16 research outputs found
Motion Compensated Unsupervised Deep Learning for 5D MRI
We propose an unsupervised deep learning algorithm for the motion-compensated
reconstruction of 5D cardiac MRI data from 3D radial acquisitions. Ungated
free-breathing 5D MRI simplifies the scan planning, improves patient comfort,
and offers several clinical benefits over breath-held 2D exams, including
isotropic spatial resolution and the ability to reslice the data to arbitrary
views. However, the current reconstruction algorithms for 5D MRI take very long
computational time, and their outcome is greatly dependent on the uniformity of
the binning of the acquired data into different physiological phases. The
proposed algorithm is a more data-efficient alternative to current
motion-resolved reconstructions. This motion-compensated approach models the
data in each cardiac/respiratory bin as Fourier samples of the deformed version
of a 3D image template. The deformation maps are modeled by a convolutional
neural network driven by the physiological phase information. The deformation
maps and the template are then jointly estimated from the measured data. The
cardiac and respiratory phases are estimated from 1D navigators using an
auto-encoder. The proposed algorithm is validated on 5D bSSFP datasets acquired
from two subjects.Comment: MICCAI 2023 conference pape
HyperSLICE: HyperBand optimized spiral for low-latency interactive cardiac examination
PURPOSE: Interactive cardiac MRI is used for fast scan planning and MR-guided interventions. However, the requirement for real-time acquisition and near-real-time visualization constrains the achievable spatio-temporal resolution. This study aims to improve interactive imaging resolution through optimization of undersampled spiral sampling and leveraging of deep learning for low-latency reconstruction (deep artifact suppression). METHODS: A variable density spiral trajectory was parametrized and optimized via HyperBand to provide the best candidate trajectory for rapid deep artifact suppression. Training data consisted of 692 breath-held CINEs. The developed interactive sequence was tested in simulations and prospectively in 13 subjects (10 for image evaluation, 2 during catheterization, 1 during exercise). In the prospective study, the optimized framework-HyperSLICE- was compared with conventional Cartesian real-time and breath-hold CINE imaging in terms quantitative and qualitative image metrics. Statistical differences were tested using Friedman chi-squared tests with post hoc Nemenyi test (p < 0.05). RESULTS: In simulations the normalized RMS error, peak SNR, structural similarity, and Laplacian energy were all statistically significantly higher using optimized spiral compared to radial and uniform spiral sampling, particularly after scan plan changes (structural similarity: 0.71 vs. 0.45 and 0.43). Prospectively, HyperSLICE enabled a higher spatial and temporal resolution than conventional Cartesian real-time imaging. The pipeline was demonstrated in patients during catheter pull back, showing sufficiently fast reconstruction for interactive imaging. CONCLUSION: HyperSLICE enables high spatial and temporal resolution interactive imaging. Optimizing the spiral sampling enabled better overall image quality and superior handling of image transitions compared with radial and uniform spiral trajectories
Development of whole-heart myocardial perfusion magnetic resonance imaging
Myocardial perfusion imaging is of huge importance for the detection of
coronary artery disease (CAD), one of the leading causes of morbidity
and mortality worldwide, as it can provide non-invasive detection at the
early stages of the disease. Magnetic resonance imaging (MRI) can assess
myocardial perfusion by capturing the rst-pass perfusion (FPP) of a
gadolinium-based contrast agent (GBCA), which is now a well-established
technique and compares well with other modalities. However, current MRI
methods are restricted by their limited coverage of the left ventricle. Interest
has therefore grown in 3D volumetric \whole-heart" FPP by MRI, although
many challenges currently limit this. For this thesis, myocardial perfusion
assessment in general, and 3D whole-heart FPP in particular, were reviewed
in depth, alongside MRI techniques important for achieving 3D FPP. From
this, a 3D `stack-of-stars' (SOS) FPP sequence was developed with the aim
of addressing some current limitations. These included the breath-hold
requirement during GBCA rst-pass, long 3D shot durations corrupted by
cardiac motion, and a propensity for artefacts in FPP. Parallel imaging and
compressed sensing were investigated for accelerating whole-heart FPP, with
modi cations presented to potentially improve robustness to free-breathing.
Novel sequences were developed that were capable of individually improving
some current sequence limits, including spatial resolution and signal-to-noise
ratio, although with some sacri ces. A nal 3D SOS FPP technique was
developed and tested at stress during free-breathing examinations of CAD
patients and healthy volunteers. This enabled the rst known detection of an
inducible perfusion defect with a free-breathing, compressed sensing, 3D FPP
sequence; however, further investigation into the diagnostic performance is
required. Simulations were performed to analyse potential artefacts in 3D
FPP, as well as to examine ways towards further optimisation of 3D SOS
FPP. The nal chapter discusses some limitations of the work and proposes
opportunities for further investigation.Open Acces