Accuracy and precision in super-resolution MRI: Enabling spherical tensor diffusion encoding at ultra-high b-values and high resolution

Vis, Geraline; Nilsson, Markus; Westin, Carl Fredrik; Szczepankiewicz, Filip

doi:10.1016/j.neuroimage.2021.118673

Cited by 16 publications

(8 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multi-slice (MS) super-resolution reconstruction (SRR) methods have been proposed to improve the inherent trade-off between spatial resolution, signal-to-noise ratio (SNR), and scan time in magnetic resonance imaging (MRI) (Greenspan, 2008;Plenge et al, 2012;Poot et al, 2012;Van Dyck et al, 2020;Askin Incebacak et al, 2021;Vis et al, 2021) and to replace direct 3D acquisition methods when these are not effective or possible, as is often the case, for example, in T2-weighted imaging (Greenspan, 2008;Plenge et al, 2012). MS-SRR consists in estimating a 3D isotropic high-resolution (HR) image, hereafter referred to as MS-SRR image, from a series of anisotropic MS images with a low through-plane resolution, where each of the low resolution MS images is acquired in a distinct fashion to ensure that the acquired dataset contains complementary resolution information about the HR image to be reconstructed (Van Reeth et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

To shift or to rotate? Comparison of acquisition strategies for multi-slice super-resolution magnetic resonance imaging

et al. 2022

View full text Add to dashboard Cite

Multi-slice (MS) super-resolution reconstruction (SRR) methods have been proposed to improve the trade-off between resolution, signal-to-noise ratio and scan time in magnetic resonance imaging. MS-SRR consists in the estimation of an isotropic high-resolution image from a series of anisotropic MS images with a low through-plane resolution, where the anisotropic low-resolution images can be acquired according to different acquisition schemes. However, it is yet unclear how these schemes compare in terms of statistical performance criteria, especially for regularized MS-SRR. In this work, the estimation performance of two commonly adopted MS-SRR acquisition schemes based on shifted and rotated MS images respectively are evaluated in a Bayesian framework. The maximum a posteriori estimator, which introduces regularization by incorporating prior knowledge in a statistically well-defined way, is put forward as the estimator of choice and its accuracy, precision, and Bayesian mean squared error (BMSE) are used as performance criteria. Analytic calculations as well as Monte Carlo simulation experiments show that the rotated scheme outperforms the shifted scheme in terms of precision, accuracy, and BMSE. Furthermore, the superior performance of the rotated scheme is confirmed in real data experiments and in retrospective simulation experiments with and without inter-image motion. Results show that the rotated scheme allows regularized MS-SRR with a higher accuracy and precision than the shifted scheme, besides being more resilient to motion.

show abstract

Section: Introductionmentioning

confidence: 99%

To shift or to rotate? Comparison of acquisition strategies for multi-slice super-resolution magnetic resonance imaging

et al. 2022

View full text Add to dashboard Cite

show abstract

“…It is known that signal accuracy concerns the distance between the average signal to the true value, while signal precision concerns the spread of the signal (Vis et al, 2021 ). Both terms are influenced by the distribution that characterizes the MR signal.…”

Section: Discussionmentioning

confidence: 99%

Both noise‐floor and tissue compartment difference in diffusivity contribute to FA dependence on b‐value in diffusion MRI

Yao

Tendler

Zhou

et al. 2022

Human Brain Mapping

View full text Add to dashboard Cite

Noninvasive diffusion magnetic resonance imaging (dMRI) has been widely employed in both clinical and research settings to investigate brain tissue microstructure. Despite the evidence that dMRI‐derived fractional anisotropy (FA) correlates with white matter properties, the metric is not specific. Recent studies have reported that FA is dependent on the b ‐value, and its origin has primarily been attributed to either the influence of microstructure or the noise‐floor effect. A systematic investigation into the inter‐relationship of these two effects is however still lacking. This study aims to quantify contributions of the reported differences in intra‐ and extra‐neurite diffusivity to the observed changes in FA, in addition to the noise in measurements. We used in‐vivo and post‐mortem human brain imaging, as well as numerical simulations and histological validation, for this purpose. Our investigations reveal that the percentage difference of FA between b ‐values (pdFA) has significant positive associations with neurite density index (NDI), which is derived from in‐vivo neurite orientation dispersion and density imaging (NODDI), or Bielschowsky's silver impregnation (BIEL) staining sections of fixed post‐mortem human brain samples. Furthermore, such an association is found to be varied with Signal‐to‐Noise Ratio (SNR) level, indicating a nonlinear interaction effect between tissue microstructure and noise. Finally, a multicompartment model simulation revealed that these findings can be driven by differing diffusivities of intra‐ and extra‐neurite compartments in tissue, with the noise‐floor further amplifying the effect. In conclusion, both the differences in intra‐ and extra‐neurite diffusivity and noise‐floor effects significantly contribute to the FA difference associated with the b ‐value.

show abstract

“…The improved conspicuity can be explained by opposite tendencies of glioma tumor tissue and white matter from the perspective of the signal attenuation ( Figures 3A,B ): STE-DWI attenuates anisotropic tissue components leaving only isotropic low-diffusion components contributing to the diffusion-weighed signal, which is prominent at higher b -values ( Figure 2 ). Thus, STE-DWI emphasizes tissues, such as dense glioma tumors, with spherical and tightly packed cells that exhibit low diffusivity in all directions ( Szczepankiewicz et al, 2015 ; Lundell et al, 2019 ; Tax et al, 2020 ; Vis et al, 2021 ). Signal attenuation in white matter was further illustrated in a patient without relevant MRI abnormalities ( Figure 1 , C1) and in a patient with a non-enhancing WHO IV glioblastoma without a DWI-hyperintensity ( Figure 1 , patient C2).…”

Section: Discussionmentioning

confidence: 99%

“…Note that STE-DWI cannot be obtained from conventional LTE-DWI such as by the trace of the diffusion tensor imaging (DTI). The STE-DWI has previously been used to highlight the difference in cell density between the cortices of the cerebrum and cerebellum ( Lundell et al, 2019 ; Tax et al, 2020 ; Vis et al, 2021 ) or for fast MD mapping ( Mori and Van Zijl, 1995 ; Wong et al, 1995 ; Ianuş and Shemesh, 2018 ; Lasič et al, 2020 ; Szczepankiewicz et al, 2021a ), but has not yet been systematically explored in gliomas.…”

Section: Introductionmentioning

confidence: 99%

Separating Glioma Hyperintensities From White Matter by Diffusion-Weighted Imaging With Spherical Tensor Encoding

et al. 2022

Self Cite

View full text Add to dashboard Cite

BackgroundTumor-related hyperintensities in high b-value diffusion-weighted imaging (DWI) are radiologically important in the workup of gliomas. However, the white matter may also appear as hyperintense, which may conflate interpretation.PurposeTo investigate whether DWI with spherical b-tensor encoding (STE) can be used to suppress white matter and enhance the conspicuity of glioma hyperintensities unrelated to white matter.Materials and MethodsTwenty-five patients with a glioma tumor and at least one pathology-related hyperintensity on DWI underwent conventional MRI at 3 T. The DWI was performed both with linear and spherical tensor encoding (LTE-DWI and STE-DWI). The LTE-DWI here refers to the DWI obtained with conventional diffusion encoding and averaged across diffusion-encoding directions. Retrospectively, the differences in contrast between LTE-DWI and STE-DWI, obtained at a b-value of 2,000 s/mm2, were evaluated by comparing hyperintensities and contralateral normal-appearing white matter (NAWM) both visually and quantitatively in terms of the signal intensity ratio (SIR) and contrast-to-noise ratio efficiency (CNReff).ResultsThe spherical tensor encoding DWI was more effective than LTE-DWI at suppressing signals from white matter and improved conspicuity of pathology-related hyperintensities. The median SIR improved in all cases and on average by 28%. The median (interquartile range) SIR was 1.9 (1.6 – 2.1) for STE and 1.4 (1.3 – 1.7) for LTE, with a significant difference of 0.4 (0.3 –0.5) (p < 10–4, paired U-test). In 40% of the patients, the SIR was above 2 for STE-DWI, but with LTE-DWI, the SIR was below 2 for all patients. The CNReff of STE-DWI was significantly higher than of LTE-DWI: 2.5 (2 – 3.5) vs. 2.3 (1.7 – 3.1), with a significant difference of 0.4 (−0.1 –0.6) (p < 10–3, paired U-test). The STE improved CNReff in 70% of the cases. We illustrate the benefits of STE-DWI in three patients, where STE-DWI may facilitate an improved radiological description of tumor-related hyperintensity, including one case that could have been missed out if only LTE-DWI was inspected.ConclusionThe contrast mechanism of high b-value STE-DWI results in a stronger suppression of white matter than conventional LTE-DWI, and may, therefore, be more sensitive and specific for assessment of glioma tumors and DWI-hyperintensities.

show abstract

Accuracy and precision in super-resolution MRI: Enabling spherical tensor diffusion encoding at ultra-high b-values and high resolution

Cited by 16 publications

References 52 publications

To shift or to rotate? Comparison of acquisition strategies for multi-slice super-resolution magnetic resonance imaging

To shift or to rotate? Comparison of acquisition strategies for multi-slice super-resolution magnetic resonance imaging

Both noise‐floor and tissue compartment difference in diffusivity contribute to FA dependence on b‐value in diffusion MRI

Separating Glioma Hyperintensities From White Matter by Diffusion-Weighted Imaging With Spherical Tensor Encoding

Contact Info

Product

Resources

About