Using medical images acquired during routine clinical care for research purposes: a comprehensive medical informatics approach applied to the study of brain development from birth through 2 years of age as indexed by diffusion MRI

Biologic variability and dramatic changes of brain development in children aged 0 to 2 years make it challenging to accurately detect subtle abnormalities in single Magnetic Resonance Imaging (MRI) scans. Diffusion MRI (dMRI) indices such as Apparent Diffusion Coefficient (ADC) are reliable measures of water content in the brain and thus an excellent surrogate marker for brain development. Developing robust age-specific diffusion biomarkers for quantitative measurement of normative brain evolution would enhance our ability to detect subtle alterations due to tissue injuries or neuropathological disorders. Obtaining significant numbers of normative MRI scans for this age group means redirecting clinical data from hospital databases for research purposes. Therefore, this pilot project demonstrates the feasibility of identifying, retrieving and analyzing pediatric clinical dMRI data to investigate normal brain development from birth to 2 years. Research Patient Data Registry (RPDR) at Massachusetts General Hospital (MGH) was used to collect patient medical information and identify healthy children according to radiology reports. Corresponding MRI data were retrieved from MGH Picture Archiving and Communication System (PACS) using the prototype of Medical Imaging Informatics Bench to Bedside (mi2b2) software. A specific pipeline was created to handle the volume of studies and extract technical scan information used to identify comparable diffusion series; 193 studies were used for analysis. Two markers, whole brain average of ADC and Fractional Anisotropy (FA) values (WBAADC and WBAFA), were computed for each patient, their age-evolution across patients was investigated with different models. WBAADC and WBAFA seem to exhibit biexponential decay and increase respectively and might be gender-specific. These results have clinical implications for potentially determining the health status of an unknown individual, and research utility for continued development of these tools.