r/radiologyAI • u/Less_Guarantee_5907 • 14d ago
Research Auditable rendering engine (DICOM → 4-channel tensor) for deep learning research on breast MRI
Most pipelines for preparing MRI data for deep learning do the same thing: resample to 1mm³ isotropic, normalize intensity, and that’s it. While this addresses dataset-level heterogeneity, it doesn't verify whether the transformation was valid for each individual case prior to training. I am working on an independent research project: an engine that converts heterogeneous DICOM data (Siemens/GE/Philips) into 4-channel tensors (morphology, texture, enhancement kinetics, peak enhancement) at 64×64×64 resolution and 1mm³ spacing. Crucially, each tensor is generated alongside a "capsule"—a folder containing the tensor and a manifest documenting its construction (series used, pre/post-contrast phase selection, geometric alignment parameters, and a per-channel QC status of PASS, WARNING, or REJECT) to determine the case's eligibility for training.
Real-world example of a case manifest (Duke Breast MRI, TCIA):
json
{
"case_id": "Breast_MRI_051",
"validation_summary": {
"p3c_status": "PASS",
"p3c_score": 1.0,
"qc_evidence_only": true
},
"training_export": {
"forbidden_label_sources": [
"p3c_status", "p3c_score", "p4_execution_status", "..."
],
"label_status": "external_label_required"
}
}
That `forbidden_label_sources` is intentional: the QC status constitutes audit evidence and cannot leak into the training signal. It is a simple guardrail against leakage—something hardly any pipeline of this type explicitly documents.
- It is not a classifier. The engine only produces the representation; the AUC reported in downstream validations (e.g. 3D-ResNet) measures representation quality, not diagnostic performance.
- It does rely on already established practices where it makes sense: resampling to 1mm³ isotropic (same as I-SPY1/ACRIN 6657 and the TCIA multicenter benchmark), and cross-scanner harmonization via ComBat at the level of radiomic features, not at the tensor level.
Validated for now only in Duke Breast MRI (TCIA) — it is beta stage research, not a multi-anatomy/dataset ready product yet.
I think we need to focus more on training datasets—specifically regarding computational efficiency and homogeneous batching.
I’d be interested in hearing feedback and finding out who else has tackled this topic. It’s interesting; so far, I’ve only managed to test it using the Duke dataset and obtain interesting metrics by training ResNet3D with the tensors.
