Methods: Thirty-one subjects with non-AD pathologic changes (mean age: 64.6±11.3 yrs) underwent three-dimensional MRI, fluoro-2-deoxyglucose (FDG)-(PET), Pittsburgh compound B (PiB)-amyloid PET, and THK5351-tau PET. All subjects completed the MMSE (mean±SD score: 27.0±4.1), and 20 subjects (mean age: 63.8±11.9 yrs) also completed the ADAS (9.7±9.3). A voxel-wise statistical analysis using statistical parametric mapping determined the correlations between each subject's FDG and THK imaging results and their MMSE and ADAS scores. Results: The voxel-wise statistical analysis revealed that FDG accumulations in the left temporal cortices were significantly positively correlated with the subjects' MMSE scores and significantly negatively correlated with their ADAS scores. A negative correlation was observed between the subjects' MMSE scores and THK5351 accumulations in the left temporal and medial frontal cortices and the right temporal pole. A positive correlation was observed between the subjects' ADAS scores and THK5351 accumulations in the left temporal cortices and the right anterior temporal lobe. Conclusions: Compared to FDG accumulation, THK5351 accumulations correlated more strongly with neuropsychological test scores in non-AD pathologic brains, likely due to THK5351's affinity for both tau and monoamine oxidase-B. THK5351 measurements may be valuable for clarifying the pathophysiology of non-AD pathologic changes in the brain. - Neuromyelitis optica spectrum disorder mimicking cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy with symmetrical lesions in the temporal poles and external capsules on MRI(in press)Yasuyuki Kojita; Atsushi Kono; Takahiro Yamada; Minoru Yamada; Sung-Woon Im; Takenori Kozuka; Hayato Kaida; Motoi Kuwahara; Yoshitaka Nagai; Kazunari IshiiNeuroradiology 2024年 [査読有り]
Purpose: Symmetrical lesions in the temporal poles and external capsules are recognized as distinctive radiological markers of cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), yet similar lesions have been reported in neuromyelitis optica spectrum disorder (NMOSD). This study aimed to investigate the prevalence of such lesions in NMOSD patients' brain MRIs and compare it with the frequency of other characteristic NMOSD brain lesions. Methods: We conducted a retrospective study on consecutive NMOSD patients who underwent MRI from July 2013 to July 2023, focusing on those who met the 2015 International Panel for NMO Diagnosis (IPND) criteria and were aquaporin-4 antibodies (AQP4-Ab) positive. Brain MRI lesions were classified into 11 categories, encompassing both typical NMOSD findings and CADASIL-like lesions in the temporal poles and external capsules, to assess their prevalence. Results: The study comprised 55 NMOSD patients with no family history of CADASIL. Brain lesions were detected in 33 patients, of whom 2 (6%) had symmetrical lesions in both the temporal poles and external capsules, showing a prevalence similar to other specific NMOSD-related lesions such as Dorsal medullary lesions. Furthermore, one patient (3%) showed symmetrical lesions exclusively in the external capsules. Conclusion: Symmetrical lesions in the temporal poles and external capsules, previously identified as hallmark imaging features of CADASIL. However, similar - Validation of a glucose metabolism to tau deposition ratio image in the Alzheimer’s continuum(in press)Kazunari Ishii; Takahiro Yamada; Kohei Hanaoka; Hayato Kaida; Yasuyuki; Kojita; Atsushi Kono; Kazushi Hanada; Kazumasa Saigo; Shizuka Sakuda; Mamoru Hashimoto; Takashi Kato; Akinori Nakamura; for BATON; Study GroupJournal of Alzheimer's Disease in press 2024年 [査読有り]
- Development and validation of the surmising model for volumetric breast density using X-ray exposure conditions in digital mammography (in press)Mika Yamamuro; Yoshiyuki Asai; Takahiro Yamada; Yuichi Kimura; Kazunari Ishii; Yohan KondoMedical & Biological Engineering & Computing 2024年 [査読有り]
The use of breast density as a biomarker for breast cancer treatment has not been well established owing to the difficulty in measuring time-series changes in breast density. In this study, we developed a surmising model for breast density using prior mammograms through a multiple regression analysis, enabling a time series analysis of breast density. We acquired 1320 mediolateral oblique view mammograms to construct the surmising model using multiple regression analysis. The dependent variable was the breast density of the mammary gland region segmented by certified radiological technologists, and independent variables included the compressed breast thickness (CBT), exposure current times exposure second (mAs), tube voltage (kV), and patients’ age. The coefficient of determination of the surmising model was 0.868. After applying the model, the correlation coefficients of the three groups based on the CBT (thin group, 18–36 mm; standard group, 38–46 mm; and thick group, 48–78 mm) were 0.913, 0.945, and 0.867, respectively, suggesting that the thick breast group had a significantly low correlation coefficient (p=0.00231). In conclusion, breast density can be accurately surmised using the CBT, mAs, tube voltage, and patients’ age, even in the absence of a mammogram image. - Extensive Image Augmentation Using Fractals for Medical Image Diagnosis and Deep Learning(in press)Hitoshi Habe; Yuken Yoshioka; Daichi Ikefuji; Tomokazu Funatsu; Takashi Nagaoka; Takenori Kozuka; Mitsutaka Nemoto; Takahiro Yamada; Yuichi Kimura; Kazunari IshiiAdvanced Biomedical Engineering 2024年 [査読有り]
Abstract We propose data augmentation using fractal images to train deep-learning models for medical image diagnosis. Deep learning models for image classification typically demand large datasets, obtaining which can be challenging in medical image diagnosis. Current approaches often involve pre-training of model parameters on natural image databases like ImageNet and fine-tuning of the parameters with specific medical image data. However, natural and medical images have distinct characteristics, which question the suitability of pre-training on natural image data. Moreover, the scalability of natural image databases is limited; thus, acquiring sufficient data for large-scale deep-learning modelsis difficult. In contrast, Kataoka et al. introduced a mathematical model for generating image data and demonstrated its effectiveness in pre-training for natural image classification.In this study, we employed a pre-trained model utilizing fractals among mathematical models and experimentally classified CT images of COVID-19 pneumonia. The experimental results demonstrated that this fractal-based pretraining model can achieve accuracy comparable to conventional natural image-based approaches. Fractal images are easily generated compared to the natural images. Furthermore, by adjusting the parameters, generating appropriate data for specific applications can be possible. This flexibility in generating data allows for customization and optimization of the model for different scenarios or specific requirements. We believe this approach holds promise in medical image diagnosis, where the number of samples is often limited. - Tetsuya Kobayashi; Yui Shigeki; Yoshiyuki Yamakawa; Yumi Tsutsumida; Tetsuro Mizuta; Kohei Hanaoka; Shota Watanabe; Daisuke Morimoto‐Ishikawa; Takahiro Yamada; Hayato Kaida; Kazunari IshiiJournal of Imaging Informatics in Medicine 37 167 - 179 2024年01月 [査読有り]
Abstract Deep learning (DL) has recently attracted attention for data processing in positron emission tomography (PET). Attenuation correction (AC) without computed tomography (CT) data is one of the interests. Here, we present, to our knowledge, the first attempt to generate an attenuation map of the human head via Sim2Real DL-based tissue composition estimation from model training using only the simulated PET dataset. The DL model accepts a two-dimensional non-attenuation-corrected PET image as input and outputs a four-channel tissue-composition map of soft tissue, bone, cavity, and background. Then, an attenuation map is generated by a linear combination of the tissue composition maps and, finally, used as input for scatter+random estimation and as an initial estimate for attenuation map reconstruction by the maximum likelihood attenuation correction factor (MLACF), i.e., the DL estimate is refined by the MLACF. Preliminary results using clinical brain PET data showed that the proposed DL model tended to estimate anatomical details inaccurately, especially in the neck-side slices. However, it succeeded in estimating overall anatomical structures, and the PET quantitative accuracy with DL-based AC was comparable to that with CT-based AC. Thus, the proposed DL-based approach combined with the MLACF is also a promising CT-less AC approach. - Takashi Nakata; Kenichi Shimada; Akiko Iba; Haruhiko Oda; Akira Terashima; Yutaka Koide; Ryota Kawasaki; Takahiro Yamada; Kazunari IshiiJapanese Journal of Radiology 42 308 - 318 2023年10月 [査読有り]
Abstract Purpose Predicting progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) or dementia with Lewy bodies (DLB) is important. We evaluated morphological and functional differences between MCI with Lewy bodies (MCI-LB) and MCI due to AD (MCI-AD), and a method for differentiating between these conditions using brain MRI and brain perfusion SPECT. Methods A continuous series of 101 subjects, who had visited our memory clinic and met the definition of MCI, were enrolled retrospectively. They were consisted of 60 MCI-LB and 41 MCI-AD subjects. Relative cerebral blood flow (rCBF) on SPECT images and relative brain atrophy on MRI images were evaluated. We performed voxel-based analysis and visually inspected brain perfusion SPECT images for regional brain atrophy, occipital hypoperfusion and the cingulate island sign (CIS), for differential diagnosis of MCI-LB and MCI-AD. Results MRI showed no significant differences in regional atrophy between the MCI-LB and MCI-AD groups. In MCI-LB subjects, occipital rCBF was significantly decreased compared with MCI-AD subjects (p < 0.01, family wise error [FWE]-corrected). Visual inspection of occipital hypoperfusion had sensitivity, specificity, and accuracy values of 100%, 73.2% and 89.1%, respectively, for differentiating MCI-LB and MCI-AD. Occipital hypoperfusion was offered higher diagnostic utility than the CIS. Conclusions The occipital lobe was the region with significantly decreased rCBF in MCI-LB compared with MCI-AD subjects. Occipital hypoperfusion on brain perfusion SPECT may be a more useful imaging biomarker than the CIS for visually differentiating MCI-LB and MCI-AD. - Initial study of an algorithm for estimating the presence of amyloid accumulation from 18F-FDG PET images using machine learningTakahiro Yamada; Kohei Hanaoka; Hayato Kaida; Kazunari IshiiJournal of Cerebral Blood Flow & Metabolism 43 1_suppl 167 - 167 2023年07月 [査読有り]
- Mitsutaka Nemoto; Kazuyuki Ushifusa; Yuichi Kimura; Takashi Nagaoka; Takahiro Yamada; Takeharu YoshikawaApplied Sciences 13 14 8330 Publisher of Open Access Journals 2023年07月 [査読有り]
Abstract There are growing expectations for AI computer-aided diagnosis: computer-aided diagnosis (CAD) systems can be used to improve the accuracy of diagnostic imaging. However, it is not easy to collect large amounts of disease medical image data with lesion area annotations for the supervised learning of CAD systems. This study proposes an unsupervised local image feature extraction method running without such disease medical image datasets. Local image features are one of the key determinants of system performance. The proposed method requires only a normal image dataset that does not include lesions and can be collected easier than a disease dataset. The unsupervised features are extracted by applying multiple convolutional autoencoders to analyze various 2.5-dimensional images. The proposed method is evaluated by two kinds of problems: the detection of cerebral aneurysms in head MRA images and the detection of lung nodules in chest CT images. In both cases, the performance is high, showing an AUC of more than 0.96. These results show that the proposed method can automatically learn features that are useful for lesion recognition from lesion-free normal data, regardless of the type of image or lesion. - Validation of a Glucose Metabolism to Tau Deposition Ratio Image in the Alzheimer’s ContinuumKazunari Ishii; Takahiro Yamada; Kohei Hanaoka; Hayato Kaida; Kenji Ishii; Takashi Kato; Akinori Nakamura; Study Group BATONJournal of Nuclear Medicine 64 supplement 1 315 - 315 2023年06月 [査読有り]
Introduction: Alzheimer’ disease (AD) is characterized by amyloid and tau deposition in prodromal stage and subsequent decrease of cerebral glucose metabolism as disease progresses. We previously proposed the voxel-wise metabolism to amyloid deposits ratio (MAR) image and evaluated its reliability for the diagnosis of AD. This time we produced the voxel-wise metabolism to tau deposits ratio (MTR) image and evaluated the utility of MTR image. Methods: Subjects with AD continuum, who underwent 18F-FDG-PET, 18F-flutemetamol amyloid PET and 18F-MK-6240 tau-PET for BATON (Blood-based Amyloid, Tau and Other Neuropathological Biomarkers Project) at our institution were included to this study. Consecutive 25 subjects with AD continuum (age: 73.7 ± 7.2, MMSE: 22.1 ± 3.7) who had positive amyloid deposition shown by amyloid PET were selected. After normalizing to a standard stereotactic space, the MTR image was created by dividing each FDG-PET image by corresponding tau-PET image using with voxel-wise interimage computation. We performed paired t test voxel wise comparison between individual FDG-PET image and MTR image. We also evaluated the correlations between the regional FDG standardized up take value ratio (SUVR), MK-6240 SUVR, and MTR value and the patients' MMSE scores. Results: In the voxel wise comparison, the MTR images showed more severely affected region in bilateral inferior temporal gyri (p < 0.001, FWE) than FDG image (Fig.1). The bilateral parietal and posterior cingulate/precuneus, and right frontal FDG SUVR and MMSE scores were positively correlated, and the bilateral occipital and central gyri MK6240 SUVR were negatively correlated with MMSE scores. There were significant positive correlations between bilateral frontal, parietal and left posterior cingulate/precuneus MTR value and MMSE scores. Conclusions: MTR image reflected not only cerebral tau deposition but the glucose hypometabolism and demonstrated that cerebral glucose metabolism decreases beyond the degree of tau accumulation in inferior temporal area of AD continuum brain. There are regional differences in glucose metabolism and tau deposition that correlate with cognitive dysfunction in AD continuum. - Kohei Hanaoka; Shota Watanabe; Daisuke Ishikawa; Hayato Kaida; Takahiro Yamada; Masakazu Yasuda; Yoshitaka Iwanaga; Gaku Nakazawa; Kazunari IshiiJournal of Nuclear Cardiology 2023年03月 [査読有り]
Background The aim of this study was to estimate the impact of respiratory and electrocardiogram (ECG)-gated FDG positron emission tomography (PET)/computed tomography (CT) on the diagnosis of cardiac sarcoidosis (CS). Methods and Results Imaging from thirty-one patients was acquired on a PET/CT scanner equipped with a respiratory- and ECG-gating system. Non-gated PET images and three kinds of gated PET/CT images were created from identical list-mode clinical PET data: respiratory-gated PET during expiration (EX), ECG-gated PET at end diastole (ED), and ECG-gated PET at end systole (ES). The maximum standardized uptake value (SUVmax) and cardiac metabolic volume (CMV) were measured, and the locations of FDG accumulation were analyzed using a polar map. The mean SUVmax of the subjects was significantly higher after application of either respiratory-gated or ECG-gated reconstruction. Conversely, the mean CMV was significantly lower following the application of respiratory-gated or ECG-gated reconstruction. The segment showing maximum accumulation was shifted to the adjacent segment in 25.8%, 38.7%, and 41.9% of cases in EX, ED, and ES images, respectively. Conclusion In FDG PET/CT scanning for the diagnosis of CS, gated scanning is likely to increase quantitative accuracy, but the effect depends on the location and synchronization method. - Yuken Yoshioka; Daichi Ikefuji; Tomokazu Funatsu; Takashi Nagaoka; Takenori Kozuka; Mitsutaka Nemoto; Takahiro Yamada; Yuichi Kimura; Kazunari Ishii; Hitoshi HabeThe 29th International Workshop on Frontiers of Computer Vision (IW-FCV2023) Online Proceeding 2023年02月 [査読有り]
Abstract. Recently, the number of images for pre-training of deep learning models has been increasing, and large-scale data sets contain inappropriate images such as ethically inappropriate images, copyright infringement, and labeling errors. A method to solve these is by using a fractal database that generates images by mathematical formulas without using natural images. Our goal is to show that the classification accuracy obtained by pre-training with fractal images is comparable to natural images. In the experiments, we compare the performance on the tasks to classify CT images of COVID-19 pneumonia and regular pneumonia. - SungWoon Im; Kohei Hanaoka; Takahiro Yamada; Kazunari IshiiAsia Oceania Journal of Nuclear Medicine and Biology 11 1 37 - 43 2023年01月 [査読有り]
Objective: We evaluated the relationship between regional accumulations of the tau positron emission tomography (PET) tracer THK5351 and cognitive dysfunction in the Alzheimer's disease (AD) continuum. Methods: The cases of 18 patients with AD or mild cognitive impairment (MCI) due to AD who underwent three-dimensional MRI, fluoro-2-deoxyglucose (FDG)-(PET), Pittsburgh compound B (PiB)-amyloid PET, and THK5351-tau PET were analyzed. Their mean age was 70.6 ± 11.3, their mean Mini-Mental State Examination (MMSE) score was 22.3 ± 6.8, and their mean Alzheimer Disease Assessment Scale-Cognitive Subtest (ADAS) score was 12.5 ± 7.3. To determine the correlation between each patient's four imaging results and their MMSE and ADAS scores, we performed a voxel-wise statistical analysis with statistical parametric mapping (SPM). Results: The SPM analysis showed that the bilateral parietotemporal FDG accumulations and MMSE scores were positively correlated, and the bilateral parietotemporal FDG accumulations were negatively correlated with ADAS scores. There were significant correlations between bilateral parietotemporal and left posterior cingulate/precuneus THK5351 accumulations and MMSE/ADAS scores. Conclusion: In the AD brain, THK5351 correlates with neuropsychological test scores as well as or more additional than FDG due to its affinity for both tau and monoamine oxidase-B (MAO-B), and measurements of THK5351 may thus be useful in estimating the progression of AD. - Daisuke Morimoto-Ishikawa; Kohei Hanaoka; Shota Watanabe; Takahiro Yamada; Yoshiyuki Yamakawa; Suzuka Minagawa; Shiho Takenouchi; Atsushi Ohtani; Tetsuro Mizuta; Hayato Kaida; Kazunari IshiiEJNMMI Physics 9 88 1 - 12 2022年12月 [査読有り]
Abstract Background This study evaluated the physical performance of a positron emission tomography (PET) system dedicated to the head and breast according to the National Electrical Manufacturers Association (NEMA) NU2-2012 standard. Methods The spatial resolution, sensitivity, scatter fraction, count rate characteristics, corrections for count losses and randoms, and image quality of the system were determined. All measurements were performed according to the NEMA NU2-2012 acquisition protocols, but image quality was assessed using a brain-sized phantom. Furthermore, scans of the three-dimensional (3D) Hoffmann brain phantom and mini-Derenzo phantom were acquired to allow visual evaluation of the imaging performance for small structures. Results The tangential, radial, and axial full width at half maximum (FWHM) at a 10-mm offset in half the axial field of view were measured as 2.3, 2.5, and 2.9 mm, respectively. The average system sensitivity at the center of the field of view and at a 10-cm radial offset was 7.18 and 8.65 cps/kBq, respectively. The peak noise-equivalent counting rate was 35.2 kcps at 4.8 kBq/ml. The corresponding scatter fraction at the peak noise-equivalent counting rate was 46.8%. The peak true rate and scatter fraction at 8.6 kBq/ml were 127.8 kcps and 54.3%, respectively. The percent contrast value for a 10-mm sphere was approximately 50%. On the 3D Hoffman brain phantom image, the structures of the thin layers composing the phantom were visualized on the sagittal and coronal images. On the mini-Derenzo phantom, each of the 1.6-mm rods was clearly visualized. Conclusion Taken together, these results indicate that the head- and breast-dedicated PET system has high resolution and is well suited for clinical PET imaging. - Comparison with regional glucose metabolism, amyloid deposition and tau deposition between dementia with Lewy bodies and Alzheimer disease brainsAyumi Hirayama; Takahiro Yamada; Kazunari IshiiActa Medica Kindai University 47 2 11 - 16 2022年12月 [査読有り]
Purpose: Although some dementia with Lewy bodies (DLB) patients have Alzheimer disease (AD) pathology, there are no reports comparing AD and DLB by amyloid PET and tau PET in the same patient. The aim of this study was to investigate the regional difference of metabolic reduction, amyloid deposition and tau deposition between DLB and AD brain. Methods: Five consecutive subjects with DLB and five age and Mini-mental state examination score matched AD patients, who underwent both fluoro-2-deoxyglucose (FDG)-PET, Pittsburgh compound B (PiB)-amyloid PET and THK5351 (THK)-tau PET, were included in this study. Voxel-based statistical analysis was performed with Statistical Parametric Mapping for comparison with FDG, amyloid and tau accumulation between the DLB and AD brains. Results: FDG PET demonstrated that occipital metabolism in DLB group was significantly lower than that in AD group. PiB PET demonstrated that in the AD group the anterior to posterior cingulate cortices, supplementary motor area, and thalamic amyloid depositions were significantly higher than those in the DLB group. THK PET demonstrated that in the AD group the posterior cingulate cortices, posterior internal capsules, thalamic and peri-brainstem regions tau depositions were significantly higher than those in the DLB group. Conclusions: In the DLB brain, it is suggested that decreased regional glucose metabolism is unrelated to amyloid and tau depositions, and the amyloid and tau depositions in the cingulate gyri and thalamic regions in the AD brain are higher than those in DLB brain. Key words: dementia with Lewy bodies (DLB), glucose metabolism, amyloid, tau, positron emission tomography (PET). - Tetsuro Mizuta; Yoshiyuki Yamakawa; Suzuka Minagawa; Tetsuya Kobayashi; Atsushi Ohtani; Shiho Takenouchi; Kohei Hanaoka; Shota Watanabe; Daisuke Morimoto-Ishikawa; Takahiro Yamada; Hayato Kaida; Kazunari IshiiAnnals of Nuclear Medicine 36 998 - 1006 2022年09月 [査読有り]
Objectives This study evaluates the phantom attenuation correction (PAC) method as an alternative to maximum-likelihood attenuation correction factor (ML-ACF) correction in time-of-flight (TOF) brain positron emission tomography (PET) studies. Methods In the PAC algorithm, a template emission image λRef and a template attenuation coefficient image μRef are prepared as a data set based on phantom geometry. Position-aligned attenuation coefficient image μAcq is derived by aligning μRef using parameters that match the template emission image λRef to measured emission image λAcq. Then, attenuation coefficient image μAcq combined with a headrest image is used for scatter and attenuation correction in the image reconstruction. To evaluate the PAC algorithm as an alternative to ML-ACF, Hoffman 3D brain and cylindrical phantoms were measured to obtain the image quality indexes of contrast and uniformity. These phantoms were also wrapped with a radioactive sheet to obtain attenuation coefficient images using ML-ACF. Emission images were reconstructed with attenuation correction by PAC and ML-ACF, and the results were compared using contrast and uniformity as well as visual assessment. CT attenuation correction (CT-AC) was also applied as a reference. Results The contrast obtained by ML-ACF was slightly overestimated due to its unique experimental condition for applying ML-ACF in Hoffman 3D brain phantom but the uniformity was almost equivalent among ML-ACF, CT-AC, and PAC. PAC showed reasonable result without overestimation compared to ML-ACF and CT-AC. Conclusions PAC is an attenuation correction method that can ensure the performance in phantom test, and is considered to be a reasonable alternative to clinically used ML-ACF-based attenuation correction. - Mitsutaka Nemoto; Atsuko Tanaka; Hayato Kaida; Yuichi Kimura; Takashi Nagaoka; Takahiro Yamada; Kohei Hanaoka; Kazuhiro Kitajima; Tatsuya Tsutitani; Kazunari IshiiPhysics in Medicine and Biology 67 19 195013 2022年09月 [査読有り]
Abstract We propose a method to detect primary and metastatic lesions with Fluorine−18 fluorodeoxyglucose (FDG) accumulation in the lung field, neck, mediastinum, and bony regions on the FDG-PET/CT images. To search for systemic lesions, various anatomical structures must be considered. The proposed method is addressed by using an extraction process for anatomical regions and a uniform lesion detection approach. The uniform approach does not utilize processes that reflect any region-specific anatomical aspects but has a machine-learnable framework. Therefore, it can work as a lesion detection process for a specific anatomical region if it machine-learns the specific region data. In this study, three lesion detection processes for the whole-body bone region, lung field, or neck-mediastinum region are obtained. These detection processes include lesion candidate detection and false positive (FP) candidate elimination. The lesion candidate detection is based on a voxel anomaly detection with a one-class support vector machine. The FP candidate elimination is performed using an AdaBoost classifier ensemble. The image features used by the ensemble are selected sequentially during training and are optimal for candidate classification. Three-fold cross-validation was used to detect performance with the 54 diseased FDG-PET/CT images. The mean sensitivity for detecting primary and metastatic lesions at 3 FPs per case was 0.89 with a 0.10 standard deviation (SD) in the bone region, 0.80 with a 0.10 SD in the lung field, and 0.87 with a 0.10 SD in the neck region. The average areas under the ROC curve were 0.887 with a 0.125 SD for detecting bone metastases, 0.900 with a 0.063 SD for detecting pulmonary lesions, and 0.927 with a 0.035 SD for detecting the neck-mediastinum lesions. These detection performances indicate that the proposed method could be applied clinically. These results also show that the uniform approach has high versatility for providing various lesion detection processes. - Kazunari Ishii; Kohei Hanaoka; Shota Watanabe; Daisuke Morimoto-Ishikawa; Takahiro Yamada; Hayato Kaida; Yoshiyuki Yamakawa; Suzuka Minagawa; Shiho Takenouchi; Atsushi Ohtani; Tetsuro MizutaJournal of Nuclear Medicine 64 1 153 - 158 2022年07月 [査読有り]
Abstract We acquired brain positron emission tomography (PET) images of fluorodeoxyglucose (FDG) and flutemetamol PET using a time-of-flight-PET system dedicated for the head (dhPET) and a conventional whole-body PET/computed tomography (wbPET) system and evaluated the clinical superiority of dhPET over wbPET. Methods: There were 18 subjects for the FDG-PET study and 17 subjects for the flutemetamol PET study. FDG-PET images were first obtained using wbPET, followed by dhPET. Flutemetamol PET images were first obtained using wbPET, followed by dhPET. Images acquired using dhPET and wbPET were compared by visual inspection, voxel-wise analysis, and standard uptake value ratio (SUVR). Results: All FDG and flutemetamol images acquired using dhPET were judged as better by visual inspection than those acquired using wbPET. The voxel-wise analysis demonstrated that accumulations in the cerebellum, lateral occipital cortices, and around the central sulcus area in dhPET FDG images were lower than those in wbPET FDG images, whereas accumulations around the ventricle systems were higher in dhPET FDG images than those in wbPET FDG images. Accumulations in the cerebellar dentate nucleus, midbrain, lateral occipital cortices, and around the central sulcus area in dhPET images were lower than those in wbPET images, whereas accumulations around the ventricle systems were higher in dhPET flutemetamol images than those in wbPET flutemetamol images. Mean cortical SUVRs of FDG and flutemetamol dhPET images were significantly higher than those of FDG and flutemetamol wbPET images, respectively. Conclusion: The dhPET images had better image quality by visual inspection and higher SUVRs than wbPET images. Although there were several regional accumulation differences between dhPET and wbPET images, understanding this phenomenon will enable full use of the features of this dhPET system in clinical practice. - Comparison between the μ-maps of different PET tracers: 18F-FDG and 18F-flutemetamol, generated by the attenuation correction method without external radiation sourceTakahiro Yamada; Kohei Hanaoka; Yoshiyuki Yamakawa; Suzuka Minagawa; Atsushi Ohtani; Tetsuro Mizuta; Hayato Kaida; Kazunari IshiiJournal of Nuclear Medicine 63 supplement 2 4127 - 4127 2022年06月 [査読有り]
- Mika Yamamuro; Yoshiyuki Asai; Naomi Hashimoto; Nao Yasuda; Hiroto Kimura; Takahiro Yamada; Mitsutaka Nemoto; Yuichi Kimura; Hisashi Handa; Hisashi Yoshida; Koji Abe; Masahiro Tada; Hitoshi Habe; Takashi Nagaoka; Seiun Nin; Kazunari Ishii; Yongbum LeeBiomedical Physics & Engineering Express 8 4 045016 2022年06月 [査読有り]
Abstract This study investigates the equivalence or compatibility between U-Net and visual segmentations of fibroglandular tissue regions by mammography experts for calculating the breast density and mean glandular dose (MGD). A total of 703 mediolateral oblique-view mammograms were used for segmentation. Two region types were set as the ground truth (determined visually) :(1) one type included only the region where fibroglandular tissue was identifiable (called the dense region); (2) the other type included the region where the fibroglandular tissue may have existed in the past, provided that apparent adipose-only parts, such as the retromammary space, are excluded (the diffuse region). U-Net was trained to segment the fibroglandular tissue region with an adaptive moment estimation optimiser, five-fold cross-validated with 400 training and 100 validation mammograms, and tested with 203 mammograms. The breast density and MGD were calculated using the van Engeland and Dance formulas, respectively, and compared between U-Net and the ground truth with the Dice similarity coefficient and Bland–Altman analysis. Dice similarity coefficients between U-Net and the ground truth were 0.895 and 0.939for the dense and diffuse regions, respectively. In the Bland–Altman analysis, no proportional or fixed errors were discovered in either the dense or diffuse region for breast density, whereas a slight proportional error was discovered in both regions for the MGD (the slopes of the regression lines were −0.0299 and−0.0443 for the dense and diffuse regions, respectively). Consequently, the U-Net and ground truth were deemed equivalent(interchangeable)for breast density and compatible (interchange-able following four simple arithmetic operations) for MGD. U-Net-based segmentation of the fibroglandular tissue region was satisfactory for both regions, providing reliable segmentation for breast density and MGD calculations. U-Net will be useful in developing a reliable individualised screening mammography programme, instead of relying on the visual judgement of mammography experts. - Takashi Nagaoka; Takenori Kozuka; Takahiro Yamada; Hitoshi Habe; Mitsutaka Nemoto; Masahiro Tada; Koji Abe; Hisashi Handa; Hisashi Yoshida; Kazunari Ishii; Yuichi KimuraAdvanced Biomedical Engineering 11 76 - 86 2022年03月 [査読有り]
Abstract Objective: The objective of the current study was to develop a novel, artificial intelligence (AI)-based system to diagnose coronavirus disease (COVID-19) using computed tomography (CT) slice images. Prior research has demonstrated that, if not focused on the lungs, AI diagnoses COVID-19 using information outside the lungs. The inclusion of CT training data from multiple facilities and CT models may also cause AI to diagnose COVID-19 with features that are irrelevant to COVID-19. Thus, the objective of the current study was to evaluate a combination of lung mask images and CT slice images from a single facility, using a single CT model, and use AI to differentiate COVID-19 from other types of pneumonia based solely on information related to the lungs. Method: By superimposing lung mask images on image feature output using an existing AI structure, it was possible to exclude image features other than those around the lungs. The results of this model were also compared with the slice image findings from which only the lung region was extracted. The system adopted an ensemble approach. The outputs of multiple AIs were averaged to differentiate COVID-19 cases from other types of pneumonia, based on CT slice images. Results: The system evaluated 132 scans of COVID-19 cases and 62 scans of non-COVID-19 cases taken at the single facility using a single CT model. The initial sensitivity, specificity, and accuracy of our system, using a threshold value of 0.50, was shown to be 95%, 53%, and 81%, respectively. Setting the threshold value to 0.84 adjusted the sensitivity and specificity to clinically usable values of 76% and 84%, respectively. Conclusion: The system developed in the current study was able to differentiate between pneumonia due to COVID-19 and other types of pneumonia with sufficient accuracy for use in clinical practice. This was accomplished without the inclusion of images of clinically meaningless regions and despite the application of more stringent conditions, compared to prior studies. - Takashi Nakata; Kenichi Shimada; Akiko Iba; Haruhiko Oda; Akira Terashima; Yutaka Koide; Ryota Kawasaki; Takahiro Yamada; Kazunari IshiiAnnals of Nuclear Medicine 36 3 384 - 392 Springer Science and Business Media LLC 2022年03月 [査読有り]
Abstract Objective This study aimed at investigating the correlation between recurrent visual hallucinations (VHs) and regional cerebral blood flow (rCBF) in patients with dementia with Lewy bodies (DLB). Methods In 147 DLB patients, the correlation between noise pareidolia scores and rCBF in brain perfusion single photon emission computed tomography (SPECT) was evaluated. The 147 subjects comprised 52 probable and 95 possible DLB patients, of whom 107 did not have visual hallucinations and 40 had visual hallucinations. Brain perfusion SPECT was then performed, and memory impairment was assessed using the Mini-Mental State Examination (MMSE), while the optical illusion “pareidolia” (the tendency to see a specific image in a random visual pattern) was evaluated using noise pareidolia test. The correlations between rCBF and MMSE or noise pareidolia scores were then analyzed. Results The rCBF and MMSE scores were positively correlated, and rCBF was correlated with MMSE scores in a region that was consistent with a previously reported memory-related site. There was no correlation between noise pareidolia scores and occipital CBF, but there were weak correlations between noise pareidolia scores and rCBF in the bilateral frontal lobes (Brodmann area [BA]8 and BA9), left cingulate cortex (BA31), and left angular and supramarginal gyri (BA39 and BA40) in DLB patients. Conclusion Weak correlation was found between noise pareidolia scores and rCBF in several sites (BA8, BA9, BA31, BA39 and BA40) other than in occipital lobe. These findings suggest that DLB hallucinations may be manifested by more complex brain network disorders, rather than by primary visual cortex disorders alone. - Tetsuro Mizuta; Tetsuya Kobayashi; Yoshiyuki Yamakawa; Kohei Hanaoka; Shota Watanabe; Daisuke Morimoto-Ishikawa; Takahiro Yamada; Hayato Kaida; Kazunari IshiiAnnals of Nuclear Medicine 36 2 420 - 426 2022年02月 [査読有り]
Abstract Aim The aim of this study was to evaluate an image reconstruction algorithm, including a new maximum-likelihood attenuation correction factor (ML-ACF) for time of flight (TOF) brain positron emission tomography (PET). Methods The implemented algorithm combines an ML-ACF method that simultaneously estimates both the emission image and attenuation sinogram from TOF emission data, and a scaling method based on anatomical features. To evaluate the algorithm’s quantitative accuracy, three-dimensional brain phantom images were acquired and soft-tissue attenuation coefficients and emission values were analyzed. Results The heterogeneous distributions of attenuation coefficients in soft tissue, skull, and nasal cavity were sufficiently visualized. The attenuation coefficient of soft tissue remained within 5% of theoretical value. Attenuation-corrected emission showed no lateral differences, and significant differences among soft tissue were within the error range. Conclusion The ML-ACF-based attenuation correction implemented for TOF brain PET worked well and obtained practical levels of accuracy. - Application of correlated component analysis to dynamic PET time-activity curves denoisingPaulus Kapundja Shigwedha; Takahiro Yamada; Kohei Hanaoka; Kazunari Ishii; Yuichi Kimura; Yutaka Fukuoka43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2021年10月 [査読有り]
- Tanyaluck Thientunyakit; Thonnapong Thongpraparn; Chakmeedaj Sethanandha; Takahiro Yamada; Yuichi Kimura; Weerasak Muangpaisan; Kazunari IshiiJapanese Journal of Radiology 39 10 984 - 993 2021年05月 [査読有り]
Abstract Purpose To determine the association between occipital amyloid-PET uptake and neurocognitive performance in Alzheimer’s disease (AD). Materials and methods Fifty-eight participants with normal aged, mild cognitive impairment (MCI) due to AD and AD subjects who underwent F-18 florbetapir brain PET/CT scans were divided into four groups (A, normal; B, MCI; C, mild AD; and D, moderate/severe AD). Semiquantitative analyses of SUVR images were performed. The differences between groups and the correlations between florbetapir uptake and Thai Mental State Examination (TMSE) scores were determined. Significant differences were defined using a P < 0.001, uncorrected, or a P < 0.05, FWE for the voxel-based analyses with Statistical Parametric Mapping (SPM). Results There was a slightly higher florbetapir uptake in the precuneus, parietal, and occipital association cortices in Group B > A. The occipital florbetapir uptake in Groups C and D was significantly higher than in Group A, in addition to the precuneus, anterior cingulate, posterior cingulate, temporoparietal, and frontal cortices. There was a strong negative correlation between TMSE scores and florbetapir uptake in the occipital lobe. Conclusions Occipital amyloid uptake is associated with clinically advanced AD, and is inversely correlated with neurocognitive performance and may be useful for evaluating AD severity. - Paulus K. Shigwedha; Takahiro Yamada; Kohei Hanaoka; Kazunari Ishii; Yuichi Kimura; Yutaka FukuokaBiomedical Physics & Engineering Express 7 035003 2021年04月 [査読有り]
Abstract Logan graphical analysis (LGA) is a method for in vivo quantification of tracer kinetics in positron emission tomography (PET). The shortcoming of LGA is the presence of a negative bias in the estimated parameters for noisy data. Various approaches have been proposed to address this issue. We recently applied an alternative regression method called least-squares cubic (LSC), which considers the errors in both the predictor and response variables to estimate the LGA slope. LSC reduced the bias in non-displaceable binding potential estimates while causing slight increases in the variance. In this study, we combined LSC with a principal component analysis (PCA) denoising technique to counteract the effects of variance on parametric image quality, which was assessed in terms of the contrast between gray and white matter. Tissue time–activity curves were denoised through PCA, prior to estimating the regression parameters using LSC. We refer to this approach as LSC–PCA. LSC–PCA was assessed against OLS–PCA (PCA with ordinary least-squares (OLS)), LSC, and conventional OLS-based LGA. Comparisons were made for simulated 11 C-carfentanil and 11C Pittsburgh compound B (11C-PiB) data, and clinical 11 C-PiB PET images. PCA-based methods were compared over a range of principal components, varied by the percentage variance they account for in the data. The results showed reduced variances in distribution volume ratio estimates in the simulations for LSC–PCA compared to LSC, and lower bias compared to OLS–PCA and OLS. Contrasts were not significantly improved in clinical data, but they showed a significant improvement in simulation data —indicating a potential advantage of LSC–PCA over OLS–PCA. The effects of bias reintroduction when many principal components are used were also observed in OLS–PCA clinical images. We therefore encourage the use of LSC–PCA. LSC–PCA can allow the use of many principal components with minimal risk of bias, thereby strengthening the interpretation of PET parametric images. - How to select training data to segment mammary gland region using a deep-learning approach for reliable individualized screening mammographyMika Yamamuro; Yoshiyuki Asai; Naomi Hashimoto; Nao Yasuda; Takahiro Yamada; Mitsutaka Nemoto; Yuichi Kimura; Hisashi Handa; Hisashi Yoshida; Koji Abe; Masahiro Tada; Hitoshi Habe; Takashi Nagaoka; Yoshiaki Ozaki; Seiun Nin; Kazunari Ishii; Yongbum LeeSPIE Medical Imaging 2021 2021年02月 [査読有り]
- Detection of cerebral aneurysms on MR angiography using generated features by unsupervised deep learning for multiple 2.5-dimensional imagesKazuyuki Ushifusa; Mitsutaka Nemoto; Yuichi Kimura; Takashi Nagaoka; Takahiro Yamada; Naoto HayashiInternational Forum on Medical Imaging in Asia 2021 2021年01月 [査読有り]
- Kazunari Ishii; Takahiro Yamada; Kohei Hanaoka; Hayato Kaida; Koichi Miyazaki; Masami Ueda; Kazushi Hanada; Kazumasa Saigoh; Julia Sauerbeck; Axel Rominger; Peter Bartenstein; Yuichi KimuraAnnals of Nuclear Medicine 34 11 856 - 863 2020年11月 [査読有り]
Purpose It is usually easy to judge whether amyloid PET images should be interpreted as positive or negative for amyloid deposits by visual inspection or quantitative measurement standard uptake value ratio (SUVR), but the findings are equivocal in some cases. As conventional mean cortical SUVR (mcSUVR) measures accumulation in both gray matter (GM) and white matter, it may mis-estimate amyloid deposits. The purpose of the study was to develop a regional GM-dedicated SUVR measuring (GMSUVR) system for amyloid PET images with 3D-MRI, and evaluate its utility for detecting amyloid deposits in equivocal cases. Methods Of 126 subjects who underwent amyloid PET with 11C-PiB and 3D-MRI, the area of amyloid-positive regions and the critical regional GMSUVR thresholds were first determined in 15 amyloid-positive and 15 amyloid-negative patients, using the automatic volumetric measurement of segmented brain images system. We then tested 36 amyloid-negative, 60 amyloid-positive, and 13 equivocal subjects with this GMSUVR system and with conventional mcSUVR. Results Sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) were 100%, 92%, 97%, 95%, and 100% for the GMSUVR system; and 97%, 86%, 93%, 92% and 94%, respectively, for mcSUVR. In 24 cases in which the findings were equivocal or discordant, the sensitivity, specificity, accuracy, PPV, and NPV were all 100% for the GMSUVR system; and were 90%, 33%, 83%, 90%, and 33%, respectively, for mcSUVR. Conclusion The regional GMSUVR measurement method was well able to discriminate between amyloid-positive and -negative subjects, even in cases where amyloid deposition was equivocal. - Yuichi Kimura; Aya Watanabe; Takahiro Yamada; Shogo Watanabe; Takashi Nagaoka; Mitsutaka Nemoto; Koichi Miyazaki; Kohei Hanaoka; Hayato Kaida; Kazunari IshiiAnnals of Nuclear Medicine 34 7 512 - 515 2020年07月 [査読有り]
Abstract Objective An artificial intelligence (AI)-based algorithm typically requires a considerable amount of training data; however, few training images are available for dementia with Lewy bodies and frontotemporal lobar degeneration. Therefore, this study aims to present the potential of cycle-consistent generative adversarial networks (CycleGAN) to obtain enough number of training images for AI-based computer-aided diagnosis (CAD) algorithms for diagnosing dementia. Methods We trained CycleGAN using 43 amyloid-negative and 45 positive images in slice-by-slice. Results The CycleGAN can be used to synthesize reasonable amyloid-positive images, and the continuity of slices was preserved. Discussion Our results show that CycleGAN has the potential to generate a sufficient number of training images for CAD of dementia. - A generalized image feature generation based on unsupervised deep learning with small scale normal datasetKazuyuki Ushifusa; Miysutaka Nemoto; Yuichi Kimura; Takashi Nagaoka; Takahiro Yamada; Atsuko Tanaka; Naoto HayashiInternational Journal of Computer Assisted Radiology and Surgery 15 Suppl 1 210 - 212 2020年06月 [査読有り]
- Automatic detection of cervical and thoracic lesions on FDG-PET/CT by organ specific one-class SVMsAtsuko Tanaka; Mitsutaka Nemoto; Hayato Kaida; Yuichi Kimura; Takashi Nagaoka; Takahiro Yamada; Kazuyuki Ushifusa; Kohei Hanaoka; Kazuhiro Kitajima; Tatsuya Tsuchitani; Kazunari IshiiInternational Journal of Computer Assisted Radiology and Surgery 15 Suppl 1 208 - 209 2020年06月 [査読有り]
- Yuichi Kimura; Takahiro Yamada; Mai Hatano; Waki Nakajima; Tomoyuki Miyazaki; Takuya TakahashiJournal of Nuclear Medicine 61 supplement 1 1577 - 1577 2020年05月 [査読有り]
- Paulus K. Shigwedha; Takahiro Yamada; Kohei Hanaoka; Kazunari Ishii; Yuichi Kimura; Yutaka FukuokaBMC Medical Imaging 20 15 15 - 15 2020年02月 [査読有り]
Background The Logan graphical analysis (LGA) algorithm is widely used to quantify receptor density for parametric imaging in positron emission tomography (PET). Estimating receptor density, in terms of the non-displaceable binding potential (BPND), from the LGA using the ordinary least-squares (OLS) method has been found to be negatively biased owing to noise in PET data. This is because OLS does not consider errors in the X-variable (predictor variable). Existing bias reduction methods can either only reduce the bias slightly or reduce the bias accompanied by increased variation in the estimates. In this study, we addressed the bias reduction problem by applying a different regression method. Methods We employed least-squares cubic (LSC) linear regression, which accounts for errors in both variables as well as the correlation of these errors. Noise-free PET data were simulated, for 11C-carfentanil kinetics, with known BPND values. Statistical noise was added to these data and the BPNDs were re-estimated from the noisy data by three methods, conventional LGA, multilinear reference tissue model 2 (MRTM2), and LSC-based LGA; the results were compared. The three methods were also compared in terms of beta amyloid (A β) quantification of 11C-Pittsburgh compound B brain PET data for two patients with Alzheimer’s disease and differing A β depositions. Results Amongst the three methods, for both synthetic and actual data, LSC was the least biased, followed by MRTM2, and then the conventional LGA, which was the most biased. Variations in the LSC estimates were smaller than those in the MRTM2 estimates. LSC also required a shorter computational time than MRTM2. Conclusions The results suggest that LSC provides a better trade-off between the bias and variability than the other two methods. In particular, LSC performed better than MRTM2 in all aspects; bias, variability, and computational time. This makes LSC a promising method for BPND parametric imaging in PET studies. - Takahiro Yamada; Shogo Watanabe; Takashi Nagaoka; Mitsutaka Nemoto; Kohei Hanaoka; Hayato Kaida; Kazunari Ishii; Yuichi KimuraAnnals of nuclear medicine 34 2 102 - 107 2020年02月 [査読有り]
OBJECTIVE: This study aims to develop an algorithm named AutoRef to delineate a reference region for quantitative PET amyloid imaging. METHODS: AutoRef sets the reference region automatically using a distinguishing feature in the kinetics of reference region. This is reflected in the shapes of the tissue time activity curve. A statistical shape recognition algorithm of the gaussian mixture model is applied with considering spatial and temporal information on a reference region. We evaluate the BPND with manually set reference region and AutoRef using 86 cases (43 positive cases, 10 equivocal cases, and 33 negative cases) of dynamically scanned 11C-Pittsburgh Compound-B. RESULTS: From the Bland-Altman plot, the difference between two BPND is 0.099 ± 0.21 as standard deviation, and no significant systematic error is observed between the BPND with AutoRef and with manual definition of a reference region. Although a proportional error is detected, it is smaller than the 95% limits of agreement. Therefore, the proportional error is negligibly small. CONCLUSIONS: AutoRef presents the same performance as the manual definition of the reference region. Further, since AutoRef is more algorithmic than the ordinary manual definition of the reference region, there are few operator-oriented uncertainties in AutoRef. We thus conclude that AutoRef can be applied as an automatic delineating algorithm for the reference region in amyloid imaging. - Clustering-Based Data Reduction Algorithm with Simplified Reference Tissue Model to Generate Parametric Images in Amyloid ImagingTakahiro Yamada; Yuichi Kimura; Muneyuki Sakata; Takashi Nagaoka; Mitsutaka Nemoto; Kohei Hanaoka; Hayato Kaida; Kazunari IshiiJournal of Cerebral Blood Flow & Metabolism 39 1_suppl 591 - 592 2019年07月 [査読有り]
- Clinical Protocol to Quantify AMPA Receptor Using Novel 11C-labeled PET Tracer of K2Yuichi Kimura; Takahiro Yamada; Mai Hatano; Waki Nakajima; Tomoyuki Miyazaki; Takuya TakahashiJournal of Nuclear Medicine 60 supplement 1 325 - 325 2019年05月 [査読有り]
- Noise Reduction Algorithm for Amyloid Imaging to Preserve the Contrast Between Gray and White Matters Using Simplified Reference Tissue ModelTakahiro Yamada; Yuichi Kimura; Kohsuke Fujii; Shougo Watanabe; Takashi Nagaoka; Mitsutaka Nemoto; Kohei Hanaoka; Hayato Kaida; Chisa Hosokawa; Kazunari Ishii12th World Congress of the World Federation of Nuclear Medicine and Biology 2018年04月 [査読有り]
- Noise Reduction Algorithm for Amyloid Image Preserving Image Resolution. - Quantitative Evaluation Using Clinical Images -Takahiro Yamada; Yuichi Kimura; Kohsuke Fujii; Shougo Watanabe; Takashi Nagaoka; Mitsutaka Nemoto; Kohei Hanaoka; Hayato Kaida; Chisa Hosokawa; Kazunari IshiiHuman Amyloid Imaging 2018 80 - 81 2018年01月 [査読有り]
- Performance Evaluation of Kinetics-Based Denoising Algorithm for PET Amyloid ImagingKohsuke Fujii; Yuichi kimura; Takahiro YamadaInternational Forum on Medical Imaging in Asia 2017 2017年01月 [査読有り]
- Algorithm for automated delineation of reference regions using the pattern recognition scheme and kinetics of administered tracer. - Considering number of clustering -Takahiro Yamada; Yuichi Kimura; Takashi Nagaoka; Chisa Hosokawa; Takamichi Murakami; Kazunari IshiiHuman Amyloid Imaging 2017 36 - 37 2017年01月 [査読有り]
- Noise reduction algorithm for amyloid imaging without loss of image resolutionYuichi Kimura; Kohsuke Fujii; Takahiro Yamada; Takashi Nagaoka; Chisa Hosokawa; Takamichi Murakami; Kazunari IshiiHuman Amyloid Imaging 2017 54 - 54 2017年01月 [査読有り]
- Yuichi Kimura; Takahiro Yamada; Chisa Hosokawa; Shima Okada; Takashi Nagaoka; Kazunari IshiiJournal of Nuclear Medicine 57 supplement 2 311 - 311 2016年05月 [査読有り]
講演・口頭発表等
- 頭部・乳房専用 PET が有用であった中枢神経系原発悪性リンパ腫の一例 [通常講演]甲斐田 勇人; 花岡 宏平; 山田 穣; 任 誠雲; 山田 誉大; 小路田 泰之; 吉岡 宏真; 奥田 武司; 石井 一成第6回日本核医学会近畿支部会 2024年07月 口頭発表(一般) 大阪
- フラクタル画像で事前学習した Vision Transformer による胸部 CT 画像の肺炎診断堀 孝輔; 小塚 健倫; 山田 誉大; 木村 裕一; 石井 一成; 波部 斉第30回画像センシングシンポジウム(SSII2024) 2024年06月 口頭発表(一般) 横浜
- 溶骨性転移を考慮したU-Net 2.5次元処理によるFDG-PET/CT像上骨領域抽出 [通常講演]佐原 詢之佑; 根本 充貴; 甲斐田 勇人; 木村 裕一; 永岡 隆; 三上 勝大; 山田 誉大; 花岡 宏平; 槌谷 達也; 北島 一宏; 石井 一成第63回日本生体医工学会大会 2024年05月 口頭発表(一般) 鹿児島
- 3次元Pix2Pix画像モダリティ変換を用いたFDG-PET/CT像上の半教師あり胸部病変検出 [通常講演]大谷 和暉; 吉田 昂平; 根本 充貴; 甲斐田 勇人; 木村 裕一; 永岡 隆; 三上 勝大; 山田 誉大; 花岡 宏平; 槌谷 達也; 北島 一宏; 石井 一成医用画像研究会(MI) 2024年03月 口頭発表(一般) 沖縄
- PET/CT像上の上腹部臓器抽出および病変検出のマルチタスク学習に関する基礎検討 [通常講演]吉田 昂平; 根本 充貴; 大谷 和暉; 甲斐田 勇人; 木村 裕一; 永岡 隆; 三上 勝大; 山田 誉大; 花岡 宏平; 槌谷 達也; 北島 一宏; 石井 一成医用画像研究会(MI) 2024年03月 口頭発表(一般) 沖縄
- Unsupervised lung lesion detection on FDG-PET/CT images by deep image transformation-based 2.5-dimensional local anomaly detection. [通常講演]Arata Segawa; Mitsutaka Nemoto; Hayato Kaida; Yuichi Kimura; Takashi Nagaoka; Katsuhiro Mikami; Takahiro Yamada; Kohei Hanaoka; Tatsuya Tsuchitani; Kazuhiro Kitajima; Kazunari IshiiSPIE Medical Imaging 2024 2024年02月 ポスター発表 San Diego
- 血中マイクロRNAを用いた認知症疾患の層別化岩崎 千絵; 大森 智織; 須藤 祐子; 平賀 経太; 勝野 雅央; 山田 誉大; 石井 一成; 新飯田 俊平; 文堂 昌彦; 加藤 隆司; 中村 昭範第42回日本認知症学会学術集会 2023年11月 奈良
- AIで生成したアミロイド画像におけるスライス間の連続性の評価渡邉 綾; 山田 誉大; 永岡 隆; 根本 充貴; 渡部 浩司; 茨木 正信; 松原 佳亮; 花岡 宏平; 甲斐田 勇人; 石井 一成; 木村 裕一第63回日本核医学会学術総会 2023年11月 大阪
- 生成系AIを用いた読影医によるアノテーション不要なFDG-PET/CT像の肺病変強調手法大谷 和暉; 根本 充貴; 甲斐田 勇人; 瀬川 新; 木村 裕一; 永岡 隆; 三上 勝大; 山田 誉大; 花岡 宏平; 槌谷 達也; 北島 一宏; 石井 一成第63回日本核医学会学術総会 2023年11月 大阪
- 教師無し深層画像生成を用いたFDG-PET/CT像上の肺野内病変検出瀬川 新; 根本 充貴; 甲斐田 勇人; 木村 裕一; 永岡 隆; 三上 勝大; 山田 誉大; 花岡 宏平; 槌谷 達也; 北島 一宏; 石井 一成日本生体医工学シンポジウム2023 2023年09月 熊本
- Surmise of breast density using mammographic X-ray exposure conditionsMika Yamamuro; Takahiro Yamada; Yuichi Kimura; Kazunari Ishii; Yohan KondoInternational Congress for Radiation Research 2023 2023年08月 ポスター発表 モントリオール
- Identification of the uncompressed region on digital mammograms for volumetric breast density measurementYoshiyuki Asai; Takahiro Yamada; Yuichi Kimura; Kazunari Ishii; Yohan KondoInternational Congress for Radiation Research 2023 2023年08月 ポスター発表 モントリオール
- 乳房 PET 検査のための再構成手法花岡 宏平; 石川 大介; 山川 善之; 小林 哲哉; 大谷 篤; 熊川 志帆; 位藤 俊一; 菰池 佳史; 山田 誉大; 甲斐田 勇人; 石井 一成第5回日本核医学会近畿支部会 2023年07月 口頭発表(一般) 大阪
- 生成系AIを用いた読影医によるアノテーション不要なFDG-PET/CT像の肺病変強調手法大谷 和暉; 根本 充貴; 甲斐田 勇人; 瀬川 新; 木村 裕一; 永岡 隆; 三上 勝大; 山田 誉大; 花岡 宏平; 槌谷 達也; 北島 一宏; 石井 一成第5回日本核医学会近畿支部会 2023年07月 口頭発表(一般) 大阪
- アミロイド PET 撮像判断のための生成系 AI による FDG 画像からのアミロイド画像合成の検討本田 実沙; 山田 誉大; 永岡 隆; 三上 勝大; 根本 充貴; 花岡 宏平; 甲斐田 勇人; 石井 一成; 木村 裕一第5回日本核医学会近畿支部会 2023年07月 口頭発表(一般) 大阪
- U-Netを用いた全身CT像上骨領域抽出~ FDG-PET/CT像上がん骨転移検出AIに向けた検討 ~佐原 詢之佑; 根本 充貴; 甲斐田 勇人; 木村 裕一; 永岡 隆; 三上 勝大; 山田 誉大; 花岡 宏平; 槌谷 達也; 北島 一宏; 石井 一成和歌山県臨床工学技士会学術集会・第28回学術集会 2023年06月 口頭発表(一般) 和歌山
- Initial study of an algorithm for estimating the presence of amyloid accumulation from 18F-FDG PET images using machine learning [通常講演]Takahiro Yamada; Kohei Hanaoka; Hayato Kaida; Kazunari IshiiBrain & Brain PET 2023 2023年06月 ポスター発表 Brisbane
- Validation of a Glucose Metabolism to Tau Deposition Ratio Image in the Alzheimer’s Continuum [通常講演]Kazunari Ishii; Takahiro Yamada; Kohei Hanaoka; Hayato Kaida; Kenji Ishii; Takashi Kato; Akinori NakamuraSNMMI2023 Annual Meeting 2023年06月 ポスター発表
- Pix2Pix 画像スタイル変換を用いた教師無し異常検知による FDG-PET/CT 像上肺病変強調大谷 和暉; 根本 充貴; 甲斐田 勇人; 瀬川 新; 中前 由香子; 村中 皓紀; 吉田 昂平; 木村 裕一; 永岡 隆; 三上 勝大; 山田 誉大; 花岡 宏平; 槌谷 達也; 北島 一宏; 石井 一成第62回日本生体医工学会大会 2023年05月 口頭発表(一般) 名古屋
- U-Net を用いた CT 像上肝臓および腎臓領域の同時抽出~腹部 PET/CT 像上病変検出に向けた検討~吉田 昂平; 根本 充貴; 甲斐田 勇人; 瀬川 新; 中前 有香子; 村中 晧紀; 大谷 和暉木村; 永岡 隆; 山田 誉大; 花岡 宏平; 槌谷 達也; 北島 一宏; 石井 一成第62回日本生体医工学会大会 2023年05月 口頭発表(一般) 名古屋
- フラクタルデータベースを用いた胸部 CT 画像の肺炎識別器の事前学習吉岡 雄健; 舩津 朋和; 永岡 隆; 小塚 健倫; 根本 充貴; 山田 誉大; 木村 裕一; 石井 一成; 波部 斉情報処理学会 第85回全国大会 2023年03月 口頭発表(一般) 東京画像診断の多くは医師による目視で行われているが,病変部の見落としによる治療の遅れが問題となっている.そこで,医師の負担を減らし病気の早期発見,診断の補助を目的とし,機械学習を用いた胸部CT画像における肺炎識別を行う.機械学習で十分な精度を得るためには,十分な数の事前学習用データセットが必要になる.本研究では,自然画像を用いず数式により事前学習用画像を生成するフラクタルデータベースを用いて従来の自然画像を用いた事前学習と遜色ない結果が得られることを示す.
- Pre-training of Pneumonia Classifier for Chest CT images using Fractal DatabaseYuken Yoshioka; Daichi Ikefuji; Tomokazu Funatsu; Takashi Nagaoka; Takenori Kozuka; Mitsutaka Nemoto; Takahiro Yamada; Yuichi Kimura; Kazunari Ishii; Hitoshi HabeThe 29th International Workshop on Frontiers of Computer Vision 2023年02月 ポスター発表 ヨスAbstract. Recently, the number of images for pre-training of deep learning models has been increasing, and large-scale data sets contain inappropriate images such as ethically inappropriate images, copyright infringement, and labeling errors. A method to solve these is by using a fractal database that generates images by mathematical formulas without using natural images. Our goal is to show that the classification accuracy obtained by pre-training with fractal images is comparable to natural images. In the experiments, we compare the performance on the tasks to classify CT images of COVID-19 pneumonia and regular pneumonia.
- AI学習用のPET画像の定量評価のためのスライス方向の連続性を考慮した画像類似性尺度の導入渡邉 綾; 山田 誉大; 石井 一成; 木村 裕一第12回核医学画像解析研究会 2022年11月 口頭発表(一般) 秋田
- Fibrolamellar Hepatocellular Carcinoma の一例 [通常講演]山田 穣; 浜川 岳文; 藤谷 哲也; 浦瀬 篤史; 上月 瞭平; 小寺 卓; 鈴木 絢子; 平山 歩; 石田 愛; 若林 雄一; 関 紳一郎; 山田 誉大; 松久保 祐子; 任 誠雲; 兵頭 朋子; 甲斐田 勇人; 小塚 健倫; 鶴﨑 正勝; 石井 一成第332回日本医学放射線学会関西地方会 2022年10月 口頭発表(一般)
- Evaluation on Applicability of Generative Adversarial Network to Synthesize Training Images for AI-Based Computer Aided Diagnostic Algorithm to DementiaAya Watanabe; Takahiro Yamada; Takashi Nagaoka; Mitsutaka Nemoto; Hiroshi Watabe; Masanobu Ibaraki; Keisuke Matsubara; Kohei Hanaoka; Kazunari Ishii; Yuichi Kimura13th World Congress of the World Federation of Nuclear Medicine and Biology 2022年09月 ポスター発表 京都
- Study for detecting pulmonary nodules on FDG-PET/CT images with training small datasetArata Segawa; Mitsutaka Nemoto; Hayato Kaida; Yuichi Kimura; Takashi Nagaoka; Haruno Yamaguti; Yukako Nakamae; Takahiro Yamada; Kohei Hanaoka; Kazuhiro Kitajima; Tatsuya Tsuchitani; Kazunari Ishii13th World Congress of the World Federation of Nuclear Medicine and Biology 2022年09月 ポスター発表 京都
- Detection of Bone Metastasis on FDG-PET/CT Images using Multi-step Anomaly Voxel Detection and Local Patch analysis with Unsupervised Deep Features and Image TexturesHaruno Yamaguchi; Mitsutaka Nemoto; Hayato Kaida; Yuichi Kimura; Takashi Nagaoka; Takahiro Yamada; Kohei Hanaoka; Kazuhiro Kitajima; Tatsuya Tsuchitani; Kazunari Ishii13th World Congress of the World Federation of Nuclear Medicine and Biology 2022年09月 ポスター発表 京都
- FDG-PET/CT で集積を認めた膵臓アミロイドーシスの⼀例 [通常講演]甲斐田勇人; 山田穣; 関紳一郎; 松久保祐子; 任誠雲; 花岡宏平; 山田誉大; 兵頭朋子; 鶴崎正勝; 細野眞; 石井一成第4回日本核医学会近畿支部会 2022年07月 口頭発表(一般) 大阪
- 深層画像生成技術を用いた FDG-PET/CT 像異常検知による病変強調 [通常講演]瀬川新; 根本充貴; 甲斐田勇人; 山口明乃; 木村裕一; 永岡隆; 山田誉大; 北島一宏; 石井一成第61回日本生体医工学会大会 2022年06月 口頭発表(一般) 新潟
- 画素異常検知と深層教師なし特徴抽出による FDG-PET/CT 像上がん骨転移検出 [通常講演]山口明乃; 根本充貴; 甲斐田勇人; 木村裕一; 永岡隆; 山田誉大; 花岡宏平; 北島一宏; 槌谷達也; 石井一成第61回日本生体医工学会大会 2022年06月 ポスター発表
- 敵対的生成ネットワークで合成した PET 画像における定量性に対する学習条件の影響 [通常講演]渡邉綾; 山田誉大; 石井一成; 木村裕一第61回日本生体医工学会大会 2022年06月 口頭発表(一般) 新潟
- Comparison between the μ-maps of different PET tracers: 18F-FDG and 18F-flutemetamol, generated by the attenuation correction method without external radiation source [通常講演]Takahiro Yamada; Kohei Hanaoka; Yoshiyuki Yamakawa; Suzuka Minagawa; Atsushi Ohtani; Tetsuro Mizuta; Hayato Kaida; Kazunari IshiiSNMMI2022 Annual Meeting 2022年06月 口頭発表(一般) Vancouver
- Robustness of a U-net model for different image processing types in segmentation of the mammary gland regionMika Yamamuro; Yoshiyuki Asai; Naomi Hashimoto; Nao Yasuda; Hiroto Kimura; Takahiro Yamada; Mitsutaka Nemoto; Yuichi Kimura; Hisashi Handa; Hisashi Yoshida; Koji Abe; Masahiro Tada; Hitoshi Habe; Takashi Nagaoka; Seiun Nin; Kazunari Ishii; Yongbum Lee16th International Workshop on Breast Imaging 2022年05月 ポスター発表 Leuven, BelgiumMany studies have assessed breast density in clinical practice. However, calculation of breast density requires segmentation of the mammary gland region, and deep learning has only recently been applied. Thus, the robustness of the deep learning model for different image processing types has not yet been reported. We investigated the accuracy of segmentation of the U-net for mammograms made with variousimage processing types. We used 478 mediolateral oblique view mammograms. The mammograms were divided into 390 training images and 88 testing images. The ground truth of the mammary gland region made by mammary experts was used for the training and testing datasets. Four types of image processing (Types 1–4) were applied to the testing images to compare breast density in the segmented mammary gland regions with that of ground truths. The shape agreement between ground truth and the segmented mammary gland region by U-net of Types 1–4 was assessed using the Dice coefficient, and the equivalence or compatibility of breast density with ground truth was assessed by Bland-Altman analysis. The mean Dice coefficients between the ground truth and U-net were 0.952, 0.948, 0.948, and 0.947 for Types 1, 2, 3, and 4, respectively. By Bland-Altman analysis, the equivalence of breast density between ground truth and U-net was confirmed for Types 1 and 2, and compatibility was confirmed for Types 3 and 4. We concluded that the robustness of the U-net for segmenting the mammary gland region was confirmed for different image processing types.
- 敵対的生成ネットワークで合成したPET画像における定量性の検討 [通常講演]渡邉 綾; 木村 裕一; 山田 誉大; 渡辺 翔吾; 永岡 隆; 根本 充貴; 宮崎 晃一; 花岡 宏平; 甲斐田 勇人; 石井 一成第61回日本核医学会学術総会 2021年11月 口頭発表(一般) 名古屋
- 食道癌の化学放射線療法に対するDeauville scoreでの予後検討 [通常講演]甲斐田 勇人; 稲田 正浩; 山田 穣; 松久保 祐子; 任 誠雲; 花岡 宏平; 山田 誉大; 細野 眞; 西村 恭昌; 石井 一成第61回日本核医学会学術総会 2021年11月 口頭発表(一般) 名古屋
- 2種類の異常検知を用いたFDG-PET/CT像上のがん骨転移病変自動検出 [通常講演]山口 明乃; 根本 充貴; 甲斐田 勇人; 木村 裕一; 永岡 隆; 山田 誉大; 花岡 宏平; 北島 一宏; 槌谷 達也; 石井 一成第61回日本核医学会学術総会 2021年11月 ポスター発表 名古屋
- AIの医用画像応用と最近の動向 [通常講演]山田 誉大第20回近畿放射線医学フォーラム 2021年10月 公開講演,セミナー,チュートリアル,講習,講義等
- 画像処理の違いがディープラーニングによる乳腺領域自動抽出及び乳腺密度算出精度に及ぼす影響 [通常講演]山室 美佳; 浅井 義行; 橋本 直美; 安田 奈生; 木村 浩都; 任 誠雲; 石井 一成; 山田 誉大; 根本 充貴; 木村 裕一; 半田 久志; 吉田 久; 阿部 孝司; 多田 昌裕; 波部 斉; 永岡 隆; 李 鎔範第191回医用画像情報学会 2021年10月 口頭発表(一般)
- 11C-methonine PET/CTで高集積を認めた傍鞍部肉芽腫の一例 [通常講演]甲斐田 勇人; 奥田 武司; 山田 穣; 関 紳一郎; 松久保 祐子; 任 誠雲; 花岡 宏平; 山田 誉大; 兵頭 朋子; 鶴崎 正勝; 細野 眞; 石井 一成第3回日本核医学会近畿支部会 2021年07月 口頭発表(一般) 京都
- 新型コロナウィルス肺炎診断支援臨床実装システムの開発~初期報告 [通常講演]小塚 健倫; 永岡 隆; 石井 一成; 山田 誉大; 根本 充貴; 半田 久志; 阿部 孝司; 波部 斉; 多田 昌裕; 吉田 久; 木村 裕一第328回日本医学放射線学会関西地方会 2021年06月 口頭発表(一般) 大阪
- CT画像上のテクスチャ解析によるESWL治療可能性な尿路結石の自動認識中前 有香子; 根本 充貴; 木村 裕一; 永岡 隆; 山田 誉大; 出口 龍良; 山下 真平; 柑本 康夫; 原 勲第60回日本生体医工学会大会 2021年06月 口頭発表(一般)
- 2種類の教師無しAI異常検知処理を用いたFDG-PET/CT像上のがん骨転移検出 [通常講演]山口 明乃; 根本 充貴; 甲斐田 勇人; 木村 裕一; 永岡 隆; 山田 誉大; 花岡 宏平; 北島 一宏; 槌谷 達也; 石井 一成第60回日本生体医工学会大会 2021年06月 口頭発表(一般) 京都
- 認知症診断AIアルゴリズム学習のためのCycleGANで合成したPET画像の定量的検証 [通常講演]渡邉 綾; 山田 誉大; 渡辺 翔吾; 永岡 隆根本; 充; 宮崎 晃一; 花岡 宏平; 甲斐田 勇人; 石井 一成; 木村 裕一第60回日本生体医工学会大会 2021年06月 口頭発表(一般) 京都
- CT画像からのCOVID性肺炎診断支援システムKindAI-COVIDの開発 [通常講演]永岡 隆; 小塚 健倫; 根本 充貴; 波部 斉; 山田 誉大; 吉田 久; 木村 裕一; 石井 一成第60回日本生体医工学会大会 2021年06月 口頭発表(一般) 京都
- 有DESH所見患者の脳ドパミントランスポーターシンチグラフィ所見 [通常講演]宮崎 晃一; 山田 誉大; 花岡 宏平; 甲斐田 勇人; 石井 一成第22回日本正常圧水頭症学会 2021年02月 口頭発表(一般)
- How to select training data to segment mammary gland region using a deep-learning approach for reliable individualized screening mammography [通常講演]Mika Yamamuro; Yoshiyuki Asai; Naomi Hashimoto; Nao Yasuda; Takahiro Yamada; Mitsutaka Nemoto; Yuichi Kimura; Hisashi Handa; Hisashi Yoshida; Koji Abe; Masahiro Tada; Hitoshi Habe; Takashi Nagaoka; Yoshiaki Ozaki; Seiun Nin; Kazunari Ishii; Yongbum LeeSPIE Medical Imaging 2021年02月 ポスター発表 オンライン SPIE Medical Imaging
- Detection of cerebral aneurysms on MR angiography using generated features by unsupervised deep learning for multiple 2.5-dimensional images [通常講演]Kazuyuki Ushifusa; Mitsutaka Nemoto; Yuichi Kimura; Takashi Nagaoka; Takahiro Yamada; Naoto HayashiInternational Forum on Medical Imaging in Asia (IFMIA) 2021 2021年01月 ポスター発表 台北 International Forum on Medical Imaging in Asia (IFMIA)
- Deep learning segmentationでの複数の放射線技師による教師データ作成の有用性 [通常講演]山室 美佳; 浅井 義行; 橋本 直美; 安田 奈生; 尾崎 吉明; 任 誠雲; 石井 一成; 山田 誉大; 根本 充貴; 木村 裕一; 吉田 久; 半田 久志; 李 鎔範第30回日本乳癌検診学会学術総会 2020年11月
- Deep learningを用いた非高濃度乳房に対する乳腺領域の自動抽出 [通常講演]山室 美佳; 浅井 義行; 橋本 直美; 安田 奈生; 尾崎 吉明; 任 誠雲; 石井 一成; 山田 誉大; 根本 充貴; 木村 裕一; 吉田 久; 半田 久志; 李 鎔範第30回日本乳癌検診学会学術総会 2020年11月
- 認知症自動診断AIアルゴリズム学習のための深層学習により加増したアミロイド画像の検証 [通常講演]渡邉 綾; 木村 裕一; 山田 誉大; 渡辺 翔吾; 永岡 隆; 根本 充貴; 宮崎 晃一; 花岡 宏平; 甲斐田 勇人; 石井 一成第60回日本核医学会学術総会 2020年11月 口頭発表(一般)
- One-class SVMを用いた異常検知によるPET/CT上の骨転移病変自動検出 [通常講演]田中 敦子; 根本 充貴; 甲斐田 勇人; 木村 裕一; 永岡 隆; 牛房 和之; 山田 誉大; 花岡 宏平; 北島 一宏; 槌谷 達也; 石井 一成第60回日本核医学会学術総会 2020年11月 口頭発表(一般)
- Quantification of AMPA Receptors Using 11C-K2 Considering White Matter as Reference Region [通常講演]Yuichi Kimura; Takahiro Yamada; Mai Hatano; Waki Nakajima; Tomoyuki Miyazaki; Takuya TakahashiSNMMI 2020 Annual Meeting 2020年07月 口頭発表(一般)
- A generalized image feature generation based on unsupervised deep learning with small scale normal dataset [通常講演]K.Ushifusa; M.Nemoto; Y.Kimura; T.Nagaoka; T.Yamada; A.Tanaka; N.HayashiCARS 2020 (Computer Assisted Radiology and Surgery) 2020年06月 ポスター発表 Munich
- Automatic detection of cervical and thoracic lesions on FDG-PET/CT by organ specific one-class SVMs [通常講演]A.Tanaka; M.Nemoto; H.Kaida; Y.Kimura; T.Nagaoka; T.Yamada; K.Ushifusa; K.Hanaoka; K.Kitajima; T.Tsuchitani; K.IshiiCARS 2020 (Computer Assisted Radiology and Surgery) 2020年06月 ポスター発表 Munich
- One-class SVM を用いた病変強調によるFDG-PET/CT上の頸胸部AI異常検知に基づくPET/CT上の頸胸部病変の自動認識 [通常講演]田中 敦子; 根本 充貴; 木村 裕一; 永岡 隆; 山田 誉大; 牛房 和之; 花岡 宏平; 北島 一宏; 槌谷 達也; 石井 一成医用画像研究会 2020年01月 口頭発表(一般) 那覇 電子情報通信学会
- 正常データセットの教師なし学習に基づく病変検出支援システム画像特徴量の汎用的生成に関する検討 – 小規模なデータセットを用いた特徴量生成の実験的検証 - [通常講演]牛房 和之; 根本 充貴; 木村 裕一; 永岡 隆; 山田 誉大; 田中 敦子; 林 直人医用画像研究会 2020年01月 口頭発表(一般) 那覇 電子情報通信学会
- 認知症自動診断AIアルゴリズム学習のための深層学習によるアミロイド画像の加増手法の検討 [通常講演]渡邉 綾; 山田 誉大; 永岡 隆; 根本 充貴; 花岡 宏平; 甲斐田 勇人; 石井 一成; 木村 裕一第59回日本核医学会学術総会 2019年11月 ポスター発表 松山 日本核医学会
- AI異常検知に基づくPET/CT上の頸胸部病変の自動認識 [通常講演]田中 敦子; 根本 充貴; 甲斐田 勇人; 木村 裕一; 山田 誉大; 牛房 和之; 花岡 宏平; 北島 一宏; 槌谷 達也; 石井 一成第59回日本核医学会学術総会 2019年11月 口頭発表(一般) 松山 日本核医学会
- 11C標識新規トレーサーによるAMPA受容体の定量画像化のための臨床プロトコル [通常講演]木村 裕一; 山田 誉大; 波多野 麻衣; 中島 和希; 宮崎 智之; 高橋 琢哉第59回日本核医学会学術総会 2019年11月 口頭発表(一般) 松山 日本核医学会
- Weighting Function for Kinetics-Based Noise Reduction in PET Amyloid Imaging [通常講演]Takahiro Yamada; Shougo Watanabe; Takashi Nagaoka; Mitsutaka Nemoto; Kohei Hanaoka; Hayato Kaida; Kazunari Ishii; Yuichi Kimura日本生体医工学シンポジウム2019 2019年09月 ポスター発表 徳島 日本生体医工学会
- Application of least-squares cubic linear regression to the Logan graphical analysis to reduce the underestimation of BPND for neuroreceptor parametric imaging in positron emission tomography studies [通常講演]Paulus Kapundja Shigwedha; Takahiro Yamada; Kohei Hanaoka; Kazunari Ishii; Yuichi Kimura; Yutaka Fukuoka日本生体医工学シンポジウム2019 2019年09月 ポスター発表 徳島 日本生体医工学会
- 病変検出アルゴリズムにおける局所画像特徴量の汎用的な自動生成-健常データのみの学習による特徴量生成の検討- [通常講演]牛房 和之; 根本 充貴; 木村 裕一; 永岡 隆; 山田 誉大; 林 直人第38回日本医用画像工学大会 2019年07月 口頭発表(一般) 奈良 日本医用画像工学会
- Clustering-Based Data Reduction Algorithm with Simplified Reference Tissue Model to Generate Parametric Images in Amyloid Imaging [通常講演]Takahiro Yamada; Yuichi Kimura; Muneyuki Sakata; Takashi Nagaoka; Mitsutaka Nemoto; Kohei Hanaoka; Hayato Kaida; Kazunari IshiiBrain & Brain PET 2019 2019年07月 ポスター発表 横浜 The International Symposium on Cerebral Blood Flow, Metabolism and Function
- Clinical Protocol to Quantify AMPA Receptor Using Novel 11C-labeled PET Tracer of K2 [通常講演]Yuichi Kimura; Takahiro Yamada; Mai Hatano; Waki Nakajima; Tomoyuki Miyazaki; Takuya TakahashiSNMMI 2019 Annual Meeting 2019年06月 口頭発表(一般) Anaheim SNMMI
- AI異常検知を用いたPET/CT上の頸胸部原発性・転移性病変の検出 [通常講演]田中 敦子; 根本 充貴; 甲斐田 勇人; 木村 裕一; 永岡 隆; 山田 誉大; 花岡 宏平; 石井 一成第58回日本生体医工学会大会 2019年06月 ポスター発表 那覇 日本生体医工学会
- 深層畳み込みオートエンコーダを用いた健常画像データからの局所画像特徴量の自動生成手法の提案 [通常講演]牛房 和之; 根本 充貴; 木村 裕一; 永岡 隆; 山田 誉大; 林 直人第58回日本生体医工学会大会 2019年06月 ポスター発表 那覇 日本生体医工学会
- 空間分解能を温存したノイズ低減アルゴリズムを用いたアミロイドイメージングの動態撮像の短時間化 [通常講演]山田 誉大; 木村 裕一; 北西 巧; 坂田 宗之; 根本 充貴; 永岡 隆; 花岡 宏平; 甲斐田 勇人; 石井 一成第8回核医学画像解析研究会 2018年12月 口頭発表(一般) 秋田 日本核医学会 核医学理工分科会
- SRTM法を用いたアミロイドイメージングに対する空間分解能を損なわないノイズ低減アルゴリズムの検討 [通常講演]山田 誉大; 木村 裕一; 根本 充貴; 永岡 隆; 花岡 宏平; 甲斐田 勇人; 石井 一成第58回日本核医学会学術総会 2018年11月 口頭発表(一般) 那覇 日本核医学会
- AI異常検知を用いたPET/CT上の頸胸部原発性・転移性病変の検出 [通常講演]根本 充貴; 甲斐田 勇人; 田中 敦子; 牛房 和之; 山田 誉大; 木村 裕一; 花岡 宏平; 石井 一成第58回日本核医学会学術総会 2018年11月 口頭発表(一般) 那覇 日本核医学会
- 深層学習を用いたDatSCANにおけるパーキンソン症候群の画像診断 [通常講演]渡辺 翔吾; 木村 裕一; 三品 雅洋; 石井 一成; 山田 誉大; 杉本 直三第58回日本核医学会学術総会 2018年11月 口頭発表(一般) 那覇 日本核医学会
- PETアミロイドイメージングにおける臨床画像に対して空間分解能を損なわないノイズ低減アルゴリズムの評価 [通常講演]山田 誉大; 木村 裕一; 藤井 康介; 渡辺 翔吾; 永岡 隆; 根本 充貴; 花岡 宏平; 甲斐田 勇人; 石井 一成第57回日本生体医工学会大会 2018年06月 口頭発表(一般) 札幌 日本生体医工学会
- Noise Reduction Algorithm for Amyloid Imaging to Preserve the Contrast Between Gray and White Matters Using Simplified Reference Tissue Model [通常講演]Takahiro Yamada; Yuichi Kimura; Kohsuke Fujii; Shougo Watanabe; Takashi Nagaoka; Mitsutaka Nemoto; Kohei Hanaoka; Hayato Kaida; Chisa Hosokawa; Kazunari Ishii12th World Congress of the World Federation of Nuclear Medicine and Biology 2018年04月 ポスター発表 Melbourne World Federation of Nuclear Medicine and Biology
- Noise Reduction Algorithm for Amyloid Image Preserving Image Resolution. - Quantitative Evaluation Using Clinical Images - [通常講演]Takahiro Yamada; Yuichi Kimura; Kohsuke Fujii; Shougo Watanabe; Takashi Nagaoka; Mitsutaka Nemoto; Kohei Hanaoka; Hayato Kaida; Chisa Hosokawa; Kazunari IshiiHuman Amyloid Imaging 2018 2018年01月 ポスター発表 Miami Human Amyloid Imaging
- PETアミロイドイメージングによるアルツハイマー病の早期画像診断のための雑音除去アルゴリズムの性能評価 [通常講演]山田 誉大; 藤井 康介; 渡辺 翔吾; 木村 裕一第7回核医学画像解析研究会 2017年11月 口頭発表(一般) 福島 日本核医学会 核医学理工分科会
- 動態に基づいたアミロイドイメージング雑音除去アルゴリズムの性能評価 [通常講演]山田 誉大; 木村 裕一; 藤井 康介; 渡辺 翔吾; 永岡 隆; 花岡 宏平; 細川 知紗; 石井 一成; 村上 卓道第57回日本核医学会学術総会 2017年10月 口頭発表(一般) 横浜 日本核医学会
- 機械学習によるDatSCANのPD・健常の自動鑑別 [通常講演]渡辺 翔吾; 木村 裕一; 根本 充貴; 山田 誉大; 藤井 康介; 林 俊行; 三品 雅洋第57回日本核医学会学術総会 2017年10月 口頭発表(一般) 横浜 日本核医学会
- PETアミロイドイメージングにおける参照領域自動設定アルゴリズムの開発 –時間情報及びクラスタ数の再検討- [通常講演]山田 誉大; 木村 裕一; 永岡 隆; 花岡 宏平; 細川 知紗; 石井 一成、第56回日本生体医工学大会 2017年05月 口頭発表(一般) 仙台 日本生体医工学会
- Performance Evaluation of Kinetics-Based Denoising Algorithm for PET Amyloid Imaging [通常講演]Kohsuke Fujii; Yuichi kimura; Takahiro YamadaIFMIA2017 2017年01月 ポスター発表 那覇 IFMIA
- Algorithm for automated delineation of reference regions using the pattern recognition scheme and kinetics of administered tracer. - Considering number of clustering - [通常講演]Takahiro Yamada; Yuichi Kimura; Takashi Nagaoka; Chisa Hosokawa; Takamichi Murakami; Kazunari IshiiHuman Amyloid Imaging 2017 2017年01月 ポスター発表 Miami Human Amyloid Imaging
- Noise reduction algorithm for amyloid imaging without loss of image resolution [通常講演]Yuichi Kimura; Kohsuke Fujii; Takahiro Yamada; Takashi Nagaoka; Chisa Hosokawa; Takamichi Murakami; Kazunari IshiiHuman Amyloid Imaging 2017 2017年01月 ポスター発表 Miami Human Amyloid Imaging
- 臨床データを用いたPETアミロイドイメージングにおける参照領域自動設定法の性能評価 [通常講演]山田 誉大; 木村 裕一; 永岡 隆; 岡田 志麻; 細川 知紗; 石井 一成日本生体医工学シンポジウム2016 2016年09月 ポスター発表 旭川 日本生体医工学会
- Deliniation Algorithm on Reference Region for Amyloid Imaging Using a Time History of Radioactivity [通常講演]Yuichi Kimura; Takahiro Yamada; Chisa Hosokawa; Shima Okada; Takashi Nagaoka; Kazunari IshiiSNMMI 2016 Annual Meeting 2016年06月 ポスター発表 San Diego SNMMI
- 薬剤動態に基づいたPETアミロイドイメージングの参照領域自動設定法の精度検証 [通常講演]山田 誉大; 木村 裕一; 永岡 隆; 岡田 志麻; 細川 知紗; 石井 一成第55回日本生体医工学会大会 2016年04月 口頭発表(一般) 富山 日本生体医工学会
- パターン認識に基づいた薬剤動態の判別によるPETアミロイドイメージングの参照領域自動設定法 -臨床データに基づいた性能評価- [通常講演]山田 誉大; 木村 裕一; 永岡 隆; 岡田 志麻; 細川 知紗; 石井 一成医用画像研究会 2016年01月 ポスター発表 那覇 電子情報通信学会
- パターン認識に基づいた薬剤動態の判別によるPETアミロイドイメージングの参照領域の頑健性山田誉大; 木村裕一; 永岡隆; 岡田志麻; 細川知紗; 石井一成ジョイント研究会 2015年11月 口頭発表(一般) 大阪
- パターン認識に基づいた薬剤動態の判別によるPETアミロイドイメージングの参照領域自動設定法 [通常講演]山田 誉大; 木村 裕一; 永岡 隆; 岡田 志麻; 細川 知紗; 石井 一成医用画像研究会 2015年11月 口頭発表(一般) 奈良 電子情報通信学会
- アミロイドイメージングにおける組織放射能時間曲線に対するパターン認識による参照領域設定アルゴリズム [通常講演]木村 裕一; 山田 誉大; 永岡 隆; 岡田 志麻; 細川 知紗; 石井 一成第55回日本核医学会学術総会 2015年11月 口頭発表(一般) 東京 日本核医学会
- PETアミロイドイメージングにおける参照領域自動設定アルゴリズムの開発 [通常講演]山田 誉大; 木村 裕一; 永岡 隆; 岡田 志麻; 細川 知紗; 石井 一成日本生体医工学シンポジウム2015 2015年09月 ポスター発表 岡山 日本生体医工学会
共同研究・競争的資金等の研究課題
- 定性画像のその先へ、定量性を兼ね備えた発症率が低い認知症疾患群のPET画像生成手法日本学術振興会:科学研究費助成事業研究期間 : 2023年04月 -2026年03月代表者 : 山田 誉大
- 脳FDG-PETによる脳アミロイド沈着予測株式会社日本メジフィジックス:2022年度研究者主導臨床研究支援プログラム研究助成研究期間 : 2023年 -2025年代表者 : 石井 一成; 山田 誉大
- 非COVID-19肺炎の胸部CTの特徴的所見近畿大学:近畿大学学内助成(令和3年度オール近大新型コロナウイルス感染症対策支援プロジェクト)研究期間 : 2021年 -2023年代表者 : 小塚 健倫; 山田 誉大; 石井 一成
- AI によるコロナウイルス肺炎診断支援システム(KindAI COVID19)の臨床実験開発近畿大学:近畿大学学内助成(令和2年度オール近大新型コロナウイルス感染症対策支援プロジェクト)研究期間 : 2020年 -2022年代表者 : 木村 裕一; 永岡 隆; 石井 一成; 小塚 健倫; 山田 誉大; 根本 充貴; 半田 久志; 阿部 孝司; 波部 斉; 多田 昌裕; 吉田 久