White matter hyperintensity shape and location feature analysis on brain MRI; proof of principle study in patients with diabetes

Neuro Imaging Researc

The Impact of Strategic White Matter Hyperintensity Lesion Location on Language

Objective: The impact of white matter hyperintensities (WMH) on language possibly depends on lesion location through disturbance of strategic white matter tracts. We examined the impact of WMH location on language in elderly Asians. Design: Cross-sectional. Setting: Population-based. Participants: Eight-hundred nineteen residents of Singapore, ages (≥65 years). Measurements: Clinical, cognitive and 3T magnetic resonance imaging assessments were performed on all participants. Language was assessed using the Modified Boston Naming Test (MBNT) and Verbal Fluency (VF). Hypothesis-free region-of-interest-based (ROI) analyses based on major white matter tracts were used to determine the association between WMH location and language. Conditional dependencies between the regional WMH volumes and language were examined using Bayesian-network analysis. Results: ROI-based analyses showed that WMH located within the anterior thalamic radiation (mean difference: −0.12, 95% confidence interval [CI]: −0.22; −0.02, p = 0.019) and uncinate fasciculus (mean difference: −0.09, 95% CI: −0.18; −0.01, p = 0.022) in the left hemisphere were significantly associated with worse VF but did not survive multiple testing. Conversely, WMH volume in the left cingulum of cingulate gyrus was significantly associated with MBNT performance (mean difference: −0.09, 95% CI: −0.17; −0.02, p = 0.016). Bayesian-network analyses confirmed the left cingulum of cingulate gyrus as a direct determinant of MBNT performance. Conclusion: Our findings identify the left cingulum of cingulate gyrus as a strategic white matter tract for MBNT, suggesting that language – is sensitive to subcortical ischemic damage. Future studies on the role of sporadic ischemic lesions and vascular cognitive impairment should not only focus on total WMH volume but should also take WMH lesion location into account when addressing language

Cortical microinfarcts in memory clinic patients are associated with reduced cerebral perfusion

Cerebral cortical microinfarcts (CMIs) are small ischemic lesions associated with cognitive impairment and dementia. CMIs are frequently observed in cortical watershed areas suggesting that hypoperfusion contributes to their development. We investigated if presence of CMIs was related to a decrease in cerebral perfusion, globally or specifically in cortex surrounding CMIs. In 181 memory clinic patients (mean age 72 ± 9 years, 51% male), CMI presence was rated on 3-T magnetic resonance imaging (MRI). Cerebral perfusion was assessed from cortical gray matter of the anterior circulation using pseudo-continuous arterial spin labeling parameters cerebral blood flow (CBF) (perfusion in mL blood/100 g tissue/min) and spatial coefficient of variation (CoV) (reflecting arterial transit time (ATT)). Patients with CMIs had a 12% lower CBF (beta = −.20) and 22% higher spatial CoV (beta =.20) (both p <.05) without a specific regional pattern on voxel-based CBF analysis. CBF in a 2 cm region-of-interest around the CMIs did not differ from CBF in a reference zone in the contralateral hemisphere. These findings show that CMIs in memory clinic patients are primarily related to global reductions in cerebral perfusion, thus shedding new light on the etiology of vascular brain injury in dementia

How Do Different Forms of Vascular Brain Injury Relate to Cognition in a Memory Clinic Population: The TRACE-VCI Study

Background: Memory clinic patients frequently present with different forms of vascular brain injury due to different etiologies, often co-occurring with Alzheimer’s disease (AD) pathology. / Objective: We studied how cognition was affected by different forms of vascular brain injury, possibly in interplay with AD pathology. / Methods: We included 860 memory clinic patients with vascular brain injury on magnetic resonance imaging (MRI), receiving a standardized evaluation including cerebrospinal fluid (CSF) biomarker analyses (n = 541). The cognitive profile of patients with different forms of vascular brain injury on MRI (moderate/severe white matter hyperintensities (WMH) (n = 398), microbleeds (n = 368), lacunar (n = 188) and non-lacunar (n = 96) infarct(s), macrobleeds (n = 16)) was assessed by: 1) comparison of all these different forms of vascular brain injury with a reference group (patients with only mild WMH (n = 205) without other forms of vascular brain injury), using linear regression analyses also stratified for CSF biomarker AD profile and 2) multivariate linear regression analysis. / Results: The cognitive profile was remarkably similar across groups. Compared to the reference group effect sizes on all domains were <0.2 with narrow 95% confidence intervals, except for non-lacunar infarcts on information processing speed (age, sex, and education adjusted mean difference from reference group (β: – 0.26, p = 0.05). Results were similar in the presence (n = 300) or absence (n = 241) of biomarker co-occurring AD pathology. In multivariate linear regression analysis, higher WMH burden was related to a slightly worse performance on attention and executive functioning (β: – 0.08, p = 0.02) and working memory (β: – 0.08, p = 0.04). / Conclusion: Although different forms of vascular brain injury have different etiologies and different patterns of cerebral damage, they show a largely similar cognitive profile in memory clinic patients regardless of co-occurring AD pathology

The Meta VCI Map consortium for meta-analyses on strategic lesion locations for vascular cognitive impairment using lesion-symptom mapping: design and multicenter pilot study

Introduction: The Meta VCI Map consortium performs meta-analyses on strategic lesion locations for vascular cognitive impairment using lesion-symptom mapping. Integration of data from different cohorts will increase sample sizes, to improve brain lesion coverage and support comprehensive lesion-symptom mapping studies. Methods: Cohorts with available imaging on white matter hyperintensities or infarcts and cognitive testing were invited. We performed a pilot study to test the feasibility of multicenter data processing and analysis and determine the benefits to lesion coverage. Results: Forty-seven groups have joined Meta VCI Map (stroke n = 7800 patients; memory clinic n = 4900; population-based n = 14,400). The pilot study (six ischemic stroke cohorts, n = 878) demonstrated feasibility of multicenter data integration (computed tomography/magnetic resonance imaging) and achieved marked improvement of lesion coverage. Discussion: Meta VCI Map will provide new insights into the relevance of vascular lesion location for cognitive dysfunction. After the successful pilot study, further projects are being prepared. Other investigators are welcome to join

Image processing techniques for quantification and assessment of brain MRI

Magnetic resonance imaging (MRI) is a widely used technique to acquire digital images of the human brain. A variety of acquisition protocols is available to generate images in vivo and noninvasively, giving great opportunities to study the anatomy and physiology of the human brain. In my thesis, image processing techniques for quantification and assessment of these images are discussed. These techniques are applied in the context of brain anatomy and pathology, in particular small vessel disease. Advances in MR imaging have led to a tremendous increase in the amount of data and level of detail that can be acquired. This causes manual assessment of images to become increasingly difficult and time-consuming, thereby threatening the quality of such assessments. Image processing techniques are indispensable to human observers in achieving the best possible results. With the use of 7T MRI, cerebral microbleeds can be visualized on gradient echo images. Owing to their small size, manual detection is time-consuming, rater-dependent, and has a limited robustness and reproducibility. In my thesis, a new method to determine the quality of manual microbleed detection is presented, which is suited for images acquired at 7T and for patients that have multiple microbleeds. Since manual detection is difficult, semi-automated detection is likely to improve the quality of ratings. In my thesis, a semi-automated detection technique for microbleeds is presented. This technique detects potential microbleed locations and its findings are presented to a human observer for the final identification of true microbleeds. By using this technique, the sensitivity and quality of microbleed detection increased and the required human observer time was reduced. The most important finding was the detection of extra microbleeds that were initially missed by human observers, but were confirmed as true microbleeds. This demonstrates the high difficulty involved with manual microbleed detection and stresses the importance of robust and reliable (semi-)automated techniques. A similar semi-automated approach was applied to the detection of cortical cerebral microinfarcts, also known as the “invisible lesion”. Microinfarcts receive high interest, because of their relation with cerebrovascular disease and dementia. Recently, microinfarcts were visualized with 7 T MRI. Manual detection of microinfarcts required approximately 30 to 60 min per patient. A semi-automatic detection technique is presented in this thesis that reduced the required rating time to 5-20 min. Next to this, extra microinfarcts were detected that were initially missed by the human observer. Fully automated image processing techniques were applied to extract the midsagittal plane from brain MR images. The midsagittal plane separates the two hemispheres and is used for many analyses that compare one hemisphere with the other, as well as a preprocessing step for further image processing. Multiple techniques to extract the midsagittal plane were evaluated and compared to manual delineations. In addition, a technique to extract the (curved) midsagittal surface is presented. Since the interhemispheric fissure that separates the two hemispheres is not exactly planar, a plane is not a correct separation. The midsagittal surface captures the natural shape of the interhemispheric fissure and provides a better separation

Automatic classification of focal liver lesions based on clinical DCE-MR and T2-weighted images: a feasibility study

Focal liver lesion classification is an important part of diagnostics. In clinical practice, T2-weighted (T2W) and dynamic contrast enhanced (DCE) MR images are used to determine the type of lesion. For automatic liver lesion classification only T2W images are exploited. In this feasibility study, a multi-modal approach for automatic lesion classification of five lesion classes (adenoma, cyst, haemangioma, HCC, and metastasis) is studied. Features are derived from four sets: (A) non-corrected, and (B) motion corrected DCE-MRI, (C) T2W images, and (D) B+C combined, originating from 43 patients. An extremely randomized forest is used as classifier. The results show that motion corrected DCE-MRI features are a valuable addition to the T2W features, and improve the accuracy in discriminating benign and malignant lesions, as well as the classification of the five lesion classes. The multimodal approach shows promising results for an automatic liver lesion classification

Computer assisted analysis of lung tumor regression during radiotherapy

International audienc

Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI

Automatic segmentation of brain tissues and white matter hyperintensities of presumed vascular origin (WMH) in MRI of older patients is widely described in the literature. Although brain abnormalities and motion artefacts are common in this age group, most segmentation methods are not evaluated in a setting that includes these items. In the present study, our tissue segmentation method for brain MRI was extended and evaluated for additional WMH segmentation. Furthermore, our method was evaluated in two large cohorts with a realistic variation in brain abnormalities and motion artefacts. The method uses a multi-scale convolutional neural network with a T1-weighted image, a T2-weighted fluid attenuated inversion recovery (FLAIR) image and a T1-weighted inversion recovery (IR) image as input. The method automatically segments white matter (WM), cortical grey matter (cGM), basal ganglia and thalami (BGT), cerebellum (CB), brain stem (BS), lateral ventricular cerebrospinal fluid (lvCSF), peripheral cerebrospinal fluid (pCSF), and WMH. Our method was evaluated quantitatively with images publicly available from the MRBrainS13 challenge (n = 20), quantitatively and qualitatively in relatively healthy older subjects (n = 96), and qualitatively in patients from a memory clinic (n = 110). The method can accurately segment WMH (Overall Dice coefficient in the MRBrainS13 data of 0.67) without compromising performance for tissue segmentations (Overall Dice coefficients in the MRBrainS13 data of 0.87 for WM, 0.85 for cGM, 0.82 for BGT, 0.93 for CB, 0.92 for BS, 0.93 for lvCSF, 0.76 for pCSF). Furthermore, the automatic WMH volumes showed a high correlation with manual WMH volumes (Spearman's ρ = 0.83 for relatively healthy older subjects). In both cohorts, our method produced reliable segmentations (as determined by a human observer) in most images (relatively healthy/memory clinic: tissues 88%/77% reliable, WMH 85%/84% reliable) despite various degrees of brain abnormalities and motion artefacts. In conclusion, this study shows that a convolutional neural network-based segmentation method can accurately segment brain tissues and WMH in MR images of older patients with varying degrees of brain abnormalities and motion artefacts