Ross
63ea4cdf23
- Implemented asynchronous downsample task for series with progress tracking. - Added new URL endpoint for downsample status. - Updated Series model to include source_series_instance_uid. - Enhanced case display template with new buttons for series actions. - Added password reset functionality in user profile with appropriate alerts. - Created migration for new source_series_instance_uid field in Series model.
583 lines
22 KiB
Python
583 lines
22 KiB
Python
from time import sleep
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from django.core.mail import send_mail
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from django.http import HttpResponse
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from django.shortcuts import get_object_or_404
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from enum import Enum
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try:
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from django_tasks import task
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HAS_DJANGO_TASKS = True
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except ImportError:
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from celery import shared_task
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from celery.result import AsyncResult
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HAS_DJANGO_TASKS = False
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class _CompatTaskResultStatus(Enum):
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READY = "READY"
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RUNNING = "RUNNING"
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FAILED = "FAILED"
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SUCCESSFUL = "SUCCESSFUL"
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class _CompatError:
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def __init__(self, traceback):
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self.traceback = traceback
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class _CompatTaskResult:
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def __init__(self, async_result):
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self._async_result = async_result
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@property
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def id(self):
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return self._async_result.id
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@property
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def status(self):
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state = self._async_result.state
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if state in ("PENDING",):
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return _CompatTaskResultStatus.READY
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if state in ("STARTED", "RETRY"):
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return _CompatTaskResultStatus.RUNNING
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if state == "SUCCESS":
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return _CompatTaskResultStatus.SUCCESSFUL
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if state == "FAILURE":
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return _CompatTaskResultStatus.FAILED
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return _CompatTaskResultStatus.RUNNING
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def refresh(self):
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return self
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@property
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def return_value(self):
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return self._async_result.result
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@property
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def errors(self):
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if self._async_result.state == "FAILURE" and self._async_result.traceback:
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return [_CompatError(self._async_result.traceback)]
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return []
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def task(func=None, *, takes_context=False, **kwargs):
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def decorator(inner_func):
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if takes_context:
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@shared_task(bind=True)
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def wrapped(self, *args, **inner_kwargs):
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class _Ctx:
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class _TaskResultRef:
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id = self.request.id
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task_result = _TaskResultRef()
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attempt = 1
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return inner_func(_Ctx(), *args, **inner_kwargs)
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else:
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wrapped = shared_task(inner_func)
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wrapped.enqueue = wrapped.delay
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wrapped.get_result = lambda result_id: _CompatTaskResult(AsyncResult(result_id))
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return wrapped
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if func is not None:
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return decorator(func)
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return decorator
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from atlas.models import Case, Series, SeriesImage
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from generic.models import CimarCase
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from rad.settings import REMOTE_URL, CIMAR_USERNAME, CIMAR_PASSWORD
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from helpers.cimar import CimarAPI, NotFoundError
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from pydicom.uid import generate_uid
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from django.contrib.auth.models import User
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from django.core.files.base import ContentFile
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from django.core.cache import cache
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import copy
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import io
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@task(takes_context=True)
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def series_downsample_task(context, series_id, user_id, downsample_pct):
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"""Downsample a series asynchronously while preserving hash continuity."""
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from loguru import logger
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from atlas import views as atlas_views
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progress_key = f"series_downsample_progress:{context.task_result.id}"
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cache.set(
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progress_key,
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{"current": 0, "total": 0, "message": "Preparing downsample..."},
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timeout=60 * 60,
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)
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series = get_object_or_404(Series, pk=series_id)
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user = get_object_or_404(User, pk=user_id)
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if not series.check_user_can_edit(user):
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raise PermissionError("Permission denied")
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images = list(series.get_images())
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total_images = len(images)
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replacement_series_uid = generate_uid()
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source_series_uid = series.series_instance_uid
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updated_images = []
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cache.set(
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progress_key,
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{"current": 0, "total": total_images, "message": "Downsample started"},
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timeout=60 * 60,
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)
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for idx, image in enumerate(images, start=1):
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ds = atlas_views._read_series_image_dataset(image)
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if ds is None:
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cache.set(
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progress_key,
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{"current": idx, "total": total_images, "message": f"Skipping unreadable image ({idx}/{total_images})"},
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timeout=60 * 60,
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)
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continue
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try:
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original_hash = image.image_blake3_hash
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ds_out = atlas_views._downsample_dicom_dataset(
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ds,
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int(downsample_pct),
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series_instance_uid=replacement_series_uid,
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source_series_instance_uid=source_series_uid,
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series_description=series.description,
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)
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out_io = io.BytesIO()
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ds_out.save_as(out_io, write_like_original=False)
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out_io.seek(0)
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image.image.save(
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f"downsample_{downsample_pct}_{image.pk}.dcm",
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ContentFile(out_io.getvalue()),
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save=False,
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)
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image.image_blake3_hash = original_hash
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image.save(update_fields=["image", "image_blake3_hash"])
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updated_images.append(image.pk)
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except Exception as exc:
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logger.warning("Downsample failed for image {}: {}", image.pk, exc)
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cache.set(
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progress_key,
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{
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"current": idx,
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"total": total_images,
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"message": f"Downsampling image {idx}/{total_images}",
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},
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timeout=60 * 60,
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)
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if updated_images:
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series.modified = Series.SeriesModifiedChocies.RE
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series.source_series_instance_uid = source_series_uid
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series.series_instance_uid = replacement_series_uid
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series.save(update_fields=["modified", "source_series_instance_uid", "series_instance_uid"])
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cache.set(
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progress_key,
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{
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"current": total_images,
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"total": total_images,
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"message": "Downsample complete",
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},
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timeout=10 * 60,
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)
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return {
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"series_id": series.pk,
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"downsample_pct": int(downsample_pct),
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"updated_images": len(updated_images),
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"series_instance_uid": replacement_series_uid,
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"source_series_instance_uid": source_series_uid,
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}
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@task
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def push_case_to_cimar_task(case_id):
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"""Sends an email when the feedback form has been submitted."""
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case = get_object_or_404(Case, pk=case_id)
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api = CimarAPI()
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api.login(username=CIMAR_USERNAME, password=CIMAR_PASSWORD)
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# We use the same study_uid for all images in a case
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study_uid = generate_uid()
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for series in case.get_series():
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print(f"Upload series: {series}")
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for image in series.images.filter(removed=False):
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data = api.upload_dicom(image.image.path, study_uid=study_uid)
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retries = 5
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delay = 20
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for attempt in range(retries):
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print("attempt 1")
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try:
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cimar_uuid = api.get_study_by_study_uid(data["study_uid"])["uuid"]
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break
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except NotFoundError:
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if attempt < retries - 1:
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sleep(delay)
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delay *= 2
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else:
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cimar_uuid = api.get_study_by_study_uid(data["study_uid"])["uuid"]
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case.cimar_uuid = cimar_uuid
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case.save()
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cimar_case, created = CimarCase.objects.get_or_create(uuid=cimar_uuid)
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cimar_case.refresh_study()
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return 10
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@task(takes_context=True)
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def series_reconstruct_task(
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context,
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series_id,
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user_id,
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recon_planes,
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slice_thickness_val=None,
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slice_spacing_val=None,
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recon_thickness_mode="mean",
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):
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"""Generate reconstructions asynchronously for a series with progress updates."""
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import numpy as np
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from loguru import logger
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from atlas import views as atlas_views
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progress_key = f"series_reconstruct_progress:{context.task_result.id}"
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cache.set(
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progress_key,
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{"current": 0, "total": 0, "message": "Preparing reconstruction..."},
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timeout=60 * 60,
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)
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series = get_object_or_404(Series, pk=series_id)
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user = get_object_or_404(User, pk=user_id)
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if not series.check_user_can_edit(user):
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raise PermissionError("Permission denied")
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images = list(series.get_images())
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dicom_items = []
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for image in images:
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ds = atlas_views._read_series_image_dataset(image)
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if ds is None:
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continue
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try:
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arr = ds.pixel_array
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if arr.ndim != 2:
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continue
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dicom_items.append((image, ds, arr))
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except Exception:
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continue
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if len(dicom_items) < 2:
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raise ValueError("Need at least 2 valid DICOM images in series for reconstruction")
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base_shape = dicom_items[0][2].shape
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dicom_items = [item for item in dicom_items if item[2].shape == base_shape]
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if len(dicom_items) < 2:
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raise ValueError("Not enough consistently-sized slices for reconstruction")
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geom = atlas_views._extract_recon_geometry(dicom_items)
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sorted_items = geom["sorted_items"]
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volume = np.stack([item[2] for item in sorted_items], axis=0)
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template_ds = sorted_items[0][1]
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origin_ipp = geom["origin_ipp"]
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row_dir = geom["row_dir"]
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col_dir = geom["col_dir"]
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normal_dir = geom["normal_dir"]
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native_row_spacing = geom["row_spacing"]
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native_col_spacing = geom["col_spacing"]
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native_z_spacing = geom["native_z_spacing"]
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requested_slice_spacing = float(slice_spacing_val) if slice_spacing_val is not None else None
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requested_slab_thickness = float(slice_thickness_val) if slice_thickness_val is not None else None
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# Determine source acquisition plane from normal vector.
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dominant_normal_axis = int(np.argmax(np.abs(normal_dir)))
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source_plane = {0: "sagittal", 1: "coronal", 2: "axial"}.get(dominant_normal_axis, "unknown")
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# Reorient the reconstructed volume into patient axes order [z, y, x] so
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# generated plane names are anatomically correct regardless of source plane.
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# NOTE: DICOM stores ImageOrientationPatient as:
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# - first triplet: direction across columns (x-axis in pixel grid)
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# - second triplet: direction across rows (y-axis in pixel grid)
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# In _extract_recon_geometry names are historically row_dir/col_dir, so we
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# map axis spacing/indexing explicitly to avoid directional mixups.
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source_axis_dirs = [normal_dir, col_dir, row_dir]
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source_axis_spacings = [float(native_z_spacing), float(native_row_spacing), float(native_col_spacing)]
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source_axis_sizes = [int(volume.shape[0]), int(volume.shape[1]), int(volume.shape[2])]
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source_for_patient_axis = {}
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sign_for_patient_axis = {}
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patient_axes_claimed = set()
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for src_axis, vec in enumerate(source_axis_dirs):
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patient_axis = int(np.argmax(np.abs(vec)))
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if patient_axis in patient_axes_claimed:
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raise ValueError("Could not infer unique source orientation axes for reconstruction")
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patient_axes_claimed.add(patient_axis)
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source_for_patient_axis[patient_axis] = src_axis
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sign_for_patient_axis[patient_axis] = 1 if float(vec[patient_axis]) >= 0 else -1
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src_x = source_for_patient_axis[0]
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src_y = source_for_patient_axis[1]
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src_z = source_for_patient_axis[2]
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spacing_x = source_axis_spacings[src_x]
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spacing_y = source_axis_spacings[src_y]
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spacing_z = source_axis_spacings[src_z]
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vol_zyx = np.transpose(volume, (src_z, src_y, src_x))
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if sign_for_patient_axis[2] < 0:
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vol_zyx = np.flip(vol_zyx, axis=0)
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if sign_for_patient_axis[1] < 0:
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vol_zyx = np.flip(vol_zyx, axis=1)
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if sign_for_patient_axis[0] < 0:
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vol_zyx = np.flip(vol_zyx, axis=2)
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def source_indices_from_patient_zyx(iz, iy, ix):
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src_indices = [0, 0, 0]
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z_index = float(iz)
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y_index = float(iy)
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x_index = float(ix)
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if sign_for_patient_axis[2] < 0:
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z_index = source_axis_sizes[src_z] - 1 - z_index
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if sign_for_patient_axis[1] < 0:
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y_index = source_axis_sizes[src_y] - 1 - y_index
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if sign_for_patient_axis[0] < 0:
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x_index = source_axis_sizes[src_x] - 1 - x_index
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src_indices[src_z] = z_index
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src_indices[src_y] = y_index
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src_indices[src_x] = x_index
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return src_indices
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def position_from_patient_zyx(iz, iy, ix):
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src_k, src_r, src_c = source_indices_from_patient_zyx(iz, iy, ix)
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pos = (
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np.asarray(origin_ipp, dtype=float)
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+ (np.asarray(normal_dir, dtype=float) * (float(src_k) * float(native_z_spacing)))
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+ (np.asarray(col_dir, dtype=float) * (float(src_r) * float(native_row_spacing)))
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+ (np.asarray(row_dir, dtype=float) * (float(src_c) * float(native_col_spacing)))
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)
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return [float(pos[0]), float(pos[1]), float(pos[2])]
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spacing_x = source_axis_spacings[src_x]
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spacing_y = source_axis_spacings[src_y]
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spacing_z = source_axis_spacings[src_z]
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def _reduce_slab(slab, reduce_axis=0):
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if recon_thickness_mode == "max":
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out = np.max(slab, axis=reduce_axis)
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elif recon_thickness_mode == "min":
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out = np.min(slab, axis=reduce_axis)
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else:
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out = np.mean(slab, axis=reduce_axis)
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if np.issubdtype(volume.dtype, np.integer):
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info = np.iinfo(volume.dtype)
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out = np.clip(np.rint(out), info.min, info.max).astype(volume.dtype)
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else:
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out = out.astype(volume.dtype, copy=False)
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return out
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def _slice_indices_for_axis(axis_length, axis_spacing, output_spacing, slab_thickness):
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if output_spacing <= 0:
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raise ValueError("Slice spacing must be greater than 0")
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if slab_thickness <= 0:
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raise ValueError("Slice thickness must be greater than 0")
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axis_positions_mm = np.arange(axis_length, dtype=float) * float(axis_spacing)
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end_pos = axis_positions_mm[-1] if axis_positions_mm.size else 0.0
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start_positions_mm = np.arange(0.0, end_pos + 1e-6, output_spacing, dtype=float)
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if start_positions_mm.size == 0:
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start_positions_mm = np.array([0.0], dtype=float)
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slabs = []
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centers_idx = []
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for start_mm in start_positions_mm:
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end_mm = start_mm + slab_thickness
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mask = (axis_positions_mm >= (start_mm - 1e-6)) & (axis_positions_mm < (end_mm - 1e-6))
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if not mask.any():
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nearest = int(np.argmin(np.abs(axis_positions_mm - start_mm)))
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idxs = np.array([nearest], dtype=int)
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else:
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idxs = np.where(mask)[0].astype(int)
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slabs.append(idxs)
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center_mm = start_mm + (slab_thickness / 2.0)
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centers_idx.append(float(center_mm / axis_spacing) if axis_spacing > 0 else float(idxs[0]))
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return slabs, centers_idx
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plane_builders = {}
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for plane in recon_planes:
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plane_norm = plane.lower()
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if plane_norm == "axial":
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plane_spacing = float(requested_slice_spacing) if requested_slice_spacing is not None else float(spacing_z)
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plane_thickness = float(requested_slab_thickness) if requested_slab_thickness is not None else float(plane_spacing)
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slab_indices, slab_centers_idx = _slice_indices_for_axis(
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int(vol_zyx.shape[0]), float(spacing_z), plane_spacing, plane_thickness
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)
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plane_builders[plane_norm] = {
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"count": int(len(slab_indices)),
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"slice_fn": lambda idx, slabs=slab_indices: _reduce_slab(vol_zyx[slabs[idx], :, :]),
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"pixel_spacing_out": [float(spacing_y), float(spacing_x)],
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"spacing_between_slices_out": float(plane_spacing),
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"slice_thickness_out": float(plane_thickness),
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"image_orientation_out": [1.0, 0.0, 0.0, 0.0, 1.0, 0.0],
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"plane_normal_out": [0.0, 0.0, 1.0],
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"position_fn": lambda idx, centers=slab_centers_idx: position_from_patient_zyx(centers[idx], 0.0, 0.0),
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}
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elif plane_norm == "coronal":
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plane_spacing = float(requested_slice_spacing) if requested_slice_spacing is not None else float(spacing_y)
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plane_thickness = float(requested_slab_thickness) if requested_slab_thickness is not None else float(plane_spacing)
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slab_indices, slab_centers_idx = _slice_indices_for_axis(
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int(vol_zyx.shape[1]), float(spacing_y), plane_spacing, plane_thickness
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)
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plane_builders[plane_norm] = {
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"count": int(len(slab_indices)),
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"slice_fn": lambda idx, slabs=slab_indices: np.flipud(_reduce_slab(vol_zyx[:, slabs[idx], :], reduce_axis=1)),
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"pixel_spacing_out": [float(spacing_z), float(spacing_x)],
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"spacing_between_slices_out": float(plane_spacing),
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"slice_thickness_out": float(plane_thickness),
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"image_orientation_out": [1.0, 0.0, 0.0, 0.0, 0.0, -1.0],
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"plane_normal_out": [0.0, 1.0, 0.0],
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"position_fn": lambda idx, centers=slab_centers_idx: position_from_patient_zyx(float(vol_zyx.shape[0] - 1), centers[idx], 0.0),
|
|
}
|
|
elif plane_norm == "sagittal":
|
|
plane_spacing = float(requested_slice_spacing) if requested_slice_spacing is not None else float(spacing_x)
|
|
plane_thickness = float(requested_slab_thickness) if requested_slab_thickness is not None else float(plane_spacing)
|
|
slab_indices, slab_centers_idx = _slice_indices_for_axis(
|
|
int(vol_zyx.shape[2]), float(spacing_x), plane_spacing, plane_thickness
|
|
)
|
|
plane_builders[plane_norm] = {
|
|
"count": int(len(slab_indices)),
|
|
"slice_fn": lambda idx, slabs=slab_indices: np.flipud(_reduce_slab(vol_zyx[:, :, slabs[idx]], reduce_axis=2)),
|
|
"pixel_spacing_out": [float(spacing_z), float(spacing_y)],
|
|
"spacing_between_slices_out": float(plane_spacing),
|
|
"slice_thickness_out": float(plane_thickness),
|
|
"image_orientation_out": [0.0, 1.0, 0.0, 0.0, 0.0, -1.0],
|
|
"plane_normal_out": [1.0, 0.0, 0.0],
|
|
"position_fn": lambda idx, centers=slab_centers_idx: position_from_patient_zyx(float(vol_zyx.shape[0] - 1), 0.0, centers[idx]),
|
|
}
|
|
|
|
if not plane_builders:
|
|
raise ValueError("No valid reconstruction planes selected")
|
|
|
|
total_slices = sum(v["count"] for v in plane_builders.values())
|
|
cache.set(
|
|
progress_key,
|
|
{"current": 0, "total": total_slices, "message": "Reconstruction started"},
|
|
timeout=60 * 60,
|
|
)
|
|
|
|
processed = 0
|
|
created_series = []
|
|
|
|
for plane_norm, cfg in plane_builders.items():
|
|
recon_series = atlas_views._create_series_derivative(series, f"Recon {plane_norm.title()} ({source_plane.title()} src)")
|
|
|
|
for idx in range(int(cfg["count"])):
|
|
arr2d = cfg["slice_fn"](idx)
|
|
ds_new = copy.deepcopy(template_ds)
|
|
arr2d = np.asarray(arr2d, dtype=dicom_items[0][2].dtype)
|
|
|
|
ds_new.Rows = int(arr2d.shape[0])
|
|
ds_new.Columns = int(arr2d.shape[1])
|
|
ds_new.InstanceNumber = idx + 1
|
|
atlas_views._stamp_derived_dataset(
|
|
ds_new,
|
|
series_instance_uid=recon_series.series_instance_uid,
|
|
source_series_instance_uid=series.series_instance_uid,
|
|
series_description=recon_series.description or f"Recon {plane_norm.title()}",
|
|
derivation_description=(
|
|
f"{plane_norm.title()} reconstruction; mode={recon_thickness_mode}; "
|
|
f"thickness={float(cfg['slice_thickness_out']):.3f}mm; spacing={float(cfg['spacing_between_slices_out']):.3f}mm"
|
|
),
|
|
image_type=["DERIVED", "SECONDARY", "MPR"],
|
|
)
|
|
ds_new.PixelData = arr2d.tobytes()
|
|
ds_new.PixelSpacing = [float(cfg["pixel_spacing_out"][0]), float(cfg["pixel_spacing_out"][1])]
|
|
ds_new.ImageOrientationPatient = cfg["image_orientation_out"]
|
|
ipp = cfg["position_fn"](idx)
|
|
ds_new.ImagePositionPatient = ipp
|
|
ds_new.SliceThickness = float(cfg["slice_thickness_out"])
|
|
ds_new.SpacingBetweenSlices = float(cfg["spacing_between_slices_out"])
|
|
try:
|
|
normal = np.asarray(cfg["plane_normal_out"], dtype=float)
|
|
ds_new.SliceLocation = float(np.dot(np.asarray(ipp, dtype=float), normal))
|
|
except Exception:
|
|
pass
|
|
|
|
out_io = io.BytesIO()
|
|
ds_new.save_as(out_io, write_like_original=False)
|
|
out_io.seek(0)
|
|
|
|
recon_image = SeriesImage(
|
|
series=recon_series,
|
|
position=idx + 1,
|
|
upload_filename=f"recon_{plane_norm}_{idx + 1}.dcm",
|
|
)
|
|
recon_image.image.save(
|
|
f"recon_{plane_norm}_{recon_series.pk}_{idx + 1}.dcm",
|
|
ContentFile(out_io.getvalue()),
|
|
save=False,
|
|
)
|
|
recon_image.save()
|
|
|
|
processed += 1
|
|
cache.set(
|
|
progress_key,
|
|
{
|
|
"current": processed,
|
|
"total": total_slices,
|
|
"message": f"Generating {plane_norm} reconstruction ({processed}/{total_slices})",
|
|
},
|
|
timeout=60 * 60,
|
|
)
|
|
|
|
created_series.append(
|
|
{
|
|
"id": recon_series.pk,
|
|
"url": recon_series.get_absolute_url(),
|
|
"description": recon_series.description or str(recon_series.pk),
|
|
}
|
|
)
|
|
|
|
logger.info(
|
|
"Reconstruction task complete for series {} with {} outputs",
|
|
series.pk,
|
|
len(created_series),
|
|
)
|
|
|
|
cache.set(
|
|
progress_key,
|
|
{
|
|
"current": total_slices,
|
|
"total": total_slices,
|
|
"message": "Reconstruction complete",
|
|
},
|
|
timeout=10 * 60,
|
|
)
|
|
|
|
return {
|
|
"series_id": series.pk,
|
|
"created_series": created_series,
|
|
"target_spacing": float(requested_slice_spacing) if requested_slice_spacing is not None else None,
|
|
"slab_thickness": float(requested_slab_thickness) if requested_slab_thickness is not None else None,
|
|
"mode": recon_thickness_mode,
|
|
"source_plane": source_plane,
|
|
} |