Neuroimaging has become an essential tool for diagnosis, monitoring, and treatment planning of brain diseases, as well as for expanding our understanding of healthy and pathological brain mechanisms. Neuroinflammatory diseases, neurodegeneration, neuro-trauma, neuro-oncology, and brain development are prominent examples of conditions where robust and sensitive imaging biomarkers may hold the key to optimized patient management and improved clinical research.
Despite a wide range of applications, neuroimaging still faces hurdles when it comes to reliable and sensitive detection of pathologic conditions at the individual patient level. Moreover, in contrast to high-quality and homogenized neuroimage data collected for research studies, real-world neuroimaging data still suffers from suboptimal image quality characteristics and inter-center/inter-scanner differences.
Novel methods for neuroimage analysis should break down current barriers posed by the lack of standardization and potential artifacts, and provide convenient, sensitive, and interpretable biomarkers for routine use by clinicians and scientists.
The aim of the current Research Topic is to cover recent advances in the field of computational neuroimaging, including image processing and statistical modeling tools based on brain scans obtained from Computed Tomography, Magnetic Resonance Imaging, Positron Emission Tomography, Ultrasound... Topics include, but are not limited to:
? new computational models to reliably extract biomarkers from real-world clinical brain scans;
? brain image processing approaches achieving inter-site/inter-scanner harmonization;
? applications that use big data, including large collections of (multi-site) clinical neuroimaging datasets;
? studies showing the translational value of brain image analysis software for clinical adoption, e.g., studies comparing clinical decisions (diagnosis and treatment planning) made without and with the use of neuroimaging post-processing software;
? methods for the longitudinal/follow-up study of brain pathologies;
? experimental designs for validation and verification of brain image analysis software;
? novel technological imaging acquisitions to improve the modeling of neuroimaging biomarkers.
Since brain imaging often visualizes disease effects with much greater sensitivity than clinical observation, it holds great promise to help diagnose patients at the earliest stages of their disease, when treatment is most effective, and personalize patient care by evaluating response to a specific intervention. To this aim, recent advances in the field of neuroimaging biomarkers, including advanced imaging technologies, image processing, and artificial intelligence approaches, are pushing forward the achievement of precision medicine. Indeed, provided that the developed imaging biomarkers are sensitive and reliable.
Dr. Sima is employed by the company Icometrix, all other Topic Editors declare to have no conflict of interest with regards to the Research Topic.
Neuroimaging has become an essential tool for diagnosis, monitoring, and treatment planning of brain diseases, as well as for expanding our understanding of healthy and pathological brain mechanisms. Neuroinflammatory diseases, neurodegeneration, neuro-trauma, neuro-oncology, and brain development are prominent examples of conditions where robust and sensitive imaging biomarkers may hold the key to optimized patient management and improved clinical research.
Despite a wide range of applications, neuroimaging still faces hurdles when it comes to reliable and sensitive detection of pathologic conditions at the individual patient level. Moreover, in contrast to high-quality and homogenized neuroimage data collected for research studies, real-world neuroimaging data still suffers from suboptimal image quality characteristics and inter-center/inter-scanner differences.
Novel methods for neuroimage analysis should break down current barriers posed by the lack of standardization and potential artifacts, and provide convenient, sensitive, and interpretable biomarkers for routine use by clinicians and scientists.
The aim of the current Research Topic is to cover recent advances in the field of computational neuroimaging, including image processing and statistical modeling tools based on brain scans obtained from Computed Tomography, Magnetic Resonance Imaging, Positron Emission Tomography, Ultrasound... Topics include, but are not limited to:
? new computational models to reliably extract biomarkers from real-world clinical brain scans;
? brain image processing approaches achieving inter-site/inter-scanner harmonization;
? applications that use big data, including large collections of (multi-site) clinical neuroimaging datasets;
? studies showing the translational value of brain image analysis software for clinical adoption, e.g., studies comparing clinical decisions (diagnosis and treatment planning) made without and with the use of neuroimaging post-processing software;
? methods for the longitudinal/follow-up study of brain pathologies;
? experimental designs for validation and verification of brain image analysis software;
? novel technological imaging acquisitions to improve the modeling of neuroimaging biomarkers.
Since brain imaging often visualizes disease effects with much greater sensitivity than clinical observation, it holds great promise to help diagnose patients at the earliest stages of their disease, when treatment is most effective, and personalize patient care by evaluating response to a specific intervention. To this aim, recent advances in the field of neuroimaging biomarkers, including advanced imaging technologies, image processing, and artificial intelligence approaches, are pushing forward the achievement of precision medicine. Indeed, provided that the developed imaging biomarkers are sensitive and reliable.
Dr. Sima is employed by the company Icometrix, all other Topic Editors declare to have no conflict of interest with regards to the Research Topic.
