Designing contrasts for rapid, simultaneous parameter quantification and flow visualization with quantitative transient-state imaging
Not on the map. Why?
Abstract (arXiv)
Magnetic resonance imaging (MRI) is a remarkably powerful diagnostic technique: it generates wide-ranging information for the non-invasive study of tissue anatomy and physiology. Complementary data is normally obtained in separate measurements, either as contrast-weighted images, which are fast and simple to acquire, or as quantitative parametric maps, which offer an absolute quantification of underlying biophysical effects, such as relaxation times or flow. Here, we demonstrate how to acquire and reconstruct data in a transient-state with a dual purpose: 1 - to generate contrast-weighted images that can be adjusted to emphasise clinically relevant image biomarkers; exemplified with signal modulation according to flow to obtain angiography information, and 2 - to simultaneously infer multiple quantitative parameters with a single, highly accelerated acquisition. This is a achieved by introducing three novel elements: a model that accounts for flowing blood, a method for sequence design that incorporates both parameter encoding and signal contrast, and the reconstruction of temporally resolved contrast-weighted images. From these images we simultaneously obtain angiography projections and multiple quantitative maps. By doing so, we increase the amount of clinically relevant data without adding measurement time, creating new dimensions for biomarker exploration and adding value to MR examinations for patients and clinicians alike.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jan 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 3.
3 papers.
- 2024Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprintingcited by 3
- 2024StoDIP: Efficient 3D MRF image reconstruction with deep image priors and stochastic iterationscited by 2
- 2024MARVEL: MR Fingerprinting with Additional micRoVascular Estimates using bidirectional LSTMscited by 0
- 2024Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting29 Oct 2024
- 2024StoDIP: Efficient 3D MRF image reconstruction with deep image priors and stochastic iterations5 Aug 2024
- 2024MARVEL: MR Fingerprinting with Additional micRoVascular Estimates using bidirectional LSTMs15 Jul 2024