1.
Preconditioned total field inversion (TFI) method for quantitative susceptibility mapping
Liu, Zhe; Kee, Youngwook; Zhou, Dong ...
Magnetic resonance in medicine,
July 2017, Letnik:
78, Številka:
1
Journal Article
Purpose
To investigate systematic errors in traditional quantitative susceptibility mapping (QSM) where background field removal and local field inversion (LFI) are performed sequentially, to develop ...
a total field inversion (TFI) QSM method to reduce these errors, and to improve QSM quality in the presence of large susceptibility differences.
Theory and Methods
The proposed TFI is a single optimization problem which simultaneously estimates the background and local fields, preventing error propagation from background field removal to QSM. To increase the computational speed, a new preconditioner is introduced and analyzed. TFI is compared with the traditional combination of background field removal and LFI in a numerical simulation and in phantom, 5 healthy subjects, and 18 patients with intracerebral hemorrhage.
Results
Compared with the traditional method projection onto dipole fields+LFI, preconditioned TFI substantially reduced error in QSM along the air–tissue boundaries in simulation, generated high‐quality in vivo QSM within similar processing time, and suppressed streaking artifacts in intracerebral hemorrhage QSM. Moreover, preconditioned TFI was capable of generating QSM for the entire head including the brain, air‐filled sinus, skull, and fat.
Conclusion
Preconditioned total field inversion improves the accuracy of QSM over the traditional method where background and local fields are separately estimated. Magn Reson Med 78:303–315, 2017. © 2016 International Society for Magnetic Resonance in Medicine
več
Dostopno za:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
2.
Nonlinear formulation of the magnetic field to source relationship for robust quantitative susceptibility mapping
Liu, Tian; Wisnieff, Cynthia; Lou, Min ...
Magnetic resonance in medicine,
February 2013, Letnik:
69, Številka:
2
Journal Article
Quantitative susceptibility mapping (QSM) opens the door for measuring tissue magnetic susceptibility properties that may be important biomarkers, and QSM is becoming an increasingly active area of ...
scientific and clinical investigations. In practical applications, there are sources of errors for QSM including noise, phase unwrapping failures, and signal model inaccuracy. To improve the robustness of QSM quality, we propose a nonlinear data fidelity term for frequency map estimation and dipole inversion to reduce noise and effects of phase unwrapping failures, and a method for model error reduction through iterative tuning. Compared with the previous phase based linear QSM method, this nonlinear QSM method reduced salt and pepper noise or checkerboard pattern in high susceptibility regions in healthy subjects and markedly reduced artifacts in patients with intracerebral hemorrhages. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.
več
Dostopno za:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
3.
Flow compensated quantitative susceptibility mapping for venous oxygenation imaging
Xu, Bo; Liu, Tian; Spincemaille, Pascal ...
Magnetic resonance in medicine,
August 2014, Letnik:
72, Številka:
2
Journal Article
Purpose
Venous blood oxygen saturation is an indicator of brain oxygen consumption and can be measured directly from quantitative susceptibility mapping (QSM) by deconvolving the MR phase signal. ...
However, accurate estimation of the susceptibility of blood may be affected by flow induced phase in the presence of imaging gradient and the inhomogeneous susceptibility field gradient. The purpose of this study is to correct the flow induced error in QSM for improved venous oxygenation quantification.
Methods
Flow compensation is proposed for QSM by using a fully flow compensated multi‐echo gradient echo sequence for data acquisition. A quadratic fit of the phase with respect to echo time is employed for the flow phase in the presence of inhomogeneity field gradients. Phantom and in vivo experiments were carried out to validate the proposed method.
Results
Phantom experiments demonstrated reduced error in the estimated field map and susceptibility map. Initial data in in vivo human imaging demonstrated improvements in the quantitative susceptibility map and in the estimated venous oxygen saturation values.
Conclusion
Flow compensated multi‐echo acquisition and an adaptive‐quadratic fit of the phase images improves the quantitative susceptibility map of blood flow. The improved vein susceptibility enables in vivo measurement of venous oxygen saturation throughout the brain. Magn Reson Med 72:438–445, 2014. © 2013 Wiley Periodicals, Inc.
več
Dostopno za:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
4.
Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: Validation and application to brain imaging
de Rochefort, Ludovic; Liu, Tian; Kressler, Bryan ...
Magnetic resonance in medicine,
January 2010, Letnik:
63, Številka:
1
Journal Article
The diagnosis of many neurologic diseases benefits from the ability to quantitatively assess iron in the brain. Paramagnetic iron modifies the magnetic susceptibility causing magnetic field ...
inhomogeneity in MRI. The local field can be mapped using the MR signal phase, which is discarded in a typical image reconstruction. The calculation of the susceptibility from the measured magnetic field is an ill‐posed inverse problem. In this work, a bayesian regularization approach that adds spatial priors from the MR magnitude image is formulated for susceptibility imaging. Priors include background regions of known zero susceptibility and edge information from the magnitude image. Simulation and phantom validation experiments demonstrated accurate susceptibility maps free of artifacts. The ability to characterize iron content in brain hemorrhage was demonstrated on patients with cavernous hemangioma. Additionally, multiple structures within the brain can be clearly visualized and characterized. The technique introduces a new quantitative contrast in MRI that is directly linked to iron in the brain. Magn Reson Med, 2010. © 2009 Wiley‐Liss, Inc.
več
Dostopno za:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
5.
Calculation of susceptibility through multiple orientation sampling (COSMOS): A method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI
Liu, Tian; Spincemaille, Pascal; de Rochefort, Ludovic ...
Magnetic resonance in medicine,
January 2009, Letnik:
61, Številka:
1
Journal Article
Magnetic susceptibility differs among tissues based on their contents of iron, calcium, contrast agent, and other molecular compositions. Susceptibility modifies the magnetic field detected in the MR ...
signal phase. The determination of an arbitrary susceptibility distribution from the induced field shifts is a challenging, ill‐posed inverse problem. A method called “calculation of susceptibility through multiple orientation sampling” (COSMOS) is proposed to stabilize this inverse problem. The field created by the susceptibility distribution is sampled at multiple orientations with respect to the polarization field, B0, and the susceptibility map is reconstructed by weighted linear least squares to account for field noise and the signal void region. Numerical simulations and phantom and in vitro imaging validations demonstrated that COSMOS is a stable and precise approach to quantify a susceptibility distribution using MRI. Magn Reson Med 61:196–204, 2009. © 2008 Wiley‐Liss, Inc.
več
Dostopno za:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
6.
DEEPMIR: a deep neural network for differential detection of cerebral microbleeds and iron deposits in MRI
Rashid, Tanweer; Abdulkadir, Ahmed; Nasrallah, Ilya M. ...
Scientific reports,
07/2021, Letnik:
11, Številka:
1
Journal Article
Abstract
Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. ...
Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84–0.88 and 0.40–0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75–0.81 and 0.62–0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies.
več
Dostopno za:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
7.
A novel background field removal method for MRI using projection onto dipole fields (PDF)
Liu, Tian; Khalidov, Ildar; de Rochefort, Ludovic ...
NMR in biomedicine,
November 2011, Letnik:
24, Številka:
9
Journal Article
For optimal image quality in susceptibility‐weighted imaging and accurate quantification of susceptibility, it is necessary to isolate the local field generated by local magnetic sources (such as ...
iron) from the background field that arises from imperfect shimming and variations in magnetic susceptibility of surrounding tissues (including air). Previous background removal techniques have limited effectiveness depending on the accuracy of model assumptions or information input. In this article, we report an observation that the magnetic field for a dipole outside a given region of interest (ROI) is approximately orthogonal to the magnetic field of a dipole inside the ROI. Accordingly, we propose a nonparametric background field removal technique based on projection onto dipole fields (PDF). In this PDF technique, the background field inside an ROI is decomposed into a field originating from dipoles outside the ROI using the projection theorem in Hilbert space. This novel PDF background removal technique was validated on a numerical simulation and a phantom experiment and was applied in human brain imaging, demonstrating substantial improvement in background field removal compared with the commonly used high‐pass filtering method. Copyright © 2011 John Wiley & Sons, Ltd.
In this article, we report an observation that the magnetic field for a dipole outside a given region of interest (ROI) is approximately orthogonal to the magnetic field of a dipole inside the ROI. Accordingly, we propose a nonparametric background field removal technique based on projection onto dipole fields (PDF). This novel PDF background removal technique was validated in a numerical simulation and a phantom experiment and was applied in human brain imaging, demonstrating substantial improvement in background field removal compared with the commonly used high‐pass filtering method.
več
Dostopno za:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
8.
Quantitative mapping of cerebral metabolic rate of oxygen (CMRO2) using quantitative susceptibility mapping (QSM)
Zhang, Jingwei; Liu, Tian; Gupta, Ajay ...
Magnetic resonance in medicine,
October 2015, Letnik:
74, Številka:
4
Journal Article
Purpose
To quantitatively map cerebral metabolic rate of oxygen (
CMRO2) and oxygen extraction fraction (
OEF) in human brains using quantitative susceptibility mapping (QSM) and arterial spin ...
labeling‐measured cerebral blood flow (CBF) before and after caffeine vasoconstriction.
Methods
Using the multiecho, three‐dimensional gradient echo sequence and an oral bolus of 200 mg caffeine, whole brain
CMRO2 and
OEF were mapped at 3‐mm isotropic resolution on 13 healthy subjects. The QSM‐based
CMRO2 was compared with an
R2*‐based
CMRO2 to analyze the regional consistency within cortical gray matter (CGM) with the scaling in the
R2* method set to provide same total
CMRO2 as the QSM method for each subject.
Results
Compared to precaffeine, susceptibility increased (5.1 ± 1.1 ppb; P < 0.01) and
CBF decreased (−23.6 ± 6.7 ml/100 g/min; P < 0.01) at 25‐min postcaffeine in CGM. This corresponded to a
CMRO2 of 153.0 ± 26.4 μmol/100 g/min with an
OEF of 33.9 ± 9.6% and 54.5 ± 13.2% (P < 0.01) pre‐ and postcaffeine, respectively, at CGM, and a
CMRO2 of 58.0 ± 26.6 μmol/100 g/min at white matter.
CMRO2 from both QSM‐ and
R2*‐based methods showed good regional consistency (P > 0.05), but quantitation of
R2*‐based
CMRO2 required an additional scaling factor.
Conclusion
QSM can be used with perfusion measurements pre‐ and postcaffeine vascoconstriction to map
CMRO2 and OEF. Magn Reson Med 74:945–952, 2015. © 2014 Wiley Periodicals, Inc.
več
Dostopno za:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
9.
Susceptibility underestimation in a high‐susceptibility phantom: Dependence on imaging resolution, magnitude contrast, and other parameters
Zhou, Dong; Cho, Junghun; Zhang, Jingwei ...
Magnetic resonance in medicine,
September 2017, Letnik:
78, Številka:
3
Journal Article
Purpose
We assessed the accuracy of quantitative susceptibility mapping in a gadolinium balloon phantom with a large range of susceptibility values and imaging resolutions at 1.5 and 3 Tesla (T).
...
Theory and Methods
The phantom contained sources with susceptibility values of 0.4, 0.8, 1.6, and 3.2 ppm and was imaged at isotropic resolutions of 0.7, 0.8, 1.2, and 1.8 mm. Numerical simulations were performed to match the experimental findings. Voxel sensitivity effects were used to explain the susceptibility underestimations.
Results
Both phantom data and simulation demonstrated that systematic underestimation of the susceptibility values increased with voxel size, field strength, and object susceptibility.
Conclusion
The underestimation originates from the signal formation in a voxel, which can be described by the voxel sensitivity function. The amount of underestimation is thus affected by imaging resolution, magnitude contrast, image filtering, and details of the susceptibility inclusions such as the susceptibility value and geometry. High‐resolution imaging is therefore needed for accurate reconstruction of QSM values, especially at higher susceptibilities. Magn Reson Med 78:1080–1086, 2017. © 2016 International Society for Magnetic Resonance in Medicine
več
Dostopno za:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
10.
Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction
Zhang, Jinwei; Liu, Zhe; Zhang, Shun ...
NeuroImage (Orlando, Fla.),
05/2020, Letnik:
211
Journal Article
Deep learning (DL) is increasingly used to solve ill-posed inverse problems in medical imaging, such as reconstruction from noisy and/or incomplete data, as DL offers advantages over conventional ...
methods that rely on explicit image features and hand engineered priors. However, supervised DL-based methods may achieve poor performance when the test data deviates from the training data, for example, when it has pathologies not encountered in the training data. Furthermore, DL-based image reconstructions do not always incorporate the underlying forward physical model, which may improve performance. Therefore, in this work we introduce a novel approach, called fidelity imposed network edit (FINE), which modifies the weights of a pre-trained reconstruction network for each case in the testing dataset. This is achieved by minimizing an unsupervised fidelity loss function that is based on the forward physical model. FINE is applied to two important inverse problems in neuroimaging: quantitative susceptibility mapping (QSM) and under-sampled image reconstruction in MRI. Our experiments demonstrate that FINE can improve reconstruction accuracy.
•A new method, FINE, is introduced to improved deep learning image reconstruction.•FINE incorporates the physical model underlying the data into the network.•FINE reduces generalization errors in deep learning image reconstruction.•FINE improves MS lesion and hemorrhage susceptibility in deep learning QSM.•FINE is robust to noise and adversarial attack in deep learning under-sampled MRI.
več
Dostopno za:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
PDF
