autoPET

Database

The PSMA cohort includes pre- and/or post-therapeutic PET/CT images of male individuals with prostate carcinoma, encompassing images with (537) and without PSMA-avid tumor lesions (60). Notably, the training datasets exhibit distinct age distributions: the FDG UKT cohort spans 570 male patients (mean age: 60; std: 16) and 444 female patients (mean age: 58; std: 16), whereas the PSMA MUC cohort tends to be older, with 378 male patients (mean age: 71; std: 8). Additionally, there are variations in imaging conditions between the FDG PET/CT and PSMA PET/CT cohorts, particularly regarding the types and number of PET/CT scanners utilized for acquisition. The PSMA Munich dataset was acquired using three different scanner types (Siemens Biograph 64-4R TruePoint, Siemens Biograph mCT Flow 20, and GE Discovery 690), whereas the FDG Tübingen dataset was acquired using a single scanner (Siemens Biograph mCT).
All PET/CT data have been acquired on state-of-the-art PET/CT scanners (Siemens Biograph mCT, mCT Flow and Biograph 64, GE Discovery 690) using standardized protocols following international guidelines. CT as well as PET data are provided as 3D volumes consisting of stacks of axial slices. Data provided as part of this challenge consists of whole-body examinations. Usually, the scan range of these examinations extends from the skull base to the mid-thigh level. If clinically relevant, scans can be extended to cover the entire body including the entire head and legs/feet.

PET/CT acquisition protocol

PSMA dataset: Examinations were acquired on different PET/CT scanners (Siemens Biograph 64-4R TruePoint, Siemens Biograph mCT Flow 20, and GE Discovery 690). The imaging protocol mainly consisted of a diagnostic CT scan from the skull base to the mid-thigh using the following scan parameters: reference tube current exposure time product of 143 mAs (mean); tube voltage of 100kV or 120 kV for most cases, slice thickness of 3 mm for Biograph 64 and Biograph mCT, and 2.5 mm for GE Discovery 690 (except for 3 cases with 5 mm). Intravenous contrast enhancement was used in most studies (571), except for patients with contraindications (26).
The whole-body PSMA-PET scan was acquired on average around 74 minutes after intravenous injection of 246 MBq 18F-PSMA (mean, 369 studies) or 214 MBq 68Ga-PSMA (mean, 228 studies), respectively. The PET data was reconstructed with attenuation correction derived from corresponding CT data. For GE Discovery 690 the reconstruction process employed a VPFX algorithm with voxel size 2.73 mm × 2.73 mm × 3.27 mm, for Siemens Biograph mCT Flow 20 a PSF+TOF algorithm (2 iterations, 21 subsets) with voxel size 4.07 mm × 4.07 mm × 3.00 mm, and for Siemens Biograph 64-4R TruePoint a PSF algorithm (3 iterations, 21 subsets) with voxel size 4.07 mm × 4.07 mm × 5.00 mm.

Cohort

Training cases: 597 PSMA studies (378 patients)
A case consists of one 3D whole body FDG-PET volume, one corresponding 3D whole body CT volume and one 3D binary mask of manually segmented tumor lesions on FDG-PET of the size of the PET volume. CT and PET were acquired simultaneously on a single PET/CT scanner in one session; thus PET and CT are anatomically aligned up to minor shifts due to physiological motion.

Download

Jeblick, K., et al. A whole-body PSMA-PET/CT dataset with manually annotated tumor lesions (PSMA-PET-CT-Lesions) (Version 1) [Dataset]. The Cancer Imaging Archive, 2024. DOI: 10.7937/r7ep-3x37

Gatidis S, Kuestner T. A whole-body FDG-PET/CT dataset with manually annotated tumor lesions (FDG-PET-CT-Lesions) [Dataset]. The Cancer Imaging Archive, 2022. DOI: 10.7937/gkr0-xv29

Jeblick, K., et al. A whole-body PSMA-PET/CT dataset with manually annotated tumor lesions (PSMA-PET-CT-Lesions) (Version 1) [Dataset]. The Cancer Imaging Archive, 2024. DOI: 10.7937/r7ep-3x37

Data pre-processing and structure

If you perform the pre-processing step, the TCIA DICOM files are resampled (CT to PET imaging resolution, i.e. same matrix size) and normalized (PET converted to standardized update values; SUV). PET data is standardized by converting image units from activity counts to standardized uptake values (SUV). We recommend to use the resampled CT (CTres.nii.gz) and the PET in SUV (SUV.nii.gz). The mask (SEG.nii.gz) is binary with 1 indicating the lesion. The training and test database have the following structure:

|--- Patient 1
	|--- Study 1
		|--- SUV.nii.gz    (PET image in SUV)
		|--- CTres.nii.gz  (CT image resampled to PET)
		|--- CT.nii.gz     (Original CT image)
		|--- SEG.nii.gz    (Manual annotations of tumor lesions)
		|--- PET.nii.gz    (Original PET image as actictivity counts)
	|--- Study 2            (Potential 2nd visit of same patient)
		|--- ...
|--- Patient 2
	|--- ...

import nibabel as nib
SUV = nib.load(os.path.join(data_root_path, 'PETCT_0af7ffe12a', '08-12-2005-NA-PET-CT Ganzkoerper  primaer mit KM-96698', 'SUV.nii.gz'))

	|--- imagesTr
		|--- tracer_patient1_study1_0000.nii.gz  (CT image resampled to PET)
	
		|--- tracer_patient1_study1_0001.nii.gz  (PET image in SUV)
		|--- ...
	|--- labelsTr
		|--- tracer_patient1_study1.nii.gz       (manual annotations of tumor lesions)
	
	|--- dataset.json                             (nnUNet dataset description)
	|--- dataset_fingerprint.json                 (nnUNet dataset fingerprint)
	
	|--- splits_final.json                        (reference 5fold split)
	
	|--- psma_metadata.csv                        (metadata csv for psma)
	
	|--- fdg_metadata.csv                         (original metadata csv for fdg)

Annotation

PSMA PET/CT training and test data from LMU as well as PSMA PET/CT test data from UKT was annotated by a single reader and reviewed by a radiologist with 5 years of experience in hybrid imaging.
The following annotation protocol was defined:
Step 1: Identification of FDG-avid tumor lesions by visual assessment of PET and CT information together with the clinical examination reports.
Step 2: Manual free-hand segmentation of identified lesions in axial slices.