Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks.

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.

Deep learning for automatic brain tumour segmentation on MRI: evaluation of recommended reporting criteria via a reproduction and replication study.

OBJECTIVES: To determine the reproducibility and replicability of studies that develop and validate segmentation methods for brain tumours on MRI and that follow established reproducibility criteria; and to evaluate whether the reporting guidelines are sufficient. METHODS: Two eligible validation studies of distinct deep learning (DL) methods were identified. We implemented the methods using published information and retraced the reported validation steps. We evaluated to what extent the description of the methods enabled reproduction of the results. We further attempted to replicate reported findings on a clinical set of images acquired at our institute consisting of high-grade and low-grade glioma (HGG, LGG), and meningioma (MNG) cases. RESULTS: We successfully reproduced one of the two tumour segmentation methods. Insufficient description of the preprocessing pipeline and our inability to replicate the pipeline resulted in failure to reproduce the second method. The replication of the first method showed promising results in terms of Dice similarity coefficient (DSC) and sensitivity (Sen) on HGG cases (DSC=0.77, Sen=0.88) and LGG cases (DSC=0.73, Sen=0.83), however, poorer performance was observed for MNG cases (DSC=0.61, Sen=0.71). Preprocessing errors were identified that contributed to low quantitative scores in some cases. CONCLUSIONS: Established reproducibility criteria do not sufficiently emphasise description of the preprocessing pipeline. Discrepancies in preprocessing as a result of insufficient reporting are likely to influence segmentation outcomes and hinder clinical utilisation. A detailed description of the whole processing chain, including preprocessing, is thus necessary to obtain stronger evidence of the generalisability of DL-based brain tumour segmentation methods and to facilitate translation of the methods into clinical practice.

Multimodal Image Analysis of Apparent Brain Age Identifies Physical Fitness as Predictor of Brain Maintenance.

Maintaining a youthful brain structure and function throughout life may be the single most important determinant of successful cognitive aging. In this study, we addressed heterogeneity in brain aging by making image-based brain age predictions and relating the brain age prediction gap (BAPG) to cognitive change in aging. Structural, functional, and diffusion MRI scans from 351 participants were used to train and evaluate 5 single-modal and 4 multimodal prediction models, based on 7 regression methods. The models were compared on mean absolute error and whether they were related to physical fitness and cognitive ability, measured both currently and longitudinally, as well as study attrition and years of education. Multimodal prediction models performed at a similar level as single-modal models, and the choice of regression method did not significantly affect the results. Correlation with the BAPG was found for current physical fitness, current cognitive ability, and study attrition. Correlations were also found for retrospective physical fitness, measured 10 years prior to imaging, and slope for cognitive ability during a period of 15 years. The results suggest that maintaining a high physical fitness throughout life contributes to brain maintenance and preserved cognitive ability.

Mental fatigue in stress-related exhaustion disorder: Structural brain correlates, clinical characteristics and relations with cognitive functioning.

Emerging evidence suggests that mental fatigue is a central component of the cognitive and clinical characteristics of stress-related exhaustion disorder (ED). Yet, the underlying mechanisms of mental fatigue in this patient group are poorly understood. The aim of this study was to investigate cortical and subcortical structural neural correlates of mental fatigue in patients with ED, and to explore the association between mental fatigue and cognitive functioning. Fifty-five patients with clinical ED diagnosis underwent magnetic resonance imaging. Mental fatigue was assessed using the Concentration subscale from the Checklist Individual Strength. Patients with high levels of mental fatigue (n = 30) had smaller caudate and putamen volumes compared to patients with low-moderate levels of mental fatigue (n = 25). No statistically significant differences in cortical thickness were observed between the groups. Mediation analysis showed that mental fatigue mediated the relationship between caudate volume and working memory; specifically, smaller caudate volume was associated with higher level of mental fatigue and mental fatigue was positively associated with working memory performance. Our findings demonstrate that the structural integrity of the striatum is of relevance for the subjective perception of mental fatigue in ED, while also highlighting the complex relationship between mental fatigue, cognitive performance and its neural underpinnings.

Assessment of Cerebral Blood Flow Pulsatility and Cerebral Arterial Compliance With 4D Flow MRI.

BACKGROUND: Four-dimensional flow magnetic resonance imaging (4D flow MRI) enables efficient investigation of cerebral blood flow pulsatility in the cerebral arteries. This is important for exploring hemodynamic mechanisms behind vascular diseases associated with arterial pulsations. PURPOSE: To investigate the feasibility of pulsatility assessments with 4D flow MRI, its agreement with reference two-dimensional phase-contrast MRI (2D PC-MRI) measurements, and to demonstrate how 4D flow MRI can be used to assess cerebral arterial compliance and cerebrovascular resistance in major cerebral arteries. STUDY TYPE: Prospective. SUBJECTS: Thirty-five subjects (20 women, 79 ± 5 years, range 70-91 years). FIELD STRENGTH/SEQUENCE: 4D flow MRI (PC-VIPR) and 2D PC-MRI acquired with a 3T scanner. ASSESSMENT: Time-resolved flow was assessed in nine cerebral arteries. From the pulsatile flow waveform in each artery, amplitude (ΔQ), volume load (ΔV), and pulsatility index (PI) were calculated. To reduce high-frequency noise in the 4D flow MRI data, the flow waveforms were low-pass filtered. From the total cerebral blood flow, total PI (PItot ), total volume load (ΔVtot ), cerebral arterial compliance (C), and cerebrovascular resistance (R) were calculated. STATISTICAL TESTS: Two-tailed paired t-test, intraclass correlation (ICC). RESULTS: There was no difference in ΔQ between 4D flow MRI and the reference (0.00 ± 0.022 ml/s, mean ± SEM, P = 0.97, ICC = 0.95, n = 310) with a cutoff frequency of 1.9 Hz and 15 cut plane long arterial segments. For ΔV, the difference was -0.006 ± 0.003 ml (mean ± SEM, P = 0.07, ICC = 0.93, n = 310) without filtering. Total R was 11.4 ± 2.41 mmHg/(ml/s) (mean ± SD) and C was 0.021 ± 0.009 ml/mmHg (mean ± SD). ΔVtot was 1.21 ± 0.29 ml (mean ± SD) with an ICC of 0.82 compared with the reference. PItot was 1.08 ± 0.21 (mean ± SD). DATA CONCLUSION: We successfully assessed 4D flow MRI cerebral arterial pulsatility, cerebral arterial compliance, and cerebrovascular resistance. Averaging of multiple cut planes and low-pass filtering was necessary to assess accurate peak-to-peak features in the flow rate waveforms. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1516-1525.

Accuracy of blood flow assessment in cerebral arteries with 4D flow MRI: Evaluation with three segmentation methods.

BACKGROUND: Accelerated 4D flow MRI allows for high-resolution velocity measurements with whole-brain coverage. Such scans are increasingly used to calculate flow rates of individual arteries in the vascular tree, but detailed information about the accuracy and precision in relation to different postprocessing options is lacking. PURPOSE: To evaluate and optimize three proposed segmentation methods and determine the accuracy of in vivo 4D flow MRI blood flow rate assessments in major cerebral arteries, with high-resolution 2D PCMRI as a reference. STUDY TYPE: Prospective. SUBJECTS: Thirty-five subjects (20 women, 79 ± 5 years, range 70-91 years). FIELD STRENGTH/SEQUENCE: 4D flow MRI with PC-VIPR and 2D PCMRI acquired with a 3 T scanner. ASSESSMENT: We compared blood flow rates measured with 4D flow MRI, to the reference, in nine main cerebral arteries. Lumen segmentation in the 4D flow MRI was performed with k-means clustering using four different input datasets, and with two types of thresholding methods. The threshold was defined as a percentage of the maximum intensity value in the complex difference image. Local and global thresholding approaches were used, with evaluated thresholds from 6-26%. STATISTICAL TESTS: Paired t-test, F-test, linear correlation (P < 0.05 was considered significant) along with intraclass correlation (ICC). RESULTS: With the thresholding methods, the lowest average flow difference was obtained for 20% local (0.02 ± 15.0 ml/min, ICC = 0.97, n = 310) or 10% global (0.08 ± 17.3 ml/min, ICC = 0.97, n = 310) thresholding with a significant lower standard deviation for local (F-test, P = 0.01). For all clustering methods, we found a large systematic underestimation of flow compared with 2D PCMRI (16.1-22.3 ml/min). DATA CONCLUSION: A locally adapted threshold value gives a more stable result compared with a globally fixed threshold. 4D flow with the proposed segmentation method has the potential to become a useful reliable clinical tool for assessment of blood flow in the major cerebral arteries. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:511-518.

A Stereotactic Probabilistic Atlas for the Major Cerebral Arteries.

Improved whole brain angiographic and velocity-sensitive MRI is pushing the boundaries of noninvasively obtained cerebral vascular flow information. The complexity of the information contained in such datasets calls for automated algorithms and pipelines, thus reducing the need of manual analyses by trained radiologists. The objective of this work was to lay the foundation for such automated pipelining by constructing and evaluating a probabilistic atlas describing the shape and location of the major cerebral arteries. Specifically, we investigated how the implementation of a non-linear normalization into Montreal Neurological Institute (MNI) space improved the alignment of individual arterial branches. In a population-based cohort of 167 subjects, age 64-68 years, we performed 4D flow MRI with whole brain volumetric coverage, yielding both angiographic and anatomical data. For each subject, sixteen cerebral arteries were manually labeled to construct the atlas. Angiographic data were normalized to MNI space using both rigid-body and non-linear transformations obtained from anatomical images. The alignment of arterial branches was significantly improved by the non-linear normalization (p < 0.001). Validation of the atlas was based on its applicability in automatic arterial labeling. A leave-one-out validation scheme revealed a labeling accuracy of 96 %. Arterial labeling was also performed in a separate clinical sample (n = 10) with an accuracy of 92.5 %. In conclusion, using non-linear spatial normalization we constructed an artery-specific probabilistic atlas, useful for cerebral arterial labeling.

Automatic labeling of cerebral arteries in magnetic resonance angiography.

OBJECTIVES: In order to introduce 4D flow magnetic resonance imaging (MRI) as a standard clinical instrument for studying the cerebrovascular system, new and faster postprocessing tools are necessary. The objective of this study was to construct and evaluate a method for automatic identification of individual cerebral arteries in a 4D flow MRI angiogram. MATERIALS AND METHODS: Forty-six elderly individuals were investigated with 4D flow MRI. Fourteen main cerebral arteries were manually labeled and used to create a probabilistic atlas. An automatic atlas-based artery identification method (AAIM) was developed based on vascular-branch extraction and the atlas was used for identification. The method was evaluated by comparing automatic with manual identification in 4D flow MRI angiograms from 67 additional elderly individuals. RESULTS: Overall accuracy was 93%, and internal carotid artery and middle cerebral artery labeling was 100% accurate. Smaller and more distal arteries had lower accuracy; for posterior communicating arteries and vertebral arteries, accuracy was 70 and 89%, respectively. CONCLUSION: The AAIM enabled fast and fully automatic labeling of the main cerebral arteries. AAIM functionality provides the basis for creating an automatic and powerful method to analyze arterial cerebral blood flow in clinical routine.