Dataset Information

Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting.

ABSTRACT: Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that can simultaneously measure multiple important tissue properties of human body. Although MRF has demonstrated improved scan efficiency as compared to conventional techniques, further acceleration is still desired for translation into routine clinical practice. The purpose of this paper is to accelerate MRF acquisition by developing a new tissue quantification method for MRF that allows accurate quantification with fewer sampling data. Most of the existing approaches use the MRF signal evolution at each individual pixel to estimate tissue properties, without considering the spatial association among neighboring pixels. In this paper, we propose a spatially constrained quantification method that uses the signals at multiple neighboring pixels to better estimate tissue properties at the central pixel. Specifically, we design a unique two-step deep learning model that learns the mapping from the observed signals to the desired properties for tissue quantification, i.e.: 1) with a feature extraction module for reducing the dimension of signals by extracting a low-dimensional feature vector from the high-dimensional signal evolution and 2) a spatially constrained quantification module for exploiting the spatial information from the extracted feature maps to generate the final tissue property map. A corresponding two-step training strategy is developed for network training. The proposed method is tested on highly undersampled MRF data acquired from human brains. Experimental results demonstrate that our method can achieve accurate quantification for T1 and T2 relaxation times by using only 1/4 time points of the original sequence (i.e., four times of acceleration for MRF acquisition).

SUBMITTER: Fang Z

PROVIDER: S-EPMC6692257 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting.

Fang Zhenghan Z Chen Yong Y Liu Mingxia M Xiang Lei L Zhang Qian Q Wang Qian Q Lin Weili W Shen Dinggang D

IEEE transactions on medical imaging 20190213 10

Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that can simultaneously measure multiple important tissue properties of human body. Although MRF has demonstrated improved scan efficiency as compared to conventional techniques, further acceleration is still desired for translation into routine clinical practice. The purpose of this paper is to accelerate MRF acquisition by developing a new tissue quantification method for MRF that allows accurate quantification with fe ...[more]

PMID: 30762540

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Project description:The purpose of this work was to develop a 31 P spectroscopic magnetic resonance fingerprinting (MRF) method for fast quantification of the chemical exchange rate between phosphocreatine (PCr) and adenosine triphosphate (ATP) via creatine kinase (CK). A 31 P MRF sequence (CK-MRF) was developed to quantify the forward rate constant of ATP synthesis via CK ( kfCK), the T1 relaxation time of PCr ( T1PCr), and the PCr-to-ATP concentration ratio ( MRPCr). The CK-MRF sequence used a balanced steady-state free precession (bSSFP)-type excitation with ramped flip angles and a unique saturation scheme sensitive to the exchange between PCr and γATP. Parameter estimation was accomplished by matching the acquired signals to a dictionary generated using the Bloch-McConnell equation. Simulation studies were performed to examine the susceptibility of the CK-MRF method to several potential error sources. The accuracy of nonlocalized CK-MRF measurements before and after an ischemia-reperfusion (IR) protocol was compared with the magnetization transfer (MT-MRS) method in rat hindlimb at 9.4 T (n = 14). The reproducibility of CK-MRF was also assessed by comparing CK-MRF measurements with both MT-MRS (n = 17) and four angle saturation transfer (FAST) (n = 7). Simulation results showed that CK-MRF quantification of kfCK was robust, with less than 5% error in the presence of model inaccuracies including dictionary resolution, metabolite T2 values, inorganic phosphate metabolism, and B1 miscalibration. Estimation of kfCK by CK-MRF (0.38 ± 0.02 s-1 at baseline and 0.42 ± 0.03 s-1 post-IR) showed strong agreement with MT-MRS (0.39 ± 0.03 s-1 at baseline and 0.44 ± 0.04 s-1 post-IR). kfCK estimation was also similar between CK-MRF and FAST (0.38 ± 0.02 s-1 for CK-MRF and 0.38 ± 0.11 s-1 for FAST). The coefficient of variation from 20 s CK-MRF quantification of kfCK was 42% of that by 150 s MT-MRS acquisition and was 12% of that by 20 s FAST acquisition. This study demonstrates the potential of a 31 P spectroscopic MRF framework for rapid, accurate and reproducible quantification of chemical exchange rate of CK in vivo.

Project description:BackgroundExercise cardiovascular magnetic resonance (Ex-CMR) myocardial tagging would enable quantification of myocardial deformation after exercise. However, current electrocardiogram (ECG)-segmented sequences are limited for Ex-CMR.MethodsWe developed a highly accelerated balanced steady-state free-precession real-time tagging technique for 3 T. A 12-fold acceleration was achieved using incoherent sixfold random Cartesian sampling, twofold truncated outer phase encoding, and a deep learning resolution enhancement model. The technique was tested in two prospective studies. In a rest study of 27 patients referred for clinical CMR and 19 healthy subjects, a set of ECG-segmented for comparison and two sets of real-time tagging images for repeatability assessment were collected in 2-chamber and short-axis views with spatiotemporal resolution 2.0 × 2.0 mm2 and 29 ms. In an Ex-CMR study of 26 patients with known or suspected cardiac disease and 23 healthy subjects, real-time images were collected before and after exercise. Deformation was quantified using measures of short-axis global circumferential strain (GCS). Two experienced CMR readers evaluated the image quality of all real-time data pooled from both studies using a 4-point Likert scale for tagline quality (1-excellent; 2-good; 3-moderate; 4-poor) and artifact level (1-none; 2-minimal; 3-moderate; 4-significant). Statistical evaluation included Pearson correlation coefficient (r), intraclass correlation coefficient (ICC), and coefficient of variation (CoV).ResultsIn the rest study, deformation was successfully quantified in 90% of cases. There was a good correlation (r = 0.71) between ECG-segmented and real-time measures of GCS, and repeatability was good to excellent (ICC = 0.86 [0.71, 0.94]) with a CoV of 4.7%. In the Ex-CMR study, deformation was successfully quantified in 96% of subjects pre-exercise and 84% of subjects post-exercise. Short-axis and 2-chamber tagline quality were 1.6 ± 0.7 and 1.9 ± 0.8 at rest and 1.9 ± 0.7 and 2.5 ± 0.8 after exercise, respectively. Short-axis and 2-chamber artifact level was 1.2 ± 0.5 and 1.4 ± 0.7 at rest and 1.3 ± 0.6 and 1.5 ± 0.8 post-exercise, respectively.ConclusionWe developed a highly accelerated real-time tagging technique and demonstrated its potential for Ex-CMR quantification of myocardial deformation. Further studies are needed to assess the clinical utility of our technique.

Dataset Information

Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting.

Publications

Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting.

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