Intensity ratio to improve black hole assessment in multiple sclerosis

Adusumilli, Gautam; Trinkaus, Kathryn; Sun, Peng; Lancia, Samantha; Viox, Jeffrey D.; Wen, Jie; Naismith, Robert T.; Cross, Anne H.

doi:10.1016/j.msard.2017.11.020

Cited by 12 publications

(14 citation statements)

References 32 publications

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“…Both the Stroop test and the verbal fluency test require high performance in executive functions. This suggests that spatial distribution of degenerative white matter lesions could influence executive functions and other cognitive domains, including attention or auditory processing [35]. Similar results were found by Hojjat et al [12].…”

Section: Discussionsupporting

confidence: 82%

Spatial distribution of white matter degenerative lesions and cognitive dysfunction in relapsing-remitting multiple sclerosis patients

Nowaczyk

Kalinowska-Łyszczarz

Paprzycki

et al. 2018

Neurol Neurochir Pol.

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Aim. The aim of this study was to assess degenerative lesion localisation in the course of relapsing-remitting multiple sclerosis (RRMS) and to identify the association between localisation and the frequency of T1-hypointense lesions (black holes) with cognitive dysfunction. We also searched for neuroradiological predictors of cognitive dysfunction in patients. The clinical rationale for the study was previous research, and our own findings suggest that lesion localisation plays an important role in cognitive performance and neurological disability of MS patients. Material and methods. Forty-two patients were included in the study. All subjects underwent neuropsychological examination using Raven's Coloured Progressive Matrices, a naming task from the Brief Repeatable Battery of Neuropsychological Tests, and attention to detail tests. Magnetic resonance imaging (MRI) was acquired on 1.5 Tesla scanner and black holes were manually segmented on T1-weighted volumetric images using the FMRIB Software Library. Linear regression was applied to establish a relationship between black hole volume per lobe and cognitive parameters. Bonferroni correction of voxelwise analysis was used to correct for multiple comparisons. Results. The following associations between black hole volume and cognition were identified: frontal lobes black hole volume was associated with phonemic verbal fluency (t =-4.013, p < 0.001), parietal black hole volume was associated with attention (t =-3.776, p < 0.001), and parietal and temporal black hole volumes were associated with nonverbal intelligence (p < 0.001). The volume of parietal black holes was the best predictor of cognitive dysfunction. Conclusions. Our approach, including measurement of focal axonal loss based on T1-volumetric MRI sequence and brief neuropsychological assessment, might improve personalised diagnostic and therapeutic decisions in clinical practice.

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Section: Discussionsupporting

confidence: 82%

Spatial distribution of white matter degenerative lesions and cognitive dysfunction in relapsing-remitting multiple sclerosis patients

Nowaczyk

Kalinowska-Łyszczarz

Paprzycki

et al. 2018

Neurol Neurochir Pol.

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“…13 Another group also has found significant correlations between normalized T1w intensity values and, not only MTR but also diffusion tensor imaging parameters (axial and radial diffusivity, apparent diffusion coefficient), demonstrating the use of normalized T1w in the assessment of lesion severity. 14 Hence, T1w-normalized intensities could be used if MTR images are not available, to roughly reflect lesion severity and the degree of demyelination of a given area.…”

Section: Introductionmentioning

confidence: 99%

Conversion of diffusely abnormal white matter to focal lesions is linked to progression in secondary progressive multiple sclerosis

et al. 2020

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Background: Diffusely abnormal white matter (DAWM) regions are observed in magnetic resonance images of secondary progressive multiple sclerosis (SPMS) patients. However, their role in clinical progression is still not established. Objectives: To characterize the longitudinal volumetric and intensity evolution of DAWM and focal white matter lesions (FWML) and assess their associations with clinical outcomes and progression in SPMS. Methods: Data include 589 SPMS participants followed up for 3 years (3951 time points). FWML and DAWM were automatically segmented. Screening DAWM volumes that transformed into FWML at the last visit (DAWM-to-FWML) and normalized T1-weighted intensities (indicating severity of damage) in those voxels were calculated. Results: FWML volume increased and DAWM volume decreased with an increase in disease duration ( p < 0.001). The Expanded Disability Status Scale (EDSS) was positively associated with FWML volumes ( p = 0.002), but not with DAWM. DAWM-to-FWML volume was higher in patients who progressed (2.75 cm3 vs. 1.70 cm3; p < 0.0001). Normalized T1-weighted intensity of DAWM-to-FWML was negatively associated with progression ( p < 0.00001). Conclusion: DAWM transformed into FWML over time, and this transformation was associated with clinical progression. DAWM-to-FWML voxels had greater normalized T1-weighted intensity decrease over time, in keeping with relatively greater tissue damage. Evaluation of DAWM in progressive multiple sclerosis provides a useful measure for therapies aiming to protect this at-risk tissue with the potential to slow progression.

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“…Lesions were classified as being black hole, gray hole, or nonblack or gray hole using an objective and semiquantitative intensity ratio (IR) method developed in our previous work 9 . This method uses a protocol that allows a single investigator to reliably determine lesion types.…”

Section: Methodsmentioning

confidence: 99%

“…This method uses a protocol that allows a single investigator to reliably determine lesion types. The range of IR used for black hole, gray hole, and nonblack or gray hole were 1.00 to 1.70, 1.71 to 2.60, and > 2.60, respectively 9 . PBHs (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Based on histological correlations, PBH are considered to contain more severe axonal loss compared with other MS lesion subtypes 7 . Other MS lesions that are hyperintense on T2‐weighted imaging (T2WI) and lack hypointensity on T1WI have less severe tissue damage, and are referred to here as nonblack or gray hole (NBH) lesions 8,9 . While standard cMRI is sensitive in detecting MS lesions in WM, it requires experience to categorize the lesion subtype, in addition to longitudinal follow‐up 10 …”

Section: Introductionmentioning

confidence: 99%

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Deep learning with diffusion basis spectrum imaging for classification of multiple sclerosis lesions

George

et al. 2020

Ann Clin Transl Neurol

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Objective: Multiple sclerosis (MS) lesions are heterogeneous with regard to inflammation, demyelination, axonal injury, and neuronal loss. We previously developed a diffusion basis spectrum imaging (DBSI) technique to better address MS lesion heterogeneity. We hypothesized that the profiles of multiple DBSI metrics can identify lesion-defining patterns. Here we test this hypothesis by combining a deep learning algorithm using deep neural network (DNN) with DBSI and other imaging methods. Methods: Thirty-eight MS patients were scanned with diffusion-weighted imaging, magnetization transfer imaging, and standard conventional MRI sequences (cMRI). A total of 499 regions of interest were identified on standard MRI and labeled as persistent black holes (PBH), persistent gray holes (PGH), acute black holes (ABH), acute gray holes (AGH), nonblack or gray holes (NBH), and normal appearing white matter (NAWM). DBSI, diffusion tensor imaging (DTI), and magnetization transfer ratio (MTR) were applied to the 43,261 imaging voxels extracted from these ROIs. The optimized DNN with 10 fully connected hidden layers was trained using the imaging metrics of the lesion subtypes and NAWM. Results: Concordance, sensitivity, specificity, and accuracy were determined for the different imaging methods. DBSI-DNN derived lesion classification achieved 93.4% overall concordance with predetermined lesion types, compared with 80.2% for DTI-DNN model, 78.3% for MTR-DNN model, and 74.2% for cMRI-DNN model. DBSI-DNN also produced the highest specificity, sensitivity, and accuracy. Conclusions: DBSI-DNN improves the classification of different MS lesion subtypes, which could aid clinical decision making. The efficacy and efficiency of DBSI-DNN shows great promise for clinical applications in automatic MS lesion detection and classification.

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Intensity ratio to improve black hole assessment in multiple sclerosis

Abstract: This method of determining the degree of hypointensity within multiple sclerosis lesions can add information to conventional imaging.

Cited by 12 publications

References 32 publications

Spatial distribution of white matter degenerative lesions and cognitive dysfunction in relapsing-remitting multiple sclerosis patients

Spatial distribution of white matter degenerative lesions and cognitive dysfunction in relapsing-remitting multiple sclerosis patients

Conversion of diffusely abnormal white matter to focal lesions is linked to progression in secondary progressive multiple sclerosis

Deep learning with diffusion basis spectrum imaging for classification of multiple sclerosis lesions

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