Automated detection of cerebral microbleeds in patients with Traumatic Brain Injury

Evaluating brain volume segmentation accuracy and reliability of FreeSurfer and Neurophet AQUA at variations in MRI magnetic field strengths

We aimed to compare the accuracy and reliability of two segmentation tools for magnetic resonance (MR) volumetry (FreeSurfer and Neurophet AQUA) at two magnetic field strengths (1.5T and 3T). We included 101 patients for the 1.5T-3T dataset and 112 for the 3T-3T dataset from three hospitals and five open-source datasets. The mean volume difference and average volume difference percentage with the change in magnetic field strength were compared between the methods. The hippocampus volume was larger with FreeSurfer than the Neurophet AQUA. In most brain regions, the Neurophet AQUA yielded a smaller average volume difference percentage (all < 10%) than FreeSurfer (all > 10%). The Neurophet AQUA exhibited more stable connectivity and regularity of the segmented components. Regarding volume, the Neurophet AQUA had effect sizes and ICCs comparable to those of FreeSurfer across the magnetic field strengths. With FreeSurfer, the original volume difference was small, whereas the average volume difference percentage was small with the Neurophet AQUA. Image segmentation took 1 h with FreeSurfer and 5 min with the Neurophet AQUA. When choosing an automatic segmentation method, the differences in image processing time and volume variability due to changes in the magnetic field strength of these methods should be considered.

Real-world application of a 3D deep learning model for detecting and localizing cerebral microbleeds

BACKGROUND

Detection and localization of cerebral microbleeds (CMBs) is crucial for disease diagnosis and treatment planning. However, CMB detection is labor-intensive, time-consuming, and challenging owing to its visual similarity to mimics. This study aimed to validate the performance of a three-dimensional (3D) deep learning model that not only detects CMBs but also identifies their anatomic location in real-world settings.

METHODS

A total of 21 patients with 116 CMBs and 12 without CMBs were visited in the neurosurgery outpatient department between January 2023 and October 2023. Three readers, including a board-certified neuroradiologist (reader 1), a resident in radiology (reader 2), and a neurosurgeon (reader 3) independently reviewed SWIs of 33 patients to detect CMBs and categorized their locations into lobar, deep, and infratentorial regions without any AI assistance. After a one-month washout period, the same datasets were redistributed randomly, and readers reviewed them again with the assistance of the 3D deep learning model. A comparison of the diagnostic performance between readers with and without AI assistance was performed.

RESULTS

All readers with an AI assistant (reader 1:0.991 [0.930-0.999], reader 2:0.922 [0.881-0.905], and reader 3:0.966 [0.928-0.984]) tended to have higher sensitivity per lesion than readers only (reader 1:0.905 [0.849-0.942], reader 2:0.621 [0.541-0.694], and reader 3:0.871 [0.759-0.935], p = 0.132, 0.017, and 0.227, respectively). In particular, radiology residents (reader 2) showed a statistically significant increase in sensitivity per lesion when using AI. There was no statistically significant difference in the number of FPs per patient for all readers with AI assistant (reader 1: 0.394 [0.152-1.021], reader 2: 0.727 [0.334-1.582], reader 3: 0.182 [0.077-0.429]) and reader only (reader 1: 0.364 [0.159-0.831], reader 2: 0.576 [0.240-1.382], reader 3: 0.121 [0.038-0.383], p = 0.853, 0.251, and 0.157, respectively). Our model accurately categorized the anatomical location of all CMBs.

CONCLUSIONS

Our model demonstrated promising potential for the detection and anatomical localization of CMBs, although further research with a larger and more diverse population is necessary to establish clinical utility in real-world settings.

An Efficient CNN-Based Method for Intracranial Hemorrhage Segmentation from Computerized Tomography Imaging

Intracranial hemorrhage (ICH) resulting from traumatic brain injury is a serious issue, often leading to death or long-term disability if not promptly diagnosed. Currently, doctors primarily use Computerized Tomography (CT) scans to detect and precisely locate a hemorrhage, typically interpreted by radiologists. However, this diagnostic process heavily relies on the expertise of medical professionals. To address potential errors, computer-aided diagnosis systems have been developed. In this study, we propose a new method that enhances the localization and segmentation of ICH lesions in CT scans by using multiple images created through different data augmentation techniques. We integrate residual connections into a U-Net-based segmentation network to improve the training efficiency. Our experiments, based on 82 CT scans from traumatic brain injury patients, validate the effectiveness of our approach, achieving an IOU score of 0.807 ± 0.03 for ICH segmentation using 10-fold cross-validation.

Naïve Bayes classifier assisted automated detection of cerebral microbleeds in susceptibility-weighted imaging brain images

Cerebral microbleeds (CMBs) in the brain are the essential indicators of critical brain disorders such as dementia and ischemic stroke. Generally, CMBs are detected manually by experts, which is an exhaustive task with limited productivity. Since CMBs have complex morphological nature, manual detection is prone to errors. This paper presents a machine learning-based automated CMB detection technique in the brain susceptibility-weighted imaging (SWI) scans based on statistical feature extraction and classification. The proposed method consists of three steps: (1) removal of the skull and extraction of the brain; (2) thresholding for the extraction of initial candidates; and (3) extracting features and applying classification models such as random forest and naïve Bayes classifiers for the detection of true positive CMBs. The proposed technique is validated on a dataset consisting of 20 subjects. The dataset is divided into training data that consist of 14 subjects with 104 microbleeds and testing data that consist of 6 subjects with 63 microbleeds. We were able to achieve 85.7% sensitivity using the random forest classifier with 4.2 false positives per CMB, and the naïve Bayes classifier achieved 90.5% sensitivity with 5.5 false positives per CMB. The proposed technique outperformed many state-of-the-art methods proposed in previous studies.

Deep learning-assisted IoMT framework for cerebral microbleed detection

The Internet of Things (IoT), big data, and artificial intelligence (AI) are all key technologies that influence the formation and implementation of digital medical services. Building Internet of Medical Things (IoMT) systems that combine advanced sensors with AI-powered insights is critical for intelligent medical systems. This paper presents an IoMT framework for brain magnetic resonance imaging (MRI) analysis to lessen the unavoidable diagnosis and therapy faults that occur in human clinical settings for the accurate detection of cerebral microbleeds (CMBs). The problems in accurate CMB detection include that CMBs are tiny dots 5-10 mm in diameter; they are similar to healthy tissues and are exceedingly difficult to identify, necessitating specialist guidance in remote and underdeveloped medical centers. Secondly, in the existing studies, computer-aided diagnostic (CAD) systems are designed for accurate CMB detection, however, their proposed approaches consist of two stages. Potential candidate CMBs from the complete MRI image are selected in the first stage and then passed to the phase of false-positive reduction. These pre-and post-processing steps make it difficult to build a completely automated CAD system for CMB that can produce results without human intervention. Hence, as a key goal of this work, an end-to-end enhanced UNet-based model for effective CMB detection and segmentation for IoMT devices is proposed. The proposed system requires no pre-processing or post-processing steps for CMB segmentation, and no existing research localizes each CMB pixel from the complete MRI image input. The findings indicate that the suggested method outperforms in detecting CMBs in the presence of contrast variations and similarities with other normal tissues and yields a good dice score of 0.70, an accuracy of 99 %, as well as a false-positive rate of 0.002 %. © 2017 Elsevier Inc. All rights reserved.

Automated detection of cerebral microbleeds on MR images using knowledge distillation framework

Introduction

Cerebral microbleeds (CMBs) are associated with white matter damage, and various neurodegenerative and cerebrovascular diseases. CMBs occur as small, circular hypointense lesions on T2*-weighted gradient recalled echo (GRE) and susceptibility-weighted imaging (SWI) images, and hyperintense on quantitative susceptibility mapping (QSM) images due to their paramagnetic nature. Accurate automated detection of CMBs would help to determine quantitative imaging biomarkers (e.g., CMB count) on large datasets. In this work, we propose a fully automated, deep learning-based, 3-step algorithm, using structural and anatomical properties of CMBs from any single input image modality (e.g., GRE/SWI/QSM) for their accurate detections.

Methods

In our method, the first step consists of an initial candidate detection step that detects CMBs with high sensitivity. In the second step, candidate discrimination step is performed using a knowledge distillation framework, with a multi-tasking teacher network that guides the student network to classify CMB and non-CMB instances in an offline manner. Finally, a morphological clean-up step further reduces false positives using anatomical constraints. We used four datasets consisting of different modalities specified above, acquired using various protocols and with a variety of pathological and demographic characteristics.

Results

On cross-validation within datasets, our method achieved a cluster-wise true positive rate (TPR) of over 90% with an average of <2 false positives per subject. The knowledge distillation framework improves the cluster-wise TPR of the student model by 15%. Our method is flexible in terms of the input modality and provides comparable cluster-wise TPR and better cluster-wise precision compared to existing state-of-the-art methods. When evaluating across different datasets, our method showed good generalizability with a cluster-wise TPR >80 % with different modalities. The python implementation of the proposed method is openly available.

Assessing the Level of Understanding (Knowledge) and Awareness of Diagnostic Imaging Students in Ghana on Artificial Intelligence and Its Applications in Medical Imaging

Introduction

Recent advancements in technology have propelled the applications of artificial intelligence (AI) in various sectors, including healthcare. Medical imaging has benefited from AI by reducing radiation risks through algorithms used in examinations, referral protocols, and scan justification. This research work assessed the level of knowledge and awareness of 225 second- to fourth-year medical imaging students from public universities in Ghana about AI and its prospects in medical imaging.

Methods

This was a cross-sectional quantitative study design that used a closed-ended questionnaire with dichotomous questions, designed on Google Forms, and distributed to students through their various class WhatsApp platforms. Responses were entered into an Excel spreadsheet and analyzed with the Statistical Package for the Social Sciences (SPSS) software version 25.0 and Microsoft Excel 2016 version.

Results

The response rate was 80.44% (181/225), out of which 97 (53.6%) were male, 82 (45.3%) were female, and 2 (1.1%) preferred not to disclose their gender. Among these, 133 (73.5%) knew that AI had been incorporated into current imaging modalities, and 143 (79.0%) were aware of AI's emergence in medical imaging. However, only 97 (53.6%) were aware of the gradual emergence of AI in the radiography industry in Ghana. Furthermore, 160 people (88.4%) expressed an interest in learning more about AI and its applications in medical imaging. Less than one-third (32%) knew about the general basic application of AI in patient positioning and protocol selection. And nearly two-thirds (65%) either felt threatened or unsure about their job security due to the incorporation of AI technology in medical imaging equipment. Less than half (38% and 43%) of the participants acknowledged that current clinical internships helped them appreciate the role of AI in medical imaging or increase their level of knowledge in AI, respectively. Discussion. Generally, the findings indicate that medical imaging students have fair knowledge about AI and its prospects in medical imaging but lack in-depth knowledge. However, they lacked the requisite awareness of AI's emergence in radiography practice in Ghana. They also showed a lack of knowledge of some general basic applications of AI in modern imaging equipment. Additionally, they showed some level of misconception about the role AI plays in the job of the radiographer.

Conclusion

Decision-makers should implement educational policies that integrate AI education into the current medical imaging curriculum to prepare students for the future. Students should also be practically exposed to the various incorporations of AI technology in current medical imaging equipment.

Diagnosis of cerebral microbleed via VGG and extreme learning machine trained by Gaussian map bat algorithm

Cerebral microbleed (CMB) is a serious public health concern. It is associated with dementia, which can be detected with brain magnetic resonance image (MRI). CMBs often appear as tiny round dots on MRIs, and they can be spotted anywhere over brain. Therefore, manual inspection is tedious and lengthy, and the results are often short in reproducible. In this paper, a novel automatic CMB diagnosis method was proposed based on deep learning and optimization algorithms, which used the brain MRI as the input and output the diagnosis results as CMB and non-CMB. Firstly, sliding window processing was employed to generate the dataset from brain MRIs. Then, a pre-trained VGG was employed to obtain the image features from the dataset. Finally, an ELM was trained by Gaussian-map bat algorithm (GBA) for identification. Results showed that the proposed method VGG-ELM-GBA provided better generalization performance than several state-of-the-art approaches.

Analysis of Clinical Characteristic and Risk Factors for Short-Term Prognosis of Moyamoya Disease with Intraventricular Hemorrhage in Adults

BACKGROUND

Intraventricular hemorrhage (IVH) is the most common type of hemorrhage in moyamoya disease (MMD) with intracerebral hemorrhage (ICH), but the risk factors affecting the short-term prognosis of MMD with IVH in adults are still unclear.

METHODS

We retrospectively analyzed patients of MMD with IVH between January 1, 2018 and January 31, 2020 in the First Affiliated Hospital of Zhengzhou University. According to the modified Rankin Scale (mRS) score at 3 months after discharge, the patients were divided into mRS score ≤2 (good prognosis) group and mRS score >2 (poor prognosis) groups. Univariate and multivariate logistics regression analysis was used to analyze the risk factors affecting the short-term prognosis of adult MMD with IVH.

RESULTS

Univariable analyses showed that patients in the poor prognosis group had a significantly older age of onset (48.48 ± 8.34 vs. 43.74 ± 5.44 years; P = 0.002), a higher percentage of hypertension (57.97% vs. 33.33%; P = 0.014), a higher percentage of tracheotomy (23.19% vs. 2.56%; P = 0.005), a lower Glasgow Coma Scale (GCS) score (7.90 ± 3.58 vs. 11.19 ± 2.56; P = 0.000), a higher Graeb score (7.46 ± 4.04 vs. 5.23 ± 1.93; P = 0.002), and treatment methods (P = 0.000). Multiple logistic regression analysis showed that the lower GCS score (odds ratio [OR], 1.761; P = 0.001) and higher Graeb score (OR, 1.767; P = 0.002) were independently associated with the poor prognosis of MMD with IVH, and surgery treatment (OR, 0.032; P = 0.000) was independently related to the good prognosis of MMD with IVH.

CONCLUSIONS

Among patients with MMD with IVH, the lower GCS score and higher Graeb score are independent risk factors for poor prognosis, whereas in patients with MMD with IVH, surgery treatment acts as a protective factor.

Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review

Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.

CMB-HUNT: Automatic detection of cerebral microbleeds using a deep neural network

Cerebral microbleeds (CMBs) are gaining increasing interest due to their importance in diagnosing cerebral small vessel diseases. However, manual inspection of CMBs is time-consuming and prone to human error. Existing automated or semi-automated solutions still have insufficient detection sensitivity and specificity. Furthermore, they frequently use more than one magnetic resonance imaging modality, but these are not always available. The majority of AI-based solutions use either numeric or image data, which may not provide sufficient information about the true nature of CMBs. This paper proposes a deep neural network with multi-type input data for automated CMB detection (CMB-HUNT) using only susceptibility-weighted imaging data (SWI). Combination of SWIs and radiomic-type numerical features allowed us to identify CMBs with high accuracy without the need for additional imaging modalities or complex predictive models. Two independent datasets were used: one with 304 patients (39 with CMBs) for training and internal system validation and one with 61 patients (21 with CMBs) for external validation. For the hold-out testing dataset, CMB-HUNT reached a sensitivity of 90.0%. As results of testing showed, CMB-HUNT outperforms existing methods in terms of the number of FPs per case, which is the lowest reported thus far (0.54 FPs/patient). The proposed system was successfully applied to the independent validation set, reaching a sensitivity of 91.5% with 1.9 false positives per patient and proving its generalization potential. The results were comparable to previous studies. Our research confirms the usefulness of deep learning solutions for CMB detection based only on one MRI modality.

Using transfer learning for automated microbleed segmentation

INTRODUCTION

Cerebral microbleeds are small perivascular hemorrhages that can occur in both gray and white matter brain regions. Microbleeds are a marker of cerebrovascular pathology and are associated with an increased risk of cognitive decline and dementia. Microbleeds can be identified and manually segmented by expert radiologists and neurologists, usually from susceptibility-contrast MRI. The latter is hard to harmonize across scanners, while manual segmentation is laborious, time-consuming, and subject to interrater and intrarater variability. Automated techniques so far have shown high accuracy at a neighborhood ("patch") level at the expense of a high number of false positive voxel-wise lesions. We aimed to develop an automated, more precise microbleed segmentation tool that can use standardizable MRI contrasts.

METHODS

We first trained a ResNet50 network on another MRI segmentation task (cerebrospinal fluid vs. background segmentation) using T1-weighted, T2-weighted, and T2* MRIs. We then used transfer learning to train the network for the detection of microbleeds with the same contrasts. As a final step, we employed a combination of morphological operators and rules at the local lesion level to remove false positives. Manual segmentation of microbleeds from 78 participants was used to train and validate the system. We assessed the impact of patch size, freezing weights of the initial layers, mini-batch size, learning rate, and data augmentation on the performance of the Microbleed ResNet50 network.

RESULTS

The proposed method achieved high performance, with a patch-level sensitivity, specificity, and accuracy of 99.57, 99.16, and 99.93%, respectively. At a per lesion level, sensitivity, precision, and Dice similarity index values were 89.1, 20.1, and 0.28% for cortical GM; 100, 100, and 1.0% for deep GM; and 91.1, 44.3, and 0.58% for WM, respectively.

DISCUSSION

The proposed microbleed segmentation method is more suitable for the automated detection of microbleeds with high sensitivity.

Computer-Aided Extraction of Select MRI Markers of Cerebral Small Vessel Disease: A Systematic Review

Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. Imaging remains the most promising method for in vivo studies of CSVD. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD from MRI scans. We aimed to summarise published computer-aided methods for the examination of three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB), dilated perivascular spaces (PVS), and lacunes of presumed vascular origin. Seventy classical image processing, classical machine learning, and deep learning studies were identified. Transfer learning and weak supervision techniques have been applied to accommodate the limitations in the training data. While good performance metrics were achieved in local datasets, there have not been generalisable pipelines validated in different research and/or clinical cohorts. Future studies could consider pooling data from multiple sources to increase data size and diversity, and evaluating performance using both image processing metrics and associations with clinical measures.

Automated detection of cerebral microbleeds via segmentation in susceptibility-weighted images of patients with traumatic brain injury

Cerebral microbleeds (CMBs) are a recognised biomarker of traumatic axonal injury (TAI). Their number and location provide valuable information in the long-term prognosis of patients who sustained a traumatic brain injury (TBI). Accurate detection of CMBs is necessary for both research and clinical applications. CMBs appear as small hypointense lesions on susceptibility-weighted magnetic resonance imaging (SWI). Their size and shape vary markedly in cases of TBI. Manual annotation of CMBs is a difficult, error-prone, and time-consuming task. Several studies addressed the detection of CMBs in other neuropathologies with convolutional neural networks (CNNs). In this study, we developed and contrasted a classification (Patch-CNN) and two segmentation (Segmentation-CNN, U-Net) approaches for the detection of CMBs in TBI cases. The models were trained using 45 datasets, and the best models were chosen according to 16 validation sets. Finally, the models were evaluated on 10 TBI and healthy control cases, respectively. Our three models outperform the current status quo in the detection of traumatic CMBs, achieving higher sensitivity at low false positive (FP) counts. Furthermore, using a segmentation approach allows for better precision. The best model, the U-Net, achieves a detection rate of 90% at FP counts of 17.1 in TBI patients and 3.4 in healthy controls.

Performance of three freely available methods for extracting white matter hyperintensities: FreeSurfer, UBO Detector, and BIANCA

White matter hyperintensities (WMH) of presumed vascular origin are frequently found in MRIs of healthy older adults. WMH are also associated with aging and cognitive decline. Here, we compared and validated three algorithms for WMH extraction: FreeSurfer (T1w), UBO Detector (T1w + FLAIR), and FSL's Brain Intensity AbNormality Classification Algorithm (BIANCA; T1w + FLAIR) using a longitudinal dataset comprising MRI data of cognitively healthy older adults (baseline N = 231, age range 64-87 years). As reference we manually segmented WMH in T1w, three-dimensional (3D) FLAIR, and two-dimensional (2D) FLAIR images which were used to assess the segmentation accuracy of the different automated algorithms. Further, we assessed the relationships of WMH volumes provided by the algorithms with Fazekas scores and age. FreeSurfer underestimated the WMH volumes and scored worst in Dice Similarity Coefficient (DSC = 0.434) but its WMH volumes strongly correlated with the Fazekas scores (rs  = 0.73). BIANCA accomplished the highest DSC (0.602) in 3D FLAIR images. However, the relations with the Fazekas scores were only moderate, especially in the 2D FLAIR images (rs  = 0.41), and many outlier WMH volumes were detected when exploring within-person trajectories (2D FLAIR: ~30%). UBO Detector performed similarly to BIANCA in DSC with both modalities and reached the best DSC in 2D FLAIR (0.531) without requiring a tailored training dataset. In addition, it achieved very high associations with the Fazekas scores (2D FLAIR: rs  = 0.80). In summary, our results emphasize the importance of carefully contemplating the choice of the WMH segmentation algorithm and MR-modality.

Automated Detection of Candidate Subjects With Cerebral Microbleeds Using Machine Learning

Cerebral microbleeds (CMBs) appear as small, circular, well defined hypointense lesions of a few mm in size on T2^*-weighted gradient recalled echo (T2^*-GRE) images and appear enhanced on susceptibility weighted images (SWI). Due to their small size, contrast variations and other mimics (e.g., blood vessels), CMBs are highly challenging to detect automatically. In large datasets (e.g., the UK Biobank dataset), exhaustively labelling CMBs manually is difficult and time consuming. Hence it would be useful to preselect candidate CMB subjects in order to focus on those for manual labelling, which is essential for training and testing automated CMB detection tools on these datasets. In this work, we aim to detect CMB candidate subjects from a larger dataset, UK Biobank, using a machine learning-based, computationally light pipeline. For our evaluation, we used 3 different datasets, with different intensity characteristics, acquired with different scanners. They include the UK Biobank dataset and two clinical datasets with different pathological conditions. We developed and evaluated our pipelines on different types of images, consisting of SWI or GRE images. We also used the UK Biobank dataset to compare our approach with alternative CMB preselection methods using non-imaging factors and/or imaging data. Finally, we evaluated the pipeline's generalisability across datasets. Our method provided subject-level detection accuracy > 80% on all the datasets (within-dataset results), and showed good generalisability across datasets, providing a consistent accuracy of over 80%, even when evaluated across different modalities.

The influence of cerebrovascular disease in dementia with Lewy bodies and Parkinson's disease dementia

INTRODUCTION

Lewy body dementia (LBD), including dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD), is a common form of neurodegenerative dementia. The frequency and influence of comorbid cerebrovascular disease is not understood but has potentially important clinical management implications.

METHODS

A systematic literature search was conducted (Medline and Embase) for studies including participants with DLB and/or PDD assessing cerebrovascular lesions (imaging and pathological studies). They included white matter changes, cerebral amyloid angiopathy (CAA), cerebral microbleeds (CMB), macroscopic infarcts, micro-infarcts and intracerebral haemorrhage.

RESULTS

Of 4411 articles, 63 studies were included. Cerebrovascular lesions commonly studied included white matter changes (41 studies) and CMB (18 studies). There was an increased severity of white matter changes on magnetic resonance imaging (visualized as white matter hyperintensities, WMH), but not neuropathology, in LBD compared to PD without dementia and age-matched controls. CMB prevalence in DLB was highly variable but broadly similar to Alzheimer's disease (AD) (0-48%), with a lobar predominance. No relationship was found between large cortical or small subcortical infarcts or intracerebral haemorrhage and presence of LBD.

CONCLUSION

The underlying mechanisms of WMH in LBD require further exploration, as their increased severity in LBD was not supported by neuropathological examination of white matter. CMB in LBD had a similar prevalence as AD. There is a need for larger studies assessing the influence of cerebrovascular lesions on clinical symptoms, disease progression and outcomes.

The radiological interpretation of possible microbleeds after moderate or severe traumatic brain injury: a longitudinal study

Introduction

In order to augment the certainty of the radiological interpretation of “possible microbleeds” after traumatic brain injury (TBI), we assessed their longitudinal evolution on 3-T SWI in patients with moderate/severe TBI.

Methods

Standardized 3-T SWI and T1-weighted imaging were obtained 3 and 26 weeks after TBI in 31 patients. Their microbleeds were computer-aided detected and classified by a neuroradiologist as no, possible, or definite at baseline and follow-up, separately (single-scan evaluation). Thereafter, the classifications were re-evaluated after comparison between the time-points (post-comparison evaluation). We selected the possible microbleeds at baseline at single-scan evaluation and recorded their post-comparison classification at follow-up.

Results

Of the 1038 microbleeds at baseline, 173 were possible microbleeds. Of these, 53.8% corresponded to no microbleed at follow-up. At follow-up, 30.6% were possible and 15.6% were definite. Of the 120 differences between baseline and follow-up, 10% showed evidence of a pathophysiological change over time. Proximity to extra-axial injury and proximity to definite microbleeds were independently predictive of becoming a definite microbleed at follow-up. The reclassification level differed between anatomical locations.

Conclusions

Our findings support disregarding possible microbleeds in the absence of clinical consequences. In selected cases, however, a follow-up SWI-scan could be considered to exclude evolution into a definite microbleed.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00234-021-02839-z.

Interrater Reliability of National Institutes of Health Traumatic Brain Injury Imaging Common Data Elements for Brain Magnetic Resonance Imaging in Mild Traumatic Brain Injury

The National Institutes of Health/National Institute of Neurological Disorders and Stroke (NIH-NINDS) Traumatic Brain Injury (TBI) Imaging Common Data Elements (CDEs) are standardized definitions for pathological intracranial lesions based on their appearance on neuroimaging studies. The NIH-NINDS TBI Imaging CDEs were designed to be as consistent as possible with the U.S. Food and Drug Administration (FDA) definition of biomarkers as "an indicator of normal biological processes, pathogenic processes, or biological responses to an exposure or intervention." However, the FDA qualification process for biomarkers requires proof of reliable biomarker test measurements. We determined the interrater reliability of TBI Imaging CDEs on subacute brain magnetic resonance imaging (MRI) performed on 517 mild TBI patients presenting to 11 U.S. level 1 trauma centers. Three U.S. board-certified neuroradiologists independently evaluated brain MRI performed 2 weeks post-injury for the following CDEs: traumatic axonal injury (TAI), diffuse axonal injury (DAI), and brain contusion. We found very high interrater agreement for brain contusion, with prevalence- and bias-adjusted kappa (PABAK) values for pairs of readers from 0.92 [95% confidence interval, 0.88-0.95] to 0.94 [0.90-0.96]. We found intermediate agreement for TAI and DAI, with PABAK values of 0.74-0.78 [0.70-0.82]. The near-perfect agreement for subacute brain contusion is likely attributable to the high conspicuity and distinctive appearance of these lesions on T1-weighted images. Interrater agreement for TAI and DAI was lower, because signal void in small vascular structures, and artifactual foci of signal void, can be difficult to distinguish from the punctate round or linear areas of slight hemorrhage that are a common hallmark of TAI/DAI on MRI.

Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks

With the rapid growth and increasing use of brain MRI, there is an interest in automated image classification to aid human interpretation and improve workflow. We aimed to train a deep convolutional neural network and assess its performance in identifying abnormal brain MRIs and critical intracranial findings including acute infarction, acute hemorrhage and mass effect. A total of 13,215 clinical brain MRI studies were categorized to training (74%), validation (9%), internal testing (8%) and external testing (8%) datasets. Up to eight contrasts were included from each brain MRI and each image volume was reformatted to common resolution to accommodate for differences between scanners. Following reviewing the radiology reports, three neuroradiologists assigned each study to abnormal vs normal, and identified three critical findings including acute infarction, acute hemorrhage, and mass effect. A deep convolutional neural network was constructed by a combination of localization feature extraction (LFE) modules and global classifiers to identify the presence of 4 variables in brain MRIs including abnormal, acute infarction, acute hemorrhage and mass effect. Training, validation and testing sets were randomly defined on a patient basis. Training was performed on 9845 studies using balanced sampling to address class imbalance. Receiver operating characteristic (ROC) analysis was performed. The ROC analysis of our models for 1050 studies within our internal test data showed AUC/sensitivity/specificity of 0.91/83%/86% for normal versus abnormal brain MRI, 0.95/92%/88% for acute infarction, 0.90/89%/81% for acute hemorrhage, and 0.93/93%/85% for mass effect. For 1072 studies within our external test data, it showed AUC/sensitivity/specificity of 0.88/80%/80% for normal versus abnormal brain MRI, 0.97/90%/97% for acute infarction, 0.83/72%/88% for acute hemorrhage, and 0.87/79%/81% for mass effect. Our proposed deep convolutional network can accurately identify abnormal and critical intracranial findings on individual brain MRIs, while addressing the fact that some MR contrasts might not be available in individual studies.

Automated detection of cerebral microbleeds on T2*-weighted MRI

Cerebral microbleeds, observed as small, spherical hypointense regions on gradient echo (GRE) or susceptibility weighted (SWI) magnetic resonance imaging (MRI) sequences, reflect small hemorrhagic infarcts, and are associated with conditions such as vascular dementia, small vessel disease, cerebral amyloid angiopathy, and Alzheimer’s disease. The current gold standard for detecting and rating cerebral microbleeds in a research context is visual inspection by trained raters, a process that is both time consuming and subject to poor reliability. We present here a novel method to automate microbleed detection on GRE and SWI images. We demonstrate in a community-based cohort of older adults that the method is highly sensitive (greater than 92% of all microbleeds accurately detected) across both modalities, with reasonable precision (fewer than 20 and 10 false positives per scan on GRE and SWI, respectively). We also demonstrate that the algorithm can be used to identify microbleeds over longitudinal scans with a higher level of sensitivity than visual ratings (50% of longitudinal microbleeds correctly labeled by the algorithm, while manual ratings was 30% or lower). Further, the algorithm identifies the anatomical localization of microbleeds based on brain atlases, and greatly reduces time spent completing visual ratings (43% reduction in visual rating time). Our automatic microbleed detection instrument is ideal for implementation in large-scale studies that include cross-sectional and longitudinal scanning, as well as being capable of performing well across multiple commonly used MRI modalities.

Cerebral microbleeds: from depiction to interpretation

Cerebral microbleeds (CMBs) are defined as hypointense foci visible on T2*-weighted and susceptible-weighted MRI sequences. CMBs are increasingly recognised with the widespread use of MRI in healthy individuals as well as in the context of cerebrovascular disease or dementia. They can also be encountered in major critical medical conditions such as in patients requiring extracorporeal mechanical oxygenation. The advent of MRI-guided postmortem neuropathological examinations confirmed that, in the context of cerebrovascular disease, the vast majority of CMBs correspond to recent or old microhaemorrhages. Detection of CMBs is highly influenced by MRI parameters, in particular field strength, postprocessing methods used to enhance T2* contrast and three dimensional sequences. Despite recent progress, harmonising imaging parameters across research studies remains necessary to improve cross-study comparisons. CMBs are helpful markers to identify the nature and the severity of the underlying chronic small vessel disease. In daily clinical practice, presence and numbers of CMBs often trigger uncertainty for clinicians especially when antithrombotic treatments and acute reperfusion therapies are discussed. In the present review, we discuss those clinical dilemmas and address the value of CMBs as diagnostic and prognostic markers for future vascular events.

Magnetic resonance imaging manifestations of cerebral small vessel disease: automated quantification and clinical application

The common cerebral small vessel disease (CSVD) neuroimaging features visible on conventional structural magnetic resonance imaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. The CSVD neuroimaging features have shared and distinct clinical consequences, and the automatic quantification methods for these features are increasingly used in research and clinical settings. This review article explores the recent progress in CSVD neuroimaging feature quantification and provides an overview of the clinical consequences of these CSVD features as well as the possibilities of using these features as endpoints in clinical trials. The added value of CSVD neuroimaging quantification is also discussed for researches focused on the mechanism of CSVD and the prognosis in subjects with CSVD.

Rate of radiation-induced microbleed formation on 7T MRI relates to cognitive impairment in young patients treated with radiation therapy for a brain tumor

BACKGROUND

Radiation therapy (RT) is essential to the management of many brain tumors, but has been known to lead to cognitive decline and vascular injury in the form of cerebral microbleeds (CMBs).

PURPOSE

In a subset of children, adolescents, and young adults recruited from a larger trial investigating arteriopathy and stroke risk after RT, we evaluated the prevalence of CMBs after RT, examined risk factors for CMBs and cognitive impairment, and related their longitudinal development to cognitive performance changes.

METHODS

Twenty-five patients (mean 17 years, range: 10-25 years) underwent 7-Tesla MRI and cognitive assessment. Nineteen patients were treated with whole-brain or focal RT 1-month to 20-years prior, while 6 non-irradiated patients with posterior-fossa tumors served as controls. CMBs were detected on 7T susceptibility-weighted imaging (SWI) using semi-automated software, a first use in this population.

RESULTS

CMB detection sensitivity with 7T SWI was higher than previously reported at lower field strengths, with one or more CMBs detected in 100% of patients treated with RT at least 1-year prior. CMBs were localized to dose-targeted brain volumes with risk factors including whole-brain RT (p = 0.05), a higher RT dose (p = 0.01), increasing time since RT (p = 0.03), and younger age during RT (p = 0.01). Apart from RT dose, these factors were associated with impaired memory performance. Follow-up data in a subset of patients revealed a proportional increase in CMB count with worsening verbal memory performance (r = -0.85, p = 0.03).

CONCLUSIONS

Treatment with RT during youth is associated with the chronic development of CMBs that evolve with memory impairment over time.

Synaptic Organization of the Human Temporal Lobe Neocortex as Revealed by High-Resolution Transmission, Focused Ion Beam Scanning, and Electron Microscopic Tomography

Modern electron microscopy (EM) such as fine-scale transmission EM, focused ion beam scanning EM, and EM tomography have enormously improved our knowledge about the synaptic organization of the normal, developmental, and pathologically altered brain. In contrast to various animal species, comparably little is known about these structures in the human brain. Non-epileptic neocortical access tissue from epilepsy surgery was used to generate quantitative 3D models of synapses. Beside the overall geometry, the number, size, and shape of active zones and of the three functionally defined pools of synaptic vesicles representing morphological correlates for synaptic transmission and plasticity were quantified. EM tomography further allowed new insights in the morphological organization and size of the functionally defined readily releasable pool. Beside similarities, human synaptic boutons, although comparably small (approximately 5 µm), differed substantially in several structural parameters, such as the shape and size of active zones, which were on average 2 to 3-fold larger than in experimental animals. The total pool of synaptic vesicles exceeded that in experimental animals by approximately 2 to 3-fold, in particular the readily releasable and recycling pool by approximately 2 to 5-fold, although these pools seemed to be layer-specifically organized. Taken together, synaptic boutons in the human temporal lobe neocortex represent unique entities perfectly adapted to the “job” they have to fulfill in the circuitry in which they are embedded. Furthermore, the quantitative 3D models of synaptic boutons are useful to explain and even predict the functional properties of synaptic connections in the human neocortex.

Mechanisms of Blood-Brain Barrier Dysfunction in Traumatic Brain Injury

Traumatic brain injuries (TBIs) account for the majority of injury-related deaths in the United States with roughly two million TBIs occurring annually. Due to the spectrum of severity and heterogeneity in TBIs, investigation into the secondary injury is necessary in order to formulate an effective treatment. A mechanical consequence of trauma involves dysregulation of the blood–brain barrier (BBB) which contributes to secondary injury and exposure of peripheral components to the brain parenchyma. Recent studies have shed light on the mechanisms of BBB breakdown in TBI including novel intracellular signaling and cell–cell interactions within the BBB niche. The current review provides an overview of the BBB, novel detection methods for disruption, and the cellular and molecular mechanisms implicated in regulating its stability following TBI.

Traumatic microbleeds persist for up to five years following traumatic brain injury despite resolution of other acute findings on MRI

OBJECTIVE

The primary objective of this study was to track the incidence and progression of traumatic microbleeds (TMBs) for up to five years following traumatic brain injury (TBI).

METHODS

Thirty patients with mild, moderate, or severe TBI received initial MRI within 48 h of injury and continued in a longitudinal study for up to five years. The incidence and progression of MRI findings was assessed across the five year period. In addition to TMBs, we noted the presence of other imaging findings including diffusion weighted imaging (DWI) lesions, extra-axial and intraventricular hemorrhage, hematoma, traumatic meningeal enhancement (TME), fluid-attenuated inversion recovery (FLAIR) hyperintensities, and encephalomalacia.

RESULTS

TMBs were observed in 60% of patients at initial presentation. At one-year follow-up, TMBs were more persistent than other neuroimaging findings, with 83% remaining visible on MRI. In patients receiving serial MRI 2-5 years post-injury, acute TMBs were visible on all follow-up scans. In contrast, most other imaging markers of TBI had either resolved or evolved into ambiguous abnormalities on imaging by one year post-injury.

CONCLUSIONS

These findings suggest that TMBs may serve as a uniquely persistent indicator of TBI and reinforce the importance of acute post-injury imaging for accurate characterization of persistent imaging findings.

Patients with Mild Traumatic Brain Injury Recruited from Both Hospital and Primary Care Settings: A Controlled Longitudinal Magnetic Resonance Imaging Study

With an emphasis on traumatic axonal injury (TAI), frequency and evolution of traumatic intracranial lesions on 3T clinical magnetic resonance imaging (MRI) were assessed in a combined hospital and community-based study of patients with mild traumatic brain injury (mTBI). The findings were related to post-concussion symptoms (PCS) at 3 and 12 months. Prospectively, 194 patients (16–60 years of age) were recruited from the emergency departments at a level 1 trauma center and a municipal outpatient clinic into the Trondheim mTBI follow-up study. MRI was acquired within 72 h (n = 194) and at 3 (n = 165) and 12 months (n = 152) in patients and community controls (n = 78). The protocol included T2, diffusion weighted imaging, fluid attenuated inversion recovery (FLAIR), and susceptibility weighted imaging (SWI). PCS was assessed with British Columbia Post Concussion Symptom Inventory in patients and controls. Traumatic lesions were present in 12% on very early MRI, and in 5% when computed tomography (CT) was negative. TAI was found in 6% and persisted for 12 months on SWI, whereas TAI lesions on FLAIR disappeared or became less conspicuous on follow-up. PCS occurred in 33% of patients with lesions on MRI and in 19% in patients without lesions at 3 months (p = 0.12) and in 21% with lesions and 14% without lesions at 12 months (p = 0.49). Very early MRI depicted cases of TAI in patients with mTBI with microbleeds persisting for 12 months. Patients with traumatic lesions may have a more protracted recovery, but the study was underpowered to detect significant differences for PCS because of the low frequency of trauma-related MRI lesions.

Characterizing Signals Within Lesions and Mapping Brain Network Connectivity After Traumatic Axonal Injury: A 7 Tesla Resting-State FMRI Study

Resting-state functional magnetic resonance imaging (RS-FMRI) has been widely used to map brain functional connectivity, but it is unclear how to probe connectivity within and around lesions. In this study, we characterize RS-FMRI signal time course properties and evaluate different seed placements within and around hemorrhagic traumatic axonal injury (hTAI) lesions. RS-FMRI was performed on a 7 Tesla scanner in a patient who recovered consciousness after traumatic coma and in three healthy controls. Eleven lesions in the patient were characterized in terms of (1) temporal signal-to-noise ratio (tSNR); (2) physiological noise, through comparison of noise regressors derived from the white matter (WM), cerebrospinal fluid (CSF), and gray matter (GM); and (3) seed-based functional connectivity. Temporal SNR at the center of the lesions was 38.3% and 74.1% lower compared with the same region in the contralesional hemisphere of the patient and in the ipsilesional hemispheres of the controls, respectively. Within the lesions, WM noise was more prominent than CSF and GM noise. Lesional seeds did not produce discernable networks, but seeds in the contralesional hemisphere revealed networks whose nodes appeared to be shifted or obscured due to overlapping or nearby lesions. Single-voxel seed analysis demonstrated that placing a seed within a lesion's periphery was necessary to identify networks associated with the lesion region. These findings provide evidence of resting-state network changes in the human brain after recovery from traumatic coma. Furthermore, we show that seed placement within a lesion's periphery or in the contralesional hemisphere may be necessary for network identification in patients with hTAI.

Cerebral Micro-Bleeding Detection Based on Densely Connected Neural Network

Cerebral micro-bleedings (CMBs) are small chronic brain hemorrhages that have many side effects. For example, CMBs can result in long-term disability, neurologic dysfunction, cognitive impairment and side effects from other medications and treatment. Therefore, it is important and essential to detect CMBs timely and in an early stage for prompt treatment. In this research, because of the limited labeled samples, it is hard to train a classifier to achieve high accuracy. Therefore, we proposed employing Densely connected neural network (DenseNet) as the basic algorithm for transfer learning to detect CMBs. To generate the subsamples for training and test, we used a sliding window to cover the whole original images from left to right and from top to bottom. Based on the central pixel of the subsamples, we could decide the target value. Considering the data imbalance, the cost matrix was also employed. Then, based on the new model, we tested the classification accuracy, and it achieved 97.71%, which provided better performance than the state of art methods.

Cerebral microbleeds: hearing through the silence-a narrative review

OBJECTIVE

The term cerebral microbleed (CMB) refers to lesions documented as unexpected findings during computed tomography or magnetic resonance imaging examination of the brain. Initially, a CMB was thought to represent hemosiderin-laden macrophages marking an area of a tiny hemorrhage. Recently, histopathologic studies have shown that the structure of a CMB can be variable. To aid in dealing with this finding and judging its clinical significance, this review addresses important aspects of a CMB, including the definition, prevalence, and incidence in various populations, end-organ damage, associated conditions, and whether any action or treatment by the clinician might be indicated.

METHODS

PubMed Medline, EMBASE, BIOSIS, Current Contents, and Derwent Drug Files databases were searched for the keywords "microbleeds-detection-damage", "silent bleeds", "microbleeds", or "silent bleeds AND hemophilia" from 2011-2016. References of retrieved articles were also reviewed and included if applicable.

RESULTS

The published data are found primarily in the imaging literature and focus on diagnostic techniques. Some publications address relationships with diverse, co-existing clinical conditions and implications for treatment, especially in stroke, intracranial hemorrhage, and antithrombotic therapy.

CONCLUSIONS

It is critical for non-radiologist clinicians (primary care, internists, neurologists, hematologists) to be aware of the potential importance of the finding of a CMB, and the fact that these lesions are not always truly silent or without important clinical consequences. As additional studies appear, clinicians may be able to "hear" more clearly through the silence of the CMB and understand potential clinical implications in patients.

Risk factors of radiotherapy-induced cerebral microbleeds and serial analysis of their size compared with white matter changes: A 7T MRI study in 113 adult patients with brain tumors

BACKGROUND

Although radiation therapy (RT) contributes to survival benefit in many brain tumor patients, it has also been associated with long-term brain injury. Cerebral microbleeds (CMBs) represent an important manifestation of radiation-related injury.

PURPOSE

To characterize the change in size and number of CMBs over time and to evaluate their relationship to white matter structural integrity as measured using diffusion MRI indices.

STUDY TYPE

Longitudinal, retrospective, human cohort.

POPULATION

In all, 113 brain tumor patients including patients treated with focal RT (n = 91, 80.5%) and a subset of nonirradiated controls (n = 22, 19.5%).

FIELD STRENGTH/SEQUENCE

Single and multiecho susceptibility-weighted imaging (SWI) and multiband, shell, and direction diffusion tensor imaging (DTI) at 7 T.

ASSESSMENT

Patients were scanned either once or serially. CMBs were detected and quantified on SWI images using a semiautomated approach. Local and global fractional anisotropy (FA) were measured from DTI data for a subset of 35 patients.

STATISTICAL TESTS

Potential risk factors for CMB development were determined by multivariate linear regression and using linear mixed-effect models. Longitudinal FA was quantitatively and qualitatively evaluated for trends.

RESULTS

All patients scanned at 1 or more years post-RT had CMBs. A history of multiple surgical resections was a risk factor for development of CMBs. The total number and volume of CMBs increased by 18% and 11% per year, respectively, although individual CMBs decreased in volume over time. Simultaneous to these microvascular changes, FA decreased by a median of 6.5% per year. While the majority of nonirradiated controls had no CMBs, four control patients presented with fewer than five CMBs.

DATA CONCLUSION

Identifying patients who are at the greatest risk for CMB development, with its likely associated long-term cognitive impairment, is an important step towards developing and piloting preventative and/or rehabilitative measures for patients undergoing RT.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;50:868-877.

Macroscale White Matter Alterations Due to Traumatic Cerebral Microhemorrhages Are Revealed by Diffusion Tensor Imaging

With the advent of susceptibility-weighted imaging (SWI), the ability to identify cerebral microbleeds (CMBs) associated with mild traumatic brain injury (mTBI) has become increasingly commonplace. Nevertheless, the clinical significance of post-traumatic CMBs remains controversial partly because it is unclear whether mTBI-related CMBs entail brain circuitry disruptions which, although structurally subtle, are functionally significant. This study combines magnetic resonance and diffusion tensor imaging (MRI and DTI) to map white matter (WM) circuitry differences across 6 months in 26 healthy control volunteers and in 26 older mTBI victims with acute CMBs of traumatic etiology. Six months post-mTBI, significant changes (p < 0.001) in the mean fractional anisotropy of perilesional WM bundles were identified in 21 volunteers, and an average of 47% (σ = 21%) of TBI-related CMBs were associated with such changes. These results suggest that CMBs can be associated with lasting changes in perilesional WM properties, even relatively far from CMB locations. Future strategies for mTBI care will likely rely on the ability to assess how subtle circuitry changes impact neural/cognitive function. Thus, assessing CMB effects upon the structural connectome can play a useful role when studying CMB sequelae and their potential impact upon the clinical outcome of individuals with concussion.

A user-guided tool for semi-automated cerebral microbleed detection and volume segmentation: Evaluating vascular injury and data labelling for machine learning

Background and purpose

With extensive research efforts in place to address the clinical relevance of cerebral microbleeds (CMBs), there remains a need for fast and accurate methods to detect and quantify CMB burden. Although some computer-aided detection algorithms have been proposed in the literature with high sensitivity, their specificity remains consistently poor. More sophisticated machine learning methods appear to be promising in their ability to minimize false positives (FP) through high-level feature extraction and the discrimination of hard-mimics. To achieve superior performance, these methods require sizable amounts of precisely labelled training data. Here we present a user-guided tool for semi-automated CMB detection and volume segmentation, offering high specificity for routine use and FP labelling capabilities to ease and expedite the process of generating labelled training data.

Materials and methods

Existing computer-aided detection methods reported by our group were extended to include fully-automated segmentation and user-guided CMB classification with FP labelling. The algorithm's performance was evaluated on a test set of ten patients exhibiting radiotherapy-induced CMBs on MR images.

Results

The initial algorithm's base sensitivity was maintained at 86.7%. FP's were reduced to inter-rater variations and segmentation results were in 98% agreement with ground truth labelling. There was an approximate 5-fold reduction in the time users spent evaluating CMB burden with the algorithm versus without computer aid. The Intra-class Correlation Coefficient for inter-rater agreement was 0.97 CI[0.92,0.99].

Conclusions

This development serves as a valuable tool for routine evaluation of CMB burden and data labelling to improve CMB classification with machine learning. The algorithm is available to the public on GitHub (https://github.com/LupoLab-UCSF/CMB_labeler).

Longitudinal changes in brain volumetry and cognitive functions after moderate and severe diffuse axonal injury

BACKGROUND AND OBJECTIVE

Diffuse axonal injury (DAI) induces a long-term process of brain atrophy and cognitive deficits. The goal of this study was to determine whether there are correlations between brain volume loss, microhaemorrhage load (MHL) and neuropsychological performance during the first year after DAI.

METHODS

Twenty-four patients with moderate or severe DAI were evaluated at 2, 6 and 12 months post-injury. MHL was evaluated at 3 months, and brain volumetry was evaluated at 3, 6 and 12 months. The trail making test (TMT) was used to evaluate executive function (EF), and the Hopkins verbal learning test (HVLT) was used to evaluate episodic verbal memory (EVM) at 6 and 12 months.

RESULTS

There were significant white matter volume (WMV), subcortical grey matter volume and total brain volume (TBV) reductions during the study period (p < 0.05). MHL was correlated only with WMV reduction. EF and EVM were not correlated with MHL but were, in part, correlated with WMV and TBV reductions.

CONCLUSIONS

Our findings suggest that MHL may be a predictor of WMV reduction but cannot predict EF or EVM in DAI. Brain atrophy progresses over time, but patients showed better EF and EVM in some of the tests, which could be due to neuroplasticity.

Cerebral Microbleeds: Imaging and Clinical Significance

Cerebral microbleeds (CMBs), also referred to as microhemorrhages, appear on magnetic resonance (MR) images as hypointense foci notably at T2*-weighted or susceptibility-weighted (SW) imaging. CMBs are detected with increasing frequency because of the more widespread use of high magnetic field strength and of newer dedicated MR imaging techniques such as three-dimensional gradient-echo T2*-weighted and SW imaging. The imaging appearance of CMBs is mainly because of changes in local magnetic susceptibility and reflects the pathologic iron accumulation, most often in perivascular macrophages, because of vasculopathy. CMBs are depicted with a true-positive rate of 48%-89% at 1.5 T or 3.0 T and T2*-weighted or SW imaging across a wide range of diseases. False-positive "mimics" of CMBs occur at a rate of 11%-24% and include microdissections, microaneurysms, and microcalcifications; the latter can be differentiated by using phase images. Compared with postmortem histopathologic analysis, at least half of CMBs are missed with premortem clinical MR imaging. In general, CMB detection rate increases with field strength, with the use of three-dimensional sequences, and with postprocessing methods that use local perturbations of the MR phase to enhance T2* contrast. Because of the more widespread availability of high-field-strength MR imaging systems and growing use of SW imaging, CMBs are increasingly recognized in normal aging, and are even more common in various disorders such as Alzheimer dementia, cerebral amyloid angiopathy, stroke, and trauma. Rare causes include endocarditis, cerebral autosomal dominant arteriopathy with subcortical infarcts, leukoencephalopathy, and radiation therapy. The presence of CMBs in patients with stroke is increasingly recognized as a marker of worse outcome. Finally, guidelines for adjustment of anticoagulant therapy in patients with CMBs are under development. ^© RSNA, 2018.

Clinical vascular imaging in the brain at 7T

Stroke and related cerebrovascular diseases are a major cause of mortality and disability. Even at standard-field-strengths (1.5T), MRI is by far the most sensitive imaging technique to detect acute brain infarctions and to characterize incidental cerebrovascular lesions, such as white matter hyperintensities, lacunes and microbleeds. Arterial time-of-flight (TOF) MR angiography (MRA) can depict luminal narrowing or occlusion of the major brain feeding arteries, and this without the need for contrast administration. Compared to 1.5T MRA, the use of high-field strength (3T) and even more so ultra-high-field strengths (7T), enables the visualization of the lumen of much smaller intracranial vessels, while adding a contrast agent to TOF MRA at 7T may enable the visualization of even more distal arteries in addition to veins and venules. Moreover, with 3T and 7T, the arterial vessel walls beyond the circle of Willis become visible with high-resolution vessel wall imaging. In addition, with 7T MRI, the brain parenchyma can now be visualized on a submillimeter scale. As a result, high-resolution imaging studies of the brain and its blood supply at 7T have generated new concepts of different cerebrovascular diseases. In the current article, we will discuss emerging clinical applications and future directions of vascular imaging in the brain at 7T MRI.

Early detection of cerebral microbleeds following traumatic brain injury using MRI in the hyper-acute phase

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Traumatic cerebral microbleeds (TCMBS) can be identified using susceptibility weighted imaging in the first few hours after injury.

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TCMBs are a useful indicator of severity in this time frame.

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The presence of TCMBs is an early indicator of injury severity following trauma.

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There is a relationship between decreasing size of TCMBs and recovery.

Background

Traumatic brain injury (TBI) is a leading cause of death and disability in people under 45. Advanced imaging techniques to identify injury and classify severity in the first few hours and days following trauma could improve patient stratification and aid clinical decision making. Traumatic cerebral microbleeds (TCMBs), detectable on magnetic resonance susceptibility weighted imaging (SWI), can be used as markers of long-term clinical outcome. However, the relationship between TCMBs and injury severity in the first few hours after injury, and their natural evolution, is unknown.

Methods

We obtained SWI scans in 10 healthy controls, and 13 patients scanned 3–24 h following TBI and again at 7–15 days. TCMBs were identified and total volume quantified for every lesion in each scan.

Results

TCMBs were present in 6 patients, all with more severe injury classified by GCS. No lesions were identified in patients with an initial GCS of 15. Improvement in GCS in the first 15 days following injury was significantly associated with a reduction in microbleed volume over the same time-period.

Conclusion

MRI is feasible in severely injured patients in the first 24 h after trauma. Detection of TCMBs using SWI provides an objective early marker of injury severity following trauma. TCMBs revealed in this time frame, offer the potential to help determine the degree of injury, improving stratification, in order to identify patients who require admission to hospital, transfer to a specialist center, or an extended period of intubation on intensive care.

Automated algorithm for counting microbleeds in patients with familial cerebral cavernous malformations

PURPOSE

Familial cerebral cavernous malformation (CCM) patients present with multiple lesions that can grow both in number and size over time and are reliably detected on susceptibility-weighted imaging (SWI). Manual counting of lesions is arduous and subject to high variability. We aimed to develop an automated algorithm for counting CCM microbleeds (lesions <5 mm in diameter) on SWI images.

METHODS

Fifty-seven familial CCM type-1 patients were included in this institutional review board-approved study. Baseline SWI (n = 57) and follow-up SWI (n = 17) were performed on a 3T Siemens MR scanner with lesions counted manually by the study neuroradiologist. We modified an algorithm for detecting radiation-induced microbleeds on SWI images in brain tumor patients, using a training set of 22 manually delineated CCM microbleeds from two random scans. Manual and automated counts were compared using linear regression with robust standard errors, intra-class correlation (ICC), and paired t tests. A validation analysis comparing the automated counting algorithm and a consensus read from two neuroradiologists was used to calculate sensitivity, the proportion of microbleeds correctly identified by the automated algorithm.

RESULTS

Automated and manual microbleed counts were in strong agreement in both baseline (ICC = 0.95, p < 0.001) and longitudinal (ICC = 0.88, p < 0.001) analyses, with no significant difference between average counts (baseline p = 0.11, longitudinal p = 0.29). In the validation analysis, the algorithm correctly identified 662 of 1325 microbleeds (sensitivity=50%), again with strong agreement between approaches (ICC = 0.77, p < 0.001).

CONCLUSION

The automated algorithm is a consistent method for counting microbleeds in familial CCM patients that can facilitate lesion quantification and tracking.