The first step for neuroimaging data analysis: DICOM to NIfTI conversion
X Li, PS Morgan, J Ashburner, J Smith… - Journal of neuroscience …, 2016 - Elsevier
Journal of neuroscience methods, 2016•Elsevier
Background Clinical imaging data are typically stored and transferred in the DICOM format,
whereas the NIfTI format has been widely adopted by scientists in the neuroimaging
community. Therefore, a vital initial step in processing the data is to convert images from the
complicated DICOM format to the much simpler NIfTI format. While there are a number of
tools that usually handle DICOM to NIfTI conversion seamlessly, some variations can disrupt
this process. New method We provide some insight into the challenges faced with image …
whereas the NIfTI format has been widely adopted by scientists in the neuroimaging
community. Therefore, a vital initial step in processing the data is to convert images from the
complicated DICOM format to the much simpler NIfTI format. While there are a number of
tools that usually handle DICOM to NIfTI conversion seamlessly, some variations can disrupt
this process. New method We provide some insight into the challenges faced with image …
Background
Clinical imaging data are typically stored and transferred in the DICOM format, whereas the NIfTI format has been widely adopted by scientists in the neuroimaging community. Therefore, a vital initial step in processing the data is to convert images from the complicated DICOM format to the much simpler NIfTI format. While there are a number of tools that usually handle DICOM to NIfTI conversion seamlessly, some variations can disrupt this process.
New method
We provide some insight into the challenges faced with image conversion. First, different manufacturers implement the DICOM format differently which complicates the conversion. Second, different modalities and sub-modalities may need special treatment during conversion. Lastly, the image transferring and archiving can also impact the DICOM conversion.
Results
We present results in several error-prone domains, including the slice order for functional imaging, phase encoding direction for distortion correction, effect of diffusion gradient direction, and effect of gantry correction for some imaging modality.
Comparison with existing methods
Conversion tools are often designed for a specific manufacturer or modality. The tools and insight we present here are aimed at different manufacturers or modalities.
Conclusions
The imaging conversion is complicated by the variation of images. An understanding of the conversion basics can be helpful for identifying the source of the error. Here we provide users with simple methods for detecting and correcting problems. This also serves as an overview for developers who wish to either develop their own tools or adapt the open source tools created by the authors.
Elsevier