BACKGROUND The purpose of imaging is multifaceted, ranging from selecting the most appropriate patients for treatment, to avoiding those who are unlikely to benefit. In the present situation, imaging methods basically include cross-sectional imaging by computed tomography (CT) or magnetic resonance imaging (MRI). The target of the assessment is the vessels that supply the brain parenchyma and its associated part at the same / distant perfusion level. In this study, we wanted to evaluate the diagnostic accuracy of diffusion-weighted MR imaging with nonenhanced CT in the diagnosis of hyperacute stroke. METHODS This prospective study was conducted in Radiology Department at Narayan Medical College Rohtas, Bihar. The study group includes a sample of 45 patients who had come to the Department of Radiology within 6 hours of onset of stroke symptoms. Non-enhanced computed tomography (NECT) and MRI were done in all the patients and the results were studied. Study subjects were recruited as following inclusion and exclusion criteria. Data was collected, entered and analysed using Microsoft Excel, Epi Info and statistical package for social sciences (SPSS) software. RESULTS The hyperintense ischemic lesions on diffusion-weighted imaging (DWI) were typically more visually distinct and easier to distinguish than the EIS on CT scans, resulting in better overall values. When the five regions were looked at separately, DWI had higher sensitivity than CT studies, which was close to the overall EIS ranking. The basal ganglia and the insular ribbon had the greatest sensitivity in both modalities. Eight of the 14 patients were classified in the consensus rating of CT and DW imaging, resulting in a sensitivity of 57 percent for both methods, with a bad value of 0.40 for CT and a good value of 0.68 for DW imaging. CONCLUSIONS DW imaging had a higher sensitivity and interrater agreement than CT imaging in detecting early infarction. KEYWORDS Stroke, Computed tomography, MRI, Ischaemia
Comparison of Maximum Intensity Projection and Volume Rendering in Detecting Pulmonary Nodules on Multidetector Computed Tomography
