Signal‐to‐noise assessment for diffusion tensor imaging with single data set and validation using a difference image method with data from a multicenter study

Wang, Zhiyue; Chia, Jonathan M.; Ahmed, Shaheen; Rollins, Nancy

doi:10.1118/1.4893195

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…SNR assessment is important for reliable quantification of diffusional metrics [ 71 ]. When SNR is low, Rician noise does not only cause random fluctuations but also a signal dependent bias to the data, which may lead to difficulty in postprocessing such as tensor calculation.…”

Section: Discussionmentioning

confidence: 99%

Non-local means based Rician noise filtering for diffusion tensor and kurtosis imaging in human brain and spinal cord

et al. 2021

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Background To investigate the effect of using a Rician nonlocal means (NLM) filter on quantification of diffusion tensor (DT)- and diffusion kurtosis (DK)-derived metrics in various anatomical regions of the human brain and the spinal cord, when combined with a constrained linear least squares (CLLS) approach. Methods Prospective brain data from 9 healthy subjects and retrospective spinal cord data from 5 healthy subjects from a 3 T MRI scanner were included in the study. Prior to tensor estimation, registered diffusion weighted images were denoised by an optimized blockwise NLM filter with CLLS. Mean kurtosis (MK), radial kurtosis (RK), axial kurtosis (AK), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD) and fractional anisotropy (FA), were determined in anatomical structures of the brain and the spinal cord. DTI and DKI metrics, signal-to-noise ratio (SNR) and Chi-square values were quantified in distinct anatomical regions for all subjects, with and without Rician denoising. Results The averaged SNR significantly increased with Rician denoising by a factor of 2 while the averaged Chi-square values significantly decreased up to 61% in the brain and up to 43% in the spinal cord after Rician NLM filtering. In the brain, the mean MK varied from 0.70 (putamen) to 1.27 (internal capsule) while AK and RK varied from 0.58 (corpus callosum) to 0.92 (cingulum) and from 0.70 (putamen) to 1.98 (corpus callosum), respectively. In the spinal cord, FA varied from 0.78 in lateral column to 0.81 in dorsal column while MD varied from 0.91 × 10−3 mm2/s (lateral) to 0.93 × 10−3 mm2/s (dorsal). RD varied from 0.34 × 10−3 mm2/s (dorsal) to 0.38 × 10−3 mm2/s (lateral) and AD varied from 1.96 × 10−3 mm2/s (lateral) to 2.11 × 10−3 mm2/s (dorsal). Conclusions Our results show a Rician denoising NLM filter incorporated with CLLS significantly increases SNR and reduces estimation errors of DT- and KT-derived metrics, providing the reliable metrics estimation with adequate SNR levels.

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Section: Discussionmentioning

confidence: 99%

Non-local means based Rician noise filtering for diffusion tensor and kurtosis imaging in human brain and spinal cord

et al. 2021

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“…SNR is known to have a major impact on computed scalar indices and tractography (Farrell et al, 2007;Polders et al, 2011;Wang, Chia, Ahmed, & Rollins, 2014). An SNR >10 has been suggested to be adequate for diffusion MRI-based tractography with some diffusion encoding directions (Descoteaux, Deriche, Knosche, & Anwander, 2009), although others indicate that an SNR >3 is sufficient (Jones, Knosche, & Turner, 2013).…”

Section: Signal-to-noise Comparisonmentioning

confidence: 99%

Mesoscale diffusion magnetic resonance imaging of the ex vivo human hippocampus

Foley

Manivannan

et al. 2020

Human Brain Mapping

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Mesoscale diffusion magnetic resonance imaging (MRI) endeavors to bridge the gap between macroscopic white matter tractography and microscopic studies investigating the cytoarchitecture of human brain tissue. To ensure a robust measurement of diffusion at the mesoscale, acquisition parameters were arrayed to investigate their effects on scalar indices (mean, radial, axial diffusivity, and fractional anisotropy) and streamlines (i.e., graphical representation of axonal tracts) in hippocampal layers. A mesoscale resolution afforded segementation of the pyramidal cell layer (CA1‐4), the dentate gyrus, as well as stratum moleculare, radiatum, and oriens. Using ex vivo samples, surgically excised from patients with intractable epilepsy (n = 3), we found that shorter diffusion times (23.7 ms) with a b‐value of 4,000 s/mm2 were advantageous at the mesoscale, providing a compromise between mean diffusivity and fractional anisotropy measurements. Spatial resolution and sample orientation exerted a major effect on tractography, whereas the number of diffusion gradient encoding directions minimally affected scalar indices and streamline density. A sample temperature of 15°C provided a compromise between increasing signal‐to‐noise ratio and increasing the diffusion properties of the tissue. Optimization of the acquisition afforded a system's view of intra‐ and extra‐hippocampal connections. Tractography reflected histological boundaries of hippocampal layers. Individual layer connectivity was visualized, as well as streamlines emanating from individual sub‐fields. The perforant path, subiculum and angular bundle demonstrated extra‐hippocampal connections. Histology of the samples confirmed individual cell layers corresponding to ROIs defined on MR images. We anticipate that this ex vivo mesoscale imaging will yield novel insights into human hippocampal connectivity.

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“…SNR assessment is important for reliable quanti cation of diffusional metrics (71). When SNR is low, Rician noise does not only cause random uctuations but also a signal dependent bias to the data, which may lead to di culty in postprocessing such as tensor calculation.…”

Section: Measurement Of Snrmentioning

confidence: 99%

Non-local means based Rician noise filtering for diffusion tensor and kurtosis imaging in human brain and spinal cord

Zhang

Vernekar

Qian

et al. 2020

Preprint

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Background To investigate the effect of using an Rician nonlocal means (NLM) filter on quantification of diffusion tensor (DT)- and diffusion kurtosis (DK)-derived metrics in various anatomical regions of the human brain and the spinal cord, when combined with a constrained linear least squares (CLLS) approach. Methods Prospective brain data from 9 healthy subjects and retrospective spinal cord data from 5 healthy subjects from a 3T MRI scanner were included in the study. Prior to tensor estimation, registered diffusion weighted images were denoised by an optimized blockwise NLM filter with CLLS. Mean kurtosis (MK), radial kurtosis (RK), axial kurtosis (AK), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD) and fractional anisotropy (FA), were determined in anatomical structures of the brain and the spinal cord. DTI and DKI metrics, signal-to-noise ratio (SNR) and Chi-square values were quantified in distinct anatomical regions for all subjects, with and without Rician denoising. Results The averaged SNR significantly increased with Rician denoising by a factor of 2 while the averaged Chi-square values significantly decreased up to 61% in the brain and up to 43% in the spinal cord after Rician NLM filtering. In the brain, the mean MK varied from 0.70 (putamen) to 1.27 (internal capsule) while AK and RK varied from 0.58 (corpus callosum) to 0.92 (cingulum) and from 0.70 (putamen) to 1.98 (corpus callosum), respectively. In the spinal cord, FA varied from 0.78 in lateral column to 0.81 in dorsal column while MD varied from 0.91 × 10− 3 mm2/s (lateral) to 0.93 × 10− 3 mm2/s (dorsal). RD varied from 0.34 × 10− 3 mm2/s (dorsal) to 0.38 × 10− 3 mm2/s (lateral) and AD varied from 1.96 × 10− 3 mm2/s (lateral) to 2.11 × 10− 3 mm2/s (dorsal). Conclusions Our results show Rician denoising NLM filter incorporated with CLLS significantly increases SNR and reduces estimation errors of DT- and KT-derived metrics, providing the reliable metrics estimation with adequate SNR levels.

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Signal‐to‐noise assessment for diffusion tensor imaging with single data set and validation using a difference image method with data from a multicenter study

Abstract: An FT-based high-pass filtering method can be used for local area SNR assessment using only one DTI data set. This method could be used to evaluate SNR for patient studies in a multicenter setting.

Cited by 5 publications

References 27 publications

Non-local means based Rician noise filtering for diffusion tensor and kurtosis imaging in human brain and spinal cord

Non-local means based Rician noise filtering for diffusion tensor and kurtosis imaging in human brain and spinal cord

Mesoscale diffusion magnetic resonance imaging of the ex vivo human hippocampus

Non-local means based Rician noise filtering for diffusion tensor and kurtosis imaging in human brain and spinal cord

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