Direct comparison of in-vivo and post-mortem spin-echo based diffusion tensor imaging in the porcine heart

Stoeck, Christian T.; Deuster, Constantin von; Cesarovic, Nikola; Genet, Martin; Emmert, Maximilian Y.; Kozerke, Sebastian

doi:10.1186/1532-429x-17-s1-p76

Cited by 1 publication

(4 citation statements)

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“…Although this work has focused on in‐plane off‐resonance correction, through‐slice off‐resonance effects remain to be addressed. The slice thickness used in most cDTI studies promotes through‐slice intravoxel dephasing due to off‐resonances 1,4,11,13,39,41,50,51 . Current 2D off‐resonance correction methods do not consider this effect, leading to residual signal‐attenuation and image distortions.…”

Section: Discussionmentioning

confidence: 99%

“…Example MRXCAT object image can be seen in Figure 3B. Tensors were simulated with the following properties obtained from literature: transmurally linear helix angle (HA) range −60° to +60°, absolute E2 sheet angle (

(||, normalE 2 normalA)

) 35°, eigenvalues

λ_{1}

λ_{2}

λ_{3}

2 × 10 −3 /1.25 × 10 −3 /0.9 × 10 −3 mm 2 /s, 37 mean diffusivity (MD) 1.45 × 10 −3 mm 2 /s, and fractional anisotropy (FA) 0.38 11,38,39 . Diffusion contrast was imposed to the proton‐density object images using

ρ^{'} false(\overset{r}{true} false) = ρ false(\overset{r}{true} false) \cdot \frac{()(, 1 - e^{- \frac{TR}{T_{1} (\overset{false\to}{r})}})}{()(, 1 + \cos ()(, α) e^{- \frac{TR}{T_{1} (\overset{false\to}{r})}})} \cdot \sin (α) \cdot e^{- \frac{italicTE}{T_{2} false(\overset{r}{true} false)}} \cdot e^{- b {\overset{g}{true}}^{T} \bar{\overset{D}{true}} false(\overset{r}{true} false) \overset{false\to}{g}},

where

T_{1}

and

T_{2}

denote tissue‐dependent parameters,

α

flip angle,

\overset{\bar{D}}{true}

local diffusion tensor,

b

b‐value, and

g

normalized diffusion gradients.…”

Section: Methodsmentioning

confidence: 99%

“…Tensors were simulated with the following properties obtained from literature: transmurally linear helix angle (HA) range −60° to +60°, absolute E2 sheet angle (|E2A|) 35°, eigenvalues 1 / 2 / 3 2 × 10 −3 /1.25 × 10 −3 /0.9 × 10 −3 mm 2 /s, 37 mean diffusivity (MD) 1.45 × 10 −3 mm 2 /s, and fractional anisotropy (FA) 0.38. 11,38,39 Diffusion contrast was imposed to the proton-density object images using where T 1 and T 2 denote tissue-dependent parameters, flip angle, D local diffusion tensor, b b-value, and g normalized diffusion gradients. After multiplication with coil-sensitivities C c , each coil image was encoded separately using the forward model where t denotes time of data acquisition relative to the TE, and T * 2 denotes transverse relaxation including field effects.…”

Section: F I G U R Ementioning

confidence: 99%

“…The slice thickness used in most cDTI studies promotes through-slice intravoxel dephasing due to off-resona nces. 1,4,11,13,39,41,50,51 Current 2D off-resonance correction methods do not consider this effect, leading to residual signal-attenuation and image distortions. Data acquisition at isotropic spatial resolutions could alleviate this issue.…”

Section: F I G U R Ementioning

confidence: 99%

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Analysis and correction of off‐resonance artifacts in echo‐planar cardiac diffusion tensor imaging

Gorkum

Deuster

Guenthner

et al. 2020

Magnetic Resonance in Med

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Purpose Cardiac diffusion tensor imaging using EPI readout is prone to image distortions in the presence of field inhomogeneities. In this work, a framework to analyze and correct image distortions in cardiac diffusion tensor imaging is presented. Methods A multi‐coil reconstruction framework was implemented to enable field map‐based off‐resonance correction. Numerical simulations were used to examine reconstruction performance for EPI phase‐encode directions blip up‐down and down‐up for different degrees of off‐resonance gradients and varying field map resolution. The impact of coil encoding was analyzed using the g‐factor and normalized RMSE. Finally, the proposed method was tested on free‐breathing in vivo cardiac diffusion tensor imaging data acquired in healthy subjects at 3 Tesla. Results Depending on the local field map gradient strength and polarity and the selected phase‐encode direction, field inhomogeneities lead to either local spatial compression or stretching with standard image reconstruction. Although spatial compression results in loss of image resolution upon field map‐based reconstruction, spatial stretching can be recovered once multiple receive coils are utilized. Multi‐coil reconstruction was found to reduce the normalized RMSE from 34.3% to 8.1% for image compression, and 33.6% to 1.8% for image stretching, with resulting average g‐factors 14.7 ± 2.9 and 1.2 ± 0.1, respectively. In vivo, multi‐coil field map‐based reconstruction yielded improved alignment of angle maps with anatomical cine data. Conclusion Multi‐coil, field map‐based image reconstruction for echo‐planar cardiac diffusion tensor imaging allows accurate image reconstruction provided that the phase‐encode direction and polarity is chosen to principally align with the direction and polarity of the prominent gradients of field inhomogeneities.

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Section: Discussionmentioning

confidence: 99%