Add FMAP conversion capability for ADNI

clinica/iotools/bids_utils.py

@@ -888,13 +888,17 @@ def run_dcm2niix(
     completed_process = subprocess.run(command, capture_output=True)
 
     if completed_process.returncode != 0:
+        if completed_process.stdout is not None:
+            output_message = completed_process.stdout.decode('utf-8')
+        else:
+            output_message = ""
         cprint(
             msg=(
                 "DICOM to BIDS conversion with dcm2niix failed:\n"
                 f"command: {' '.join(command)}\n"
-                f"{completed_process.stdout.decode('utf-8')}"
+                f"{output_message}"
             ),
-            lvl="warning",
+            lvl="warning"
         )
         return False
     cprint(

clinica/iotools/converters/adni_to_bids/adni_modalities/adni_av45_fbb_pet.py

@@ -116,6 +116,7 @@ def compute_av45_fbb_pet_paths(source_dir, csv_dir, subjs_list, conversion_dir):
     pet_meta_list = load_clinical_csv(csv_dir, "PET_META_LIST")
 
     for subj in subjs_list:
+
         # PET images metadata for subject
         subject_pet_meta = pet_meta_list[pet_meta_list["Subject"] == subj]
 

clinica/iotools/converters/adni_to_bids/adni_modalities/adni_dwi.py

@@ -119,6 +119,7 @@ def compute_dwi_paths(source_dir, csv_dir, subjs_list, conversion_dir):
     ]
 
     for subj in subjs_list:
+
         # Filter ADNIMERGE, MRI_LIST and QC for only one subject and sort the rows/visits by examination date
         adnimerge_subj = adni_merge[adni_merge.PTID == subj]
         adnimerge_subj = adnimerge_subj.sort_values("EXAMDATE")

clinica/iotools/converters/adni_to_bids/adni_modalities/adni_fdg_pet.py

@@ -86,6 +86,7 @@ def _convert_adni_fdg_pet(
 
     import pandas as pd
 
+
     from clinica.iotools.converters.adni_to_bids.adni_utils import (
         load_clinical_csv,
         paths_to_bids,
@@ -94,7 +95,6 @@ def _convert_adni_fdg_pet(
 
     if subjects is None:
         adni_merge = load_clinical_csv(csv_dir, "ADNIMERGE")
-
         subjects = list(adni_merge.PTID.unique())
     cprint(
         "Calculating paths of FDG PET images. "
@@ -103,6 +103,7 @@ def _convert_adni_fdg_pet(
     images = _compute_fdg_pet_paths(
         source_dir, csv_dir, subjects, conversion_dir, preprocessing_step
     )
+
     cprint("Paths of FDG PET images found. Exporting images into BIDS ...")
     modality = _get_modality_from_adni_preprocessing_step(preprocessing_step)
     paths_to_bids(
@@ -172,6 +173,7 @@ def _compute_fdg_pet_paths(
     from clinica.utils.pet import Tracer
 
     pet_fdg_df = _get_pet_fdg_df(Path(csv_dir), subjects, preprocessing_step)
+
     images = find_image_path(pet_fdg_df, source_dir, "FDG", "I", "Image_ID")
     images.to_csv(
         Path(conversion_dir) / f"{Tracer.FDG.value}_pet_paths.tsv",

clinica/iotools/converters/adni_to_bids/adni_modalities/adni_flair.py

@@ -34,7 +34,7 @@ def convert_adni_flair(
     mod_to_update : bool
         If True, pre-existing images in the BIDS directory
         will be erased and extracted again.
-
+    
     n_procs : int, optional
         The requested number of processes.
         If specified, it should be between 1 and the number of available CPUs.
@@ -103,9 +103,10 @@ def compute_flair_paths(source_dir, csv_dir, subjs_list, conversion_dir):
     # Loading needed .csv files
     adni_merge = load_clinical_csv(csv_dir, "ADNIMERGE")
     mayo_mri_qc = load_clinical_csv(csv_dir, "MAYOADIRL_MRI_IMAGEQC_12_08_15")
-    mayo_mri_qc = mayo_mri_qc[mayo_mri_qc.series_type == "AFL"]
     mri_list = load_clinical_csv(csv_dir, "MRILIST")
 
+    mayo_mri_qc = mayo_mri_qc[mayo_mri_qc.series_type == "AFL"]
+
     # Selecting FLAIR DTI images that are not MPR
     mri_list = mri_list[mri_list.SEQUENCE.str.contains("flair", case=False, na=False)]
     unwanted_sequences = ["_MPR_"]