Inner‐volume echo volumar imaging (<scp>IVEVI</scp>) for robust fetal brain imaging

Nunes, Rita G.; Ferrazzi, Giulio; Price, Anthony N.; Hutter, Jana; Gaspar, Andreia S.; Rutherford, Mary; Hajnal, Joseph V.

doi:10.1002/mrm.26998

Cited by 5 publications

(9 citation statements)

References 40 publications

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“…This approach's challenge is that obtaining high-quality subject-specific high-resolution anatomical images presents a different set of challenges that have been addressed elsewhere (Studholme, 2015). Prior studies of fetal anatomical development (Nunes et al, 2018) demonstrate that even when trained experts apply the most advanced techniques to these data, there is still significant data loss and image blurring where motion effects, image artifacts, or lack of tissue contrast compromise data quality. This example extends to other parts of the pipeline, where alternative optimized preprocessing strategies could be used.…”

Section: Discussionmentioning

confidence: 99%

Automated Brain Masking of Fetal Functional MRI with Open Data

et al. 2021

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Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses, we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open-source workflow, with openly shared code and data, for fetal functional MRI data preprocessing.

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Section: Discussionmentioning

confidence: 99%

Automated Brain Masking of Fetal Functional MRI with Open Data

et al. 2021

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“…The fly‐back EPI‐like excitation k ‐space trajectory used in our 2D RF pulse design provides robustness against gradient infidelities (particularly those caused by eddy currents) than bidirectional EPI‐like gradient waveform 26 . In addition, the fly‐back k ‐space trajectory allows 1D RF pulses (sub‐pulses and their envelope RF pulse) to be designed separately, which provides independent control of two out of the three dimensions of the excited volume compared with a spiral trajectory 27,28 . A major drawback is that the fly‐back EPI‐like excitation k ‐space trajectory can lead to an increased duration of the 2D RF pulse, lengthening the minimum TE and TR.…”

Section: Discussionmentioning

confidence: 99%

“…26 In addition, the fly-back k-space trajectory allows 1D RF pulses (sub-pulses and their envelope RF pulse) to be designed separately, which provides independent control of two out of the three dimensions of the excited volume compared with a spiral trajectory. 27,28 A major drawback is that the fly-back EPI-like excitation k-space trajectory can lead to an increased duration of the 2D RF pulse, lengthening the minimum TE and TR. Other 2D RF pulse design strategies also exist, such as those using a discretized matrix.…”

Section: Discussionmentioning

confidence: 99%

Three‐dimensional reduced field‐of‐view imaging (3D‐rFOVI)

Sun

Zhong

Dan

et al. 2021

Magnetic Resonance in Med

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This study aimed at developing a 3D reduced field-of-view imaging (3D-rFOVI) technique using a 2D radiofrequency (RF) pulse, and demonstrating its ability to achieve isotropic high spatial resolution and reduced image distortion in echo planar imaging (EPI). Methods:The proposed 3D-rFOVI technique takes advantage of a 2D RF pulse to excite a slab along the conventional slice-selection direction (i.e., z-direction) while limiting the spatial extent along the phase-encoded direction (i.e., y-direction) within the slab. The slab is phase-encoded in both through-slab and in-slab phase-encoded directions. The 3D-rFOVI technique was implemented at 3T in gradient-echo and spin-echo EPI pulse sequences for functional MRI (fMRI) and diffusion-weighted imaging (DWI), respectively. 3D-rFOVI experiments were performed on a phantom and human brain to illustrate image distortion reduction, as well as isotropic high spatial resolution, in comparison with 3D full-FOV imaging. Results: In both the phantom and the human brain, image voxel dislocation was substantially reduced by 3D-rFOVI when compared with full-FOV imaging. In the fMRI experiment with visual stimulation, 3D isotropic spatial resolution of (2 × 2 × 2 mm 3 ) was achieved with an adequate signal-to-noise ratio (81.5) and blood oxygen level-dependent (BOLD) contrast (2.5%). In the DWI experiment, diffusion-weighted brain images with an isotropic resolution of (1 × 1 × 1 mm 3 ) was obtained without appreciable image distortion. Conclusion:This study indicates that 3D-rFOVI is a viable approach to 3D neuroimaging over a zoomed region.

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“…The challenge of this approach is that obtaining high-quality subject-specific high-resolution anatomical images presents a different set of challenges that have been addressed elsewhere [55]. Prior studies of fetal anatomical development [56] demonstrate that even when trained experts apply the most advanced techniques to these data, there is still significant data loss and image blurring where motion effects, image artifacts, or lack of tissue contrast compromise data quality. This example extends to other parts of the pipeline, where alternative optimized preprocessing strategies could be used.…”

Section: Discussionmentioning

confidence: 99%

Automated Brain Masking of Fetal Functional MRI

Rutherford

Sturmfels

Angstadt

et al. 2019

Preprint

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Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite rapid and widespread growth of this approach, at present we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses (gestational age 24-39 weeks, M=30.9, SD=4.2), we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open source workflow for fetal functional MRI data preprocessing.

show abstract

Inner‐volume echo volumar imaging (IVEVI) for robust fetal brain imaging

Cited by 5 publications

References 40 publications

Automated Brain Masking of Fetal Functional MRI with Open Data

Automated Brain Masking of Fetal Functional MRI with Open Data

Three‐dimensional reduced field‐of‐view imaging (3D‐rFOVI)

Automated Brain Masking of Fetal Functional MRI

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