Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2110.02317

Cartesian dictionary-based native T1 and T2 mapping of the myocardium

Markus Henningsson
View original source ↗Read source document ↗Export metadata ↓

Abstract

Purpose: To implement and evaluate a new dictionary-based technique for native myocardial T1 and T2 mapping using Cartesian sampling. Methods: The proposed technique (Multimapping) consisted of single-shot Cartesian image acquisitions in 10 consecutive cardiac cycles, with inversion pulses in cycle 1 and 5, and T2 preparation (TE: 30ms, 50ms and 70ms) in cycles 8-10. Multimapping was simulated for different T1 and T2, where entries corresponding to the k-space centers were matched to acquired data. Experiments were performed in a phantom, 16 healthy subjects and three patients with cardiovascular disease. Results: Multimapping phantom measurements showed good agreement with reference values for both T1 and T2, with no discernable heart-rate dependency for T1 and T2 within the range of myocardium. In vivo mean T1 in healthy subjects was significantly higher using Multimapping (T1=1114+/-14ms) compared to the reference (T1=991+/-26ms) (p<0.01). Mean Multimapping T2 (47.1+/-1.3ms) and T2 spatial variability (5.8+/-1.0ms) was significantly lower compared to the reference (T2=54.7+/-2.2ms, p<0.001; spatial variability=8.4+/-2.0ms, p<0.01). Increased T1 and T2 was detected in all patients using Multimapping. Conclusions: Multimapping allows for simultaneous native myocardial T1 and T2 mapping with a conventional Cartesian trajectory, demonstrating promising in vivo image quality and parameter quantification results.

Explore related subjects

physics.med-pheess.IV

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Markus Henningsson. 2021-11-22. Cartesian dictionary-based native T1 and T2 mapping of the myocardium. https://doi.org/10.1002/mrm.29143

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Prediction of biological radiation effects based on ionization clusters (nanodosimetry)

This article reviews approaches that link the formation of ionization clusters in nanometric volumes to radiobiological effectiveness. The corresponding models were developed as the field of nanodosimetry developed. Some address early biological radiation effects, such as DNA damage, while most aim to predict cell survival or inactivation. The models also differ in the nanodosimetric quantities considered, with many based on the probability distribution of ionization cluster formation in a single target. Some models account for the synergistic effects of pairs of ionization clusters formed in different targets. Several models feature macroscopic aggregation frameworks based on particle fluence, which are proposed for use in radiotherapy treatment planning, particularly in ion-beam radiotherapy. The models are presented here using harmonized terminology and notation for nanodosimetric quantities. An extension of the conceptual framework of nanodosimetry is also discussed. This extension transitions from a target-centered description to a track-centered description. It also introduces nanodosimetry-based analogs of dosimetric concepts, such as dose and linear energy transfer. This paper traces and summarizes the historical development of nanodosimetry-based biological effect models and discusses conceptual aspects of the models to reveal their underlying assumptions and the extent to which they are mechanistic or merely elucidate correlations. Eventually, an attempt is made to identify the key open questions in this field that still need to be addressed.

physics.med-ph↗
arXiv2026

Contextual Cellular Growth (ConCeG) of neural cells for realistic grey matter tissue generation for diffusion MRI simulations

Accurate interpretation of diffusion magnetic resonance imaging (dMRI) signals in grey matter (GM) remains challenging due to the complex, heterogeneous, and densely packed cellular environment. Numerical phantoms provide a controlled framework for investigating the relationship between microstructure and diffusion signals, yet existing approaches often lack the morphological realism and multi-cellular organisation required to faithfully represent GM tissue. In this work, we introduce Contextual Cellular Growth (ConCeG), a generative framework for creating individual cells or constructing dense, three-dimensional, multi-cellular GM substrates informed by real neuronal and glial morphologies. The method combines topological neuron synthesis with a spatially constrained growth network, allowing for the controlled generation of heterogeneous cellular environments with realistic intra- and extracellular compartments. Synthetic cells are generated using morphological and topological characteristics derived from biological reconstructions. We validate the framework through comparisons of structural features with real cellular data, demonstrating strong agreement in branch order, length, angle, and tortuosity distributions. Power spectrum analysis further shows that both intracellular compartments reproduce the spatial correlations observed in biological tissue. Together, these results show ConCeG provides a biologically grounded framework for generating grey matter substrates suitable for large scale diffusion MRI simulation.

physics.med-ph↗
arXiv2026

Magnetic Field of Firing Neuron in Humans: Measurable by Quantum Sensing MRI?

Firing neurons generate action potentials that propagate along axons to transmit signals supporting cognitive functions. These electrical currents generate magnetic fields, yet direct detection of these neuronal magnetic fields by MRI remains elusive. This Mini Review investigates why this goal has been proven difficult to achieve and whether an emerging approach, quantum sensing MRI, can overcome the challenge.

physics.med-ph↗