Applications of artificial intelligence for DWI and PWI data processing in acute ischemic stroke: Current practices and future directions

The novel imaging methods in diagnosis and assessment of cerebrovascular diseases: an overview

Cerebrovascular diseases, including ischemic strokes, hemorrhagic strokes, and vascular malformations, are major causes of morbidity and mortality worldwide. The advancements in neuroimaging techniques have revolutionized the field of cerebrovascular disease diagnosis and assessment. This comprehensive review aims to provide a detailed analysis of the novel imaging methods used in the diagnosis and assessment of cerebrovascular diseases. We discuss the applications of various imaging modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and angiography, highlighting their strengths and limitations. Furthermore, we delve into the emerging imaging techniques, including perfusion imaging, diffusion tensor imaging (DTI), and molecular imaging, exploring their potential contributions to the field. Understanding these novel imaging methods is necessary for accurate diagnosis, effective treatment planning, and monitoring the progression of cerebrovascular diseases.

The montage method improves the classification of suspected acute ischemic stroke using the convolution neural network and brain MRI

This study investigated the usefulness of the montage method that combines four different magnetic resonance images into one images for automatic acute ischemic stroke (AIS) diagnosis with deep learning method. The montage image was consisted from diffusion weighted image (DWI), fluid attenuated inversion recovery (FLAIR), arterial spin labeling (ASL), and apparent diffusion coefficient (ASL). The montage method was compared with pseudo color map (pCM) which was consisted from FLAIR, ASL and ADC. 473 AIS patients were classified into four categories: mechanical thrombectomy, conservative therapy, hemorrhage, and other diseases. The results showed that the montage image significantly outperformed pCM in terms of accuracy (montage image = 0.76 ± 0.01, pCM = 0.54 ± 0.05) and the area under the curve (AUC) (montage image = 0.94 ± 0.01, pCM = 0.76 ± 0.01). This study demonstrates the usefulness of the montage method and its potential for overcoming the limitations of pCM.

Utilizing deep learning via the 3D U-net neural network for the delineation of brain stroke lesions in MRI image

The segmentation of acute stroke lesions plays a vital role in healthcare by assisting doctors in making prompt and well-informed treatment choices. Although Magnetic Resonance Imaging (MRI) is a time-intensive procedure, it produces high-fidelity images widely regarded as the most reliable diagnostic tool available. Employing deep learning techniques for automated stroke lesion segmentation can offer valuable insights into the precise location and extent of affected tissue, enabling medical professionals to effectively evaluate treatment risks and make informed assessments. In this research, a deep learning approach is introduced for segmenting acute and sub-acute stroke lesions from MRI images. To enhance feature learning through brain hemisphere symmetry, pre-processing techniques are applied to the data. To tackle the class imbalance challenge, we employed a strategy of using small patches with balanced sampling during training, along with a dynamically weighted loss function that incorporates f1-score and IOU-score (Intersection over Union). Furthermore, the 3D U-Net architecture is used to generate predictions for complete patches, employing a high degree of overlap between patches to minimize the requirement for subsequent post-processing steps. The 3D U-Net model, utilizing ResnetV2 as the pre-trained encoder for IOU-score and Seresnext101 for f1-score, stands as the leading state-of-the-art (SOTA) model for segmentation tasks. However, recent research has introduced a novel model that surpasses these metrics and demonstrates superior performance compared to other backbone architectures. The f1-score and IOU-score were computed for various backbones, with Seresnext101 achieving the highest f1-score and ResnetV2 performing the highest IOU-score. These calculations were conducted using a threshold value of 0.5. This research proposes a valuable model based on transfer learning for the classification of brain diseases in MRI scans. The achieved f1-score using the recommended classifiers demonstrates the effectiveness of the approach employed in this study. The findings indicate that Seresnext101 attains the highest f1-score of 0.94226, while ResnetV2 achieves the best IOU-score of 0.88342, making it the preferred architecture for segmentation methods. Furthermore, the study presents experimental results of the 3D U-Net model applied to brain stroke lesion segmentation, suggesting prospects for researchers interested in segmenting brain strokes and enhancing 3D U-Net models.

Diagnostic test accuracy study of a commercially available deep learning algorithm for ischemic lesion detection on brain MRIs in suspected stroke patients from a non-comprehensive stroke center

PURPOSE

To estimate the ability of a commercially available artificial intelligence (AI) tool to detect acute brain ischemia on Magnetic Resonance Imaging (MRI), compared to an experienced neuroradiologist.

METHODS

We retrospectively included 1030 patients with brain MRI, suspected of stroke from January 6th, 2020 to 1st of April 2022, based on these criteria: Age ≥ 18 years, symptoms within four weeks before the scan. The neuroradiologist reinterpreted the MRI scans and subclassified ischemic lesions for reference. We excluded scans with interpretation difficulties due to artifacts or missing sequences. Four MRI scanner models from the same vendor were used. The first 800 patients were included consecutively, remaining enriched for less frequent lesions. The index test was a CE-approved AI tool (Apollo version 2.1.1 by Cerebriu).

RESULTS

The final analysis cohort comprised 995 patients (mean age 69 years, 53 % female). A case-based analysis for detecting acute ischemic lesions showed a sensitivity of 89 % (95 % CI: 85 %-91 %) and specificity of 90 % (95 % CI: 87 %-92 %). We found no significant difference in sensitivity or specificity based on sex, age, or comorbidities. Specificity was reduced in cases with DWI artifacts. Multivariate analysis showed that increasing ischemic lesion size and fragmented lesions were independently associated with higher sensitivity, while non-acute lesion ages lowered sensitivity.

CONCLUSIONS

The AI tool exhibits high sensitivity and specificity in detecting acute ischemic lesions on MRI compared to an experienced neuroradiologist. While sensitivity depends on the ischemic lesions' characteristics, specificity depends on the image quality.

Assessment Precision of CT Perfusion Imaging in the Detection of Acute Ischemic Stroke: A Systematic Review and Meta-Analysis

Stroke, a prevalent medical emergency, comprises ischemic and hemorrhagic subtypes, with acute ischemic stroke (AIS) being a predominant type. The application of computed tomography perfusion (CTP) imaging has gained prominence due to its rapidity and accessibility in stroke evaluation. This study systematically reviews and conducts a meta-analysis of existing literature to assess the diagnostic accuracy of CTP in detecting AIS and predicting hemorrhagic transformation (HT). Employing Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, an extensive search was conducted across electronic databases and relevant radiology journals. Studies conducted between 2007 and 2023 that fulfilled predetermined inclusion criteria underwent quality assessment using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS 2) tool. Cochrane diagnostic accuracy tools were used for data extraction. Thirteen studies involving a total of 1014 patients were included in the analysis. The diagnostic performance of CTP in predicting HT demonstrated high sensitivity (86.7%) and moderate specificity (77.8%), resulting in an overall accuracy of 79.1%. The negative predictive value (NPV) was notably high (92.9%), signifying its efficacy in excluding patients at risk of HT. The positive predictive value (PPV) was comparatively lower (60.3%), highlighting the need for clinical context when making thrombolysis decisions. The false positive rate was 16.2%, while the false negative rate was minimal (9.8%). Subgroup analysis underscored consistent sensitivity and specificity across diverse imaging metrics. The findings of this study emphasize the promising diagnostic accuracy of CTP imaging in predicting HT subsequent to AIS. This non-invasive technique can aid treatment decisions and patient management strategies. By effectively assessing perfusion status and offering predictive insights, CTP imaging improves stroke intervention choices, especially in identifying patients with a lower risk of HT.

Discrimination between progressive penumbra and benign oligemia of the diffusion-perfusion mismatch region by amide proton transfer-weighted imaging

PURPOSE

Amide proton transfer-weighted (APTw) imaging was an effective tool to reveal the tissue acidosis of acute ischemic stroke. This study aimed to evaluate the ability of APTw MRI to distinguish progressive penumbra and benign oligemia in the diffusion-perfusion mismatch region.

MATERIALS AND METHODS

38 acute cerebral infarction patients who underwent a comprehensive MRI examination, including diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI), APT imaging, and a follow-up scan in one week were recruited. There were 12 DWI/PWI match cases. The DWI/PWI mismatch patients were divided into 10 progressive cases and 16 stable cases according to the lesion size on the follow-up DWI image compared to the admission scan. Three ROIs: infarction lesion, peripheral, and contralateral normal regions were measured on each subject's MTRasym map. The Friedman test was used to compare the changes of MTRasym among three different regions within each group. The Kruskal-Wallis ANOVA test was used to compare them among the same region of different groups. The correlation between the MTRasym of the peripheral region and the lesion enlargement was analyzed by the Spearman test.

RESULTS

The MTRasym at the infarction lesion of all three groups showed significant decrease to the contralateral normal tissue. In the progressive mismatch group, the MTRasym at the peripheral region within the DWI/PWI mismatch showed a significant difference with the contralateral normal region and no difference with the infarct core. Whereas both the MTRasym at the peripheral region of the stable mismatch and match groups had no significant difference with the contralateral side, but the differences were significant from those of the central core. When comparing the peripheral region of three groups, the MTRasym of the progressive mismatch group showed a significant decrease to that of the stable mismatch and match groups. The MTRasym of the peripheral region showed a negative correlation with lesion enlargement.

CONCLUSION

APTw imaging is promising to stratify the heterogeneous PWI/DWI mismatch region and benefit the clinical treatment.

Image-to-image generative adversarial networks for synthesizing perfusion parameter maps from DSC-MR images in cerebrovascular disease

Stroke is a major cause of death or disability. As imaging-based patient stratification improves acute stroke therapy, dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) is of major interest in image brain perfusion. However, expert-level perfusion maps require a manual or semi-manual post-processing by a medical expert making the procedure time-consuming and less-standardized. Modern machine learning methods such as generative adversarial networks (GANs) have the potential to automate the perfusion map generation on an expert level without manual validation. We propose a modified pix2pix GAN with a temporal component (temp-pix2pix-GAN) that generates perfusion maps in an end-to-end fashion. We train our model on perfusion maps infused with expert knowledge to encode it into the GANs. The performance was trained and evaluated using the structural similarity index measure (SSIM) on two datasets including patients with acute stroke and the steno-occlusive disease. Our temp-pix2pix architecture showed high performance on the acute stroke dataset for all perfusion maps (mean SSIM 0.92-0.99) and good performance on data including patients with the steno-occlusive disease (mean SSIM 0.84-0.99). While clinical validation is still necessary for future studies, our results mark an important step toward automated expert-level perfusion maps and thus fast patient stratification.

Paradoxical Anxiety Level Reduction in Animal Chronic Stress: A Unique Role of Hippocampus Neurobiology

A paradoxical reduction in anxiety levels in chronic predator stress paradigm (PS) in Sprague–Dawley rats has recently been shown in previous works. In this paper, we studied the possible neurobiological mechanism of this phenomenon. We segregated PS-exposed Sprague–Dawley rats into the high- and low-anxiety phenotypes. The long-lasting effects of PS on corticosterone levels, blood flow speed in the carotid arteries, diffusion coefficient, and 1H nuclear magnetic resonance spectra in the hippocampus were compared in the high-anxiety and low-anxiety rats. In addition, we evaluated the gene BDNF expression in the hippocampus which is considered to be a main factor of neuroplasticity. We demonstrated that in low-anxiety rats, the corticosterone level was decreased and carotid blood flow speed was increased. Moreover, in the hippocampus of low-anxiety rats compared to the control group and high-anxiety rats, the following changes were observed: (a) a decrease in N-acetyl aspartate levels with a simultaneous increase in phosphoryl ethanol amine levels; (b) an increase in lipid peroxidation levels; (c) a decrease in apparent diffusion coefficient value; (d) an increase in BDNF gene expression. Based on these findings, we proposed that stress-induced anxiety reduction is associated with the elevation of BDNF gene expression directly. Low corticosterone levels and a rise in carotid blood flow speed might facilitate BDNF gene expression. Meanwhile, the decrease in apparent diffusion coefficient value and decrease in N-acetyl aspartate levels, as well as an increase in the lipid peroxidation levels, in the hippocampus possibly reflected destructive changes in the hippocampus. We suggested that in Sprague–Dawley rats, these morphological alterations might be considered as an impetus for further increase in neuroplasticity in the hippocampus.