Patent Filed · Application 202611033808

Bidirectional
CT ↔ MRI
Brain Translation

QuadGAN is a novel 8-neural-network generative AI framework that translates brain scans bidirectionally between CT and MRI — producing 4 diverse outputs per input for clinician selection, with no paired training data required.

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0
% Brightness
Improvement
0
% Noise
Reduction
1.23
Cohen's d
Effect Size
Real CT brain scan — QuadGAN input Real MRI brain scan — QuadGAN output
CT SCAN
CT → MRI
Input Scan
CT → MRI
Generator G
MRI → CT
Generator F
<1s
CPU Inference
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The Innovation

What is QuadGAN?

A multi-domain generative AI framework enabling simultaneous, unpaired image translation across four imaging domains — a first in medical AI.

Quad-Generator Architecture

Four independent U-Net generators with identical architecture but divergent learned weights produce 4 diverse outputs per input — giving clinicians multiple options to choose the most diagnostically accurate result.

Bidirectional Translation

Simultaneous CT→MRI and MRI→CT translation across four domains (CT, Primary MRI, Auxiliary MRI, Auxiliary CT) using redundant cyclic supervision and multi-scale adversarial learning.

No Paired Training Data

Operates entirely on unpaired CT and MRI datasets — eliminating the need for matched patient scans, which are rarely available in clinical practice. Cycle-consistency loss ensures anatomical fidelity.

The Clinical Problem

CT and MRI are
complementary, not interchangeable

CT
Computed Tomography
Excels at visualizing bones, calcifications, and providing electron-density maps for radiation planning. Fast, widely available.
MRI
Magnetic Resonance Imaging
Provides superior soft-tissue contrast for neurological assessment. Expensive, time-consuming, contraindicated in some patients.
QuadGAN Solution
Synthesizes the missing modality from available scans — reducing patient burden, cost, and enabling multi-modal analysis from a single scan.
Performance Comparison: QuadGAN vs. Standard CycleGAN
Under the Hood

8-Network Architecture

Four generators and four discriminators working in concert through redundant cyclic supervision and cross-cycle consistency constraints.

Input Domains
🧠
Domain X
CT Brain Images
🔬
Domain Y
Primary MRI
📡
Domain Z
Auxiliary MRI
Neural Networks
G
CT→MRI
F
MRI→CT
I
CT→AuxMRI
J
AuxMRI→CT
DX
CT Auth
DY
MRI Auth
DZ
AuxMRI Auth
DA
AuxCT Auth
4 Generators + 4 Discriminators
4 Diverse Outputs
Output G — Synthetic MRI (CT→MRI Primary)
Output F — Synthetic CT (MRI→CT Primary)
Output I — Auxiliary MRI (CT→MRI Aux)
Output J — Auxiliary CT (MRI→CT Aux)
Clinician selects best result

Symmetric U-Net Architecture

6
Encoder layers with LeakyReLU (α=0.3)
6
Decoder layers with ReLU + skip connections
IN
Instance Normalization (patient-specific)
D
50% Dropout in first 3 decoder layers only
T
Tanh output activation

PatchGAN Discriminator

Receptive Field70×70 pixels
Output Shape8×8 patch map
PurposeLocal texture fidelity
AdvantagePrevents blurry outputs
Medical benefitVessel/lesion detail

Loss Weight Configuration

λcyc — Cycle-Consistency 10.0
λid — Identity Loss 5.0
λcross — Cross-Cycle 2.0

High λcyc prioritizes anatomical preservation — critical in medical imaging where hallucinated anatomy is clinically dangerous.

Total Loss Formula

LG = Ladv + λcyc·Lcyc
     + λid·Lid
     + λcross·Lcross
LD = LD_Xadv + LD_Yadv
     + LD_Zadv + LD_Wadv
5000
Total Epochs
18 hours total training time
652
Checkpoint Used
13% completion — intentional early stop
4×
NVIDIA A100
40GB each · 160GB total GPU memory
Optimizer: Adam
β₁0.5
β₂0.999
ε1×10⁻⁸
Base LR0.0002
Two-Phase Learning Rate Schedule
Phase 1 (1–2500)
Constant LR = 0.0002
Free exploration phase
Phase 2 (2501–5000)
Linear decay to 0
Solution refinement phase

12-Stage Deterministic Denoising Pipeline

Applied to all outputs post-generation for clinical-grade image quality.

Stage 1
Lanczos Preprocessing
Stage 2
Gaussian Smoothing
Stage 3
Bilateral Filter
Stage 4
Median Filter
Stage 5
Wavelet Denoise
Stage 6
Histogram Equalization
Stages 7–12
Advanced artifact suppression + quality refinement
Result
68% noise reduction · 53% brightness gain
Performance

Validated Results

QuadGAN outperforms standard CycleGAN across all perceptual and clinically relevant metrics, validated through rigorous statistical analysis.

0
%
Brightness Consistency
Improvement over CycleGAN
0
%
Noise Reduction
Post-processing pipeline
1.23
Cohen's d
Effect Size
Large effect (>0.8 threshold)
<0.01
p-value
Statistical Significance
Highly significant (p < 0.01)

Perceptual Metrics Comparison

Clinical Quality Measures

Built on a Decade of GAN Research

A comprehensive review of 35+ studies (2015–2025) informed the QuadGAN design. The framework directly addresses limitations identified across the literature: narrow one-to-one modality mappings, poor generalization to diverse datasets, and lack of multi-parametric sequence support. QuadGAN's multi-domain approach represents a significant architectural leap beyond existing CycleGAN variants.

Live Conversion Demo

See QuadGAN in Action

Experience real-time CT ↔ MRI translation powered by QuadGAN. Both directions are available — try the live conversion tool hosted on our platform.

CT MRI

Powered by Khush-AI & Art  ·  Patent 202611033808

Network Architecture

QuadGAN 4-Generator Architecture

A novel Quad-Path GAN with 4 generators (G, F, I, J) and 4 discriminators (DX, DY, DZ, DA) enabling simultaneous bidirectional translation across CT, MRI, Synthetic MRI and Synthetic CT domains — with cycle-consistency and cross-domain synthesis paths.

G & F — Bidirectional CT ↔ MRI I — CT → Synthetic MRI J — MRI → Synthetic CT λ_cyc = 10 · λ_id = 5 · λ_cross = 2
QuadGAN Architecture — 4 Generators G F I J and 4 Discriminators DX DY DZ DA with cycle-consistency paths, loss labels, and patent details

Figure 1. QuadGAN Architecture Diagram  ·  Patent Application No. 202611033808  ·  Applicant: Khush-AI & Art  ·  Attorney: United & United

Download Diagram
4
Generators
G · F · I · J
4
Discriminators
DX · DY · DZ · DA
8
Neural Networks
U-Net architecture
4
Image Domains
CT · MRI · Syn.MRI · Syn.CT
Stakeholders

Who Benefits from QuadGAN?

QuadGAN creates value across the entire healthcare and research ecosystem — from bedside to boardroom.

👨‍⚕️

Radiologists & Doctors

Receive 4 candidate synthetic images per scan and select the most diagnostically appropriate output. Reduce patient radiation exposure and scan costs.

Multi-output clinical selection
Anatomical fidelity guaranteed
Sub-second inference speed
Learn more
🏥

Healthcare Providers

Deploy QuadGAN in resource-limited settings where MRI access is restricted. Synthesize missing modalities from available scans to support multi-modal workflows.

Reduce imaging bottlenecks
Works on unpaired datasets
CPU-deployable (no GPU needed)
Learn more
💼

Investors & Partners

Patent-filed technology (Application 202611033808) by Khush-AI & Art targeting a multi-billion dollar medical imaging AI market. Scalable, modality-agnostic architecture.

Filed patent protection
Scalable multi-domain framework
Clear clinical translation path
Learn more
🫀

Patients

Potentially avoid repeat scans when one modality is unavailable or contraindicated. Benefit from faster diagnosis and reduced healthcare costs through AI-synthesized imaging.

Fewer repeat imaging sessions
Reduced radiation exposure
Faster diagnostic turnaround
Learn more
🎓

Academicians & Researchers

A novel multi-domain GAN architecture with full technical documentation. Validated against 35+ prior studies. Open for collaborative research, citation, and extension.

Novel architecture — publishable
Full technical documentation
Reproducible methodology
Learn more

Ready to Collaborate?

Whether you're a clinician, researcher, investor, or patient advocate — we invite you to join the QuadGAN journey.

Get in Touch
Intellectual Property

Patent Protection

Patent Application Filed
Application No. 202611033808

Multi-Generator Diversity System

Four generators with identical U-Net architecture but divergent learned weights, providing multiple conversion options for a single input image.

Instance Normalization for Medical Imaging

Normalization method that processes each patient scan independently, preserving patient-specific intensity characteristics and improving generalization.

Strategic Dropout Placement

Regularization method applying dropout only to the first three decoder layers at 50% rate, balancing overfitting prevention with spatial precision.

Early-Stage Convergence Checkpoint

Training methodology that intentionally stops at 13% completion to optimize the trade-off between quality, diversity, and training time.

IP Summary

Applicant Khush-AI & Art
Application No. 202611033808
Patent Attorney Shravan Kumar Bansal
United & United
Technology Class Medical AI / GAN Architecture
Core Claims 4 primary claims
Status Filed & Active
Vision

Development Roadmap

✓ Done
Phase 1

Research Prototype

  • • QuadGAN architecture design
  • • Training on CT/MRI datasets
  • • Performance benchmarking
  • • Patent application filed
Active
Phase 2

Clinical Validation

  • • Radiologist evaluation studies
  • • Higher resolution training
  • • Multi-site dataset expansion
  • • Peer-reviewed publication
Phase 3

Product Development

  • • Clinical-grade UI/UX
  • • DICOM integration
  • • PACS compatibility
  • • Regulatory pathway planning
Phase 4

Market Deployment

  • • Hospital partnerships
  • • Regulatory clearance
  • • Commercial licensing
  • • Global scale-up
Get Involved

Join the QuadGAN Journey

Whether you're a clinician, researcher, investor, or patient advocate — we want to hear from you. Help us bring QuadGAN from prototype to clinical reality.

🔬

Research Collaboration

Partner with us on clinical validation studies, dataset expansion, and publication.

💡

Investment & Funding

Support the transition from research prototype to clinical-grade medical AI product.

🏥

Clinical Deployment

Pilot QuadGAN in your hospital or imaging center and provide real-world feedback.

Email
atantrad@atantradkhushai.com
Mobile
+91 99100 16981