MRI FLAIR lesion segmentation in multiple sclerosis: Does automated segmentation hold up with manual annotation?

Automatic lesion detection at Multiple Sclerosis patients - Comparison of 2D- and 3D-FLAIR-datasets

BACKGROUND

Multiple Sclerosis (MS) is a common autoimmune inflammatory disease of the central nervous system (CNS). Magnetic Resonance Imaging (MRI) allows a sensitive assessment of the CNS and is established for diagnostic, prognostic and (therapy-) monitoring purposes. Especially lesion counting in T2- or Fluid Attenuated Inversion Recovery (FLAIR)-weighted images plays a decisive role in clinical routine. Software-packages allowing an automatic evaluation of image data are increasingly established aiming a faster and improved workflow. These programs allow e.g. the counting, spatial attribution and volumetry of MS-lesions in FLAIR-weighted images. Research has shown that 3D-FLAIR-sequences are superior to 2D-FLAIR-sequences in visual evaluation of lesion burden in MS. An influence on the automatic analysis is expectable but not yet systematically studied. This work will therefore investigate the influence of 2D- and 3D datasets on the results of an automatic assessment.

MATERIAL AND METHODS

In this prospective study, 80 Multiple Sclerosis patients underwent a clinically indicated routine MRI examination. The clinical routine protocol already including a 3D-FLAIR sequence was adapted by an additional 2D-FLAIR sequence also conform to the 2021 MAGNIMS-CMSCNAIMS consensus recommendations. To obtain a quantitative analysis for assessment of amount, dissemination and volume of the lesions, the acquired MR images were post-processed using the CE-certified Software mdbrain (mediaire, Berlin, Germany). The resulting data were statistically analysed using the paired t-test for normally distributed data and the Wilcoxon-signed-rank-test for not normally distributed data respectively. Demographic data and data such as the subtype, duration, severity and therapy of the disease were collected, pseudonymized and evaluated.

RESULTS

There is a significant difference concerning the total number and lesion volume with more lesions being detected (2D: 29.7, +/- 20.22 sd; 3D: 40.1 +/- 31.67 sd; p < 0.0001) but lower total volume (2D: 6.24 +/- 6.11 sd; 3D: 5.39 +/- 6.37 sd; p < 0.0001) when using the 3D- sequence. Especially significantly more small lesions in the unspecific white matter and infratentorial region were detected by using the 3D-FLAIR sequence (p < 0.0001) compared to the 2D-FLAIR image. Main reason for the lower total volume in the 3D-FLAIR sequence was the calculated volume for periventricular lesions which was significantly beneath the calculated volume from the 2D-FLAIR sequence (p < 0.0001).

CONCLUSION

Automatic lesion counting and volumetry is feasible with both 2D- and 3D-weightend FLAIR images. Still, it leads to partly significant differences even between two sequences that both are conform to the 2021 MAGNIMS-CMSCNAIMS consensus recommendations. This study contributes valuable insights into the impact of using different input data from the same patient for automated MS lesion evaluation.

AI supported detection of cerebral multiple sclerosis lesions decreases radiologic reporting times

PURPOSE

Multiple Sclerosis (MS) is a common autoimmune disease of the central nervous system. MRI plays a crucial role in diagnosing as well as in disease and treatment monitoring. Therefore, evaluation of cerebral MRI of MS patients is part of daily clinical routine. A growing number of companies offer commercial software to support the reporting with automated lesion detection. Aim of this study was to evaluate the effect of such a software with AI supported lesion detection to the radiologic reporting.

METHOD

Four radiologist each counted MS-lesions in MRI examinations of 50 patients separated by the locations periventricular, cortical/juxtacortical, infrantentorial and unspecific white matter. After at least six weeks they repeated the evaluation, this time using the AI based software mdbrain for lesion detection. In both settings the required time was documented. Further the radiologists evaluated follow-up MRI of 50 MS-patients concerning new and enlarging lesions in the same manner.

RESULTS

To determine the lesion-load the average reporting time decreased from 286.85 sec to 196.34 sec (p > 0.001). For the evaluation of the follow-up images the reporting time dropped from 196.17 sec to 120.87 sec (p < 0.001). The interrater reliabilities showed no significant differences for the determination of lesion-load (0.83 without vs. 0.8 with software support) and for the detection of new/enlarged lesions (0.92 without vs. 0.82 with software support).

CONCLUSION

For the evaluation of MR images of MS patients, an AI-based support for image-interpretation can significantly decreases reporting times.

Motor function in multiple sclerosis assessed by navigated transcranial magnetic stimulation mapping

INTRODUCTION

Impaired motor function is a major cause of disability in multiple sclerosis (MS), involving various neuroplasticity processes typically assessed by neuroimaging. This study aimed to determine whether navigated transcranial magnetic stimulation (nTMS) could also provide biomarkers of motor cortex plasticity in patients with MS (pwMS).

METHODS

nTMS motor mapping was performed for hand and leg muscles bilaterally. nTMS variables included the amplitude and latency of motor evoked potentials (MEPs), corticospinal excitability measures, and the size of cortical motor maps (CMMs). Clinical assessment included disability (Expanded Disability Status Scale, EDSS), strength (MRC scale, pinch and grip), and dexterity (9-hole Pegboard Test).

RESULTS

nTMS motor mapping was performed in 68 pwMS. PwMS with high disability (EDSS ≥ 3) had enlarged CMMs with less dense distribution of MEPs and various MEP parameter changes compared to pwMS with low disability (EDSS < 3). Patients with progressive MS had also various MEP parameter changes compared to pwMS with relapsing remitting form. MRC score correlated positively with MEP amplitude and negatively with MEP latency, pinch strength correlated negatively with CMM volume and dexterity with MEP latency.

CONCLUSIONS

This is the first study to perform 4-limb cortical motor mapping in pwMS using a dedicated nTMS procedure. By quantifying the cortical surface representation of a given muscle and the variability of MEP within this representation, nTMS can provide new biomarkers of motor function impairment in pwMS. Our study opens perspectives for the use of nTMS as an objective method for assessing pwMS disability in clinical practice.

Decoding the best automated segmentation tools for vascular white matter hyperintensities in the aging brain: a clinician's guide to precision and purpose

White matter hyperintensities of vascular origin (WMH) are commonly found in individuals over 60 and increase in prevalence with age. The significance of WMH is well-documented, with strong associations with cognitive impairment, risk of stroke, mental health, and brain structure deterioration. Consequently, careful monitoring is crucial for the early identification and management of individuals at risk. Luckily, WMH are detectable and quantifiable on standard MRI through visual assessment scales, but it is time-consuming and has high rater variability. Addressing this issue, the main aim of our study is to decipher the utility of quantitative measures of WMH, assessed with automatic tools, in establishing risk profiles for cerebrovascular deterioration. For this purpose, first, we work to determine the most precise WMH segmentation open access tool compared to clinician manual segmentations (LST-LPA, LST-LGA, SAMSEG, and BIANCA), offering insights into methodology and usability to balance clinical precision with practical application. The results indicated that supervised algorithms (LST-LPA and BIANCA) were superior, particularly in detecting small WMH, and can improve their consistency when used in parallel with unsupervised tools (LST-LGA and SAMSEG). Additionally, to investigate the behavior and real clinical utility of these tools, we tested them in a real-world scenario (N = 300; age > 50 y.o. and MMSE > 26), proposing an imaging biomarker for moderate vascular damage. The results confirmed its capacity to effectively identify individuals at risk comparing the cognitive and brain structural profiles of cognitively healthy adults above and below the resulted threshold.

Decoding the Best Automated Segmentation Tools for Vascular White Matter Hyperintensities in the Aging Brain: A Clinician's Guide to Precision and Purpose

Cerebrovascular damage from small vessel disease (SVD) occurs in healthy and pathological aging. SVD markers, such as white matter hyperintensities (WMH), are commonly found in individuals over 60 and increase in prevalence with age. WMHs are detectable on standard MRI by adhering to the STRIVE criteria. Currently, visual assessment scales are used in clinical and research scenarios but is time-consuming and has rater variability, limiting its practicality. Addressing this issue, our study aimed to determine the most precise WMH segmentation software, offering insights into methodology and usability to balance clinical precision with practical application. This study employed a dataset comprising T1, FLAIR, and DWI images from 300 cognitively healthy older adults. WMHs in this cohort were evaluated using four automated neuroimaging tools: Lesion Prediction Algorithm (LPA) and Lesion Growth Algorithm (LGA) from Lesion Segmentation Tool (LST), Sequence Adaptive Multimodal Segmentation (SAMSEG), and Brain Intensity Abnormalities Classification Algorithm (BIANCA). Additionally, clinicians manually segmented WMHs in a subsample of 45 participants to establish a gold standard. The study assessed correlations with the Fazekas scale, algorithm performance, and the influence of WMH volume on reliability. Results indicated that supervised algorithms were superior, particularly in detecting small WMHs, and can improve their consistency when used in parallel with unsupervised tools. The research also proposed a biomarker for moderate vascular damage, derived from the top 95th percentile of WMH volume in healthy individuals aged 50 to 60. This biomarker effectively differentiated subgroups within the cohort, correlating with variations in brain structure and behavior.

Comparison between R2'-based and R2*-based χ-separation methods: A clinical evaluation in individuals with multiple sclerosis

Susceptibility source separation, or χ-separation, estimates diamagnetic (χdia) and paramagnetic susceptibility (χpara) signals in the brain using local field and R2' (= R2* - R2) maps. Recently proposed R2*-based χ-separation methods allow for χ-separation using only multi-echo gradient echo (ME-GRE) data, eliminating the need for additional data acquisition for R2 mapping. Although this approach reduces scan time and enhances clinical utility, the impact of missing R2 information remains a subject of exploration. In this study, we evaluate the viability of two previously proposed R2*-based χ-separation methods as alternatives to their R2'-based counterparts: model-based R2*-χ-separation versus χ-separation and deep learning-based χ-sepnet-R2* versus χ-sepnet-R2'. Their performances are assessed in individuals with multiple sclerosis (MS), comparing them with their corresponding R2'-based counterparts (i.e., R2*-χ-separation vs. χ-separation and χ-sepnet-R2* vs. χ-sepnet-R2'). The evaluations encompass qualitative visual assessments by experienced neuroradiologists and quantitative analyses, including region of interest analyses and linear regression analyses. Qualitatively, R2*-χ-separation tends to report higher χpara and χdia values compared with χ-separation, leading to less distinct lesion contrasts, while χ-sepnet-R2* closely aligns with χ-sepnet-R2'. Quantitative analysis reveals a robust correlation between both R2*-based methods and their R2'-based counterparts (r ≥ 0.88). Specifically, in the whole-brain voxels, χ-sepnet-R2* exhibits higher correlation and better linearity than R2*-χ-separation (χdia/χpara from R2*-χ-separation: r = 0.88/0.90, slope = 0.79/0.86; χdia/χpara from χ-sepnet-R2*: r = 0.90/0.92, slope = 0.99/0.97). In MS lesions, both R2*-based methods display comparable correlation and linearity (χdia/χpara from R2*-χ-separation: r = 0.90/0.91, slope = 0.98/0.91; χdia/χpara from χ-sepnet-R2*: r = 0.88/0.88, slope = 0.91/0.95). Notably, χ-sepnet-R2* demonstrates negligible offsets, whereas R2*-χ-separation exhibits relatively large offsets (0.02 ppm in the whole brain and 0.01 ppm in the MS lesions), potentially indicating the false presence of myelin or iron in MS lesions. Overall, both R2*-based χ-separation methods demonstrated their viability as alternatives to their R2'-based counterparts. χ-sepnet-R2* showed better alignment with its R2'-based counterpart with minimal susceptibility offsets, compared with R2*-χ-separation that reported higher χpara and χdia values compared with R2'-based χ-separation.

Assessing robustness and generalization of a deep neural network for brain MS lesion segmentation on real-world data

OBJECTIVES

Evaluate the performance of a deep learning (DL)-based model for multiple sclerosis (MS) lesion segmentation and compare it to other DL and non-DL algorithms.

METHODS

This ambispective, multicenter study assessed the performance of a DL-based model for MS lesion segmentation and compared it to alternative DL- and non-DL-based methods. Models were tested on internal (n = 20) and external (n = 18) datasets from Latin America, and on an external dataset from Europe (n = 49). We also examined robustness by rescanning six patients (n = 6) from our MS clinical cohort. Moreover, we studied inter-human annotator agreement and discussed our findings in light of these results. Performance and robustness were assessed using intraclass correlation coefficient (ICC), Dice coefficient (DC), and coefficient of variation (CV).

RESULTS

Inter-human ICC ranged from 0.89 to 0.95, while spatial agreement among annotators showed a median DC of 0.63. Using expert manual segmentations as ground truth, our DL model achieved a median DC of 0.73 on the internal, 0.66 on the external, and 0.70 on the challenge datasets. The performance of our DL model exceeded that of the alternative algorithms on all datasets. In the robustness experiment, our DL model also achieved higher DC (ranging from 0.82 to 0.90) and lower CV (ranging from 0.7 to 7.9%) when compared to the alternative methods.

CONCLUSION

Our DL-based model outperformed alternative methods for brain MS lesion segmentation. The model also proved to generalize well on unseen data and has a robust performance and low processing times both on real-world and challenge-based data.

CLINICAL RELEVANCE STATEMENT

Our DL-based model demonstrated superior performance in accurately segmenting brain MS lesions compared to alternative methods, indicating its potential for clinical application with improved accuracy, robustness, and efficiency.

KEY POINTS

• Automated lesion load quantification in MS patients is valuable; however, more accurate methods are still necessary. • A novel deep learning model outperformed alternative MS lesion segmentation methods on multisite datasets. • Deep learning models are particularly suitable for MS lesion segmentation in clinical scenarios.

The glymphatic system as a potential biomarker and therapeutic target in secondary progressive multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) is a refractory immune-mediated inflammatory disease of the central nervous system, and some cases of the major subtype, relapsing-remitting (RR), transition to secondary progressive (SP). However, the detailed pathogenesis, biomarkers, and effective treatment strategies for secondary progressive multiple sclerosis have not been established. The glymphatic system, which is responsible for waste clearance in the brain, is an intriguing avenue for investigation and is primarily studied through diffusion tensor image analysis along the perivascular space (DTI-ALPS). This study aimed to compare DTI-ALPS indices between patients with RRMS and SPMS to uncover potential differences in their pathologies and evaluate the utility of the glymphatic system as a possible biomarker.

METHODS

A cohort of 26 patients with MS (13 RRMS and 13 SPMS) who met specific criteria were enrolled in this prospective study. Magnetic resonance imaging (MRI), including diffusion MRI, 3D T1-weighted imaging, and relaxation time quantification, was conducted. The ALPS index, a measure of glymphatic function, was calculated using diffusion-weighted imaging data. Demographic variables, MRI metrics, and ALPS indices were compared between patients with RRMS and those with SPMS.

RESULTS

The ALPS index was significantly lower in the SPMS group. Patients with SPMS exhibited longer disease duration and higher Expanded Disability Status Scale (EDSS) scores than those with RRMS. Despite these differences, the correlations between the EDSS score, disease duration, and ALPS index were minimal, suggesting that the impact of these clinical variables on ALPS index variations was negligible.

DISCUSSION

Our study revealed the potential microstructural and functional differences between RRMS and SPMS related to glymphatic system impairment. Although disease severity and duration vary among subtypes, their influence on ALPS index differences appears to be limited. This highlights the stronger association between SP conversion and changes in the ALPS index. These findings align with those of previous research, indicating the involvement of the glymphatic system in the progression of MS.

CONCLUSION

Although the causality remains uncertain, our study suggests that a reduced ALPS index, reflecting glymphatic system dysfunction, may contribute to MS progression, particularly in SPMS. This suggests the potential of the ALPS index as a diagnostic biomarker for SPMS and underscores the potential of the glymphatic system as a therapeutic target to mitigate MS progression. Future studies with larger cohorts and pathological validation are necessary to confirm these findings. This study provides new insights into the pathogenesis of SPMS and the potential for innovative therapeutic strategies.

Image harmonization improves consistency of intra-rater delineations of MS lesions in heterogeneous MRI

Clinical magnetic resonance images (MRIs) lack a standard intensity scale due to differences in scanner hardware and the pulse sequences used to acquire the images. When MRIs are used for quantification, as in the evaluation of white matter lesions (WMLs) in multiple sclerosis, this lack of intensity standardization becomes a critical problem affecting both the staging and tracking of the disease and its treatment. This paper presents a study of harmonization on WML segmentation consistency, which is evaluated using an object detection classification scheme that incorporates manual delineations from both the original and harmonized MRIs. A cohort of ten people scanned on two different imaging platforms was studied. An expert rater, blinded to the image source, manually delineated WMLs on images from both scanners before and after harmonization. It was found that there is closer agreement in both global and per-lesion WML volume and spatial distribution after harmonization, demonstrating the importance of image harmonization prior to the creation of manual delineations. These results could lead to better truth models in both the development and evaluation of automated lesion segmentation algorithms.

Glymphatic System Dysfunction in Myelin Oligodendrocyte Glycoprotein Immunoglobulin G Antibody-Associated Disorders: Association with Clinical Disability

BACKGROUND AND PURPOSE

Impaired glymphatic function has been suggested to be implicated in the pathophysiology of MS and aquaporin-4 immunoglobulin G-positive neuromyelitis optica spectrum disorder. This study aimed to investigate the interstitial fluid dynamics in the brain in patients with myelin oligodendrocyte glycoprotein antibody disorders (MOGAD), another demyelinating disorder, using a noninvasive imaging technique called the diffusivity along the perivascular space (ALPS) index.

MATERIALS AND METHODS

A prospective study was conducted on 16 patients with MOGAD in remission and 22 age- and sex-matched healthy control subjects. MR imaging was performed using a 3T scanner, and the ALPS index was calculated using diffusion MR imaging data with a b-value of 1000 s/mm2. The ALPS index and gray matter volumes were compared between the 2 groups, and these parameters were correlated with the Expanded Disability Status Scale.

RESULTS

The mean ALPS index of patients with MOGAD was significantly lower than that of healthy controls (Cohen d = 0.93, false discovery rate-corrected P = .02). The lower mean ALPS index was significantly associated with a worse Expanded Disability Status Scale score (Spearman ρ = -0.51; 95% CI, -0.85 to -0.02; P = .03). However, cortical volume and deep gray matter volume were not significantly different between the 2 groups, and they were not correlated with the Expanded Disability Status Scale.

CONCLUSIONS

This study suggests that patients with MOGAD may have impaired glymphatic function, as measured by the ALPS index, which is associated with patient disability. Further study is warranted with a larger sample size.

Tumor classification of gastrointestinal liver metastases using CT-based radiomics and deep learning

OBJECTIVES

The goal of this study is to demonstrate the performance of radiomics and CNN-based classifiers in determining the primary origin of gastrointestinal liver metastases for visually indistinguishable lesions.

METHODS

In this retrospective, IRB-approved study, 31 pancreatic cancer patients with 861 lesions (median age [IQR]: 65.39 [56.87, 75.08], 48.4% male) and 47 colorectal cancer patients with 435 lesions (median age [IQR]: 65.79 [56.99, 74.62], 63.8% male) were enrolled. A pretrained nnU-Net performed automated segmentation of 1296 liver lesions. Radiomics features for each lesion were extracted using pyradiomics. The performance of several radiomics-based machine-learning classifiers was investigated for the lesions and compared to an image-based deep-learning approach using a DenseNet-121. The performance was evaluated by AUC/ROC analysis.

RESULTS

The radiomics-based K-nearest neighbor classifier showed the best performance on an independent test set with AUC values of 0.87 and an accuracy of 0.67. In comparison, the image-based DenseNet-121-classifier reached an AUC of 0.80 and an accuracy of 0.83.

CONCLUSIONS

CT-based radiomics and deep learning can distinguish the etiology of liver metastases from gastrointestinal primary tumors. Compared to deep learning, radiomics based models showed a varying generalizability in distinguishing liver metastases from colorectal cancer and pancreatic adenocarcinoma.

BIANCA-MS: An optimized tool for automated multiple sclerosis lesion segmentation

In this work we present BIANCA-MS, a novel tool for brain white matter lesion segmentation in multiple sclerosis (MS), able to generalize across both the wide spectrum of MRI acquisition protocols and the heterogeneity of manually labeled data. BIANCA-MS is based on the original version of BIANCA and implements two innovative elements: a harmonized setting, tested under different MRI protocols, which avoids the need to further tune algorithm parameters to each dataset; and a cleaning step developed to improve consistency in automated and manual segmentations, thus reducing unwanted variability in output segmentations and validation data. BIANCA-MS was tested on three datasets, acquired with different MRI protocols. First, we compared BIANCA-MS to other widely used tools. Second, we tested how BIANCA-MS performs in separate datasets. Finally, we evaluated BIANCA-MS performance on a pooled dataset where all MRI data were merged. We calculated the overlap using the DICE spatial similarity index (SI) as well as the number of false positive/negative clusters (nFPC/nFNC) in comparison to the manual masks processed with the cleaning step. BIANCA-MS clearly outperformed other available tools in both high- and low-resolution images and provided comparable performance across different scanning protocols, sets of modalities and image resolutions. BIANCA-MS performance on the pooled dataset (SI: 0.72 ± 0.25, nFPC: 13 ± 11, nFNC: 4 ± 8) were comparable to those achieved on each individual dataset (median across datasets SI: 0.72 ± 0.28, nFPC: 14 ± 11, nFNC: 4 ± 8). Our findings suggest that BIANCA-MS is a robust and accurate approach for automated MS lesion segmentation.

Serum neurofilament light chain is more strongly associated with T2 lesion volume than with number of T2 lesions in patients with multiple sclerosis

BACKGROUND AND PURPOSE

MR imaging provides information on the number and extend of focal lesions in multiple sclerosis (MS) patients. This study explores whether total brain T2 lesion volume or lesion number shows a better correlation with serum and cerebrospinal fluid (CSF) biomarkers of disease activity.

MATERIALS AND METHODS

In total, 52 patients suffering from clinically isolated syndrome (CIS)/relapsing-remitting multiple sclerosis (RRMS) were assessed including MRI markers (total brain T2 lesion volume semi-automatically outlined on 3D DIR/FLAIR sequences, number of lesions), serum and CSF biomarkers at the time of neuroimaging (neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP)), and clinical parameters. After log-transformation and partial correlations adjusted for the covariates patients' age, BMI, EDSS-score and diagnosis, the Fisher's r-to-Z transformation was used to compare different correlation coefficients.

RESULTS

The correlation between lesion volume and serum NfL (r = 0.6, p < 0.001) was stronger compared to the association between the number of T2 lesions and serum NfL (r = 0.4, p < 0.01) (z = -2.0, p < 0.05). With regard to CSF NfL, there was a moderate, positive relationship for both number of T2 lesions and lesion volume (r = 0.5 respectively, p < 0.01). We found no significant association between MRI markers and GFAP levels.

CONCLUSION

Our findings suggest that there is a stronger association between serum NfL and T2 lesion volume, than there is between serum NfL and T2 lesion number. Improving robustness and accuracy of fully-automated lesion volume segmentation tools can expedite implementation into clinical routine and trials.

A Multicenter Longitudinal MRI Study Assessing LeMan-PV Software Accuracy in the Detection of White Matter Lesions in Multiple Sclerosis Patients

BACKGROUND

Detecting new and enlarged lesions in multiple sclerosis (MS) patients is needed to determine their disease activity. LeMan-PV is a software embedded in the scanner reconstruction system of one vendor, which automatically assesses new and enlarged white matter lesions (NELs) in the follow-up of MS patients; however, multicenter validation studies are lacking.

PURPOSE

To assess the accuracy of LeMan-PV for the longitudinal detection NEL white-matter MS lesions in a multicenter clinical setting.

STUDY TYPE

Retrospective, longitudinal.

SUBJECTS

A total of 206 patients with a definitive MS diagnosis and at least two follow-up MRI studies from five centers participating in the Swiss Multiple Sclerosis Cohort study. Mean age at first follow-up = 45.2 years (range: 36.9-52.8 years); 70 males.

FIELD STRENGTH/SEQUENCE

Fluid attenuated inversion recovery (FLAIR) and T1-weighted magnetization prepared rapid gradient echo (T1-MPRAGE) sequences at 1.5 T and 3 T.

ASSESSMENT

The study included 313 MRI pairs of datasets. Data were analyzed with LeMan-PV and compared with a manual "reference standard" provided by a neuroradiologist. A second rater (neurologist) performed the same analysis in a subset of MRI pairs to evaluate the rating-accuracy. The Sensitivity (Se), Specificity (Sp), Accuracy (Acc), F1-score, lesion-wise False-Positive-Rate (aFPR), and other measures were used to assess LeMan-PV performance for the detection of NEL at 1.5 T and 3 T. The performance was also evaluated in the subgroup of 123 MRI pairs at 3 T.

STATISTICAL TESTS

Intraclass correlation coefficient (ICC) and Cohen's kappa (CK) were used to evaluate the agreement between readers.

RESULTS

The interreader agreement was high for detecting new lesions (ICC = 0.97, Pvalue < 10-20 , CK = 0.82, P value = 0) and good (ICC = 0.75, P value < 10-12 , CK = 0.68, P value = 0) for detecting enlarged lesions. Across all centers, scanner field strengths (1.5 T, 3 T), and for NEL, LeMan-PV achieved: Acc = 61%, Se = 65%, Sp = 60%, F1-score = 0.44, aFPR = 1.31. When both follow-ups were acquired at 3 T, LeMan-PV accuracy was higher (Acc = 66%, Se = 66%, Sp = 66%, F1-score = 0.28, aFPR = 3.03).

DATA CONCLUSION

In this multicenter study using clinical data settings acquired at 1.5 T and 3 T, and variations in MRI protocols, LeMan-PV showed similar sensitivity in detecting NEL with respect to other recent 3 T multicentric studies based on neural networks. While LeMan-PV performance is not optimal, its main advantage is that it provides automated clinical decision support integrated into the radiological-routine flow.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Age-, sex-, and pathology-related variability in brain structure and cognition

This work aimed to investigate potential pathways linking age and imaging measures to early age- and pathology-related changes in cognition. We used [18F]-Flutemetamol (amyloid) and [18F]-Flortaucipir (tau) positron emission tomography (PET), structural MRI, and neuropsychological assessment from 232 elderly individuals aged 50-89 years (46.1% women, 23% APOE-ε4 carrier, 23.3% MCI). Tau-PET was available for a subsample of 93 individuals. Structural equation models were used to evaluate cross-sectional pathways between age, amyloid and tau burden, grey matter thickness and volumes, white matter hyperintensity volume, lateral ventricle volume, and cognition. Our results show that age is associated with worse outcomes in most of the measures examined and had similar negative effects on episodic memory and executive functions. While increased lateral ventricle volume was consistently associated with executive function dysfunction, participants with mild cognitive impairment drove associations between structural measures and episodic memory. Both age and amyloid-PET could be associated with medial temporal lobe tau, depending on whether we used a continuous or a dichotomous amyloid variable. Tau burden in entorhinal cortex was related to worse episodic memory in individuals with increased amyloid burden (Centiloid >12) independently of medial temporal lobe atrophy. Testing models for sex differences revealed that amyloid burden was more strongly associated with regional atrophy in women compared with men. These associations were likely mediated by higher tau burden in women. These results indicate that influences of pathological pathways on cognition and sex-specific vulnerabilities are dissociable already in early stages of neuropathology and cognitive impairment.

How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review

INTRODUCTION

Over the past few years, the deep learning community has developed and validated a plethora of tools for lesion detection and segmentation in Multiple Sclerosis (MS). However, there is an important gap between validating models technically and clinically. To this end, a six-step framework necessary for the development, validation, and integration of quantitative tools in the clinic was recently proposed under the name of the Quantitative Neuroradiology Initiative (QNI).

AIMS

Investigate to what extent automatic tools in MS fulfill the QNI framework necessary to integrate automated detection and segmentation into the clinical neuroradiology workflow.

METHODS

Adopting the systematic Cochrane literature review methodology, we screened and summarised published scientific articles that perform automatic MS lesions detection and segmentation. We categorised the retrieved studies based on their degree of fulfillment of QNI's six-steps, which include a tool's technical assessment, clinical validation, and integration.

RESULTS

We found 156 studies; 146/156 (94%) fullfilled the first QNI step, 155/156 (99%) the second, 8/156 (5%) the third, 3/156 (2%) the fourth, 5/156 (3%) the fifth and only one the sixth.

CONCLUSIONS

To date, little has been done to evaluate the clinical performance and the integration in the clinical workflow of available methods for MS lesion detection/segmentation. In addition, the socio-economic effects and the impact on patients' management of such tools remain almost unexplored.

A feasibility and safety study of afamelanotide in acute stroke patients - an open label, proof of concept, phase iia clinical trial

BACKGROUND

Neuroprotective agents have the potential to improve the outcomes of revascularisation therapies in acute ischemic stroke patients (AIS) and in those unable to receive revascularisation. Afamelanotide, a synthetic α-melanocyte stimulating hormone analogue, is a potential novel neuroprotective agent. We set out to assess the feasibility and safety of afamelanotide for the first time in AIS patients.

METHODS

AIS patients within 24 h of onset, with perfusion abnormality on imaging (Tmax) and otherwise ineligible for revascularisation therapies were enrolled. Afamelanotide 16 mg implants were administered subcutaneously on Day 0 (D0, day of recruitment), D1 and repeated on D7 and D8, if not well recovered. Treatment emergent adverse events (TEAEs) and neurological assessments were recorded regularly up to D42. Magnetic resonance imaging (MRI) with FLAIR sequences were also performed on D3 and D9.

RESULTS

Six patients (5 women, median age 81, median NIHSS 6) were recruited. Two patients received 4 doses and four patients received 2. One patient (who received 2 doses), suffered a fatal recurrent stroke on D9 due to a known complete acute internal carotid artery occlusion, assessed as unrelated to the study drug. There were no other local or major systemic TEAEs recorded. In all surviving patients, the median NIHSS improved from 6 to 2 on D7. The median Tmax volume on D0 was 23 mL which was reduced to a FLAIR volume of 10 mL on D3 and 4 mL on D9.

CONCLUSIONS

Afamelanotide was well tolerated and safe in our small sample of AIS patients. It also appears to be associated with good recovery and radiological improvement of salvageable tissue which needs to be tested in randomized studies.

CLINICALTRIALS

GOV IDENTIFIER

NCT04962503, First posted 15/07/2021.

AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis

BACKGROUND

Contrast-enhancing (CE) lesions are an important finding on brain magnetic resonance imaging (MRI) in patients with multiple sclerosis (MS) but can be missed easily. Automated solutions for reliable CE lesion detection are emerging; however, independent validation of artificial intelligence (AI) tools in the clinical routine is still rare.

METHODS

A three-dimensional convolutional neural network for CE lesion segmentation was trained externally on 1488 datasets of 934 MS patients from 81 scanners using concatenated information from FLAIR and T1-weighted post-contrast imaging. This externally trained model was tested on an independent dataset comprising 504 T1-weighted post-contrast and FLAIR image datasets of MS patients from clinical routine. Two neuroradiologists (R1, R2) labeled CE lesions for gold standard definition in the clinical test dataset. The algorithmic output was evaluated on both patient- and lesion-level.

RESULTS

On a patient-level, recall, specificity, precision, and accuracy of the AI tool to predict patients with CE lesions were 0.75, 0.99, 0.91, and 0.96. The agreement between the AI tool and both readers was within the range of inter-rater agreement (Cohen's kappa; AI vs. R1: 0.69; AI vs. R2: 0.76; R1 vs. R2: 0.76). On a lesion-level, false negative lesions were predominately found in infratentorial location, significantly smaller, and at lower contrast than true positive lesions (p < 0.05).

CONCLUSIONS

AI-based identification of CE lesions on brain MRI is feasible, approaching human reader performance in independent clinical data and might be of help as a second reader in the neuroradiological assessment of active inflammation in MS patients.

CRITICAL RELEVANCE STATEMENT

Al-based detection of contrast-enhancing multiple sclerosis lesions approaches human reader performance, but careful visual inspection is still needed, especially for infratentorial, small and low-contrast lesions.

Scanner agnostic large-scale evaluation of MS lesion delineation tool for clinical MRI

Introduction

Patients with MS are MRI scanned continuously throughout their disease course resulting in a large manual workload for radiologists which includes lesion detection and size estimation. Though many models for automatic lesion segmentation have been published, few are used broadly in clinic today, as there is a lack of testing on clinical datasets. By collecting a large, heterogeneous training dataset directly from our MS clinic we aim to present a model which is robust to different scanner protocols and artefacts and which only uses MRI modalities present in routine clinical examinations.

Methods

We retrospectively included 746 patients from routine examinations at our MS clinic. The inclusion criteria included acquisition at one of seven different scanners and an MRI protocol including 2D or 3D T2-w FLAIR, T2-w and T1-w images. Reference lesion masks on the training (n = 571) and validation (n = 70) datasets were generated using a preliminary segmentation model and subsequent manual correction. The test dataset (n = 100) was manually delineated. Our segmentation model https://github.com/CAAI/AIMS/ was based on the popular nnU-Net, which has won several biomedical segmentation challenges. We tested our model against the published segmentation models HD-MS-Lesions, which is also based on nnU-Net, trained with a more homogenous patient cohort. We furthermore tested model robustness to data from unseen scanners by performing a leave-one-scanner-out experiment.

Results

We found that our model was able to segment MS white matter lesions with a performance comparable to literature: DSC = 0.68, precision = 0.90, recall = 0.70, f1 = 0.78. Furthermore, the model outperformed HD-MS-Lesions in all metrics except precision = 0.96. In the leave-one-scanner-out experiment there was no significant change in performance (p < 0.05) between any of the models which were only trained on part of the dataset and the full segmentation model.

Conclusion

In conclusion we have seen, that by including a large, heterogeneous dataset emulating clinical reality, we have trained a segmentation model which maintains a high segmentation performance while being robust to data from unseen scanners. This broadens the applicability of the model in clinic and paves the way for clinical implementation.

Using a semi-automated approach to quantify Unidentified Bright Objects in Neurofibromatosis type 1 and linkages to cognitive and academic outcomes

Neurofibromatosis type 1 (NF1) is an autosomal dominant neurocutaneous syndrome that affects multiple organ systems resulting in widespread symptoms, including cognitive deficits. In addition to the criteria required for an NF1 diagnosis, approximately 70% of children with NF1 present with Unidentified Bright Objects (UBOs) or Focal Areas of Signal Intensity, which are hyperintense bright spots seen on T2-weighted magnetic resonance images and seen more prominently on FLAIR magnetic resonance images (Sabol et al., 2011). Current findings relating the presence/absence, quantities, sizes, and locations of these bright spots to cognitive abilities are mixed. To contribute to and hopefully disentangle some of these mixed findings, we explored potential relationships between metrics related to UBOs and cognitive abilities in a sample of 28 children and adolescents with NF1 (M=12.52 years; SD=3.18 years; 16 male). We used the Lesion Segmentation Tool (LST) to automatically detect and segment the UBOs. The LST was able to qualitatively and quantitatively reliably detect UBOs in images of children with NF1. Using these automatically detected and segmented lesions, we found that while controlling for age, biological sex, perceptual IQ, study, and scanner, "total UBO volume", defined as the sum of all the voxels representing all of the UBOs for each participant, helped explain differences in word reading, phonological awareness, and visuospatial skills. These findings contribute to the emerging NF1 literature and help parse the specific deficits that children with NF1 have, to then help improve the efficacy of reading interventions for children with NF1.

A critical guide to the automated quantification of perivascular spaces in magnetic resonance imaging

The glymphatic system is responsible for waste clearance in the brain. It is comprised of perivascular spaces (PVS) that surround penetrating blood vessels. These spaces are filled with cerebrospinal fluid and interstitial fluid, and can be seen with magnetic resonance imaging. Various algorithms have been developed to automatically label these spaces in MRI. This has enabled volumetric and morphological analyses of PVS in healthy and disease cohorts. However, there remain inconsistencies between PVS measures reported by different methods of automated segmentation. The present review emphasizes that importance of voxel-wise evaluation of model performance, mainly with the Sørensen Dice similarity coefficient. Conventional count correlations for model validation are inadequate if the goal is to assess volumetric or morphological measures of PVS. The downside of voxel-wise evaluation is that it requires manual segmentations that require large amounts of time to produce. One possible solution is to derive these semi-automatically. Additionally, recommendations are made to facilitate rigorous development and validation of automated PVS segmentation models. In the application of automated PVS segmentation tools, publication of image quality metrics, such as the contrast-to-noise ratio, alongside descriptive statistics of PVS volumes and counts will facilitate comparability between studies. Lastly, a head-to-head comparison between two algorithms, applied to two cohorts of astronauts reveals how results can differ substantially between techniques.

Improving the detection of new lesions in multiple sclerosis with a cascaded 3D fully convolutional neural network approach

Longitudinal magnetic resonance imaging (MRI) has an important role in multiple sclerosis (MS) diagnosis and follow-up. Specifically, the presence of new lesions on brain MRI scans is considered a robust predictive biomarker for the disease progression. New lesions are a high-impact prognostic factor to predict evolution to MS or risk of disability accumulation over time. However, the detection of this disease activity is performed visually by comparing the follow-up and baseline scans. Due to the presence of small lesions, misregistration, and high inter-/intra-observer variability, this detection of new lesions is prone to errors. In this direction, one of the last Medical Image Computing and Computer Assisted Intervention (MICCAI) challenges was dealing with this automatic new lesion quantification. The MSSEG-2: MS new lesions segmentation challenge offers an evaluation framework for this new lesion segmentation task with a large database (100 patients, each with two-time points) compiled from the OFSEP (Observatoire français de la sclérose en plaques) cohort, the French MS registry, including 3D T2-w fluid-attenuated inversion recovery (T2-FLAIR) images from different centers and scanners. Apart from a change in centers, MRI scanners, and acquisition protocols, there are more challenges that hinder the automated detection process of new lesions such as the need for large annotated datasets, which may be not easily available, or the fact that new lesions are small areas producing a class imbalance problem that could bias trained models toward the non-lesion class. In this article, we present a novel automated method for new lesion detection of MS patient images. Our approach is based on a cascade of two 3D patch-wise fully convolutional neural networks (FCNNs). The first FCNN is trained to be more sensitive revealing possible candidate new lesion voxels, while the second FCNN is trained to reduce the number of misclassified voxels coming from the first network. 3D T2-FLAIR images from the two-time points were pre-processed and linearly co-registered. Afterward, a fully CNN, where its inputs were only the baseline and follow-up images, was trained to detect new MS lesions. Our approach obtained a mean segmentation dice similarity coefficient of 0.42 with a detection F1-score of 0.5. Compared to the challenge participants, we obtained one of the highest precision scores (PPVL = 0.52), the best PPVL rate (0.53), and a lesion detection sensitivity (SensL of 0.53).

Thalamic atrophy moderates associations among aerobic fitness, cognitive processing speed, and walking endurance in persons with multiple sclerosis

BACKGROUND AND OBJECTIVES

Thalamic atrophy (TA) represents a biomarker of neurodegeneration and associated dysfunction/decline in physical and cognitive functioning among persons with multiple sclerosis (MS). Aerobic fitness, as an end point of exercise training, represents a promising target for restoring function in MS, but it is unknown if such effects differ by TA. This cross-sectional study examined whether aerobic fitness was differentially associated with cognitive processing speed and walking endurance in persons with MS who present with and without TA.

METHODS

44 fully ambulatory persons with MS completed a graded exercise test for measuring aerobic fitness (VO_2peak) and underwent 3T MRI for measuring TA, the Symbol Digit Modalities Test (SDMT), and the 6-min walk (6MW). We performed Spearman correlations (r_s) among VO_2peak, SDMT, and 6MW scores overall, and in persons with and without TA. We applied Fisher's z-test for comparing correlations based on TA status.

RESULTS

When controlling for age, EDSS score, and global MRI measures of atrophy, VO_2peak was strongly associated with SDMT scores (pr_s = 0.74, p < 0.01) and 6MW performance (pr_s = 0.77, p < 0.01) in persons with TA, whereas VO_2peak was not associated with SDMT scores (pr_s =  - 0.01, p = 0.99) or 6MW performance (pr_s = 0.25, p = 0.38) in those without TA. The correlations between VO_2peak and SDMT (z = 2.86, p < 0.01) and VO_2peak and 6MW (z = 2.33, p = 0.02) were significantly stronger in the TA group.

DISCUSSION

This study provides initial evidence of strong, selective associations among aerobic fitness, cognitive processing speed, and walking endurance in persons with TA as a biomarker for MS-related neurodegeneration. Such data support TA as a moderator of the association among aerobic fitness, cognitive processing speed, and walking endurance in persons with MS. Future research should carefully consider the role of TA when designing trials of aerobic exercise, cognition, and mobility in MS.

Analysis of the extent of limbic system changes in multiple sclerosis using FreeSurfer and voxel-based morphometry approaches

Background and purpose

The limbic brain is involved in diverse cognitive, emotional, and autonomic functions. Injury of the various parts of the limbic system have been correlated with clinical deficits in MS. The purpose of this study was to comprehensively examine different regions of the subcortical limbic system to assess the extent of damage within this entire system as it may be pertinent in correlating with specific aspects of cognitive and behavioral dysfunction in MS by using a fully automated, unbiased segmentation approach.

Methods

Sixty-seven subjects were included in this study, including 52 with multiple sclerosis (MS) and 15 healthy controls. Only patients with stable MS disease, without any relapses, MRI activity, or disability progression were included. Subcortical limbic system segmentation was performed using the FreeSurfer pipeline ScLimbic, which provides volumes for fornix, mammillary bodies, hypothalamus, septal nuclei, nucleus accumbens, and basal forebrain. Hippocampus and anterior thalamic nuclei were added as additional components of the limbic circuitry, also segmented through FreeSurfer. Whole limbic region mask was generated by combining these structures and used for Voxel-based morphometry (VBM) analysis.

Results

The mean [95% confidence interval] of the total limbic system volume was lower (0.22% [0.21–0.23]) in MS compared to healthy controls (0.27%, [0.25–0.29], p < .001). Pairwise comparisons of individual limbic regions between MS and controls was significant in the nucleus accumbens (0.046%, [0.043–0.050] vs. 0.059%, [0.051–0.066], p = .005), hypothalamus (0.062%, [0.059–0.065] vs. 0.074%, [0.068–0.081], p = .001), basal forebrain (0.038%, [0.036–0.040] vs. 0.047%, [0.042–0.051], p = .001), hippocampus (0.47%, [0.45–0.49] vs. 0.53%, [0.49–0.57], p = .004), and anterior thalamus (0.077%, [0.072–0.082] vs. 0.093%, [0.084–0.10], p = .001) after Bonferroni correction. Volume of several limbic regions was significantly correlated with T2 lesion burden and brain parenchymal fraction (BPF). Multiple regression model showed minimal influence of BPF on limbic brain volume and no influence of other demographic and disease state variables. VBM analysis showed cluster differences in the fornix and anterior thalamic nuclei at threshold p < 0.05 after adjusting for covariates but the results were insignificant after family-wise error corrections.

Conclusions

The results show evidence that brain volume loss is fairly extensive in the limbic brain. Given the significance of the limbic system in many disease states including MS, such volumetric analyses can be expanded to studying cognitive and emotional disturbances in larger clinical trials. FreeSurfer ScLimbic pipeline provided an efficient and reliable methodology for examining many of the subcortical structures related to the limbic brain.

Triplanar U-Net with lesion-wise voting for the segmentation of new lesions on longitudinal MRI studies

We present a deep learning method for the segmentation of new lesions in longitudinal FLAIR MRI sequences acquired at two different time points. In our approach, the 3D volumes are processed slice-wise across the coronal, axial, and sagittal planes and the predictions from the three orientations are merged using an optimized voting strategy. Our method achieved best F1 score (0.541) among all participating methods in the MICCAI 2021 challenge Multiple sclerosis new lesions segmentation (MSSEG-2). Moreover, we show that our method is on par with the challenge's expert neuroradiologists: on an unbiased ground truth, our method achieves results comparable to those of the four experts in terms of detection (F1 score) and segmentation accuracy (Dice score).

New MS lesion segmentation with deep residual attention gate U-Net utilizing 2D slices of 3D MR images

Multiple sclerosis (MS) is an autoimmune disease that causes lesions in the central nervous system of humans due to demyelinating axons. Magnetic resonance imaging (MRI) is widely used for monitoring and measuring MS lesions. Automated methods for MS lesion segmentation have usually been performed on individual MRI scans. Recently, tracking lesion activity for quantifying and monitoring MS disease progression, especially detecting new lesions, has become an important biomarker. In this study, a unique pipeline with a deep neural network that combines U-Net, attention gate, and residual learning is proposed to perform better new MS lesion segmentation using baseline and follow-up 3D FLAIR MR images. The proposed network has a similar architecture to U-Net and is formed from residual units which facilitate the training of deep networks. Networks with fewer parameters are designed with better performance through the skip connections of U-Net and residual units, which facilitate information propagation without degradation. Attention gates also learn to focus on salient features of the target structures of various sizes and shapes. The MSSEG-2 dataset was used for training and testing the proposed pipeline, and the results were compared with those of other proposed pipelines of the challenge and experts who participated in the same challenge. According to the results over the testing set, the lesion-wise F1 and dice scores were obtained as a mean of 48 and 44.30%. For the no-lesion cases, the number of tested and volume of tested lesions were obtained as a mean of 0.148 and 1.488, respectively. The proposed pipeline outperformed 22 proposed pipelines and ranked 8th in the challenge.

Higher white matter hyperintensity load adversely affects pre-post proximal cognitive training performance in healthy older adults

Cognitive training has shown promise for improving cognition in older adults. Age-related neuroanatomical changes may affect cognitive training outcomes. White matter hyperintensities are one common brain change in aging reflecting decreased white matter integrity. The current study assessed (1) proximal cognitive training performance following a 3-month randomized control trial and (2) the contribution of baseline whole-brain white matter hyperintensity load, or total lesion volume (TLV), on pre-post proximal training change. Sixty-two healthy older adults were randomized to either adaptive cognitive training or educational training control interventions. Repeated-measures analysis of covariance revealed two-way group × time interactions such that those assigned cognitive training demonstrated greater improvement on proximal composite (total training composite) and sub-composite (processing speed training composite, working memory training composite) measures compared to education training counterparts. Multiple linear regression showed higher baseline TLV associated with lower pre-post change on processing speed training sub-composite (β = -0.19, p = 0.04), but not other composite measures. These findings demonstrate the utility of cognitive training for improving post-intervention proximal performance in older adults. Additionally, pre-post proximal processing speed training change appears to be particularly sensitive to white matter hyperintensity load versus working memory training change. These data suggest that TLV may serve as an important factor for consideration when planning processing speed-based cognitive training interventions for remediation of cognitive decline in older adults.

MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies

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Sharing data from cooperative studies is essential to develop new biomarkers in MS.

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Differences in MRI acquisition, analysis, storage represent a substantial constraint.

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We review the state of the art and developments in the harmonization of MRI.

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We provide recommendations to harmonize large MRI datasets in the MS field.

There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resources.

Deformation-based morphometry identifies deep brain structures protected by ocrelizumab

BACKGROUND

Despite advancements in treatments for multiple sclerosis, insidious disease progression remains an area of unmet medical need, for which atrophy-based biomarkers may help better characterize the progressive biology.

METHODS

We developed and applied a method of longitudinal deformation-based morphometry to provide voxel-level assessments of brain volume changes and identified brain regions that were significantly impacted by disease-modifying therapy.

RESULTS

Using brain MRI data from two identically designed pivotal trials of relapsing multiple sclerosis (total N = 1483), we identified multiple deep brain regions, including the thalamus and brainstem, where volume loss over time was reduced by ocrelizumab (p < 0.05), a humanized anti-CD20 + monoclonal antibody approved for the treatment of multiple sclerosis. Additionally, identified brainstem shrinkage, as well as brain ventricle expansion, was associated with a greater risk for confirmed disability progression (p < 0.05).

CONCLUSIONS

The identification of deep brain structures has a strong implication for developing new biomarkers of brain atrophy reduction to advance drug development for multiple sclerosis, which has an increasing focus on targeting the progressive biology.

Validation of an automatic tool for the rapid measurement of brain atrophy and white matter hyperintensity: QyScore®

OBJECTIVES

QyScore® is an imaging analysis tool certified in Europe (CE marked) and the US (FDA cleared) for the automatic volumetry of grey and white matter (GM and WM respectively), hippocampus (HP), amygdala (AM), and white matter hyperintensity (WMH). Here we compare QyScore® performances with the consensus of expert neuroradiologists.

METHODS

Dice similarity coefficient (DSC) and the relative volume difference (RVD) for GM, WM volumes were calculated on 50 3DT1 images. DSC and the F1 metrics were calculated for WMH on 130 3DT1 and FLAIR images. For each index, we identified thresholds of reliability based on current literature review results. We hypothesized that DSC/F1 scores obtained using QyScore® markers would be higher than the threshold. In contrast, RVD scores would be lower. Regression analysis and Bland-Altman plots were obtained to evaluate QyScore® performance in comparison to the consensus of three expert neuroradiologists.

RESULTS

The lower bound of the DSC/F1 confidence intervals was higher than the threshold for the GM, WM, HP, AM, and WMH, and the higher bounds of the RVD confidence interval were below the threshold for the WM, GM, HP, and AM. QyScore®, compared with the consensus of three expert neuroradiologists, provides reliable performance for the automatic segmentation of the GM and WM volumes, and HP and AM volumes, as well as WMH volumes.

CONCLUSIONS

QyScore® represents a reliable medical device in comparison with the consensus of expert neuroradiologists. Therefore, QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

KEY POINTS

• QyScore® provides reliable automatic segmentation of brain structures in comparison with the consensus of three expert neuroradiologists. • QyScore® automatic segmentation could be performed on MRI images using different vendors and protocols of acquisition. In addition, the fast segmentation process saves time over manual and semi-automatic methods. • QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

Scalp HFO rates are higher for larger lesions

High‐frequency oscillations (HFO) in scalp EEG are a new and promising noninvasive epilepsy biomarker, providing added prognostic value, particularly in pediatric lesional epilepsy. However, it is unclear if lesion characteristics, such as lesion volume, depth, type, and localization, impact scalp HFO rates. We analyzed scalp EEG from 13 children and adolescents with focal epilepsy associated with focal cortical dysplasia (FCD), low‐grade tumors, or hippocampal sclerosis. We applied a validated automated detector to determine HFO rates in bipolar channels. We identified the lesion characteristics in MRI. Larger lesions defined by MRI volumetric analysis corresponded to higher cumulative scalp HFO rates (P = .01) that were detectable in a higher number of channels (P = .05). Both superficial and deep lesions generated HFO detectable in the scalp EEG. Lesion type (FCD vs tumor) and lobar localization (temporal vs extratemporal) did not affect scalp HFO rates in our study. Our observations support that all lesions may generate HFO detectable in scalp EEG, irrespective of their characteristics, whereas larger epileptogenic lesions generate higher scalp HFO rates over larger areas that are thus more accessible to detection. Our study provides crucial insight into scalp HFO detectability in pediatric lesional epilepsy, facilitating their implementation as an epilepsy biomarker in a clinical setting.

Longitudinal changes of deep gray matter shape in multiple sclerosis

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Specific shape changes over time occur at the bilateral ventrolateral pallidal and the left posterolateral striatal surface in relapse-onset multiple sclerosis.

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These shape changes over time were not associated with disease progression.

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The average shape of deep gray matter structures was associated with the patients’ average disease severity as well as white matter lesion-load.

Objective

This study aimed to investigate longitudinal deep gray matter (DGM) shape changes and their relationship with measures of clinical disability and white matter lesion-load in a large multiple sclerosis (MS) cohort.

Materials and Methods

A total of 230 MS patients (179 relapsing-remitting, 51 secondary progressive; baseline age 44.5 ± 11.3 years; baseline disease duration 12.99 ± 9.18) underwent annual clinical and MRI examinations over a maximum of 6 years (mean 4.32 ± 2.07 years). The DGM structures were segmented on the T1-weighted images using the “Multiple Automatically Generated Templates” brain algorithm. White matter lesion-load was measured on T2-weighted MRI. Clinical examination included the expanded disability status scale, 9-hole peg test, timed 25-foot walk test, symbol digit modalities test and paced auditory serial addition test. Vertex‐wise longitudinal analysis of DGM shapes was performed using linear mixed effect models and evaluated the association between average/temporal changes of DGM shapes with average/temporal changes of clinical measurements, respectively.

Results

A significant shrinkage over time of the bilateral ventrolateral pallidal and the left posterolateral striatal surface was observed, whereas no significant shape changes over time were observed at the bilateral thalamic and right striatal surfaces. Higher average lesion-load was associated with an average inwards displacement of the global thalamic surface with relative sparing on the posterior side (slight left-side predominance), the antero-dorso-lateral striatal surfaces bilaterally (symmetric on both sides) and the antero-lateral pallidal surface (left-side predominance). There was also an association between shrinkage of large lateral DGM surfaces with higher clinical motor and cognitive disease severity. However, there was no correlation between any DGM shape changes over time and measurements of clinical progression or lesion-load changes over time.

Conclusions

This study showed specific shape change of DGM structures occurring over time in relapse-onset MS. Although these shape changes over time were not associated with disease progression, we demonstrated a link between DGM shape and the patients’ average disease severity as well as white matter lesion-load.

Deep Attention and Graphical Neural Network for Multiple Sclerosis Lesion Segmentation from MR Imaging Sequences

The segmentation of multiple sclerosis (MS) lesions from MR imaging sequences remains a challenging task, due to the characteristics of variant shapes, scattered distributions and unknown numbers of lesions. However, the current automated MS segmentation methods with deep learning models face the challenges of (1) capturing the multiple scattered lesions in multiple regions and (2) delineating the global contour of variant lesions. To address these challenges, in this paper, we propose a novel attention and graph-driven network (DAG-Net), which incorporates (1) the spatial correlations for embracing the lesions in distant regions and (2) the global context for better representing lesions of variant features in a unified architecture. Firstly, the novel local attention coherence mechanism is designed to construct dynamic and expansible graphs for the spatial correlations between pixels and their proximities. Secondly, the proposed spatial-channel attention module enhances features to optimize the global contour delineation, by aggregating relevant features. Moreover, with the dynamic graphs, the learning process of the DAG-Net is interpretable, which in turns support the reliability of segmentation results. Extensive experiments were conducted on a public ISBI2015 dataset and an in-house dataset in comparison to state-of-the-art methods, based on the geometrical and clinical metrics. The experimental results validate the effectiveness of the proposed DAG-Net on segmenting variant and scatted lesions in multiple regions.

Multiple Sclerosis Lesion Segmentation in Brain MRI Using Inception Modules Embedded in a Convolutional Neural Network

Multiple sclerosis (MS) is a chronic and autoimmune disease that forms lesions in the central nervous system. Quantitative analysis of these lesions has proved to be very useful in clinical trials for therapies and assessing disease prognosis. However, the efficacy of these quantitative analyses greatly depends on how accurately the MS lesions have been identified and segmented in brain MRI. This is usually carried out by radiologists who label 3D MR images slice by slice using commonly available segmentation tools. However, such manual practices are time consuming and error prone. To circumvent this problem, several automatic segmentation techniques have been investigated in recent years. In this paper, we propose a new framework for automatic brain lesion segmentation that employs a novel convolutional neural network (CNN) architecture. In order to segment lesions of different sizes, we have to pick a specific filter or size 3 × 3 or 5 × 5. Sometimes, it is hard to decide which filter will work better to get the best results. Google Net has solved this problem by introducing an inception module. An inception module uses 3 × 3, 5 × 5, 1 × 1 and max pooling filters in parallel fashion. Results show that incorporating inception modules in a CNN has improved the performance of the network in the segmentation of MS lesions. We compared the results of the proposed CNN architecture for two loss functions: binary cross entropy (BCE) and structural similarity index measure (SSIM) using the publicly available ISBI-2015 challenge dataset. A score of 93.81 which is higher than the human rater with BCE loss function is achieved.

Evaluation of Ultrafast Wave-Controlled Aliasing in Parallel Imaging 3D-FLAIR in the Visualization and Volumetric Estimation of Cerebral White Matter Lesions

BACKGROUND AND PURPOSE

Our aim was to evaluate an ultrafast 3D-FLAIR sequence using Wave-controlled aliasing in parallel imaging encoding (Wave-FLAIR) compared with standard 3D-FLAIR in the visualization and volumetric estimation of cerebral white matter lesions in a clinical setting.

MATERIALS AND METHODS

Forty-two consecutive patients underwent 3T brain MR imaging, including standard 3D-FLAIR (acceleration factor = 2, scan time = 7 minutes 50 seconds) and resolution-matched ultrafast Wave-FLAIR sequences (acceleration factor  = 6, scan time = 2 minutes 45 seconds for the 20-channel coil; acceleration factor = 9, scan time  = 1 minute 50 seconds for the 32-channel coil) as part of clinical evaluation for demyelinating disease. Automated segmentation of cerebral white matter lesions was performed using the Lesion Segmentation Tool in SPM. Student t tests, intraclass correlation coefficients, relative lesion volume difference, and Dice similarity coefficients were used to compare volumetric measurements among sequences. Two blinded neuroradiologists evaluated the visualization of white matter lesions, artifacts, and overall diagnostic quality using a predefined 5-point scale.

RESULTS

Standard and Wave-FLAIR sequences showed excellent agreement of lesion volumes with an intraclass correlation coefficient of 0.99 and mean Dice similarity coefficient of 0.97 (SD, 0.05) (range, 0.84-0.99). Wave-FLAIR was noninferior to standard FLAIR for visualization of lesions and motion. The diagnostic quality for Wave-FLAIR was slightly greater than for standard FLAIR for infratentorial lesions (P < .001), and there were fewer pulsation artifacts on Wave-FLAIR compared with standard FLAIR (P < .001).

CONCLUSIONS

Ultrafast Wave-FLAIR provides superior visualization of infratentorial lesions while preserving overall diagnostic quality and yields white matter lesion volumes comparable with those estimated using standard FLAIR. The availability of ultrafast Wave-FLAIR may facilitate the greater use of 3D-FLAIR sequences in the evaluation of patients with suspected demyelinating disease.

18F-florbetapir PET/MRI for quantitatively monitoring myelin loss and recovery in patients with multiple sclerosis: A longitudinal study

BACKGROUND

Amyloid positron emission tomography (PET) can measure in-vivo demyelination in patients with multiple sclerosis (MS). However, the value of 18F-labeled amyloid PET tracer, 18F-florbetapir in the longitudinal study for monitoring myelin loss and recovery has not been confirmed.

METHODS

From March 2019 to September 2020, twenty-three patients with MS and nine healthy controls (HCs) underwent a hybrid PET/MRI at baseline and expanded disability status scale (EDSS) assessment, and eight of 23 patients further underwent follow-up PET/MRI. The distribution volume ratio (DVR) and standard uptake value ratio (SUVR) of 18F-florbetapir in damaged white matter (DWM) and normal-appearance white matter (NAWM) were obtained from dynamic and static PET acquisition. Diffusion tensor imaging-derived parameters were also calculated. Data were expressed as mean ± standard deviation with 99% confidence interval (99%CI).

FINDING

The mean DVR (1.08 ± 0.12, 99%CI [1.02 ~ 1.14]) but not the mean SUVR of DWM lesions was lower than that of NAWM in patients with MS (1.25 ± 0.10, 99%CI [1.20 ~ 1.31]) and HCs (1.29 ± 0.08, 99%CI [1.23 ~ 1.36]). A trend toward lower mean fractional anisotropy (374.95 ± 45.30 vs. 419.07 ± 4.83) and higher mean radial diffusivity (0.45 ± 0.05 vs. 0.40 ± 0.01) of NAWM in patients with MS than those in HCs was found. DVR decreased in DWM lesions with higher MD (rho = -0.261, 99%CI [-0.362 ~ -0.144]), higher AD (rho = -0.200, 99%CI [-0.318 ~ -0.070]) and higher RD (rho = -0.198, 99%CI [-0.313 ~ -0.075]). Patients' EDSS scores were reduced (B = 0.04, 99%CI [-0.005 ~ 0.084]) with decreased index of global demyelination in the longitudinal study.

INTERPRETATION

Our exploratory study suggests that dynamic 18F-florbetapir PET/MRI may be a very promising tool for quantitatively monitoring myelin loss and recovery in patients with MS.

FUNDING

Shanghai Pujiang Program, Shanghai Municipal Key Clinical Specialty, Shanghai Shuguang Plan Project, Shanghai Health and Family Planning Commission Research Project, Clinical Research Plan of SHDC, French-Chinese program "Xu Guangqi".

Increased extracellular fluid is associated with white matter fiber degeneration in CADASIL: in vivo evidence from diffusion magnetic resonance imaging

Background

White matter hyperintensities (WMHs) are one of the hallmarks of cerebral small vessel disease (CSVD), but the pathological mechanisms underlying WMHs remain unclear. Recent studies suggest that extracellular fluid (ECF) is increased in brain regions with WMHs. It has been hypothesized that ECF accumulation may have detrimental effects on white matter microstructure. To test this hypothesis, we used cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) as a unique CSVD model to investigate the relationships between ECF and fiber microstructural changes in WMHs.

Methods

Thirty-eight CADASIL patients underwent 3.0 T MRI with multi-model sequences. Parameters of free water (FW) and apparent fiber density (AFD) obtained from diffusion-weighted imaging (b = 0 and 1000 s/mm²) were respectively used to quantify the ECF and fiber density. WMHs were split into four subregions with four levels of FW using quartiles (FWq1 to FWq4) for each participant. We analyzed the relationships between FW and AFD in each subregion of WMHs. Additionally, we tested whether FW of WMHs were associated with other accompanied CSVD imaging markers including lacunes and microbleeds.

Results

We found an inverse correlation between FW and AFD in WMHs. Subregions of WMHs with high-level of FW (FWq3 and FWq4) were accompanied with decreased AFD and with changes in FW-corrected diffusion tensor imaging parameters. Furthermore, FW was also independently associated with lacunes and microbleeds.

Conclusions

Our study demonstrated that increased ECF was associated with WM degeneration and the occurrence of lacunes and microbleeds, providing important new insights into the role of ECF in CADASIL pathology. Improving ECF drainage might become a therapeutic strategy in future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12987-021-00264-1.

Arterial stiffness and progression of cerebral white matter hyperintensities in patients with type 2 diabetes and matched controls: a 5-year cohort study

BACKGROUND

Stroke is a serious complication in patients with type 2 diabetes (T2DM). Arterial stiffness may improve stroke prediction. We investigated the association between carotid-femoral pulse wave velocity [PWV] and the progression of cerebral white matter hyperintensities (WMH), a marker of stroke risk, in patients with T2DM and controls.

METHODS

In a 5-year cohort study, data from 45 patients and 59 non-diabetic controls were available for analysis. At baseline, participants had a mean (± SD) age of 59  ±  10 years and patients had a median (range) diabetes duration of 1.8 (0.8-3.2) years. PWV was obtained by tonometry and WMH volume by an automated segmentation algorithm based on cerebral T2-FLAIR and T1 MRI (corrected by intracranial volume, cWMH). High PWV was defined above 8.94 m/s (corresponding to the reference of high PWV above 10 m/s using the standardized path length method).

RESULTS

Patients with T2DM had a higher PWV than controls (8.8  ±  2.2 vs. 7.9  ±  1.4 m/s, p  <  0.01). WMH progression were similar in the two groups (p  =  0.5). One m/s increase in baseline PWV was associated with a 16% [95% CI 1-32%], p  <  0.05) increase in cWMH volume at 5 years follow-up after adjustment for age, sex, diabetes, pulse pressure and smoking. High PWV was associated with cWMH progression in the combined cohort (p  <  0.05). We found no interaction between diabetes and PWV on cWMH progression.

CONCLUSIONS

PWV is associated with cWMH progression in patients with type 2 diabetes and non-diabetic controls. Our results indicate that arterial stiffness may be involved early in the pathophysiology leading to cerebrovascular diseases.

Segmentation of MRI head anatomy using deep volumetric networks and multiple spatial priors

Purpose: Conventional automated segmentation of the head anatomy in magnetic resonance images distinguishes different brain and nonbrain tissues based on image intensities and prior tissue probability maps (TPMs). This works well for normal head anatomies but fails in the presence of unexpected lesions. Deep convolutional neural networks (CNNs) leverage instead spatial patterns and can learn to segment lesions but often ignore prior probabilities. Approach: We add three sources of prior information to a three-dimensional (3D) convolutional network, namely, spatial priors with a TPM, morphological priors with conditional random fields, and spatial context with a wider field-of-view at lower resolution. We train and test these networks on 3D images of 43 stroke patients and 4 healthy individuals which have been manually segmented. Results: We demonstrate the benefits of each source of prior information, and we show that the new architecture, which we call Multiprior network, improves the performance of existing segmentation software, such as SPM, FSL, and DeepMedic for abnormal anatomies. The relevance of the different priors was compared, and the TPM was found to be most beneficial. The benefit of adding a TPM is generic in that it can boost the performance of established segmentation networks such as the DeepMedic and a UNet. We also provide an out-of-sample validation and clinical application of the approach on an additional 47 patients with disorders of consciousness. We make the code and trained networks freely available. Conclusions: Biomedical images follow imaging protocols that can be leveraged as prior information into deep CNNs to improve performance. The network segmentations match human manual corrections performed in 3D and are comparable in performance to human segmentations obtained from scratch in 2D for abnormal brain anatomies.

Radius-optimized efficient template matching for lesion detection from brain images

Computer-aided detection of brain lesions from volumetric magnetic resonance imaging (MRI) is in demand for fast and automatic diagnosis of neural diseases. The template-matching technique can provide satisfactory outcome for automatic localization of brain lesions; however, finding the optimal template size that maximizes similarity of the template and the lesion remains challenging. This increases the complexity of the algorithm and the requirement for computational resources, while processing large MRI volumes with three-dimensional (3D) templates. Hence, reducing the computational complexity of template matching is needed. In this paper, we first propose a mathematical framework for computing the normalized cross-correlation coefficient (NCCC) as the similarity measure between the MRI volume and approximated 3D Gaussian template with linear time complexity, [Formula: see text], as opposed to the conventional fast Fourier transform (FFT) based approach with the complexity [Formula: see text], where [Formula: see text] is the number of voxels in the image and [Formula: see text] is the number of tried template radii. We then propose a mathematical formulation to analytically estimate the optimal template radius for each voxel in the image and compute the NCCC with the location-dependent optimal radius, reducing the complexity to [Formula: see text]. We test our methods on one synthetic and two real multiple-sclerosis databases, and compare their performances in lesion detection with FFT and a state-of-the-art lesion prediction algorithm. We demonstrate through our experiments the efficiency of the proposed methods for brain lesion detection and their comparable performance with existing techniques.

An exploratory study into the influence of laterality and location of hippocampal sclerosis on seizure prognosis and global cortical thinning

In mesial temporal lobe epilepsy (mTLE), the correlation between disease duration, seizure laterality, and rostro-caudal location of hippocampal sclerosis has not been examined in the context of seizure severity and global cortical thinning. In this retrospective study, we analyzed structural 3 T MRI from 35 mTLE subjects. Regions of FLAIR hyperintensity (as an indicator of sclerosis)—based on 2D coronal FLAIR sequences—in the hippocampus were manually segmented, independently and in duplicate; degree of segmentation agreement was confirmed using the DICE index. Segmented lesions were used for separate analyses. First, the correlation of cortical thickness with disease duration and seizure focus laterality was explored using linear model regression. Then, the relationship between the rostro-caudal location of the FLAIR hyperintense signal and seizure severity, based on the Cleveland Clinic seizure freedom score (ccSFS), was explored using probabilistic voxel-wise mapping and functional connectivity analysis from normative data. The mean DICE Index was 0.71 (range 0.60–0.81). A significant correlation between duration of epilepsy and decreased mean whole brain cortical thickness was identified, regardless of seizure laterality (p < 0.05). The slope of cortical volume loss over time, however, was greater in subjects with right seizure focus. Based on probabilistic voxel-wise mapping, FLAIR hyperintensity in the posterior hippocampus was significantly associated with lower ccSFS scores (greater seizure severity). Finally, the right hippocampus was found to have greater brain-wide connectivity, compared to the left side, based on normative connectomic data. We have demonstrated a significant correlation between duration of epilepsy and right-sided seizure focus with global cortical thinning, potentially due to greater brain-wide connectivity. Sclerosis along the posterior hippocampus was associated with greater seizure severity, potentially serving as an important biomarker of seizure outcome after surgery.

Cortical Thinning in the Medial Temporal Lobe and Precuneus Is Related to Cognitive Deficits in Patients With Subcortical Ischemic Vascular Disease

Subcortical ischemic vascular disease (SIVD) is a major cause of vascular cognitive impairment (CI) and features extensive atrophy in the cerebral cortex. We aimed to test the hypothesis that cognitive deficits in SIVD are linked to decreased cortical thickness in specific brain regions, which may constitute neuroimaging biomarkers of CI. Sixty-seven SIVD patients without (SIVD-NC, n = 35) and with (SIVD-CI, n = 32) CI and a group of healthy controls (HCs, n = 36) underwent structural magnetic resonance imaging (MRI) and cognitive functional assessments. FreeSurfer was used to preprocess structural MRI data and to calculate and compare cortical thickness. The correlation between cortical thickness and cognitive scores was examined in SIVD patients. Significantly altered cortical thickness in the bilateral insula, middle and inferior temporal lobes, precuneus, and medial temporal lobe (MTL) was identified among the three groups (p < 0.05, Monte Carlo simulation corrected). Post hoc results showed significantly decreased thickness in the bilateral insula and temporal lobe in SIVD-NC and SIVD-CI patients compared with HCs. However, the areas with reduced cortical thickness were larger in SIVD-CI than SIVD-NC patients. SIVD-CI patients had significantly reduced thickness in the bilateral precuneus and left MTL (Bonferroni corrected) compared with SIVD-NC patients when we extracted the mean thickness for each region of interest. In SIVD patients, the thicknesses of the left MTL and bilateral precuneus were positively correlated with immediate recall in the memory test. SIVD might lead to extensive cerebral cortical atrophy, while atrophy in the MTL and precuneus might be associated with memory deficits.

Lateralizing magnetic resonance imaging findings in mesial temporal sclerosis and correlation with seizure and neurocognitive outcome after temporal lobectomy

BACKGROUND

Mesial temporal sclerosis (MTS) is the most common cause of temporal lobe epilepsy (TLE). While MTS is associated with a high cure rate after temporal lobectomy (TL), postoperative neurocognitive deficits are common, and a subset of patients may continue to have refractory seizures.

OBJECTIVE

To use magnetic resonance (MR) volumetry to identify features of the mesial temporal lobe in patients with MTS that correlate with seizure and neurocognitive outcome after temporal lobectomy.

METHODS

Thirty-five patients with unilateral MTS, high-resolution MR imaging, and at least one year of postoperative assessments were retrospectively examined. Volumetric analysis of the hippocampus, parahippocampal gyrus (PHG) and FLAIR hyperintensity of the affected temporal lobe was performed. TL resections were manually segmented, and resection heat maps reflecting seizure outcome were produced. The degree of preoperative atrophy of the affected mesial structures relative to the unaffected side were related to preoperative and postoperative component scores of verbal and visuospatial memory as well as confrontation naming.

RESULTS

Greater FLAIR hyperintense volume was associated with favorable seizure outcome at one year and last follow-up. Resections extending most medial and posteriorly were associated with favorable seizure outcome. In patients with left MTS, less atrophy of the affected PHG was predictive of higher preoperative naming scores and greater postoperative naming deficit, while less hippocampal atrophy was predictive of higher preoperative verbal memory component scores.

CONCLUSION

Greater hippocampal FLAIR volume is associated with favorable surgical outcome. Hippocampal volume correlates with preoperative verbal memory, while PHG volume is implicated in confrontation naming ability.

Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema

OBJECTIVES

To evaluate for the first time the performance of a deep learning method based on no-new-Net for fully automated segmentation and volumetric measurements of intracerebral hemorrhage (ICH), intraventricular extension of intracerebral hemorrhage (IVH), and perihematomal edema (PHE) in primary ICH on CT.

METHODS

Three hundred and eighty primary ICH patients who underwent CT at hospital arrival were divided into a training cohort (n = 300) and a validation cohort (n = 80). An independent cohort with 80 patients was used for testing. Ground truth (segmentation masks) was manually generated by radiologists. Model performance on lesion segmentation and volumetric measurement of ICH, IVH, and PHE were evaluated by comparing the model results with the segmentations performed by radiologists.

RESULTS

In the test cohort, the Dice scores of lesion segmentation were 0.92, 0.79, and 0.71 for ICH, IVH, and PHE, respectively. The sensitivities were 0.93 for ICH, 0.88 for IVH, and 0.81 for PHE. The positive predictive values were 0.92, 0.76, and 0.69 for ICH, IVH, and PHE, respectively. Excellent concordance (concordance correlation coefficients [CCCs] ≥ 0.98) of ICH and IVH and good concordance of PHE (CCCs ≥ 0.92) were demonstrated between manually and automatically measured volumes. The model took approximately 15 s to provide automatic segmentation and volume analysis for each patient.

CONCLUSION

Our model demonstrates good reliability for automatic segmentation and volume measurement of ICH, IVH, and PHE in primary ICH, which can be useful to reduce the effort and time of doctors to calculate volumes of ICH, IVH, and PHE.

KEY POINTS

• Deep learning algorithms can provide automatic and reliable assessment of intracerebral hemorrhage, intraventricular hemorrhage, and perihematomal edema on CT. • Non-contrast CT-based deep learning method can be helpful to provide efficient and accurate measurements of ICH, IVH, and PHE in primary ICH patients, thereby reducing the effort and time of doctors to segment and calculate volumes of ICH, IVH, and PHE in primary ICH patients.

Biomimetic phantom with anatomical accuracy for evaluating brain volumetric measurements with magnetic resonance imaging

Purpose: Brain image volumetric measurements (BVM) methods have been used to quantify brain tissue volumes using magnetic resonance imaging (MRI) when investigating abnormalities. Although BVM methods are widely used, they need to be evaluated to quantify their reliability. Currently, the gold-standard reference to evaluate a BVM is usually manual labeling measurement. Manual volume labeling is a time-consuming and expensive task, but the confidence level ascribed to this method is not absolute. We describe and evaluate a biomimetic brain phantom as an alternative for the manual validation of BVM. Methods: We printed a three-dimensional (3D) brain mold using an MRI of a three-year-old boy diagnosed with Sturge-Weber syndrome. Then we prepared three different mixtures of styrene-ethylene/butylene-styrene gel and paraffin to mimic white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The mold was filled by these three mixtures with known volumes. We scanned the brain phantom using two MRI scanners, 1.5 and 3.0 Tesla. Our suggestion is a new challenging model to evaluate the BVM which includes the measured volumes of the phantom compartments and its MRI. We investigated the performance of an automatic BVM, i.e., the expectation-maximization (EM) method, to estimate its accuracy in BVM. Results: The automatic BVM results using the EM method showed a relative error (regarding the phantom volume) of 0.08, 0.03, and 0.13 ( ± 0.03 uncertainty) percentages of the GM, CSF, and WM volume, respectively, which was in good agreement with the results reported using manual segmentation. Conclusions: The phantom can be a potential quantifier for a wide range of segmentation methods.

Frontal White Matter Hyperintensities and Executive Functioning Performance in Older Adults

Frontal lobe structures decline faster than most other brain regions in older adults. Age-related change in the frontal lobe is associated with poorer executive function (e.g., working memory, switching/set-shifting, and inhibitory control). The effects and presence of frontal lobe white matter hyperintensities (WMH) on executive function in normal aging is relatively unknown. The current study assessed relationships between region-specific frontal WMH load and cognitive performance in healthy older adults using three executive function tasks from the NIH Toolbox (NIHTB) Cognition Battery. A cohort of 279 healthy older adults ages 65-88 completed NIHTB and 3T T1-weighted and FLAIR MRI. Lesion Segmentation Toolbox quantified WMH volume and generated lesion probability maps. Individual lesion maps were registered to the Desikan-Killiany atlas in FreeSurfer 6.0 to define regions of interest (ROI). Independent linear regressions assessed relationships between executive function performance and region-specific WMH in frontal lobe ROIs. All models included age, sex, education, estimated total intracranial volume, multi-site scanner differences, and cardiovascular disease risk using Framingham criteria as covariates. Poorer set-shifting performance was associated with greater WMH load in three frontal ROIs including bilateral superior frontal (left β = -0.18, FDR-p = 0.02; right β = -0.20, FDR-p = 0.01) and right medial orbitofrontal (β = -0.17, FDR-p = 0.02). Poorer inhibitory performance associated with higher WMH load in one frontal ROI, the right superior frontal (right β = -0.21, FDR-p = 0.01). There were no significant associations between working memory and WMH in frontal ROIs. Our study demonstrates that location and pattern of frontal WMH may be important to assess when examining age-related differences in cognitive functions involving switching/set-shifting and inhibition. On the other hand, working memory performance was not related to presence of frontal WMH in this sample. These data suggest that WMH may contribute selectively to age-related declines in executive function. Findings emerged beyond predictors known to be associated with WMH presence, including age and cardiovascular disease risk. The spread of WMH within the frontal lobes may play a key role in the neuropsychological profile of cognitive aging. Further research should explore whether early intervention on modifiable vascular factors or cognitive interventions targeted for executive abilities may help mitigate the effect of frontal WMH on executive function.

Accuracy and reproducibility of automated white matter hyperintensities segmentation with lesion segmentation tool: A European multi-site 3T study

Brain vascular damage accumulate in aging and often manifest as white matter hyperintensities (WMHs) on MRI. Despite increased interest in automated methods to segment WMHs, a gold standard has not been achieved and their longitudinal reproducibility has been poorly investigated. The aim of present work is to evaluate accuracy and reproducibility of two freely available segmentation algorithms. A harmonized MRI protocol was implemented in 3T-scanners across 13 European sites, each scanning five volunteers twice (test-retest) using 2D-FLAIR. Automated segmentation was performed using Lesion segmentation tool algorithms (LST): the Lesion growth algorithm (LGA) in SPM8 and 12 and the Lesion prediction algorithm (LPA). To assess reproducibility, we applied the LST longitudinal pipeline to the LGA and LPA outputs for both the test and retest scans. We evaluated volumetric and spatial accuracy comparing LGA and LPA with manual tracing, and for reproducibility the test versus retest. Median volume difference between automated WMH and manual segmentations (mL) was -0.22[IQR = 0.50] for LGA-SPM8, -0.12[0.57] for LGA-SPM12, -0.09[0.53] for LPA, while the spatial accuracy (Dice Coefficient) was 0.29[0.31], 0.33[0.26] and 0.41[0.23], respectively. The reproducibility analysis showed a median reproducibility error of 20%[IQR = 41] for LGA-SPM8, 14% [31] for LGA-SPM12 and 10% [27] with the LPA cross-sectional pipeline. Applying the LST longitudinal pipeline, the reproducibility errors were considerably reduced (LGA: 0%[IQR = 0], p < 0.001; LPA: 0% [3], p < 0.001) compared to those derived using the cross-sectional algorithms. The DC using the longitudinal pipeline was excellent (median = 1) for LGA [IQR = 0] and LPA [0.02]. LST algorithms showed moderate accuracy and good reproducibility. Therefore, it can be used as a reliable cross-sectional and longitudinal tool in multi-site studies.

Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3D convolutional neural networks

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A fully automated segmentation of new or enlarged multiple sclerosis (MS) lesions.

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3D convolutional neural network (CNN) with U-net-like encoder-decoder architecture.

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Simultaneous processing of baseline and follow-up scan of the same patient.

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Trained on 3253 patient data from over 103 different MR scanners.

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Fast (<1min), robust algorithm with segmentation results in inter-rater variability.

The quantification of new or enlarged lesions from follow-up MRI scans is an important surrogate of clinical disease activity in patients with multiple sclerosis (MS). Not only is manual segmentation time consuming, but inter-rater variability is high. Currently, only a few fully automated methods are available. We address this gap in the field by employing a 3D convolutional neural network (CNN) with encoder-decoder architecture for fully automatic longitudinal lesion segmentation.

Input data consist of two fluid attenuated inversion recovery (FLAIR) images (baseline and follow-up) per patient. Each image is entered into the encoder and the feature maps are concatenated and then fed into the decoder. The output is a 3D mask indicating new or enlarged lesions (compared to the baseline scan). The proposed method was trained on 1809 single point and 1444 longitudinal patient data sets and then validated on 185 independent longitudinal data sets from two different scanners. From the two validation data sets, manual segmentations were available from three experienced raters, respectively. The performance of the proposed method was compared to the open source Lesion Segmentation Toolbox (LST), which is a current state-of-art longitudinal lesion segmentation method.

The mean lesion-wise inter-rater sensitivity was 62%, while the mean inter-rater number of false positive (FP) findings was 0.41 lesions per case. The two validated algorithms showed a mean sensitivity of 60% (CNN), 46% (LST) and a mean FP of 0.48 (CNN), 1.86 (LST) per case. Sensitivity and number of FP were not significantly different (p < 0.05) between the CNN and manual raters.

New or enlarged lesions counted by the CNN algorithm appeared to be comparable with manual expert ratings. The proposed algorithm seems to outperform currently available approaches, particularly LST. The high inter-rater variability in case of manual segmentation indicates the complexity of identifying new or enlarged lesions. An automated CNN-based approach can quickly provide an independent and deterministic assessment of new or enlarged lesions from baseline to follow-up scans with acceptable reliability.