Add FMAP conversion capability for ADNI

.gitignore

@@ -27,6 +27,7 @@ share/python-wheels/
 .installed.cfg
 *.egg
 MANIFEST
+Miniconda3-latest-Linux-x86_64.sh
 
 # PyInstaller
 #  Usually these files are written by a python script from a template

clinica/iotools/bids_utils.py

@@ -68,7 +68,6 @@ def create_participants_df(
     import numpy as np
     import pandas as pd
 
-    from clinica.iotools.converters.adni_to_bids.adni_utils import load_clinical_csv
     from clinica.utils.stream import cprint
 
     fields_bids = ["participant_id"]
@@ -92,8 +91,9 @@ def create_participants_df(
     participant_df = pd.DataFrame(columns=fields_bids)
 
     for i in range(0, len(participant_fields_db)):
+        pfdbi = participant_fields_db[i]
         # If a field not empty is found
-        if not pd.isnull(participant_fields_db[i]):
+        if not pd.isnull(pfdbi):
             # Extract the file location of the field and read the value from the file
             tmp = field_location[i].split("/")
             location = tmp[0]
@@ -112,28 +112,27 @@ def create_participants_df(
                 if file_ext == ".xlsx":
                     file_to_read = pd.read_excel(file_to_read_path, sheet_name=sheet)
                 elif file_ext == ".csv":
-                    file_to_read = load_clinical_csv(
-                        clinical_data_dir, location.split(".")[0]
-                    )
+                    file_to_read = pd.read_csv(file_to_read_path)
                 prev_location = location
                 prev_sheet = sheet
 
             field_col_values = []
             # For each field in fields_dataset extract all the column values
             for j in range(0, len(file_to_read)):
                 # Convert the alternative_id_1 to string if is an integer/float
-                value_to_read = file_to_read[participant_fields_db[i]]
+                import time
+                value_to_read = file_to_read[pfdbi]
                 if participant_fields_bids[i] == "alternative_id_1" and (
                     value_to_read.dtype == np.float64 or value_to_read.dtype == np.int64
                 ):
-                    if not pd.isnull(file_to_read.at[j, participant_fields_db[i]]):
+                    if not pd.isnull(file_to_read.at[j, pfdbi]):
                         value_to_append = str(
-                            file_to_read.at[j, participant_fields_db[i]]
+                            file_to_read.at[j, pfdbi]
                         ).rstrip(".0")
                     else:
                         value_to_append = np.NaN
                 else:
-                    value_to_append = file_to_read.at[j, participant_fields_db[i]]
+                    value_to_append = file_to_read.at[j, pfdbi]
                 field_col_values.append(value_to_append)
             # Add the extracted column to the participant_df
             participant_df[participant_fields_bids[i]] = pd.Series(field_col_values)
@@ -246,6 +245,15 @@ def create_sessions_dict_OASIS(
                 file_to_read = pd.read_excel(file_to_read_path, sheet_name=sheet)
             elif file_ext == ".csv":
                 file_to_read = pd.read_csv(file_to_read_path)
+            # if file_ext == ".xlsx":
+            #     file_to_read = pd.read_excel(file_to_read_path, sheet_name=sheet)
+            # if file_ext == ".csv":
+            #     file_to_read = pd.read_csv(file_to_read_path)
+            #     with open('/home/[email protected]/Desktop/Code/participant/debug_column_names.txt', 'w') as f:
+            #         f.write(f"Columns in {file_to_read_path}: \n")
+            #         for col in file_to_read.columns:
+            #             f.write(f"{col}\n")
+            #     file_to_read.columns = file_to_read.columns.str.replace('"', '')
 
             for r in range(0, len(file_to_read.values)):
                 # Extracts the subject ids columns from the dataframe
@@ -533,7 +541,7 @@ def write_modality_agnostic_files(
     _write_bidsignore(bids_dir)
 
 
-def write_sessions_tsv(bids_dir: Union[str, Path], sessions_dict: dict) -> None:
+def write_sessions_tsv(bids_dir: str, sessions_dict: dict) -> None:
     """Create <participant_id>_sessions.tsv files.
 
     Basically writes the content of the function
@@ -617,7 +625,7 @@ def _get_pet_tracer_from_filename(filename: str) -> str:
 
 
 def write_scans_tsv(
-    bids_dir: Union[str, Path], participant_ids: List[str], scans_dict: dict
+    bids_dir: str, participant_ids: List[str], scans_dict: dict
 ) -> None:
     """Write the scans dict into TSV files.
 
@@ -883,16 +891,20 @@ def run_dcm2niix(
     command = _build_dcm2niix_command(
         input_dir, output_dir, output_fmt, compress, bids_sidecar
     )
-    completed_process = subprocess.run(command, capture_output=True)
+    completed_process = subprocess.run(command)
 
     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

@@ -10,7 +10,6 @@ def convert_adni_av45_fbb_pet(
     conversion_dir: PathLike,
     subjects: Optional[List[str]] = None,
     mod_to_update: bool = False,
-    n_procs: int = 1,
 ):
     """Convert AV-45 and Florbetaben PET images of ADNI into BIDS format.
 
@@ -34,24 +33,18 @@ def convert_adni_av45_fbb_pet(
     mod_to_update : bool
         If True, pre-existing images in the BIDS directory
         will be erased and extracted again.
-
-    n_procs : int, default=1
-        The requested number of processes.
-        If specified, it should be between 1 and the number of available CPUs.
-        Default=1.
     """
     from os import path
 
     import pandas as pd
 
-    from clinica.iotools.converters.adni_to_bids.adni_utils import (
-        load_clinical_csv,
-        paths_to_bids,
-    )
+    from clinica.iotools.converters.adni_to_bids.adni_utils import paths_to_bids
     from clinica.utils.stream import cprint
 
     if not subjects:
-        adni_merge = load_clinical_csv(csv_dir, "ADNIMERGE")
+        adni_merge_path = path.join(csv_dir, "ADNIMERGE.csv")
+        adni_merge = pd.read_csv(adni_merge_path, delimiter='","')
+        adni_merge.columns = adni_merge.columns.str.strip('"')
         subjects = list(adni_merge.PTID.unique())
 
     cprint(
@@ -61,13 +54,7 @@ def convert_adni_av45_fbb_pet(
     cprint(
         "Paths of AV45 and Florbetaben PET images found. Exporting images into BIDS ..."
     )
-    paths_to_bids(
-        images,
-        destination_dir,
-        "av45_fbb",
-        mod_to_update=mod_to_update,
-        n_procs=n_procs,
-    )
+    paths_to_bids(images, destination_dir, "av45_fbb", mod_to_update=mod_to_update)
     cprint(msg="AV45 and Florbetaben PET conversion done.", lvl="debug")
 
 
@@ -91,7 +78,6 @@ def compute_av45_fbb_pet_paths(source_dir, csv_dir, subjs_list, conversion_dir):
     from clinica.iotools.converters.adni_to_bids.adni_utils import (
         find_image_path,
         get_images_pet,
-        load_clinical_csv,
     )
 
     pet_amyloid_col = [
@@ -111,22 +97,27 @@ def compute_av45_fbb_pet_paths(source_dir, csv_dir, subjs_list, conversion_dir):
     pet_amyloid_dfs_list = []
 
     # Loading needed .csv files
-    av45qc = load_clinical_csv(csv_dir, "AV45QC")
-    amyqc = load_clinical_csv(csv_dir, "AMYQC")
-    pet_meta_list = load_clinical_csv(csv_dir, "PET_META_LIST")
+    av45qc = pd.read_csv(path.join(csv_dir, "AV45QC.csv"), sep=",", low_memory=False)
+    amyqc = pd.read_csv(path.join(csv_dir, "AMYQC.csv"), sep=",", low_memory=False)
+    pet_meta_list = pd.read_csv(
+        path.join(csv_dir, "PET_META_LIST.csv"), sep=",", low_memory=False
+    )
 
     for subj in subjs_list:
+
+        ssubj = subj.replace('"','')
+
         # PET images metadata for subject
-        subject_pet_meta = pet_meta_list[pet_meta_list["Subject"] == subj]
+        subject_pet_meta = pet_meta_list[pet_meta_list["Subject"] == ssubj]
 
         if subject_pet_meta.empty:
             continue
 
         # QC for AV45 PET images for ADNI 1, GO and 2
-        av45_qc_subj = av45qc[(av45qc.PASS == 1) & (av45qc.RID == int(subj[-4:]))]
+        av45_qc_subj = av45qc[(av45qc.PASS == 1) & (av45qc.RID == int(ssubj[-4:]))]
 
         # QC for Amyloid PET images for ADNI 3
-        amy_qc_subj = amyqc[(amyqc.SCANQLTY == 1) & (amyqc.RID == int(subj[-4:]))]
+        amy_qc_subj = amyqc[(amyqc.SCANQLTY == 1) & (amyqc.RID == int(ssubj[-4:]))]
         amy_qc_subj.insert(0, "EXAMDATE", amy_qc_subj.SCANDATE.to_list())
 
         # Concatenating visits in both QC files

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

@@ -10,7 +10,6 @@ def convert_adni_dwi(
     conversion_dir: PathLike,
     subjects: Optional[List[str]] = None,
     mod_to_update: bool = False,
-    n_procs: Optional[int] = 1,
 ):
     """Convert DW images of ADNI into BIDS format.
 
@@ -34,24 +33,18 @@ def convert_adni_dwi(
     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.
-        Default=1.
     """
     from os import path
 
     import pandas as pd
 
-    from clinica.iotools.converters.adni_to_bids.adni_utils import (
-        load_clinical_csv,
-        paths_to_bids,
-    )
+    from clinica.iotools.converters.adni_to_bids.adni_utils import paths_to_bids
     from clinica.utils.stream import cprint
 
     if not subjects:
-        adni_merge = load_clinical_csv(csv_dir, "ADNIMERGE")
+        adni_merge_path = path.join(csv_dir, "ADNIMERGE.csv")
+        adni_merge = pd.read_csv(adni_merge_path, delimiter='","')
+        adni_merge.columns = adni_merge.columns.str.strip('"')
         subjects = list(adni_merge.PTID.unique())
 
     cprint(
@@ -60,9 +53,7 @@ def convert_adni_dwi(
     images = compute_dwi_paths(source_dir, csv_dir, subjects, conversion_dir)
     cprint("Paths of DWI images found. Exporting images into BIDS ...")
     # dwi_paths_to_bids(images, dest_dir)
-    paths_to_bids(
-        images, destination_dir, "dwi", mod_to_update=mod_to_update, n_procs=n_procs
-    )
+    paths_to_bids(images, destination_dir, "dwi", mod_to_update=mod_to_update)
     cprint(msg="DWI conversion done.", lvl="debug")
 
 
@@ -84,7 +75,6 @@ def compute_dwi_paths(source_dir, csv_dir, subjs_list, conversion_dir):
 
     from clinica.iotools.converters.adni_to_bids.adni_utils import (
         find_image_path,
-        load_clinical_csv,
         visits_to_timepoints,
     )
 
@@ -103,11 +93,19 @@ def compute_dwi_paths(source_dir, csv_dir, subjs_list, conversion_dir):
     dwi_dfs_list = []
 
     # 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")
+    # adni_merge_path = path.join(csv_dir, "ADNIMERGE.csv")
+    # adni_merge = pd.read_csv(adni_merge_path, delimiter='","')
+    # adni_merge.columns = adni_merge.columns.str.strip('"')
+    adni_merge = pd.read_csv(path.join(csv_dir, "ADNIMERGE2.csv"), sep=",", engine='python')
+
+    mayo_mri_qc = pd.read_csv(
+        path.join(csv_dir, "MAYOADIRL_MRI_IMAGEQC_12_08_15.csv"),
+        sep=",",
+        low_memory=False,
+    )
     mayo_mri_qc = mayo_mri_qc[mayo_mri_qc.series_type == "DTI"]
 
-    mri_list = load_clinical_csv(csv_dir, "MRILIST")
+    mri_list = pd.read_csv(path.join(csv_dir, "MRILIST.csv"), sep=",", low_memory=False)
 
     # Selecting only DTI images that are not Multiband, processed or enhanced images
     mri_list = mri_list[mri_list.SEQUENCE.str.contains("dti", case=False, na=False)]
@@ -119,14 +117,19 @@ def compute_dwi_paths(source_dir, csv_dir, subjs_list, conversion_dir):
     ]
 
     for subj in subjs_list:
+
+        ssubj = subj.replace('"','')
+
         # 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")
 
-        mri_list_subj = mri_list[mri_list.SUBJECT == subj]
+        mri_list_subj = mri_list[mri_list.SUBJECT == ssubj]
         mri_list_subj = mri_list_subj.sort_values("SCANDATE")
 
-        mayo_mri_qc_subj = mayo_mri_qc[mayo_mri_qc.RID == int(subj[-4:])]
+        ssubj = subj[-4:]
+        ssubj = ssubj.replace('"','')
+        mayo_mri_qc_subj = mayo_mri_qc[mayo_mri_qc.RID == int(ssubj)]
 
         # Obtain corresponding timepoints for the subject visits
         visits = visits_to_timepoints(subj, mri_list_subj, adnimerge_subj, "DWI")