No abstract

A surgical guidance and visualization system is presented, which uniquely integrates capabilities for data analysis and on-line interventional guidance into the setting of interventional MRI. Various pre-operative scans (T1-and T2-weighted MRI, MR angiography, and functional MRI (fMRI)) are fused and automatically aligned with the operating field of the interventional MR system. Both pre-surgical and intra-operative data may be segmented to generate three-dimensional surface models of key anatomical and functional structures. Models are combined in a three-dimensional scene along with reformatted slices that are driven by a tracked surgical device. Thus, pre-operative data augments interventional imaging to expedite tissue characterization and precise localization and targeting. As the surgery progresses, and anatomical changes subsequently reduce the relevance of preoperative data, interventional data is refreshed for software navigation in true real time. The system has been applied in Index terms: neurosurgical planning; image guided surgery; image fusion; 3D visualization; interventional MRI IMAGE-GUIDED SURGERY SYSTEMS strive to enhance the surgeon's capability to utilize medical imagery to decrease the invasiveness of surgical procedures and increase their accuracy and safety. These systems can be categorized into performing one or more of the following functions: data analysis (2,3,4), surgical planning (2,3,4), surgical guidance (5,6,7,8,9,10), and surgical guidance with intra-operative updates (11,12,13,14). The systems focused on surgical guidance tend to present the surgeon with data that was gathered prior to surgery, track surgical instruments within the operating field, and render the tracked devices along with the data. For more difficult surgeries, it is beneficial to present the surgeon with not just one diagnostic scan, but with an array of information derived from fusing data sets with information on morphology, cortical function, and metabolic activity. These varied data sets are acquired in different coordinate systems and need to be aligned, or registered, to a common framework for surgical planning before that framework is in turn registered to the patient for surgical guidance. The latter registration allows the surgeon to establish a correspondence between the patient lying on the operating table and the images rendered on a nearby computer screen.The major shortcoming of image guided surgery systems is that the use of pre-surgically acquired data does not account for intra-operative changes in brain morphology. The systems with intra-operative updates have been introduced to fill that void, but they have fallen short of achieving perfect interactivity and full information disclosure to the surgeon. In particular, the benefits of interventional MRI could be amplified by focusing on five issues: image quality, imaging time, multi-modal fusion, faster localization, and three-dimensional visualization. The need for better image quality arises because some anatomical structures are difficult...

Abstract. We present a software package which uniquely integrates several facets of image-guided medicine into a single portable, extendable environment. It provides capabilities for automatic registration, semiautomatic segmentation, 3D surface model generation, 3D visualization, and quantitative analysis of various medical scans. We describe its system architecture, wide range of applications, and novel integration with an interventional Magnetic Resonance (MR) scanner to augment intraoperative imaging with pre-operative data. Analysis previously reserved for pre-operative data can now be applied to exploring the anatomical changes as the surgery progresses. Surgical instruments are tracked and used to drive the location of reformatted slices. Real-time scans are visualized as slices in the same 3D view along with the pre-operative slices and surface models. The system has been applied in over 20 neurosurgical cases at Brigham and Women's Hospital, and continues to be routinely used for 1-3 cases per week.

Muscle morphology, signal intensity, and volume is relatively uniform among healthy young women.

Magnetic resonance (MR) imaging--guided prostate biopsy in a 0.5-T open imager is described, validated in phantom studies, and performed in two patients. The needles are guided by using fast gradient-recalled echo and T2-weighted fast spin-echo images. Surgical navigation software provided T2-weighted images critical to targeting the peripheral zone and the tumor. MR imaging can be used to guide prostate biopsy.

Brain shift is a continuous dynamic process that evolves differently in distinct brain regions. Therefore, only serial imaging or continuous data acquisition can provide consistently accurate image guidance. Furthermore, only serial intraoperative magnetic resonance imaging provides an accurate basis for the computational analysis of brain deformations, which might lead to an understanding and eventual simulation of brain shift for intraoperative guidance.

Abstract.A framework is proposed for the segmentation of brain tumors from MRI. Instead of training on pathology, the proposed method trains exclusively on healthy tissue. The algorithm attempts to recognize deviations from normalcy in order to compute a fitness map over the image associated with the presence of pathology. The resulting fitness map may then be used by conventional image segmentation techniques for honing in on boundary delineation. Such an approach is applicable to structures that are too irregular, in both shape and texture, to permit construction of comprehensive training sets. The technique is an extension of EM segmentation that considers information on five layers: voxel intensities, neighborhood coherence, intra-structure properties, inter-structure relationships, and user input. Information flows between the layers via multi-level Markov random fields and Bayesian classification. A simple instantiation of the framework has been implemented to perform preliminary experiments on synthetic and MRI data.

The National Alliance for Medical Image Computing (NA-MIC) was launched in 2004 with the goal of investigating and developing an open source software infrastructure for the extraction of information and knowledge from medical images using computational methods. Several leading research and engineering groups participated in this effort that was funded by the US National Institutes of Health through a variety of infrastructure grants. This effort transformed 3D Slicer from an internal, Boston-based, academic research software application into a professionally maintained, robust, open source platform with an international leadership and developer and user communities. Critical improvements to the widely used underlying open source libraries and tools -- VTK, ITK, CMake, CDash, DCMTK -- were an additional consequence of this effort. This project has contributed to close to a thousand peer-reviewed publications and a growing portfolio of US and international funded efforts expanding the use of these tools in new medical computing applications every year. In this editorial, we discuss what we believe are gaps in the way medical image computing is pursued today; how a well-executed research platform can enable discovery, innovation and reproducible science (“Open Science”); and how our quest to build such a software platform has evolved into a productive and rewarding social engineering exercise in building an open-access community with a shared vision.