Diffusion Tensor-Derived Properties of Benign Oligemia, True "at Risk" Penumbra, and Infarct Core during the First Three Hours of Stroke Onset: A Rat Model

Estimating the volume of penumbra in rodents using DTI and stack-based ensemble machine learning framework

BACKGROUND

This study investigates the potential of diffusion tensor imaging (DTI) in identifying penumbral volume (PV) compared to the standard gadolinium-required perfusion-diffusion mismatch (PDM), utilizing a stack-based ensemble machine learning (ML) approach with enhanced explainability.

METHODS

Sixteen male rats were subjected to middle cerebral artery occlusion. The penumbra was identified using PDM at 30 and 90 min after occlusion. We used 11 DTI-derived metrics and 14 distance-based features to train five voxel-wise ML models. The model predictions were integrated using stack-based ensemble techniques. ML-estimated and PDM-defined PVs were compared to evaluate model performance through volume similarity assessment, the Pearson correlation analysis, and Bland-Altman analysis. Feature importance was determined for explainability.

RESULTS

In the test rats, the ML-estimated median PV was 106.4 mL (interquartile range 44.6-157.3 mL), whereas the PDM-defined median PV was 102.0 mL (52.1-144.9 mL). These PVs had a volume similarity of 0.88 (0.79-0.96), a Pearson correlation coefficient of 0.93 (p < 0.001), and a Bland-Altman bias of 2.5 mL (2.4% of the mean PDM-defined PV), with 95% limits of agreement ranging from -44.9 to 49.9 mL. Among the features used for PV prediction, the mean diffusivity was the most important feature.

CONCLUSIONS

Our study confirmed that PV can be estimated using DTI metrics with a stack-based ensemble ML approach, yielding results comparable to the volume defined by the standard PDM. The model explainability enhanced its clinical relevance. Human studies are warranted to validate our findings.

RELEVANCE STATEMENT

The proposed DTI-based ML model can estimate PV without the need for contrast agent administration, offering a valuable option for patients with kidney dysfunction. It also can serve as an alternative if perfusion map interpretation fails in the clinical setting.

KEY POINTS

• Penumbral volume can be estimated by DTI combined with stack-based ensemble ML. • Mean diffusivity was the most important feature used for predicting penumbral volume. • The proposed approach can be beneficial for patients with kidney dysfunction.

Simulating Cerebral Edema and Ischemia After Traumatic Acute Subdural Hematoma Using Triphasic Swelling Biomechanics

Poor outcome following traumatic acute subdural hematoma (ASDH) is associated with the severity of the primary injury and secondary injury including cerebral edema and ischemia. However, the underlying secondary injury mechanism contributing to elevated intracranial pressure (ICP) and high mortality rate remains unclear. Cerebral edema occurs in response to the exposure of the intracellular fixed charge density (FCD) after cell death, causing ICP to increase. The increased ICP from swollen tissue compresses blood vessels in adjacent tissue, restricting blood flow and leading to ischemic damage. We hypothesize that the mass occupying effect of ASDH exacerbates the ischemic injury, leading to ICP elevation, which is an indicator of high mortality rate in the clinic. Using FEBio (febio.org) and triphasic swelling biomechanics, this study modeled clinically relevant ASDHs and simulated post-traumatic brain swelling and ischemia to predict ICP. Results showed that common convexity ASDH significantly increased ICP by exacerbating ischemic injury, and surgical removal of the convexity ASDH may control ICP by preventing ischemia progression. However, in cases where the primary injury is very severe, surgical intervention alone may not effectively decrease ICP, as the contribution of the hematoma to the elevated ICP is insignificant. In addition, interhemispheric ASDH, located between the cerebral hemispheres, does not significantly exacerbate ischemia, supporting the conservative surgical management generally recommended for interhemispheric ASDH. The joint effect of the mass occupying effect of the blood clot and resulting ischemia contributes to elevated ICP which may increase mortality. Our novel approach may improve the fidelity of predicting patient outcome after motor vehicle crashes and traumatic brain injuries due to other causes.

Impaired T1 mapping and Tmax during the first 7 days after ischemic stroke. A retrospective observational study

OBJECTIVE

To measure the relative T1 (rT1) value in different hypo-perfused regions after ischemic stroke using T1 mapping derived by Strategically Acquired Gradient Echo (STAGE) and assess its relationship with onset time and severity of ischemia.

MATERIALS AND METHODS

Sixty-three patients with acute anterior circulation ischemic stroke from 2017 to 2022 who underwent STAGE, diffusion weighted imaging (DWI) and dynamic susceptibility contrast perfusion weighted imaging (DSC-PWI) within 7 days were retrospectively enrolled. The areas with reduced diffusion and hypo-perfusion were segmented based on apparent diffusion coefficient (ADC) value < 0.62 × 10-3mm2/s and time-to-maximum (Tmax) thresholds (4, 6, 8, and 10 seconds). We measured the T1 value in the diffusion reduced and every 2 s Tmax strata regions and calculated rT1 (T1ipsi/T1contra) to explore the relationship between rT1 value, Tmax, and onset time.

RESULTS

rT1 value was increased in diffusion reduced (1.42) and hypo-perfused regions (1.02, 1.06, 1.12, 1.27, Tmax 4-6 s, 6-8 s, 8-10 s, > 10 s, respectively; all different from 1, P < 0.001). rT1 value was positively correlated with Tmax (rs = 0.61, P < 0.001) and onset time in area with reduced diffusion (rs = 0.39, P = 0.014).

CONCLUSIONS

Increased rT1 value in different hypo-perfused brain regions using T1 mapping derived by STAGE may reflect the edema; it was associated with the severity of Tmax and showed a weak correlation with the onset time in diffusion reduced areas.

Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives

Biomarker discovery and development are popular for detecting the subtle diseases. However, biomarkers are needed to be validated and approved, and even fewer are ever used clinically. Imaging biomarkers have a crucial role in the treatment of cancer patients because they provide objective information on tumor biology, the tumor's habitat, and the tumor's signature in the environment. Tumor changes in response to an intervention complement molecular and genomic translational diagnosis as well as quantitative information. Neuro-oncology has become more prominent in diagnostics and targeted therapies. The classification of tumors has been actively updated, and drug discovery, and delivery in nanoimmunotherapies are advancing in the field of target therapy research. It is important that biomarkers and diagnostic implements be developed and used to assess the prognosis or late effects of long-term survivors. An improved realization of cancer biology has transformed its management with an increasing emphasis on a personalized approach in precision medicine. In the first part, we discuss the biomarker categories in relation to the courses of a disease and specific clinical contexts, including that patients and specimens should both directly reflect the target population and intended use. In the second part, we present the CT perfusion approach that provides quantitative and qualitative data that has been successfully applied to the clinical diagnosis, treatment and application. Furthermore, the novel and promising multiparametric MR imageing approach will provide deeper insights regarding the tumor microenvironment in the immune response. Additionally, we briefly remark new tactics based on MRI and PET for converging on imaging biomarkers combined with applications of bioinformatics in artificial intelligence. In the third part, we briefly address new approaches based on theranostics in precision medicine. These sophisticated techniques merge achievable standardizations into an applicatory apparatus for primarily a diagnostic implementation and tracking radioactive drugs to identify and to deliver therapies in an individualized medicine paradigm. In this article, we describe the critical principles for imaging biomarker characterization and discuss the current status of CT, MRI and PET in finiding imaging biomarkers of early disease.

Altered diffusivity of the subarachnoid cisterns in the rat brain following neurological disorders

BACKGROUND

Although changes in diffusion characteristics of the brain parenchyma in neurological disorders are widely studied and used in clinical practice, the change in diffusivity in the cerebrospinal fluid (CSF) system is rarely reported. In this study, free water diffusion in the subarachnoid cisterns and ventricles of the rat brain was examined using diffusion magnetic resonance imaging (MRI), and the effects of neurological disorders on diffusivity in CSF system were investigated.

METHODS

Diffusion MRI and T₂-weighted images were obtained in the intact rats, 24 h after ischemic stroke, and 50 days after mild traumatic brain injury (mTBI). We conducted the assessment of diffusivity in the rat brain in the subarachnoid cisterns around the midbrain, as well as the lateral ventricles. One-way ANOVA and Kruskal-Wallis test were used to evaluate the change in mean diffusivity (MD) and MD histogram, respectively, in CSF system following different neurological disease.

RESULTS

A significant decrease in the mean MD value of the subarachnoid cisterns was observed in the stroke rats compared with the intact and mTBI rats (p < 0.005). In addition, the skewness (p < 0.002), maximum MD (p < 0.002), and MD percentiles (p < 0.002) in the stroke rats differed significantly from those in the intact and mTBI rats. By contrast, no difference was observed in the mean MD value of the lateral ventricles among three groups of rats. We proposed that the assessment of the subarachnoid cisterns, rather than the lateral ventricles, in the rat brain would be useful in providing diffusion information in the CSF system.

CONCLUSIONS

Alterations in MD parameters of the subarachnoid cisterns after stroke provide evidence that brain injury may alter the characteristics of free water diffusion not only in the brain parenchyma but also in the CSF system.

Brain Abnormalities in Pontine Infarction: A Longitudinal Diffusion Tensor Imaging and Functional Magnetic Resonance Imaging study

OBJECTIVES

The aim of this study was to make a reasonable and accurate assessment of the prognosis of patients with pontine infarction. We assessed the changes in structure and function in the whole brain after pontine infarction from the acute to chronic phase using diffustion tensor imaging and functional magnetic resonance imaging.

MATERIALS AND METHODS

Sixteen individuals with a recent pontine infarction and sixteen healthy controls were recruited and underwent 3.0T DTI, resting-state fMRI and upper extremity Fugl-Myer (UE-FM) functional evaluation at five time points: within one week (T1), half a month (T2), one month (T3), three months (T4), and six months (T5) after onset. Tract-based spatial statistics was used to conduct a voxelwise analysis.

RESULTS

The fractional anisotropy (FA) values were significantly lower in the pontine infarction group than in the control group. Then, specific ROIs were analyzed. The FA values of 10 regions of interest were significantly increased at T2 compared with those at T1. The FA value of the corticospinal tract was significantly increased at T3 compared with that at T2. Regional brain activity results showed that the amplitude of low frequency fluctuations value of the frontal lobe decreased at T1, then increased. Finally, The UE-FM scores showed the same increased trend.

CONCLUSION

These findings show that the microstructure changes most significantly within half a month after pontine infarction and stabilizes after one month. The recovery of motor function in the later period is mainly caused by changes in the cortex. This facilitates more treatment options.

Early neuroimaging and ultrastructural correlates of injury outcome after neonatal hypoxic-ischaemia

Hypoxic ischaemia encephalopathy is the major cause of brain injury in new-borns. However, to date, useful biomarkers which may be used to early predict neurodevelopmental impairment for proper commencement of hypothermia therapy is still lacking. This study aimed to determine whether the early neuroimaging characteristics and ultrastructural correlates were associated with different injury progressions and brain damage severity outcomes after neonatal hypoxic ischaemia. Longitudinal 7 T MRI was performed within 6 h, 24 h and 7 days after hypoxic ischaemia in rat pups. The brain damage outcome at 7 days post-hypoxic ischaemia assessed using histopathology and MRI were classified as mild, moderate and severe. We found there was a spectrum of different brain damage severity outcomes after the same duration of hypoxic ischaemia. The severity of brain damage determined using MRI correlated well with that assessed by histopathology. Quantitative MRI characteristics denoting water diffusivity in the tissue showed significant differences in the apparent diffusion coefficient deficit volume and deficit ratios within 6 h, at 24 h and 7 days after hypoxic ischaemia among the 3 different outcome groups. The susceptible brain areas to hypoxic ischaemia were revealed by the temporal changes in regional apparent diffusion coefficient values among three outcome groups. Within 6 h post-hypoxic ischaemia, a larger apparent diffusion coefficient deficit volume and deficit ratios and lower apparent diffusion coefficient values were highly associated with adverse brain damage outcome. In the apparent diffusion coefficient deficit areas detected early after hypoxic ischaemia which were highly associated with severe damage outcome, transmission electron microscopy revealed fragmented nuclei; swollen rough endoplasmic reticulum and degenerating mitochondria in the cortex and prominent myelin loss and axon detraction in the white matter. Taken together, different apparent diffusion coefficient patterns obtained early after hypoxic ischaemia are highly associated with different injury progression leading to different brain damage severity outcomes, suggesting the apparent diffusion coefficient characteristics may be applicable to early identify the high-risk neonates for hypothermia therapy.

Machine learning-based segmentation of ischemic penumbra by using diffusion tensor metrics in a rat model

Background

Recent trials have shown promise in intra-arterial thrombectomy after the first 6–24 h of stroke onset. Quick and precise identification of the salvageable tissue is essential for successful stroke management. In this study, we examined the feasibility of machine learning (ML) approaches for differentiating the ischemic penumbra (IP) from the infarct core (IC) by using diffusion tensor imaging (DTI)-derived metrics.

Methods

Fourteen male rats subjected to permanent middle cerebral artery occlusion (pMCAO) were included in this study. Using a 7 T magnetic resonance imaging, DTI metrics such as fractional anisotropy, pure anisotropy, diffusion magnitude, mean diffusivity (MD), axial diffusivity, and radial diffusivity were derived. The MD and relative cerebral blood flow maps were coregistered to define the IP and IC at 0.5 h after pMCAO. A 2-level classifier was proposed based on DTI-derived metrics to classify stroke hemispheres into the IP, IC, and normal tissue (NT). The classification performance was evaluated using leave-one-out cross validation.

Results

The IC and non-IC can be accurately segmented by the proposed 2-level classifier with an area under the receiver operating characteristic curve (AUC) between 0.99 and 1.00, and with accuracies between 96.3 and 96.7%. For the training dataset, the non-IC can be further classified into the IP and NT with an AUC between 0.96 and 0.98, and with accuracies between 95.0 and 95.9%. For the testing dataset, the classification accuracy for IC and non-IC was 96.0 ± 2.3% whereas for IP and NT, it was 80.1 ± 8.0%. Overall, we achieved the accuracy of 88.1 ± 6.7% for classifying three tissue subtypes (IP, IC, and NT) in the stroke hemisphere and the estimated lesion volumes were not significantly different from those of the ground truth (p = .56, .94, and .78, respectively).

Conclusions

Our method achieved comparable results to the conventional approach using perfusion–diffusion mismatch. We suggest that a single DTI sequence along with ML algorithms is capable of dichotomizing ischemic tissue into the IC and IP.