Dataset Information

Combined diffusion-relaxometry MRI to identify dysfunction in the human placenta.

ABSTRACT:

Purpose

A combined diffusion-relaxometry MR acquisition and analysis pipeline for in vivo human placenta, which allows for exploration of coupling between T2* and apparent diffusion coefficient (ADC) measurements in a sub 10-minute scan time.

Methods

We present a novel acquisition combining a diffusion prepared spin echo with subsequent gradient echoes. The placentas of 17 pregnant women were scanned in vivo, including both healthy controls and participants with various pregnancy complications. We estimate the joint T2* -ADC spectra using an inverse Laplace transform.

Results

T2* -ADC spectra demonstrate clear quantitative separation between normal and dysfunctional placentas.

Conclusions

Combined T2* -diffusivity MRI is promising for assessing fetal and maternal health during pregnancy. The T2* -ADC spectrum potentially provides additional information on tissue microstructure, compared to measuring these two contrasts separately. The presented method is immediately applicable to the study of other organs.

SUBMITTER: Slator PJ

PROVIDER: S-EPMC6519240 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Publications

Combined diffusion-relaxometry MRI to identify dysfunction in the human placenta.

Slator Paddy J PJ Hutter Jana J Palombo Marco M Jackson Laurence H LH Ho Alison A Panagiotaki Eleftheria E Chappell Lucy C LC Rutherford Mary A MA Hajnal Joseph V JV Alexander Daniel C DC

Magnetic resonance in medicine 20190318 1

<h4>Purpose</h4>A combined diffusion-relaxometry MR acquisition and analysis pipeline for in vivo human placenta, which allows for exploration of coupling between T2* and apparent diffusion coefficient (ADC) measurements in a sub 10-minute scan time.<h4>Methods</h4>We present a novel acquisition combining a diffusion prepared spin echo with subsequent gradient echoes. The placentas of 17 pregnant women were scanned in vivo, including both healthy controls and participants with various pregnancy ...[more]

PMID: 30883915

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Project description:PURPOSE:This study demonstrated a magnetic resonance (MR) signal multitask learning method for three-dimensional (3D) simultaneous segmentation and relaxometry of human brain tissues. MATERIALS AND METHODS:A 3D inversion-prepared balanced steady-state free precession sequence was used for acquiring in vivo multicontrast brain images. The deep neural network contained three residual blocks, and each block had 8 fully connected layers with sigmoid activation, layer norm, and 256 neurons in each layer. Online-synthesized MR signal evolutions and labels were used to train the neural network batch-by-batch. Empirically defined ranges of T1 and T2 values for the normal gray matter, white matter, and cerebrospinal fluid (CSF) were used as the prior knowledge. MRI brain experiments were performed on three healthy volunteers. The mean and standard deviation for the T1 and T2 values in vivo were reported and compared to literature values. Additional animal (N = 6) and prostate patient (N = 1) experiments were performed to compare the estimated T1 and T2 values with those from gold standard methods and to demonstrate clinical applications of the proposed method. RESULTS:In animal validation experiment, the differences/errors (mean difference ± standard deviation of difference) between the T1 and T2 values estimated from the proposed method and the ground truth were 113 ± 486 and 154 ± 512 ms for T1, and 5 ± 33 and 7 ± 41 ms for T2, respectively. In healthy volunteer experiments (N = 3), whole brain segmentation and relaxometry were finished within ~ 5 s. The estimated apparent T1 and T2 maps were in accordance with known brain anatomy, and not affected by coil sensitivity variation. Gray matter, white matter, and CSF were successfully segmented. The deep neural network can also generate synthetic T1- and T2-weighted images. CONCLUSION:The proposed multitask learning method can directly generate brain apparent T1 and T2 maps, as well as synthetic T1- and T2-weighted images, in conjunction with segmentation of gray matter, white matter, and CSF.

Dataset Information

Combined diffusion-relaxometry MRI to identify dysfunction in the human placenta.

Purpose

Methods

Results

Conclusions

Publications

Combined diffusion-relaxometry MRI to identify dysfunction in the human placenta.

Similar Datasets

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