On a Siemens Trio, the first volume in a diffusion set is always b=0. Typically, b=0 volumes are acquired in the same series as b-weighted images, but a user can also acquire a series with only b=0 volumes. It is generally a good idea to embed your b=0 images in the same series as the b-weighted images, as this ensures the scanner does not optimize other parameters. However, this requires the user to install a custom gradient table - the default Siemens gradient tables only include a single b=0 volume (the first one). An example might be a clinical user who does not have the research license required to install custom gradient tables. In this case, the user might specify a b-weighted series as well as a b=0 series to boost the SNR of the b=0 data. Another reason for a b=0 series is acquisition of a polarity reversed series for TOPUP/eddy.
@nateConnors can you upload one of the BIDS JSON files from the mysterious images? I wonder if they are derived rather than original raw images. You can usually see this by looking for the term
DERIVED in the
"ImageType": ["ORIGINAL", "PRIMARY", "DIFFUSION", "NONE", "ND", "MOSAIC"],
The DICOM format demands that derived images are saved as a separate series from the raw data. On the Siemens Trio console the user can specify to save derived data (Trace, MD, ColFA) on the
Diffusion tab in the MRI console. While a Trace or MD image might look a bit like a b=0 volume, they are derived from all of the b-weighted volumes.
If your images are derived, you want to discard them (with dcm2niix you can use
-i y to ignore derived data). For future data, set the console to not generate the derived images. You will be able to create much nicer derived images after you preprocess your data (denoise, deGibbs, eddy, TOPUP).
If the images are not derived, but rather a unique set of b=0 volumes, I would suggest concatenating them (e.g. fslmerge for images, and zero pad bvec/bval files). Additional b=0 images will improve your SNR for several derived diffusion parameters (ADC, MD, MK, etc). While b=0 volumes have inherently higher SNR than the b-weighted images, these are the denominator for several of these derived parameters, so an accurate measure helps (e.g. averaging four b=0 volumes will improve the b=0 SNR by a factor of two relative to a single measurement).
You may want to look at the PowerPoint file and DTI tutorials for my Image to Inference class.