Browsing by Subject "Computed tomography angiography"

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  • Öman, Olli; Mäkelä, Teemu; Salli, Eero; Savolainen, Sauli; Kangasniemi, Marko (Springer International Publishing, 2019)
    Abstract Background The aim of this study was to investigate the feasibility of ischemic stroke detection from computed tomography angiography source images (CTA-SI) using three-dimensional convolutional neural networks. Methods CTA-SI of 60 patients with a suspected acute ischemic stroke of the middle cerebral artery were randomly selected for this study; 30 patients were used in the neural network training, and the subsequent testing was performed using the remaining 30 patients. The training and testing were based on manually segmented lesions. Cerebral hemispheric comparison CTA and non-contrast computed tomography (NCCT) were studied as additional input features. Results All ischemic lesions in the testing data were correctly lateralized, and a high correspondence to manual segmentations was achieved. Patients with a diagnosed stroke had clinically relevant regions labeled infarcted with a 0.93 sensitivity and 0.82 specificity. The highest achieved voxel-wise area under receiver operating characteristic curve was 0.93, and the highest Dice similarity coefficient was 0.61. When cerebral hemispheric comparison was used as an input feature, the algorithm performance improved. Only a slight effect was seen when NCCT was included. Conclusion The results support the hypothesis that an acute ischemic stroke lesion can be detected with 3D convolutional neural network-based software from CTA-SI. Utilizing information from the contralateral hemisphere appears to be beneficial for reducing false positive findings.
  • Uusitalo, Valtteri; Kamperidis, Vasileios; de Graaf, Michiel A.; Maaniitty, Teemu; Stenstrom, Iida; Broersen, Alexander; Dijkstra, Jouke; Scholte, Arthur J.; Saraste, Antti; Bax, Jeroen J.; Knuuti, Juhani (2017)
    Background: We evaluated the prognostic value of an integrated atherosclerosis risk score combining the markers of coronary plaque burden, location and composition as assessed by computed tomography angiography (CTA). Methods: 922 consecutive patients underwent CTA for suspected coronary artery disease (CAD). Patients without atherosclerosis (n = 261) and in whom quantitative CTA analysis was not feasible due to image quality, step-artefacts or technical factors related to image acquisition or data storage (n = 153) were excluded. Thus, final study group consisted of 508 patients aged 63 9 years. Coronary plaque location, severity and composition for each coronary segment were identified using automated CTA quantification software and integrated in a single CTA score (0-42). Adverse events (AE) including death, myocardial infarction (MI) and unstable angina (UA) were obtained from the national healthcare statistics. Results: There were a total of 20 (4%) AE during a median follow-up of 3.6 years (9 deaths, 5 MI and 6 UA). The CTA risk score was divided into tertiles: 0-6.7, 6.8-14.8 and > 14.8, respectively. All MI (n = 5) and most of the other AE occurred in the highest risk score tertile (3 vs. 3 vs. 14, p = 0.002). After correction for age and gender, the CTA risk score remained independently associated with AE. Conclusions: Comprehensive CIA risk score integrating the location, burden and composition of coronary atherosclerosis predicts future cardiac events in patients with suspected CAD. (C) 2017 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.
  • Hokkinen, Lasse M I; Mäkelä, Teemu Olavi; Savolainen, Sauli; Kangasniemi, Marko Matti (2021)
    Background Computed tomography angiography (CTA) imaging is needed in current guideline-based stroke diagnosis, and infarct core size is one factor in guiding treatment decisions. We studied the efficacy of a convolutional neural network (CNN) in final infarct volume prediction from CTA and compared the results to a CT perfusion (CTP)-based commercially available software (RAPID, iSchemaView). Methods We retrospectively selected 83 consecutive stroke cases treated with thrombolytic therapy or receiving supportive care that presented to Helsinki University Hospital between January 2018 and July 2019. We compared CNN-derived ischaemic lesion volumes to final infarct volumes that were manually segmented from follow-up CT and to CTP-RAPID ischaemic core volumes. Results An overall correlation of r = 0.83 was found between CNN outputs and final infarct volumes. The strongest correlation was found in a subgroup of patients that presented more than 9 h of symptom onset (r = 0.90). A good correlation was found between the CNN outputs and CTP-RAPID ischaemic core volumes (r = 0.89) and the CNN was able to classify patients for thrombolytic therapy or supportive care with a 1.00 sensitivity and 0.94 specificity. Conclusions A CTA-based CNN software can provide good infarct core volume estimates as observed in follow-up imaging studies. CNN-derived infarct volumes had a good correlation to CTP-RAPID ischaemic core volumes.
  • Vainio, Tuomas J V; Mäkelä, Teemu Olavi; Savolainen, Sauli; Kangasniemi, Marko Matti (2021)
    Background Chronic pulmonary embolism (CPE) is a life-threatening disease easily misdiagnosed on computed tomography. We investigated a three-dimensional convolutional neural network (CNN) algorithm for detecting hypoperfusion in CPE from computed tomography pulmonary angiography (CTPA). Methods Preoperative CTPA of 25 patients with CPE and 25 without pulmonary embolism were selected. We applied a 48%-12%-40% training-validation-testing split (12 positive and 12 negative CTPA volumes for training, 3 positives and 3 negatives for validation, 10 positives and 10 negatives for testing). The median number of axial images per CTPA was 335 (min-max, 111-570). Expert manual segmentations were used as training and testing targets. The CNN output was compared to a method in which a Hounsfield unit (HU) threshold was used to detect hypoperfusion. Receiver operating characteristic area under the curve (AUC) and Matthew correlation coefficient (MCC) were calculated with their 95% confidence interval (CI). Results The predicted segmentations of CNN showed AUC 0.87 (95% CI 0.82-0.91), those of HU-threshold method 0.79 (95% CI 0.74-0.84). The optimal global threshold values were CNN output probability >= 0.37 and