Quality Control Considerations for the Effective Integration of Neuroimaging Data

Ensuring image quality control (QC) for data acquired in a multi-modality context offers substantial advantages for both multi-level and multi-point analysis. Although a variety of neuroimage analysis algorithms exist, the tasks of multimodal neuroimaging data QC and integration remain challenging because image quality can be affected by numerous factors. Here, we discuss the challenges of the QC and integration of neuroimaging data and provide two examples of often-neglected and potentially under-appreciated problems related to the QC of diffusion tensor imaging (DTI) data and to their integration with other modalities. Specifically, we illustrate the challenges of (1) DTI/MRI co-registration and (2) scanner vibration artifacts, both being representative examples of difficulties involving both data QC and its integration. Additionally, we highlight the need for automatic methods which can address neuroimaging data QC which allows for its successful integration.

Authors and Affiliations

1. Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA

   Sumiko Abe, Andrei Irimia & John Darrell Van Horn

Corresponding author

Correspondence to John Darrell Van Horn .

Editors and Affiliations

1. USC Stevens Neuroimaging and Informatics Institute, Los Angeles, California, USA

   Naveen Ashish

2. USC Information Sciences Institute, Marina del Rey, California, USA

   Jose-Luis Ambite

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