Artificial double inversion recovery images for (juxta)cortical lesion visualization in multiple sclerosis

Bouman, Piet M; Strijbis, Victor I. J.; Jonkman, Laura E.; Hulst, Hanneke E.; Geurts, Jeroen J. G.; Steenwijk, Martijn D.

doi:10.1177/13524585211029860

Cited by 15 publications

(20 citation statements)

References 27 publications

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“…On the other hand, no significant difference in the number of detected plaques between both sequences in the deep white matter (p = 0.344) and deep gray matter MS plaques (p = 0.104) (Figs. 1,2,3,4,5,6).…”

Section: Resultsmentioning

confidence: 99%

“…We recommended that using artificial DIR, which can create high contrast images from standard input, can detect cortical lesions with high reliability, significantly improving lesion detection in MS patients [5,24].…”

Section: Discussionmentioning

confidence: 99%

“…Cortical lesions have, thus, been included in the recent 2017 revised Mcdonald's diagnostic criteria for MS due to their specificity and pivotal role in the pathophysiology of MS [4]. Cortical lesions may remain undetected with conventional imaging sequences as T2 and FLAIR, yet it is more discernible and clearly seen with the double inversion recovery (DIR) sequence [5]. DIR is a special pulse sequence that nulls the signal from cerebrospinal fluid (CSF) and white matter of the brain making it easier to detect cortical lesions and useful to detect MS at its earliest stages [6].…”

Section: Introductionmentioning

confidence: 99%

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Diagnostic utility of 3D DIR MRI in the estimation of MS lesions overall load with special emphasis on cortical subtypes

Abdelrahman

Khater

Barakat

2022

Egypt J Radiol Nucl Med

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Background A number of magnetic resonance imaging studies have shown that a significant proportion of multiple sclerosis (MS) lesions are located in the cerebral cortex and correlate with the patient's disability level. Double inversion recovery (DIR) has a higher sensitivity to detect cortical lesions and their different types. The aim of our study was to assess the superiority of 3D DIR as compared to FLAIR in detecting the different cortical subtypes and to enhance its role as a test for the clinical disability of the patient. Results The current study included 82 MS patients (60 female and 22 male with mean age of 34.5 ± 8.2 years); 59 cases having relapsing–remitting MS, 5 having secondary progressive MS, and 4 having primary progressive MS, as well as eight clinically isolated syndrome cases, and six radiologically isolated syndrome cases. DIR was superior to FLAIR in MS plaque number detection of cortical plaque-types 1, 2, 3 and 4 with a p value < 0.001. DIR was superior to FLAIR for detection of overall cortical types, juxtacortical and juxtacortical extended MS plaques with p = < 0.001, 0.010 and < 0.001, respectively. DIR was significantly better than FLAIR for MS plaque number detection in the periventricular white matter, subcortical white matter, infratentorial region and the overall MS plaques burden with a p < 0.001, 0.038, < 0.001, < 0.001, respectively. No significant difference was noted between both sequences for plaques number detection in deep white matter and deep gray matter. A significant correlation between MS disease duration, number of attacks and EDSS and the overall cortical MS plaques types and the overall MS plaque burden. Conclusion 3D DIR is a reliable tool and provides superior delineation in detecting cortical subtypes. In addition, DIR is of value in assessing the degree of clinical and cognitive impairment of MS affected patients.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Diagnostic utility of 3D DIR MRI in the estimation of MS lesions overall load with special emphasis on cortical subtypes

Abdelrahman

Khater

Barakat

2022

Egypt J Radiol Nucl Med

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“…The ability of synthDIR to outperform FLAIR, a sequence still considered gold-standard in MS imaging, has been shown for a multi-modal input (T1w, T2w, and FLAIR) in a monocentric setting ( Finck et al, 2020 ; Bouman et al, 2021 ). In doing so, relevant reductions in scan times are feasible as the physical acquisition of 3D and isotropic DIR may take up to 7 min ( Eichinger et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…Recently, it has been shown that synthesizing DIR images with generative adversarial networks (GANs), a deep learning (DL) architecture with great potential for image synthesis, is feasible and improves lesion detection compared to FLAIR and T2w sequences ( Finck et al, 2020 ; Bouman et al, 2021 ). Nonetheless, and in particular as MS lesions typically are small, GANs are at risk to synthesize images of high morphologic similarity to the target image, while failing to translate the clinically important MS lesions.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty-Aware and Lesion-Specific Image Synthesis in Multiple Sclerosis Magnetic Resonance Imaging: A Multicentric Validation Study

Finck

Schlaeger

et al. 2022

Front. Neurosci.

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Generative adversarial networks (GANs) can synthesize high-contrast MRI from lower-contrast input. Targeted translation of parenchymal lesions in multiple sclerosis (MS), as well as visualization of model confidence further augment their utility, provided that the GAN generalizes reliably across different scanners. We here investigate the generalizability of a refined GAN for synthesizing high-contrast double inversion recovery (DIR) images and propose the use of uncertainty maps to further enhance its clinical utility and trustworthiness. A GAN was trained to synthesize DIR from input fluid-attenuated inversion recovery (FLAIR) and T1w of 50 MS patients (training data). In another 50 patients (test data), two blinded readers (R1 and R2) independently quantified lesions in synthetic DIR (synthDIR), acquired DIR (trueDIR) and FLAIR. Of the 50 test patients, 20 were acquired on the same scanner as training data (internal data), while 30 were scanned at different scanners with heterogeneous field strengths and protocols (external data). Lesion-to-Background ratios (LBR) for MS-lesions vs. normal appearing white matter, as well as image quality parameters were calculated. Uncertainty maps were generated to visualize model confidence. Significantly more MS-specific lesions were found in synthDIR compared to FLAIR (R1: 26.7 ± 2.6 vs. 22.5 ± 2.2 p < 0.0001; R2: 22.8 ± 2.2 vs. 19.9 ± 2.0, p = 0.0005). While trueDIR remained superior to synthDIR in R1 [28.6 ± 2.9 vs. 26.7 ± 2.6 (p = 0.0021)], both sequences showed comparable lesion conspicuity in R2 [23.3 ± 2.4 vs. 22.8 ± 2.2 (p = 0.98)]. Importantly, improvements in lesion counts were similar in internal and external data. Measurements of LBR confirmed that lesion-focused GAN training significantly improved lesion conspicuity. The use of uncertainty maps furthermore helped discriminate between MS lesions and artifacts. In conclusion, this multicentric study confirms the external validity of a lesion-focused Deep-Learning tool aimed at MS imaging. When implemented, uncertainty maps are promising to increase the trustworthiness of synthetic MRI.

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Machine Learning in Multiple Sclerosis

Jasperse¹,

Barkhof

2023

Neuromethods

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Multiple sclerosis (MS) is characterized by inflammatory activity and neurodegeneration, leading to the accumulation of damage to the central nervous system resulting in the accumulation of disability. MRI depicts an important part of the pathology of this disease and therefore plays a key part in diagnosis and disease monitoring. Still, major challenges exist with regard to the differential diagnosis, adequate monitoring of disease progression, quantification of CNS damage, and prediction of disease progression. Machine learning techniques have been employed in an attempt to overcome these challenges. This chapter aims to give an overview of how machine learning techniques are employed in MS with applications for diagnostic classification, lesion segmentation, improved visualization of relevant brain pathology, characterization of neurodegeneration, and prognostic subtyping.

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Artificial double inversion recovery images for (juxta)cortical lesion visualization in multiple sclerosis

Cited by 15 publications

References 27 publications

Diagnostic utility of 3D DIR MRI in the estimation of MS lesions overall load with special emphasis on cortical subtypes

Diagnostic utility of 3D DIR MRI in the estimation of MS lesions overall load with special emphasis on cortical subtypes

Uncertainty-Aware and Lesion-Specific Image Synthesis in Multiple Sclerosis Magnetic Resonance Imaging: A Multicentric Validation Study

Machine Learning in Multiple Sclerosis

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