Abstract
Intracranial stereotactic radiation therapy (SRS/SRT) with robotic systems demands sub-millimetric geometric accuracy. As image guidance relies on Digitally Reconstructed Radiographs (DRRs) derived from the planning CT for skull tracking along the bone-tissue gradient, accurate reconstruction of this interface drives treatment precision. MRI-only workflows would remove the MRI-CT registration error, but they shift onto the synthesis model the full responsibility of reproducing correct spatial information and Hounsfield Unit (HU) values. We present a modular pipeline for MRI-to-synthetic-CT (sCT) generation, built around a 3D Attention U-Net trained on the SynthRAD2023 Brain dataset (180 brain MRI/CT pairs). The pipeline combines additive attention gates on skip connections with a bone-weighted composite loss, a cranial-specific HU normalization window ([-1000, 2000], bone defined above 300 HU), bone-aware patch sampling, Exponential Moving Average (EMA) of the weights, and Hann-weighted sliding window inference. We compared four architectures under one shared protocol: a conditional GAN, a denoising diffusion probabilistic model (DDPM), a standard 3D U-Net, and the proposed 3D Attention U-Net. Attention U-Net provided the best HU accuracy, with a global MAE of ~106 HU and a bone-region MAE of ~195 HU. A per-patient reliability assessment module then combines bone geometry, DRR consistency, and HU accuracy into a single confidence score, providing a structured retrospective framework for assessing case-wise suitability in stereotactic workflows.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MIART_025.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: Not Available
BibTex
@InProceedings{PagDan_MRItoSynthetic_MICCAISAT2026,
author = { Paglialunga, Danilo AND Belardo, Alfonso AND Ubeira Gabellini, Maria Giulia AND Raggio, Simone AND Broggi, Sara AND Palazzo, Gabriele AND Bordigoni, Bianca AND Gragnaniello, Gianluca AND del Vecchio, Antonella AND Placidi, Lorenzo AND Fiorino, Claudio},
title = { { MRI-to-Synthetic CT for Intracranial Stereotactic Radiotherapy: a Comparative Study of Deep Learning Architectures with Focus on Bone Fidelity } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17274},
month = {pending},
page = {pending}
}