Dataset Information

Quantitative Susceptibility Mapping Using Structural Feature Based Collaborative Reconstruction (SFCR) in the Human Brain.

ABSTRACT: The reconstruction of MR quantitative susceptibility mapping (QSM) from local phase measurements is an ill posed inverse problem and different regularization strategies incorporating a priori information extracted from magnitude and phase images have been proposed. However, the anatomy observed in magnitude and phase images does not always coincide spatially with that in susceptibility maps, which could give erroneous estimation in the reconstructed susceptibility map. In this paper, we develop a structural feature based collaborative reconstruction (SFCR) method for QSM including both magnitude and susceptibility based information. The SFCR algorithm is composed of two consecutive steps corresponding to complementary reconstruction models, each with a structural feature based l 1 norm constraint and a voxel fidelity based l 2 norm constraint, which allows both the structure edges and tiny features to be recovered, whereas the noise and artifacts could be reduced. In the M-step, the initial susceptibility map is reconstructed by employing a k -space based compressed sensing model incorporating magnitude prior. In the S-step, the susceptibility map is fitted in spatial domain using weighted constraints derived from the initial susceptibility map from the M-step. Simulations and in vivo human experiments at 7T MRI show that the SFCR method provides high quality susceptibility maps with improved RMSE and MSSIM. Finally, the susceptibility values of deep gray matter are analyzed in multiple head positions, with the supine position most approximate to the gold standard COSMOS result.

SUBMITTER: Bao L

PROVIDER: S-EPMC5495149 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Quantitative Susceptibility Mapping Using Structural Feature Based Collaborative Reconstruction (SFCR) in the Human Brain.

Bao Lijun L Li Xu X Cai Congbo C Chen Zhong Z van Zijl Peter C M PC

IEEE transactions on medical imaging 20160322 9

The reconstruction of MR quantitative susceptibility mapping (QSM) from local phase measurements is an ill posed inverse problem and different regularization strategies incorporating a priori information extracted from magnitude and phase images have been proposed. However, the anatomy observed in magnitude and phase images does not always coincide spatially with that in susceptibility maps, which could give erroneous estimation in the reconstructed susceptibility map. In this paper, we develop ...[more]

PMID: 27019480

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Project description:Quantitative Susceptibility Mapping (QSM) is an established Magnetic Resonance Imaging (MRI) technique with high potential in brain iron studies associated to several neurodegenerative diseases. Unlike other MRI techniques, QSM relies on phase images to estimate tissue's relative susceptibility, therefore requiring a reliable phase data. Phase images from a multi-channel acquisition should be reconstructed in a proper way. On this work it was compared the performance of combination of phase matching algorithms (MCPC3D-S and VRC) and phase combination methods based on a complex weighted sum of phases, considering the magnitude at different powers (k = 0 to 4) as the weighting factor. These reconstruction methods were applied in two datasets: a simulated brain dataset for a 4-coil array and data of 22 postmortem subjects acquired at a 7T scanner using a 32 channels coil. For the simulated dataset, differences between the ground truth and the Root Mean Squared Error (RMSE) were evaluated. For both simulated and postmortem data, the mean (MS) and standard deviation (SD) of susceptibility values of five deep gray matter regions were calculated. For the postmortem subjects, MS and SD were statistically compared across all subjects. A qualitative analysis indicated no differences between methods, except for the Adaptive approach on postmortem data, which showed intense artifacts. In the 20% noise level case, the simulated data showed increased noise in central regions. Quantitative analysis showed that both MS and SD were not statistically different when comparing k = 1 and k = 2 on postmortem brain images, however visual inspection showed some boundaries artifacts on k = 2. Furthermore, the RMSE decreased (on regions near the coils) and increased (on central regions and on overall QSM) with increasing k. In conclusion, for reconstruction of phase images from multiple coils with no reference available, alternative methods are needed. In this study it was found that overall, the phase combination with k = 1 is preferred over other powers of k.

Project description: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 relating the tissue phase to the underlying susceptibility distribution. 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 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 generalized and learned the underlying mathematical relationship between magnetic field perturbation and magnetic susceptibility. Quantitative and qualitative comparisons were performed between autoQSM and other two-step QSM methods. 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 autoQSM's potential for future applications.

Project description:PurposeCartilage canal vessels are critical to the normal function of epiphyseal (growth) cartilage and damage to these vessels is demonstrated or suspected in several important developmental orthopaedic diseases. High-resolution, three-dimensional (3-D) visualization of cartilage canals has recently been demonstrated using susceptibility weighted imaging (SWI). In the present study, a quantitative susceptibility mapping (QSM) approach is evaluated for 3-D visualization of the cartilage canals. It is hypothesized that QSM post-processing improves visualization of the cartilage canals by resolving artifacts present in the standard SWI post-processing while retaining sensitivity to the cartilage canals.MethodsEx vivo distal femoral specimens from 3- and 8-week-old piglets and a 1-month-old human cadaver were scanned at 9.4 T with a 3-D gradient recalled echo sequence suitable for SWI and QSM post-processing. The human specimen and the stifle joint of a live, 3-week-old piglet also were scanned at 7.0 T. Datasets were processed using the standard SWI method and truncated k-space division QSM approach. To compare the post-processing methods, minimum/maximum intensity projections and 3-D reconstructions of the processed datasets were generated and evaluated.ResultsCartilage canals were successfully visualized using both SWI and QSM approaches. The artifactual splitting of the cartilage canals that occurs due to the dipolar phase, which was present in the SWI post-processed data, was eliminated by the QSM approach. Thus, orientation-independent visualization and better localization of the cartilage canals was achieved with the QSM approach. Combination of GRE with a mask based on QSM data further improved visualization.ConclusionsImproved and artifact-free 3-D visualization of the cartilage canals was demonstrated by QSM processing of the data, especially by utilizing susceptibility data as an enhancing mask. Utilizing tissue-inherent contrast, this method allows noninvasive assessment of the vasculature in the epiphyseal cartilage in the developing skeleton and potentially increases the opportunity to diagnose disease of this tissue in the preclinical stages, when treatment likely will have increased efficacy.

Dataset Information

Quantitative Susceptibility Mapping Using Structural Feature Based Collaborative Reconstruction (SFCR) in the Human Brain.

Publications

Quantitative Susceptibility Mapping Using Structural Feature Based Collaborative Reconstruction (SFCR) in the Human Brain.

Similar Datasets

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