2026 - Thesis - Accelerate 3D Brain Mapping

Readings

• 📍 Demos
• 📍 2019 - Point-Voxel CNN for Efficient 3D Deep Learning, NIPS
• 2020 - Searching efficient 3d architectures with sparse point-voxel convolution
• 2026 - nvidia/NV-Generate-MR-Brain

1. Comparison between Conventional Structural MRI and Quantitative Susceptibility Mapping (QSM MRI)

2. Model Structures

- Comparison of Contributions under Medical Saliency Perspective (ADNI, Amyloid Mapping)
- The core model is a voxel-wise mapping fθ: X → Y, where learning implicitly assigns region-aware
saliency weights over 3D brain structures to disentangle amyloid-related signals from QSM inputs.

3. Medical Principles for Amyloid Prediction

4. Coding

• 2026 - Let your training 8hrs -> 📍 13mins
• 📍 Armadillo
• Differentiable Physics with Nvidia - Warp 1.11.1

5. Alzheimer’s Disease Neuroimaging Initiative (ADNI)

• A large-scale longitudinal multi-center study initiated in 2004. The dataset includes 3D brain MRI and PET images with associated diagnostic labels and clinical metadata, and is publicly available via the ADNI Image and Data Archive under a data use agreement
• ADNI Database
• The essence of Alzheimer’s disease (AD) is the breakdown of neuronal connections caused by the deposition of amyloid plaques at the microscopic level, PATHFINDER (bioRxiv 2025) addresses how to precisely reconstruct damaged neurons, QSM/MRI Framework (Arxiv 2503) addresses how to quantify plaque burden in vivo using imaging
• Data alignment: Microscopic data (PATHFINDER) and MRI data (ADNI) differ in spatial scale by several orders of magnitude. Instead of directly feeding them into the same model, you need to learn their representation mapping, 3D U-Net or A Medical GAN
• Python + PyTorch (deep learning) + ANTs (image registration) + MEDI (QSM reconstruction)
• Based on 3D deep learning, Spatial Mapping Reconstruction from QSM magnetic signals to Amyloid pathological signals is achieved, Why:
  • PET scan: Can directly visualize amyloid plaques in the brain, but it is expensive, involves radiation, and is not available in many hospitals
  • QSM MRI (Input): A newer MRI technique, highly sensitive to magnetic materials in the brain (such as iron deposits and plaques). It is inexpensive and safe
  • Thesis task: Use AI to find patterns between QSM signals and PET plaque distribution.

ADNI Cohort
│
├── QSM MRI (in vivo)
│     ├── QSM reconstruction & normalization
│     ├── Spatial registration to PET space
│     └── 3D volume cropping / resampling
│
├── Amyloid PET (reference standard)
│     ├── ADNI-standard preprocessing
│     ├── Intensity normalization
│     └── Co-registration with QSM
│
▼
3D QSM Volume
│
▼
Encoder: BrainIAC-Pretrained 3D Vision Transformer
│   (global contextual representation learning)
│
▼
Latent Cross-Modal Representation
│
▼
Alignment Module
│   (Conditional Diffusion or GAN-based refinement)
│
▼
Decoder
│
▼
Predicted Amyloid Burden Map
(continuous voxel-wise 3D estimate)
│
▼
Loss Optimization
│   ├── Structural Similarity (SSIM)
│   ├── Perceptual Loss (VGG-based)
│   └── Intensity Consistency Loss
│
▼
Voxel-wise Quantification of Cerebral Amyloid Plaque Burden

Overview of the ADNI Dataset

Participant Identifiers and Longitudinal Indexing

Diagnostic Group Distribution

Neuroimaging Data (Raw and Processed)

Brain Signals (Why Median + MAD)

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References

• If a team / mentor can tolerate you saying "This has no information" and listen carefully to the rest of your sentence, then it is a very good peer / team

• Earthdata Plugin
• DiffusionDrive, CVPR highlight 2025.
• Development of the Nervous System, Prof. Dr. Stoeckli Esther