Add FMAP conversion capability for ADNI

clinica/iotools/bids_utils.py

@@ -888,11 +888,15 @@ 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",
         )

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

@@ -94,7 +94,6 @@ 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 +102,7 @@ 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 +172,7 @@ 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

@@ -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_"]