Quantitative susceptibility mapping through model-based deep image prior (MoDIP).
Neuroimage
; 291: 120583, 2024 May 01.
Article
de En
| MEDLINE
| ID: mdl-38554781
ABSTRACT
The data-driven approach of supervised learning methods has limited applicability in solving dipole inversion in Quantitative Susceptibility Mapping (QSM) with varying scan parameters across different objects. To address this generalization issue in supervised QSM methods, we propose a novel training-free model-based unsupervised method called MoDIP (Model-based Deep Image Prior). MoDIP comprises a small, untrained network and a Data Fidelity Optimization (DFO) module. The network converges to an interim state, acting as an implicit prior for image regularization, while the optimization process enforces the physical model of QSM dipole inversion. Experimental results demonstrate MoDIP's excellent generalizability in solving QSM dipole inversion across different scan parameters. It exhibits robustness against pathological brain QSM, achieving over 32 % accuracy improvement than supervised deep learning methods. It is also 33 % more computationally efficient and runs 4 times faster than conventional DIP-based approaches, enabling 3D high-resolution image reconstruction in under 4.5 min.
Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Encéphale
/
Félodipine
Limites:
Humans
Langue:
En
Journal:
Neuroimage
Sujet du journal:
DIAGNOSTICO POR IMAGEM
Année:
2024
Type de document:
Article
Pays d'affiliation:
Australie
Pays de publication:
États-Unis d'Amérique