Question
Question Posted 02/24/26:
Dear ADNI Support Team,
I am a PhD researcher working on ASL-MRI denoising and acquisition time reduction for early Alzheimer’s disease diagnosis.
After downloading 3D pCASL data (Axial 3DpCASL 1260, Siemens Prisma), the series contains 80 DICOM files and converts to a 128×128×40×2 NIfTI dataset. This indicates two temporal volumes rather than a full dynamic control–label time series.
For my research, it is essential to access the complete dynamic ASL acquisition containing multiple control–label pairs (e.g., 20–30 pairs), in order to simulate shortened acquisitions and evaluate denoising performance.
I would appreciate clarification on:
Whether full 4D pCASL control–label dynamics are available in ADNI.
How to correctly identify and download these raw time-series datasets (if available).
Whether only averaged control/label or perfusion-weighted reconstructions are distributed.
Thank you very much for your assistance.
Kind regards,
Kajuboju Tejaswi
Dear ADNI Support Team,
I am a PhD researcher working on ASL-MRI denoising and acquisition time reduction for early Alzheimer’s disease diagnosis.
After downloading 3D pCASL data (Axial 3DpCASL 1260, Siemens Prisma), the series contains 80 DICOM files and converts to a 128×128×40×2 NIfTI dataset. This indicates two temporal volumes rather than a full dynamic control–label time series.
For my research, it is essential to access the complete dynamic ASL acquisition containing multiple control–label pairs (e.g., 20–30 pairs), in order to simulate shortened acquisitions and evaluate denoising performance.
I would appreciate clarification on:
Whether full 4D pCASL control–label dynamics are available in ADNI.
How to correctly identify and download these raw time-series datasets (if available).
Whether only averaged control/label or perfusion-weighted reconstructions are distributed.
Thank you very much for your assistance.
Kind regards,
Kajuboju Tejaswi
Response posted 02/25/26 by ADNI MRI Core:
Thank you for your question to the ADNI MRI Core.
There are only two temporal volumes for pCASL in ADNI4. In ADNI3 you may want to look at the single PLD PASL scans on Siemens. Those had 10 averages (which is what I think you mean by time series). Alternatively, if you are talking about multiple post-labelling-delays those will be found in separate series for pCASL scans in ADNI4. A complete description of all the types of ASL used in ADNI are given in these two papers:
https://alz-journals.onlinelibrary.wiley.com/doi/pdf/10.1002/alz.14310
https://alz-journals.onlinelibrary.wiley.com/doi/pdf/10.1002/alz.14162
Once you narrow down which acquisitions or if there are acquisition you are interested from the papers above someone from our team or LONI will be able to help you download the scans of interest. They can be reached at: data.coordinator@loni.usc.edu
There are multiple ASL protocols, some will have multiple averages, and some will have multiple measurements without averaging. If I had to guess I think the best match is ADNI3 PASL scans on Siemens.
Good luck!
There are only two temporal volumes for pCASL in ADNI4. In ADNI3 you may want to look at the single PLD PASL scans on Siemens. Those had 10 averages (which is what I think you mean by time series). Alternatively, if you are talking about multiple post-labelling-delays those will be found in separate series for pCASL scans in ADNI4. A complete description of all the types of ASL used in ADNI are given in these two papers:
https://alz-journals.onlinelibrary.wiley.com/doi/pdf/10.1002/alz.14310
https://alz-journals.onlinelibrary.wiley.com/doi/pdf/10.1002/alz.14162
Once you narrow down which acquisitions or if there are acquisition you are interested from the papers above someone from our team or LONI will be able to help you download the scans of interest. They can be reached at: data.coordinator@loni.usc.edu
There are multiple ASL protocols, some will have multiple averages, and some will have multiple measurements without averaging. If I had to guess I think the best match is ADNI3 PASL scans on Siemens.
Good luck!
