The value of whole-brain CT perfusion imaging combined with dynamic CT angiography in the evaluation of pial collateral circulation with middle cerebral artery occlusion

Multi-phase computed tomography angiography combined with inflammation index to predict clinical functional prognosis in patients with acute ischemic stroke

AIM

In this study, we investigated the feasibility of the Alberta Stroke Program Early CT Score (ASPECTS) and multiphase computed tomography angiography (mCTA) lateral branch circulation grading combined with clinical and laboratory indicators to predict the clinical prognosis of patients with acute ischemic stroke after 90 days.

MATERIALS AND METHODS

The clinical data of 80 patients with acute anterior circulation ischemic stroke were retrospectively analyzed and divided into the good prognosis (37 cases) and poor prognosis groups (43 cases) according to their clinical function score at 90 days after discharge. Various factors, including basic imaging parameters (ASPECTS), occluded vessel location, affected side location and clinical indicators (time from onset to computed tomography examination, height, weight, body mass index, previous hypertension, and degree of hypertension and diabetes mellitus), laboratory blood rutine, and biochemical tests (white blood count, neutrophil count, lymphocyte count, neutrophil-to-lymphocyte ratio, hematocrit test, platelet count, international normalized ratio, blood glucose, triglycerides, uric acid, and D-dimer) were considered in the analysis.

RESULTS

Logistic regression analysis showed that the mCTA score, hypertension, and neutrophil count were significant independent predictors.

CONCLUSION

A nomogram of the mCTA score, hypertension, and neutrophil count may predict functional recovery after 90 days in patients with acute ischemic stroke.

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.

Enhanced CT imaging artificial neural network coronary artery calcification score assisted diagnosis

BACKGROUND

The study of coronary artery calcification (CAC) may assist in identifying additional coronary artery problem protective factors. On the contrary side, due to the wide variety of CAC as individuals, CAC research is difficult. Due to this, evaluating data for investigation is becoming complicated.

OBJECTIVE

To use a multi-layer perceptron, we investigated the accuracy and reliability of synthetic CAC coursework or hazard classification in pre or alors chest computerized tomography (CT) of arrangements resolutions in this analysis.

method

Photographs of the chest from similar individuals as well as calcium-just and non-gated pictures were incorporated. This cut thickness ordered CT pictures (bunch A: 1 mm; bunch B: 3 mm). The CAC rating was determined utilizing calcification score picture information, and became standard for tests. While the control treatment's machine learning program was created using 170 computed tomography pictures and evaluated using 144 scans, group A's machine learning algorithm was created using 150 chest CT diagnostic tests.

RESULTS

334 external related pictures (100 μm: 117; 0.5 mm x: 117) of 117 individuals and 612 inside design organizing (1 mm: 294; mm3: 314) of 406 patients were surveyed. Pack B had 0.94, however, tests An and b had 0.90 (95% CI: 0.85-0.93) ICCs between significant learning and gold expenses (0.92-0.96). Dull Altman plots agreed well. A machine teaching approach successfully identified 71% of cases in category A is 81% of patients in section B again for cardiac risk class.

CONCLUSION

Regression risk evaluation algorithms could assist in categorizing cardiorespiratory individuals into distinct risk groups and conveniently personalize the treatments to the patient's circumstances. The models would be based on information gathered through CAC. On both 1 and 3-mm scanners, the automatic determination of a CAC value and cardiovascular events categorization that used a depth teaching approach was reliable and precise. The layer thickness of 0.5 mm on chest CT was slightly less accurate in CAC detection and risk evaluation.