Effects of restricted diffusion in a biological phantom: a q-space diffusion MRI study of asparagus stems at a 3T clinical scanner

Lätt, Jimmy; Nilsson, Markus; Rydhög, Anna; Wirestam, Ronnie; Ståhlberg, Freddy; Brockstedt, Sara

doi:10.1007/s10334-007-0085-z

Cited by 34 publications

(30 citation statements)

References 53 publications

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“…The mean parameter value in the two ROIs was FA intact = 0.50 and FA puree = 0.06, and μFA intact = 0.75 and μFA puree = 0.50, respectively. The FA value of intact asparagus is in agreement with other experiments that have employed similar diffusion times [41]. The distributions of parameter values are presented in histograms in Figures 8D,I.…”

Section: Whole-body Scannersupporting

confidence: 89%

“…Here we implement a numerically optimized version of the q-MAS pulse sequence [37] on a high-performance microimaging system, limited to specimens with maximum 10 mm diameter, and on a standard whole-body clinical scanner. The efficiency of the q-MAS sequence is demonstrated using two materials with pronounced water diffusion anisotropy: lyotropic liquid crystals [24,25,27,34,[38][39][40] and pureed asparagus [41][42][43][44]. For contrast, a yeast cells suspension is used, exhibiting two isotropic diffusion components [34,[45][46][47].…”

Section: Introductionmentioning

confidence: 99%

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Microanisotropy imaging: quantification of microscopic diffusion anisotropy and orientational order parameter by diffusion MRI with magic-angle spinning of the q-vector

et al. 2014

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Diffusion tensor imaging (DTI) is the method of choice for non-invasive investigations of the structure of human brain white matter (WM). The results are conventionally reported as maps of the fractional anisotropy (FA), which is a parameter related to microstructural features such as axon density, diameter, and myelination. The interpretation of FA in terms of microstructure becomes ambiguous when there is a distribution of axon orientations within the image voxel. In this paper, we propose a procedure for resolving this ambiguity by determining a new parameter, the microscopic fractional anisotropy (μFA), which corresponds to the FA without the confounding influence of orientation dispersion. In addition, we suggest a method for measuring the orientational order parameter (OP) for the anisotropic objects. The experimental protocol is capitalizing on a recently developed diffusion nuclear magnetic resonance (NMR) pulse sequence based on magic-angle spinning of the q-vector. Proof-of-principle experiments are carried out on microimaging and clinical MRI equipment using lyotropic liquid crystals and plant tissues as model materials with high μFA and low FA on account of orientation dispersion. We expect the presented method to be especially fruitful in combination with DTI and high angular resolution acquisition protocols for neuroimaging studies of gray and white matter.

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Section: Whole-body Scannersupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Microanisotropy imaging: quantification of microscopic diffusion anisotropy and orientational order parameter by diffusion MRI with magic-angle spinning of the q-vector

et al. 2014

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“…Even though the method does not require very high qvalues, the limited maximum magnetic field gradient strength of a clinical scanner prevents achievement of the infinitely short gradient pulse duration. Studies of the effect of long gradient pulse duration by simulation and experiments 19,25,[42][43][44] have shown echo attenuation shifted toward a higher q value, resulting in a narrowed PDF and underestimated root mean square displacement. In addition, interpretation is more complicated using the twocompartment model in this method.…”

Section: In Vivo Experiments In Healthy Volunteersmentioning

confidence: 99%

Estimation of the Mean Axon Diameter and Intra-axonal Space Volume Fraction of the Human Corpus Callosum: Diffusion q-space Imaging with Low q-values

Suzuki

Hori

Kamiya

et al. 2016

MRMS

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Purpose: Q-space imaging (QSI) is a diffusion-weighted imaging (DWI) technique that enables investigation of tissue microstructure. However, for sufficient displacement resolution to measure the microstructure, QSI requires high q-values that are usually difficult to achieve with a clinical scanner. The recently introduced "low q-value method" fits the echo attenuation to only low q-values to extract the root mean square displacement. We investigated the clinical feasibility of the low q-value method for estimating the microstructure of the human corpus callosum using a 3.0-tesla clinical scanner within a clinically feasible scan time.Methods: We performed a simulation to explore the acceptable range of maximum qvalues for the low q-value method. We simulated echo attenuations caused by restricted diffusion in the intra-axonal space (IAS) and hindered diffusion in the extra-axonal space (EAS) assuming 100,000 cylinders with various diameters, and we estimated mean axon diameter, IAS volume fraction, and EAS diffusivity by fitting echo attenuations with different maximum q-values. Furthermore, we scanned the corpus callosum of 7 healthy volunteers and estimated the mean axon diameter and IAS volume fraction.Results: Good agreement between estimated and defined values in the simulation study with maximum q-values of 700 and 800 cm ¹1 suggested that the maximum q-value used in the in vivo experiment, 737 cm ¹1 , was reasonable. In the in vivo experiment, the mean axon diameter was larger in the body of the corpus callosum and smaller in the genu and splenium, and this anterior-to-posterior trend is consistent with previously reported histology, although our mean axon diameter seems larger in size. On the other hand, we found an opposite anterior-to-posterior trend, with high IAS volume fraction in the genu and splenium and a lower fraction in the body, which is similar to the fiber density reported in the histology study.Conclusion: The low q-value method may provide insights into tissue microstructure using a 3T clinical scanner within clinically feasible scan time.

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“…We identify a region of interest (ROI) in the DW-MRI data containing one of the vascular bundles (Fig.1a), which we cut from the stem and image with CLSM (Fig.1b) to obtain a stack of images. The vascular bundles in the asparagus consist of highlyorganised cylindrical fibres with thick walls that exhibit anisotropic diffusion and the distribution of capillary sizes is similar to brain white matter tissue [12]. We construct the three-dimensional mesh model ( Fig.1c) with the marching cubes algorithm [13] and use it as a substrate in Monte-Carlo simulations [10] to synthesize DW-MRI data.…”

Section: Methodsmentioning

confidence: 99%

“…We capture the three-dimensional structure of biological tissue in a more natural way than previous studies [8,11]. We demonstrate the method using a biological phantom (asparagus) which is a useful model with similar microstructure to white matter [12]. Experiments vary simulation parameters and mesh properties to optimize the precision of the synthesized data while minimizing computational complexity.…”

Section: Introductionmentioning

confidence: 99%

High-Fidelity Meshes from Tissue Samples for Diffusion MRI Simulations

Panagiotaki

Hall

Zhang

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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Abstract. This paper presents a method for constructing detailed geometric models of tissue microstructure for synthesizing realistic diffusion MRI data. We construct three-dimensional mesh models from confocal microscopy image stacks using the marching cubes algorithm. Randomwalk simulations within the resulting meshes provide synthetic diffusion MRI measurements. Experiments optimise simulation parameters and complexity of the meshes to achieve accuracy and reproducibility while minimizing computation time. Finally we assess the quality of the synthesized data from the mesh models by comparison with scanner data as well as synthetic data from simple geometric models and simplified meshes that vary only in two dimensions. The results support the extra complexity of the three-dimensional mesh compared to simpler models although sensitivity to the mesh resolution is quite robust.

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Effects of restricted diffusion in a biological phantom: a q-space diffusion MRI study of asparagus stems at a 3T clinical scanner

Abstract: A new method allowing tensor analysis of FWHM was derived and yielded accurate results. In conclusion, we found it possible to measure the effects of restricted diffusion with q-space MRI using a clinical MRI scanner.

Cited by 34 publications

References 53 publications

Microanisotropy imaging: quantification of microscopic diffusion anisotropy and orientational order parameter by diffusion MRI with magic-angle spinning of the q-vector

Microanisotropy imaging: quantification of microscopic diffusion anisotropy and orientational order parameter by diffusion MRI with magic-angle spinning of the q-vector

Estimation of the Mean Axon Diameter and Intra-axonal Space Volume Fraction of the Human Corpus Callosum: Diffusion q-space Imaging with Low q-values

High-Fidelity Meshes from Tissue Samples for Diffusion MRI Simulations

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