ILV: Iterative Latent Volumes for Fast and Accurate Sparse-View CT Reconstruction
ILV: Iterative Latent Volumes for Fast and Accurate Sparse-View CT Reconstruction explores ILV is a novel framework for fast and accurate 3D reconstruction from sparse-view CT projections, enhancing clinical imaging workflows.. Commercial viability score: 8/10 in Medical Imaging.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
MVP Investment
$9K - $13K
6-10 weeksEngineering
$8,000GPU Compute
$800SaaS Stack
$300Domain & Legal
$1006mo ROI
0.5-1x
3yr ROI
6-15x
GPU-heavy products have higher costs but premium pricing. Expect break-even by 12mo, then 40%+ margins at scale.
Related Papers
- Builds On ThisA Diffusion-Based Generative Prior Approach to Sparse-view Computed TomographyViability: 2.0
- Builds On ThisHICT: High-precision 3D CBCT reconstruction from a single X-rayViability: 7.0
- Builds On ThisRevis: Sparse Latent Steering to Mitigate Object Hallucination in Large Vision-Language ModelsViability: 6.0
- Builds On ThisRVLM: Recursive Vision-Language Models with Adaptive DepthViability: 7.0
- Builds On ThisMAESIL: Masked Autoencoder for Enhanced Self-supervised Medical Image Learning