8,139 research outputs found
Learning-based Single-step Quantitative Susceptibility Mapping Reconstruction Without Brain Extraction
Quantitative susceptibility mapping (QSM) estimates the underlying tissue
magnetic susceptibility from MRI gradient-echo phase signal and typically
requires several processing steps. These steps involve phase unwrapping, brain
volume extraction, background phase removal and solving an ill-posed inverse
problem. The resulting susceptibility map is known to suffer from inaccuracy
near the edges of the brain tissues, in part due to imperfect brain extraction,
edge erosion of the brain tissue and the lack of phase measurement outside the
brain. This inaccuracy has thus hindered the application of QSM for measuring
the susceptibility of tissues near the brain edges, e.g., quantifying cortical
layers and generating superficial venography. To address these challenges, we
propose a learning-based QSM reconstruction method that directly estimates the
magnetic susceptibility from total phase images without the need for brain
extraction and background phase removal, referred to as autoQSM. The neural
network has a modified U-net structure and is trained using QSM maps computed
by a two-step QSM method. 209 healthy subjects with ages ranging from 11 to 82
years were employed for patch-wise network training. The network was validated
on data dissimilar to the training data, e.g. in vivo mouse brain data and
brains with lesions, which suggests that the network has generalized and
learned the underlying mathematical relationship between magnetic field
perturbation and magnetic susceptibility. AutoQSM was able to recover magnetic
susceptibility of anatomical structures near the edges of the brain including
the veins covering the cortical surface, spinal cord and nerve tracts near the
mouse brain boundaries. The advantages of high-quality maps, no need for brain
volume extraction and high reconstruction speed demonstrate its potential for
future applications.Comment: 26 page
Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group
This article provides recommendations for implementing quantitative susceptibility mapping (QSM) for clinical brain research. It is a consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available give rise to the need in the neuroimaging community for guidelines on implementation. This article describes relevant considerations and provides specific implementation recommendations for all steps in QSM data acquisition, processing, analysis, and presentation in scientific publications. We recommend that data be acquired using a monopolar 3D multi-echo GRE sequence, that phase images be saved and exported in DICOM format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields should be removed within the brain mask using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of whole brain as a region of interest in the analysis, and QSM results should be reported with - as a minimum - the acquisition and processing specifications listed in the last section of the article. These recommendations should facilitate clinical QSM research and lead to increased harmonization in data acquisition, analysis, and reporting
NeXtQSM -- A complete deep learning pipeline for data-consistent quantitative susceptibility mapping trained with hybrid data
Deep learning based Quantitative Susceptibility Mapping (QSM) has shown great
potential in recent years, obtaining similar results to established
non-learning approaches. Many current deep learning approaches are not data
consistent, require in vivo training data or solve the QSM problem in
consecutive steps resulting in the propagation of errors. Here we aim to
overcome these limitations and developed a framework to solve the QSM
processing steps jointly. We developed a new hybrid training data generation
method that enables the end-to-end training for solving background field
correction and dipole inversion in a data-consistent fashion using a
variational network that combines the QSM model term and a learned regularizer.
We demonstrate that NeXtQSM overcomes the limitations of previous deep learning
methods. NeXtQSM offers a new deep learning based pipeline for computing
quantitative susceptibility maps that integrates each processing step into the
training and provides results that are robust and fast
Plug-and-Play Latent Feature Editing for Orientation-Adaptive Quantitative Susceptibility Mapping Neural Networks
Quantitative susceptibility mapping (QSM) is a post-processing technique for
deriving tissue magnetic susceptibility distribution from MRI phase
measurements. Deep learning (DL) algorithms hold great potential for solving
the ill-posed QSM reconstruction problem. However, a significant challenge
facing current DL-QSM approaches is their limited adaptability to magnetic
dipole field orientation variations during training and testing. In this work,
we propose a novel Orientation-Adaptive Latent Feature Editing (OA-LFE) module
to learn the encoding of acquisition orientation vectors and seamlessly
integrate them into the latent features of deep networks. Importantly, it can
be directly Plug-and-Play (PnP) into various existing DL-QSM architectures,
enabling reconstructions of QSM from arbitrary magnetic dipole orientations.
Its effectiveness is demonstrated by combining the OA-LFE module into our
previously proposed phase-to-susceptibility single-step instant QSM (iQSM)
network, which was initially tailored for pure-axial acquisitions. The proposed
OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a
self-supervised manner on a specially-designed simulation brain dataset.
Comprehensive experiments are conducted on simulated and in vivo human brain
datasets, encompassing subjects ranging from healthy individuals to those with
pathological conditions. These experiments involve various MRI platforms (3T
and 7T) and aim to compare our proposed iQSM+ against several established QSM
reconstruction frameworks, including the original iQSM. The iQSM+ yields QSM
images with significantly improved accuracies and mitigates artifacts,
surpassing other state-of-the-art DL-QSM algorithms.Comment: 13pages, 9figure
Accelerating Quantitative Susceptibility Mapping using Compressed Sensing and Deep Neural Network
Quantitative susceptibility mapping (QSM) is an MRI phase-based
post-processing method that quantifies tissue magnetic susceptibility
distributions. However, QSM acquisitions are relatively slow, even with
parallel imaging. Incoherent undersampling and compressed sensing
reconstruction techniques have been used to accelerate traditional
magnitude-based MRI acquisitions; however, most do not recover the full phase
signal due to its non-convex nature. In this study, a learning-based Deep
Complex Residual Network (DCRNet) is proposed to recover both the magnitude and
phase images from incoherently undersampled data, enabling high acceleration of
QSM acquisition. Magnitude, phase, and QSM results from DCRNet were compared
with two iterative and one deep learning methods on retrospectively
undersampled acquisitions from six healthy volunteers, one intracranial
hemorrhage and one multiple sclerosis patients, as well as one prospectively
undersampled healthy subject using a 7T scanner. Peak signal to noise ratio
(PSNR), structural similarity (SSIM) and region-of-interest susceptibility
measurements are reported for numerical comparisons. The proposed DCRNet method
substantially reduced artifacts and blurring compared to the other methods and
resulted in the highest PSNR and SSIM on the magnitude, phase, local field, and
susceptibility maps. It led to 4.0% to 8.8% accuracy improvements in deep grey
matter susceptibility than some existing methods, when the acquisition was
accelerated four times. The proposed DCRNet also dramatically shortened the
reconstruction time by nearly 10 thousand times for each scan, from around 80
hours using conventional approaches to only 30 seconds.Comment: 10 figure
- …