UC2: AI Neuroimaging Enhancement

📡 Three-Panel MRI Quality Comparison

3T Input

Standard clinical scanner

AI Enhanced

3D U-Net + Diffusion Model

7T Ground Truth

Research-grade reference

Before / After Comparison — Drag to reveal

7T Ground Truth

3T Input

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📊 Image Quality Metrics

PSNR

Peak Signal-to-Noise Ratio (higher = better)

28 dB

34 dB

02040 dB

3T Baseline

28.0 dB

Enhanced

34.0 dB

SSIM

Structural Similarity Index (1.0 = perfect)

0.82

0.94

00.51.0

3T Baseline

0.82

Enhanced

0.94

LPIPS

Perceptual Similarity (lower = better)

0.18

0.06

00.150.30

3T Baseline

0.18

Enhanced

0.06

🔀 Training Pipeline — Domain Adaptation Strategy

✅

Phase 1: Baseline

Complete

Adult paired 3T/7T data (30 subjects)
3D U-Net backbone training
HCP dataset (Human Connectome)
PSNR: 31.2 dB achieved

⏳

Phase 2: Adaptation

In Progress — 65%

Pediatric domain adaptation
dHCP (developing Human Connectome)
Calgary Preschool Dataset
HCP-Development cohort

🕐

Phase 3: Fine-tuning

Pending

Diffusion model refinement
Synthetic paired generation
Pediatric-specific anatomies
Target: PSNR ≥ 34 dB

☁️ AWS Training Architecture

🗄️

Amazon S3

NIfTI volumes

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💾

FSx Lustre

High-throughput I/O

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💻

SageMaker HyperPod

p4d.24xlarge • 8×A100

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📦

Model Registry

Versioned artifacts

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✨

Custom Model Import

Bedrock inference

⚡ Checkpointless Training: Automatic recovery in minutes (not hours) — critical for multi-day 3D volume training runs on 8-GPU clusters

💲 Cost & Access Impact

📡

Traditional 7T MRI Scan

$3,000–5,000

Per scan session

Only ~100 machines worldwide

Requires specialized facility

2-4 week scheduling lead time

1,500×–2,500× cheaper

per scan

→

✨

AI-Enhanced 3T → 7T

~$2

Per inference on AWS

Any hospital with a 3T scanner

Results in minutes

Scalable to any volume

🎯 Model Confidence & Uncertainty Estimation

Confidence Heatmap — Axial Slice

Low → High Confidence

Dual-Output Architecture

The model outputs both an enhanced image AND a per-voxel confidence map. Regions where the model is uncertain are explicitly flagged for neuroradiologist review.

High confidence (>0.9) — White matter, ventricles, well-represented anatomy

Medium confidence (0.7-0.9) — Cortical boundaries, sulcal depth

Low confidence (<0.7) — Pathological tissue, motion artifacts

⚠️ Known Limitations & Constraints

Training Data Bias

Primarily trained on adult data. Pediatric myelination patterns (ages 0-5) may have reduced quality until Phase 3 fine-tuning.

Pathological Tissue

Enhancement may not generalize to lesions or malformations absent from training data. Confidence map flags these regions.

Motion Artifacts

Significant motion corruption produces unreliable enhancements. QC pipeline rejects inputs below quality threshold.

Not Diagnostic Standalone

Research tool, not FDA-cleared diagnostic. Clinical decisions require neuroradiologist review and AI Review Board approval.