Abstract. This paper presents a method to extract heart structures from CTA and MRA data sets, in particular the left atrium. First, the segmented blood pool is subdivided at narrowings in small components. Second, these basic components are merged automatically so that they represent the different heart structures. The resulting cutting surfaces have a relatively small diameter compared to the diameter of the neighboring heart chambers. Both steps are controlled by only one fixed parameter. The method is fast and allows interactive postprocessing by the user. Experiments on various data sets show the accuracy, robustness and repeatability of this approach.

The use of vasopressin infusion or arterial embolization in the treatment of 87 patients with gastrointestinal hemorrhage is reviewed. A bleeding point was identified angiographically in 46 patients (53%), with a higher success rate in those with upper gastrointestinal hemorrhage (63%) than in those with lower (39%) gastrointestinal hemorrhage. Vasopressin infusion in 33 patients completely stopped hemorrhage in 14 and slowed hemorrhage pending surgery in another 5. Gelfoam embolization was successful as definitive therapy in 12 of 15 patients. Mortality as a result of hemorrhage or its sequelae was 40% in patients with upper gastrointestinal hemorrhage and 21% in those with lower gastrointestinal hemorrhage.

Registration of atrial high-resolution CT and MR images with a cardiac mapping system can provide real-time electrical activation information, catheter tracking, and recording of lesion position. The cardiac mapping and navigation system comprises a miniature passive magnetic field sensor, an external ultralow magnetic field emitter (location pad), and a processing unit (CARTO, BiosenseWebster). We developed a progressive methodology for both interactively and automatically registering high-resolution 3D atrial images (MR or CT) with the corresponding electrophysiological (EP) points of 3D electro-anatomical (EA) maps. This methodology consists of four types of registration algorithms ranging from landmark-based to surface-based registration. We evaluated the methodology through phantom and patient studies. In the phantom study, we obtain a CT scan of a transparent heart phantom, and then use the CARTO system to visually pick a number of points inside the transparent phantom. After segmenting the atrium into a 3D surface, we register it to the measured EA map. The results are compared to the manual EA point measurements. In the 13-patient study, the four types of registrations are evaluated: visual alignment, landmark registration (three EA points are used), surface-based registration (all EA points are used), and local surface-based registration (a subset of the EA points is used, and one specific point is given a higher weight for a better "local registration"). Surface-based registration proves to be clearly superior to visual alignment. This new registration methodology may help in creating a novel and more visually interactive workflow for EP procedures, with more accurate EA map acquisitions. This may improve the ablation accuracy in atrial fibrillation (AFib) procedures, decrease the dependency on fluoroscopy, and also lead to less radiation delivered to the patient.

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Summary: Aim: Although the fusion of images from different modalities may improve diagnostic accuracy, it is rarely used in clinical routine work due to logistic problems. Therefore we evaluated performance and time needed for fusing MRI and SPECT images using a semiautomated dedicated software. Patients, material and Method: In 32 patients regional cerebral blood flow was measured using 99mTc ethylcystein dimer (ECD) and the three-headed SPECT camera MultiSPECT 3. MRI scans of the brain were performed using either a 0,2 T Open or a 1,5 T Sonata. Twelve of the MRI data sets were acquired using a 3D-T1w MPRAGE sequence, 20 with a 2D acquisition technique and different echo sequences. Image fusion was performed on a Syngo workstation using an entropy minimizing algorithm by an experienced user of the software. The fusion results were classified. We measured the time needed for the automated fusion procedure and in case of need that for manual realignment after automated, but insufficient fusion. Results: The mean time of the automated fusion procedure was 123 s. It was for the 2D significantly shorter than for the 3D MRI datasets. For four of the 2D data sets and two of the 3D data sets an optimal fit was reached using the automated approach. The remaining 26 data sets required manual correction. The sum of the time required for automated fusion and that needed for manual correction averaged 320 s (50-886 s). Conclusion: The fusion of 3D MRI data sets lasted significantly longer than that of the 2D MRI data. The automated fusion tool delivered in 20% an optimal fit, in 80% manual correction was necessary. Nevertheless, each of the 32 SPECT data sets could be merged in less than 15 min with the corresponding MRI data, which seems acceptable for clinical routine use.

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