(study-pams-conversion)= # Conversion Load the following modules: > ```bash > module use ${root_dir}/server/modulefiles > module load bashHelperKennedyRodrigue > module load containers/r/4.2.1-quarto > module load study-pams/convert_mri/latest > ``` For more details or updates, check the [github page.](https://github.com/epongpipat/convert-mri_study-pams) (data-entry)= ## Data Entry 1. Enter BP measurements to the Cognitive Data Sheet, and file subject's receipts into the receipt bin. ```bash ${root_dir}/study-pams/Demographic_and_Task_Data/Cognitive_Data_MCI.xlsx ``` 2. Copy physiological measures to the server and rename the files using the following template: ```bash ${root_dir}/study-pams/sourcedata/physio/KENROD_PAMS_20230101_0001_1/sub-0001_ses-01.puls ${root_dir}/study-pams/sourcedata/physio/KENROD_PAMS_20230101_0001_1/sub-0001_ses-01.resp ``` 3. Copy the exam card to the server using the following template: ```bash ${root_dir}/study-pams/sourcedata/exam_cards/KENROD_PAMS_20230101_0001_1.pdf ``` (study-pams-convert)= ## Convert 1. After receiving the {{BHIC}} Data Transfer email, run the following script to unzip data: ```bash dcm_unzip.sh --data_ref abcdefgh-12345678 --sub 0001 --ses 1 --date 20230101 ``` 2. After the files have been unzipped, convert the DICOM images to NIFTI with the following script: ```bash dcm2niix_wrapper.sh --sub 0001 --ses 1 --date 20230101 ``` (qc-parameters)= ## QC Parameters 1. QC the data and check for any images that have bad quality or were cut off. - Create a `scanner_reconstruction` directory for the reconstructed files (acq. 11,13,15,18,19 for ref.). - Create a `bad` directory within the subject directory for bad scans if necessary. 2. After QC, run the QC uber script: ```bash qc_uber.sh --sub 0001 --ses 1 --date 20230101 ``` 3. Combine the QC logs together with the following script: ```bash qc_combine_all_sub.sh ``` 4. Then run the following script locally (via RStudio) ```bash qc_render.sh --study pams --overwrite 1 ```