rbyrct-mart-model

RBYRCT — Multiplicative Algebraic Reconstruction Technique (MART)

This repository hosts a reference implementation of the MART algorithm for use in Ray-by-Ray Computed Tomography (RBYRCT) — a next-generation imaging framework being developed by Janus Sphere Innovations.

RBYRCT integrates steerable X-ray beams and Janus spheres to focus radiation precisely where needed, enabling earlier detection of breast cancer with minimal dose.

✳️ Model Overview

MART is an iterative multiplicative update algorithm that reconstructs an image from sparse or photon-limited X-ray projections.

import numpy as np

def mart(A, b, x0, iters=20, eps=1e-8):
    """
    A: projection matrix [M, N]
    b: measured data [M]
    x0: initial estimate [N]
    """
    x = x0.copy()
    row_sums = A.sum(axis=1) + eps
    for _ in range(iters):
        for i in range(A.shape[0]):
            proj = A[i] @ x + eps
            r = (b[i] + eps) / proj
            x *= np.power(r, A[i] / row_sums[i])
        x = np.clip(x, 0, None)
    return x

📊 Inputs and Outputs

🧠 Applications

• Low-dose breast cancer imaging
• Limited-angle tomography
• Sparse-view reconstruction
• Real-time diagnostic modeling

🧩 Linked Assets

📜 Citation

Gordon, R., Ather, S.H., et al. (2024). Electronic Steering of X-rays (RBYRCT): A Plan to End Breast Cancer. Journal of Artificial Intelligence Research (JAIR).

💡 About Janus Sphere Innovations

Janus Sphere Innovations is an open-science company exploring the intersection of optics, AI, and life — from photon-limited tomography to quantum imaging.

“I know of no better life purpose than to perish in attempting the great and the impossible.” — F. Nietzsche