Comparison of non-parametric T2 relaxometry methods for myelin water quantification

Canales-Rodríguez, Erick Jorge; Pizzolato, Marco; Piredda, Gian Franco; Hilbert, Tom; Kunz, Nicolas; Pot, Caroline; Yu, Thomas; Salvador, Raymond; Pomarol‐Clotet, Edith; Kober, Tobias; Thiran, Jean Philippe; Daducci, Alessandro

doi:10.1016/j.media.2021.101959

Cited by 18 publications

(16 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This naturally comes at the cost of an additional, inevitable trade-off between bias and variance of the estimates. Such a trade-off could be optimized using adaptive regularization criteria such as those discussed, for instance, for 2 relaxometry (Canales-Rodríguez, Pizzolato, Piredda, Hilbert, Kunz, Pot, Yu, Salvador, Pomarol-Clotet, Kober et al, 2021a;Canales-Rodríguez, Pizzolato, Yu, Piredda, Hilbert, Radua, Kober and Thiran, 2021b). Moreover, in the future, the integration of necessary constraints (Haije, Özarslan and Feragen, 2020) while fitting the signal could ensure that the optimization of Eq.…”

Section: Discussionmentioning

confidence: 99%

Axial and radial axonal diffusivities from single encoding strongly diffusion-weighted MRI

Pizzolato¹,

Canales‐Rodríguez²,

Andersson³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

We enable the estimation of the per-axon axial diffusivity from single encoding, strongly diffusion-weighted, pulsed gradient spin echo data. Additionally, we improve the estimation of the per-axon radial diffusivity compared to estimates based on spherical averaging. The use of strong diffusion weightings in magnetic resonance imaging (MRI) allows to approximate the signal in white matter as the sum of the contributions from axons. At the same time, spherical averaging leads to a major simplification of the modeling by removing the need to explicitly account for the unknown orientation distribution of axons. However, the spherically averaged signal acquired at strong diffusion weightings is not sensitive to the axial diffusivity, which cannot therefore be estimated. After revising existing theory, we introduce a new general method for the estimation of both axonal diffusivities at strong diffusion weightings based on zonal harmonics modeling. We additionally show how this could lead to estimates that are free from partial volume bias with, for instance, gray matter. We test the method on publicly available data from the MGH Adult Diffusion Human Connectome project dataset. We report reference values of axonal diffusivities based on 34 subjects, and derive estimates of axonal radii. We address the estimation problem also from the angle of the required data preprocessing, the presence of biases related to modeling assumptions, current limitations, and future possibilities.

show abstract

Section: Discussionmentioning

confidence: 99%

Axial and radial axonal diffusivities from single encoding strongly diffusion-weighted MRI

Pizzolato¹,

Canales‐Rodríguez²,

Andersson³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since the problem is ill-posed and prone to overfitting, a second order Laplacian regularization term was added to favor smoothness between neighboring weights 𝚫w, as described previously. 49,50 The solution of the NNLS problem is the k × 1 array of weights, which can be used to compute the 2 N × 1 best fit signal given by the weighted sum of all the signals in the dictionary (D⋅w). The final weight matrix is obtained by representing the array of weights in its original 2D shape, with relaxation time ratio and off-resonance frequencies as dimensions.…”

Section: 23mentioning

confidence: 99%

SPARCQ: A new approach for fat fraction mapping using asymmetries in the phase‐cycled balanced SSFP signal profile

Rossi

Mackowiak

et al. 2023

Magnetic Resonance in Med

Self Cite

View full text Add to dashboard Cite

PurposeTo develop SPARCQ (Signal Profile Asymmetries for Rapid Compartment Quantification), a novel approach to quantify fat fraction (FF) using asymmetries in the phase‐cycled balanced SSFP (bSSFP) profile.MethodsSPARCQ uses phase‐cycling to obtain bSSFP frequency profiles, which display asymmetries in the presence of fat and water at certain TRs. For each voxel, the measured signal profile is decomposed into a weighted sum of simulated profiles via multi‐compartment dictionary matching. Each dictionary entry represents a single‐compartment bSSFP profile with a specific off‐resonance frequency and relaxation time ratio. Using the results of dictionary matching, the fractions of the different off‐resonance components are extracted for each voxel, generating quantitative maps of water and FF and banding‐artifact‐free images for the entire image volume. SPARCQ was validated using simulations, experiments in a water‐fat phantom and in knees of healthy volunteers. Experimental results were compared with reference proton density FFs obtained with 1H‐MRS (phantoms) and with multiecho gradient‐echo MRI (phantoms and volunteers). SPARCQ repeatability was evaluated in six scan‐rescan experiments.ResultsSimulations showed that FF quantification is accurate and robust for SNRs greater than 20. Phantom experiments demonstrated good agreement between SPARCQ and gold standard FFs. In volunteers, banding‐artifact‐free quantitative maps and water‐fat‐separated images obtained with SPARCQ and ME‐GRE demonstrated the expected contrast between fatty and non‐fatty tissues. The coefficient of repeatability of SPARCQ FF was 0.0512.ConclusionSPARCQ demonstrates potential for fat quantification using asymmetries in bSSFP profiles and may be a promising alternative to conventional FF quantification techniques.

show abstract

“…and MD (63%-68%) may be combined with metrics derived from T1w data to build multimodality classifiers for the improved automatic classification of BD patients (e.g., see Salvador et al, 2019). Another potentially helpful metric to enhance the accuracy is the myelin water fraction, which can be estimated from multicomponent T2 relaxometry techniques and multi-echo T2 data (Piredda et al, 2021, Yu et al, 2021, Canales-Rodríguez et al, 2021a, 2021b, 2021c . Nevertheless, in our study, we did not observe a significant improvement in the performance when the four diffusion metrics were employed together in the classification.…”

Section: Is Fa the Most Sensitive Diffusion Metric In Bd?mentioning

confidence: 99%

Widespread intra‐axonal signal fraction abnormalities in bipolar disorder from multicompartment diffusion MRI: Sensitivity to diagnosis, association with clinical features and pharmacologic treatment

Canales‐Rodríguez,

Verdolini,

Alonso‐Lana

et al. 2023

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

Despite diffusion tensor imaging (DTI) evidence for widespread fractional anisotropy (FA) reductions in the brain white matter of patients with bipolar disorder, questions remain regarding the specificity and sensitivity of FA abnormalities as opposed to other diffusion metrics in the disorder. We conducted a whole-brain voxel-based Raymond Salvador and Edith Pomarol-Clotet are joint last author (equal contribution, shared senior authorship).

show abstract

Comparison of non-parametric T2 relaxometry methods for myelin water quantification

Cited by 18 publications

References 58 publications

Axial and radial axonal diffusivities from single encoding strongly diffusion-weighted MRI

Axial and radial axonal diffusivities from single encoding strongly diffusion-weighted MRI

SPARCQ: A new approach for fat fraction mapping using asymmetries in the phase‐cycled balanced SSFP signal profile

Widespread intra‐axonal signal fraction abnormalities in bipolar disorder from multicompartment diffusion MRI: Sensitivity to diagnosis, association with clinical features and pharmacologic treatment

Contact Info

Product

Resources

About