A 3T Phase-Sensitive Inversion Recovery MRI Sequence Improves Detection of Cervical Spinal Cord Lesions and Shows Active Lesions in Patients with Multiple Sclerosis

Fechner, Alix; Savatovsky, Julien; Methni, Jonathan El; Sadik, Jean-Claude; Gout, Olivier; Deschamps, Romain; Guéguen, Antoine

doi:10.3174/ajnr.a5941

Cited by 14 publications

(22 citation statements)

References 36 publications

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“…Moreover, as the number and extent of SC lesions were assessed only on sagittal T2-weighted images and only in the segments C1 to Th4, the number of lesions in might be underestimated. 3D phase-sensitive inversion recovery significantly improves detection of cervical spinal cord lesions (39). A healthy control group was not included in the present study, deeming it impossible to determine sex-, and age-specific cut-offs for spinal cord volumes.…”

Section: Discussionmentioning

confidence: 99%

Additive Effect of Spinal Cord Volume, Diffuse and Focal Cord Pathology on Disability in Multiple Sclerosis

et al. 2019

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Introduction: Spinal cord (SC) pathology is strongly associated with disability in multiple sclerosis (MS). We aimed to evaluate the association between focal and diffuse SC abnormalities and spinal cord volume and to assess their contribution to physical disability in MS patients. Methods: This large sample-size cross-sectional study investigated 1,249 patients with heterogeneous MS phenotypes. Upper cervical-cord cross-sectional area (MUCCA) was calculated on an axial 3D-T2w-FatSat sequence acquired at 3T using a novel semiautomatic edge-finding tool. SC images were scored for the presence of sharply demarcated hyperintense areas (focal lesions) and homogenously increased signal intensity (diffuse changes). Patients were dichotomized according EDSS in groups with mild (EDSS up to 3.0) and moderate (EDSS ≥ 3.5) physical disability. Analysis of covariance was used to identify factors associated with dichotomized MUCCA. In binary logistic regression, the SC imaging parameters were entered in blocks to assess their individual contribution to risk of moderate disability. In order to assess the risk of combined SC damage in terms of atrophy and lesional pathology on disability, secondary analysis was carried out where patients were divided into four categories (SC phenotypes) according to median dichotomized MUCCA and presence/absence of focal and/or diffuse changes. Results: MUCCA was strongly associated with total intracranial volume, followed by presence of diffuse SC pathology, and disease duration. Compared to the reference group (normally appearing SC, MUCCA>median), patients with the most severe SC changes (SC affected with focal and/or diffuse lesions, MUCCA

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Section: Discussionmentioning

confidence: 99%

Additive Effect of Spinal Cord Volume, Diffuse and Focal Cord Pathology on Disability in Multiple Sclerosis

et al. 2019

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“…The second one was the “3D-FGAPSIR dataset”. The 3D-FGAPSIR was developed combining a Fast Grey Matter Acquisition T1 Inversion Recovery and a 3D-PSIR sequence optimized for spinal cord visualization [ 13 , 20 ]. Starting from the 3D-PSIR sequence, we modified the Turbo Field Echo inversion prepulse and the shot duration in order to increase the possible T1 contrast range and to obtain a Fast Grey Matter Acquisition T1 Inversion Recovery contrast on the magnitude image.…”

Section: Methodsmentioning

confidence: 99%

“…The sample size was calculated based on the minimum expected mean difference of at least one lesion per patient in the 3D-FGAPSIR dataset as compared to the conventional dataset and a common standard deviation of 2.5 lesions. Assumptions were based on data from literature [ 13 , 20 ]. The statistical power was set at 0.8, and the significance criterion was set to 0.05, with a two-tailed analysis.…”

Section: Methodsmentioning

confidence: 99%

“…Magnetization prepared rapid gradient echo (MPRAGE) allows for more accurate calculation of lesion load whereas double-inversion-recovery (DIR) provides better spatial delineation of lesions [ 11 , 12 ]. Among them, Phase-Sensitive Inversion-Recovery (PSIR) showed promise by increasing detection of spinal cord lesions and active enhancement in the cord, paving the way for an all-in-one sequence [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

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3D-Fast Gray Matter Acquisition with Phase Sensitive Inversion Recovery Magnetic Resonance Imaging at 3 Tesla: Application for detection of spinal cord lesions in patients with multiple sclerosis

et al. 2021

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Background and purpose To compare 3D-Fast Gray Matter Acquisition with Phase Sensitive Inversion Recovery (3D-FGAPSIR) with conventional 3D-Short-Tau Inversion Recovery (3D-STIR) and sagittal T1-and T2-weighted MRI dataset at 3 Tesla when detecting MS spinal cord lesions. Material and methods This prospective single-center study was approved by an institutional review board and enrolled participants from December 2016 to August 2018. Two neuroradiologists blinded to all data, individually analyzed the 3D-FGAPSIR and the conventional datasets separately and in random order. Discrepancies were resolved by consensus by a third neuroradiologist. The primary judgment criterion was the number of MS spinal cord lesions. Secondary judgment criteria included lesion enhancement, lesion delineation, reader-reported confidence and lesion-to-cord-contrast-ratio. A Wilcoxon’s test was used to compare the two datasets. Results 51 participants were included. 3D-FGAPSIR detected significantly more lesions than the conventional dataset (344 versus 171 respectively, p<0.001). Two participants had no detected lesion on the conventional dataset, whereas 3D-FGAPSIR detected at least one lesion. 3/51 participants had a single enhancing lesion detected by both datasets. Lesion delineation and reader-reported confidence were significantly higher with 3D-FGAPSIR: 4.5 (IQR 1) versus 2 (IQR 0.5), p<0.0001 and 4.5 (IQR 1) versus 2.5 (IQR 0.5), p<0.0001. Lesion-to-cord-contrast-ratio was significantly higher using 3D-FGAPSIR as opposed to 3D-STIR and T2: 1.4 (IQR 0,3) versus 0.4 (IQR 0,1) and 0.3 (IQR 0,1)(p = 0.04). Correlations with clinical data and inter- and intra-observer agreements were higher with 3D-FGAPSIR. Conclusion 3D-FGAPSIR improved overall MS spinal cord lesion detection as compared to conventional set and detected all enhancing lesions.

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“…Multiple Sclerosis is an inflammatory disease in which myelin sheaths of the nerve cells in the brain and spinal cord are damaged [1][2][3][4][5][6]. This damage can disrupt the ability of parts of the nervous system which are responsible for communication and causes many signs and symptoms such as physical problems [7].…”

Section: Introductionmentioning

confidence: 99%

Supervised meta-heuristic extreme learning machine for multiple sclerosis detection based on multiple feature descriptors in MR images

Rezaee

Haddadnia

et al. 2020

SN Appl. Sci.

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In this study, we propose a hybrid approach involving feature extraction, feature selection, and optimized learning for the diagnosis of multiple sclerosis (MS), which can detect the lesion caused by MS plaques in the brain using magnetic resonance imaging analysis. A major challenge associated with lesion diagnosis by neurologists is that it is a timeconsuming process and demands high expertise; therefore, researchers have been stimulated to find an auto-diagnose method of the disease. Given the high resemblance of MS plaque-induced lesions and other lesions such as Alzheimer's or dementia, scant research has explored the diagnosis of MS-induced lesions, most of which suffering from the lack of an efficient and accurate method. Informed by the need for a precise hybrid model for the classification of MS plaques and other comparable lesions, a solution is proposed that utilizes an efficient model. In this method, after image preprocessing, the feature vector is created by applying fractal and Pseudo-Zernike Moments descriptors. Feature selection using the Difference Evolution) algorithm to select the minimum subset of features will reduce the number of Extreme Learning Machine (ELM) inputs for classification. To improve the classification effect, the ELM wavelet kernel parameters are also regulated by the Shuffled Frog-Leaping Algorithm. By applying the proposed model to a set of brain MR images obtained from healthy subjects and MS patients during different experimental iterations, an average accuracy of 97% was obtained. The results of the method were estimated under specific conditions, and finally the proposed model yielded desirable outputs compared to similar methods.

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A 3T Phase-Sensitive Inversion Recovery MRI Sequence Improves Detection of Cervical Spinal Cord Lesions and Shows Active Lesions in Patients with Multiple Sclerosis

Cited by 14 publications

References 36 publications

Additive Effect of Spinal Cord Volume, Diffuse and Focal Cord Pathology on Disability in Multiple Sclerosis

Additive Effect of Spinal Cord Volume, Diffuse and Focal Cord Pathology on Disability in Multiple Sclerosis

3D-Fast Gray Matter Acquisition with Phase Sensitive Inversion Recovery Magnetic Resonance Imaging at 3 Tesla: Application for detection of spinal cord lesions in patients with multiple sclerosis

Supervised meta-heuristic extreme learning machine for multiple sclerosis detection based on multiple feature descriptors in MR images

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