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We discuss the basic principles of clinical image segmentation, including coregistration of multiple imaging datasets to create an anatomic model of interest.
Therefore, the aim of the present study is to synthesize multiple imaging datasets addressing ASD and schizophrenia within a common framework for a detailed exploration.
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While the idea of a large and representative dataset is appealing, a challenge with data collected over multiple imaging sites is the variability in the resulting images that is not due to subject differences, but simply due to the scanner and imaging parameters i.e., increased noise that could swamp more subtle disease-specific effects.
Multiple imaging techniques are routinely used in diagnosing stress fractures.
Oncologic imaging seems to be the easier setting for the collection of imaging biomarkers from multiple imaging modalities.
Three-dimensional imaging might overcome the problem of multiple imaging planes, saving time [76, 77].
Because functional imaging datasets are so large, however, this approach is impractical.
Multiple imaging in certain instances is diagnostically warranted; however, 400,000 patients being subjected to unnecessary and expensive procedures performed by non-radiology physicians/greedy businessmen is a problem, and the imaging tests -- multiple or singular -- are not the appropriate target.
The majority of previous imaging strategies have focused on incorporating reporter gene using a single imaging modality whereas; our unique virus incorporates the potential for multiple imaging modalities.
The individual MS imaging datasets were aligned to each other.
The following OAI datasets were assessed in this study: baseline clinical dataset 0.2.2 and baseline imaging datasets 0.E.1 and 0.C.2.
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