Single-Shot ${\text{T}}_{{2}}$ Mapping Through OverLapping-Echo Detachment (OLED) Planar Imaging

Cai, Congbo; Zeng, Yiqing; Zhuang, Yuchuan; Cai, Shuhui; Chen, Lin; Ding, Xinghao; Bao, Lijun; Zhong, Jianhui; Chen, Zhong

doi:10.1109/tbme.2017.2661840

Cited by 18 publications

(40 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, setting the refocusing slice thickness smaller than excitation slice thickness can also be helpful to reduce the effect of nonideal excitation pulses. Overall, the effect of nonideal pulses on DM‐OLED is the same as that on T 2 OLED , so the DM‐OLED ADC map is also quite insensitive to B 1 field variation.…”

Section: Discussionmentioning

confidence: 93%

“…Double‐refocusing is used to let the two echoes have the same echo time (TE), and the effect of T 2 attenuation can be neglected in the signal expression because the two echoes have same T 2 weighting. Because δ < < T 1 is usually satisfied, we have ignored the T 1 effect between the two excitation pulses . In fact, there will be three echoes in the acquisition period:

false{\begin{array}{l} S_{1} = true \int_{\overset{r}{true}} ρ (\vec{r}) \frac{1}{8} | \sin α | (1 + \cos α) | - \cos^{2} β + 2 \cos β - 1 | e^{i (k_{2} + k_{1})} e^{- b • D (\overset{r}{true})} d \overset{r}{true}, the first echo \\ S_{2} = true \int_{\overset{r}{true}} ρ (\vec{r}) \frac{1}{4} | \sin α \cos α | | - \cos^{2} β + 2 \cos β - 1 | e^{i k_{2}} d \vec{r}, the second echo \\ S_{3} = true \int_{\overset{r}{true}} ρ (\vec{r}) \frac{1}{8} | \sin α | (1 - \cos α) | - \cos^{2} β + 2 \cos β - 1 | e^{i (k_{2} - k_{1})} e^{- b • D (\overset{r}{true})} d \vec{r}, \end{array}

…”

Section: Methodsmentioning

confidence: 99%

“…Based on previous knowledge of structure similarity and edge sparsity, the following energy function is minimized :

{{boldx}_{1}, {boldx}_{2}} = \underset{{boldx}_{1}, {boldx}_{2}}{\arg \min} [| | {boldx}_{0} - {boldx}_{1} - {boldx}_{2} | |_{2}^{2} + λ_{1} | | \nabla {boldx}_{1} | |_{1} + λ_{2} | | \nabla {boldx}_{2} | |_{1} + λ_{3} | | \nabla ({boldx}_{1} - ζ {boldx}_{2}) | |_{1}]

…”

Section: Methodsmentioning

confidence: 99%

“…The k‐space is divided along the frequency‐encoding dimension, using the iterative partial Fourier transform method. The total variation extrapolation is applied to improve the quality of the separated images . Finally, the ADC value for every pixel can be calculated directly by the following formula:

D (\overset{r}{true}) = - \ln (μ \frac{x_{1} (\overset{r}{true})}{x_{2} (\overset{r}{true})}) \cdot b^{- 1}

where

μ = 2 \cos α / (1 + \cos α)

is the correction factor according to Equation .…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Motion‐tolerant diffusion mapping based on single‐shot overlapping‐echo detachment (OLED) planar imaging

Cai

Yang

et al. 2017

Magnetic Resonance in Med

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 93%

false{\begin{array}{l} S_{1} = true \int_{\overset{r}{true}} ρ (\vec{r}) \frac{1}{8} | \sin α | (1 + \cos α) | - \cos^{2} β + 2 \cos β - 1 | e^{i (k_{2} + k_{1})} e^{- b • D (\overset{r}{true})} d \overset{r}{true}, the first echo \\ S_{2} = true \int_{\overset{r}{true}} ρ (\vec{r}) \frac{1}{4} | \sin α \cos α | | - \cos^{2} β + 2 \cos β - 1 | e^{i k_{2}} d \vec{r}, the second echo \\ S_{3} = true \int_{\overset{r}{true}} ρ (\vec{r}) \frac{1}{8} | \sin α | (1 - \cos α) | - \cos^{2} β + 2 \cos β - 1 | e^{i (k_{2} - k_{1})} e^{- b • D (\overset{r}{true})} d \vec{r}, \end{array}

…”

Section: Methodsmentioning

confidence: 99%

“…Based on previous knowledge of structure similarity and edge sparsity, the following energy function is minimized :

{{boldx}_{1}, {boldx}_{2}} = \underset{{boldx}_{1}, {boldx}_{2}}{\arg \min} [| | {boldx}_{0} - {boldx}_{1} - {boldx}_{2} | |_{2}^{2} + λ_{1} | | \nabla {boldx}_{1} | |_{1} + λ_{2} | | \nabla {boldx}_{2} | |_{1} + λ_{3} | | \nabla ({boldx}_{1} - ζ {boldx}_{2}) | |_{1}]

…”

Section: Methodsmentioning

confidence: 99%

D (\overset{r}{true}) = - \ln (μ \frac{x_{1} (\overset{r}{true})}{x_{2} (\overset{r}{true})}) \cdot b^{- 1}

where

μ = 2 \cos α / (1 + \cos α)

is the correction factor according to Equation .…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Motion‐tolerant diffusion mapping based on single‐shot overlapping‐echo detachment (OLED) planar imaging

Cai

Yang

et al. 2017

Magnetic Resonance in Med

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, slow data acquisition speed usually hinders the practical applications of T 2 mapping, especially in the cases that high temporal resolution is required or irregular motions exist. In our early work, a single‐shot T 2 mapping sequence based on spin echo (SE)‐EPI acquisition scheme was proposed . Two overlapped echo signals with different T 2 weighting are obtained simultaneously by using two excitation pulses and corresponding echo‐shifting gradients (ESGs) and a detachment algorithm was proposed.…”

Section: Introductionmentioning

confidence: 99%

Single‐shot T₂ mapping using overlapping‐echo detachment planar imaging and a deep convolutional neural network

Cai

Wang

Zeng

et al. 2018

Magnetic Resonance in Med

Self Cite

View full text Add to dashboard Cite

show abstract

Single‐shot T₂ mapping via multi‐echo‐train multiple overlapping‐echo detachment planar imaging and multitask deep learning

et al. 2022

Self Cite

View full text Add to dashboard Cite

Background: Quantitative magnetic resonance imaging provides robust biomarkers in clinics. Nevertheless, the lengthy scan time reduces imaging throughput and increases the susceptibility of imaging results to motion. In this context, a single-shot T 2 mapping method based on multiple overlapping-echo detachment (MOLED) planar imaging was presented, but the relatively small echo time range limits its accuracy, especially in tissues with large T 2 . Purpose: In this work we proposed a novel single-shot method, Multi-Echo-Train Multiple OverLapping-Echo Detachment (METMOLED) planar imaging, to accommodate a large range of T 2 quantification without additional measurements to rectify signal degeneration arisen from refocusing pulse imperfection. Methods: Multiple echo-train techniques were integrated into the MOLED sequence to capture larger TE information. Maps of T 2 , B 1 , and spin density were reconstructed synchronously from acquired METMOLED data via multitask deep learning. A typical U-Net was trained with 3000/600 synthetic data with geometric/brain patterns to learn the mapping relationship between MET-MOLED signals and quantitative maps. The refocusing pulse imperfection was settled through the inherent information of METMOLED data and auxiliary tasks. Results: Experimental results on the digital brain (structural similarity (SSIM) index = 0.975/0.991/0.988 for MOLED/METMOLED-2/METMOLED-3, hyphenated number denotes the number of echo-trains), physical phantom (the slope of linear fitting with reference T 2 map = 1.047/1.017/1.006 for MOLED/METMOLED-2/METMOLED-3), and human brain (Pearson's correlation coefficient (PCC) = 0.9581/0.9760/0.9900 for MOLED/METMOLED-2/METMOLED-3) demonstrated that the METMOLED improved the quantitative accuracy and the tissue details in contrast to the MOLED. These improvements were more pronounced in tissues with large T 2 and in application scenarios with high temporal resolution (PCC = 0.8692/0.9465/0.9743 for MOLED/METMOLED-2/METMOLED-3). Moreover, the METMOLED could rectify the signal deviations induced by the non-ideal slice profiles of refocusing pulses without additional measurements. A preliminary measurement also demonstrated that the METMOLED is highly repeatable (mean coefficient of variation (CV) = 1.65%). Conclusions: METMOLED breaks the restriction of echo-train length to TE and implements unbiased T 2 estimates in an extensive range. Furthermore, it

show abstract

Single-Shot ${\text{T}}_{{2}}$ Mapping Through OverLapping-Echo Detachment (OLED) Planar Imaging

Abstract: Reliable T mapping was achieved with a single shot for the first time. The proposed method will facilitate real-time dynamic and quantitative MR imaging.

Cited by 18 publications

References 47 publications

Motion‐tolerant diffusion mapping based on single‐shot overlapping‐echo detachment (OLED) planar imaging

Motion‐tolerant diffusion mapping based on single‐shot overlapping‐echo detachment (OLED) planar imaging

Single‐shot T₂ mapping using overlapping‐echo detachment planar imaging and a deep convolutional neural network

Single‐shot T₂ mapping via multi‐echo‐train multiple overlapping‐echo detachment planar imaging and multitask deep learning

Contact Info

Product

Resources

About

Single-Shot ${\text{T}}_{{2}}$ Mapping Through OverLapping-Echo Detachment (OLED) Planar Imaging

Abstract: Reliable T mapping was achieved with a single shot for the first time. The proposed method will facilitate real-time dynamic and quantitative MR imaging.

Cited by 18 publications

References 47 publications

Motion‐tolerant diffusion mapping based on single‐shot overlapping‐echo detachment (OLED) planar imaging

Motion‐tolerant diffusion mapping based on single‐shot overlapping‐echo detachment (OLED) planar imaging

Single‐shot T2 mapping using overlapping‐echo detachment planar imaging and a deep convolutional neural network

Single‐shot T2 mapping via multi‐echo‐train multiple overlapping‐echo detachment planar imaging and multitask deep learning

Contact Info

Product

Resources

About

Single‐shot T₂ mapping using overlapping‐echo detachment planar imaging and a deep convolutional neural network

Single‐shot T₂ mapping via multi‐echo‐train multiple overlapping‐echo detachment planar imaging and multitask deep learning