Biology subjects

Setsompop, K.

Publications and source records attributed to Setsompop, K..

5 recordsLinked to original sources

bioRxiv2021

SNR-efficient distortion-free diffusion relaxometry imaging using ACcelerated Echo-train shifted EPTI (ACE-EPTI)

PurposeTo develop an efficient acquisition technique for distortion-free diffusion MRI and diffusion-relaxometry. MethodsA new ACcelerated Echo-train shifted Echo-Planar Time-resolved Imaging (ACE-EPTI) technique is developed to achieve high-SNR, distortion- and blurring-free diffusion and diffusion-relaxometry imaging. ACE-EPTI employs a newly designed variable density spatiotemporal encoding with self-navigation capability, that allows submillimeter in-plane resolution using only 3-shot. Moreover, an echo-train-shifted acquisition is developed to achieve minimal TE, together with an SNR-optimal readout length, leading to ~30% improvement in SNR efficiency over single-shot EPI. To recover the highly accelerated data with high image quality, a tailored subspace image reconstruction framework is developed, that corrects for odd/even-echo phase difference, shot-to-shot phase variation, and the B0 field changes due to field drift and eddy currents across different dynamics. After the phase-corrected subspace reconstruction, artifacts-free high-SNR diffusion images at multiple TEs are obtained with varying T2* weighting. ResultsSimulation, phantom and in-vivo experiments were performed, which validated the 3-shot spatiotemporal encoding provides accurate reconstruction at submillimeter resolution. The use of echo-train shifting and optimized readout length improves the SNR-efficiency by 27-36% over single-shot EPI. The reconstructed multi-TE diffusion images were demonstrated to be free from distortion (susceptibility and eddy currents) and phase/field variation induced artifacts. These improvements of ACE-EPTI enable improved diffusion tensor imaging and rich multi-TE information for diffusion-relaxometry analysis. ConclusionACE-EPTI was demonstrated to be an efficient and powerful technique for high-resolution diffusion imaging and diffusion-relaxometry, which provides high SNR, distortion- and blurring-free, and time-resolved multi-echo images by a fast 3-shot acquisition.

bioengineering↗

bioRxiv2021

Simultaneous pure T2 and varying T2'-weighted BOLD fMRI using Echo Planar Time-resolved Imaging (EPTI) for mapping laminar fMRI responses

Spin-echo (SE) BOLD fMRI has high microvascular specificity, and thus provides a more reliable means to localize neural activity compared to conventional gradient-echo BOLD fMRI. However, the most common SE BOLD acquisition method, SE-EPI, is known to suffer from T2' contrast contamination with undesirable draining vein bias. To address this, in this study, we extended a recently developed distortion/blurring-free multi-shot EPI technique, Echo-Planar Time-resolved Imaging (EPTI), to cortical-depth dependent SE-fMRI at 7T to test whether it could provide purer SE BOLD contrast with minimal T2' contamination for improved neuronal specificity. From the same acquisition, the time-resolved feature of EPTI also provides a series of asymmetric SE (ASE) images with varying T2' weightings, and enables extraction of data equivalent to conventional SE EPI with different echo train lengths (ETLs). This allows us to systematically examine how T2'-contribution affects different SE acquisition strategies using a single dataset. A low-rank spatiotemporal subspace reconstruction was implemented for the SE-EPTI acquisition, which incorporates corrections for both shot-to-shot phase variations and dynamic B0 drifts. SE-EPTI was used in a visual task fMRI experiment to demonstrate that i) the pure SE image provided by EPTI results in the highest microvascular specificity; ii) the ASE EPTI series, with a graded introduction of T2' weightings at time points farther away from the pure SE, show a gradual sensitivity increase along with increasing draining vein bias; iii) the longer ETL seen in conventional SE EPI acquisitions will induce more draining vein bias. Consistent results were observed across multiple subjects, demonstrating the robustness of the proposed technique for SE-BOLD fMRI with high specificity.

neuroscience↗

bioRxiv2021

Ultrafast high-resolution multi-parametric brain MRI using 3D Echo Planar Time-resolved Imaging

Multi-parametric quantitative MRI has shown great potential to improve the sensitivity and specificity of clinical diagnosis and to enhance our understanding of complex brain processes, but suffers from long scan time especially at high spatial resolution. To address this long-standing challenge, we introduce a novel approach, termed 3D Echo Planar Time-resolved Imaging (3D-EPTI), which significantly increases the acceleration capacity of MRI sampling, and provides high acquisition efficiency for multi-parametric MRI. This is achieved by exploiting the spatiotemporal correlation of MRI data at multiple timescales through new encoding strategies within and between efficient continuous readouts. Specifically, an optimized spatiotemporal CAIPI encoding within the readouts combined with a radial-block sampling strategy across the readouts enables an acceleration rate of 800 folds in the k-t space. A subspace reconstruction was employed to resolve thousands of artifact-free high-quality multi-contrast images spaced at a time interval of ~1 ms. We have demonstrated the ability of 3D-EPTI to provide robust and repeatable whole-brain simultaneous T1, T2, T2*, PD and B1+ mapping at high isotropic resolution within minutes (e.g., 1-mm isotropic resolution in 3 minutes), and to enable submillimeter multi-parametric imaging to study detailed brain structures. HighlightsO_LIUltra-fast acquisition for 3D multi-parametric quantitative MRI. C_LIO_LISimultaneous T1 T2 T2* PD and B1+ mapping. C_LIO_LI3-minute scan at 1-mm isotropic resolution with whole-brain coverage. C_LIO_LIMulti-parametric mapping at 700-m isotropic resolution in 10 minutes. C_LIO_LIRepeatable quantification and cortical-depth analysis. C_LI

bioengineering↗

bioRxiv2021

High-fidelity fast volumetric brain MRI using a hybrid denoising generative adversarial network (HDnGAN)

PurposeReducing scan times is important for wider adoption of high-resolution volumetric MRI in research and clinical practice. Emerging fast imaging and deep learning techniques provide promising strategies to accelerate volumetric MRI without compromising image quality. In this study, we aim to leverage an advanced fast imaging technique, wave-controlled aliasing in parallel imaging (Wave-CAIPI), and a novel denoising generative adversarial network (GAN) to achieve accelerated high-fidelity, high-signal-to-noise-ratio (SNR) volumetric MRI. Methods3D T2-weighted fluid-attenuated inversion recovery (FLAIR) image data were acquired on 33 multiple sclerosis (MS) patients using a prototype Wave-CAIPI sequence (acceleration factor R=3x2, 2.75 minutes) and a standard T2-SPACE FLAIR sequence (R=2, 7.25 minutes). A hybrid denoising GAN entitled "HDnGAN" composed of a 3D generator (i.e., a modified 3D U-Net entitled MU-Net) and a 2D discriminator was proposed to denoise Wave-CAIPI images with the standard FLAIR images as the target. HDnGAN was trained and validated on data from 25 MS patients by minimizing a combined content loss (i.e., mean squared error (MSE)) and adversarial loss with adjustable weight{lambda} , and evaluated on data from 8 patients unseen during training. The quality of HDnGAN-denoised images was compared to those from other denoising methods including AONLM, BM4D, MU-Net, and 3D GAN in terms of their similarity to standard FLAIR images, quantified using MSE and VGG perceptual loss. The images from different methods were assessed by two neuroradiologists using a five-point score regarding sharpness, SNR, lesion conspicuity, and overall quality. Finally, the performance of these denoising methods was compared at higher noise levels using simulated data with added Rician noise. ResultsHDnGAN effectively denoised noisy Wave-CAIPI images with sharpness and rich textural details, which could be adjusted by controlling{lambda} . Quantitatively, HDnGAN ({lambda}=10-3) achieved low MSE (7.43 x10-4{+/-}0.94x10-4) and the lowest VGG perceptual loss (1.09x10-2{+/-}0.18x10-2). The reader study showed that HDnGAN ({lambda}=10-3) significantly improved the SNR of Wave-CAIPI images (4.19{+/-}0.39 vs. 2.94{+/-}0.24, P<0.001), outperformed AONLM (4.25{+/-}0.56 vs. 3.75{+/-}0.90, P=0.015), BM4D (3.31{+/-}0.46, P<0.001), MU-Net (3.13{+/-}0.99, P<0.001) and 3D GAN ({lambda}=10-3) (3.31{+/-}0.46, P<0.001) regarding image sharpness, and outperformed MU-Net (4.21{+/-}0.67 vs. 3.29{+/-}1.28, P<0.001) and 3D GAN ({lambda}=10-3) (3.5{+/-}0.82, P=0.001) regarding lesion conspicuity. The overall quality score of HDnGAN ({lambda}=10-3) (4.25{+/-}0.43) was significantly higher than those from Wave-CAIPI (3.69{+/-}0.46, P=0.003), BM4D (3.50{+/-}0.71, P=0.001), MU-Net (3.25{+/-}0.75, P<0.001), and 3D GAN ({lambda}=10-3) (3.50{+/-}0.50, P<0.001), with no significant difference compared to standard FLAIR images (4.38{+/-}0.48, P=0.333). The advantages of HDnGAN over other methods were more obvious at higher noise levels. ConclusionHDnGAN provides robust and feasible denoising while preserving rich textural detail in empirical volumetric MRI data and is superior on both quantitative and qualitative evaluation compared to the original Wave-CAIPI images and images denoised using standard methods. HDnGAN concurrently benefits from the improved image synthesis performance of the 3D convolution and the increased number of samples for training the 2D discriminator from a limited number of subjects. Our study supports the use of HDnGAN in combination with modern fast imaging techniques such as Wave-CAIPI to achieve high-fidelity fast volumetric MRI.

neuroscience↗

bioRxiv2020

In vivo human whole-brain Connectom diffusion MRI dataset at 760 μm isotropic resolution

We present a whole-brain in vivo diffusion MRI (dMRI) dataset acquired at 760 m isotropic resolution and sampled at 1260 q-space points across 9 two-hour sessions on a single healthy subject. The creation of this benchmark dataset is possible through the synergistic use of advanced acquisition hardware and software including the high-gradient-strength Connectom scanner, a custom-built 64-channel phased-array coil, a personalized motion-robust head stabilizer, a recently developed SNR-efficient dMRI acquisition method, and parallel imaging reconstruction with advanced ghost reduction algorithm. With its unprecedented resolution, SNR and image quality, we envision that this dataset will have a broad range of investigational, educational, and clinical applications that will advance the understanding of human brain structures and connectivity. This comprehensive dataset can also be used as a test bed for new modeling, sub-sampling strategies, denoising and processing algorithms, potentially providing a common testing platform for further development of in vivo high resolution dMRI techniques. Whole brain anatomical T1-weighted and T2-weighted images at submillimeter scale along with field maps are also made available.

neuroscience↗