CT-fluoroscopy (CTF) is an efficient imaging technique for guiding percutaneous lung intervention such as biopsy and ablation. In CTF-guided procedures, four to ten axial images are captured in a very short time period during breath holding to provide near real-time feedback of patients' anatomy so that physicians can adjust the needle as it is advanced toward a target lesion. Although popularly used in clinics, this procedure requires frequent scans to guide the needle, which may cause increased procedure time, complication rates, and radiation exposure to both clinicians and patients. In addition, CTF only generates a limited number of 2-D axial images and does not provide sufficient 3-D anatomical information. Therefore, how to provide volumetric anatomical information using CTF while reducing intraoperative scan is an important and challenging problem. In this paper, we propose a fast CT-CTF deformable registration algorithm that warps the inhale preprocedural CT onto the intraprocedural CTF for guidance in 3-D. In the algorithm, the deformation in the transverse plane is modeled using 2-D B-Spline, and the deformation along z-direction is regularized by smoothness constraint. A respiratory motion compensation framework is also incorporated for accurate registration. A parallel implementation strategy is adopted to accomplish the registration in several seconds. With electromagnetic tracking, the needle position can be superimposed onto the deformed inhale CT image, thereby providing 3-D image guidance during breath holding. Experiments were conducted using both simulated CTF images with known deformation and real CTF images captured during lung cancer biopsy studies. The experiments demonstrated satisfactory registration results of our proposed fast CT-CTF registration algorithm.
Statistical models of deformations (SMD) capture the variability of deformations from the template image onto a group of sample images and can be used to constrain the traditional deformable registration algorithms to improve their robustness and accuracy. This paper employs a wavelet-PCA-based SMD to constrain the traditional deformable registration based on the Bayesian framework. The template image is adaptively warped by an intermediate deformation field generated based on the SMD during the registration procedure, and the traditional deformable registration is performed to register the intermediate template image with the input subject image. Since the intermediate template image is much more similar to the subject image, and the deformation is relatively small and local, it is less likely to be stuck into undesired local minimum using the same deformable registration in this framework. Experiments show that the proposed statistically-constrained deformable registration framework is more robust and accurate than the conventional registration.
CT-fluoroscopy (CTF) is an efficient imaging method for guiding percutaneous lung interventions such as biopsy. During CTF-guided biopsy procedure, four to ten axial sectional images are captured in a very short time period to provide nearly real-time feedback to physicians, so that they can adjust the needle as it is advanced toward the target lesion. Although popularly used in clinics, this traditional CTF-guided intervention procedure may require frequent scans and cause unnecessary radiation exposure to clinicians and patients. In addition, CTF only generates limited slices of images and provides limited anatomical information. It also has limited response to respiratory movements and has narrow local anatomical dynamics. To better utilize CTF guidance, we propose a fast CT-CTF registration algorithm with respiratory motion estimation for image-guided lung intervention using electromagnetic (EM) guidance. With the pre-procedural exhale and inhale CT scans, it would be possible to estimate a series of CT images of the same patient at different respiratory phases. Then, once a CTF image is captured during the intervention, our algorithm can pick the best respiratory phase-matched 3D CT image and performs a fast deformable registration to warp the 3D CT toward the CTF. The new 3D CT image can be used to guide the intervention by superimposing the EM-guided needle location on it. Compared to the traditional repetitive CTF guidance, the registered CT integrates both 3D volumetric patient data and nearly real-time local anatomy for more effective and efficient guidance. In this new system, CTF is used as a nearly real-time sensor to overcome the discrepancies between static pre-procedural CT and the patient's anatomy, so as to provide global guidance that may be supplemented with electromagnetic (EM) tracking and to reduce the number of CTF scans needed. In the experiments, the comparative results showed that our fast CT-CTF algorithm can achieve better registration accuracy.
Abstract Patient falls during hospitalization can lead to severe injuries and remain one of the most vexing patient-safety problems facing hospitals. They lead to increased medical care costs, lengthened hospital stays, more litigation, and even death. Existing methods and technology to address this problem mostly focus on stratifying inpatients at risk, without predicting fall severity or injuries. Here, a retrospective cohort study was designed and performed to predict the severity of inpatient falls, based on a machine learning classifier integrating multi-view ensemble learning and model-based missing data imputation method. As input, over two thousand inpatient fall patients’ demographic characteristics, diagnoses, procedural data, and bone density measurements were retrieved from the HMH clinical data warehouse from two separate time periods. The predictive classifier developed based on multi-view ensemble learning with missing values (MELMV) outperformed other three baseline models; achieved a cross-validated AUC of 0.713 (95% CI, 0.701–0.725), an AUC of 0.808 (95% CI, 0.740–0.876) on the separate testing set. Our studies show the efficacy of integrative machine-learning based classifier model in dealing with multi-source patient data, which in this case delivers robust predictive performance on the severity of patient falls. The severe fall index provided by the MELMV classifier is calculated to identify inpatients who are at risk of having severe injuries if they fall, thus triggering additional steps of intervention to prevent a harmful fall, beyond the standard-of-care procedure for all high-risk fall patients.
Segmenting medical images into different tissues is an important task in medical image analysis, e.g., classifying every voxel of input image into different tissue types: CSF, gray matter and white matter. This paper investigates the fully-tuned radial basis function (RBF) and compares it with the traditional fuzzy c-mean (FCM) clustering algorithm in MR image segmentation. It turns out that FCM is not only biased by the number of voxels in different groups, but also by the intensity differences between different tissue groups, while the fully-tuned RBF captures the multi-Gaussian distribution of the image intensities very well and thus it can be used to segment image intensities accurately. Moreover, in order to generate spatially smooth segmentation results, a Markov random field model is applied to the segmentation results of the fully-tuned RBF algorithm. Experimental results show that fully-tuned RBF method can capture the tissue intensity distribution more accurately than the FCM algorithm
The automated vehicle license-plate locating, is an important part in the intelligence traffic system. It is a key step in the Vehicle License Plate Recognition (LPR). A method for the recognition of images of different backgrounds and different illuminations is proposed. The gray beep points of the row is used to get the horizontal projection of the image first. Then the edge characteristic of the image line is used to get the vertical projection and locate the vehicle license plate. Theoretical analyses and experimental results show that the method is quite effective, which is excellent in accuracy, robust and valuable in practical application.
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