Richard Osuala
Universitat de Barcelona, Spain
Challenge Co-LeadContext
Dynamic contrast-enhanced MRI (DCE-MRI) plays a central role in breast cancer management, but its reliance on gadolinium-based contrast agents raises various concerns. MAMA-SYNTH introduces a standardized, clinically informed benchmark for evaluating generative models, with the goal of advancing the development of contrast-reduced and contrast-free breast MRI protocols.
Why?64
Gd
Gadolinium
Side Effects
Gadolinium deposits, even from chelated agents, can lead to long-term accumulation, potential neurotoxicity and trigger nephrogenic systemic fibrosis.
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Contamination
Gadolinium is detected in drinking water supplies worldwide and beverages, raising concerns about long-term exposure and environmental health impacts.
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Accessibility
Gadolinium contrast agents significantly increase the cost of MRI examinations, limiting accessibility in resource-constrained settings.
Task
The task of the challenge is to synthesize single-timepoint 2-dimensional post-contrast breast DCE-MRI slices from corresponding pre-contrast T1-weighted MRI inputs using paired clinical data. Participating algorithms operate on pre-contrast images and generate synthetic peak enhancement post-contrast output.
Pre-contrast
🤖
→
Peak-enhancement
Data
The challenge utilizes diverse datasets to ensure algorithmic generalizability across different scanners and populations:
Training Cohort: MAMA-MIA Dataset
This dataset contains pre-treatment DCE-MRI from 1,506 patients from 25 + centers across the United States. The MAMA-MIA dataset was utilized as the training set in the first edition of MAMA Challenges for Primary Tumor Segmentation and Pathologic Complete Response Prediction. Learn more about this benchmark at the MAMA-MIA Challenge 2025 and access the complete dataset at MAMA-MIA Dataset.
Acquisition Plane
Axial
84.4%
Sagittal
15.6%
Magnetic Field Strength
1.5T
72.1%
3T
27.9%
Scanner Manufacturers
GE
64.1%
Siemens
27.3%
Philips
8.6%
Testing Cohorts
Radboud University Medical Centre
The Netherlands
Dataset: 200 pre-contrast and 200 post-contrast DCE-MRI scans.
Acquisition Parameters:
• Plane: Axial (99.5%), Unspecified (0.5%)
• Magnetic Field Strength: 3T (100%)
• Fat Suppression: Yes (100.0%)
• Scanner Manufacturers: Siemens (100%)
• Acquisition Protocol: t1_fl3d_tra_Dixon_W (100%)
• Contrast Agents: DOTAREM (99.0%), GADOVIST (0.5%), Unspecified (0.5%)
• Imaging Parameters: TR 5–10 ms, TE 2–5 ms, flip angle 8–15°, slice thickness ~1 mm
• Image Dimensions: 416×416 pixels (100.0%)
• Pixel Spacing: 0.8654–0.9615 mm (mean: 0.8689 mm, median: 0.8654 mm, SD: 0.0157 mm)
• Slice Thickness Distribution: 1.0 mm (65.7%), 1.1 mm (23%), 1.2 mm (9.0%), 1.3 mm (1.5%), 1.4 mm (1.0%)
Instituto Alexander Fleming
Argentina
Dataset: 100 pre-contrast and 100 post-contrast DCE-MRI scans.
Acquisition Parameters:
• Plane: Axial
• Magnetic Field Strength: 1.5T
• Fat Suppression: Yes (FAT, FAT Classic, Spectral, Water)
• Scanner Manufacturer: GE
• Contrast Agents: DOTAREM, GADOVIST
• Imaging Parameters: TR 4.2 ms, TE 2 ms, flip angle 12°, slice thickness 1.1 mm
• Image Matrix: 512 × 512 pixels
Evaluation Framework
Timeline
| May 1 | Validation Phase Opens | |
| June 15 | Test Phase Opens | |
| June 30 | Last Submission Deadline | |
| August 1 | Official Results Release | |
| October 8 | Winners Announcement at Deep-Breath Workshop (MICCAI 2026) | |
Organization Team
Smriti Joshi
Universitat de Barcelona, Spain
Challenge Co-Lead
Jarek van Dijk
Radboud University Medical Centre, Netherlands
Challenge Co-Lead
Luyi Han
Radboud University Medical Centre, Netherlands
Maria Laura Cosaka
Instituto Alexander Fleming, Argentina
Daniel Mysler
Instituto Alexander Fleming, Argentina
Lidia Garrucho
Universitat de Barcelona, Spain
Karim Lekadir
Universitat de Barcelona & ICREA, Spain
Simone Balocco
Universitat de Barcelona & CVC, Spain
Oliver Diaz
Universitat de Barcelona & CVC, Spain
Partners & Institutions
Contact
For inquiries about the MAMA-SYNTH challenge, feel free to reach out to Richard Osuala (richard.osuala@ub.edu), and Smriti Joshi (smriti.joshi@ub.edu).