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

The prognostic importance of traumatic axonal injury on early MRI: the Trondheim TAI-MRI grading and quantitative models

OBJECTIVES

We analysed magnetic resonance imaging (MRI) findings after traumatic brain injury (TBI) aiming to improve the grading of traumatic axonal injury (TAI) to better reflect the outcome.

METHODS

Four-hundred sixty-three patients (8-70 years) with mild (n = 158), moderate (n = 129), or severe (n = 176) TBI and early MRI were prospectively included. TAI presence, numbers, and volumes at predefined locations were registered on fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging, and presence and numbers on T2*GRE/SWI. Presence and volumes of contusions were registered on FLAIR. We assessed the outcome with the Glasgow Outcome Scale Extended. Multivariable logistic and elastic-net regression analyses were performed.

RESULTS

The presence of TAI differed between mild (6%), moderate (70%), and severe TBI (95%). In severe TBI, bilateral TAI in mesencephalon or thalami and bilateral TAI in pons predicted worse outcomes and were defined as the worst grades (4 and 5, respectively) in the Trondheim TAI-MRI grading. The Trondheim TAI-MRI grading performed better than the standard TAI grading in severe TBI (pseudo-R2 0.19 vs. 0.16). In moderate-severe TBI, quantitative models including both FLAIR volume of TAI and contusions performed best (pseudo-R2 0.19-0.21). In patients with mild TBI or Glasgow Coma Scale (GCS) score 13, models with the volume of contusions performed best (pseudo-R2 0.25-0.26).

CONCLUSIONS

We propose the Trondheim TAI-MRI grading (grades 1-5) with bilateral TAI in mesencephalon or thalami, and bilateral TAI in pons as the worst grades. The predictive value was highest for the quantitative models including FLAIR volume of TAI and contusions (GCS score <13) or FLAIR volume of contusions (GCS score ≥ 13), which emphasise artificial intelligence as a potentially important future tool.

CLINICAL RELEVANCE STATEMENT

The Trondheim TAI-MRI grading reflects patient outcomes better in severe TBI than today's standard TAI grading and can be implemented after external validation. The prognostic importance of volumetric models is promising for future use of artificial intelligence technologies.

KEY POINTS

Traumatic axonal injury (TAI) is an important injury type in all TBI severities. Studies demonstrating which MRI findings that can serve as future biomarkers are highly warranted. This study proposes the most optimal MRI models for predicting patient outcome at 6 months after TBI; one updated pragmatic model and a volumetric model. The Trondheim TAI-MRI grading, in severe TBI, reflects patient outcome better than today's standard grading of TAI and the prognostic importance of volumetric models in all severities of TBI is promising for future use of AI.

Imaging Findings in Acute Traumatic Brain Injury: a National Institute of Neurological Disorders and Stroke Common Data Element-Based Pictorial Review and Analysis of Over 4000 Admission Brain Computed Tomography Scans from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Study

In 2010, the National Institute of Neurological Disorders and Stroke (NINDS) created a set of common data elements (CDEs) to help standardize the assessment and reporting of imaging findings in traumatic brain injury (TBI). However, as opposed to other standardized radiology reporting systems, a visual overview and data to support the proposed standardized lexicon are lacking. We used over 4000 admission computed tomography (CT) scans of patients with TBI from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study to develop an extensive pictorial overview of the NINDS TBI CDEs, with visual examples and background information on individual pathoanatomical lesion types, up to the level of supplemental and emerging information (e.g., location and estimated volumes). We documented the frequency of lesion occurrence, aiming to quantify the relative importance of different CDEs for characterizing TBI, and performed a critical appraisal of our experience with the intent to inform updating of the CDEs. In addition, we investigated the co-occurrence and clustering of lesion types and the distribution of six CT classification systems. The median age of the 4087 patients in our dataset was 50 years (interquartile range, 29-66; range, 0-96), including 238 patients under 18 years old (5.8%). Traumatic subarachnoid hemorrhage (45.3%), skull fractures (37.4%), contusions (31.3%), and acute subdural hematoma (28.9%) were the most frequently occurring CT findings in acute TBI. The ranking of these lesions was the same in patients with mild TBI (baseline Glasgow Coma Scale [GCS] score 13-15) compared with those with moderate-severe TBI (baseline GCS score 3-12), but the frequency of occurrence was up to three times higher in moderate-severe TBI. In most TBI patients with CT abnormalities, there was co-occurrence and clustering of different lesion types, with significant differences between mild and moderate-severe TBI patients. More specifically, lesion patterns were more complex in moderate-severe TBI patients, with more co-existing lesions and more frequent signs of mass effect. These patients also had higher and more heterogeneous CT score distributions, associated with worse predicted outcomes. The critical appraisal of the NINDS CDEs was highly positive, but revealed that full assessment can be time consuming, that some CDEs had very low frequencies, and identified a few redundancies and ambiguity in some definitions. Whilst primarily developed for research, implementation of CDE templates for use in clinical practice is advocated, but this will require development of an abbreviated version. In conclusion, with this study, we provide an educational resource for clinicians and researchers to help assess, characterize, and report the vast and complex spectrum of imaging findings in patients with TBI. Our data provides a comprehensive overview of the contemporary landscape of TBI imaging pathology in Europe, and the findings can serve as empirical evidence for updating the current NINDS radiologic CDEs to version 3.0.

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.

Clinical considerations in early-onset cerebral amyloid angiopathy

Cerebral amyloid angiopathy (CAA) is an important cerebral small vessel disease associated with brain haemorrhage and cognitive change. The commonest form, sporadic amyloid-β CAA, usually affects people in mid- to later life. However, early-onset forms, though uncommon, are increasingly recognized and may result from genetic or iatrogenic causes that warrant specific and focused investigation and management. In this review, we firstly describe the causes of early-onset CAA, including monogenic causes of amyloid-β CAA (APP missense mutations and copy number variants; mutations of PSEN1 and PSEN2) and non-amyloid-β CAA (associated with ITM2B, CST3, GSN, PRNP and TTR mutations), and other unusual sporadic and acquired causes including the newly-recognized iatrogenic subtype. We then provide a structured approach for investigating early-onset CAA, and highlight important management considerations. Improving awareness of these unusual forms of CAA amongst healthcare professionals is essential for facilitating their prompt diagnosis, and an understanding of their underlying pathophysiology may have implications for more common, late-onset, forms of the disease.

Cerebral amyloid angiopathy-related cardiac injury: Focus on cardiac cell death

Cerebral amyloid angiopathy (CAA) is a kind of disease in which amyloid β (Aβ) and other amyloid protein deposits in the cerebral cortex and the small blood vessels of the brain, causing cerebrovascular and brain parenchymal damage. CAA patients are often accompanied by cardiac injury, involving Aβ, tau and transthyroxine amyloid (ATTR). Aβ is the main injury factor of CAA, which can accelerate the formation of coronary artery atherosclerosis, aortic valve osteogenesis calcification and cardiomyocytes basophilic degeneration. In the early stage of CAA (pre-stroke), the accompanying locus coeruleus (LC) amyloidosis, vasculitis and circulating Aβ will induce first hit to the heart. When the CAA progresses to an advanced stage and causes a cerebral hemorrhage, the hemorrhage leads to autonomic nervous function disturbance, catecholamine surges, and systemic inflammation reaction, which can deal the second hit to the heart. Based on the brain-heart axis, CAA and its associated cardiac injury can create a vicious cycle that accelerates the progression of each other.