Dataset Information

High-resolution 3D MR Fingerprinting using parallel imaging and deep learning.

ABSTRACT: MR Fingerprinting (MRF) is a relatively new imaging framework capable of providing accurate and simultaneous quantification of multiple tissue properties for improved tissue characterization and disease diagnosis. While 2D MRF has been widely available, extending the method to 3D MRF has been an actively pursued area of research as a 3D approach can provide a higher spatial resolution and better tissue characterization with an inherently higher signal-to-noise ratio. However, 3D MRF with a high spatial resolution requires lengthy acquisition times, especially for a large volume, making it impractical for most clinical applications. In this study, a high-resolution 3D MR Fingerprinting technique, combining parallel imaging and deep learning, was developed for rapid and simultaneous quantification of T₁ and T₂ relaxation times. Parallel imaging was first applied along the partition-encoding direction to reduce the amount of acquired data. An advanced convolutional neural network was then integrated with the MRF framework to extract features from the MRF signal evolution for improved tissue characterization and accelerated mapping. A modified 3D-MRF sequence was also developed in the study to acquire data to train the deep learning model that can be directly applied to prospectively accelerate 3D MRF scans. Our results of quantitative T₁ and T₂ maps demonstrate that improved tissue characterization can be achieved using the proposed method as compared to prior methods. With the integration of parallel imaging and deep learning techniques, whole-brain (26?×?26?×?18?cm³) quantitative T₁ and T₂ mapping with 1-mm isotropic resolution were achieved in ~7?min. In addition, a ~7-fold improvement in processing time to extract tissue properties was also accomplished with the deep learning approach as compared to the standard template matching method. All of these improvements make high-resolution whole-brain quantitative MR imaging feasible for clinical applications.

SUBMITTER: Chen Y

PROVIDER: S-EPMC7136033 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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High-resolution 3D MR Fingerprinting using parallel imaging and deep learning.

Chen Yong Y Fang Zhenghan Z Hung Sheng-Che SC Chang Wei-Tang WT Shen Dinggang D Lin Weili W

NeuroImage 20191102

MR Fingerprinting (MRF) is a relatively new imaging framework capable of providing accurate and simultaneous quantification of multiple tissue properties for improved tissue characterization and disease diagnosis. While 2D MRF has been widely available, extending the method to 3D MRF has been an actively pursued area of research as a 3D approach can provide a higher spatial resolution and better tissue characterization with an inherently higher signal-to-noise ratio. However, 3D MRF with a high ...[more]

PMID: 31689536

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Project description:Background and purposeTranscranial MR imaging-guided focused ultrasound is a promising novel technique to treat multiple disorders and diseases. Planning for transcranial MR imaging-guided focused ultrasound requires both a CT scan for skull density estimation and treatment-planning simulation and an MR imaging for target identification. It is desirable to simplify the clinical workflow of transcranial MR imaging-guided focused ultrasound treatment planning. The purpose of this study was to examine the feasibility of deep learning techniques to convert MR imaging ultrashort TE images directly to synthetic CT of the skull images for use in transcranial MR imaging-guided focused ultrasound treatment planning.Materials and methodsThe U-Net neural network was trained and tested on data obtained from 41 subjects (mean age, 66.4 ± 11.0 years; 15 women). The derived neural network model was evaluated using a k-fold cross-validation method. Derived acoustic properties were verified by comparing the whole skull-density ratio from deep learning synthesized CT of the skull with the reference CT of the skull. In addition, acoustic and temperature simulations were performed using the deep learning CT to predict the target temperature rise during transcranial MR imaging-guided focused ultrasound.ResultsThe derived deep learning model generates synthetic CT of the skull images that are highly comparable with the true CT of the skull images. Their intensities in Hounsfield units have a spatial correlation coefficient of 0.80 ± 0.08, a mean absolute error of 104.57 ± 21.33 HU, and a subject-wise correlation coefficient of 0.91. Furthermore, deep learning CT of the skull is reliable in the skull-density ratio estimation (r = 0.96). A simulation study showed that both the peak target temperatures and temperature distribution from deep learning CT are comparable with those of the reference CT.ConclusionsThe deep learning method can be used to simplify workflow associated with transcranial MR imaging-guided focused ultrasound.

Project description:PurposeTo develop high-spatial-resolution magnetic resonance (MR) microangiography techniques to image the rat ocular circulation.Materials and methodsAnimal experiments were performed with institutional Animal Care Committee approval. MR microangiography (resolution, 84×84×84 μm or 42×42×84 μm) of the rat eye (eight rats) was performed by using a custom-made small circular surface coil with an 11.7-T MR unit before and after monocrystalline iron oxide nanoparticle (MION) injection. MR microangiography measurements were made during air, oxygen, and carbogen inhalation. From three-dimensional MR microangiography, the retina was virtually flattened to enable en face views of various retinal depths, including the retinal and choroidal vascular layers. Signal intensity changes within the retinal or choroidal arteries and veins associated with gas challenges were analyzed. Statistical analysis was performed by using paired t tests, with P<.05 considered to indicate a significant difference. Bonferroni correction was used to adjust for multiple comparisons.ResultsThe central retinal artery, long posterior ciliary arteries, and choroidal vasculature could be distinguished on MR microangiograms of the eye. With MR microangiography, retinal arteries and veins could be distinguished on the basis of blood oxygen level-dependent contrast. Carbogen inhalation-enhanced MR microangiography signal intensity in both the retina (P=.001) and choroid (P=.027) compared with oxygen inhalation. Carbogen inhalation showed significantly higher signal intensity changes in the retinal arteries (P=.001, compared with oxygen inhalation), but not in the veins (P=.549). With MION administration, MR microangiography depicted retinal arterial vasoconstriction when the animals were breathing oxygen (P=.02, compared with animals breathing air).ConclusionMR microangiography of the eye allows depth-resolved imaging of small angiographic details of the ocular circulation. This approach may prove useful in studying microvascular pathologic findings and neurovascular dysfunction in the eye and retina.

Dataset Information

High-resolution 3D MR Fingerprinting using parallel imaging and deep learning.

Publications

High-resolution 3D MR Fingerprinting using parallel imaging and deep learning.

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