xCAT®, (Xoran Technologies, LLC., Ann Arbor, MI) is a CT imaging device that has been used for minimally invasive surgeries. Designed with flat panel and cone-beam imaging technique, it provides a fast, low-dose CT imaging alternative for diagnosis and examination purposes at hospitals. With its unique compact and mobile characteristics, it allows scanning inside crowded operating rooms (OR). The xCAT allows acquisition of images in the OR that show the most recent morphology during the procedure. This can potentially improve outcomes of surgical procedures such as deep brain stimulation (DBS) and other neurosurgeries, since brain displacement and deformation (brain shift) often occur between pre-operative imaging and electrode placement during surgery. However, the small gantry size of the compact scanner obstructs scanning of patients with stereotactic frames or skull clamp. In this study, we explored a novel method, in which we first utilized the xCAT to obtain CT images with fiducial markers, registered the stereotactic frame with those markers, and finally, target measurements were calculated and set up on the frame. The new procedure workflow provides a means to use CT images obtained inside of OR for stereotactic surgery and can be used in current intraoperative settings. Our phantom validation study in lab shows that the procedure workflow with this method is easy to conduct.
Dual-energy (DE) X-ray computed tomography (CT) has shown promise for material characterization and for providing quantitatively accurate CT values in a variety of applications. However, DE-CT has not been used routinely in medicine to date, primarily due to dose considerations. Most methods for DE-CT have used the filtered backprojection method for image reconstruction, leading to suboptimal noise/dose properties. This paper describes a statistical (maximum-likelihood) method for dual-energy X-ray CT that accommodates a wide variety of potential system configurations and measurement noise models. Regularized methods (such as penalized-likelihood or Bayesian estimation) are straightforward extensions. One version of the algorithm monotonically decreases the negative log-likelihood cost function each iteration. An ordered-subsets variation of the algorithm provides a fast and practical version.
The availability of new digital detector technologies and high speed computer processing has led to the development of CAD (computer-aided diagnostic) tools that assist radiologists in detecting and characterizing mammographic lesions. To meet the challenge of developing and implementing algorithms that are computationally intensive, it is desirable to develop reusable components that can execute in a distributed environment. It is well know that the Common Object Request Broker Architecture (CORBA) provides an open solution in distributed computing. We have implemented a hybrid component model consisting of a CORBA server and a Contract Net Protocol (CNP) algorithm for distributing tasks to multiple computers for enhanced processing. Support classes were developed to wrap algorithms developed in C to operate within the distributed framework. CORBA provides communication between agents on different computers and computer platforms and the CNP algorithm is used to select the 'optimal' computer for performing a task. We have evaluated this framework with CAD processing applied to digitized mammograms by transparently scheduling and distributing multiple tasks on three server computers. We achieved a significant reduction in processing times compared to processing on a single computer.