ms_detection

MS Detection & Symptom Mapping — Patient-Owned MRI Intelligence for Multiple Sclerosis

This project is an open, modular pipeline designed to analyze a patient’s brain MRI and return two high-value outputs:

1. Whether MS-like lesions are present
2. Which cognitive, emotional, or physical symptom domains those lesions are most likely to affect

The goal is not diagnosis.
The goal is patient-owned insight — giving individuals clarity on their MRI scans over time while keeping a clinician in the loop.

The repository includes the full processing pipeline: ingestion, harmonization, preprocessing, lesion detection, atlas overlap, symptom-domain mapping, and human-readable reporting.

---

🚀 Core Idea

MS patients often undergo years of MRI scans without clear explanations of:

• What changed
• Why it matters
• What symptoms to expect

This tool fills that gap by providing:

• Longitudinal lesion tracking
• Template-aligned, harmonized brain images
• Region-based lesion interpretation
• Symptom inference based on neuroscientific mapping
• Simple, patient-readable summaries

This is the foundation for a future clinical-grade, one-click MRI intelligence engine.

---

🧠 Pipeline Overview

A 6-stage neuroimaging + AI workflow:

---

1. Ingest & Harmonize

• Converts DICOM → NIfTI
• Standardizes voxel spacing and orientation
• Ensures comparability across scanners

File: 01_ingest_and_harmonize.py

---

2. Preprocess & Register to MNI

• N4 bias-field correction
• Skull stripping / brain extraction
• Registers to MNI152 template
• Produces clean, normalized volumes for AI

Files:
02_preprocess_and_register.py
Includes: MNI152_T1_1mm.nii.gz templates

---

3. AI Lesion Detection (Model Inference)

• UNet / nnU-Net–style deep learning
• Generates voxel-level lesion masks
• Returns lesion presence, volume, and distribution

Files:
03_train_lesion_model.py
04_inference_and_longitudinal_report.py

---

4. Longitudinal Lesion Tracking

• Aggregates MRI results across dates
• Computes lesion count, lesion load, and emergence of new lesions
• Builds patient-level progression profiles

Included in: 04_inference_and_longitudinal_report.py

---

5. Symptom–Lesion Mapping

Maps lesions → brain regions → symptom domains.

Lesion–Atlas Overlap

Intersects lesion masks with anatomical atlases.
File: 4_1_loadAtlasAndComputeRegionOverlaps.py

Region → Symptom Mapping

Maps affected regions to cognitive/emotional/motor functions.
File: 4_2_mapRegionsToSymptomDomains.py

Example domains:

• Executive function
• Emotional regulation
• Sensory processing
• Motor coordination
• Memory systems

---

6. Human-Readable Summary

Generates clinical-style interpretation:

Lesions Detected: Yes Most likely affected domains:

Executive function

Emotional regulation

Sensory processing

File: 4_3_scanSummary.py

---

🔥 One-Function API (High-Level Entry Point)

A single call runs the entire pipeline:

from analyze_brain_for_symptoms_image import analyze_brain_for_symptoms

has_lesions, symptoms = analyze_brain_for_symptoms("example.nii.gz")

print("Lesions present:", has_lesions)
print("Likely symptoms:", symptoms)


Example Output:

Lesions present: True
Likely symptoms: ['executive dysfunction', 'emotional regulation challenges']

📁 Repo Structure
ms_detection/
│── 01_ingest_and_harmonize.py
│── 02_preprocess_and_register.py
│── 03_train_lesion_model.py
│── 04_inference_and_longitudinal_report.py
│── 4_1_loadAtlasAndComputeRegionOverlaps.py
│── 4_2_mapRegionsToSymptomDomains.py
│── 4_3_scanSummary.py
│── analyze_brain_for_symptoms_image.py
│── MNI152_T1_1mm.nii.gz
│── data/
└── README.md

🧪 Demo Instructions

• To run a single-image analysis:

'''python analyze_brain_for_symptoms_image.py /path/to/your_mri.nii.gz'''

Notes:

No GPU required for demonstrating the pipeline structure

Lesion model can be:

• A real trained checkpoint
• A stub for demonstration / IP review

🏥 Clinical Vision A future version of this tool supports: Upload MRI (NIfTI or DICOM) Auto-run lesion detection Auto-map regions → symptoms Auto-generate PDF summaries Longitudinal tracking across years

Optional clinician review for safety

A clinical-grade version would include:

One-click PDF export Time-series lesion progression Symptom-domain probability mapping Secure patient data handling (HIPAA-compliant)

🧩 Why This Matters

MS imaging is complex. Patients deserve clarity.

This tool offers:

A sense of control

Explanations for symptoms

A way to track change over time

Better communication with clinicians

It is not diagnostic — it is empowerment through understanding.

📚 Future Work

Train/finetune models on public MS datasets

Expand symptom ontology

Add FLAIR + diffusion support

Build a web app wrapper

Integrate one-click PDF export

Deploy as fog/edge compute module for patient-owned devices

👤 Author

Ali Payne Entrepreneur, engineer, and researcher exploring patient-centered neurotechnology.

Pipeline Diagram
          ┌───────────────────────────┐
          │  Patient-owned MRI data  │
          │   (DICOM from hospitals) │
          └───────────┬──────────────┘
                      │
                      ▼
     ┌───────────────────────────────────────┐
     │  1. Ingest & Harmonize               │
     │  - DICOM → NIfTI                     │
     │  - Standardize orientation/voxel size│
     └───────────────────┬──────────────────┘
                         │
                         ▼
     ┌────────────────────────────────────────────┐
     │  2. Preprocess & Normalize                │
     │  - Bias correction (ANTs N4)              │
     │  - Brain extraction                       │
     │  - Register to MNI                        │
     └───────────────────┬──────────────────────┘
                         │
                         ▼
     ┌────────────────────────────────────────────┐
     │  3. AI Lesion Detection                    │
     │  - UNet / nnU-Net                          │
     │  - Output: voxelwise lesion masks          │
     └───────────────────┬──────────────────────┘
                         │
                         ▼
     ┌────────────────────────────────────────────┐
     │  4. Lesion Quantification & Longitudinal  │
     │  - Lesion count, volume, location         │
     │  - Time-series progression                │
     └───────────────────┬──────────────────────┘
                         │
                         ▼
     ┌────────────────────────────────────────────┐
     │  5. Symptom–Lesion Mapping                │
     │  - Atlas intersection                     │
     │  - Map regions → cognitive/emotional fxn  │
     │  - Generate likely symptom domains        │
     └───────────────────┬──────────────────────┘
                         │
                         ▼
     ┌────────────────────────────────────────────┐
     │  6. Clinical Review & Reporting           │
     │  - Overlays + regional labels             │
     │  - Narrative summary                      │
     └───────────────────────────────────────────┘

# Single-Function Example
image_path = "/mnt/data/your_mri_image.nii.gz"

has_lesions, symptoms = analyze_brain_for_symptoms(image_path)

print("Lesions present:", has_lesions)
print("Likely symptoms:", symptoms)


# Example Output:

Lesions present: True
Likely symptoms: [
    "executive dysfunction",
    "emotional regulation issues",
    "anxiety-like symptoms",
    "interoceptive disturbance",
    "memory issues"
]