Longitudinal QSM: Enhancing consistency of multiple time point susceptibility maps via simultaneous reconstruction
Jiye Kim, Hwihun Jeong, Taechang Kim, Eunseon Jeong, Jinhee Jang + 2 more
TLDR
Longitudinal QSM is a new simultaneous reconstruction method that improves the consistency and accuracy of susceptibility maps over multiple time points.
Key contributions
- Introduces Longitudinal QSM, a simultaneous reconstruction framework for multi-time point susceptibility mapping.
- Jointly estimates susceptibility maps across time points, enforcing spatial sparsity of temporal changes.
- Significantly reduces inter-scan variability and accurately recovers simulated lesion changes.
- Stabilizes non-lesion variability while preserving critical lesion-related temporal changes in patient data.
Why it matters
Longitudinal QSM addresses critical limitations in current QSM methods, which struggle with repeatability and sensitivity in tracking subtle brain changes over time. By improving consistency, it offers a more reliable tool for monitoring neurodegenerative diseases, aging, and development.
Original Abstract
Quantitative susceptibility mapping (QSM) has been increasingly applied in longitudinal studies of neurodegenerative diseases and aging to assess temporal alterations in brain iron and myelin. The accuracy of such investigations depends on the repeatability and sensitivity of measurements. However, the ill-posed nature of the QSM processing steps makes the reconstruction vulnerable to background field changes, head orientation changes, noise, and imperfect registration, which compromise repeatability and sensitivity and hinder reliable detection of true changes. To address these limitations, we propose Longitudinal QSM, a simultaneous reconstruction framework that jointly estimates susceptibility maps across time points while enforcing spatial sparsity of temporal changes. The method was evaluated through simulations and in-vivo experiments and compared with conventional reconstruction methods. Longitudinal QSM consistently reduced inter-scan variability and accurately recovered simulated lesion changes. Application to stroke patient and multiple sclerosis patient data further demonstrated that the framework stabilizes non-lesion variability while preserving lesion-related temporal changes. This approach offers a promising tool for monitoring subtle temporal changes in brain iron and myelin in various neurodegenerative diseases as well as throughout aging and development.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.