The prevalence of neurodegenerative diseases is increasing as human longevity increases. The objective biomarkers that enable the staging and early diagnosis of neurodegenerative diseases are eagerly anticipated. It h...The prevalence of neurodegenerative diseases is increasing as human longevity increases. The objective biomarkers that enable the staging and early diagnosis of neurodegenerative diseases are eagerly anticipated. It has recently become possible to determine pathological changes in the brain without autopsy with the advancement of diffusion magnetic resonance imaging techniques. Diffusion magnetic resonance imaging is a robust tool used to evaluate brain microstructural complexity and integrity, axonal order, density, and myelination via the micron-scale displacement of water molecules diffusing in tissues. Diffusion tensor imaging, a type of diffusion magnetic resonance imaging technique is widely utilized in clinical and research settings;however, it has several limitations. To overcome these limitations, cutting-edge diffusion magnetic resonance imaging techniques, such as diffusional kurtosis imaging, neurite orientation dispersion and density imaging, and free water imaging, have been recently proposed and applied to evaluate the pathology of neurodegenerative diseases. This review focused on the main applications, findings, and future directions of advanced diffusion magnetic resonance imaging techniques in patients with Alzheimer's and Parkinson's diseases, the first and second most common neurodegenerative diseases, respectively.展开更多
Diffusion tensor tractography was used to evaluate whether diffusion metrics in the nigrostriatal pathway could diagnose Parkinson disease. Diffusion tensor imaging was performed on 30 patients with Parkinson disease ...Diffusion tensor tractography was used to evaluate whether diffusion metrics in the nigrostriatal pathway could diagnose Parkinson disease. Diffusion tensor imaging was performed on 30 patients with Parkinson disease and 32 healthy controls by using a 3.0 Tesla magnetic resonance imaging system. Diffusion tensor tractography was used for both groups to visualize the nigrostriatal and corticospinal tracts. The fractional anisotropy (FA) and mean diffusivity (MD) of the tracts were evaluated. Receiver operating characteristic (ROC) analysis was used to determine whether diffusion metrics of the nigrostriatal pathway could be used to diagnose Parkinson disease. Mean FA values (±SD) of the nigrostriatal tract in Parkinson disease patients (0.41 ± 0.025) were significantly lower than those in the control group (0.43 ± 0.022;p = 0.00068) and showed a sensitivity of 66.7% and specificity of 60%. There were no significant differences in the MD values of the nigrostriatal tract or the FA and MD values of the corticospinal tract between Parkinson disease patients and the control group. FA values of the nigrostriatal pathway in Parkinson disease patients were significantly lower than those in normal, healthy individuals. Reduced FA was generally thought to reflect neuronal loss, gliosis, or demyelination of nerve fibers. This result might provide a useful measure for diagnosing Parkinson disease and evaluating patients’ clinical condition.展开更多
Purpose: The Prostate Imaging Reporting and Data System (PI-RADS) was introduced to standardize prostate cancer diagnosis by MRI. However, the inter-reader agreement by PI-RADS scoring is not always high. The purpose ...Purpose: The Prostate Imaging Reporting and Data System (PI-RADS) was introduced to standardize prostate cancer diagnosis by MRI. However, the inter-reader agreement by PI-RADS scoring is not always high. The purpose of this study was to validate a deep-learning-based diagnostic algorithm of PI-RADS. Methods: We applied a Siemens Healthineers Prostate Artificial Intelligence (AI) prototype (work in progress) for fully automated prostate lesion detection, classification and reporting. More than 2000 bi-parametric MRI studies along with the PI-RADS reports were included as training, validation, and test data. This prospective validation study includes 101 consecutive patients suspected of prostate cancer, and 100 patients were included in the analysis. All subjects underwent a noncontrast-enhanced bi-parametric MRI including T2-weighted and diffusion-weighted imaging. Two board-certified radiologists independently scored the PI-RADS, and if there were disagreements;another radiologist confirmed the diagnosis. We compared the AI results with the interpretation results by the radiologists. Results: The sensitivity of our AI model for PI-RADS ≥ 4 was 0.76, and the specificity was 0.76. For the cases with PI-RADS ≥ 3, the sensitivity was 0.69, and the specificity was 0.76. In the lesion-based analysis, AI detection rates of PI-RADS 3, 4, 5 lesions in the peripheral zone were 43%, 63%, and 100%, respectively. In the transition zone, AI detection rates of PI-RADS 3, 4, 5 were 30%, 54%, and 100%, respectively. Conclusion: Our deep-learning-based algorithm has been validated and shown to help score PI-RADS.展开更多
基金supported by research grants from the program for Brain/MINDS Beyond program from the Japan Agency for Medical Research and Development(AMED)under Grant Number JP18dm0307024(to KK)MEXT-Supported Program for the Private University Research Branding Project+1 种基金ImPACT Program of Council for Science,Technology and Innovation(Cabinet Office,Government of Japan)JSPS KAKENHI Grant Number JP16K10327(to KK)
文摘The prevalence of neurodegenerative diseases is increasing as human longevity increases. The objective biomarkers that enable the staging and early diagnosis of neurodegenerative diseases are eagerly anticipated. It has recently become possible to determine pathological changes in the brain without autopsy with the advancement of diffusion magnetic resonance imaging techniques. Diffusion magnetic resonance imaging is a robust tool used to evaluate brain microstructural complexity and integrity, axonal order, density, and myelination via the micron-scale displacement of water molecules diffusing in tissues. Diffusion tensor imaging, a type of diffusion magnetic resonance imaging technique is widely utilized in clinical and research settings;however, it has several limitations. To overcome these limitations, cutting-edge diffusion magnetic resonance imaging techniques, such as diffusional kurtosis imaging, neurite orientation dispersion and density imaging, and free water imaging, have been recently proposed and applied to evaluate the pathology of neurodegenerative diseases. This review focused on the main applications, findings, and future directions of advanced diffusion magnetic resonance imaging techniques in patients with Alzheimer's and Parkinson's diseases, the first and second most common neurodegenerative diseases, respectively.
文摘Diffusion tensor tractography was used to evaluate whether diffusion metrics in the nigrostriatal pathway could diagnose Parkinson disease. Diffusion tensor imaging was performed on 30 patients with Parkinson disease and 32 healthy controls by using a 3.0 Tesla magnetic resonance imaging system. Diffusion tensor tractography was used for both groups to visualize the nigrostriatal and corticospinal tracts. The fractional anisotropy (FA) and mean diffusivity (MD) of the tracts were evaluated. Receiver operating characteristic (ROC) analysis was used to determine whether diffusion metrics of the nigrostriatal pathway could be used to diagnose Parkinson disease. Mean FA values (±SD) of the nigrostriatal tract in Parkinson disease patients (0.41 ± 0.025) were significantly lower than those in the control group (0.43 ± 0.022;p = 0.00068) and showed a sensitivity of 66.7% and specificity of 60%. There were no significant differences in the MD values of the nigrostriatal tract or the FA and MD values of the corticospinal tract between Parkinson disease patients and the control group. FA values of the nigrostriatal pathway in Parkinson disease patients were significantly lower than those in normal, healthy individuals. Reduced FA was generally thought to reflect neuronal loss, gliosis, or demyelination of nerve fibers. This result might provide a useful measure for diagnosing Parkinson disease and evaluating patients’ clinical condition.
文摘Purpose: The Prostate Imaging Reporting and Data System (PI-RADS) was introduced to standardize prostate cancer diagnosis by MRI. However, the inter-reader agreement by PI-RADS scoring is not always high. The purpose of this study was to validate a deep-learning-based diagnostic algorithm of PI-RADS. Methods: We applied a Siemens Healthineers Prostate Artificial Intelligence (AI) prototype (work in progress) for fully automated prostate lesion detection, classification and reporting. More than 2000 bi-parametric MRI studies along with the PI-RADS reports were included as training, validation, and test data. This prospective validation study includes 101 consecutive patients suspected of prostate cancer, and 100 patients were included in the analysis. All subjects underwent a noncontrast-enhanced bi-parametric MRI including T2-weighted and diffusion-weighted imaging. Two board-certified radiologists independently scored the PI-RADS, and if there were disagreements;another radiologist confirmed the diagnosis. We compared the AI results with the interpretation results by the radiologists. Results: The sensitivity of our AI model for PI-RADS ≥ 4 was 0.76, and the specificity was 0.76. For the cases with PI-RADS ≥ 3, the sensitivity was 0.69, and the specificity was 0.76. In the lesion-based analysis, AI detection rates of PI-RADS 3, 4, 5 lesions in the peripheral zone were 43%, 63%, and 100%, respectively. In the transition zone, AI detection rates of PI-RADS 3, 4, 5 were 30%, 54%, and 100%, respectively. Conclusion: Our deep-learning-based algorithm has been validated and shown to help score PI-RADS.
