[ENH] Skip visits for which processed images exist

clinica/pipelines/engine.py

@@ -11,6 +11,7 @@
 from nipype.interfaces.utility import IdentityInterface
 from nipype.pipeline.engine import Node, Workflow
 
+from clinica.utils.bids import Visit
 from clinica.utils.check_dependency import SoftwareDependency, ThirdPartySoftware
 from clinica.utils.group import GroupID, GroupLabel
 from clinica.utils.stream import log_and_warn
@@ -593,6 +594,18 @@ def sessions(self, value: List[str]):
         self._sessions = value
         self.is_built = False
 
+    @property
+    def visits(self) -> list[Visit]:
+        return [
+            Visit(subject, session)
+            for subject, session in zip(self.subjects, self.sessions)
+        ]
+
+    @visits.setter
+    def visits(self, value: list[Visit]):
+        self.subjects = [v.subject for v in value]
+        self.sessions = [v.session for v in value]
+
     @property
     def tsv_file(self) -> Optional[Path]:
         return self._tsv_file
@@ -601,24 +614,31 @@ def tsv_file(self) -> Optional[Path]:
     def info_file(self) -> Path:
         return self._info_file
 
-    @staticmethod
-    def get_processed_images(
-        caps_directory: Path, subjects: List[str], sessions: List[str]
-    ) -> List[str]:
-        """Extract processed image IDs in `caps_directory` based on `subjects`_`sessions`.
+    def determine_subject_and_session_to_process(self):
+        """Query expected output files in the CAPS folder in order to process only those missing.
 
-        Todo:
-            [ ] Implement this static method in all pipelines
-            [ ] Make it abstract to force overload in future pipelines
+        If expected output files already exist in the CAPS folder for some subjects and sessions,
+        then do not process those again.
         """
-        from clinica.utils.exceptions import ClinicaException
-        from clinica.utils.stream import cprint
+        from clinica.utils.stream import log_and_warn
 
-        cprint(msg="Pipeline finished with errors.", lvl="error")
-        cprint(msg="CAPS outputs were not found for some image(s):", lvl="error")
-        raise ClinicaException(
-            "Implementation on which image(s) failed will appear soon."
+        visits_already_processed = self.get_processed_visits()
+        if len(visits_already_processed) == 0:
+            return
+        message = (
+            f"In the provided CAPS folder {self.caps_directory}, Clinica found already processed "
+            f"images for {len(visits_already_processed)} visit(s):\n- "
         )
+        message += "\n- ".join([str(visit) for visit in visits_already_processed])
+        message += "\nThose visits will be ignored by Clinica."
+        log_and_warn(message, UserWarning)
+        self.visits = [
+            visit for visit in self.visits if visit not in visits_already_processed
+        ]
+
+    def get_processed_visits(self) -> list[Visit]:
+        """Examine the files present in the CAPS output folder and return the visits for which processing has already been done."""
+        return []
 
     def _init_nodes(self) -> None:
         """Init the basic workflow and I/O nodes necessary before build."""
@@ -691,6 +711,7 @@ def build(self):
             self._check_dependencies()
             self._check_pipeline_parameters()
             if not self.has_input_connections():
+                self.determine_subject_and_session_to_process()
                 self._build_input_node()
             self._build_core_nodes()
             if not self.has_output_connections():

clinica/pipelines/pet/linear/pipeline.py

@@ -1,10 +1,12 @@
 # Use hash instead of parameters for iterables folder names
 # Otherwise path will be too long and generate OSError
+from pathlib import Path
 from typing import List
 
 from nipype import config
 
 from clinica.pipelines.pet.engine import PETPipeline
+from clinica.utils.bids import Visit
 
 cfg = dict(execution={"parameterize_dirs": False})
 config.update_config(cfg)
@@ -30,6 +32,53 @@ def _check_custom_dependencies(self) -> None:
         """Check dependencies that can not be listed in the `info.json` file."""
         pass
 
+    def get_processed_visits(self) -> list[Visit]:
+        """Return a list of visits for which the pipeline is assumed to have run already.
+
+        Before running the pipeline, for a given visit, if both the PET SUVR registered image
+        and the rigid transformation files already exist, then the visit is added to this list.
+        The pipeline will further skip these visits and run processing only for the remaining
+        visits.
+        """
+        from functools import reduce
+
+        from clinica.utils.filemanip import extract_visits
+        from clinica.utils.input_files import (
+            pet_linear_nii,
+            pet_linear_transformation_matrix,
+        )
+        from clinica.utils.inputs import clinica_file_reader
+
+        if not self.caps_directory.is_dir():
+            return []
+        pet_registered_image, _ = clinica_file_reader(
+            self.subjects,
+            self.sessions,
+            self.caps_directory,
+            pet_linear_nii(
+                acq_label=self.parameters["acq_label"],
+                suvr_reference_region=self.parameters["suvr_reference_region"],
+                uncropped_image=self.parameters.get("uncropped_image", False),
+            ),
+        )
+        visits = [set(extract_visits(pet_registered_image))]
+        transformation, _ = clinica_file_reader(
+            self.subjects,
+            self.sessions,
+            self.caps_directory,
+            pet_linear_transformation_matrix(tracer=self.parameters["acq_label"]),
+        )
+        visits.append(set(extract_visits(transformation)))
+        if self.parameters.get("save_PETinT1w", False):
+            pet_image_in_t1w_space, _ = clinica_file_reader(
+                self.subjects,
+                self.sessions,
+                self.caps_directory,
+                pet_linear_nii(acq_label=self.parameters["acq_label"], space="T1w"),
+            )
+            visits.append(set(extract_visits(pet_image_in_t1w_space)))
+        return sorted(list(reduce(lambda x, y: x.intersection(y), visits)))
+
     def get_input_fields(self) -> List[str]:
         """Specify the list of possible inputs of this pipeline.
 

clinica/pipelines/t1_linear/anat_linear_pipeline.py

@@ -6,6 +6,7 @@
 from nipype import config
 
 from clinica.pipelines.engine import Pipeline
+from clinica.utils.bids import Visit
 from clinica.utils.check_dependency import ThirdPartySoftware
 from clinica.utils.stream import log_and_warn
 
@@ -67,24 +68,46 @@ def __init__(
             caps_name=caps_name,
         )
 
-    @staticmethod
-    def get_processed_images(
-        caps_directory: Path, subjects: List[str], sessions: List[str]
-    ) -> List[str]:
-        from clinica.utils.filemanip import extract_image_ids
-        from clinica.utils.input_files import T1W_LINEAR_CROPPED
+    def get_processed_visits(self) -> list[Visit]:
+        """Return a list of visits for which the pipeline is assumed to have run already.
+
+        Before running the pipeline, for a given visit, if both the T1w image registered
+        to the MNI152NLin2009cSym template and the affine transformation estimated with ANTs
+        already exist, then the visit is added to this list.
+        The pipeline will further skip these visits and run processing only for the remaining
+        visits.
+        """
+        from clinica.utils.filemanip import extract_visits
+        from clinica.utils.input_files import (
+            T1W_LINEAR,
+            T1W_LINEAR_CROPPED,
+            T1W_TO_MNI_TRANSFORM,
+        )
         from clinica.utils.inputs import clinica_file_reader
 
-        image_ids: List[str] = []
-        if caps_directory.is_dir():
-            cropped_files, _ = clinica_file_reader(
-                subjects,
-                sessions,
-                caps_directory,
-                T1W_LINEAR_CROPPED,
+        if not self.caps_directory.is_dir():
+            return []
+        images, _ = clinica_file_reader(
+            self.subjects,
+            self.sessions,
+            self.caps_directory,
+            T1W_LINEAR
+            if self.parameters.get("uncropped_image", False)
+            else T1W_LINEAR_CROPPED,
+        )
+        visits_having_image = extract_visits(images)
+        transformation, _ = clinica_file_reader(
+            self.subjects,
+            self.sessions,
+            self.caps_directory,
+            T1W_TO_MNI_TRANSFORM,
+        )
+        visits_having_transformation = extract_visits(transformation)
+        return sorted(
+            list(
+                set(visits_having_image).intersection(set(visits_having_transformation))
             )
-            image_ids = extract_image_ids(cropped_files)
-        return image_ids
+        )
 
     def _check_custom_dependencies(self) -> None:
         """Check dependencies that can not be listed in the `info.json` file."""
@@ -119,8 +142,6 @@ def _build_input_node(self):
         import nipype.interfaces.utility as nutil
         import nipype.pipeline.engine as npe
 
-        from clinica.utils.exceptions import ClinicaBIDSError, ClinicaException
-        from clinica.utils.filemanip import extract_subjects_sessions_from_filename
         from clinica.utils.image import get_mni_template
         from clinica.utils.input_files import T1W_NII, Flair_T2W_NII
         from clinica.utils.inputs import clinica_file_filter
@@ -131,27 +152,6 @@ def _build_input_node(self):
             "t1" if self.name == "t1-linear" else "flair"
         )
 
-        # Display image(s) already present in CAPS folder
-        # ===============================================
-        processed_ids = self.get_processed_images(
-            self.caps_directory, self.subjects, self.sessions
-        )
-        if len(processed_ids) > 0:
-            cprint(
-                msg=f"Clinica found {len(processed_ids)} image(s) already processed in CAPS directory:",
-                lvl="warning",
-            )
-            for image_id in processed_ids:
-                cprint(msg=f"{image_id.replace('_', ' | ')}", lvl="warning")
-            cprint(msg=f"Image(s) will be ignored by Clinica.", lvl="warning")
-            input_ids = [
-                f"{p_id}_{s_id}" for p_id, s_id in zip(self.subjects, self.sessions)
-            ]
-            to_process_ids = list(set(input_ids) - set(processed_ids))
-            self.subjects, self.sessions = extract_subjects_sessions_from_filename(
-                to_process_ids
-            )
-
         # Inputs from anat/ folder
         # ========================
         # anat image file:

clinica/utils/bids.py

@@ -12,11 +12,31 @@
     "BIDS_VERSION",
     "Extension",
     "Suffix",
+    "Visit",
 ]
 
 BIDS_VERSION = Version("1.7.0")
 
 
+@dataclass(frozen=True)
+class Visit:
+    subject: str
+    session: str
+
+    def __lt__(self, obj):
+        return (self.subject < obj.subject) or (
+            self.subject == obj.subject and self.session < obj.session
+        )
+
+    def __gt__(self, obj):
+        return (self.subject > obj.subject) or (
+            self.subject == obj.subject and self.session > obj.session
+        )
+
+    def __str__(self) -> str:
+        return f"{self.subject} {self.session}"
+
+
 class Extension(str, Enum):
     """Possible extensions in BIDS file names."""
 

clinica/utils/filemanip.py

@@ -2,12 +2,15 @@
 from pathlib import Path
 from typing import Callable, List, Optional, Union
 
+from .bids import Visit
+
 __all__ = [
     "UserProvidedPath",
     "delete_directories",
     "delete_directories_task",
     "extract_crash_files_from_log_file",
     "extract_image_ids",
+    "extract_visits",
     "extract_metadata_from_json",
     "extract_subjects_sessions_from_filename",
     "get_filename_no_ext",
@@ -365,6 +368,13 @@ def extract_image_ids(bids_or_caps_files: list[str]) -> list[str]:
     return id_bids_or_caps_files
 
 
+def extract_visits(bids_or_caps_files: list[str]) -> list[Visit]:
+    return [
+        Visit(*image_id.split("_"))
+        for image_id in extract_image_ids(bids_or_caps_files)
+    ]
+
+
 def extract_subjects_sessions_from_filename(
     bids_or_caps_files: list[str],
 ) -> tuple[list[str], list[str]]: