--- license: cc0-1.0 size_categories: - 1K T-SYNTH is a synthetic digital breast tomosynthesis (DBT) dataset with four breast fibroglandular density distributions imaged using Monte Carlo x-ray simulations with the publicly available [Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE)](https://github.com/DIDSR/VICTRE) toolkit. ## Dataset Details The dataset has the following characteristics: * Breast density: dense, heterogeneously dense, scattered, fatty * Mass radius (mm): 5.00, 7.00, 9.00 * Mass density: 1.0, 1.06, 1.1 (ratio of mass radiodensity to that of fibroglandular tissue) ### Dataset Description - **Curated by:** [Christopher Wiedeman](https://www.linkedin.com/in/christopher-wiedeman-a0b01014b), [Anastasiia Sarmakeeva](https://www.linkedin.com/in/anastasiia-sarmakeeva/), [Elena Sizikova](https://elenasizikova.github.io/), [Daniil Filienko](https://www.linkedin.com/in/daniil-filienko-800160215/), [Miguel Lago](https://www.linkedin.com/in/milaan/), [Jana Gut Delfino](https://www.linkedin.com/in/janadelfino/), [Aldo Badano](https://www.linkedin.com/in/aldobadano/) - **License:** Creative Commons 1.0 Universal License (CC0) ## Data Acquisition Details **Imaging Modality:** Paired 2D digital mammography (DM) and 3D digital breast tomosynthesis (DBT) images. The DBT images are projected into C-VIEW via the method of (Klein, 2023). **Manufacturer and Model:** Replica of the Siemens detector based on MC-GPU (Badal and Badano, 2009). **Demographics:** All breast phantoms are synthetic and do not represent real human subjects. **Cohort Description:** 9,000 synthetic digital breast tomosynthesis (DBT) samples, distributed across four breast density categories: | Breast Density | Fatty | Scattered | Hetero | Dense | **Total** | | --------- | --------- | --------- | ------- | ------- | --------- | | **Les.-free / Les.-present** | 1350/1350 | 1350/1350 | 900/900 | 900/900 | 4500/4500 | **Annotation Protocols:** Lesion masks and bounding boxes were generated automatically from the phantom. **Data Format and Structure:** Image files are in .raw format. ### Dataset Sources - **Code:** [https://github.com/DIDSR/tsynth-release](https://github.com/DIDSR/tsynth-release) - **Arxiv:** [https://arxiv.org/abs/2507.04038](https://arxiv.org/abs/2507.04038) ## Intended Use T-SYNTH is intended to facilitate testing of AI with pre-computed synthetic digital breast tomosynthesis (DBT) data, complementing the M-SYNTH synthetic mammography dataset. ## Ethical Considerations This work is using synthetically generated data and does not include any real patient-identifiable information. Publication of synthetic data aims to promote transparency, reproducibility, and fairness in medical AI research. We recommend avoiding training models only on synthetic data without appropriate validation. ## Dataset Structure Directory layout: ``` T-SYNTH/data/ ├── cview ├── embed_metadata ├── pretrained_models ├── results └── volumes_subset ``` Descriptions: * **`cview/`** -- contains T-SYNTH C-VIEW images. * **`embed_metadata/`** -- Configuration files needed to reproduce experiments. * **`pretrained_models/`** -- Pretrained models for ```DBT```, ```DM``` and ```diffusion``` experiments to reproduce results from the paper. Note to reproduce you need files from **`embed_metadata/`**. * **`results/`** -- Output data and plots used in the paper (see [T-SYNTH repository](https://github.com/DIDSR/tsynth-release/tree/main/code/notebooks)). Description for each experiment could be found [here](https://github.com/DIDSR/tsynth-release/blob/main/code/README.md#experiment-configuration-map). * **`volumes_subset/`** -- example of volumetric data. The full data set can be downloaded via the following [instructions](https://github.com/DIDSR/tsynth-release/blob/main/code/README.md#optional-download-all-volumes). The data is organized by lesion size, breast density and lesion density. Folder names follow the convention: ```output_cview_det_Victre/device_data_VICTREPhantoms_spic_[LESION_DENSITY]/[BREAST_DENSITY]/2/[LESION_SIZE]/SIM.zip```. Example path in `volumes_subset`: ``` device_data_VICTREPhantoms_spic_1.1/fatty/2/5.0/SIM/D2_5.0_fatty.1/1/ ├── reconstruction1.loc # Lesion coordinates ├── reconstruction1.mhd # Metadata (raw format) ├── reconstruction1.raw # Raw image data └── reconstruction1_mask.h5 # Pixel-level segmentation masks for lesions and tissues ``` ## How to use it The description how to use it could be found [here](https://github.com/DIDSR/tsynth-release/blob/main/code/README.md). ## Citation ```bibtex @article{t-synth, title={T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Images}, author={Christopher Wiedeman, Anastasiia Sarmakeeva, Elena Sizikova, Daniil Filienko, Miguel Lago, Jana G. Delfino, Aldo Badano}, journal={}, volume={}, pages={}, year={2025}, url={https://huggingface.co/papers/2507.04038} } ``` ## Related Links 1. [Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE)](https://cdrh-rst.fda.gov/victre-silico-breast-imaging-pipeline). 2. [M-SYNTH: A Dataset for the Comparative Evaluation of Mammography AI](https://cdrh-rst.fda.gov/m-synth-dataset-comparative-evaluation-mammography-ai). 3. A. Kim*, N. Saharkhiz*, E. Sizikova*, M. Lago, B. Sahiner, J. G. Delfino, A. Badano. [S-SYNTH: Knowledge-Based, Synthetic Generation of Skin Images](https://github.com/DIDSR/ssynth-release). MICCAI 2024. 4. [FDA Catalog of Regulatory Science Tools to Help Assess New Medical Devices](https://www.fda.gov/medical-devices/science-and-research-medical-devices/catalog-regulatory-science-tools-help-assess-new-medical-devices).