ScanVerse360 aims to open source and standardize Total-Body PET/CT datasets. We have utilized a proposed standardization methodology from MedUniWien as a baseline approach and updated accordingly in our efforts to contribute to the open-source initiative. It's designed to ensure easy navigation, consistent representation, and programmable data retrieval.
sv360_metadata.json
cohort_name_a__99990130__99990230/
├── ...
├── ...
│ ├── ...
cohort_name_b__99990130__99990230/
├── metadata.json
├── sub001/
│ ├── scan01/
│ │ ├── pt/
│ │ │ ├── static/
│ │ │ ├── dynamic/
│ │ │ ├── listmode/
│ │ │ ├── parametric/
│ │ ├── acct/
│ ├── scan02/
│ │ ├── pt/
│ │ │ ├── static/
│ │ │ ├── dynamic/
│ │ │ ├── listmode/
│ │ │ ├── parametric/
│ │ ├── acct/
├── sub002/
│ ├── scan01/
│ │ ├── pt/
│ │ │ ├── static/
│ │ │ ├── dynamic/
│ │ │ ├── listmode/
│ │ │ ├── parametric/
│ │ ├── acct/
│ ├── scan02/
│ │ ├── pt/
│ │ │ ├── static/
│ │ │ ├── dynamic/
│ │ │ ├── listmode/
│ │ │ ├── parametric/
│ │ ├── acct/
Top level directory structure consists of cohort (or Project Folders); these
folders serve as groupings for studies. Folder naming should be unique and
follow the naming convention of: PROJECTNAME_STARTDATE_ENDDATE.
Both start date and end date should be in the format of: YYYYMMDD
The sv360_metadata.json file serves as a comprehensive overview & description
of available projects within the collective open source data set:
[
{
"project_name": "Project 1",
"short_desc": "Lorem ipsum 1",
"start": "YYYYMMDD1",
"end": "YYYYMMDD1",
"objective": "Lorem Ipsum 1",
"principal_investigator": "Name 1",
"principal_investigator_contact": "contact 1",
"avail_data": "Lorem Ipsum 1",
"institutions": [
{
"name": "Institution 1",
"ct_parm": {
"machine": "SIEMENS",
"settings": "120kV,80mAs",
"modality": "CT",
"study_type": "STATIC (3D)"
},
"pt_parm": {
"machine": "SIEMEN",
"modality": "PT",
"radiosotope": "F-18 FDG",
"injected_dose": "3.7-5.2 MBq/kg",
"scan_duration": "60-90 MIN POST INJECTION"
},
"sub_demographic": {
"age_range": "45-75",
"gender_distribution": "55% MALE, 45% Female",
"total_sub": "> 800"
}
},
{
"name": "Institution 2",
"ct_parm": {
"machine": "SIEMENS",
"settings": "Custom settings for Institution 2",
"modality": "CT",
"study_type": "Custom study type for Institution 2"
},
"pt_parm": {
"machine": "SIEMEN",
"modality": "PT",
"radiosotope": "Custom radiosotope for Institution 2",
"injected_dose": "Custom dose for Institution 2",
"scan_duration": "Custom duration for Institution 2"
},
"sub_demographic": {
"age_range": "Custom age range for Institution 2",
"gender_distribution": "Custom gender distribution for Institution 2",
"total_sub": "Custom total subjects for Institution 2"
}
}
]
},
{
"project_name": "Project 2",
"short_desc": "Lorem ipsum 2",
"start": "YYYYMMDD2",
"end": "YYYYMMDD2",
"objective": "Lorem Ipsum 2",
"principal_investigator": "Name 2",
"principal_investigator_contact": "contact 2",
"avail_data": "Lorem Ipsum 2",
"institutions": [
{
"name": "Institution 3",
"ct_parm": {
"machine": "SIEMENS",
"settings": "Settings for Institution 3",
"modality": "CT",
"study_type": "Study type for Institution 3"
},
"pt_parm": {
"machine": "SIEMEN",
"modality": "PT",
"radiosotope": "Radiosotope for Institution 3",
"injected_dose": "Dose for Institution 3",
"scan_duration": "Duration for Institution 3"
},
"sub_demographic": {
"age_range": "Age range for Institution 3",
"gender_distribution": "Gender distribution for Institution 3",
"total_sub": "Total subjects for Institution 3"
}
}
]
}
]
- PT: Positron Emission Tomography
- ACCT: Computed Tomography - Attenuation Correction
- static: Static PET data
- dynamic: Dynamic PET data
- listmode: List-mode PET data
- parametric: Parametric PET images
The metadata.json file serves as a comprehensive overview of the dataset for
each cohort/project:
{
"cohort": "LungCancerPatients",
"description": "A collection of PET/CT datasets for lung cancer patients.",
"directory_key": {
"pet": "Positron Emission Tomography",
"acct": "Computed Tomography - Attenuation Correction",
"static": "Static PET data",
"dynamic": "Dynamic PET data",
"listmode": "List-mode PET data",
"parametric": "Parametric PET images"
},
"expected_datasets": ["static", "dynamic", "listmode", "parametric", "ACCT"],
"subjects": [
{
"id": "sub001",
"scans": [
{
"scan_id": "scan01",
"datasets": {
"static": true,
"dynamic": true,
"listmode": true,
"parametric": true,
"ACCT": true
}
},
{
"scan_id": "scan02",
"datasets": {
"static": true,
"dynamic": true,
"listmode": true,
"parametric": true,
"ACCT": true
}
}
]
},
{
"id": "sub002",
"scans": [
{
"scan_id": "scan01",
"datasets": {
"static": true,
"dynamic": true,
"listmode": true,
"parametric": true,
"acct": true
}
},
{
"scan_id": "scan02",
"datasets": {
"static": true,
"dynamic": false,
"listmode": false,
"parametric": true,
"acct": true
}
}
]
}
]
}Before diving into generating metadata.json, there are naming conventions to
be followed for subjects and their scans:
-
Subjects: Each subject should have a name in the format
subXXXXwhereXXXXis a unique identifier for the subject. For example:sub0012,sub1204. -
Scans: Each longitudinal scan for a subject should be named as
scanXXXwhereXXXis the identifier for that scan. For example:scan001,scan032.
Adhering to these conventions ensures accurate and consistent metadata
generation. We provide a convenient Python script to generate metadata.json
directly from your dataset's root folder. It works as long as the folder
conventions we've described are adhered to.
Make sure you have Python installed on your machine.
- Copy the script below:
import os
import json
def generate_metadata(root_folder):
metadata = {
"cohort": os.path.basename(root_folder),
"description": f"A collection of PET/CT datasets for {os.path.basename(root_folder)}.",
"directory_key": {
"pt": "Positron Emission Tomography",
"acct": "Computed Tomography - Attenuation Correction",
"static": "Static PET data",
"dynamic": "Dynamic PET data",
"listmode": "List-mode PET data",
"parametric": "Parametric PET images"
},
"expected_datasets": ["static", "dynamic", "listmode", "parametric", "ACCT"],
"subjects": []
}
for subject in sorted(os.listdir(root_folder)):
subject_path = os.path.join(root_folder, subject)
if os.path.isdir(subject_path) and "sub" in subject:
subject_data = {
"id": subject,
"scans": []
}
for scan in sorted(os.listdir(subject_path)):
scan_path = os.path.join(subject_path, scan)
if os.path.isdir(scan_path) and "scan" in scan:
scan_data = {
"scan_id": scan,
"datasets": {
"static": os.path.exists(os.path.join(scan_path, "PET", "static")),
"dynamic": os.path.exists(os.path.join(scan_path, "PET", "dynamic")),
"listmode": os.path.exists(os.path.join(scan_path, "PET", "listmode")),
"parametric": os.path.exists(os.path.join(scan_path, "PET", "parametric")),
"acct": os.path.exists(os.path.join(scan_path, "ACCT"))
}
}
subject_data["scans"].append(scan_data)
metadata["subjects"].append(subject_data)
with open(os.path.join(root_folder, 'metadata.json'), 'w') as f:
json.dump(metadata, f, indent=4)
print(f"metadata.json generated in {root_folder}")
# To execute the script, just call the generate_metadata function with your root folder path.
# For example: generate_metadata('/path/to/your/CohortName')- Save the script in a
.pyfile, e.g.,generate_metadata.py. - Run the script and provide the root folder path.
Example:
python generate_metadata.py- After execution,
metadata.jsonwill be generated in the provided root folder.