Statistical estimation of T1 relaxation times using conventional magnetic resonance imaging

Mejia, Amanda F.; Sweeney, Elizabeth; Nair, Govind; Sati, Pascal; Shea, Colin D.; Reich, Daniel S.; Shinohara, Russell T.

doi:10.1016/j.neuroimage.2015.12.037

Cited by 16 publications

(22 citation statements)

References 55 publications

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“…While there are limitations to the development of biomarkers using the quantitative information of conventional MRI intensities, previous studies have shown that a great deal of quantitative information in the MRI intensities can be harnessed to study disease [Sweeney et al, 2016, Reich et al, 2015, Ghassemi et al, 2015b, Mejia et al, 2015, Meier et al, 2007]. The present results show that the development of biomarkers using MRI studies in many neurological and psychiatric disorders could benefit from the RAVEL scan-effect correction tool.…”

Section: Resultsmentioning

confidence: 66%

“…Shinohara et al [2014] used a NAWM stripe to estimate a scaling and shifting parameter in their z -score normalization method. In Mejia et al [2015], in the context of estimating quantitative T 1 maps ( qT 1 ) from conventional MRI, the authors proposed an adaptation of the z -score normalization method by using a combination of NAWM and cerebellar gray matter (CBGM), where the NAWM was used for the scaling parameter and the CBGM was used for the shifting parameter. In Ghassemi et al [2015b], the authors used the median GM intensity for the shifting parameter, and the difference between the median intraconal orbital fat intensity and the median GM intensity for the scaling parameter.…”

Section: Discussionmentioning

confidence: 99%

“…In Sweeney et al [2013], the authors use the whole brain to estimate the scale and shift parameters. We note that the different versions of the z-score transformation used in Shinohara et al [2014], Sweeney et al [2013], Mejia et al [2015], Ghassemi et al [2015b] only leave room for the choice of two control regions at maximum, corresponding to the mean and scale parameters. While this improves comparability between subjects in comparison to the unnormalized intensities, as shown in Figure 4b, we have shown that RAVEL improves dramatically upon a z-score transformation only.…”

Section: Discussionmentioning

confidence: 99%

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Removing inter-subject technical variability in magnetic resonance imaging studies

et al. 2016

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Magnetic resonance imaging (MRI) intensities are acquired in arbitrary units, making scans non-comparable across sites and between subjects. Intensity normalization is a first step for the improvement of comparability of the images across subjects. However, we show that unwanted inter-scan variability associated with imaging site, scanner effect and other technical artifacts is still present after standard intensity normalization in large multi-site neuroimaging studies. We propose RAVEL (Removal of Artificial Voxel Effect by Linear regression), a tool to remove residual technical variability after intensity normalization. As proposed by SVA and RUV [Leek and Storey, 2007, 2008, Gagnon-Bartsch and Speed, 2012], two batch effect correction tools largely used in genomics, we decompose the voxel intensities of images registered to a template into a biological component and an unwanted variation component. The unwanted variation component is estimated from a control region obtained from the cerebrospinal fluid (CSF), where intensities are known to be unassociated with disease status and other clinical covariates. We perform a singular value decomposition (SVD) of the control voxels to estimate factors of unwanted variation. We then estimate the unwanted factors using linear regression for every voxel of the brain and take the residuals as the RAVEL-corrected intensities. We assess the performance of RAVEL using T1-weighted (T1-w) images from more than 900 subjects with Alzheimer’s disease (AD) and mild cognitive impairment (MCI), as well as healthy controls from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We compare RAVEL to two intensity-normalization-only methods: histogram matching and White Stripe. We show that RAVEL performs best at improving the replicability of the brain regions that are empirically found to be most associated with AD, and that these regions are significantly more present in structures impacted by AD (hippocampus, amygdala, parahippocampal gyrus, enthorinal area and fornix stria terminals). In addition, we show that the RAVEL-corrected intensities have the best performance in distinguishing between MCI subjects and healthy subjects using the mean hippocampal intensity (AUC=67%), a marked improvement compared to results from intensity normalization alone (AUC=63% and 59% for histogram matching and White Stripe, respectively). RAVEL is promising for many other imaging modalities.

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Section: Resultsmentioning

confidence: 66%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Removing inter-subject technical variability in magnetic resonance imaging studies

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“…MCPHEE and WILMAN Researchers have sought to acquire quantitative maps rapidly by either directly fitting standard weighted images [12][13][14] or through use of specialized or nonstandard sequences (e.g., fingerprinting, 15,16 DESPOT1 and DESPOT2, 17,18 QRAPMASTER, 19,20 or multislice inversion recovery 21 ). In some cases, dictionary methods are used for parameter quantification.…”

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confidence: 99%

T₁ and T₂ quantification from standard turbo spin echo images

McPhee

Wilman

2018

Magnetic Resonance in Med

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Purpose To extract longitudinal and transverse (T1 and T2) relaxation maps from standard MRI methods. Methods Bloch simulations were used to model relative signal amplitudes from standard turbo spin‐echo sequences: proton density weighted, T2‐weighted, and either T2‐weighted fluid attenuated inversion recovery or T1‐weighted images. Simulations over a range of expected parameter values yielded a look‐up table of relative signal intensities of these sequences. Weighted images and flip angle maps were acquired in 8 subjects at 3 T using both single and multislice acquisitions. The T1 and T2 maps were fit by comparing the weighted images to the look‐up table, given the measured flip angles. Results were compared with inversion recovery and multi‐echo spin‐echo experiments. Results A region analysis showed that relaxation maps computed from single‐slice proton density, T2 and T1 weighting provided a mean T1 error of 4% in gray matter and 11% in white matter, and a mean T2 error of 3% and 4%, respectively, in comparison to reference measurements. In multislice acquisitions that are optimized to reduce cross‐talk and incidental magnetization transfer, the mean T1 error was 7% in gray matter and 1% in white matter, and the mean T2 errors were 3% and 4%, respectively. The best T1 results were achieved using proton density, T2 and T1 weighting rather than the fluid attenuated inversion recovery, although T2 maps were largely unaffected by this choice. Incidental magnetization transfer reduced T1 accuracy in standard interleaved multislice acquisitions. Conclusion Through exact sequence modeling and separate flip angle measurement, T2 and T1 may be quantified from a turbo spin‐echo brain protocol with proton density, T2, and T1 weighting.

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“…However, recent advances in neuroimaging analysis have emphasized multimodal techniques in order to include covariance modeling across modalities [11] [12] [13]. This relationship, which we refer to as coupling or inter-modal coupling (IMCo), is known to differ across tissue types [14] [15]. However, it is unknown whether IMCo is disrupted in pathological conditions such as MS. We propose to leverage IMCo information as features for lesion segmentation in order to quantify the coherent changes as tissue damage and repair occur across imaging modalities.…”

Section: Mimosa and Imco Regressionmentioning

confidence: 99%

MIMoSA: A Method for Inter-Modal Segmentation Analysis

Valcarcel

Linn

Vandekar

et al. 2017

Preprint

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Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WML) in multiple sclerosis. While these lesions have been studied for over two decades using MRI technology, automated segmentation remains challenging. Although the majority of statistical techniques for the automated segmentation of WML are based on a single imaging modality, recent advances have used multimodal techniques for identifying WML. Complementary imaging modalities emphasize different tissue properties, which can help identify and characterize interrelated features of lesions. However, prior work has ignored relationships between imaging modalities, which may be informative in this clinical context. To harness the coherent changes in these measurements, we utilized inter-modal coupling regression (IMCo) to estimate the covariance structure across modalities. We then used a local logistic regression, MIMoSA, which leverages new covariance features from IMCo regression as well as the mean structure of each imaging modality in order to model the probability that any voxel is part of a lesion. Finally, we introduced a novel thresholding algorithm to fully automate the estimation of the probability maps to generate fully automated segmentations masks for 94 subjects. To evaluate the performance of the automated segmentations generated using MIMoSA we compared results with gold standard manual segmentations. We demonstrate the superiority of MIMoSA to other automated segmentation techniques by comparing it to the OASIS algorithm as well as LesionTOADS. MIMoSA resulted in statistically significant improvement in lesion segmentation.

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Statistical estimation of T1 relaxation times using conventional magnetic resonance imaging

Cited by 16 publications

References 55 publications

Removing inter-subject technical variability in magnetic resonance imaging studies

Removing inter-subject technical variability in magnetic resonance imaging studies

T₁ and T₂ quantification from standard turbo spin echo images

MIMoSA: A Method for Inter-Modal Segmentation Analysis

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Statistical estimation of T1 relaxation times using conventional magnetic resonance imaging

Cited by 16 publications

References 55 publications

Removing inter-subject technical variability in magnetic resonance imaging studies

Removing inter-subject technical variability in magnetic resonance imaging studies

T1 and T2 quantification from standard turbo spin echo images

MIMoSA: A Method for Inter-Modal Segmentation Analysis

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T₁ and T₂ quantification from standard turbo spin echo images