Reproducibility for carotid wall segmentation using T1-weighted DANTE-SPACE sequence on high-resolution 3-T carotid MRI.

BACKGROUND: T1-weighted (T1W) magnetic resonance imaging (MRI) using the delay alternating with nutation for excitation-sampling perfection with application-optimized contrasts using different flip angle evolution (DANTE-SPACE) is the preferred imaging technique for evaluation of the vessel wall. PURPOSE: To evaluate the intra- and inter-rater reproducibility of carotid wall segmentation on T1W DANTE-SPACE in patients with symptomatic (acute stroke or transient ischemic attack) internal carotid artery (ICA) stenosis. MATERIAL AND METHODS: This prospective study included 25 patients with acute (≤3 months) stroke or transient ischemic attack and 50%-99% stenosis of the ICA. All patients underwent 3.0-T high-resolution carotid MRI. Two radiologists independently performed the manual segmentation of the vessel wall and inner lumen of the bilateral carotid artery on DANTE-SPACE. The intraclass correlation coefficient (ICC), Dice similarity coefficient (DSC), and Hausdorff distance (HD) were calculated. RESULTS: The ICCs for intra-rater reproducibility of carotid wall volume, inner lumen volume, and normalized wall index were 0.965, 0.990, and 0.962, respectively. The ICCs for inter-rater reproducibility of carotid wall volume, inner lumen, and normalized wall index were 0.856, 0.981, and 0.904. DSC and HD for intra- and inter-rater reproducibility of carotid wall segmentation were as follows: 0.873 and 0.809 (DSC); and 0.079 and 0.118 (HD), respectively. For evaluation of reproducibility only in the carotid artery with symptomatic stenosis, the ICCs for intra- and inter-rater reproducibility indicated all perfect agreement. CONCLUSION: T1W DANTE-SPACE is a reproducible sequence for evaluation of the carotid wall using carotid MRI in patients with symptomatic ICA stenosis.

Risk Factors for the Recurrence of Distal Anterior Cerebral Artery Aneurysms After Endovascular Treatment.

OBJECTIVE: We aimed to investigate risk factors for the recurrence of distal anterior cerebral artery (DACA) aneurysms after endovascular treatment (EVT). METHODS: The clinical and radiologic outcomes of DACA aneurysms treated with endovascular methods at a single tertiary hospital from September 2008 to December 2021 were retrospectively reviewed. We measured the angle between 2 distal branches of DACA aneurysms and categorized the angle as follows: 1) wide-angle (≥180°), and 2) narrow-angle type configuration (<180°). Univariate and multivariate analyses were performed to demonstrate the relationships between characteristics of DACA aneurysm and recurrence risk. RESULTS: In total, 132 DACA aneurysms were treated in our institution. Among these, 47 DACA aneurysms after EVT were included in this study. Forty patients underwent coil embolization without stent, 7 for stent-assisted coil embolization. At the last follow-up (mean 30.2 ± 24.2 months), overall recurrence rate was 23.4% (n = 11). Recurrence rate of the wide-angle type (9 of 23, 39.1%) was significantly greater than narrow-angle type (2 of 24, 8.3%) (P = 0.041; odds ratio 8.174, 95% confidence interval 1.094-61.066). Irregular shape of the DACA aneurysm also showed significantly greater recurrence rate (P = 0.011; odds ratio 10.663, 95% confidence interval 1.701-66.838) after endovascular treatment. CONCLUSIONS: The wide-angle between 2 distal branches of DACA aneurysm and irregular shape might be independent risk factors for the recurrence after endovascular treatment for DACA aneurysms.

Reconfigurable Hanging Drop Microarray Platform for On-Demand Preparation and Analysis of Spheroid Array.

In response to the increasing demand for spheroid-based cancer research, the importance of developing integrated platforms that can simultaneously facilitate high-throughput spheroid production and multiplexed analysis is emphasized. In addition, the understanding of how the size and cellular composition of tumors directly influence their internal structures and functionalities underlines the critical need to produce spheroids of diverse sizes and compositions on a large scale. To address this rising demand, this work presents a configurable and linkable in vitro three-dimensional (3D) cell culture kit (CLiCK) for spheroids, termed CLiCK-Spheroid. This platform consists of three primary components: a hanging drop microarray (HDMA), a concave pillar microarray (CPMA), and gradient blocks. The HDMA alone produces a homogeneous spheroid array, while its combination with the gradient block enables one-step generation of a size-gradient spheroid array. Using the size-gradient spheroid arrays, the occurrence of necrotic cores based on spheroid size is demonstrated. Additionally, spheroids in a single batch can be conveniently compartmentalized and regrouped using a CPMA, enhancing the versatility of spheroid arrays and enabling multiplexed drug treatments. By combining the different assembly methods, this work has achieved high-throughput production of cell composition-gradient spheroid arrays, with noticeable variations in morphology and vascularization based on cell compositions.

Deep learning improves quality of intracranial vessel wall MRI for better characterization of potentially culprit plaques.

Intracranial vessel wall imaging (VWI), which requires both high spatial resolution and high signal-to-noise ratio (SNR), is an ideal candidate for deep learning (DL)-based image quality improvement. Conventional VWI (Conv-VWI, voxel size 0.51 × 0.51 × 0.45 mm3) and denoised super-resolution DL-VWI (0.28 × 0.28 × 0.45 mm3) of 117 patients were analyzed in this retrospective study. Quality of the images were compared qualitatively and quantitatively. Diagnostic performance for identifying potentially culprit atherosclerotic plaques, using lesion enhancement and presence of intraplaque hemorrhage (IPH), was evaluated. DL-VWI significantly outperformed Conv-VWI in all image quality ratings (all P < .001). DL-VWI demonstrated higher SNR and contrast-to-noise ratio (CNR) than Conv-VWI, both in normal walls (basilar artery; SNR 4.83 ± 1.23 vs. 3.02 ± 0.59, P < .001) and lesions (contrast-enhanced images; SNR 22.12 ± 11.68 vs. 8.33 ± 3.26, P < .001). In the assessment of 86 lesions, DL-VWI showed higher confidence of detection (4.56 ± 0.55 vs. 2.62 ± 0.77, P < .001), more concordant IPH characterization (Cohen's Kappa 0.85 vs. 0.59) and greater enhancement. For culprit plaque identification, IPH exhibited higher sensitivity in DL-VWI compared to Conv-VWI (70.6% vs. 23.5%) and excellent specificity (94.3% vs. 94.3%). Deep learning application of intracranial vessel wall images successfully improved the quality and resolution of the images. This aided in detecting vessel wall lesions and intraplaque hemorrhage, and in identifying potentially culprit atherosclerotic plaques.