Benchmark dataset for clot detection in ischemic stroke vessel-based imaging: CODEC-IV.

We present an annotated dataset for the purposes of creating a benchmark in Artificial Intelligence for automated clot detection. While there are commercial tools available for automated clot detection on computed tomographic (CT) angiographs, they have not been compared in a standardized manner whereby accuracy is reported on a publicly available benchmark dataset. Furthermore, there are known difficulties in automated clot detection - namely, cases where there is robust collateral flow, or residual flow and occlusions of the smaller vessels - and it is necessary to drive an initiative to overcome these challenges. Our dataset contains 159 multiphase CTA patient datasets, derived from CTP and annotated by expert stroke neurologists. In addition to images where the clot is marked, the expert neurologists have provided information about clot location, hemisphere and the degree of collateral flow. The data is available on request by researchers via an online form, and we will host a leaderboard where the results of clot detection algorithms on the dataset will be displayed. Participants are invited to submit an algorithm to us for evaluation using the evaluation tool, which is made available at together with the form at https://github.com/MBC-Neuroimaging/ClotDetectEval.

DNA-PKcs modulates progenitor cell proliferation and fibroblast senescence in idiopathic pulmonary fibrosis.

BACKGROUND: Recent studies have highlighted the contribution of senescent mesenchymal and epithelial cells in Idiopathic Pulmonary Fibrosis (IPF), but little is known regarding the molecular mechanisms that regulate the accumulation of senescent cells in this disease. Therefore, we addressed the hypothesis that the loss of DNA repair mechanisms mediated by DNA protein kinase catalytic subunit (DNA-PKcs) in IPF, promoted the accumulation of mesenchymal progenitors and progeny, and the expression of senescent markers by these cell types. METHODS: Surgical lung biopsy samples and lung fibroblasts were obtained from patients exhibiting slowly, rapidly or unknown progressing IPF and lung samples lacking any evidence of fibrotic disease (i.e. normal; NL). The expression of DNA-Pkcs in lung tissue was assessed by quantitative immunohistochemical analysis. Chronic inhibition of DNA-PKcs kinase activity was mimicked using a highly specific small molecule inhibitor, Nu7441. Proteins involved in DNA repair (stage-specific embryonic antigen (SSEA)-4+ cells) were determined by quantitative Ingenuity Pathway Analysis of transcriptomic datasets (GSE103488). Lastly, the loss of DNA-PKc was modeled in a humanized model of pulmonary fibrosis in NSG SCID mice genetically deficient in PRKDC (the transcript for DNA-PKcs) and treated with Nu7441. RESULTS: DNA-PKcs expression was significantly reduced in IPF lung tissues. Chronic inhibition of DNA-PKcs by Nu7441 promoted the proliferation of SSEA4+ mesenchymal progenitor cells and a significant increase in the expression of senescence-associated markers in cultured lung fibroblasts. Importantly, mesenchymal progenitor cells and their fibroblast progeny derived from IPF patients showed a loss of transcripts encoding for DNA damage response and DNA repair components. Further, there was a significant reduction in transcripts encoding for PRKDC (the transcript for DNA-PKcs) in SSEA4+ mesenchymal progenitor cells from IPF patients compared with normal lung donors. In SCID mice lacking DNA-PKcs activity receiving IPF lung explant cells, treatment with Nu7441 promoted the expansion of progenitor cells, which was observed as a mass of SSEA4+ CgA+ expressing cells. CONCLUSIONS: Together, our results show that the loss of DNA-PKcs promotes the expansion of SSEA4+ mesenchymal progenitors, and the senescence of their mesenchymal progeny.

Generalised Cornu spirals: an experimental study using hard x-rays.

The Cornu spiral is a graphical aid that has been used historically to evaluate Fresnel integrals. It is also the Argand-plane mapping of a monochromatic complex scalar plane wave diffracted by a hard edge. We have successfully reconstructed a Cornu spiral due to diffraction of hard x-rays from a piece of Kapton tape. Additionally, we have explored the generalisation of the Cornu spiral by observing the Argand-plane mapping of complex scalar electromagnetic fields diffracted by a cylinder and a sphere embedded within a cylinder.

Automatic segmentation of hemorrhagic transformation on follow-up non-contrast CT after acute ischemic stroke.

Background: Hemorrhagic transformation (HT) following reperfusion therapies is a serious complication for patients with acute ischemic stroke. Segmentation and quantification of hemorrhage provides critical insights into patients' condition and aids in prognosis. This study aims to automatically segment hemorrhagic regions on follow-up non-contrast head CT (NCCT) for stroke patients treated with endovascular thrombectomy (EVT). Methods: Patient data were collected from 10 stroke centers across two countries. We propose a semi-automated approach with adaptive thresholding methods, eliminating the need for extensive training data and reducing computational demands. We used Dice Similarity Coefficient (DSC) and Lin's Concordance Correlation Coefficient (Lin's CCC) to evaluate the performance of the algorithm. Results: A total of 51 patients were included, with 28 Type 2 hemorrhagic infarction (HI2) cases and 23 parenchymal hematoma (PH) cases. The algorithm achieved a mean DSC of 0.66 ± 0.17. Notably, performance was superior for PH cases (mean DSC of 0.73 ± 0.14) compared to HI2 cases (mean DSC of 0.61 ± 0.18). Lin's CCC was 0.88 (95% CI 0.79-0.93), indicating a strong agreement between the algorithm's results and the ground truth. In addition, the algorithm demonstrated excellent processing time, with an average of 2.7 s for each patient case. Conclusion: To our knowledge, this is the first study to perform automated segmentation of post-treatment hemorrhage for acute stroke patients and evaluate the performance based on the radiological severity of HT. This rapid and effective tool has the potential to assist with predicting prognosis in stroke patients with HT after EVT.

Machine learning segmentation of core and penumbra from acute stroke CT perfusion data.

Introduction: Computed tomography perfusion (CTP) imaging is widely used in cases of suspected acute ischemic stroke to positively identify ischemia and assess suitability for treatment through identification of reversible and irreversible tissue injury. Traditionally, this has been done via setting single perfusion thresholds on two or four CTP parameter maps. We present an alternative model for the estimation of tissue fate using multiple perfusion measures simultaneously. Methods: We used machine learning (ML) models based on four different algorithms, combining four CTP measures (cerebral blood flow, cerebral blood volume, mean transit time and delay time) plus 3D-neighborhood (patch) analysis to predict the acute ischemic core and perfusion lesion volumes. The model was developed using 86 patient images, and then tested further on 22 images. Results: XGBoost was the highest-performing algorithm. With standard threshold-based core and penumbra measures as the reference, the model demonstrated moderate agreement in segmenting core and penumbra on test images. Dice similarity coefficients for core and penumbra were 0.38 ± 0.26 and 0.50 ± 0.21, respectively, demonstrating moderate agreement. Skull-related image artefacts contributed to lower accuracy. Discussion: Further development may enable us to move beyond the current overly simplistic core and penumbra definitions using single thresholds where a single error or artefact may lead to substantial error.

Risk factors of hemorrhagic transformation in acute ischaemic stroke: A systematic review and meta-analysis.

Background: Hemorrhagic transformation (HT) following reperfusion therapies for acute ischaemic stroke often predicts a poor prognosis. This systematic review and meta-analysis aims to identify risk factors for HT, and how these vary with hyperacute treatment [intravenous thrombolysis (IVT) and endovascular thrombectomy (EVT)]. Methods: Electronic databases PubMed and EMBASE were used to search relevant studies. Pooled odds ratio (OR) with 95% confidence interval (CI) were estimated. Results: A total of 120 studies were included. Atrial fibrillation and NIHSS score were common predictors for any intracerebral hemorrhage (ICH) after reperfusion therapies (both IVT and EVT), while a hyperdense artery sign (OR = 2.605, 95% CI 1.212-5.599, I 2 = 0.0%) and number of thrombectomy passes (OR = 1.151, 95% CI 1.041-1.272, I 2 = 54.3%) were predictors of any ICH after IVT and EVT, respectively. Common predictors for symptomatic ICH (sICH) after reperfusion therapies were age and serum glucose level. Atrial fibrillation (OR = 3.867, 95% CI 1.970-7.591, I 2 = 29.1%), NIHSS score (OR = 1.082, 95% CI 1.060-1.105, I 2 = 54.5%) and onset-to-treatment time (OR = 1.003, 95% CI 1.001-1.005, I 2 = 0.0%) were predictors of sICH after IVT. Alberta Stroke Program Early CT score (ASPECTS) (OR = 0.686, 95% CI 0.565-0.833, I 2 =77.6%) and number of thrombectomy passes (OR = 1.374, 95% CI 1.012-1.866, I 2 = 86.4%) were predictors of sICH after EVT. Conclusion: Several predictors of ICH were identified, which varied by treatment type. Studies based on larger and multi-center data sets should be prioritized to confirm the results. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=268927, identifier: CRD42021268927.

Automated occlusion detection for the diagnosis of acute ischemic stroke: A detailed performance review.

INTRODUCTION: Stroke is a leading cause of adult disability and death worldwide. Automated detection of stroke on brain imaging has promise in a time critical environment. We present a method for the automated detection of intracranial occlusions on dynamic CT Angiography (CTA) causing acute ischemic stroke. METHODS: We derived dynamic CTA images from CT Perfusion (CTP) data and utilised advanced image processing to enhance and display major cerebral blood vessels for symmetry analysis. We reviewed the performance of the algorithm on a cohort of 207 patients from the International Stroke Perfusion Imaging Registry (INSPIRE), with Large Vessel Occlusion (LVO) and non-LVO strokes. Included in the data were images with chronic stroke, various artefacts, incomplete vessel occlusions, and images of poorer quality. All images were annotated by stroke experts. In addition, each image was graded in terms of the difficulty of the task of occlusion detection. Performance was evaluated on the overall cohort, and with respect to occlusion location, collateral grade, and task difficulty. We also evaluated the impact of including additional perfusion data. RESULTS: Images with a rating of lower difficulty achieved a sensitivity and specificity of 96% and 90%, respectively, while images with a moderate difficulty rating achieved 88% and 50%, respectively. For cases of high difficulty, where more than two experts or additional data were required to reach consensus, sensitivity and specificity was 53% and 11%. The addition of perfusion data to the dCTA images increased the specificity by 38%. CONCLUSION: We have provided an unbiased interpretation of algorithm performance. Further developments include generalising to conventional CTA and employing the algorithm in a clinical setting for prospective studies.

Quantification of muco-obstructive lung disease variability in mice via laboratory X-ray velocimetry.

To effectively diagnose, monitor and treat respiratory disease clinicians should be able to accurately assess the spatial distribution of airflow across the fine structure of lung. This capability would enable any decline or improvement in health to be located and measured, allowing improved treatment options to be designed. Current lung function assessment methods have many limitations, including the inability to accurately localise the origin of global changes within the lung. However, X-ray velocimetry (XV) has recently been demonstrated to be a sophisticated and non-invasive lung function measurement tool that is able to display the full dynamics of airflow throughout the lung over the natural breathing cycle. In this study we present two developments in XV analysis. Firstly, we show the ability of laboratory-based XV to detect the patchy nature of cystic fibrosis (CF)-like disease in ß-ENaC mice. Secondly, we present a technique for numerical quantification of CF-like disease in mice that can delineate between two major modes of disease symptoms. We propose this analytical model as a simple, easy-to-interpret approach, and one capable of being readily applied to large quantities of data generated in XV imaging. Together these advances show the power of XV for assessing local airflow changes. We propose that XV should be considered as a novel lung function measurement tool for lung therapeutics development in small animal models, for CF and for other muco-obstructive diseases.

Real-time in vivo imaging of regional lung function in a mouse model of cystic fibrosis on a laboratory X-ray source.

Most measures of lung health independently characterise either global lung function or regional lung structure. The ability to measure airflow and lung function regionally would provide a more specific and physiologically focused means by which to assess and track lung disease in both pre-clinical and clinical settings. One approach for achieving regional lung function measurement is via phase contrast X-ray imaging (PCXI), which has been shown to provide highly sensitive, high-resolution images of the lungs and airways in small animals. The detailed images provided by PCXI allow the application of four-dimensional X-ray velocimetry (4DxV) to track lung tissue motion and provide quantitative information on regional lung function. However, until recently synchrotron facilities were required to produce the highly coherent, high-flux X-rays that are required to achieve lung PCXI at a high enough frame rate to capture lung motion. This paper presents the first translation of 4DxV technology from a synchrotron facility into a laboratory setting by using a liquid-metal jet microfocus X-ray source. This source can provide the coherence required for PCXI and enough X-ray flux to image the dynamics of lung tissue motion during the respiratory cycle, which enables production of images compatible with 4DxV analysis. We demonstrate the measurements that can be captured in vivo in live mice using this technique, including regional airflow and tissue expansion. These measurements can inform physiological and biomedical research studies in small animals and assist in the development of new respiratory treatments.