Background: We performed a prospective study on patients with middle cerebral artery(MCA) ischemic stroke to evaluate the accuracy of perfusion-CT imaging(PCT) to predict the development of malignant brain infarction (MBI).
Methods: 106 patients(women 37 %, mean age 65 years)underwent native cranial computed tomography (CCT), CT angiography(CTA) and PCT after a median of 2 h after stroke onset. We assessed the patency of the MCA and the area of tissue ischemia (AIT)according to cerebral blood flow(CBF), cerebral blood volume (CBV) and time-to-peak (TTP)maps. Optimum sensitivity, specificity,positive (PPV) and negative predictive values (NPV) were calculated for the end-point MBI (= midline shift > 5 mm or decompressive surgery) by means of receiver operating characteristics(ROC).
Results: 20 patients (19 %)developed a MBI. In these patients,a larger AIT was found in all perfusion maps as compared to the remaining patients (p < 0.001). All perfusion maps had a very high NPV (95.4-98.4 %), a high sensitivity (85-95 %) and specificity (71.6-77.9 %) and only a moderate PPV (44-47.4 %). Best prediction was found for CBF maps with AIT of > 27.9 % of the hemisphere.
Conclusion: PCT allows the discrimination of patients without a relevant risk for MBI from those having a 50 % risk of MBI development. Due to the high sensitivity and specificity, PCT is a reliable tool in detecting MBI. Because of PCT's better availability, it is the method of choice at present for an early risk stratification of acute stroke patients.
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http://dx.doi.org/10.1007/s00415-008-0802-1 | DOI Listing |
Radiol Med
December 2024
Department of Radiology, ASST Papa Giovanni XXIII Hospital, Piazza OMS, 1, 24127, Bergamo, Italy.
Coronary computed tomography angiography (CCTA) is a powerful tool to rule out coronary artery disease (CAD). In the last decade, myocardial perfusion CT (CTP) technique has been developed for the evaluation of myocardial ischemia, thereby increasing positive predictive value for diagnosis of obstructive CAD. A diagnostic strategy combining CCTA and perfusion acquisitions provides both anatomical coronary evaluation and functional evaluation of the stenosis, increasing the specificity and the positive predictive value of cardiac CT.
View Article and Find Full Text PDFEur Radiol
December 2024
Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Objectives: To develop and validate deep learning (DL)-models that denoise late iodine enhancement (LIE) images and enable accurate extracellular volume (ECV) quantification.
Methods: This study retrospectively included patients with chest discomfort who underwent CT myocardial perfusion + CT angiography + LIE from two hospitals. Two DL models, residual dense network (RDN) and conditional generative adversarial network (cGAN), were developed and validated.
Abdom Radiol (NY)
December 2024
All India Institute of Medical Sciences, Ansari Nagar East, New Delhi, 110029, India.
Purpose: To assess diagnostic accuracy of perfusion CT (pCT) based biomarkers in differentiating clear-cell renal cell carcinoma (ccRCC) from non-ccRCC.
Materials And Method: This retrospective study comprised 95 patients with RCCs (70 ccRCCs and 25 non-ccRCCs) who had perfusion CT (pCT) before surgery between January 2017 and December 2022. Two readers independently recorded PCT parameters [blood flow (BF), blood volume (BV), mean transit time (MTT), and time to peak (TTP)] by drawing a circular ROI on the tumor.
Int J Cardiovasc Imaging
January 2025
Department of Cardiology, Radboud University Medical Center, Nijmegen, The Netherlands.
Coronary CT angiography (CCTA) and dynamic stress CT myocardial perfusion (CT-MPI) are established modalities in the analysis of patients with chronic coronary syndromes. Their role in patients with suspected non-ST elevation myocardial infarction (NSTEMI) is unknown. CCTA with CT-MPI might assist in the triage of NSTEMI patients to the Cath lab.
View Article and Find Full Text PDFEur J Radiol
December 2024
Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckstr. 1, 55131 Mainz, Germany. Electronic address:
Purpose: To examine the impact of deep learning-augmented contrast enhancement on image quality and diagnostic accuracy of poorly contrasted CT angiography in patients with suspected stroke.
Methods: This retrospective single-centre study included 102 consecutive patients who underwent CT imaging for suspected stroke between 01/2021 and 12/2022, including whole brain volume perfusion CT (VPCT) and, specifically, a poorly contrasted CT angiography (defined as < 350HU in the proximal MCA). CT angiography imaging data was reconstructed using i.
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