wip2

clinica/iotools/converters/ixi_to_bids/ixi_to_bids.py

@@ -5,7 +5,8 @@
 
 import nibabel as nb
 import numpy as np
-from ixi_to_bids_utils import read_ixi_clinical_data
+from iotools.bids_utils import write_modality_agnostic_files
+from ixi_to_bids_utils import *
 
 from clinica.utils.filemanip import UserProvidedPath
 
@@ -36,8 +37,30 @@ def convert(
             lvl="warning",
         )
 
-    path_to_clinical = "/Users/alice.joubert/Downloads"  # todo : testing only here
     clinical_data = read_ixi_clinical_data(path_to_clinical)
 
-    # todo : convert here
+    subjects = subjects if subjects else get_subjects_list_from_data(path_to_dataset)
+    subjects = filter_subjects_list(subjects, clinical_data)
+
+    # todo : treat DTI different (load and merge)
+
+    image_data = get_img_data_df(path_to_dataset)
+
+    for subject in subjects:
+        write_subject(select_subject_data(image_data, subject), bids_dir)
+
+    readme_data = {
+        "link": "https://brain-development.org/ixi-dataset/",
+        "desc": (
+            "IXI is the nickname for the Information eXtraction from Images project, "
+            "which issued a dataset of nearly 600 images from healthy subjects. The MR"
+            "acquisition protocol includes T1,T2, PD weighted, MRA and diffusion-weighted"
+            "images. Three hospitals in London were involved in data collection."
+        ),
+    }
+
+    write_modality_agnostic_files(
+        study_name=StudyName.IXI, readme_data=readme_data, bids_dir=bids_dir
+    )
+
     cprint("Conversion to BIDS succeeded.", lvl="info")

clinica/iotools/converters/ixi_to_bids/ixi_to_bids_utils.py

@@ -1,9 +1,13 @@
 # todo : verify typing compatibility with user input
+import json
 import re
+import shutil
 from pathlib import Path
 from typing import List
 
+import nibabel as nib
 import pandas as pd
+from nilearn.image import concat_imgs
 
 from clinica.iotools.bids_utils import StudyName, bids_id_factory
 
@@ -27,6 +31,9 @@ def filter_subjects_list(
     ]
 
 
+# todo : filter img data based on this
+
+
 def read_ixi_clinical_data(clinical_data_path: Path) -> pd.DataFrame:
     clinical_data = pd.read_excel(clinical_data_path / "IXI.xls")
     clinical_data.dropna(inplace=True)
@@ -53,12 +60,19 @@ def rename_ixi_modalities(input_mod: str) -> str:
         )
 
 
-def get_data_df(data_directory: Path) -> pd.DataFrame:
+def define_magnetic_field(hospital: str) -> str:
+    if hospital == "Guys" or hospital == "IOP":
+        return "1.5"
+    if hospital == "HH":
+        return "3"
+
+
+def get_img_data_df(data_directory: Path) -> pd.DataFrame:
     # todo : works for all except DTI !!! (I mean we should drop it after this)
     df = pd.DataFrame(
         {"img_path": [path for path in data_directory.rglob(pattern="IXI*.nii.gz")]}
     )
-    # todo : rename modalities inside
+    # todo : regex pattern for 3 groups IXI\d{3}(-\w*){3}.nii.gz vs 4 groups IXI\d{3}(-\w*){4}.nii.gz
     df = (
         df.assign(img_name=lambda df: df.img_path.apply(lambda x: x.name))
         .assign(img_name_no_ext=lambda df: df.img_name.apply(lambda x: x.split(".")[0]))
@@ -74,27 +88,54 @@ def get_data_df(data_directory: Path) -> pd.DataFrame:
                 lambda x: rename_ixi_modalities(x.split("-")[3])
             )
         )
+        .assign(field=lambda df: df.hospital.apply(lambda x: define_magnetic_field(x)))
         .assign(session="ses-M000")
     )
     return df
 
 
 def select_subject_data(data_df: pd.DataFrame, subject: str) -> pd.DataFrame:
+    """Select subject images to copy based on IXI id"""
     return data_df[data_df["subject"] == subject]
 
 
 def bids_filename_from_ixi(img: pd.Series) -> str:
-    # todo : adapter for modalities
-    return f"{img['bids_id']}_{img['session']}"
+    # todo : has to work for both nifti and json
+    return f"{img['bids_id']}_{img['session']}_{img['modality']}"
 
 
 def write_subject(subject_df: pd.DataFrame, bids_path: Path) -> None:
-    # todo : place in subject folder, anat folder (T1,T2,PD) or dwi (dwi) folder if modality there ; where angio ??
+    for _, row in subject_df.iterrows():
+        filename = bids_filename_from_ixi(row)
+
+        if row["modality"] in ["T1w", "T2w", "PDw", "angio"]:
+            data_path = bids_path / row["bids_id"] / row["session"] / "anat"
+        elif row["modality"] == "dwi":
+            data_path = bids_path / row["bids_id"] / row["session"] / "func"
+        else:
+            raise ValueError(
+                f"Modality {row['modality']} not recognized for IXI dataset."
+            )
+
+        data_path.mkdir(parents=True, exist_ok=True)
+        shutil.copy2(row["img_path"], f"{data_path}/{filename}.nii.gz")
+
+        with open(f"{data_path}/{filename}.json", "w") as f:
+            json.dump(
+                {
+                    "InstitutionName": row["hospital"],
+                    "MagneticFieldStrength (T)": row["field"],
+                },
+                f,
+                indent=4,
+            )
+
+
+def merge_dti(dti_df: pd.DataFrame) -> pd.DataFrame:
     pass
 
 
-# todo : function to place in files
-# todo : function to create jsons
+# todo : write only one json per subject ?
 # todo : test all
-
 # todo : question DTI - sessions différentes ou images différentes ?
+# todo : dataset descr? scans/sessions/...

clinica/pipelines/t1_linear/anat_linear_utils.py

@@ -43,10 +43,6 @@ def _get_substitutions_datasink(bids_image_id: str, suffix: str) -> list:
             f"{bids_image_id}Warped_cropped.nii.gz",
             f"{bids_image_id_without_suffix}_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_{suffix}.nii.gz",
         ),
-        (
-            f"{bids_image_id}0GenericAffine.mat",
-            f"{bids_image_id_without_suffix}_space-MNI152NLin2009cSym_res-1x1x1_affine.mat",
-        ),
         (
             f"{bids_image_id}Warped.nii.gz",
             f"{bids_image_id_without_suffix}_space-MNI152NLin2009cSym_res-1x1x1_{suffix}.nii.gz",