Publications by authors named "M Doneva"
Purpose: This study proposes a novel, contrast-free Magnetic Resonance Fingerprinting (MRF) method using balanced Steady-State Free Precession (bSSFP) sequences for the quantification of cerebral blood volume (CBV), vessel radius (R), and relaxometry parameters (T , T , T *) in the brain.
Methods: The technique leverages the sensitivity of bSSFP sequences to intra-voxel frequency distributions in both transient and steady-state regimes. A dictionary-matching process is employed, using simulations of realistic mouse microvascular networks to generate the MRF dictionary.
Background: Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment of heart disease; however, limitations of CMR include long exam times and high complexity compared to other cardiac imaging modalities. Recently advancements in artificial intelligence (AI) technology have shown great potential to address many CMR limitations. While the developments are remarkable, translation of AI-based methods into real-world CMR clinical practice remains at a nascent stage and much work lies ahead to realize the full potential of AI for CMR.
View Article and Find Full Text PDFPurpose: T mapping is a widely used quantitative MRI technique, but its tissue-specific values remain inconsistent across protocols, sites, and vendors. The ISMRM Reproducible Research and Quantitative MR study groups jointly launched a challenge to assess the reproducibility of a well-established inversion-recovery T mapping technique, using acquisition details from a seminal T mapping paper on a standardized phantom and in human brains.
Methods: The challenge used the acquisition protocol from Barral et al.
Purpose: Liver T mapping techniques typically require long breath holds or long scan time in free-breathing, need correction for inhomogeneities and process composite (water and fat) signals. The purpose of this work is to accelerate the multi-slice acquisition of liver water selective T (wT) mapping in a single breath hold, improving the k-space sampling efficiency.
Methods: The proposed continuous inversion-recovery (IR) Look-Locker methodology combines a single-shot gradient echo spiral readout, Dixon processing and a dictionary-based analysis for liver wT mapping at 3 T.
Article Synopsis
- MRI acquisition is prone to motion artifacts, which can cause significant scanning issues like blurring and ghosting, often requiring rescans.
- Recent developments in AI-based reconstruction techniques aim to enhance MRI efficiency but typically assume no patient movement.
- A new deep learning model was created to detect and quantify motion artifacts in brain MRI, achieving high accuracy rates in distinguishing between motion-corrupted and motion-free scans, and allowing adjustments in reconstruction methods based on detected motion.