Characteristics of misclassified ct perfusion ischemic core in patients with acute ischemic stroke

Background CT perfusion (CTP) is used to estimate the extent of ischemic core and penumbra in patients with acute ischemic stroke. CTP reliability, however, is limited. This study aims to identify regions misclassified as ischemic core on CTP, using infarct on follow-up noncontrast CT. We aim to assess differences in volumetric and perfusion characteristics in these regions compared to areas that ended up as infarct on follo

Stroke Etiology and Thrombus Computed Tomography Characteristics in Patients With Acute Ischemic Stroke:A MR CLEAN Registry Substudy

Background and Purpose - If a relationship between stroke etiology and thrombus computed tomography characteristics exists, assessing these characteristics in clinical practice could serve as a useful additional diagnostic tool for the identification of stroke subtype. Our purpose was to study the association of stroke etiology and thrombus computed tomography characteristics in patients with acute ischemic stroke due to a large vessel occlusion. Methods - For 1429 consecutive patients enrolled in the MR CLEAN Registry, we determined stroke cause as defined by the TOAST (Trial of ORG 10172 in Acute Stroke Treatment) criteria. The association of stroke etiology with the hyperdense artery sign, clot burden score, and thrombus location was estimated with univariable and multivariable binary and ordinal logistic regression. Additionally, for 367 patients with available thin-section imaging, we assessed the association of stroke etiology with absolute and relative thrombus attenuation, distance from internal carotid artery-terminus to thrombus, thrombus length, and thrombus attenuation increase with univariable and multivariable linear regression. Results - Compared with cardioembolic strokes, noncardioembolic strokes were associated with presence of hyperdense artery sign (odds ratio, 2.2 [95% CI, 1.6-3.0]), lower clot burden score (common odds ratio, 0.4 [95% CI, 0.3-0.6]), shift towards a more proximal thrombus location (common odds ratio, 0.2 [95% CI, 0.2-0.3]), higher absolute thrombus attenuation (β, 3.6 [95% CI, 0.9-6.4]), decrease in distance from the ICA-terminus (β, -5.7 [95% CI, -8.3 to -3.0]), and longer thrombi (β, 8.6 [95% CI, 6.5-10.7]), based on univariable analysis. Thrombus characteristics of strokes with undetermined cause were similar to those of cardioembolic strokes. Conclusions - Thrombus computed tomography characteristics of cardioembolic stroke are distinct from those of noncardioembolic stroke. Additionally, our study supports the general hypothesis that many cryptogenic strokes have a cardioembolic cause. Further research should focus on the use of thrombus computed tomography characteristics as a diagnostic tool for stroke cause in clinical practice

Repeated-root cyclic and negacyclic codes over a finite chain ring

AbstractWe show that repeated-root cyclic codes over a finite chain ring are in general not principally generated. Repeated-root negacyclic codes are principally generated if the ring is a Galois ring with characteristic a power of 2. For any other finite chain ring they are in general not principally generated. We also prove results on the structure, cardinality and Hamming distance of repeated-root cyclic and negacyclic codes over a finite chain ring

Automated final lesion segmentation in posterior circulation acute ischemic stroke using deep learning

Final lesion volume (FLV) is a surrogate outcome measure in anterior circulation stroke (ACS). In posterior circulation stroke (PCS), this relation is plausibly understudied due to a lack of methods that automatically quantify FLV. The applicability of deep learning approaches to PCS is limited due to its lower incidence compared to ACS. We evaluated strategies to develop a convolutional neural network (CNN) for PCS lesion segmentation by using image data from both ACS and PCS patients. We included follow-up non-contrast computed tomography scans of 1018 patients with ACS and 107 patients with PCS. To assess whether an ACS lesion segmentation generalizes to PCS, a CNN was trained on ACS data (ACS-CNN). Second, to evaluate the performance of only including PCS patients, a CNN was trained on PCS data. Third, to evaluate the performance when combining the datasets, a CNN was trained on both datasets. Finally, to evaluate the performance of transfer learning, the ACS-CNN was fine-tuned using PCS patients. The transfer learning strategy outperformed the other strategies in volume agreement with an intra-class correlation of 0.88 (95% CI: 0.83–0.92) vs. 0.55 to 0.83 and a lesion detection rate of 87% vs. 41–77 for the other strategies. Hence, transfer learning improved the FLV quantification and detection rate of PCS lesions compared to the other strategies