Grading of Malignant Potential in Brain Tumors using Proton MR Spectroscopy

科学研究費補助金研究成果報告書研究種目: 基盤研究(C)研究期間: 1998～1999課題番号: 10671294研究代表者: 椎野 顯彦（滋賀医科大学・医学部・講師

Unsupervised MRI Super-Resolution Using Deep External Learning and Guided Residual Dense Network with Multimodal Image Priors

Deep learning techniques have led to state-of-the-art single image super-resolution (SISR) with natural images. Pairs of high-resolution (HR) and low-resolution (LR) images are used to train the deep learning model (mapping function). These techniques have also been applied to medical image super-resolution (SR). Compared with natural images, medical images have several unique characteristics. First, there are no HR images for training in real clinical applications because of the limitations of imaging systems and clinical requirements. Second, other modal HR images are available (e.g., HR T1-weighted images are available for enhancing LR T2-weighted images). In this paper, we propose an unsupervised SISR technique based on simple prior knowledge of the human anatomy; this technique does not require HR images for training. Furthermore, we present a guided residual dense network, which incorporates a residual dense network with a guided deep convolutional neural network for enhancing the resolution of LR images by referring to different HR images of the same subject. Experiments on a publicly available brain MRI database showed that our proposed method achieves better performance than the state-of-the-art methods.Comment: 10 pages, 3 figure

Machine learning of brain structural biomarkers for Alzheimer\u27s disease (AD) diagnosis, prediction of disease progression, and amyloid beta deposition in the Japanese population

Introduction:We developed machine learning (ML) designed to analyze structural brain magnetic resonance imaging (MRI), and trained it on the Alzheimer\u27s Disease Neuroimaging Initiative (ADNI) database. In this study, we verified its utility in the Japanese population.Methods:A total of 535 participants were enrolled from the Japanese ADNI database, including 148 AD, 152 normal, and 235 mild cognitive impairment (MCI). Probability of AD was expressed as AD likelihood scores (ADLS).Results:The accuracy of AD diagnosis was 88.0% to 91.2%. The accuracy of predicting the disease progression in non-dementia participants over a 3-year observation was 76.0% to 79.3%. More than 90% of the participants with low ADLS did not progress to AD within 3 years. In the amyloid positron emission tomography (PET)-positive MCI, the hazard ratio of progression was 2.39 with low ADLS, and 5.77 with high ADLS. When high ADLS was defined as N+ and Pittsburgh compound B (PiB) PET positivity was defined as A+, the time to disease progression for 50% of MCI participants was 23.7 months in A+N+, whereas it was 52.3 months in A+N-.Conclusion:These results support the feasibility of our ML for the diagnosis of AD and prediction of the disease progression

Research and Development of quantitative CBF measurement with MRI

科学研究費補助金研究成果報告書研究種目: 基盤研究(C)研究期間: 2002～2004課題番号: 14571309研究代表者: 椎野 顯彦（滋賀医科大学・医学部・教授）研究分担者: 犬伏 俊郎（滋賀医科大学・MR医学総合研究センター・教授

Oxygen Metabolic Mapping by MRI-BOLD technique

科学研究費補助金研究成果報告書研究種目: 基礎研究(C)研究期間: 2003～2005課題番号: 15591519研究代表者: 松田 昌之（滋賀医科大学・医学部・教授）研究分担者: 椎野 顯彦（滋賀医科大学・医学部・講師

NMR-spectrophotometer integrated device to monitor neuronal activity

科学研究費補助金研究成果報告書研究種目: 基盤研究(B)研究期間: 1999～2000課題番号: 11694262研究代表者: 犬伏 俊郎（滋賀医科大学・分子神経科学研究センター・教授）研究分担者: 森川 茂広（滋賀医科大学・分子神経科学研究センター・助教授）研究分担者: 椎野 顯彦（滋賀医科大学・医学部・講師

Device development to measure cellular activity in brain

科学研究費補助金研究成果報告書研究種目: 国際学術研究研究期間: 1997～1998課題番号: 09044287研究代表者: 犬伏 俊郎（滋賀医科大学・分子神経生物学研究センター・教授）研究分担者: 森川 茂広（滋賀医科大学・分子神経生物学研究センター・助教授）研究分担者: 椎野 顯彦（滋賀医科大学・医学部・助手

Non-invasive analyses of brain metabolic function in dementia

科学研究費補助金研究成果報告書研究種目: 基盤研究(A)研究期間: 1998～2000課題番号: 10358017研究代表者: 犬伏 俊郎（滋賀医科大学・分子神経科学研究センター・教授）研究分担者: 森川 茂広（滋賀医科大学・分子神経科学研究センター・助教授）研究分担者: 椎野 顯彦（滋賀医科大学・医学部・助手

Machine Learning for Diagnosis of AD and Prediction of MCI Progression From Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation.

Background:With the growing momentum for the adoption of machine learning (ML) in medical field, it is likely that reliance on ML for imaging will become routine over the next few years. We have developed a software named BAAD, which uses ML algorithms for the diagnosis of Alzheimer\u27s disease (AD) and prediction of mild cognitive impairment (MCI) progression.Methods:We constructed an algorithm by combining a support vector machine (SVM) to classify and a voxel-based morphometry (VBM) to reduce concerned variables. We grouped progressive MCI and AD as an AD spectrum and trained SVM according to this classification. We randomly selected half from the total 1,314 subjects of AD neuroimaging Initiative (ADNI) from North America for SVM training, and the remaining half were used for validation to fine-tune the model hyperparameters. We created two types of SVMs, one based solely on the brain structure (SVMst), and the other based on both the brain structure and Mini-Mental State Examination score (SVMcog). We compared the model performance with two expert neuroradiologists, and further evaluated it in test datasets involving 519, 592, 69, and 128 subjects from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Aging (AIBL), Japanese ADNI, the Minimal Interval Resonance Imaging in AD (MIDIAD) and the Open Access Series of Imaging Studies (OASIS), respectively.Results:BAAD\u27s SVMs outperformed radiologists for AD diagnosis in a structural magnetic resonance imaging review. The accuracy of the two radiologists was 57.5 and 70.0%, respectively, whereas, that of the SVMst was 90.5%. The diagnostic accuracy of the SVMst and SVMcog in the test datasets ranged from 88.0 to 97.1% and 92.5 to 100%, respectively. The prediction accuracy for MCI progression was 83.0% in SVMst and 85.0% in SVMcog. In the AD spectrum classified by SVMst, 87.1% of the subjects were Aβ positive according to an AV-45 positron emission tomography. Similarly, among MCI patients classified for the AD spectrum, 89.5% of the subjects progressed to AD.Conclusion:Our ML has shown high performance in AD diagnosis and prediction of MCI progression. It outperformed expert radiologists, and is expected to provide support in clinical practice

Realization of Real-Time MR Image Guided Surgery Combined with High-Resolution 3D Image Navigation

科学研究費補助金研究成果報告書研究種目: 基盤研究(C)研究期間: 2001～2002課題番号: 13671227研究代表者: 森川 茂廣（滋賀医科大学・分子神経科学研究センター・助教授）研究分担者: 犬伏 俊郎（滋賀医科大学・分子神経科学研究センター・教授）研究分担者: 椎野 顯彦（滋賀医科大学・医学部・講師）研究分担者: 来見 良誠（滋賀医科大学・医学部・講師