In this paper, the current status of cardiac image registration methods is reviewed. The combination of information from multiple cardiac image modalities, such as magnetic resonance imaging, computed tomography, positron emission tomography, single-photon emission computed tomography, and ultrasound, is of increasing interest in the medical community for physiologic understanding and diagnostic purposes. Registration of cardiac images is a more complex problem than brain image registration because the heart is a nonrigid moving organ inside a moving body. Moreover, as compared to the registration of brain images, the heart exhibits much fewer accurate anatomical landmarks. In a clinical context, physicians often mentally integrate image information from different modalities. Automatic registration, based on computer programs, might, however, offer better accuracy and repeatability and save time.

No 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.

The combination of first-pass enhancement analysis and wall motion assessment with stress significantly increases the specificity of MR imaging in the detection of unviable sectors.

In this paper, a new approach is presented for the assessment of a 3-D anatomical and functional model of the heart including structural information from magnetic resonance imaging (MRI) and functional information from positron emission tomography (PET) and magnetocardiography (MCG). The method uses model-based co-registration of MR and PET images and marker-based registration for MRI and MCG. Model-based segmentation of MR anatomical images results in an individualized 3-D biventricular model of the heart including functional parameters from PET and MCG in an easily interpretable 3-D form.

SummaryObjectiveNavigated transcranial magnetic stimulation (nTMS) is becoming increasingly popular in noninvasive preoperative language mapping, as its results correlate well enough with those obtained by direct cortical stimulation (DCS) during awake surgery in adult patients with tumor. Reports in the context of epilepsy surgery or extraoperative DCS in adults are, however, sparse, and validation of nTMS with DCS in children is lacking. Furthermore, little is known about the risk of inducing epileptic seizures with nTMS in pediatric epilepsy patients. We provide the largest validation study to date in an epilepsy surgery population.MethodsWe compared language mapping with nTMS and extraoperative DCS in 20 epilepsy surgery patients (age range 9‐32 years; 14 children and adolescents).ResultsIn comparison with DCS, sensitivity of nTMS was 68%, specificity 76%, positive predictive value 27%, and negative predictive value 95%. Age, location of ictal‐onset zone near or within DCS‐mapped language areas or severity of cognitive deficits had no significant effect on these values. None of our patients had seizures during nTMS.SignificanceOur study suggests that nTMS language mapping is clinically useful and safe in epilepsy surgery patients, including school‐aged children and patients with extensive cognitive dysfunction. Similar to in tumor surgery, mapping results in the frontal region are most reliable. False negative findings may be slightly more likely in epilepsy than in tumor surgery patients. Mapping results should always be verified by other methods in individual patients.

To automate diagnostic chest radiograph imaging quality control (lung inclusion at all four edges, patient rotation, and correct inspiration) using convolutional neural network models. Methods: The data comprised of 2589 postero-anterior chest radiographs imaged in a standing position, which were divided into train, validation, and test sets. We increased the number of images for the inclusion by cropping appropriate images, and for the inclusion and the rotation by flipping the images horizontally. The image histograms were equalized, and the images were resized to a 512 × 512 resolution. We trained six convolutional neural networks models to detect the image quality features using manual image annotations as training targets. Additionally, we studied the inter-observer variability of the image annotation. Results: The convolutional neural networks' areas under the receiver operating characteristic curve were >0.88 for the inclusions, and >0.70 and >0.79 for the rotation and the inspiration, respectively. The inter-observer agreement between two human annotators for the assessed image-quality features were: 92%, 90%, 82%, and 88% for the inclusion at patient's left, patient's right, cranial, and caudal edges, and 78% and 89% for the rotation and inspiration, respectively. Higher inter-observer agreement was related to a smaller variance in the network confidence. Conclusions: The developed models provide automated tools for the quality control in a radiological department. Additionally, the convolutional neural networks could be used to obtain immediate feedback of the chest radiograph image quality, which could serve as an educational instrument.

A novel method is proposed for elastic matching of two data volumes. A combination of mutual information, gradient information and smoothness of transformation is used to guide the deformation of another of the volumes. The deformation is accomplished in a multiresolution process by spheres containing a vector field. Position and radius of the spheres are varied. The feasibility of the method is demonstrated in two cases: matching inter-patient MR images of the head and intra-patient cardiac MR and PET images.