Objective To explore the nurses'self-efficacy for palliative care and its related factors.Methods Data were collected via a self-constructed questionnaire,using the purposive sampling method.Subjects were nurses from two “grade-A” general hospitals in Henan province.Results The nurses' self-efficacy for palliative care stayed at a disequilibrium state,mean score of self-efficacy for physical care was 4.00,followed by family care self-efficacy 3.85,then psychological and spiritual care self-efficacy 3.70.Significant difference existed in self-efficacy for palliative care in nurses having different attitudes toward death.The cognitive level for palliative care,past experience of caring for end-stage patients were positively correlated with self-efficacy.Conclusions The main factors of the nurses' self-efficacy for palliative care related to attitudes toward death,past experience of caring for end-stage patients and the cognition level of nurses to palliative care.

Breathing motion is a rule-based motion. The traditional breathing motion is described by mathematical model which can not reflect the different properties of different patients or the different breathing periods of the same patient. So, it can not satisfy the needs of the real-time and accurate analysis. Accordingly, in this paper is proposed a method to establish the breathing model through tracking the cone beam CT images which are gained when the patient is breathing freely. The model obtained by the proposed method is similar to the traditional model, which verifies the feasibility and effectiveness of the proposed method. Simultaneously, the proposed method has "real-time" and "accurate" properties, which make it valuable in clinical application.
Cone-Beam Computed Tomography ; Diaphragm ; diagnostic imaging ; physiology ; Humans ; Models, Biological ; Movement ; Radiographic Image Interpretation, Computer-Assisted ; methods ; Respiration

In order to solve the time-consuming problem in volume CT image reconstruction field, we brought forward a fast reconstructive algorithm method to fit the clinic medicine. First, using the great parallel and Floating-Point power of graphic processing unit (GPU), we proposed a volume CT GPU- based reconstruction method. Second, in order to reduce the repetitive operation, we separated the geometry computation and pixel computation. As a result, the computation efficiency was further improved. At last, based on the background of medical engineering, the parallel of volume CT scan and reconstruction was implemented. The results demonstrated that the volume CT reconstruction time could reduce up to 70 times compared to the previous one in common PC by the fast reconstructive algorithm method.
Algorithms ; Computer Graphics ; Cone-Beam Computed Tomography ; instrumentation ; methods ; Humans ; Image Processing, Computer-Assisted ; methods ; Radiographic Image Enhancement ; methods ; Radiographic Image Interpretation, Computer-Assisted ; methods