Combined Background Field Removal and Reconstruction for Quantitative Susceptibility Mapping

2016 ◽  
pp. 8-13
Author(s):  
Maximilian März ◽  
Lars Ruthotto
Keyword(s):  
Background Field ◽  
Susceptibility Mapping ◽  
Quantitative Susceptibility Mapping

scholarly journals Abdominal single-step quantitative susceptibility mapping with spherical mean value filter and structure prior-based regularization

2020 ◽  
Author(s):  
Anton Abyzov ◽  
Bernard E. Van Beers ◽  
Philippe Garteiser
Keyword(s):  
Subcutaneous Fat ◽  
Background Field ◽  
Mean Value ◽  
Single Step ◽  
Susceptibility Mapping ◽  
Small Animals ◽  
Quantitative Susceptibility Mapping ◽  
Split Bregman ◽  
Spherical Mean ◽  
Susceptibility Maps

Abdominal quantitative susceptibility mapping (QSM), especially in small animals, is challenging because of respiratory motion and blood flow that, in addition to noise, deteriorate the quality of the input data. Efficient artefact suppression in QSM reconstruction is crucial in these conditions. Single-step QSM algorithms combine background field removal and magnetic field-to-susceptibility inverse problem regularization in a single optimization equation. Here, we propose a single-step QSM algorithm that uses spherical mean value kernels of different radii for background field removal and structure prior (consistency with magnitude image) with L1 norm for regularization. The optimization problem is solved using the split-Bregman method on the graphic processor unit. The method was compared with previously reported singlestep methods: a method using discrete Laplacian instead of spherical mean value kernels, a method using total variational penalty instead of structure prior, and a method using L2 norm for structure prior. With the proposed method relative to the previous ones, a numerical susceptibility phantom was reconstructed more precisely. In living mice, susceptibility maps with more homogeneous liver, higher contrast between liver and blood vessels, and well-preserved structural details were obtained. In patients, susceptibility maps with more homogeneous subcutaneous fat and higher contrast between subcutaneous fat and liver were obtained. These results show the potential of the proposed single-step method for abdominal QSM in small animals and humans.

Patents on Quantitative Susceptibility Mapping (QSM) of Tissue Magnetism

2019 ◽  
Vol 13 (2) ◽  
pp. 90-113
Author(s):  
Feng Lin ◽  
Martin R. Prince ◽  
Pascal Spincemaille ◽  
Yi Wang
Keyword(s):  
Neurological Diseases ◽  
Background Field ◽  
Bayesian Framework ◽  
Susceptibility Mapping ◽  
Quantitative Susceptibility Mapping ◽  
Liver Iron ◽  
Seamless Integration ◽  
Tissue Ischemia ◽  
Susceptibility Maps ◽  
Wide Range

<P>Background: Quantitative susceptibility mapping (QSM) depicts biodistributions of tissue magnetic susceptibility sources, including endogenous iron and calcifications, as well as exogenous paramagnetic contrast agents and probes. When comparing QSM with simple susceptibility weighted MRI, QSM eliminates blooming artifacts and shows reproducible tissue susceptibility maps independent of field strength and scanner manufacturer over a broad range of image acquisition parameters. For patient care, QSM promises to inform diagnosis, guide surgery, gauge medication, and monitor drug delivery. The Bayesian framework using MRI phase data and structural prior knowledge has made QSM sufficiently robust and accurate for routine clinical practice.Objective:To address the lack of a summary of US patents that is valuable for QSM product development and dissemination into the MRI community.Method:We searched the USPTO Full-Text and Image Database for patents relevant to QSM technology innovation. We analyzed the claims of each patent to characterize the main invented method and we investigated data on clinical utility. </P><P> Results: We identified 17 QSM patents; 13 were implemented clinically, covering various aspects of QSM technology, including the Bayesian framework, background field removal, numerical optimization solver, zero filling, and zero-TE phase.Conclusion:Our patent search identified patents that enable QSM technology for imaging the brain and other tissues. QSM can be applied to study a wide range of diseases including neurological diseases, liver iron disorders, tissue ischemia, and osteoporosis. MRI manufacturers can develop QSM products for more seamless integration into existing MRI scanners to improve medical care.</P>

scholarly journals The Effect of Oblique Image Acquisition on the Accuracy of Quantitative Susceptibility Mapping and a Robust Tilt Correction Method

2021 ◽  
Author(s):  
Oliver C. Kiersnowski ◽  
Anita Karsa ◽  
Stephen J. Wastling ◽  
John S. Thornton ◽  
Karin Shmueli
Keyword(s):  
Mean Squared Error ◽  
Image Acquisition ◽  
Similarity Index ◽  
Background Field ◽  
Phase Unwrapping ◽  
Susceptibility Mapping ◽  
Main Magnetic Field ◽  
Quantitative Susceptibility Mapping ◽  
Tilt Correction

Purpose: Quantitative susceptibility mapping (QSM) is increasingly used for clinical research where oblique image acquisition is commonplace but its effects on QSM accuracy are not well understood. Theory and Methods: The QSM processing pipeline involves defining the unit magnetic dipole kernel, which requires knowledge of the direction of the main magnetic field B0 with respect to the acquired image volume axes. The direction of B0 is dependent upon the axis and angle of rotation in oblique acquisition. Using both a numerical brain phantom and in-vivo acquisitions, we analysed the effects of oblique acquisition on magnetic susceptibility maps. We compared three tilt correction schemes at each step in the QSM pipeline: phase unwrapping, background field removal and susceptibility calculation, using the root-mean-squared error and QSM-tuned structural similarity index (XSIM). Results: Rotation of wrapped phase images gave severe artefacts. Background field removal with projection onto dipole fields gave the most accurate susceptibilities when the field map was first rotated into alignment with B0. LBV and VSHARP background field removal methods gave accurate results without tilt correction. For susceptibility calculation, thresholded k-space division, iterative Tikhonov regularisation and weighted linear total variation regularisation all performed most accurately when local field maps were rotated into alignment with B0 before susceptibility calculation. Conclusion: For accurate QSM, oblique acquisition must be taken into account. Rotation of images into alignment with B0 should be carried out after phase unwrapping and before background field removal. We provide open-source tilt-correction code to incorporate easily into existing pipelines: https://github.com/o-snow/QSM_TiltCorrection.git.

scholarly journals SHARQnet - Sophisticated Harmonic Artifact Reduction in Quantitative Susceptibility Mapping using a Deep Convolutional Neural Network

2019 ◽  
Author(s):  
Steffen Bollmann ◽  
Matilde Holm Kristensen ◽  
Morten Skaarup Larsen ◽  
Mathias Vassard Olsen ◽  
Mads Jozwiak Pedersen ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Background Field ◽  
Susceptibility Mapping ◽  
Deep Convolutional Neural Network ◽  
Quantitative Susceptibility Mapping ◽  
Iterative Optimization ◽  
Wide Range ◽  
Ill Posed ◽  
Background Fields

AbstractQuantitative susceptibility mapping (QSM) reveals pathological changes in widespread diseases such as Parkinson’s disease, Multiple Sclerosis, or hepatic iron overload. QSM requires multiple processing steps after the acquisition of magnetic resonance imaging (MRI) phase measurements such as unwrapping, background field removal and the solution of an ill-posed field-to-source-inversion. Current techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and lead to suboptimal or over-regularized solutions requiring a careful choice of parameters that make a clinical application of QSM challenging. We have previously demonstrated that a deep convolutional neural network can invert the magnetic dipole kernel with a very efficient feed forward multiplication not requiring iterative optimization or the choice of regularization parameters. In this work, we extended this approach to remove background fields in QSM. The prototype method, called SHARQnet, was trained on simulated background fields and tested on 3T and 7T brain datasets. We show that SHARQnet outperforms current background field removal procedures and generalizes to a wide range of input data without requiring any parameter adjustments. In summary, we demonstrate that the solution of ill-posed problems in QSM can be achieved by learning the underlying physics causing the artifacts and removing them in an efficient and reliable manner and thereby will help to bring QSM towards clinical applications.

Sign in / Sign up

Export Citation Format

Share Document